innovation in the australian wool industry: a …...technology attributes and economic rationality...
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Innovation in the Australian wool industry: A sensemaking perspective
Joanne Sneddon
Grad. Dip (Mgmt.), Adv. Dip. (Bus. Admin.) (Durham University) M.Com (Mgmt.) (CUT), MBA (UWA)
This thesis is presented for the degree of Doctor of Philosophy of The University of Western Australia
Business School The University of Western Australia
2008
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Statement of Candidate Contribution
The work presented in this PhD thesis is, to the best of the candidate’s knowledge and
belief, original and is the candidate’s own work, except as acknowledged in the text.
The material has not been submitted, either in whole or in part, for a degree at this or
another university.
Joanne Sneddon
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Abstract
Achieving the successful development, transfer and adoption of new agricultural technology is a popular issue in the innovation literature. Innovation diffusion and economic theory has informed this literature by emphasising the central role that technology attributes and economic rationality play in the adoption of new technology. In agricultural innovation context, research has traditionally taken a technological determinist perspective, assuming that technologies shape society and that all technological change is positive and progressive. As a result of limitations of the linear, determinist perspective of agricultural innovation to explain how new technologies are adopted and diffused, social constructivist approaches to agricultural innovation have emerged as a complement to this approach. However, a unifying framework of the social construction of new agricultural technologies has not been presented in the agricultural innovation literature. In this study Karl Weicks seven properties of sensemaking are used as the foundation for the development of a unifying conceptual framework for the examination of the social construction of agricultural technology. This thesis is a study of sensemaking in the context of agricultural innovation. It examines how participants in the Australian wool industry make sense of new technologies and how that sensemaking shapes their use of new technologies over time. The focal innovation initiative studied in this thesis is the development, transfer, adoption and abandonment of objective wool fibre testing technologies. This initiative commenced in the 1960s and has resulted in significant changes in the way that Australian wool is produced, marketed and processed. An interpretive research paradigm is adopted in this study. A theory-building case study approach, combining quantitative and qualitative data collection and analysis is used to capture the ongoing, iterative, enactive and social actions and interactions that occur throughout the agricultural innovation process. The case study is divided into three separate but interlocking empirical analyses which examine how industry participants’ sensemaking shaped their use of wool testing technologies at the industry, technological system and individual farm level. The findings and implications of the three empirical studies in this thesis are discussed in relation to (1) the interpretation frameworks of agricultural industry participants and technology enactment, (2) the sensemaking process, (3) the social construction of shared technology frames, and (4) the social construction of industry belief systems. This study contributes to the debate on the social construction of agricultural technology and sensemaking in the innovation process by exploring the development, transfer, adoption and abandonment of new wool fibre testing technologies by industry participants over time. It builds on theoretical and empirical agricultural innovation and sensemaking research, and draws on a theoretical framework sensitive to the social construction of technology at the individual, group and industry levels. In doing so this study develops the concept of sensemaking in the agricultural innovation process as a way of deepening our understanding of how new agricultural technologies are transferred, adopted and diffused.
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Acknowledgements
This thesis could not have been written without the contribution of many people who
are too numerous to list individually. But whether named or not, I am extremely
grateful to anyone who helped with and participated in this research by providing
information, moral and financial support. Particular thanks go to my three supervisors,
Tim Mazzarol, John Stanton and Geoff Soutar, who have guided me through this
challenging process. I would like to especially thank John Stanton and his colleagues at
the Department of Agriculture and Food, Western Australia who funded this research
and facilitated the collection of data for this study. I would also like to thank my
colleague Russell Barnett with whom I have worked closely over the last four years and
who has provided me with valuable insights into the innovation process from his own
innovation management experience.
The idea for this research topic came from my work on the adoption of new
technologies in the Australian wool industry, much of it carried out with the CSIRO and
the Department of Food and Agriculture, Western Australia. I would like to thank my
colleagues in these organisations and the farm families and consultants that I have had
the great pleasure of working with over the past eight years for their support, insights,
enthusiasm and openness.
The completion of thesis would not have been possible without the unwavering support
of my fellow PhD travellers and colleagues at The University of Western Australia
Business School and School of Animal Biology. My PhD friends, Stacie Chappell,
Lydia Kilcullen and Barb Wood enriched my research journey with friendship and
support, for this I am truly grateful.
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The last two years of this project I spent working as a lecturer at The University of
Western Australia. I would like to thank my colleagues at the Business School for their
support. In particular my gratitude goes to Geoff Soutar and Ray Fells who gave me the
opportunity to join the Business School team and to David Plowman whose comments
and questions about my research always hit the mark.
Although we are separated by several continents, my family, Margaret Collins, Bill
Turner, Mandy Whelan and Morggen Renner were also with me for every step of this
journey. Thank you for being there when I needed you and for giving me space when I
needed it. My deepest gratitude is reserved for my husband Kevin. Your love, support
and encouragement made the undertaking of this research possible. This thesis is
dedicated to you with all my love.
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Table of Contents
1 Introduction 1
1.1 Agricultural innovation 3
1.2 The aim of the present study 8
1.3 The Research Design 9
1.4 The structure of the thesis 11
2 A review of agricultural innovation research 13
2.1 The transfer of agricultural innovation: A technological determinist
perspective 14
2.2 Determinist perspectives of agricultural innovation adoption 24
2.3 Social constructivist perspectives of agricultural innovation 39
2.4 Individual and social sensemaking 47
3 Agricultural innovation: A sensemaking process 55
3.1 Agricultural innovation as an occasion for sensemaking 56
3.2 Collective sensemaking at an industry level: Sensemaking as organising 69
3.3 Making sense of agricultural innovation: an analytical framework 76
3.4 Methodological issues and an introduction to the empirical studies 81
4 The co-evolution of agricultural innovation and Australian wool industry belief
systems 92
4.1 Theoretical background 94
4.2 Research Method 96
4.3 Case Study Findings: A selective chronology of OM innovation in the
Australian wool industry 102
4.4 Discussion of the findings 127
5 Fad, fashion, compliance or efficient choice? A study of the diffusion of
technologies in the Australian wool industry 140
5.1 Theoretical background 143
5.2 Research Method 150
5.3 Research findings 162
5.4 Discussion of the findings 174
6 The enactment of new technologies on-farm: A sensemaking perspective 182
6.1 Theoretical Background 184
6.2 Research Method 185
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6.3 Research findings: Patterns of technology enactment 206
7 The enactment of new technologies on-farm: Case study findings and discussion
219
7.1 Case study findings 220
7.2 Discussion of the multiple case study findings 244
8 Contributions and implications for theory, policy and practice 261
8.1 Concluding discussion of the empirical findings 266
8.2 Contributions to theory 278
8.3 Implications for policy and practice 283
8.4 Limitations of the research 292
8.5 Directions for future research 292
REFERENCES 298
APPENDIX A 325
APPENDIX B 330
APPENDIX C 349
APPENDIX D 352
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1 Introduction
The present study examined agricultural innovation and, specifically, how industry
participants’ sensemaking shaped their use of new agricultural technologies. It focused
on how Australian wool industry participants made sense of technologies that were
developed to provide more objective measurement of greasy wool fibre and how that
sensemaking shaped their use of these technologies over time. Through this approach it
was hoped to understand the technology frames, actions and artefacts that evolved
through Australian wool industry participant sensemaking and how this sensemaking
contributed to the adoption, implementation, use and abandonment of new wool fibre
testing technologies.
For many years, technology has been considered a silver bullet that can solve the
profitability, productivity and sustainability problems that many agricultural industries
face. However, new agricultural technologies often fail to meet the expectations of
researchers, practitioners and end-users. The gap between what industry participants
expected of new agricultural technologies and what was delivered can be observed in
the highly variable levels of technology use and industry impact. For instance, an
unanticipated abandonment of non-traditional agro-export crops resulted in a return to
traditional crop production by small family farms in Guatemala. Technology
abandonment occurred despite incentives for the adoption of new cropping systems
being offered by Government and development agencies (Carletto, de Janvry &
Sadoulet 1996). Neill and Lee (2001) found that, despite approximately 65 per cent of
Honduran maize farmers using the innovative maize-mucuna rotation system in 1992,
five years later many farmers (more than 10% a year) were abandoning the system.
Similarly, a new System of Rice Intensification (SRI) adopted by many Madagascan
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landholders was later abandoned by 40 per cent of adopters (Moser & Barrett 2003).
The landholders who continued to use SRI reported that they had implemented the
system across a relatively small proportion of their productive rice land despite higher
yields being obtained by SRI than by traditional rice cultivation methods (Moser &
Barrett 2003).
In Australia’s livestock industries, improved sheep reproduction efficiency is a major
goal of State Departments of Agriculture, funding bodies and research agencies alike
(Barnett & Sneddon 2006b). In the 2000s, at least 19 extension programs were
addressing sheep reproduction efficiency and several million dollars were invested in
reproduction research and development nationally every year (Barnett & Sneddon
2006b). Programs designed to extend new sheep reproduction technologies reached no
more than 12 per cent of their target audience and only around half of these extension
program participants adopted new sheep reproduction technologies as a result of their
involvement in the program (Barnett & Sneddon 2006b). Despite sustained efforts to
develop and transfer new sheep reproduction technologies, there is little evidence in
long term trend data that lambing percentages have improved in Australia over the last
fifteen years see (Barrett, Ashton & Shafron 2003).
Evidence of limited farmer engagement and low levels of technology adoption and
impact can also be found among Australian livestock grazing management technologies.
In the mid 2000s, more than twenty applied research and extension programs developed
and extended technologies designed to improve pasture utilisation in Australian grazing
industries (Barnett & Sneddon 2006a). These research and extension programs engaged
between five and 20 per cent of their target audience and around one third of
participants adopted the new technologies. Despite the level of program participation
and adoption of new technologies, national farm survey data suggests pasture utilisation
has not increased substantially in the last two decades (Barnett & Sneddon 2006a).
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These examples of the variable use and impact of agricultural innovation raise questions
about how new successful agricultural technologies are constructed and used in
agricultural industries. In an agricultural innovation context, technologies that are
widely accepted and used are often assumed to be what Pinch and Bijker (1987) term
technologically sweet, which means their success was inevitable. Conversely, the
blame for rejection, abandonment and failure to adopt and implement new technologies
that are considered by researchers, policy makers and extensionists to be superior to
existing solutions is often placed upon farmers (Guerin & Guerin 1994; Ruttan 1996).
However, is there sufficient evidence to support these propositions? Is the success of
one agricultural technology inevitable because it is technologically sweet and the
abandonment of another the fault of end users for failing to perceive its superiority over
existing technologies? Or is agricultural innovation a more complex phenomenon than
these basic assumptions suggest? Despite a substantial body of agricultural innovation
research stretching back several decades, these assumptions continue to dominate
research and practice (Douthwaite 1999; Douthwaite, Keatinge & Park 2002; Qamar
2002).
1.1 Agricultural innovation
It has long been considered that innovation has a positive impact on the growth and
productivity of agricultural industries (Campbell 1980; Bohlen 1964; Fliegel & van Es
1983). The introduction and subsequent application of new technologies into an
economy or social system has been a classic problem in agriculture, economics,
sociology and management (Rogers 2003; Weick 1990; von Hippel 1988). Within this
broad area, one particular line of research that is relevant to this study is how to
successfully manage the application and use of a new technology in an economy or
social system. In an agricultural innovation context, this line of enquiry has typically
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been approached from a technological determinist perspective and has largely focused
on the decision to adopt or reject a new technology (Guerin & Guerin 1994; Ruttan
1996).
Some of the central tenets of technology determinism are that technology shapes society
and that technological change is positive and progressive. The dominance of
technology determinism in agricultural innovation is what drives linear, hierarchical
approaches to the transfer and adoption of new technologies. However, this approach to
agricultural innovation has been found to be problematic and has been blamed for both
the under- and the over-adoption of new technologies, contributing to uneven rural
development, environmental degradation and to farmers being overwhelmed by
unsuitable technologies (Howden et al. 1998; Röling 1988; Black 2000). Linear, top-
down innovation models have also been criticised for favouring a small proportion of
‘progressive farmers’ to the detriment of the wider farming community (Vanclay 1994;
Ruttan 1996; Dunn, Gray & Phillips 2000; Fliegel & van Es 1983; Chamala 1987;
Röling, Ashcroft & Chege 1976; Clark & Lowe 1992; Long & van der Ploeg 1989;
Röling 2004; Scoones & Thompson 1994; Kloppenburg Jr 1991; Chambers & Jiggins
1986; Chambers 1983; Biggs 1989; Horton & Prain 1989).
More recently, social-constructivist perspectives of agricultural technologies have
emerged as researchers have sought to gain deeper insights into the agricultural
innovation phenomenon (Coughenour 2003; Coughenour & Chamala 2000). In contrast
to the technological determinist perspective, this perspective views technologies as
variables that are socially constructed, with their designs manifested in different ways to
suit different industry participants (Bijker 1999; Clark & Staunton 1989). Clark and
Staunton (1989, p. 73) provided a succinct explanation of the social constructivist
perspective of technology by arguing that “at any one moment in time an innovation
configuration will present itself to potential adopters as a complex bundle of elements
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whose relevance is relational to the pre-existing features of the host organisation.” This
approach seeks to explain how individuals and groups shape the meaning of new
technologies in their own context through individual cognitions and social learning
(Prasad 1993; Fulk 1993).
In the social-constructivist perspective, new technologies are viewed as being
‘equivocal’, as they “require ongoing structuring and sensemaking if they are to be
managed” (Weick 1990, p. 2). It is important for the purposes of this study to explain
what is meant by the term sensemaking: described as a cognitive and behavioural
response to ambiguous and uncertain situations that interrupt the ongoing flow of
events (Gioia & Chittipeddi 1991), sensemaking is an ongoing process of forming
anticipations and assumptions and the subsequent interpretation of experiences that
deviate from those anticipations and assumptions (Louis 1980). Sense in this context
refers to the meaning ascribed to an event and making is the activity of creation or
construction (Weick 1995). Sensemaking frameworks reflect sensemakers’ habits and
beliefs about what is and what ought to be (Weick 1995). Sensemaking occurs at all
levels of a social system, from the individual to the industry or cultural level (Weick
1995; Beyer 1981; Wiley 1988; Porac, Ventresca & Mishina 2002).
Studies of sensemaking within the innovation process have been limited to studies of
the construction, adoption and implementation of information systems technologies
(e.g. Seligman 2000; Seligman 2006; Choo & Johnston 2004; Faraj, Kwon & Watts
2004; Guney 2004; Theoharakis & Wong 2002; Dougherty et al. 2000; Griffith 1999;
Prasad 1993; Orlikowski & Gash 1994; Barley 1986; Leonard-Barton 1988). Even
though sensemaking concepts have been cited for their theoretical and empirical
importance to our understanding of the use of new technologies, they have not been
used to examine agricultural innovation. Therefore, while many people have studied
the transfer, adoption and diffusion of new agricultural technologies, little has been said
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about the process through which agricultural industry participants make sense of new
technologies and how this sensemaking shapes the use of these technologies.
Innovation in the Australian wool industry
The present study examined innovation within the Australian wool industry, which was
of particular interest as it has a long history of innovation-based development
(Australian Bureau of Statistics 2002). The Australian wool industry was established in
the early nineteenth century and, at the turn of the twenty-first century, Australia
continues to maintain its position as the largest wool producing nation in the world
(Food and Agriculture Organisation 2002; Australian Wool Innovation 2007). In 2003,
approximately 47,000 farm businesses in Australia produced wool, contributing around
AUD$2.7 billion to the national economy (Australian Bureau of Statistics 2003;
Australian Wool Innovation 2004).
Despite the continuing importance of the Australian wool industry to the national
economy and global textile industry, Australian woolgrowers have been in the grip of a
severe “cost-price squeeze” since the 1970s (Australian Bureau of Statistics 2002). The
combination of deteriorating terms of trade and poor productivity gains has resulted in a
sharp decline in wool production and the number of farm enterprises producing wool
from the early 1990s onwards, placing a question-mark over the future of the industry
(Shafron, Martin & Ashton 2002). Australian wool industry commentators argue that
investment in innovation initiatives has not prevented a contraction in wool production
as woolgrowers have a low level of technology adoption compared with other
Australian rural industries (Wool Industry Future Directions Task Force 1999;
Woolmark Business Intelligence Group 2004). Such observations suggest a determinist
perspective of technology prevails in the Australian wool industry. However, few
studies have examined the use of new technologies in the Australian wool industry and
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there is a need to understand more about the innovation process and how it can be better
managed to improve industry outcomes.
Objective wool fibre testing technologies
The present study examined the use of novel, objective, wool fibre testing technologies
as examples of innovation in the Australian wool industry. The measurement of greasy
wool fibre attributes, known as wool metrology, provides an objective means of
measuring and controlling the impact of wool fibre variability during processing and
has become a fundamental component of modern wool textile processing and marketing
systems (Sommerville 2002). The technology that enabled objective wool fibre
measurements to be made emerged in the 1960s and continues to play a central role in
the production, marketing and processing of Australian greasy wool fibre (Sommerville
2002).
The objective fibre- testing technologies that are at the heart of the present study are
tests for wool fibre strength and length that are known as Additional Measurements
(AM) and tests for clean colour (CC). AM is widely considered to be a successful
innovation as there has been widespread acceptance and use of these technologies along
the wool supply chain. CC, however, is considered to have been an unsuccessful
innovation as most of the Australian woolgrowers that adopted it after its introduction
in the mid-1990s subsequently abandoned the technology. These two objective wool
fibre testing technologies were selected for analysis as they provided an opportunity to
compare and contrast industry participant sensemaking about an accepted and an
abandoned technology.
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1.2 The aim of the present study
The present study examined how Australian wool industry participants made sense of
objective wool fibre testing technologies and how this sensemaking shaped the use of
these technologies over time. The primary research question and the sub-question
examined were:
- How did the sensemaking of Australian wool industry participants shape the
use of objective wool fibre testing technologies?
-What are the implications of how sensemaking shaped the use of
objective wool fibre testing technologies for future advances in the
management of agricultural innovation?
Understanding how individuals, organizations and industries make sense of new
technologies is beginning to be recognized as an important issue by innovation
management and organizational behaviour researchers (e.g. Seligman 2000; Seligman
2006; Choo & Johnston 2004; Faraj, Kwon & Watts 2004; Guney 2004; Theoharakis &
Wong 2002; Dougherty et al. 2000; Griffith 1999; Prasad 1993; Orlikowski & Gash
1994; Barley 1986; Leonard-Barton 1988). Sensemaking has its conceptual roots in the
Social Construction of Technology approach (T. Pinch & W.E. Bijker 1987) and in the
socio-cognitive perspective of organisations (Porac, Ventresca & Mishina 2002).
Focusing on sensemaking, and the roles played by enactment, cognitions and social
influence in the construction of technologies, also falls within an interpretive research
paradigm (Weick 1995; T. Pinch & W. Bijker 1987; Bijker 1987), which was used in
this research. The study was a response to calls for research that integrates sensemaking
concepts into the study of situations and events at an individual, organizational and
industry level that examines tensions between social groups in collective sensemaking
processes (Weick, Sutcliffe & Obstfeld 2005; Porac, Ventresca & Mishina 2002). It
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was also a response to calls for explanations as to why some agricultural technologies
are successful and some are unsuccessful (Feder, Just & Zilberman 1985; Frank 1999;
Lindner 1987).
The study was an attempt to provide a comprehensive analysis of how participants in
the Australian wool industry made sense of objective wool fibre testing technologies
and how this sensemaking shaped the use of these technologies. This study examined
what technology frames and industry belief systems were socially constructed and
reconstructed through participant sensemaking. It also examined conflict, consensus
and compliance in industry participants’ sensemaking and the impact that sensemaking
had on the acceptance and abandonment of new wool fibre testing technologies.
1.3 The Research Design
A case study strategy was used to examine how Australian wool industry participants’
sensemaking shaped the use of objective wool fibre testing technologies. The approach
was used to capture the ongoing, iterative, enactive and social actions and interactions
that occur throughout the innovation process.
The case study was divided into three separate, but interlocking, empirical analyses.
The three studies examined how sensemaking shaped the use of wool fibre testing
technologies at an industry level, a technological system level and at an individual farm
level. The first study, ‘The co-evolution of agricultural innovation and Australian wool
industry belief systems’ examined how Objective Measurements (OM) testing
technologies were adopted and diffused in the Australian wool industry. The data used
were collected from an examination of historical industry policy documentation,
research and practice publications and testing technology use data.
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The second study, ‘Fad, fashion, compliance or efficient choice? A study of the
diffusion of new technologies in the Australian wool industry’ examined how new wool
fibre testing technologies were adopted or abandoned by Australian woolgrowers. As
already noted, the focal technologies were Additional Measurements (AM) and Clean
Colour (CC) testing technologies that were introduced into the Australian wool industry
in the mid-1980s and mid-1990s respectively. Theories of industry sensemaking were
extended to the analysis of the diffusion of new agricultural technologies. The data in
this case were collected from the Australian wool auction database, which records the
objective tests undertaken on every wool lot offered at auction in Australia between
1988 and 2005. Data were also collected from an examination of published research
articles and policy documents relating to AM and CC testing.
The third and final study, ‘The enactment of new technologies on-farm: A sensemaking
perspective’ examined the socio-cognitive mechanisms underlying the enactment of
AM and CC testing on-farm through an ethnographic study of six wool growing
families and farm enterprises located in the south west of Western Australia. Based on
data obtained from the wool auction database, a hierarchical clustering technique was
used to define groups of relatively homogeneous commercial wool production
enterprises in terms of the size of the wool enterprise and AM adoption and use
behaviour. This enabled the selection and examination of similar cases from within
clusters (to provide literal replication) and of contrasting cases from different clusters
(to provide theoretical replication).
Individual case study data were collected through observation, semi-structured
interviews and ongoing discussions. Semi-structured interviews were conducted with
adult farm family members (aged eighteen years and over) and relevant advisors who
were engaged in the management of the farm business. Typically, four interviews were
conducted with the participants in the farm business. Each interview lasted, on average,
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74 minutes. The researcher undertook all observations and interviews with family farm
business members at the farm property. Family members were interviewed together
wherever possible to support an examination of collective sensemaking. Analysis of the
data collected and generated in the cases studies involved data reduction, data display
and conclusion-drawing and verification (Miles & Huberman 1994) that were
undertaken concurrently during the study.
1.4 The structure of the thesis
The thesis continues with an overview of the literature relating to agricultural
innovation research and practice. In this chapter the dominant agricultural innovation
paradigm, technological determinism, is discussed along with emerging social
constructivist perspectives of the agricultural innovation process. In the third chapter,
sensemaking concepts are discussed and a preliminary analytical framework of the
agricultural innovation sensemaking process is presented as a theoretical account of the
construction and enactment of objective wool fibre testing technologies in the
Australian wool industry. The three empirical studies are introduced and discussed in
more detail.
In Chapter 4 the first empirical study, ‘The co-evolution of agricultural innovation and
Australian wool industry belief systems’ is presented. This study examined how
Objective Measurements (OM) testing technologies were adopted and diffused in the
Australian wool industry. This study documented different Australian wool industry
participant group’s responses to the development, introduction and adoption of new
testing technologies over time and the impact of these responses on industry belief
systems.
In the fifth chapter, the second study, ‘Fad, fashion, compliance or efficient choice? A
study of the diffusion of new technologies in the Australian wool industry’ is presented.
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This study examined how new wool fibre testing technologies were adopted or
abandoned by Australian woolgrowers over time. This study reported on whether the
dominant efficient-choice perspective of diffusion is sufficient to explain the pattern of
diffusion and abandonment of new agricultural technologies. In Chapter 6 the third
empirical study, ‘The enactment of new technologies on-farm: A sensemaking
perspective’ is introduced. In this chapter the selection of the six case study farms using
hierarchical cluster analysis is described and quantitative data relating to the adoption,
implementation and use of AM and CC on the case study farms is analysed. In the
seventh chapter of this thesis the story of the enactment of AM and CC on the six case
study farms is described and discussed and research propositions are developed in
respect of the agricultural innovation sensemaking process. In this study the
sensemaking processes of Australian woolgrowers were examined in the context of the
adoption, implementation, use and abandonment of new agricultural technologies on-
farm.
In Chapter 8, conclusions drawn from the three empirical studies are presented and
discussed in relation to the sensemaking constructs in the preliminary analytical
framework of the agricultural innovation sensemaking process presented in Chapter 3.
The limitations of the study, contributions to theory and practice and proposed direction
for future research are discussed in this chapter. The appendices contain: a full glossary
of wool industry terms; a summary of the chronological narrative data from the first
empirical study; a copy of the consent letter sent to participants in the third empirical
study; and a copy of the interview guide used in this study.
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2 A review of agricultural innovation research
A determinist view of technology pervades agricultural innovation research and practice
which may have reduced researchers’ ability to answer important questions in the field.
In the determinist perspective of agricultural innovation, the development, transfer,
adoption and use of new technologies is conceptualised as a simple, linear, hand-over of
complete new technologies from scientists to end users. In this approach, the socio-
cognitive mechanisms underlying technology actions are either assumed or overlooked
and the enormous potential for a meaningful understanding of how agricultural
innovation occurs remains largely untapped.
This chapter, which is divided into four sections, examines the literature relating to
agricultural innovation research and draws on practice to discuss dominant and
emerging perspectives of agricultural innovation. First, technology determinism, the
dominant agricultural innovation paradigm, is discussed. In this section the rationale
for the technological determinist perspective, and the limitations of this approach, are
examined. Second, the adoption and use of new agricultural technologies from the
dominant technological determinist perspective is discussed. In particular, this section
explores the limitations of this approach in the light of new empirical agricultural
innovation research. Third, the emerging perspective of the social construction of
technology is described and empirical socio-technical research in agricultural and rural
contexts is discussed. Fourth, the theory and concepts of sensemaking are introduced as
a guiding framework for examining how participants in the Australian wool industry
made sense of objective wool fibre testing technologies and how this sensemaking
shaped the use of these technologies over time. The final section provides a conclusion
of the chapter.
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2.1 The transfer of agricultural innovation: A technological
determinist perspective
It has long been suggested that innovation has a positive impact on growth and
productivity in agricultural industries (Campbell 1980; Bohlen 1964; Fliegel & van Es
1983). Innovation is distinguished from invention as the means by which new ideas are
applied in commercial and practical contexts (Schumpeter 1939). Innovation has been
defined as “an idea, practice or object that is perceived as new by an individual or other
unit of adoption” (Rogers 1983, p. 11). Indeed, Van de Ven (1986) suggested new ideas
are not usually seen as innovations unless they are used successfully. Innovation is
defined in this study as the process by which new ideas are generated and applied in a
social system, while new ideas that are the product of the innovation process are
described as technologies. Technologies are mainly identified with tangible artefacts
and products. However, they can also include ‘software,’ such as knowledge,
experience, practices and techniques (Rogers 1995).
There are two main perspectives of agricultural innovation (Ruttan 1996; Coughenour
2003; Douthwaite 1999; Douthwaite, Keatinge & Park 2002). The first is technological
determinism, which views technology as an autonomous force beyond direct human
control that is the cause of social change (Chandler 1995). The central tenets of
technology determinism are that technology shapes society, and that technological
change is positive and progressive. The second perspective is social constructivism
which is discussed in Section 2.3 of this chapter.
Technology determinists view economic growth and social change as a discontinuous
series of revolutionary technological advances. It is assumed that, in periods of rapid
technological change, society must adjust to technology, rather than technology adjust
to society. Alvin Toffler, author of Future Shock, outlined the philosophy of
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technology determinism by arguing that technology was the engine of social change and
economic growth, noting that “behind such prodigious economic facts lies that great,
growling engine of change – technology” (Toffler 1970, p. 25).
Technology determinists argue that, given a set of conditions, technological outcomes
can be predicted with some certainty (T. Pinch & W.E. Bijker 1987). The logic of
technical rationality underlies the determinist perspective, an assumption that the
technical and social worlds operate according to rules that predetermine potential
outcomes (T. Pinch & W.E. Bijker 1987). Technological determinist views of
technology include the technological imperative, in which it is assumed technological
artefacts move in a linear and inexorable fashion towards a final end state of adoption
by end-users (Edge 1995). The technological imperative perspective underlies the
development and application of linear, staged models of innovation (T. Pinch & W.E.
Bijker 1987).
The technological determinist approach has been most often used in agricultural
innovation research and practice to explain the development of technologies or to
predict their adoption (Ruttan 1996). Agricultural innovation is often seen as a simple,
linear, staged process of the development, transfer and adoption of new technologies.
Guerin and Guerin (1994, p. 550) define agricultural technology transfer as “the process
of moving technical scientific and technical knowledge, ideas, services, inventions and
products from the origin of their development to where they can be put into operation.”
New knowledge is seen to flow through a conceptual pipeline that has basic research
activities at one end and useful technologies that are adopted by farmers at the other
(Clark 1995; Biggs 1989; Horton & Prain 1989; Chambers & Jiggins 1986).
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Figure 2.1: Transfer of Technology (ToT) model (adapted from: Lionberger &
Gwin 1991)
Figure 2.1 shows a linear, determinist conceptualisation of agricultural innovation,
referred to in the literature as the Transfer of Technology (ToT) or Central Model
(Lionberger & Gwin 1991). In this model it is assumed basic research is undertaken in
universities and research organisations to extend the frontiers of knowledge (Lionberger
& Gwin 1991). The outcomes of basic research are transferred to organisations, such as
State Departments of Agriculture, that undertake applied research to translate basic
research outcomes into farming technologies (Lionberger & Gwin 1991). The
technologies developed through applied research activities are translated into extension
programs that are delivered to the farming community to bring about change (Clark
1995; Biggs 1989). Agricultural extension is seen as the science of promoting
voluntary behavioural change within social systems (Röling 1985).
The ToT model assumes more innovative farmers, the so called ‘progressive farmers’,
will adopt new technologies through direct participation in extension activities. It is
Basic Research
New Knowledge
Applied Agricultural Research
New technology
Extension
Technology transfer to Progressive Farmers
Adoption by Progressive Farmers and/or Advisors
Technology transfer to ‘other’ Farmers
Adoption by ‘other’ Farmers
Widespread technology adoption and industry change
- 17 -
further assumed that new technologies will spread through the rest of the farming
community as other farmers observe and imitate the technology actions of ‘progressive
farmers’ (Röling 1985; Kaine & Lees 1994; Dunn, Gray & Phillips 2000; Black 2000).
The linear technology transfer model is strictly a ‘technology push’ or technology
opportunity approach to innovation which ignores market demand for the development
and dissemination of new technologies.
Although the limitations of the linear ToT approach to agricultural innovation have
been discussed by researchers and practitioners since the late 1980s (e.g. Hildebrand
1993; Röling 1988; Vanclay 1994) there is evidence that the application of the ToT
model to the transfer of new agricultural technologies continues in the Australian
agricultural research sector. The dominance of top-down approaches to agricultural
innovation in Australia has been perpetuated by a crisis in Australian agricultural
research, development and extension programs reached in the mid-1990’s (Nabben,
Egerton-Warburton & van Moort 2000; Vanclay 1994). Since the mid-1990s there has
been a general movement at all levels of Government for greater accountability,
economic rationalism and the privatisation of services which has resulted in a move
towards ‘guaranteed’ program outputs with reduced funding timeframes (Nabben,
Egerton-Warburton & van Moort 2000). In many instances short funding cycles and a
greater focus on the guaranteed delivery of outputs has resulted in the pre-selection of
research, development and extension approaches based on traditional ‘top down’
innovation models (Nabben, Egerton-Warburton & van Moort 2000).
There is evidence that the ‘top down’ ToT approach to research, development and
extension continues to dominate innovation policy and practice in the Australian wool
industry. Based on the author’s own experience working on Australian wool industry
innovation initiatives since 2000, it appears that many state, national, tertiary and
- 18 -
industry organisations continue to undertake research and development in the absence
of clear market demand for new technologies. Industry research and development
initiatives often fail to include a comprehensive program to engage woolgrowers in the
innovation process and facilitate the successful adoption and effective implementation
and use of new technologies (e.g. Barnett & Sneddon 2006b; Barnett & Sneddon
2006a). A review of the performance of Australian Wool Innovation Ltd (the
Australian wool industry Research and Development Corporation) in 2006
recommended the prioritisation of market led research and development over
‘technology push’ innovation initiatives in order to improve industry engagement and
innovation outcomes (Barber & Smart 2006).
There are some notable exceptions to the application of linear technology transfer
approaches to innovation in the Australian wool industry; these include network and
grower group participatory extension programs such as 8X5 Wool Profit project,
Bestwool 2010, Bestprac and Look@Wool (Australian Wool Innovation 2003a).
However, these extension programs are relatively small and regionally focused, without
the scale and scope to engage a wide range of Australian woolgrowers and industry
stakeholders in the innovation process on an ongoing basis. Therefore, participation in
such extension programs has been limited to a small number of ‘progressive’
woolgrowers under the assumption that technologies and related industry impacts will
trickle down to the majority of woolgrowers (Welsman 2003).
The ToT model came from innovation diffusion theory (Ruttan 1996) that suggests new
technologies permeate social systems, over time through social interactions (Rogers
2003). The classical diffusion model has its conceptual roots in early sociology and
social psychology as an explanation of change in human behavior. The observations of
Gabriel Tarde (cited in Rogers 1983) suggested that the rate of innovation adoption
- 19 -
usually followed an S-shaped curve over time as knowledge of the innovation ‘diffused’
through society. Most diffusion research implies that all members of a social system
will adopt an innovation over time, ignoring issues such as dis-adoption or adaptation
(Rogers 1983). Most adoption and diffusion studies have been undertaken post hoc
with simple, successful innovations; very little is know about forecasting adoption
behaviour in complex innovation scenarios. Adoption scenarios become more complex
when the attributes, outcomes and benefits of the innovation are difficult to perceive.
This may be the case where the innovation involves several interrelated components
such as new crop varieties bundled with new fertilizers (Leathers & Smale 1991). Such
innovations may require a sequential or step-wise approach, underpinned by learning
rather than economic factors (Kaine & Lees 1994).
Agricultural economists and rural sociologists have used the classical diffusion model
as the basis for rural innovation adoption studies. Rural sociologists and agricultural
economists have tended to approach the adoption of new agricultural technologies as an
individual ‘innovativeness’ problem (Rogers 1983; Lindner 1987).
Figure 2.2 shows the central proposition of innovation diffusion theory that not all
individuals in a social system adopt a new technology at the same time. Rogers (2003)
classified members of a social system into five groups depending on when they adopted
a new technology. Innovators are the first users and are seen as venturesome and as
having a strong desire to try new ideas (Rogers & Shoemaker 1971). Early adopters,
the next set of users, are well respected and imitated by their peers. Innovators and
early adopters are usually classified as ‘progressive farmers’.
The early majority think carefully before adopting an innovation and rarely assume an
adoption leadership position. The late majority tend to be sceptical about innovations
- 20 -
and wait until innovations have been widely diffused across the social system before
adopting. Finally, ‘laggards’ either reject a new technology outright or are the last to
adopt. They have traditional values and tend to look to the past as a point of reference
for the future (Rogers & Shoemaker 1971).
Figure 2.2: Adopter segments (source: Rogers 2003)
The largest body of innovation diffusion research has examined the characteristics of
early adopters of new technologies, looking for the correlates of ‘innovativeness’
(Rogers 2003). The critical assumption behind this research is causal as it is assumed if
we can make people resemble innovators, they will adopt innovations relatively early
and at higher rates. Rogers (2003) calls this perspective a ‘pro-innovation bias’ as it
assumes all members of a social system will adopt a new technology. Assessing the
‘innovativeness’ of adopters by relating past adoption behaviour to current individual
characteristics presents a problem as individuals change over time. Therefore, it is not
always clear how or why these personal characteristics explain innovation adoption
behaviour (Rogers 2003).
Innovation adoption and diffusion studies suggest earlier adopters are influenced more
by mass media communication, whereas later adopters rely more on interpersonal
sources of communication (Rogers 2003). Earlier adopters of new technologies make
Early Adopters (13.5%)
Early Majority (34%)
Late Majority (34%)
Laggards (16%)
Time
Innovators Progressives
(2.5%)
- 21 -
more rational assessments of new technologies, have more realistic expectations of the
performance of new technologies, superior cognitive and technical skills to utilise a new
technology more effectively and are therefore less likely to be dissatisfied with it,
compared with relatively late adopters (Rogers 2003). Relatively late adopters of new
technologies are more likely to discontinue using a new technology because of their
unrealistically high expectations of the performance of the technology and their inability
to achieve expected benefits from it (Rogers 2003). For example, Carletto, de Janvry
and Sadoulet (1996) found relatively early adopters of non-traditional export crops
continued to cultivate these crops longer than did relatively late adopters. Early
adopters benefited from the high profits associated with the early adoption of new crops
and from a longer period of learning from the experience that enabled them to increase
their skills and knowledge of the new varieties.
The ToT model presents agricultural innovation as a simple hand-over of new
technologies from researchers to extensionists to farmers. This approach to innovation
is predicated on an assumption of technological necessity, in which science, technology,
markets and adopters are welded into an objective and interlocking causal chain
(Scarbrough & Corbett 1992). Implicit in this approach is the assumption that reality is
objective and that the scientific method, in the form of basic and applied research, can
be used to understand reality and translate problems into appropriate new technologies
(Röling 1996). It also assumes new technologies apply equally to all farmers and that it
is in their best interests to adopt the new technologies, thus guaranteeing a successful
technology transfer (Buttel, Larson & Gillespie 1990).
Although the linear, determinist approach to agricultural innovation emerged in
agricultural research and practice in the 1940s, it was the application of this approach to
the transfer of new crop varieties in the 1970s that assured its dominance in the field of
agricultural innovation. Agricultural research institutions had great success in the 1970s
- 22 -
in breeding and transferring new, high-yielding crop varieties using the ToT approach.
The success of these crops led to the ‘Green Revolution1’, which substantially changed
the face of crop production (Pardey, Rosebom & Anderson 1991). ‘Green Revolution’
high yielding, new crop varieties were highly observable, easy to use and provided
substantial returns on investment for primary producers. The strength of the top down
approach to transferring ‘Green Revolution’ crops suggested this approach could
achieve the rapid and widespread adoption of a new crop variety and rapid, high rates of
return on research investment (Lipton 1994).
Limitations of technology determinism in agricultural innovation
In the sociology and economics literature the appropriate theoretical approach to
understanding adoption of new agricultural technologies has been the subject of some
debate. The apparent failure of the traditional information-based diffusion model to
successfully explain the adoption of conservation practices specifically has been
attributed largely to their lack of perceived profitability (Pampel & van Es 1977).
Attempts have been made to apply the classical diffusion model to conservation
innovations. Saltiel, Bauder and Palakovich (1994), for example, overcame problems
associated with assumed profitability by incorporating farmers’ perceptions into the
adoption model. With the inclusion of variables such as farmers’ perceptions,
conservation innovation research has moved towards the use of social psychology
models to explain farming and conservation behaviour (e.g. Beedell & Rehman 2000;
Lynne, Shonkwiler & Rola 1988; Willock 1999; Willock et al. 1999).
Despite the widespread acceptance of linear, determinist approaches to agricultural
innovation in both research and practice, the successful transfer of technologies from
1 The ‘Green Revolution’ was a significant increase in agricultural productivity as a result of the introduction of high-yielding crop varieties, the use of pesticides and improved farm management practices.
- 23 -
scientists to farmers using this approach has been problematic (Howden et al. 1998;
Röling 1988; Black 2000). The application of the ToT approach has been blamed for
the failure and over-adoption of new technologies and has been seen as contributing to
uneven rural development and environmental degradation (Vanclay 1994). Some top-
down approaches to agricultural innovation have overwhelmed farmers with new
technologies, many of which are not suited to their farming context (Ruttan 1996;
Dunn, Gray & Phillips 2000). The ToT approach to agricultural innovation has also
been criticised for assuming all farmers will adopt a new technology, for holding the so-
called ‘progressive’ farmers in high regard and for viewing later adopters disparagingly
as “laggards” (Vanclay 1994; Fliegel & van Es 1983; Chamala 1987; Röling, Ashcroft
& Chege 1976; Clark & Lowe 1992; Long & van der Ploeg 1989; Röling 2004;
Scoones & Thompson 1994; Kloppenburg Jr 1991; Chambers 1983).
A major constraint on the successful transfer of new technologies through the ToT
model is a lack of feedback either up or down the innovation pipeline. Researchers
have little or no direct contact with end-users or the technology during adoption and
implementation. Given the long chain of linkages in the ToT model, feedback from
adopters may not be possible or may be corrupted in transmission (Horton & Prain
1989). Without direct contact or useful feedback from adopters, the scientific
community may fail to understand the local context in which new technologies are
adopted and implemented, resulting in poor technology and end-user fit (Chambers &
Jiggins 1986; Biggs 1989; Crouch 1981).
Understanding the context in which a new agricultural technology will be used is
critical to successful technology transfer (Aubry, Papy & Capillon 1998). Kaine and
Lees (1994, p. 2) argued that “[i]f the advantages of a new farming practice can only be
fully captured when that practice is used in conjunction with some particular mix of
existing techniques and practices, then the ‘value’ of that new practice will vary across
- 24 -
farmers depending on their mix of practices and techniques.” They defined the farming
‘context’ as the production resources, practices and technologies on a farm and the
characteristics of the farmer.
The separation of researchers from the farming context, which is inherent in the top-
down approach to agricultural innovation, can constrain the adoption and
implementation of even the simplest agricultural technology. For example, the
application of the ToT model to the transfer of new, high-yielding crop varieties did not
result in high levels of adoption of these varieties among resource-poor farmers
operating in highly variable farming contexts (Chambers & Jiggins 1986). In this
instance, researchers and extensionists did not take different farming contexts for high-
yielding crop varieties into account and so did not adapt these technologies
appropriately. However, while Frank (1995a, p. 319) argued the top down, technology
transfer approach to agricultural innovation “has little relevance in increasingly
complex agricultural systems,” this approach continues to dominate agricultural
innovation research and practice (Douthwaite 1999; Douthwaite, Keatinge & Park 2002;
Qamar 2002).
2.2 Determinist perspectives of agricultural innovation adoption
From a technological determinist perspective, the main role of technology developers
and extensionists is to ensure the adoption of new technologies. As described in the
previous section of this chapter, the underlying assumption of the technological
determinist perspective is that new technologies will replace inferior technologies by
virtue of their superiority (Rogers 2003). Therefore, a great deal of agricultural
innovation research and practice assumes the adoption and use of new technologies is a
relatively simple process (Black 2000).
- 25 -
The dominance of technology determinism in agricultural innovation research is
reflected in the linear, staged conceptualisations of the adoption of new technologies,
(e.g. Kennedy 1977; Rogers 2003; Jones 1967). Stages in adoption behaviour have
been described using various terms (Beal, Rogers & Bohlen 1957; Lindner, Pardey &
Jarrett 1982; Rogers 1962). Essentially they relate to the information status of the
potential adopter at a given point in time. In linear adoption models the adoption
process begins with the potential adopter becoming aware of the innovations existence
and ends with the decision to adopt or reject the innovation.
There is some dispute between researchers as to the number and nature of stages that
can be identified in the adoption process. Beal, Rogers and Bohlen (1957) initially
suggested that up to five stages in the adoption process may be identified from
awareness to adoption. Whereas Lindner, Pardey and Jarrett (1982) referred to three
stages in the innovation adoption process: discovery, evaluation and trial.
The most widely used staged adoption model is Rogers’ (2003) innovation-decision
model, shown in Figure 2.3, which describes five psychological states people pass
through as they become aware of a new technology. These stages are:
1. Knowledge – coming from an exposure to the technology and learning about
how it functions.
2. Persuasion - in which an attitude is formed about the technology, based on
perceptions of its attributes or characteristics.
3. Decision – when the choice is made to adopt or reject a technology “as the best
course of action available” (Rogers 1995, p. 171).
- 26 -
4. Implementation – when the first use of the technology is undertaken (Dodgson
& Bessant 1996).
5. Confirmation – when additional information about the technology is sought to
evaluate it and to make decisions about its continued use (Mason 1964; Black
1980).
Figure 2.3: The Innovation-Decision process (Source: Rogers 2003, p. 170)
In Rogers’ (2003) innovation-decision model, the decision to adopt a new technology
reflects potential adopters’ perceptions of the technology’s attributes. Perceptions are a
product of the environment, organisational processes and structures and people’s
dispositions (Weick 1995). Perceptions of the attributes of a new technology are
developed through the synthesis of acquired information, persuasion and observation of
the technology in action. The five key perceived technology attributes that underpin the
innovation-decision process are:
I. Knowledge II. Persuasion III. Decision IV. Implementation V. Confirmation
1. Adoption
2. Rejection
Continued Adoption
Later Adoption
Discontinuance
Continued Rejection Characteristics of the
decision-making unit
1. Socio-economic 2. Personality 3. Communication
behaviour
Perceived characteristics of the innovation
1. Relative advantage 2. Compatibility 3. Complexity 4. Trialability 5. Observability
Communication Channels
Prior conditions 1. Previous practice 2. Felt needs/problems 3. Innovativeness 4. Social system
norms
- 27 -
1. Relative Advantage – the degree to which a new technology is perceived as
better than the technology it supersedes (Rogers 2003). The greater the
perceived relative advantage, the more likely it is that the innovation will be
adopted (Rogers 2003; Tornatzky & Klein 1982).
2. Compatibility – the degree to which a new technology is perceived to be
consistent with the existing values, past experiences and the needs of potential
adopters (Rogers 2003). The greater the perceived compatibility, the more
likely it is the innovation will be adopted (Rogers 2003; Tornatzky & Klein
1982).
3. Complexity – the degree to which a new technology is perceived to be difficult
to understand and/or use. The more complex a new technology is perceived to
be, the less likely it is that it will be adopted (Rogers 2003; Kuhlmann &
Brodersen 2001).
4. Trialability – the degree to which a new technology can be tried before a final
decision is made. Technologies that can be trialled are more likely to be adopted
(Öhlmér, Olson & Brehmer 1998; Leathers & Smale 1991).
5. Observability – the degree to which the results of a new technology are visible
to others. The easier it is for people to see the results of a new technology, the
more likely it is they will adopt it themselves (Rogers 2003; Tornatzky & Klein
1982; Pannell 2001).
Limitations of linear innovation adoption models
Linear, staged technology adoption models generally view adopters as passive
recipients of a new technology, operating in stable environments (as can be seen in
Figure 2.3). Such models do not consider cycling between stages, end user feedback,
- 28 -
symbolic adoption or discontinuities in the innovation-decision process (Simon 1977).
Technological complexity is assumed to be the major constraint to successful adoption;
therefore the actions and context of the end user are largely assumed or ignored
(Biemans 1992). In an agricultural innovation context, these assumptions are
particularly problematic as farm businesses are complex and heterogeneous (Tanewski,
Romano & Smyrnios 2000; Gamble et al. 1995; Lloyd & Malcolm 1997; Gasson &
Errington 1993; Kaine, Crosby & Stayner 1997; Kimhi & Bollman 1999; Scott &
Cacho 2000; Burling 2000; Janelid 1975; Kebede 1993; van der Ploeg 1993). For
example, decisions made by Australian woolgrowers are influenced by price trends, the
relative return of alternative enterprises, enterprise structure, experience, historical
enterprise performance, the strategic orientation of the farmer and livestock numbers
(Murray-Prior & Wright 2001).
It is worth mentioning that Rogers (2003) offered a word of caution about the
application of linear adoption models as he argued that a definitive model of the
innovation-decision process would be difficult to develop due to the complex nature of
the adoption process and that the innovation-decision model was constructed to simplify
this complex process. Rogers (2003, pp. 187-188) argued that adopters are active,
rather than passive, participants in the innovation process as they “shape [the new
technology] by giving it meaning as they learn by using the new idea.”
Experiential Learning
Lindner (1987, p. 147) argued that the most significant problem in agricultural
innovation research is “a conceptual failure by most authors to appreciate the
significance of the dynamic learning process underpinning innovation adoption.” A
number of recent studies have highlighted the central role played by end users’
experiential learning. For example, the Grasslands Pasture Productivity project used a
- 29 -
paired paddock model that enabled farmers to compare two management systems side-
by-side on their farm. Trialling productive pasture systems on a small proportion of the
farm through the paired paddock model helped build farmers’ confidence in and
knowledge about new pasture management technologies. The researchers concluded
producers need to be involved “in a detailed action-learning experience that disposes or
modifies their existing beliefs” in order for them to integrate new technologies into their
own farming context (Trompf & Sale 2000, p. 555).
Martin and Sherington (1997) found evidence of farmers’ experimentation, learning and
adaptation in an ActionAid2 research project in which participants adapted on-farm
trials and set their own performance criteria. In this case, what was observed was how
indigenous knowledge impacted on the experimentation with and the adaptation of new
technologies. Experimentation occurred on-farm as a continuous action-reflection
process that used stimuli from personal experience and past performance assessment.
Cameron (1999) examined the dynamic, experiential learning process that influenced
the adoption of high yielding varieties of cotton seed in Indian villages. She found that
farmers were uncertain about the relative profitability of the new seeds and learned
about the new seeds from their own production experience. Knowledge about the new
seed varieties was updated after each season of cultivating the new crop. In this
instance, learning from experience on-farm positively influenced the use of the crop as
the proportion of land planted to new seed varieties increased over time.
Social Learning
In an agricultural innovation context, farmers do not act alone when deciding about new
technologies as they are embedded in evolving social networks that generate, absorb,
2 ActionAid is an international anti-poverty agency working with partner agencies in developing countries (www.actionaid.org)
- 30 -
share and transform new knowledge through social interaction (de Souza & Busch
1998). Juska and Busch (1994) argued that innovation is only likely to be effective if
networks are constructed to extend beyond the laboratory and across relevant members
of a social system and that these networks are subject to change and renegotiation.
Röling (1985) termed such networks Agricultural Knowledge and Information Systems,
which were seen as interlocking sets of sub-systems of researchers, extensionists and
end-users that generate, absorb, share and transform knowledge.
Social learning in an agricultural innovation context has usually been examined in terms
of the role external ‘change agents’, such as extensionists, play in the innovation
decision process and has linked these ‘change agents’ to the adoption and
implementation of new technologies (Wilkening 1952b; Leuthold 1967; Moser &
Barrett 2003). Wilkening (1952b) observed that contact between adopters and
‘credible’, external sources of information, such as agricultural agencies, increased the
likelihood of the continued use of a new technology, while Leuthold (1967, p. 10)
pointed out that although interactions between farmers influenced initial adoption
decisions, “communication with more technical sources may function both as an
instrumental communicatory response in affecting initial use and a consummatory
response affecting continued use.”
Foster and Rosenzweig (1995) studied the effects that learning from experience and
learning from others had on the adoption and profitability of high yielding varieties of
rice and wheat in India. Adopters with experienced neighbours were found to be more
profitable than those with inexperienced neighbours. The level of neighbour experience
had twice the impact on profitability of new seed varieties than did the adopter’s own
experience.
- 31 -
The empirical studies discussed in this section suggest the adoption and use of new
agricultural technologies is a dynamic, adaptive process that is influenced by individual
cognitions, learning from experience and social interactions (Foster & Rosenzweig
1995; Martin & Sherington 1997; Cameron 1999; Leuthold 1967; Trompf & Sale
2000). Although experiential and social learning processes have been explored in
agricultural innovation research, adoption and adaptation have been assumed to involve
unproblematic learning processes that occur after the technology has been accepted by
an end-user. Learning is seen as a by-product of innovation activity. Studies of the
adoption and adaptation of new agricultural technologies often imply situational
stability. However, the adoption of a new technology is often accompanied by
simultaneous changes, transitions or crises in the end user’s own context.
Situational variety and transition
Situational variety and transition are central features of agricultural enterprises.
Farming enterprises operate in highly variable biophysical, economic and social
environments (Aubry, Papy & Capillon 1998). They are transitional, as they are
constantly developing and evolving in response to internal pressures and externalities,
such as fluctuating commodity prices and climatic events (Crouch 1981). The
transitional nature of farm enterprises can be seen in Frank’s (1995a; 1995b; 1999)
study of Northern Australian cattlemen. He observed the ongoing transition of cattle
properties as producers responded to destabilising events and declining returns with a
range of interventions, including cost reductions, skills development and the adoption
and implementation of improved management practices. Cattlemen assessed new
technologies in terms of potential cost and benefit and, if advantageous, whether they
had the resources needed to implement the technology (Frank 1995a; Frank 1995b;
Frank 1999).
- 32 -
Kaine and Lees (1994, p. 7) described the ongoing transition of farm enterprises as a
response to emerging economic forces and new production practices and technologies.
He pointed out that farm development is not immune to disruptions that trigger the
advancement, postponement and abandonment of development plans. In a study of
French cropping systems, Aubry, Papy and Capillon (1998) found that, despite the
existence of cropping plans, there was a substantial difference between planned and
actual cropping practices as a result of changing climatic conditions, the economic
viability of plans, external system shocks (such as disease outbreaks) and farmers’
preferences. They concluded that cropping systems evolved as the farmer learned from
and adapted to changing internal and external conditions.
Predictive behavioural models and innovation
Theories and concepts from social psychology have been applied to the study of
innovation adoption behaviour in the form of predictive models of human behaviour
that seek to quantify the impact of attitudes, social norms and behavioural intentions on
actual behaviour. Many of the behavioural models developed by social psychologists
have followed an expectancy value form. That is, the expectancy or probability that an
action will be followed by a particular consequence indexed by the subjective value or
utility placed on the consequence (Feather 1982). The most widely used of the
expectancy value models is the Theory of Reasoned Action developed by Ajzen and
Fishbein (1980). The Theory of Reasoned Action (TRA) was developed to predict and
explain “virtually any human behaviour” (Ajzen & Fishbein 1980, p. 4). According to
Ajzen and Fishbein (1980) an understanding of the factors that cause behaviour is
necessary for predicting behavioural change.
According to the TRA framework shown in Figure 2.4, individuals performing a
specific behaviour will act in accordance with their intentions, so that if behavioural
- 33 -
intentions are known, actual behaviour is easy to predict. However, intention alone
does not explain the reasons for behaviour; instead an understanding of the determinants
of these intentions is critical in order to understand behaviour. In the TRA, an
individual’s intention is a function of a personal determinant and a social influence
determinant. The personal determinant is the individual’s positive or negative attitude
towards performing the behaviour, and the social influence determinant or subjective
norm is the perception that an individual has of the social pressures to perform the
behaviour. The explanatory power of the TRA lies in its ability to assess the relative
importance of personal and social determinants of behavioural intention. Therefore, this
approach provides an opportunity to understand why individuals choose to behave in a
certain way.
Figure 2.4: Theory of Reasoned Action (source: Ajzen & Fishbein 1980)
The TRA, shown in Figure 2.4, predicts intention to perform a target behaviour from
two factors:
1. Attitude towards the behaviour, which measures the degree to which an
individual has a favourable or unfavourable evaluation of the target behaviour.
2. Subjective norm, measuring the influence of significant others in respect of the
target behaviour (Ajzen & Fishbein 1980).
Beliefs
Attitude
Subjective Norm
Behavioural Intention
Behaviour
Evaluation
Normative Beliefs
Motivation to Comply
External V
ariables
- 34 -
Attitude towards the target behaviour is predicted by salient beliefs about performing
the behaviour; this is weighted by the individual’s evaluation of the likelihood that
performing the target behaviour will result in a specific outcome. Subjective norm is
predicted by normative beliefs about what the individual’s referent group would feel
about them performing the target behaviour. Subjective norm is weighted by the
individual’s motivation to comply with the beliefs of their social referents (Ajzen &
Fishbein 1980).
Ajzen and Fishbein (1980) argue that all other factors affecting the intention to perform
target behaviour such as demographics, personality and past experience are mediated
through the endogenous belief variables, making the model alone a sufficient predictor
of behavioural intention. The extent to which the TRA succeeds in predicting
behavioural intention has largely been evaluated using multiple regression analysis.
Icek Azen developed the Theory of Planned Behaviour (TPB) as an extension of the
TRA (Ajzen 1985) to address limitations of the original model in terms of the role of a
person’s volitional control over their behaviour. The TPB dealt with the issue of
volition by proposing that behavioural intentions influence behaviour if the person
performing the behaviour perceives that they have volitional control over their actions
(Ajzen 1988; 1985; 1991). In other words, they perceive that they have the ability to
decide whether or not to perform the target behaviour such as the adoption and use of a
new technology. Figure 2.5 shows the TPB as a structural model with behavioural
intention as the central factor and perceived behavioural control as an antecedent to
intention and actual behaviour.
- 35 -
Figure 2.5: Theory of planned behaviour (source Ajzen 1985)
The influence of social psychology on agricultural innovation research and practice can
be traced back to the work of Rural Sociologist Eugene A. Wilkening in the 1950s and
1960s (e.g. Wilkening & Guerrero 1969; Wilkening 1953; Wilkening 1952a;
Wilkening 1954a; Wilkening 1954b). Wilkening examined the agricultural innovation
process in relation to family values, beliefs about new technologies, farming aspirations
and the role of change agents. More recent work incorporating the social psychology
theories of Ajzen and Fishbein (1980) and Ajzen (1985) has demonstrated the influence
of attitudes on agricultural innovation adoption behaviour (e.g. Ervin & Ervin 1982;
Lynne, Shonkwiler & Rola 1988; Carr 1988; Willock 1999; Willock et al. 1999;
Beedell & Rehman 2000).
Social psychology models such as the TRA and TBP have been employed by
agricultural innovation researchers in an attempt to better understand complex
innovation scenarios such as the adoption of new conservation technologies. For
example, in a study of soil conservation technology adoption, Ervin and Ervin (1982)
Attitude toward the behaviour
Subjective norm
Perceived behavioural control
Intention Behaviour
- 36 -
proposed that farmers’ attitudes towards general environmental issues would influence
their farm conservation actions. Lynne, Shonkwiler and Rola (1988) found that attitude
to conservation was significantly related to soil management efforts.
Carr (1988) used the TRA to examine differences in attitude and social influence
between farmers and conservationalists in Bedfordshire in the early 1980s. She found
that statements of general attitudes were poor predictors of specific behaviours. There
appeared to be few differences between the groups but sharp differences did emerge
when conservation was discussed in relation to specific behaviours. Therefore, the
application of such behavioural models is more likely to be successful when applied to
the study of a specific behaviour rather than measurement of general attitudes.
Beedell and Rehman (2000) applied the TPB to an examination of the underlying
determinants of farmers’ attitudes and behaviours in response to conservation-related
issues. They found that farmers with greater environmental awareness and involvement
in conservation groups were more influenced by conservation issues than management
concerns compared with other farmers. The research of Willock et al. (1999) also
explored farmers’ attitudes and behaviours in relation to environmentally oriented
farming, production behaviour and stress. They developed scales for the measurement
of famers’ attitudes, goals and behaviours and the correlation between these domains
and farmers’ personality traits.
Although behavioural models such as the TRA and TPB have been found to be robust
and reliable predictors of intentions and behaviour (Sheppard, Hartwick & Warshaw
1988), these models are based on the assumption that human beings are rational and
make systematic use of information available to them. In other words, that the
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behaviour in question is purely volitional. Expectancy value models such as the TRA
and TPB also assume the circumstances of the individual do not change between their
intention to perform a particular behaviour and their actual behaviour. However, as has
previously been discussed, potential technology adopters are not necessarily rational,
passive actors, operating in stable environments. They may engage in experiential and
social learning and experience transition in their environment that may disrupt the
relationship between their intentions and their actual behaviour. Therefore, these
predictive models may be inappropriate when examining post-adoption behaviour.
The failure and discontinuance of agricultural technology
Although staged innovation-decision models identify a number of stages both prior to
and after the adoption of a new technology, most agricultural innovation research has
focused on the adoption decision, which is often modelled as a dichotomous dependent
variable. This is despite scholars such as Schutjer and Van der Veen (1977, p. 14)
arguing that “the major technology issues relate to the extent and intensity of use at the
individual farm level rather than the initial decision to adopt a new practice.”
Agricultural innovation research that examines post-adoption behaviour has highlighted
the significant challenge of getting adopters to implement and use a new technology
(e.g. Carletto, de Janvry & Sadoulet 1996; Neill & Lee 2001; Moser & Barrett 2003;
Barnett & Sneddon 2006b; Barnett & Sneddon 2006a). This research has examined the
factors that impact on the decision to continue or discontinue using a new agricultural
technology. As examples:
1. New non-traditional agro-export crops that were promoted to Guatemalan
farmers as a means of overcoming depressed market conditions for traditional
export crops, improving export earnings and reducing rural poverty but were
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soon abandoned because of increasing land degradation and negative market
shocks (Carletto, de Janvry & Sadoulet 1996).
2. While approximately 65 per cent of Honduran maize farmers had adopted an
innovative rotation system in the early 1990s, 45 per cent of the adopters had
abandoned the system by 1997 due to their production orientation, increasing
weed management problems and a lack of appropriate infrastructure (Neill &
Lee 2001)
3. A rice intensification (SRI) system in Madagascar with proven productivity
gains that was supported through extensive extension efforts did not succeed as
was expected. The rate of adoption of the SRI was low and discontinuance was
high, often when extension services were withdrawn from adopters’ local areas
(Moser & Barrett 2003).
The studies of adaptation, transition and discontinuance described in this section
suggest agricultural innovation is an iterative, ongoing process that is influenced by the
farming context, individual cognitions, social factors and environmental conditions. In
order to understand how the use of new agricultural technologies is shaped, a deeper
understanding of the socio-cognitive mechanisms underlying the technology actions of
participants in the innovation process is required.
Such approaches can be found in the social constructivist perspectives of technology
that have emerged since the 1970s (T. Pinch & W.E. Bijker 1987; Bijker 1987). Social
constructivists reject technological determinism and embrace indeterminacy (Bijker
1987), arguing that technologies do not emerge from a single social determinant or
through a predetermined logic and that all of the impacts of a technology cannot be
anticipated (Sproull & Keisler 1991). The social-constructivist perspectives of
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innovation examined in the following section of this chapter offer a useful complement
to the study of the innovation process.
2.3 Social constructivist perspectives of agricultural innovation
In the 1980s, technology studies began to draw on a combination of social and historical
theories and methods to generate and test theories of socio-technical change (Bijker
1987). Socio-technical systems theorists developed an interpretive approach
challenging the assumption that technological change is a linear process (T. Pinch &
W.E. Bijker 1987). The emergence of socio-technical research marked a move away
from technological determinist, linear conceptualisations of innovation and technology
in which the inventor was perceived as the ‘genius’ and end users were perceived as
passive recipients of technologies (Bijker 1987).
Most technological determinist focused innovation studies have a pro-innovation bias
(T. Pinch & W.E. Bijker 1987; Abrahamson 1991). They contribute to our
understanding of the conditions for success, but largely ignore technology failure (T.
Pinch & W.E. Bijker 1987). The pro-innovation bias and a focus on successful
technologies have led some scholars to assume ‘the success of an artefact is an
explanation of its subsequent development’ (T. Pinch & W.E. Bijker 1987, p. 22). No
further analysis is presumed to be required if a technology is successful. This
retrospective approach does not tell the full story of how and why new technologies
succeed or fail (T. Pinch & W.E. Bijker 1987). On the other hand, in socio-technical
studies, technology is seen as an explanandum (i.e. the thing to be explained) rather than
an explanans (i.e. the thing doing the explaining) (Bijker 1999).
The epistemological stance on interpretive research is that knowledge of reality is
gained only through social constructions such as languages, shared meaning and
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artefacts (Walsham 1993). The interpretive paradigm takes a subjective view of a
reality that is socially constructed (Hatch 1997). Berger and Luckmann (1966) argued
that human reality is created through interpersonal negotiations and implicit
understandings that are developed through shared histories and experiences. Patterns of
meaning are given to activities through this process of interpretation, and people then
assume that those patterns of meaning that they impose on the world stand apart from
the interpretations that produced them (Berger & Luckmann 1966). The interpretive
perspective of technology discussed in this section is referred to as ‘social
constructivist’ (T. Pinch & W. Bijker 1987, p. 19).
The context in which new products and technologies are developed, and the effects
cultural norms, social relations and power have on technology design and use are also
examined in socio-technical research (T. Pinch & W.E. Bijker 1987). Socio-technical
perspectives of innovation are based on the assumption that the social and the
technological mutually constitute each other. In other words, technological artefacts are
socially constructed and interpreted and there is no one single best way for a
technological artefact to be developed and used. By extension, different social forms
influence the development and use of different technologies (T. Pinch & W.E. Bijker
1987). In contrast to the technological determinist perspective of innovation, social
constructivists view technologies as variables that are interpretively flexible and
socially designed and constructed and recognise their use can be manifested in different
ways to suit different organisations (Bijker 1999; Clark & Staunton 1989).
Technologies can be equivocal because they combine physical artefacts with formal and
functional characteristics and with the knowledge and cognitions developed as the
technology is used, enacted and interpreted (Weick 1990). Technology includes not just
what is tangible but also what people believe is possible. Technologies embody
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routines for testing and normative values that are used to evaluate the technology as it
evolves (Faraj, Kwon & Watts 2004). This subjective component of technology is
described by Orlikowski and Gash (1994) as a technology frame. Technologies are seen
as social entities that are open to different interpretations by different people whose
interpretations are underpinned by their own frames of reference (T. Pinch & W.E.
Bijker 1987). The construction of technology “occurs within a social and historical
context encrusted with embedded interests and ideologies about what problems can or
should be solved by this technology” (Munir 2002, p. 1409).
Three approaches have been suggested by socio-technical researchers, namely:
1. Technological systems.
2. Actor-network theory (ANT).
3. The social construction of technology (SCOT) (Bijker 1987).
The systems approach emerged from studies of technology history and suggests there is
a need for technological artefacts to be seen as parts of larger systems that include an
organisational component, scientific artefacts, legislative artefacts and natural resources
(Hughes 1987). Like the technological systems approach, actor-network theory
suggests the fate of technological artefacts is determined by the clash of opposing
systems. However, ANT is distinguished from the systems approach through the
central concept of the ‘radical indeterminacy’ of network actors as no distinction is
made between human and non-human actors in a network (Callon 1987; Callon & Law
1997). Network actors can enrol other actors or be enrolled in a network by other actors
(Callon & Law 1997). Network actors are enrolled into projects by those with power by
‘boxing in’, ‘speaking on behalf of’, or ‘borrowing from others’ (Law 1986; Law &
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Bijker 1992). In an actor-network, new actors can replace incumbents by building
stronger networks (Woolgar 1996).
Within the SCOT approach, the social construction of technologies occurs not only in
terms of their use but also in terms of their design and technical content (Bijker 1987).
The SCOT approach suggests an understanding of the problems and solutions that are
attributed to technologies by relevant social groups is needed to explain why they
‘work’ (Bijker 1987) and “the meanings given by a relevant social group actually
constitute the artefact” (Bijker 1999, p. 77) (emphasis in the original). Three SCOT key
concepts relevant to the present study are interpretive flexibility, closure and relevant
social groups (Bijker 1987).
Constant (1980) suggested the development of technological artefacts is an evolutionary
process; an alternation between socially constructed variation and selection. This
evolutionary approach lets researchers examine how some artefacts fail and some
succeed. Over time, “interactions with an artefact, within and between relevant social
groups, results in the creation of a technological frame that bounds the attribution of
meanings by relevant social groups” (Bijker 1999, p. 282). Where one technological
frame dominates, new artefacts can replace previous technologies if they provide a
better solution to the problems highlighted by that frame. However, if a new
technology is constructed as a solution in one frame and a problem in another, its
success or failure will be determined by negotiation and a contesting of meanings
between relevant social groups (Bijker 1999).
Each of these social constructivist approaches to the study of technology emphasises the
importance of providing a ‘thick description’ of technological artefacts by opening the
‘black boxes’ of technology and society and examining their contents simultaneously
(Bijker 1987). Thick descriptions of the social construction of technologies enable
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detailed information about the technical, social, economic and political aspects of the
success or failure of a technology to be collected and analysed (Bijker 1987).
Empirical socio-technical research in an agricultural and rural context
Social constructivist approaches have been widely used in the study of the transfer,
adoption and implementation of new information systems technologies (e.g. Seligman
2000; Seligman 2006; Choo & Johnston 2004; Faraj, Kwon & Watts 2004; Guney
2004; Theoharakis & Wong 2002; Dougherty et al. 2000; Griffith 1999; Prasad 1993;
Orlikowski & Gash 1994; Barley 1986; Leonard-Barton 1988). However, despite calls
for constructivist approaches to rural research as early as the 1970s (Falk & Pinhey
1978), the use of social constructivist perspectives in the study of agricultural
technologies and products is a recent phenomenon and remains limited in scale and
scope. Since the 1990s, a number of scholars have examined the social construction of
landscapes and the environment (Greider & Garkovich 1994; Tax 1990), new
commodities (Tanaka, Juska & Busch 1999; de Souza & Busch 1998), conservation
practices (Coughenour 2003; Coughenour & Chamala 2000; Lockie 1997b; Lockie
1999) and farming styles (Howden & Vanclay 2000; Vanclay et al. 2006). For
example, Greider and Garkovich (1994, p. 2) suggested landscapes mean different
things to different people and are constructed as a result of “negotiating new symbols
and meanings.” The meanings given to a landscape are filtered through people’s self-
beliefs and values as “cultural groups continue to reconstruct and redefine their realities
– past, present and future – through ongoing social interactions, which may be thought
of as negotiations over meaning” (Greider & Garkovich 1994, p. 6).
Social interactions can result in shared world views about landscapes and the
environment that become deeply ingrained in how group members define themselves
(Tax 1990). This structure of beliefs and self perceptions can change; for instance,
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when triggered by the introduction of new technologies (Tax 1990). Greider and
Garkovich (1994, p. 2) argued “when events or technological innovations challenge the
meanings of…landscapes, it is our conceptions of ourselves that change through a
process of negotiating new symbols and meanings.” Therefore, interactions between
people, technologies and the environment can result in new conceptualisations of ‘self’.
Tanaka, Juska and Busch (1999) and de Souza and Busch (1998) described the social
construction of agricultural innovation in their respective studies of the construction of
rapeseed and soybean sub-sectors. In Tanaka, Juska and Busch’s (1999) study of the
globalisation of agricultural production and research in the case of the rapeseed sub-
sector they treated the relationships between agricultural research and production as
reciprocal and contingent. There were reciprocal relationships between farmers,
scientists, processors and government bureaucrats, among others. The transformation of
rapeseed production and research at the national and global levels was contingent upon
these social interactions (Tanaka, Juska & Busch 1999). De Souza and Busch (1998)
also found the introduction of soybeans into Brazil required significant changes in both
the crop and the people working with it. In other words, the technology (the soybean
crop) and the social system (the research, production and consumption networks) were
simultaneously socially constructed and reconstructed.
Coughenour (2003), Coughenour and Chamala (2000) and Lockie (1997b; 1999)
examined the social construction of new conservation technologies and practices.
Coughenour and Chamala (2000) explored the social construction of new conservation
technologies in America and Australia and found the reconstruction of farming
practices, cropping systems, farming systems, landscape visions and farmer identity
were a result of the social construction of new conservation technologies. They noted
that systems innovations, such as conservation farming, cannot be directly transferred
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from scientist to end user but need to be constructed through the social interactions of
network actors.
Coughenour (2003, p. 280) emphasized the social nature of innovation, arguing “entities
and actors take on or are ascribed meanings as a result of social interaction.” He
suggested the social construction of no-tillage cropping systems had created and
sustained new social networks and relationships between the land, farmers, advisors,
agents, new techniques and scientists. The no-tillage system that emerged from these
evolutionary actor networks combined local knowledge and new conservation cropping
technologies and science. Consequently, he concluded that when a new technology is
constructed and reconstructed in a specific farming context, the capacity of the adopter
to combine local and generic knowledge, experience and resources creates the
components of the technology (Coughenour 2003).
Participation in the construction of new conservation technologies also influences the
participants’ identity construction3. For example, the widespread involvement of farm
families and rural communities in the Australian Landcare4 movement has been
associated with changes in the identities of farm women. Lockie (1997b) observed farm
women involved in Landcare increasingly rejected being identified as ‘helpers’,
‘housewives’ and ‘offsiders’ and, instead, identified themselves as farmers in their own
right. Lockie (1997a) argued that, in the case of Landcare and similar networks,
participants adopt identities that challenge traditional notions of what constitutes good
farming practice but reinforce farmers’ innovative and progressive self-identities.
In a further study of the Australian Landcare movement, Lockie (1999) found Landcare
had become a vehicle for the social construction of ‘good’ farming practice.
3 The influences of technology on identity construction are further explored in Chapter 3, pp 57-60. 4 Landcare Australia is a partnership between the community, government and business working together to protect and repair the environment (www.landcareonline.com)
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Perceptions of ‘good’ farming practices were tied up in the promotion of conservation
farming packages. Landcare was also found to have been used as a “discursive
resource” to promote high-input farming practices and technologies by sponsors and
other program stakeholders (Lockie 1999). The Landcare movement was used as a
‘sense giving’ resource by stakeholders who wished to shape rural community views of
the landscape and to promote their own organisations, products and policy agendas.
Social constructivist theories have been applied to the ‘categorisation’ of farmers and
farming practices. In the 1990s, Van der Ploeg introduced the concept of ‘farming
styles’ as discrete styles or strategies that were observable in a farming community and
used to guide farmers’ activities. However, attempts to apply this theory in Australia
have resulted in a reconceptualisation of farming styles as social constructions, rather
than as objective categories. During focus group interviews with Australian broadacre
farmers, Howden and Vanclay (2000) found the farming styles described were laden
with social judgements and that the language used by extensionists dominated
discourse. Some styles were demonised for not being socially acceptable (e.g. ‘diesel
burner’) while some were promoted (e.g. ‘progressive’) and farmers associated
themselves with the socially desirable styles. Although researchers and extensionists
have used farming styles as a heuristic to explain farmer behaviour, they are not
empirically observable (Vanclay et al. 2006). Indeed, Howden and Vanclay (2000, pp.
306-7) argued that farming styles represent mythologised styles which provide
“parables to inform farmers’ actions.”
The empirical studies discussed in this section offer insights into how and why
landscapes, commodities, conservation technologies and farming styles are socially
constructed. These studies present agricultural innovation as an iterative, enactive,
cognitive and social process that complements the traditional linear, determinist view by
providing a deep and rich examination of how innovation occurs. In particular, the
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prior research reviewed highlights a number of issues that need to be considered when
examining the social construction of agricultural technologies, namely:
• The role of conflict, consensus and compliance in the meanings made by
different social groups (Tax 1990; Greider & Garkovich 1994).
• The importance of understanding how participants’ self-identities are
constructed and reconstructed (Coughenour 2003; Lockie 1997b; Vanclay et al.
2006).
• The roles of social groups, power and influence (Tanaka, Juska & Busch 1999;
de Souza & Busch 1998).
• The social construction and reconstruction of technology frames (Coughenour
2003).
• The reciprocal effects of technological and social construction (Coughenour
2003; Tanaka, Juska & Busch 1999; de Souza & Busch 1998).
2.4 Individual and social sensemaking
The majority of socio-technical research in the agricultural context has adopted an
Actor-Network Theory (ANT) approach (e.g. Coughenour 2003; Lockie 1997b; de
Souza & Busch 1998). This approach has been broadly criticised for disregarding
social structures, neutralising the role of human actors, a lack of political analysis, and
its focus on description rather than its capacity for explanation (Walsham 1997).
Whittle and Spicer (2005) have suggested ANT focuses on how actors participate in
networks, how technologies are made to work in organisations and the negotiation
activities between network actors. However, local knowledge and perspectives are
largely ignored and pre-existing power relations are assumed to be irrelevant. In the
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present study, local knowledge and perspectives, and power and influence relations,
were seen as central to our understanding of the socio-cognitive mechanisms that
underlie how new agricultural technologies are shaped by participants in the innovation
process. Therefore, sensemaking processes (Weick 1995) were used to guide data
collection and analysis. Sensemaking offers a unified socio-cognitive model for the
examination of agricultural innovation that includes iteration, enactment, adaptation,
transition, individual cognitions and social learning at an individual, organisational and
industry level.
As indicated previously5, Weick (1990) has argued that the equivocal nature of new
technologies requires specific cognitive models and sensemaking capabilities which
enable the user to represent and understand the events associated with them. It will be
recalled that sensemaking is defined as a cognitive and behavioural response to
situations involving ambiguity, uncertainty and arousal that interrupts the ongoing flow
of events (Gioia & Chittipeddi 1991). Under this definition, ‘sense’ is the meaning
ascribed to an event, and ‘making’ is a creation or construction activity (Weick 1995).
The term may be further refined: according to Starbuck and Milliken (1988, p. 51),
“sensemaking has many distinct aspects – comprehending, understanding, explaining,
attributing, extrapolating, and predicting, at least.” However, sensemaking involves
more than just giving meaning to an event as it is also about the construction of the
environment itself (Weick 1995). Weick (1995) also emphasizes this notion of
sensemaking as a constructive, theorising activity by describing it as an active, ongoing
process through which people give meaning to events from ‘cues’ that they extract from
their own actions and the actions of others. Paraphrasing Czarniawska-Joerges (1992)
and Weick (1995) it may be said that to study sensemaking is to enquire into how
5 Chapter 1, page 5.
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meanings are constructed and reconstructed socially, what is constructed, why is it
constructed and to what effect.
Sensemaking is, in part, controlled by people’s expectations about situations or events
(Weick 1995). People form expectations of events from their frames of reference,
experiences, interpretation and meaning (Louis & Sutton 1991). People then seek to
make sense of experiences that deviate from these expectations (Louis 1980).
Sensemaking seeks to describe the disparity between what individuals intuitively expect
and what actually happens (Murnighan 1993). Therefore, sensemaking concepts can
provide a deeper and more elaborate understanding of complex social phenomena.
Sensemaking is a cognitive process in which a person switches between automatic
(unconscious) and active (conscious) cognitive modes (Louis & Sutton 1991).
Switching from automatic to active cognitive mode is triggered by noticing cues that are
novel or discrepant or as a result of a deliberate initiative on the part of the sensemaker
(Louis & Sutton 1991; Weick 1995). Cues are seeds of information extracted from
events or the actions of the sensemaker and others in response to events. Sensemakers
switch from active to automatic cognitive mode when active thinking is no longer
necessary; for instance when novel or discrepant cues are no longer noticed, or when an
initiative has been completed.
Novel or discrepant cues that trigger the sensemaking process are extracted from
interruptions to an ongoing flow of events (Weick 1995). Huber (1987) identified three
environmental conditions for such interruptions, namely:
1. Increased information load.
2. Increased complexity or perceived uncertainty.
3. Increased turbulence or instability and randomness.
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Such interruptions to an ongoing flow of events can originate from inside or outside an
organisation (Schroeder et al. 1989). These environmental conditions can generate
events that are perceived as ambiguous and uncertain (Weick 1995). Ambiguous events
create confusion as many interpretations of the same event are possible. Uncertain
events create a sense of ignorance and occur when there is uncertainty about the
environment (Milliken 1987). Sensemakers seek more information to reduce their
perception of their own ignorance, whereas social interactions and the generation and
sensing of multiple cues are needed to reduce confusion (Daft 2003).
In the sensemaking process, people act not only as sensemakers but also as sense givers.
Sensegiving is the process through which members of a social system project their
meaning of events to a wider group of people to persuade them to construct and share
the same meaning (Gioia & Chittipeddi 1991). Sensemaking and sensegiving are
inextricably linked. People receive cues from sense givers and make sense of the cues
in their own context. Sensegiving occurs when peoples’ perceptions of their own
identities, roles and activities are influenced by cues received from others. Recipients
may modify these cues, in effect becoming sense givers themselves.
Sensemaking substance
The substance of sensemaking can be conceptualised as a frame, plus a cue, plus a
connection (Weick 1995). Frames, also known as schema, are “past moments of
socialization” and cues are “present moments of experience” (Weick 1995, p. 111).
Frames are dynamic, cognitive knowledge structures about specific concepts, artefacts
and events that people use to encode and represent cues efficiently (Markus 1977).
Frames reflect an individual’s experience, socialisation and habits and their beliefs
about ‘what is’ and ‘what ought to be’ (Harris 1994) (emphasis added). They serve as
mental maps that direct the search for and acquisition of information and guide
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subsequent behaviour in response to that information (Harris 1994; Gioia 1986;
Orlikowski & Gash 1994).
Frames guide sensemaking at two fundamental levels (Harris 1994). First, they
facilitate answering the question ‘what or who is it?’ that promotes the categorisation
and identification of cues. Second, they facilitate answering the question, ‘what should
I pay attention to?’ indicating the search for meaning is egocentric. When a person
encounters a cue, it is matched against an existing frame (Harris 1994). The elements of
a cue are imposed on the elements of a frame that, in turn, outlines expectations about
the event within certain ranges of acceptability (Dunbar 1981; Starbuck & Milliken
1988; Cantril 1941). If information is missing, default values may be inserted, allowing
the person to make judgements beyond the scope of the information available. Filling
in information gaps from frames of reference offers a more complete perception of an
event, but also increases the potential for making incorrect assumptions about cues
(Harris 1994).
The substance of sensemaking is articulated in the words, sentences, definitions,
concepts and interpretations that are drawn from an individual’s vocabularies as the
person articulates the connection of cues and frames (Weick 1995). The sense that we
make of events is often communicated to others through narratives and metaphors
(Dunford & Jones 2000; Hill & Levenhagen 1995; Keeley & Scoones 2003; Jasanoff
2005). Narratives are stories that give meaning to a flow of events and actions
(Dunford & Jones 2000). They are constructed to project some certainty onto an
uncertain future and, therefore, are central to sensemaking during times of change and
transition (Dunford & Jones 2000). Narratives bear similarities to metaphors. Hill and
Levenhagen (1995) argued that people resort to the use of metaphors in the construction
and articulation of frames in the sensemaking process, a view supported by Keeley and
Scoones (2003) and Jasanoff (2005).
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When they share the sense they make of their experiences with others, people use a
range of vocabularies based on ideology, premise controls, paradigms, theories of
action, tradition and experience (Weick 1995; Starbuck & Milliken 1988; Perrow 1986;
Pfeffer 1985; Hackbarth 2001; Hedberg 1991; Zukier 1986).
When new cues conflict with an individual’s frame of reference, they may be ignored as
an aberration or cognitively recast to fit an existing frame. However, vocabularies and
the frames they articulate are not static as they may be expanded and elaborated as an
individual incorporates new information and experiences (Bartunek & Moch 1987).
Over time, as more complex cues are encountered, the frame for that cue becomes more
complex, abstract and organised (Fiske & Taylor 1991). This process can result in the
development of highly elaborate frames of reference drawn from multiple experiences
(Bartunek & Moch 1987).
Levels of sensemaking
Sensemaking occurs at different levels of a social system (Weick 1995). Wiley (1988)
posited the following three levels of sensemaking analysis which operate beyond the
individual or intra-subjective level:
1. Inter-subjective sensemaking – individual sense is merged with others through
conversation and interaction, allowing for the interpretation of events between
people.
2. Generic subjective – organisational sensemaking and the level at which the
individual disappears into organisational roles and routines.
3. Extra-subjective – a symbolic, idealised and abstract reality where generic
selves no longer exist. This level of sensemaking is representative of trans-
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national, national, community, industry and occupational cultures (Weick 1995;
Beyer 1981; Porac, Ventresca & Mishina 2002).
A tension exists between the inter-subjective and generic subjective levels of
sensemaking as generic subjectivity ratifies routines and inter-subjectivity seeks to
modify them. Organisational ‘scripts’ that describe routines emerge at the generic
subjective level during stable periods of event flow. In organisational sensemaking
there is a constant move towards generic subjectivity (Callon & Law 1997). However,
sensemaking flexes back to the inter-subjective level when organisational scripts do not
give meaning to the events encountered at a generic level, such as in response to new
technologies. Yet generic subjectivity, in the form of organisational scripts, also shapes
inter-subjective sensemaking. Sensemaking can also be examined at an extra-subjective
or inter-firm level as the social construction of industry belief systems (Porac, Ventresca
& Mishina 2002).
The sensemaking concepts described in this section offer an opportunity to examine the
cognitive and social mechanisms that underlie the innovation process and allow for a
deeper analysis of meaning making, action and interaction at the individual,
organisational and industry levels (Weick 1995). The sensemaking process and related
constructs have been applied in conceptual and empirical innovation research, such as
studies of the construction and transfer of new technologies (Choo & Johnston 2004;
Faraj, Kwon & Watts 2004; Guney 2004; Theoharakis & Wong 2002; Dougherty et al.
2000), technology adoption (Griffith 1999; Prasad 1993; Seligman 2000; Seligman
2006) and implementation (Orlikowski & Gash 1994; Barley 1986; Leonard-Barton
1988).
The application of sensemaking concepts to innovation processes has been limited to
the study of information systems technologies. For example, Seligman (2000; 2006)
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developed a conceptual framework for intra- and inter-subjective sensemaking in the
adoption of new information systems. However, despite sensemaking concepts having
been cited for their substantial theoretical and empirical importance in the innovation
area, and specifically in relation to information systems, sensemaking concepts have not
been used in the examination of agricultural innovation processes. Therefore, in the
following Chapter, the sensemaking process as it relates to agricultural innovation is
discussed and an analytical framework for the analysis of sensemaking in the
agricultural innovation context is presented.
Conclusions
Limitations of the dominant technological determinist approach to agricultural
innovation described in sections 2.1 and 2.2, along with a growing recognition of the
heterogeneous, complex and variable nature of individual farm businesses, have
influenced the recent emergence of social constructivist perspectives of agricultural
innovation. However, gaps remain in terms of understanding the socio-cognitive
mechanisms underlying the use of new agricultural technologies. It is not clear how
participants in the innovation process make sense of new technologies in their own
context and how that sensemaking shapes the use of new agricultural technologies.
Without examining and understanding this sensemaking process at the individual and
industry levels, a deeper understanding of the agricultural innovation phenomenon is
likely to remain elusive. In the following Chapter, a comprehensive approach to
examining how participants in the agricultural innovation process make sense of new
agricultural technologies is proposed and preliminary analytical framework for
examining how sensemaking shapes the use of new agricultural technologies is
presented. In Chapter 3, methodological issues associated with sensemaking research
are discussed and the three empirical studies that make up this research are introduced.
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3 Agricultural innovation: A sensemaking process In the previous chapter, sensemaking was described as an ongoing, cognitive and social
process by which people interpret and enact their environments and which is largely
driven by expectations as people attempt to fill the gaps between what is expected and
what actually happens (Weick 1995). People act not only as sensemakers, but also as
sense givers as they persuade other members of their social system to share their
meaning of events (Gioia & Chittipeddi 1991). The sensemaking process is triggered
by cues sensemakers extract from the ambiguous and uncertain events that occur in their
organisations and environments and can be observed in different forms at various levels
of a social system from an individual (intra-subjective) level to an industry or cultural
level (extra-subjective) (Weick 1995; Wiley 1988).
Weick (1979) argues that to understand sensemaking is to address the question of what
provokes cognition in social settings. New technologies can trigger sensemaking
because they are ‘equivocal’: they can be uncertain and complex with multiple possible
or plausible interpretations (Griffith 1999). Technologies “require ongoing structuring
and sensemaking if they are to be managed” (Weick 1990, p. 2). Orlikowski and Gash
(1994, p. 175) argue that, to “interact with technology, people have to make sense of it;
and in this sense-making process, they develop particular assumptions, expectations and
knowledge of the technology, which then serve to shape subsequent actions towards it”.
In this chapter, the sensemaking process is presented as a conceptual framework
through which the socio-cognitive mechanisms underlying agricultural innovation
processes can be examined and understood. The concepts and application of
sensemaking are largely associated with Weick’s (1995) work on sensemaking in
organisations. The sensemaking concepts discussed in this Chapter are drawn from
arguments made by Weick and other sensemaking scholars whose work formed the
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basis for the development of an analytical framework for the present study. The
Chapter is divided into four sections. The first section examines how the seven key
properties of sensemaking relate to empirical agricultural innovation research. The
second section examines both sensemaking at an industry level, and the related concepts
of discourse and tension in macro-level sensemaking systems. The third section
presents a preliminary analytical sensemaking framework for the agricultural innovation
context. The fourth section introduces and describes the three individual studies
undertaken to examine agricultural innovation. In this section of the chapter, relevant
methodological issues are examined. Finally, a conclusion to the chapter is provided.
3.1 Agricultural innovation as an occasion for sensemaking
Weick (1995) associated sensemaking with other explanatory processes, such as
understanding, attribution and interpretation. However, he argued that sensemaking has
seven properties which make this process of theorising and construction unique and
provide a “rough guideline for inquiry into sensemaking in the sense that they suggest
what sensemaking is, how it works and where it can fail” (Weick 1995, p. 18). These
seven properties are:
I. Sensemaking is grounded in identity construction.
II. Sensemaking is retrospective.
III. Sensemaking is enactive of sensible environments.
IV. Sensemaking is social in nature.
V. Sensemaking is ongoing.
VI. Sensemaking is focused on and by extracted cues.
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VII. Sensemaking is driven by plausibility rather than accuracy.
In the following sub-sections, these seven sensemaking properties are described and
examined in relation to the technology sensemaking literature and to agricultural
innovation research. This literature was examined to develop a theoretical and
empirical basis for examining how participants in the Australian wool industry made
sense of objective wool fibre testing technologies and how this sensemaking shaped the
use of these technologies over time.
I. Sensemaking and identity construction
Sensemaking is grounded in identity construction as people learn who they are by their
actions and the actions of others. The establishment and maintenance of self-identity is
a core function of the sensemaking process as “[s]ensemaking begins with the self-
conscious sensemaker” (Weick 1995, p. 22). Individuals are driven by a need for a
sense of identity and have a general orientation to situations that maintain the
consistency of their self-conceptions (Erez & Earley 1993). Self-identities can change
if they are not confirmed and a failure to confirm self identity can trigger sensemaking
(Weick 1995).
An individual’s response to new technologies can be an element of his self-identity
(Gremy, Fessler & Bonnin 1999). For example, Prasad (1993) reported that some
people felt professional, organised, intelligent and respected when they adopted a new
computer system. People’s perceptions of the innovation behaviour undertaken by
other members of their social system can also affect their self-identity. For instance, a
person may see himself as highly innovative for adopting a new technology before his
peers adopt the technology.
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The establishment and maintenance of self-identity is driven by a need for self-
enhancement, self-efficacy, and self-consistency. However, such identities do not exist
as external objects of reality but are “constituted out of the process of interaction”
(Weick 1995, p. 20). Identity construction is the interplay between how the individual
sees himself, how he sees a situation, and his perceptions of how others view him. The
role others’ perceptions play in identity construction is particularly acute in an
agricultural innovation context. As was discussed in Chapter 2, ‘progressive’ farmers
tend to be highly regarded by the scientific community, elevating their social status in
the farming community (Vanclay 1994). The self-identity of such farmers as
‘progressive’ is reinforced by the scientific community’s actions as it continues to target
them with research, development and extension initiatives, often ignoring other farmers
(Howden et al. 1998).
Self-generated learning with new technologies seems to inform a person’s internal
attributions of his self-efficacy and, therefore, his identity (Carletto, de Janvry &
Sadoulet 1996; Hoch & Deighton 1989). Self-efficacy beliefs are a foundation of
human motivation, well-being and personal accomplishment as, unless people believe
their actions can produce the outcomes they desire, they have little incentive to act
(Bandura 1997). For example, a person may reject or discontinue the use of a new
technology if they believe they are unable to adopt and implement it effectively in their
own context (Rogers 2003).
The beliefs people have of their self-efficacy in adopting and implementing new
technologies, may be related to memories of past successes and failures with similar
technologies that are embedded in their frames of reference (Agarwal & Prasad 1999).
Technology frames can bias and filter information and focus adopters on the difficulties
involved in changing their behaviour in relation to a new technology (Sproull &
Hofmeister 1986; Orlikowski & Gash 1994).
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Identity construction has been explored in the agricultural context (e.g. Vanclay, Mesiti
& Howden 1998; Frost 1998; Lockie 1997b; Coughenour 2003). For example, when a
farmer receives feedback from his peers about his enterprise’s performance or farming
practices, he learns opinions, not only about his farm business, but also about his
identity as a good or bad farmer. Therefore, it is his actions, the actions of others, his
observations and his reflection on these things that contribute to his understanding of
himself as a good or bad farmer (Vanclay, Mesiti & Howden 1998).
Thus, Frost (2000, p. 511) found that farmers who were diversifying, or exiting the
Australian wool industry constructed a “new statement as to ‘who’ they are”. Their
farming legacy, social standing and acceptance in their social system as a ‘good farmer’
influenced their farming goals and decisions. Personal values, which contribute to a
person’s frames of reference, seem to be critical determinants of behaviour when
incomplete information is available, such as during the introduction of new technologies
on-farm. Further, as noted earlier6 Lockie (1997b) observed the construction of farm
women’s identities through their involvement in the Australian Landcare movement.
Such women were increasingly less willing to be thought of as ‘helpers’, ‘housewives’
and ‘offsiders’ but rather, identified themselves as farmers in their own right. Lockie
(1997b) argued that participation in Landcare enabled participants to adopt identities
that challenged traditional notions of what constitutes good farming practice.
Identity construction in an agricultural innovation context seems to be an evolutionary
process. For example, Coughenour (2003, p. 295) examined the evolution of farmers’
self-identities during the construction of new conservation technologies. He found that
prior to the adoption of no-tillage7 cropping systems, farmers self-identified as
ploughmen controlling nature, but thereafter reconstructed their identity as practical
6 Chapter 2, p.45. 7 No-tillage is a method of conservation farming in which soil is not tilled but instead crops are planted by inserting seeds in small slits in the earth and other methods of weed control are used.
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agro-ecologists striving to strike a balance between profitable systems and
environmental sustainability (Coughenour 2003) (emphasis added).
II. Sensemaking is retrospective
Sensemaking occurs retrospectively because a person can only make sense of what has
already happened, not what is happening in the instant it occurs (Weick 1995). As
sensemaking only occurs when we pay attention to something that has already
happened, we are reliant on memory and recall (Weick 1995). What is happening at the
moment of retrospection, in terms of our projects and goals, influences how we view the
past (Weick 1995; Gioia & Chittipeddi 1991). Many different meanings may be made
of an event because a sensemaker is concerned about many different projects and goals.
A crucial element of sensemaking is the notion that, although situations are
progressively clarified, this clarification often works in reverse. Rather than the
outcome of a situation fulfilling some prior definition, the outcome often influences the
definition of the situation retrospectively (Weick 1995). For example, if a person’s
attitudes towards a new technology is influenced by a set of norms and beliefs, such as
low self-efficacy, their experience with the technology may improve these beliefs and
their frames may evolve to exclude some of those original perceptions (Rice &
Contractor 1990).
The retrospective nature of sensemaking has its roots in cognitive dissonance theory
(Weick 1995), which is a widely accepted concept in innovation adoption and consumer
research (Kopalle & Lehmann 2001). Cognitive dissonance is a negative state that
occurs when a person simultaneously holds two cognitions about the same situation that
are psychologically inconsistent (Festinger 1957; Aronson 1968). Since dissonance is
presumed to be an unpleasant state, individuals are driven to reduce or remove it by
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changing their attitudes or behaviour to restore cognitive consistency (Taylor, Peplau &
Sears 1997).
In an innovation context, dissonance arising from retrospective sensemaking may be
reduced or removed by discontinuing the use of the new technology. Examples of the
discontinuance of new agricultural technologies are plentiful, as was noted in Chapter 2.
However, because of the potentially costly implications of technology discontinuance,
people often seek information to confirm and justify the decision to adopt a new
technology by changing their beliefs rather than their behaviour (Rogers 2003;
Cummings & Venkatesan 1976).
III. Sensemaking is enactive of sensible environments
Enactment is the construction of reality through action. Action is the start of the
sensemaking process because we can’t make sense of a situation until we have acted
(Weick 1995). Actions create cues that are noticed and extracted by the ‘sense-maker’.
Those cues are integrated into the frames of the sensemaker, who then selects further
actions. These actions may take the form of creation, abandonment, postponement or
adaptation, all of which generate meaning (Weick 1995). Therefore, the sensemaker
produces part of the environment that they face, which then acts to guide or constrain
their future actions (Porac, Thomas & Baden-Fuller 1989). It is through this process of
enactment that the sensemaker constructs an environment that makes sense to them, or
as Weick puts it, a sensible environment.
Weick (1979) argued that environments should not be conceptualised as fixed or
external to the enacting subject, but as dynamic processes that involve the sensemaker.
The sensemaker constructs reality by acting and the environment that is enacted is
sensible because the sensemaker labels their reality with meaning (Weick 1995).
Because of the interpretive and enactive nature of sensemaking, researchers need to be
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prepared for ‘ontological oscillation’ between subjective and objective notions of
society (Weick 1995; Burell & Morgan 1979; Neisser 1976). In enacting their
environment, people set apart or ‘bracket’ events from ongoing flows in order to make
sense of them. Bracketing switches people into a mode of ‘realism’ as they deal with
the bracketed event as something that exists out there in reality. However, the very act
of bracketing is based on subjective norms and beliefs (Weick 1995). These social
constructions may become institutionalised and frame further sensemaking. Hence,
sensemakers oscillate between the subjective and the objective as their enacted world is
simultaneously tangible and personal.
The concept of enactment provides a deeper and more complex view of the individual’s
context than is typically described in the innovation literature (Bessant 1985; Swanson
1988; Voss 1988). A new technology can confirm or modify existing patterns of action
(Weick 1990). Barley (1986, p. 81) described technologies as “occasions that trigger
social dynamics which, in turn, modify or maintain an organization’s contours” and
argued that the social dynamics triggered by new technologies are multifaceted,
dynamic and contextual. New patterns of action that arise from the introduction and use
of new technologies are incorporated into individual frames and organisational scripts
and structure. The greater the modifications to organisational scripts and structure
required, the greater the influence the new technology has on organisational structure
(Barley 1986).
Traditionally, agricultural innovation research and practice has taken a technological
determinist view of the relationship between technology and the farming context.
However, the application of sensemaking to agricultural innovation requires a more
interpretive and enactive view of this relationship. For example, an individual’s
perception of the compatibility of the technology with his environment influences his
adoption behaviour (Rogers 2003). From a sensemaking perspective, it is the individual
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who enacts part of the environment he faces and, therefore, the individual controls the
compatibility of his environment with a new technology (Weick 1995).
There is some evidence that people and groups construct and reconstruct their
environment in response to agricultural innovation. For example, Coughenour (2003)
found new social networks were enacted by participants engaged in the construction of
new no-tillage cropping technologies, while de Souza and Busch (1998) found new
soybean production and consumption patterns and social networks were enacted
through the introduction of soybeans to Brazilian farming communities.
IV. The social nature of sensemaking
Sensemaking is a social process as people make sense of the world and their own
identities in relation to their peers and colleagues and the organisations and social
systems to which they belong (Weick 1995). The social nature of sensemaking binds
people to actions that need social justification, affecting the saliency of the cues they
extract from events and providing norms and expectations against which they measure
the relevance of extracted cues (Weick 1995).
Sensemaking is never solitary because even monologues and other forms of one-way
communication presume an audience (Weick 1995). Wittgenstein made this point in his
discussions of the impossibility of private language; arguing that the meaning of a
concept is its use in language and that language is used in social contexts (Kripke 1982).
Symbolic social interaction is an important concept in sensemaking as a person’s
conduct is largely contingent on the conduct of others, whether physically present or
imagined. Because both real and imagined social interactions allow for the
development of a ‘common sense’, it follows that social interactions strengthen this
‘common sense’ and enable members of a social system to organize around it (Aydin &
Rice 1992; Brown & Eisenhardt 1997; Bettis & Prahalad 1995).
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In sensemaking, individual cognitions and social interactions are mediated by talk,
discourse and conversations (Weick 1995). Social interaction occurs throughout the
sensemaking process as people extract cues from the actions of others and modify their
own frames and actions. Social influences on sensemaking take the form of shared
meaning, overlapping views of ambiguous events, compromise and duress (Eisenberg
1984; Blumer 1969; Weick 1995).
Social influence and interaction have been widely examined in the innovation literature
as the process by which a new technology diffuses through social systems and as an
antecedent to the decision to adopt a new technology (Rogers 2003). In the innovation
literature, the adoption and implementation of new technologies is often conceptualised
as a social process in which agents of change, such as technology developers and
extensionists, play a vital role in influencing farmers to adopt and implement new
technologies (Biemans 1992; Fincham et al. 1995; Preece 1989; Fleck 1994).
In an agricultural innovation context, external change agents, such as researchers and
extensionists, have been found to play sensegiving roles in the adoption and
implementation of new agricultural technologies (Leuthold 1967; Moser & Barrett
2003; Wilkening 1954b; Douthwaite 1999). Farming peers have also been identified as
sense givers in the adoption and implementation of new agricultural technologies
(Foster & Rosenzweig 1995).
Studies of the social construction of agricultural innovation have been undertaken. For
example, Coughenour (2003, p. 285) presented the construction and reconstruction of
conservation technologies as a “process and product of new networking”. Ongoing
interactions between researchers, extensionists, farmers and other key actors provide
access to the core science and technologies required to undertake the collaborative
construction of agricultural innovation.
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V. The ongoing nature of sensemaking
Sensemaking is ongoing because the flow of events that people experience is
continuous. Sensemaking “neither starts fresh nor stops cleanly” (Weick 1995, p. 49) as
people act, makes sense of their actions and then act again, guided by the sense they
have already made (Louis & Sutton 1991). Because of the ongoing nature of
sensemaking, discrete words and sentences are inadequate ways to convey this meaning.
Sensemaking requires ongoing narratives to preserve the continuity of event flows
(Weick 1995).
Although event flows are ongoing, it does not mean this continuous stream is
monotonous. There are events of importance within the stream that ‘crystallise
meaning’, such as the introduction of a new technology. People bracket such moments
in ongoing flows and extract cues to make sense of them (Eccles, Nohria & Berkley
1992). Bracketing occurs as a result of a person’s ongoing ‘projects’ being interrupted.
Interruptions signal that important changes have occurred in the environment (Weick
1995). Positive interruptions are experienced when a sensemaker gains the ability to
complete a plan quicker than expected, while negative interruptions slow their
accomplishment (Weick 1995). Weick (1990) argued that, the longer a technology is
used, the less likely it is that positive interruptions will occur as the operator becomes
used to a new technology and it becomes part of ongoing operations and plans.
Negative stimuli are more likely to interrupt the use of a new technology, potentially
resulting in disenchantment and discontinuance. This notion of sensemaking as
bracketing events from interruptions to ongoing event flows presents a dynamic and
continuous view of the innovation context.
Farming contexts are highly variable and transitional, exposing sensemakers to
interruptions that affect the sense they make of new technologies. Examples of
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interruptions to the ongoing flow of events in the agricultural innovation context are
plentiful and vivid, including:
• market price signals (Trompf, Sale & Graetz 2000; Carletto, de Janvry &
Sadoulet 1996)
• changing climatic conditions (Trompf, Sale & Graetz 2000; Carletto, de Janvry
& Sadoulet 1996)
• changes in farming contexts (Kaine & Lees 1994; Crouch 1981; Frank 1999;
Coughenour 2003; Aubry, Papy & Capillon 1998).
A new technology that is perceived to be invaluable under certain farming conditions
may seem trivial and wasteful when those conditions change and vice versa. The
ongoing ‘sense’ that an adopter makes of a new technology may result in actions such
as postponed adoption, discontinuance, re-adoption and the periodic adoption of
innovations.
Swanson (1994) suggested the adaptation of a new technology is an ongoing,
evolutionary sensemaking act. Indeed, Coughenour and Chamala (2000) found the
‘adaptive modification’ of conservation technologies continued almost indefinitely.
Adopters of no-tillage cropping systems learned to monitor social, economic and
environmental factors and to select the most appropriate technique(s) to attain their no-
tillage cropping system goals.
VI. Cue extraction and sensemaking
Sensemaking is focused on and by extracted cues. Cues are “simple, familiar structures
that are seeds from which people develop a larger sense of what may be occurring”
(Weick 1995, p. 50). Actions create cues that are extracted for further sensemaking.
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Cue extraction is the process of ‘noticing’ and extracting what is salient and useful for
mentally representing stimuli (Starbuck & Milliken 1988). Cue extraction occurs
through environmental scanning and focused search and contributes to the maintenance
and evolution of a person’s interpretation frames (Weick 1995). Cues have three
important characteristics:
1. They are received as perceptions and are therefore subjective.
2. There is no reason to assume everyone who experiences a particular event will
extract the same cues from it, or that two people who perceive the same cue will
incorporate it in the same way into their frames.
3. Control over cues is a source of influence and power in a social system
(Smircich & Morgan 1982).
Weick (1990) described new technologies as a combination of three types of technology
events that generate cues for sensemaking. These events he termed stochastic,
continuous, and abstract. Stochastic technology events occur randomly and are
unpredictable, poorly understood and adaptive. Continuous technology events come
from the search for reliability and the maintenance of integrity and coexist with
stochastic events. Abstract technology events are embodied in technologies that operate
out of a user’s sight.
How people perceive the features and attributes of a new technology, such as its relative
advantage, influences adoption and implementation behaviour (Rogers 2003; Tornatzky
& Klein 1982; Lindner 1987; Pannell 2001). The observable actions of technology
developers and other members of a social system provide sensemaking cues for
potential adopters as they help generate a mental picture of the technology (Sproull &
Hofmeister 1986; Foster & Rosenzweig 1995; Moser & Barrett 2003). Therefore, it is
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important that technology developers have some knowledge about the cues to provide to
potential adopters to help them to make sense of a new technology in their own context
(Seligman 2000). In an agricultural innovation context, the technology developers’ and
extensionists’ ‘sensegiving’ roles have been found to decline post-adoption as end users
begin to extract cues from their own experiences with the technology (Trompf, Sale &
Graetz 2000; Martin & Sherington 1997; Cameron 1999).
VII. Sensemaking driven by plausibility
The social sensemaking process is about plausibility rather than accuracy as it involves
the embellishment of a single point of reference or an extracted cue (Weick 1995).
Fiske and Taylor (1991, p. 182) argued that most people give meanings to situations
which are ‘good enough’ to enable them to undertake effective actions. In other words,
their ‘thinking’ is good enough to serve their ‘doing’. Accuracy is not absolute and
accuracy demands are dependent on the purpose of the sensemaking process.
Expectancy of outcomes (i.e. the self-fulfilling prophecy) and consensus are often used
as a proxy for accuracy (Fiske & Taylor 1991).
The retrospective nature of sensemaking also clouds the accuracy of the sensemaking
process as people rely on memory and recall. Accuracy is often sacrificed for
expediency and is likely to be issue- or situation-specific (Weick 1995). Because of
information overload, accuracy of recall, time constraints and people’s limited data
processing capacity, sensemaking comes from interpretations of phenomena that are
socially acceptable, credible and workable (Weick 1995). The focus of sensemaking is
interpersonal, in the same way that experiential learning is open to influence and is not
simply a process of discovering an objective truth (Hoch & Deighton 1989).
Weick (1995, pp. 60-61) articulates the concept of plausibility clearly by describing
sensemaking as:
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Something that preserves plausibility and coherence, something that is reasonable and
memorable, something that embodies past experience and expectations, something that
resonates with other people, something that can be constructed retrospectively but can
also be used prospectively, something that captures both feeling and thought, something
that allows for embellishment to fit current oddities, something that is fun to construct.
In short, what is necessary is a good story.
Therefore, sensemaking is not only an understanding of what is directly observable and
accurate, but also the achievement of a level of reasonableness for a situation that is
suitable for a sensemaker’s needs (Weick 1995).
The concept of plausibility is at odds with deterministic perspectives of agricultural
innovation which assume there is an objective truth about a new technology and that
farmers accept this ‘truth’ by adopting technologies that are economically prudent.
However, as Lindner (1987, p. 149) pointed out, farmers operate in their own self-
interest regarding new technologies, although self-interest is “deceptively simple” as it
involves values and preferences and is not a measurable, objective ‘truth’.
3.2 Collective sensemaking at an industry level: Sensemaking as
organising
The foregoing overview of the seven properties of sensemaking focused on individual
socio-cognitive concepts. However, Weick, Sutcliffe and Obstfeld (2005) have argued
that these concepts and activities are related to the process of organising. They have
suggested sensemaking and organising should be conceptualised as an evolutionary
process as it is a series of “reciprocal exchanges between actors (enactment) and their
environments (ecological change) that are made meaningful (selection) and preserved
(retention)” (Weick, Sutcliffe & Obstfeld 2005, p. 414).
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Figure 3.1: Evolutionary sensemaking and organising (source: Jennings &
Greenwood 2003)
Figure 3.1 shows the reciprocal relationship between enactment and ecological change
which presents sensemaking as an ongoing process of noticing, enacting and external
influence (Jennings & Greenwood 2003; Weick, Sutcliffe & Obstfeld 2005). Enactment
incorporates noticing and bracketing activities as a sensemaker’s ongoing projects are
interrupted by ambiguous or uncertain events. According to Weick, Sutcliffe and
Obstfeld (2005) the selection of possible meanings that can be made of data that are
noticed and bracketed by a sensemaker is reduced through a process of selection.
Sensemakers’ mental models and retrospective attention to extracted cues enable them
to reduce bracketed data and to generate a story that is plausible to them (Weick,
Sutcliffe & Obstfeld 2005). This provisional, plausible story is solidified though its
retention and it is related to identity construction and used to guide further actions and
meaning making. Weick, Sutcliffe and Obstfeld (2005) have argued the enactment,
selection and retention processes that are shown in Figure 3.1 provide a “micro
foundation” for the conceptualisation of sensemaking and organising that can be easily
linked to macro-level analyses of evolutionary sensemaking processes.
Ecological Change
Enactment Selection Retention
Ongoing updating
Retrospect extracted cues
Identity plausibility
Feedback of Identity on selection and enactment
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Industry-level sensemaking
Cognitive modelling at an industry level plays an important role in understanding the
dynamics of organizational communities (Porac, Ventresca & Mishina 2002).
Cognitive structures, or industry belief systems, form part of an industry’s collective
cognitions and influence the role innovation plays in the evolution of that industry.
Industry belief system scholars argue that industries, or markets, are not just collections
of accumulated assets or patterns of asset utilisation actions. Rather, they argue,
industries and markets are enacted fields held together by desired beliefs about
products, market structures, ways of doing business and participant quality (Porac,
Ventresca & Mishina 2002). Industry beliefs are externalised through discourse
between industry participants and are enacted as the strategic choices participants make
(Porac, Ventresca & Mishina 2002).
Thus, beliefs about the product, market, competition, ways of doing business and firm
reputation are seen as social constructions that evolve from producer and buyer
communities’ activities and artefacts (Porac, Ventresca & Mishina 2002; H.C. White
1981). These activities and artefacts provide informational cues that lead to industry
interpretations and definitions that then become an explicit industry nomenclature and
categorisation for the products and services produced, how the market works, how
rivalry or competition plays out and who participates in the industry or market (Porac,
Ventresca & Mishina 2002). Porac, Ventresca and Mishina’s (2002) model, which is
shown in Figure 3.2, identifies four types of interrelated industry beliefs (product
ontology, definitions of market structures, industry recipes and organisational
reputations). These beliefs are linked through the discourse and enactment of industry
participants, as is described in detail subsequently.
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Figure 3.2: Sensemaking and industry belief systems (source: Porac, Ventresca &
Mishina 2002)
Product Ontology refers to the nature, use, cues and participant profiles of products that
define and differentiate product markets. Over time, these product beliefs shape a
product’s definition and form its nomenclature or ontology (T. Pinch & W.E. Bijker
1987). Product ontologies are externalised through market stories, documents and texts
that encourage industry participants to categorise products and services (Porac,
Ventresca & Mishina 2002; Petroski 1993).
Product ontologies are dynamic and evolve as industry participants become interested in
the problem that a product is designed to solve (Petroski 1993). Eventually a product
ontology will move towards closure in which a producer network hardens around
common beliefs about a product’s definition and market structure (T. Pinch & W.E.
Bijker 1987; Porac, Ventresca & Mishina 2002; Bijker 1999; Petroski 1993).
Boundary beliefs influence what and who participants notice or pay attention to. They
define who competitors are and establish the boundaries of a market space (Porac,
Thomas & Baden-Fuller 1989; Porac et al. 1995; Porac, Ventresca & Mishina 2002).
White (1981) highlighted the role of competitive boundaries in the social construction
of markets as firms observe the actions of other firms and define their unique position in
Reputational Rankings
Industry Recipe
Boundary Beliefs
Product Ontology
Reciprocal cognition-action
relationships
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the market in relation to each other. However, as boundary beliefs enable inter-firm
comparisons to be made and the creation of a competitive order, they can only emerge
around a stable product ontology (Porac, Ventresca & Mishina 2002). Boundary beliefs
channel the attention or noticing of firms towards comparable peers, which, in turn,
allows industry beliefs to evolve into cognitive representations that shape organisational
strategies and competition (Porac, Thomas & Baden-Fuller 1989; Porac et al. 1995;
Porac, Ventresca & Mishina 2002).
Industry recipes are formed by participants as shared assumptions about time and space,
the nature of work relationships, the relationships between the industry and the
environment, constructs used to think through strategic problems and competitive
advantage (Porac, Ventresca & Mishina 2002). Industry recipes create the bedrock of
justifications for competitive actions (Porac, Ventresca & Mishina 2002). They emerge
over time and become stable, self-reinforcing and standardised, but are also influenced
by fads and fashions. In other words, they can be adopted and then abandoned over time
(Abrahamson & Fairchild 1999).
Reputational rankings occur because some firms implement industry recipes better than
others, resulting in the formation of formal and informal opinions of firm performance
that are used to rank firm reputations (Porac, Ventresca & Mishina 2002). The relative
success of firms in using industry recipes informs other industry participants and the
broader community about a firm’s competencies and reliability. A firm’s reputation
confers a competitive advantage and provides important barriers to the mobility of firms
within an industry, as well as creating a form of normative control (Fombrun & Shanley
1990).
The environmental conditions that allow the measurement and evaluation of firm
performance are created by “stable product ontology, consensual market boundaries,
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(and) taken for granted strategic recipes” (Porac, Ventresca & Mishina 2002, p. 591).
Firm evaluation occurs formally and informally and with a range of metrics (Porac,
Ventresca & Mishina 2002). Evaluations by industry insiders often provide weak cues
about the relative quality of those firms to outsiders who do not have access to market
ontologies or nomenclature. However, there is often a regulatory and measurement
industry that translates and amplifies reputational signals for customers and other users
(Porac, Ventresca & Mishina 2002; Fombrun & Shanley 1990).
Industry level discourse and tensions
In Porac, Ventresca and Mishina’s (2002) model, lower order beliefs create the
conditions for higher order beliefs. The enactment process moves both ways, as higher
order beliefs influence lower order beliefs and vice versa. There is a constant tension
between stability and instability resulting from multilevel enactment processes that
bring beliefs and actions together over time. Industry beliefs are shared and propagated
through public and private discourse or ‘industry rhetoric’ (Porac, Ventresca & Mishina
2002; Weick 1995).
Industry discourse or rhetoric externalises internal cognitions as public interpretations
of industry events or conditions (Rosa, Judson & Porac 2005). This suggests industry
belief systems are evolutionary sensemaking schemata that reflect new contingencies,
participants, artefacts and discourse transferred from other industries and life spheres.
Porac, Ventresca and Mishina (2002) described discourse among industry participants,
such as firms, media, analysts and agencies, as the mechanisms through which stories
and meaning are created. Storytelling creates, maintains and transforms industry belief
systems in a dynamic way, as new interpretations, participants and achievements impact
on existing sensemaking (Abrahamson & Fairchild 1999; Theoharakis & Wong 2002).
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Considerable emphasis is placed on shared understanding and common sense (Weick,
Sutcliffe & Obstfeld 2005; Aydin & Rice 1992; Bettis & Prahalad 1995; Brown &
Eisenhardt 1997). In this view, a consensus of meaning between industry participants is
anticipated as an outcome of collective sensemaking. However, collective sensemaking
may also result in ambiguity, compromise and duress (Eisenberg 1984; Blumer 1969;
Weick 1995). Indeed, Orlikowski and Gash (1994) found technology frames differed
between the developers and end users of a new computing system, resulting in the
system being used in ways not envisaged by the developers.
Despite evidence of sensemaking conflict, the question raised by Lant (2002) and Porac,
Ventresca and Mishina (2002) as to whether shared beliefs are a prerequisite for
collective action and, indeed, whether the concept of collective beliefs is meaningful
remains unexplored (Weick, Sutcliffe & Obstfeld 2005). Porac, Ventresca and Mishina
(2002) have called for further research into tensions in the construction of industry
belief systems and how they are externalised through conflict, consensus and
compliance. They argue that sensemaking scholars need to assume cognitions are not
stable or complete at an individual actor level and that greater focus is needed to
understand how patterns of conflict, consensus and compliance in generic and extra-
subjective sensemaking evolve over time.
The following section presents a guiding analytical framework of sensemaking in
agricultural innovation is presented that connects individual and collective sensemaking
in the agricultural innovation process.
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3.3 Making sense of agricultural innovation: an analytical
framework
A central concern of agricultural innovation research and practice is to understand why
and how new technologies are transferred, adopted and implemented by farmers. For
agricultural innovation scholars and practitioners, the primary concern is influencing
farmers’ decisions to adopt new technologies that are expected to improve farm
productivity, profitability and sustainability. Research that provides a deeper
understanding of the relationship between cognitions, social systems and technologies is
seen as increasingly important (Coughenour 2003; Coughenour & Chamala 2000;
Lockie 1999; Lockie 2006). However, there are no studies that have investigated
agricultural innovation as an occasion for sensemaking. Therefore, a preliminary
analytical framework of the agricultural innovation sensemaking process was developed
to guide the present project.
The preliminary analytical framework of the agricultural innovation sensemaking
process was inspired by the sensemaking concepts and empirical research discussed in
the previous sections of this Chapter and in Chapter 2. The seven properties of
sensemaking that were discussed in Section 3.1 offer insights that can be applied to the
study of how participants in the Australian wool industry made sense of objective wool
fibre testing technologies and how this sensemaking shaped their use of these
technologies over time. The process by which industry belief systems are socially
constructed and reconstructed, described in Section 3.2, offers further insights that can
also be applied to the present project. The suggestion that sensemaking is a social,
evolutionary, organising process and that shared technology frames and industry belief
systems are constructed and reconstructed through individual and collective
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sensemaking has not been explored previously in the examination of agricultural
innovation.
In the present study sensemaking was conceptualised as an ongoing, evolutionary
process, in which there is reciprocal interplay between individual and collective ‘sense’.
Individual and collective sensemaking is connected and articulated in discourse between
industry participants. That industry discourse can reflect conflict, consensus and
compliance between industry participants in relation to shared technology frames and
industry beliefs. This conceptualisation of the agricultural innovation sensemaking
process can be seen in Figure 3.3.
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Figure 3.3: The Agricultural Innovation Sensemaking Process – A preliminary analytical framework
Personal Identity Assumptions & Expectations, Experience & Knowledge, Current project & goals
Enactment
Selection
Retention
Reputational Ranking
Individual interpretation Framework
Sensemaking Process Shared Technology Frame Elements
Industry Belief System Elements
Social Context Group belonging Group and Industry norms Extent of power and influence
Physical artefacts Functional
characteristics Use
Performance evaluation
Industry Recipes
Boundary Beliefs
Product Ontology
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In this model, the interpretation framework of the sensemaker has two aspects (personal
identity and social context) that are ongoing, interrelated constructions. Based on other
scholars’ contributions, the following aspects of the proposed interpretation framework
were considered relevant to the present study:
• Personal identity - Sensemakers need to establish and maintain a self-identity
(Weick 1995). Consequently, sensemakers view a technology in relation to
their own identity construction in terms of their assumptions about the functions
and use of the technology, expectations of the performance of the technology,
experience and knowledge of existing technologies and practices and current
projects and goals (Agarwal & Prasad 1999; Sproull & Hofmeister 1986;
Orlikowski & Gash 1994; Weick 1995).
• Social context - Represents the social nature of sensemaking that binds people to
actions that need social justification, affecting the saliency of cues they extract
from events and providing the norms and expectations against which extracted
cues are measured (Weick 1995). Social context involves how sensemakers
view themselves and the technology in relation to their peers, their colleagues,
their social groups, the industry to which they belong, and the norms of that
industry group (Weick 1995; Tanaka, Juska & Busch 1999; de Souza & Busch
1998; Biemans 1992; Fincham et al. 1995; Preece 1989; Fleck 1994).
Industry participants make sense of technological or environmental events in the
proposed preliminary analytical framework through an evolutionary, organising
process. The interpretation of technological or environmental events is seen as a socio-
cognitive process through which industry participants make sense of new agricultural
technologies. As technological and/or environmental changes occur, agricultural
industry participants notice and bracket cues and then select salient cues to create a
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plausible story about the technological or environmental events that guide further
actions.
In the proposed framework, the sense that industry participants make of technological
and environmental events provides ongoing updating of shared technological frames
and industry belief systems. Based on other scholars’ contributions, the following
aspects of the proposed technology frame and industry belief system concepts were
considered relevant to the present study:
• Technology frames – these include not only physical artefacts, but also what
people believe is possible about a technology (Orlikowski & Gash 1994).
Technology frames reflect perceptions of the physical artefacts, functional
characteristics, technology use and the evaluation of its performance (Faraj,
Kwon & Watts 2004; T. Pinch & W.E. Bijker 1987; Munir 2002).
• Industry belief systems - these are enacted fields held together by desired beliefs
about products, market structure, ways of doing business and the relative quality
of participants (Porac, Ventresca & Mishina 2002). Individual meanings and
industry beliefs about the new technology are articulated through industry
discourse that can result in conflict, consensus or compliance (Porac, Ventresca
& Mishina 2002; Weick 1995; H.C. White 1981).
It is argued that it is through these individual and collective sensemaking processes that
the use of new agricultural technologies is shaped and participant contexts and industry
belief systems are constructed and reconstructed. The framework shown in Figure 3.3
was used in each of the three empirical studies undertaken in the present project. In the
following section, some of the methodological issues relevant to sensemaking studies
are discussed and the three empirical studies are described.
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3.4 Methodological issues and an introduction to the empirical
studies
Depending on the philosophical assumptions accepted, research can be classified as
positivist or interpretive (Myers 1997). Positivist research involves formal propositions,
the quantifiable measurement of variables of interest and hypothesis testing (Orlikowski
& Baroudi 1990). Positivist research approaches assume the relationship between
social reality and people is independent and objective. The majority of agricultural
innovation research has been positivist. However, this approach has been criticised for
its treatment of organisational reality, which is seen as complex and not amenable to
statistical deduction (Orlikowski & Baroudi 1990). Positivism is also regarded as being
too deeply rooted in functionalism and too concerned with causal analysis at the
expense of getting close to the phenomenon being studied (Galliers 1991). This
paradigm privileges the perspectives of the researcher over the perspectives of the
research participants.
The interpretive paradigm takes a subjective view of a reality that is seen as socially
constructed (Hatch 1997). It is assumed knowledge of reality is gained only through
social constructions, such as languages, shared meaning and artefacts (Walsham 1993).
In contrast to the positivist paradigm the perspectives of research participants are
privileged over those of the researcher in interpretive research. In interpretive research,
there are no predefined dependent and independent variables, but a focus on the
complexity of human sensemaking as situations emerge (Kaplan & Maxwell 1994). In
this research paradigm the researcher attempts to understand the ‘world’ from the
research participant’s perspective and to convey that perspective authentically in
empirical findings and emergent theory.
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The present research was guided by the analytical framework, shown in Figure 3.3, in
which agricultural industry participants were proposed to make sense of new
technologies within their own interpretive frameworks and through the histories, beliefs,
artefacts and experiences they share with others. By examining how agricultural
industry participants engage with new technologies as a kind of dialogue between an
individual, industry group and technological artefacts, it was felt the sensemaking
evidenced in how the use of new technologies are shaped would become clear.
Therefore, an interpretive epistemological stance was used.
Sensemaking research methods
Sensemaking studies have tended to be ethnographic in nature (Rainbow & Sullivan
1979; Choo & Johnston 2004; Gioia & Chittipeddi 1991) or to have a grounded theory
approach (Gioia & Thomas 1996; Faraj, Kwon & Watts 2004). Ethnographical
approaches put the researcher within the unit of analysis (Rainbow & Sullivan 1979;
Gioia & Chittipeddi 1991) while grounded theory approaches rely on micro-
interpretivist data collection and systematic comparisons in data analysis to uncover
meanings (first order understanding) that reveal relevant patterns (second order
analysis).
In a review of the diverse body of sensemaking research, Weick (1995, pp. 172-3)
suggested the majority of sensemaking studies have nine characteristics, namely:
1. Investigators made an effort to preserve action that was situated in context.
2. Participants’ texts were central and there was less reliance on researcher-
specified measures.
3. Participants, rather than observers, defined the environment.
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4. Findings were described in terms of patterns rather than hypotheses.
5. Explanations were tested as much against common sense and plausibility as
against a priori theories.
6. The density of information and the vividness of meaning were as crucial as
precision and replicability.
7. There was an intensive examination of a small number of cases, rather than a
large number of cases, under the assumption that person-situation interactions
were similar across classes of people and situations.
8. Settings were chosen more because they were accessible than because they were
representative.
9. The methodologies dealt with meanings, rather than frequency counts.
These nine characteristics provided a guiding framework for the design of the present
study.
The Case Study Method
In light of the methodological advice given by Weick (1995) and others, a case study
research strategy was used to examine how participants in the Australian wool industry
made sense of objective wool fibre testing technologies and how this sensemaking
shaped the use of these technologies over time. The intention of using this research
approach was to capture the socio-cognitive mechanisms underlying the social
construction and use of objective wool fibre testing technologies and to examine them
in the context of the innovation process. The objective wool fibre testing technologies
that are the subject of the case study are described in detail in Chapter 4 and Chapter 5.
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Case study research is an effective way to investigate and understand complex
processes in their naturalistic setting (Sechrest, Stickle & Sidani 1996; Yin 1994;
General Accounts Office 1987). Yin (1994) defined a case study as an empirical
enquiry that investigates a contemporary phenomenon within its real life context when
the boundaries between phenomenon and context are not clearly defined. He argued a
“case study allows an investigation to retain the holistic and meaningful characteristics
of real-life events” (Yin 1994, p. 2). Case study methods make the capture and
understanding of action-in-context possible and can be used to achieve a variety of
research aims using diverse methods of data collection and analysis (Pare & Elam
1997). Case studies can involve single or multiple cases and numerous levels of
analysis and there can be multiple levels of analysis within a single case (Yin 1994). As
researchers have demonstrated that it is difficult to separate a new technology from its
implementation context (Orlikowski 1992), case study research is particularly well-
suited to the investigation of sensemaking in an innovation process.
Case studies are appropriate in studies such as this where the primary goal is to answer
questions about how and why phenomena occur in a certain setting as they provide
researchers with an opportunity to generate and test theory. Robson (1993) pointed out
that case studies can be classified into three main types, namely:
1. Descriptive case studies that are used to profile people, events or situations.
2. Exploratory case studies that are used to build theory by discovering what is
happening and assessing phenomena in that new light. Exploratory case studies
are used to answer questions as to how and why a phenomenon occurred and to
define constructs, propositions or hypotheses in areas where the available
literature or knowledge base is poor (Yin 1994).
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3. Explanatory case studies that are used to identify and compare patterns that may
be causal or relational (Gall, Borg & Gall 1996).
Case studies can be used to test theory (Yin 1994) or to develop theory (Eisenhardt
1989). In the present study, a theory-building approach was used in which case study
evidence generated theory from the data collected and analysed. Anderson (1994) has
suggested two different approaches to theory building case study research. A researcher
can work with an explicitly specified conceptual framework that “consists of a selection
of concepts and relations among them, grouped so as to enable its users to easily see the
major concepts simultaneously in their relations to one another” (Kochen 1985, p. 93).
Such a conceptual framework is the “researcher’s first cut at making some explicit
theoretical statements” (Miles & Huberman 1994, p. 91). Alternatively, a researcher
can try not to be constrained by prior theory and develop relevant theory, hypotheses
and constructs within the study itself.
According to Eisenhardt (1989), theory-building case study research should begin as
close as possible to the ideal of there being no a priori theory, as such theories may
limit and bias the research results, although this is virtually impossible to achieve.
Instead, like Anderson (1994), she suggests the a priori specification of constructs in a
conceptual framework can provide a “firmer empirical grounding for the emergent
theory” (Eisenhardt 1989, p. 536). However, unlike Anderson (1994), she warns
against identifying specific relationships between constructs a priori. In the present
study, a theory-building research approach was adopted that specified sensemaking
concepts as a guiding analytical framework on which to base the development of a
theory of the agricultural innovation sensemaking process and specifically how the
sensemaking of agricultural industry participants shaped the use of new technologies in
their own context.
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Strengths of case study method - One of the main strengths of the case study method is
that it allows a researcher to employ multiple data sources in order to examine a range
of historical, behavioural and attitudinal factors (Yin 1994). The use of multiple data
sources allows a triangulation of evidence, which is a process of seeing whether
different lines of enquiry converge, supporting a study’s central findings (Miles &
Huberman 1994). Patton (1987) noted triangulation can be undertaken in case study
research in four ways, namely through the use of:
1. Different data sources.
2. Different investigators.
3. Different perspectives of the same data set.
4. Different methods.
In the present study, triangulation was achieved through the collection and analysis of
multiple sources of data relating to the focal innovation initiatives and technologies.
Both quantitative and qualitative case study data were collected and analysed as
“multiple sources of evidence [that] essentially provide multiple measures of the same
phenomenon” (Yin 1994, p. 92). Industry participants were interviewed and research
and wool production data were examined to verify and triangulate the interview data.
Criticisms of case study method - The case study method has been criticised for lacking
external validity as it can be dangerous to generalise findings from single or a small
numbers of cases (Yin 1994). However, as Yin (1994), has pointed out, scientific facts
are rarely based on single experiments. They are typically based on multiple
experiments that seek to replicate a phenomenon under different conditions. This
‘multiple experiment’ approach can be used in case study research by adopting a
multiple case study design. The ‘multiple experiment’ approach is used in this thesis by
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examining the case of agricultural innovation in the Australian wool industry at the
industry, technology and individual enterprise levels.
Case study data collection - An examination of an evolving innovation process requires
an openness and sensitivity to emerging events, artefacts and data. The principle of
openness and sensitivity to emerging data is grounded in the ethnographic research
tradition. Preliminary ethnographic research was undertaken through observation and
interviews with key industry participants to identify appropriate units of analysis,
research sites and sources of data and to evaluate the relevance of the preliminary
analytical framework (shown in Figure 3.3).
The analysis of empirical innovation data required an historical perspective as it was
assumed the challenges facing participants and the belief systems through which they
filter cues were historically constructed. Sensemaking offers a framework for studying
the innovation process from an historical, social and cultural perspective and at an
individual, group and industry level. In order to understand and interpret the technology
actions observed, the empirical study was supported with studies of historical data that
showed how participants in the Australian wool industry made sense of objective wool
fibre testing technologies and how this sensemaking shaped their use of these
technologies. The case study strategy used offered a possibility for context-specific
findings and explanations and allowed the analysis of complex interactions and
relationships. The findings were used as a means of reflective dialogue between the
researcher and participants, enabling validity assessment to be built into the research
process.
The case study subject - Objective wool fibre testing technologies were studied for three
main reasons, namely:
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1. The development of ways to objectively measure wool fibre characteristics has
been a major innovation initiative in the Australian wool industry since the
1970s.
2. The introduction of objective fibre testing technologies has had a substantial
impact on the wool textile supply chain.
3. Objective wool fibre testing technologies have been subject to continuous
technological change and development since their introduction into the
Australian wool industry more than thirty years ago.
Introduction to the empirical research studies
In the present research, the empirical analysis of how participants in the Australian wool
industry made sense of objective wool fibre testing technologies and how this
sensemaking shaped their use of these technologies over time was divided into three
separate but interrelated studies, namely:
1. The co-evolution of agricultural innovation and Australian wool industry belief
systems (which is discussed in Chapter 4).
2. Fad, fashion, compliance or efficient choice? A study of the diffusion of new
technologies in the Australian wool industry (which is discussed in Chapter 5).
3. The enactment of new technologies on-farm: A sensemaking perspective (which
is discussed in Chapters 6 and 7).
The first study examined how objective wool fibre measurement innovation initiatives
and Australian wool industry belief systems co-evolved over time. The data used in this
study were collected from an analysis of historical industry policy documentation,
research and practice publications and production data. The second study was an
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historical, chronological case analysis of the acceptance and abandonment of Additional
Measurements (AM) and Clean Colour testing (CC) technologies by Australian wool
industry participants. In this study, sensemaking concepts were extended to the analysis
of patterns of industry discourse and analysis reflecting conflict, consensus and
compliance in the sensemaking of industry participants that contributed to the
acceptance of AM and abandonment of CC testing technologies. The data used were
collected from the Australian wool auction database, which has recorded details of
objective tests undertaken on every wool lot offered at auction in Australia from 1988.
Data were also collected from an analysis of published research articles and policy
documents that discussed AM and CC testing.
The third and final study examined the socio-cognitive mechanisms underlying the
enactment of AM and CC on-farm. This was an ethnographic study of six wool-
growing families and their farm enterprises that were located in the south west of
Western Australia. The cases were selected from homogeneous groups of commercial
wool production enterprises found through the clustering of a large number of farm
enterprises. The cluster analysis enabled the selection and examination of similar cases
from within clusters (to provide literal replication) and of contrasting cases from
different clusters (to provide theoretical replication). Literal replication is a case study
(or studies) that tests precisely the same outcomes, principles or predictions established
by the initial case study. In contrast, a theoretical replication is a case study that
produces contrasting results but for predictable reasons (Yin 1994).
Individual case study data were collected through observation, semi-structured
interviews and ongoing discussions. Semi-structured interviews were conducted with
adult farm family members (aged eighteen years and over) and relevant advisors who
were engaged in the management of the farm business. Typically, four interviews were
conducted with the participants in the farm business. Each interview lasted, on average,
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for 74 minutes. The researcher undertook all observations and interviews with family
farm business members at the farm property. Family members were interviewed
together wherever possible to examine the notion of collective sensemaking through
discussion and shared meaning. Analysis of the data collected and generated in the
cases studies involved data reduction, data display and conclusion-drawing and
verification (Miles & Huberman 1994) that were undertaken concurrently during the
study.
Conclusions
In this Chapter, the seven main properties of sensemaking were discussed in order to
develop a theoretical and empirical rationale for studying agricultural innovation as an
occasion for sensemaking. It was clear from the overview of the agricultural innovation
and sensemaking literature that agricultural innovation represents an occasion for social
sensemaking. However, the agricultural innovation process has not been examined
from this perspective, creating a gap in our understanding of how agricultural industry
participants make sense of new technologies in their own context and how that
sensemaking shapes technology use. In this Chapter the review of the agricultural
innovation and sensemaking literature supported the development of an analytical
framework for agricultural innovation sensemaking (shown in Figure 3.3). Through the
preliminary analytical framework it was proposed that the agricultural innovation
sensemaking process is an evolutionary, organising process and that sensemaking at the
individual, group and industry level contributes to the construction and reconstruction
of shared technology fames and industry belief systems. In the final section of this
chapter the methodological issues associated with applying the preliminary analytical
framework to the agricultural innovation process were discussed and the case study
method was proposed as a means of capturing the socio-cognitive mechanisms
underlying the social construction and use of objective wool fibre testing technologies
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in context. In this section the three empirical studies of agricultural innovation
sensemaking that form the body of the thesis were introduced.
In the following Chapter, the first of the three empirical agricultural innovation
sensemaking studies is presented. This study, ‘The co-evolution of agricultural
innovation and Australian wool industry belief systems’, examines how Objective
Measurements (OM) testing technologies were developed, introduced, adopted and
diffused in the Australian wool industry.
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4 The co-evolution of agricultural innovation and Australian wool industry belief systems
Why are some new agricultural technologies rapidly adopted and diffused, while others are
slow to diffuse or are rejected outright? What is the process by which new agricultural
technologies replace existing technologies? How can the process be managed effectively to
increase the adoption and diffusion of new agricultural technologies? These questions are
central to the study of agricultural innovation and were addressed in the present study of the
Australian wool industry.
The literature reviewed in Chapter 2 identified some shortcomings in the dominant
agricultural innovation models in which potential adopters are viewed as rational and
passive actors; the relative performance of new technologies is assumed to be knowable,
quantifiable and uncontested and the power and interest of actors within a social system are
not considered to influence adoption and diffusion. Addressing these issues is critical in
the Australian wool industry, which is characterised by a long and fragmented supply chain
that is made up of groups of industry participants with different beliefs, values, goals and
levels of power and influence.
The Australian wool industry has a long history of innovation-based development
(Australian Bureau of Statistics 2002) that has included the successful introduction of
mechanical shears (Palmer 1980), pasture improvement technologies (Balderstone et al.
1982) and the use of genetics in breeding and selection (Balderstone et al. 1982; Massey
1990). However, in more recent times, the industry has been criticised for being traditional,
conservative and slow to embrace innovation and change (Wool Industry Future Directions
Task Force 1999; International Wool Secretariat 1996).
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Australia is the largest wool-producing nation in the world (Food and Agriculture
Organisation 2002) producing 27 per cent of the world’s greasy wool in 2005-06
(Australian Wool Innovation 2007). However, Australian woolgrowers have been in the
grip of a severe “cost-price squeeze” since the 1970s (Australian Bureau of Statistics 2002).
The combination of worsening terms of trade and poor productivity gains over this period
has resulted in a sharp decline in wool production and the number of wool growing
enterprises, placing a question-mark over the future of the industry (Shafron, Martin &
Ashton 2002). While the industry contracts as a result of this cost-price squeeze and
increasingly volatile market conditions, industry bodies and the Australian Government
continue to invest woolgrower levies and taxpayer funds in research and development in
the hope that innovation will reverse the current fortunes of the industry (Woolmark
Business Intelligence Group 2004)8. However, Australian wool industry commentators
have argued that investment in innovation initiatives has not prevented a decline in wool
production and productivity as woolgrowers have a low level of technology adoption
compared with other Australian rural industries (Wool Industry Future Directions Task
Force 1999; Woolmark Business Intelligence Group 2004).
The present study presents a longitudinal historical case study, aimed at building a theory
about the co-evolution of agricultural innovation and industry belief systems through the
sensemaking of industry participants. In the study the development, introduction, adoption
and diffusion of new technologies for the Objective Measurement (OM) of wool in the
Australian wool industry from 1957 to 2001 were investigated. The research questions in
the present study were simple, namely:
8 Australian Wool Innovation Ltd (AWI) funds research, development and innovation in the Australian wool industry with a projected expenditure in 2007-08 of $48.9 million (Australian Wool Innovation Ltd, Company
Funding, AWI Ltd. Available from: http://www.woolinnovation.com.au/About_AWI/Company_funding/page__2144.aspx [25th October 2007].).
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1. How were Objective Measurements testing technologies developed,
introduced, adopted and diffused in the Australian wool industry?
2. How can the innovation process in the Australian wool industry be
improved?
These research questions were asked with a particular emphasis on industry discourse about
OM as a way to identify industry participants’ changing beliefs and actions over time and
the relationships between shared technology frames and industry belief systems. The
findings link responses to OM testing technologies with conflict, consensus and compliance
with industry beliefs, highlighting the co-evolutionary nature of agricultural innovation and
industry belief systems.
This Chapter is divided into five sections. The first section presents the theoretical
background of the study. In this section the proposition of agricultural innovation as a
social sensemaking process is advanced with particular emphasis on the reciprocal interplay
between individual and collective ‘sense’. The second section describes the research
method and introduces the Australian wool industry Objective Measurements (OM)
innovation initiatives that are the focus of this thesis. The third section presents the
findings of the case analysis as a selective chronology of the enactment of OM technology
frames and industry belief systems by Australian wool industry participants. The fourth
section discusses the case study findings and advances tentative research propositions.
Finally, a conclusion to the chapter is provided.
4.1 Theoretical background
The agricultural innovation literature can be divided into three main categories:
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1. Research explaining temporal and spatial patterns of technology diffusion (e.g.
Sheaffer 2000; Marsh, Pannell & Lindner 2000; Carr 1997; Hildebrand 1993;
Fliegel 1993; Ryan & Goss 1943; Griliches 1957; Bohlen 1964).
2. Research aimed at establishing the determinants of adoption decisions by
individuals (e.g. Rogers & Shoemaker 1971; Abolaji 1993; Nowak 1987; Findlay
1980; Wilkening 1953; Beal & Rogers 1960; Feder 1980).
3. Research that seeks to explain the process by which new technologies are adopted
(e.g. Clark 1995; Biggs 1989; Horton & Prain 1989; Chambers & Jiggins 1986).
In the agricultural innovation literature, most empirical research has examined diffusion
patterns and determinants of adoption. The present study is located in the less examined
third category that is concerned with the innovation process.
The dominant theories of agricultural innovation stem from rural sociology and agricultural
economics. These theories take assumptions of rational actors and technology determinism
for granted. Although they offer important concepts, such as the role of change agents and
how individuals make adoption decisions, they provide little insight into the process by
which agricultural innovations are developed, introduced, adopted and diffused. In these
technological determinist models of adoption and diffusion (e.g. Kennedy 1977; Rogers
2003; Jones 1967), adoption is dependent on the potential adopters’ perceptions of the
relative value of the new technology compared to an existing technology or practice. If the
relative advantage of the new technology is quantifiable, the adoption of technologies with
superior value and performance occurs. Adoption is seen as a relatively simple decision
once the performance information of the technology reaches a potential adopter (Rogers
2003). The diffusion of the new technology occurs over space and time as potential
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adopters learn about the relative advantage of the new technology through mass media
(early adopters) or word of mouth (late adopters) (Rogers & Shoemaker 1971). However,
as previously discussed9, the assumptions underpinning these innovation models are
unrealistic and problematic.
The present study attempted to address these shortcomings by approaching the agricultural
innovation process from a social constructivist perspective. Such a perspective questions
the assumptions of technology determinism that dominate agricultural innovation research.
Rather than viewing the adoption of agricultural innovations as a simple, rational,
uncontested, individual choice, in the present study innovation adoption is seen as an
ongoing, evolutionary process of social sensemaking in which there is a reciprocal interplay
between individual and collective meaning-making. This individual and collective
sensemaking is connected and articulated in industry discourse which reflects conflict,
consensus and compliance over co-evolving shared technology frames and industry belief
systems.
4.2 Research Method
As already noted, the present study attempted to address some of the gaps in the
agricultural innovation literature by taking an evolutionary sensemaking perspective. The
study investigated the proposition that agricultural innovation is socially constructed by
industry participants as they struggle to make sense of new technologies in their own
context. The study investigated the proposition that technology frames and the locus of
control of an industry decision to adopt a new technology can be the subject of conflict,
consensus and compliance among industry participants as they attempt to impose their
9 Chapter 2, pp 27-28.
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beliefs about the new technology on others. The success of industry participants in these
struggles depends largely on the power and the influence a participant has over the use of
the new technology.
The preliminary analytical framework, which is shown in Figure 3.3, guided the data
collection and analysis used to address the study’s research questions. It was suggested
Australian wool industry participants interpret technological and environmental events
relating to the objective measurement (OM) of Australian wool through their personal
identity frames and social context and their response to shared technology frames and
industry belief systems. The experience, knowledge, projects, goals, group norms and level
of power and interest of industry participants in OM influence their enactment of stimuli
relating to OM and their selection and retention of salient cues that result from their actions.
Technology frames and industry belief systems are updated as participants enact their
response to these external stimuli.
In the present study particular interest was taken in the role that industry participant’s
power and interest in OM played and how participant groups respond to updated
technology frames and industry belief systems. Porac, Ventresca and Mishina (2002)
suggested the tension between consensus and conflict in industry belief systems are the key
to understanding industry sensemaking. They argued that a time-dependent analysis that
links the form and types of industry participant’s response to industry belief systems over
time is needed. Therefore the present study focused not on the stable consensus of industry
beliefs around OM but, rather, on the changes and constellations of beliefs over time and
the links between these beliefs and the response of different groups of industry participants
to OM.
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Porac, Ventresca and Mishina (2002) argued that researchers should focus on documenting
industry consensus or dissensus in a dynamic way. Consequently, the present study was
based on a single, longitudinal, historical case study, aimed at theory-building, which
investigated the development, introduction, adoption and diffusion of OM in the Australian
wool industry. The case study spans the history of OM in the Australian wool industry
from its introduction in 1957 to recent developments in on-farm testing, up to and including
activities in 2001.
The case study of the development, introduction, adoption and diffusion of OM has unique
characteristics that make it an ideal candidate for theory development (Eisenhardt 1989;
Yin 1994). The adoption and diffusion of OM in the Australian wool industry is a natural
experiment as, before 1957, the objective measurement of wool was not undertaken. Prior
to 1957, the development and adoption of these technologies had occurred mostly outside
of Australia.10 Data on the development, introduction, adoption and diffusion of OM
technologies in the Australian wool industry are readily available as technological
developments and industry issues relating to OM were well documented. Initially, the
majority of Australian wool industry participants supported the introduction of post-sale
OM, but opposed pre-sale OM. In the 1950s and 1960s, pre-sale OM was supported by a
relatively small group of researchers. Industry participants, such as policy makers, wool
buyers and wool selling brokers, opposed the introduction of pre-sale OM as they struggled
10 The projection microscope was used in Europe as early as 1777 to measure wool fibre thickness and the relationship between crimps per inch and mean fibre thickness. The International Wool Textile Organisation was established in Europe in 1930 to standardise wool fibre testing methods across member nations. In 1937 the United States Department of Agriculture and Customs developed core-testing technologies for the objective measurement of the clean yield of imported greasy wool to apply tariffs. In the early 1950s the Wool Industry Research Association (WIRA) and British Wool Federation developed testing technologies for yield and in 1955 WIRA developed Airflow technology for objectively measuring mean wool fibre thickness (Hamilton, JM 1973, 'Modern methods of wool selling', Wool Technology and Sheep Breeding, vol. 20, no. 1, pp. 61-66, Baxter, BP 2002, 'Raw-wool metrology: Recent developments and future directions', Wool
Technology and Sheep Breeding, vol. 50, no. 4, pp. 766-779, Sommerville, PJ 2002, 'Wool metrology: Past and current trends and future requirements', Wool Technology and Sheep Breeding, vol. 50, no. 4, pp. 853-860.
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to make sense of major changes in the textile processing industry and increasing
competition from synthetic fibres. Therefore, the story of the development, introduction,
adoption and diffusion of OM in the Australian wool industry provides an excellent
opportunity to investigate the co-evolution of agricultural innovation and industry belief
systems over time.
OM testing technologies have changed considerably since 1957. Over the four decades
since its introduction into the Australian wool industry, OM has involved a wide range of
industry participant groups and has impacted on the production, preparation, marketing,
sale and processing of Australian greasy wool. OM has evolved into an important
technological system within the Australian wool industry; yet its history is marked by
technological failure and success, conflict over industry policy and dramatic structural and
operational changes in the Australian wool industry.
Drawing on numerous primary sources of data by way of industry reports, policy
documents, scientific articles and interviews with representatives of industry participant
groups, the present study documented and examined:
1. The development, introduction, adoption or rejection of new OM testing
technologies and methods by industry participants.
2. Industry beliefs related to OM and the changes in these beliefs over time.
3. The responses of industry participants to evolving technology frames and industry
belief systems.
Data were collected from a range of qualitative and quantitative sources, among which
were primary documents, secondary source accounts, technology adoption and use data and
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interviews conducted with representatives of wool industry participant groups. The
interviewees were selected to represent the main types of industry participants in the OM
innovation initiative. The following sets of participants were interviewed: two senior
executives at the Australian Wool Testing Authority (AWTA) Ltd, to represent wool
metrologists and textile scientists; a representative of the wool selling broker sector and
wool export sector to represent participants in wool marketing, buying and exporting; a
member of the wool processing industry; an animal scientist (applied research), an animal
scientist (fundamental research) and a textile scientist, all from different research
organisations. Two industry policy makers involved in OM initiatives were also
interviewed.
The interviews with representatives of wool industry participant groups were conducted by
the researcher on the interviewee business premises. These were unstructured interviews
that asked about OM innovation initiatives and lasted for approximately one hour. All
interviews were taped and verbal responses were transcribed for analysis. Transcripts from
the interviews were typed and sent to the interviewees for review to ensure they were a
correct representation of interview content. The interviews were undertaken after an initial
review of documentary evidence and technology use data and the content of these
interviews was used to triangulate the bibliographic and technology use data that were also
collected in the study.
The documents selected for review in the present study were identified through a search of
wool-fibre testing publications stored in the Australian Livestock Library.11 Documents
were also selected from the libraries of Australian wool industry organisations (i.e.
11 The Australian Livestock Library is an online database of over 22 000 livestock publications from the 1950s to the 2000s, http://www.livestocklibrary.com.au/.
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Australian Wool Innovation Ltd, Woolmark, the Australian Wool Testing Authority and the
International Wool Textile Organisation) that house archived industry policy and
technology reports. In total, 522 publications and reports relating to OM in the Australian
wool industry were identified and reviewed. Figure 4.1 shows the number of OM related
documents identified that were published each year from 1957 to 2001.
0
10
20
30
40
50
19571960
19631966
19691972
19751978
19811984
19871990
19931996
1999
Year
Nu
mb
er
of
OM
pu
bli
ca
tio
ns
Figure 4.1: The number of Objective Measurements publications per annum, 1957-
2001
The data were analysed in four stages. In the first stage, a chronological narrative account
of the events surrounding the development, introduction, adoption and diffusion of OM was
developed by ordering raw data from the documentary review and triangulating these data
with accounts given by interviewees. The data used in the chronological narrative are
summarised in Appendix B. Secondly, the narrative was coded into four broad categories
defined in the preliminary analytical framework that was shown in Figure 3.3. The a priori
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categories used for coding were Interpretation Framework, Technological and
Environmental Events, Technology Frames and Industry Belief Systems. The
categorisation of narrative accounts is also summarised in Appendix B. The four a priori
analytical categories were refined by pattern-matching the accounts made by interviewees
from industry participant groups. Thirdly, an iterative process of combining the findings
from the case study with the extant literature was undertaken to develop constructs and
relationships that were true to the events that occurred in the case and meaningful in terms
of existing theory. Finally, the new constructs and relationships that emerged from
integrating the case study findings with the literature were used to develop tentative
research propositions relating to the development, introduction, adoption and diffusion of
new agricultural technologies.
4.3 Case Study Findings: A selective chronology of OM innovation in
the Australian wool industry
This case study was not meant to be an authoritative or exhaustive account of the history of
Objective Measurements in the Australian wool industry; instead it presents details of four
major OM innovation initiatives that support the telling of the overall story of the co-
evolution of shared technology frames and industry beliefs relating to the measurement of
Australian wool from 1957 to 2001. A comprehensive glossary of wool industry terms
used in this study is provided in Appendix A.
The context for objective wool fibre measurement
From the establishment of the Australian wool industry in the late eighteenth century until
1957, the attributes of greasy wool (mainly crimp frequency and fibre thickness, fibre
length, fibre strength, style, handle and colour) were subjectively appraised by eye or hand
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by woolgrowers, classers, selling brokers, buyers and processors (Lang 1961). The
subjective appraisal of greasy wool attributes was used along the wool supply chain:
1. To prepare and class the clip in the shearing shed into lines for sale.
2. For the wool selling broker to market wool sale lots on the show floor and in
auction sale catalogues.
3. For the buyer to value wool sale lots and to prepare bulk consignments for
processors to meet processing requirements.
4. For processors to sort wool sale lots for processing (Welsman 1981).
The objective measurement (OM) of the physical properties of greasy and processed wool,
known as wool metrology, provided a scientific means by which to measure, predict and
control the impact of wool fibre variability during processing and, as such, has become a
fundamental component of modern wool textile processing and marketing systems
(Sommerville 2002). Objective Measurements have been gradually introduced into the
Australian wool industry since the 1950s through a range of innovation and policy
initiatives that replaced and supplemented traditional subjective wool appraisal practices.
The present study examined the co-evolution of innovation and industry beliefs through
four major OM initiatives from 1957 to 2001, namely:
1. The introduction of post-sale OM in 1957.
2. The development and introduction of pre-sale OM and Sale by Sample (SXS),
which started in 1969.
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3. Sale with Additional Measurements (SXAM), which was introduced in the mid-
1980s.
4. Sale by Description (SXD), which was initially proposed in 1973 but finally
abandoned without being implemented in 2001.
Australian wool industry response to increasing competition from synthetic fibres (1957-
1968)
In this period, greasy wool fibre was increasingly described by its textile properties, rather
than by its fleece or staple properties (Whan 1967). This shift in the definition of greasy
wool product was largely driven by competition from newly developed synthetic fibres. At
the start of this period there was a broad consensus within the Australian wool industry that
the competition for Australian wool was not other wool producing nations or domestic
woolgrowers, but the burgeoning manufacture and use of synthetic fibres in textile
production.12 There was considerable support in the Australian wool industry for the
introduction of the post-sale Objective Measurement (OM) of greasy wool consignments to
enable greasy wool fibre to compete with uniform, objectively specified synthetic fibres
(Gruen 1959; MacKay 1968; Chaikin 1963). It was widely believed post-sale OM would
enable wool buyers to build uniform bulk processing consignments to objective
specifications.
In 1957, the Australian Commonwealth Government established the Australian Wool
Testing Authority (AWTA) in response to demands from international and domestic wool
12 Synthetic fibres were created as a stable, uniform product processed in the same way as wool and cotton fibres. The introduction of synthetic fibres from the 1920s to the 1950s created a demand in the textile industry for uniform fibres, processing performance prediction and objective specification (MacKay, BH 1968, 'Some technical aspects of testing greasy wool on a large scale', Wool Technology and Sheep Breeding, vol. 15, no. 2, pp. 22-30, Chaikin, M 1963, 'General introduction with special reference to competitive fibres', Wool Technology and Sheep Breeding, vol. 10, no. 1, pp. 89-97.).
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processors for the more accurate measurement and specification of wool fibre
characteristics (Dixie 1958; Sommerville 2002). The Australian wool industry, through the
AWTA, joined its counterparts in the USA13 and Britain in introducing the post-sale testing
of greasy wool fibre for clean yield and mean fibre diameter (FD). Sampling and testing
technologies, developed in the USA and Britain, were adopted by the AWTA for this
purpose (Skinner 1964; Sommerville 2002).
The introduction of post-sale OM in the Australian wool industry helped to reduce contract
disputes between buyers and processors as consignments could be specified and delivered
with objective, independent test certificates (Dixie 1958). Wool buyers were also able to
request guidance tests of individual wool sale lots in order to select greasy wool for
consignments. By 1968, around forty per cent of the Australian clip was post sale tested for
yield and FD (McKenzie 1972) and it became common practice for wool combers to supply
wool tops to processors with OM specifications (MacKay 1968).
As the use of post-sale OM increased, so too did the body of Australian wool metrology
research. Researchers exposed the inaccuracy of subjective appraisal methods in the
preparation and valuation of greasy wool fibre (e.g. Pressley 1957; Gruen 1959; Boyer
1959). The use of the subjective appraisal of fibre crimp frequency as a proxy measure of
mean fibre diameter was strongly criticised (e.g. Lang 1961; Skinner 1964; Boyer 1959;
Chapman 1964). This body of research motivated wool metrology and textile researchers
from the University of New South Wales (UNSW), the Commonwealth Scientific and
Industrial Research Organisation (CSIRO) and the Bureau of Agricultural Economics
(BAE) to propose the introduction of OM along the wool supply chain from the breeding
13 In the 1940s the US Customs Bureau and Treasury Department introduced core-testing for yield to accurately levy duties on wool imports (Johnston, A 1955, 'Core-testing in the United States', Wool
Technology and Sheep Breeding, vol. 2, no. 2, pp. 93-99.).
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and selection of sheep, through clip preparation and marketing to the sale of individual lots,
consignment building and processing (e.g. Charlton 1965; Whan 1968b; Skinner 1964;
MacKay 1968; MacKay & David 1965). However, the proposal for the introduction of pre-
sale OM was rejected by the wool industry statutory authority, the Australian Wool Board
(AWB) and wool selling brokers and buyers.
The 1950s and 1960s was a period of technological uncertainty as wool metrologists
developed numerous competing greasy wool sampling and testing technologies and
methods. Various instruments and methods were developed for objectively measuring FD,
including micro projection, the micrometer, airflow or porous plug, weight-length, optical
inference, sedimentation of fibres in a liquid medium, photometric measurement,
vibroscope and electronic scanning of fibre profiles (Lang 1961). Eventually Airflow
emerged as the dominant testing technology for FD in the Australian wool industry with the
projection microscope used for instrument calibration. A range of test methods for clean
yield was also used in this period.14 The lack of industry consensus around the
technologies and methods used in post-sale OM was the cause of a great deal of frustration
among wool buyers (Douglas 1968). The CSIRO, AWTA and the New Zealand Wool
Testing Authority (NZWTA) developed manual pressure core-testing equipment suitable
for high volume objective testing of greasy wool with the potential to be used in pre-sale
OM (Taylor 1988; NSW Department of Technical and Further Education 1976).
The quality of the preparation of the Australian clip had been the subject of criticism from
the wool trade and the AWB for some time. Many buyers and processors claimed the
preparation of the Australian clip had deteriorated in the 1950s as a result of high demand 14 Five methods for testing clean yield were developed and introduced between 1957 and 1967: American Standards method, British Wool Federation method, International Wool Textile Organisation method, Japanese clean scoured yield method, and the AWTA method (Douglas, SAS 1968, 'Yield and micron testing in a commercial wool testing laboratory', Ibid., vol. 15, pp. 41-44.).
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and prices that created a marketing environment in which poorly prepared clips were not
penalised with market price discounts. In 1961 in response to criticisms about clip quality,
increasing competition from synthetic fibres and falling wool prices, the Minister for
Primary Industry appointed a Committee of Enquiry to examine Australian wool marketing
and promotion systems (Adermann 1963; Phelp, Butterfield & Merry 1962). The
Committee recommended the establishment of an OM system for the central appraisal and
sale of Australian wool through the auction system (Phelp, Butterfield & Merry 1962). The
Committee also suggested the Australian wool industry should take the lead in the
development and commercialisation of OM testing technologies and methods (Phelp,
Butterfield & Merry 1962).
The AWB rejected the recommendations of the Committee of Enquiry for a central
appraisal system and the introduction of pre-sale OM and, instead, focused on improving
clip preparation standards and practices (Anon 1963; Fraser 1969). In 1963 the AWB
reached an agreement with representatives of woolgrower, selling broker and wool buyer
groups to introduce a voluntary register of wool-classers, a clip inspection service managed
by the AWB and new clip preparation standards. By July 1963, 3000 wool classers had
registered under the voluntary scheme (Australian Wool Board 1963). Although the new
scheme was broadly supported by the trade and the Australian Government, researchers
could find little evidence that well prepared wool lots achieved price premiums (Paynter
1960; Whan 1968a; Ponting 1964; Fraser 1969). These researchers argued clip preparation
should be based on the same objective criteria used by buyers to prepare consignments for
processors.
Despite the lack of industry support for pre-sale OM, in the mid-1960s a small number of
woolgrowers began to request yield and FD guidance tests from the AWTA and objective
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fleece tests from new fleece testing services (Morgan 2003; Ward & Somerville 2003).
However, any OM data supplied with a sale lot was excluded from the sale catalogue and
could only be displayed with bales on the show floor or published separately in the press
(Whan 1968b). In effect, wool buyers and brokers controlled the use of OM along the wool
supply chain and actively blocked the exchange of OM data between woolgrowers and
processors in this period (Whiteley 1967).
The parallel use of post-sale OM and subjective appraisal in this period resulted in the
emergence of two very different and conflicting product nomenclatures for greasy wool
fibre in the Australian wool industry (Whiteley 1967; Whan 1967). Whan (1967, p. 47)
argued “there are two languages in the trade and in some cases they are difficult to
reconcile”. Researchers, processors and buyers increasingly used objective descriptions of
greasy wool fibre, with measurements of clean yield, FD and Vegetable Matter content
(VM) replacing the use of subjectively appraised ‘Type’ and ‘Quality Number’ (Skinner
1960). However, woolgrowers and wool selling brokers continued to use traditional
subjective descriptions of greasy wool fibre attributes in clip preparation and marketing.15
Wool buyers employed both nomenclatures, using objective descriptions of greasy wool
with processors and subjective descriptions in the valuation and purchase of individual sale
lots.
The introduction of pre-sale Objective Measurement and ‘Sale by Sample’ into the
Australian wool auction system (1969-1979)
15 Primarily fleece type, length, soundness, handle, colour or bloom, character, density, evenness, yield and weight (National Council of Wool Selling Brokers of Australia 1960, 'Fleece Judging', Ibid., vol. 7, pp. 117-118.).
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In 1969 the Australian Wool Board (AWB) responded to calls from researchers and the
AWTA (McMahon 1969) and investigated the feasibility of introducing pre-sale OM into
the wool selling system. Two Committees were established to report on the status and
accuracy of OM and how it might be used to improve clip preparation and the marketing
and processing of Australian wool (Ward 1969; Whan 1973; McKenzie 1972; Baxter
2002). However, there is evidence to suggest the AWB, wool buyers and wool selling
brokers continued to oppose the introduction of pre-sale OM (Higginson 1969; Fraser
1969). For example, researchers who supported the introduction of pre-sale OM were
excluded from the AWB Committees, which were made up of members of the wool trade
(Fraser 1969).
The Australian Wool Commission (AWC) replaced the AWB in 1970 and attitudes towards
the introduction of pre-sale OM changed. The AWC launched the Australian Objective
Measurement Project (AOMP) to investigate the technical and organisational feasibility of
selling wool with core-tests for yield, fibre diameter (FD) and vegetable matter content
(VM) (Morgan 2003; McKenzie 1972). The AOMP concluded that objective preparation
and pre-sale testing were applicable to much of the Australian clip and were both
technically and economically feasible (McKenzie 1972).
After successful trials of pre-sale OM in 1972, core-tests of yield, FD and VM were offered
to Australian woolgrowers (Taylor 1988). The introduction of pre-sale OM enabled
significant changes to be made to the Australian wool marketing and selling system. Under
the system of subjective appraisal, sample or show bales were displayed on the sales floor
of the wool selling brokers’ stores for wool buyers to appraise. With pre-sale OM, test
results were printed in the sale catalogue and only a small, representative sample of the sale
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lot was displayed for the buyer to subjectively appraise unmeasured characteristics
(Butcher 1983). Bids for sale lots were based on core-test results and buyers’ subjective
appraisals of the sample (Taylor 1988). This new marketing system was called ‘Sale by
Sample’ (SXS). Less than one per cent of the Australian clip was pre-sale tested and sold
by sample in 1972. However, by 1979, 86 per cent of the Australian clip was tested and
sold in this way (Welsman 1981), as can be seen in Figure 4.2, which shows the average
proportion of the Australian clip sold with OM (yield, FD and VM), Additional
Measurements (AM) and Clean Colour (CC) test results each year from 1972 to 2001.
0
10
20
30
40
50
60
70
80
90
100
1972 1975 1978 1981 1984 1987 1990 1993 1996 1999
Avera
ge %
of
the A
ustr
alian
Clip
Additional Measurements Clean Colour
Objective Measurements
Figure 4.2: The average proportion of the Australian clip with OM, AM and CC
(source: Welsman (1981) and AWEX) Woolgrowers and brokers stood to gain substantially from the introduction of pre-sale OM
and SXS through reductions in marketing, storage and handling costs. However, wool
buyers viewed this new industry recipe as a threat to their position as information brokers
in the wool supply chain (Morgan 2003; Booth 1974; James 1974). Despite buyers’
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widespread use of post-sale OM, the introduction of pre-sale OM was viewed as a
challenge to their skills and experience in valuing the Australian clip (James 1974). Wool
buyers continued to question the accuracy of OM after the introduction of pre-sale OM and
opposed the introduction of further pre-sale objective fibre measurements (Booth 1974).
In the mid 1970s, the AWC and AWTA concentrated on generating demand for OM among
wool processors in order to bypass reluctant wool buyers (Ward & Somerville 2003). The
CSIRO undertook research with processors to determine their wool classing needs.
Researchers also undertook processing trials using lots sold with OM to gain trade support
for pre-sale OM and SXS (Andrews & Rottenbury 1975; Downes 1975; Anon 1973). As
processing trials demonstrated the value of purchasing consignments made up of clips with
OM, more processors requested consignments made up of sale lots with OM. Price
premiums emerged for sale lots with OM, creating demand among woolgrowers for pre-
sale OM and SXS (Ward & Somerville 2003; Sommerville 2002).
In order for the Australian wool industry to maximise value from the introduction of pre-
sale OM and SXS, substantial changes were required to the way in which individual clips
were prepared on-farm. The aims of traditional clip preparation practices were to identify
wool for sale with different manufacturing uses16, to present buyers with lines that were
easy to appraise and to offer lines for sale that were attractive to buyers (McMahon 1987).
Traditional clip preparation practices resulted in the unnecessary fragmentation of the clip
and a relatively high number of small sale lots entering the auction system (Dalton 1972).
16 Greasy wool is used in a range of different manufacturing systems which have specific fibre attribute requirements. The diameter, strength and length of the greasy wool fibre determine which manufacturing system the wool fibre is used in. For example the woollen or worsted system, Noble or continental combing, carbonising or mechanical treatment of wool (McMahon, PR 1987, 'A new look at wool classing', Ibid., vol. 35, no. 1, pp. 30-32.).
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In 1972, the AWC OM Policy Committee recommended the introduction of Objective Clip
Preparation (OCP) (Dalton 1972). The aim of OCP was to consolidate individual clips to
produce larger wool sale lots with core tests provided at the point of sale, thereby reducing
handling costs (Dalton 1972). OCP emphasised fleece skirting, the removal of cast fleeces
and the classing of fleeces from a single mob of sheep into one line (Welsman 1976). It
was anticipated that the introduction of OCP would provide good returns to woolgrowers
and reduce testing and clip preparation costs significantly. However, by 1976, although 55
per cent of the Australian clip was sold by sample with pre-sale OM, only five per cent of
those lots were prepared using OCP (Welsman 1981; Welsman 1976). Woolgrowers who
adopted OCP were penalised in the market as wool buyers discounted OCP clips at auction
for not being visually uniform (McMahon 1972; Whiteley 1972; Johnston 1985).
However, despite the slow adoption of OCP and the negative price signals, by the end of
the 1970s the average wool lot size had increased and the number of wool lots offered at
auction had decreased (MacKenzie 1979).
The majority of testing technologies and methods required for the introduction of pre-sale
OM had been developed and adopted under the post-sale OM initiative. However, the
launch of the AOMP presented a significant opportunity for wool metrology researchers to
expand their research and development activities. Australia emerged as the centre of wool
metrology research and technology development in the 1970s (Baxter 2002). In this period
the CSIRO’s Division of Textile Physics (DTP) developed a new washer/dryer for clean
yield testing, a sonic fibre fineness tester, prototype core and grab sampling equipment,
wool base analyser and the Fibre Diameter Video Analyser (FIDIVAN) (Downes 1969;
James & Stearn 1971; Baxter 2002). The CSIRO also started work on the development of
the Almeter for measuring staple and fibre length (SL and FL) (MacKay 1972).
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After the launch of pre-sale OM and SXS, the CSIRO worked closely with the AWTA in
the development of new testing technologies, including a method for testing fibre fineness
and yellowness using radioisotope technology, methods for clean colour testing and the
Laser Fibre Fineness Distribution Analyser (FFDA) (MacKay 1973; Connell & Mackay
1973; Downes 1973; Jackson 1973; Lynch & Michie 1973; Buckenham & Stearn 1976).
The UNSW also undertook a substantial program of wool metrology research and
developed a range of testing technologies and methods, including the Vegemat technology
for measuring VM and methods for testing fleece weight using infrared reflectance
spectroscopy (Wilkins & Whiteley 1977; Scott & Roberts 1978). Of the technologies
developed in this period, FDA, FIDIVAN, grab sampling and colour measurement became
platform technologies for future testing innovation. However, many of the testing
technologies developed in this period, including the sonic fineness tester, the image
analyser system and the wool base analyser, were not commercially successful and were
abandoned (Baxter 2002).
The 1970s was a decade of significant structural, economic and political change in the
Australian wool industry. The cornerstone of the AWC’s approach to reforming the
Australian wool industry was achieving price stabilisation. Declining prices for wool in the
1960s paved the way for the introduction of the Wool Deficiency Payment Scheme
(WDPS), a temporary price stabilisation scheme that was introduced in 1970 to protect
farm business incomes during a sharp decline in the global demand for wool (Ward 1985).
In 1973, the Australian Wool Corporation (AWCorp) replaced the Commission and was
made responsible for coordinating research and promotion initiatives in the wool industry
(L. White 1981; Piggott 1998). The creation of the AWCorp brought responsibility for
research, market price protection and generic promotion in the Australian wool industry
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under the control of a single organisation for the first time. The AWCorp was a strong
supporter of price protection policies and, in 1974, oversaw the replacement of the
temporary WDPS with the permanent Wool Reserve Price Scheme (WPRS). The WRPS,
which set a floor price for wool for the season, was funded by compulsory woolgrower
levies of five per cent of the gross proceeds of wool sales (Department of Agriculture
Fisheries and Forestry 2001; Richardson 2001). Wool offered at auction that failed to reach
the minimum price set by the scheme was purchased under the WRPS and re-offered for
sale when prices improved (Department of Agriculture Fisheries and Forestry 2001).
Around 13 per cent of wool offered at auction between 1974 and 1984 was purchased
through the WRPS (Ward 1985). Wool purchased and sold under the WRPS was routinely
core-tested and sold with OM certificates.
In 1973, after the introduction of pre-sale OM and SXS, the AWCorp proposed that OM
should replace subjective appraisal practices and that Australian wool should be sold by
description only (SXD) (MacKay 1973). The introduction of SXD was supported by
researchers from the BAE, CSIRO and UNSW, who believed the transition from SXS to
SXD was inevitable and would only be constrained by technological limitations (Whan
1971; Whiteley 1975). However, the proposal for the Australian wool industry to move
towards SXD was strongly opposed by wool buyers and wool selling brokers and became
one of the most contentious issues in the wool industry (Whiteley 1975; Asimus 1976).
The move towards Sale by Description and the introduction of Sale with Additional
Measurements (1980-1990)
At the start of this period, around 96 per cent of Australian wool was sold by sample with
OM (see Figure 4.2), suggesting the Australian wool industry had reached a consensus
- 115 -
about the use of pre-sale OM and SXS. In 1980, the AWCorp established an Advisory
Committee on Objective Measurement with representatives from the CSIRO, UNSW,
AWTA, the Council of Wool Buyers, the Australian Wool Measurements Standards
Authority, Brokers and Woolgrowers associations to investigate the feasibility of
introducing Sale with Additional Measurements (SXAM) (AWC Advisory Committee on
Objective Measurement 1980). It was envisaged that SXAM would involve pre-sale testing
of individual lots for Staple Strength (SS), Staple Length (SL) and Clean Colour (CC) in
addition to core-tests and would be an incremental step towards achieving SXD (AWC
Advisory Committee on Objective Measurement 1980). The mandate of the Committee
was to trial SXAM with woolgrowers, brokers and buyers.
Although trials of SXAM were relatively successful, the significant reductions in marketing
costs achieved by pre-sale OM and SXS were not replicated with SXAM. Moreover, the
introduction of SXAM required the adoption of new tests for SS, SL and CC, whereas core-
testing for yield, FD and VM was well established in the industry prior to the introduction
of pre-sale OM and SXS. As such, there was little real demand from woolgrowers, brokers
and buyers for AM (Anon 1981b). The AWCorp recognised that for SXAM to be accepted
by the Australian wool industry they needed to generate demand for AM among wool
processors (Anon 1981b).
In 1981 the AWCorp launched TEAM (Trials Evaluating Additional Measurement) to
evaluate the potential of pre-sale AM with particular emphasis on the use of AM in the
prediction of the processing performance of greasy wool (Anon 1981b). The aim of TEAM
was to establish procedures for sampling and testing wool for AM, monitor the production
performance of tested consignments and analyse the relationship between mill results and
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greasy wool characteristics in order to develop general processing performance prediction
formulae (Anon 1981b).
TEAM was a collaborative effort with participants from the AWC, AWTA and CSIRO
working with local and overseas mills to evaluate the usefulness of AM in greasy wool
processing (Douglas et al. 1985). Wool industry participants in TEAM included 14
combing mills from nine different countries, 10 topmakers from five different countries, 26
Australian wool buying firms purchasing TEAM consignments on behalf of participating
topmakers and mills and 55 Australian brokers and private treaty merchants who provided
sale lots with AM. Mill consignments were made up of sale lots from auction centres
around Australia involving a small number of woolgrowers in sampling, valuing and selling
wool with AM (Australian Wool Corporation 1984).
In TEAM, textile scientists and wool metrologists from the AWTA sampled and tested
selected consignments for staple length, strength and clean colour (Australian Wool
Corporation 1985). Participating woollen mills returned a sample of the tested
consignment in the form of processed wool top in order for the AWTA to test the
relationship between AM and the fibre length of the top (Hauteur) and fibre wastage
(Romaine) (Australian Wool Corporation 1985). These data were used to develop general
processing prediction formulae for Hauteur and Romaine (Australian Wool Corporation
1985). The general formula for predicting the Hauteur of fleece wool combined
measurements of FD, staple strength (SS) and staple length (SL) and was the basis for the
commercialisation of pre-sale AM in Australia in 1986 (see text box below). In the final
TEAM report it was recommended that the IWTO accredit these formulae for international
use and that the TEAM database be expanded so the wool processing industry could be
confident in these formulae (Australian Wool Corporation 1985).
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The general formula for Hauteur had received wide industry acceptance by 1988.
However, the formula was simplistic, consisting of four greasy wool characteristics and
generalised across a range of wool types and mills. The relationship between the prediction
of Hauteur from AM and the actual Hauteur achieved by mills and topmakers changed
significantly after the TEAM formulae were published in 1985 (Australian Wool Testing
Authority 1988). Participating mills were combing substantially longer tops than scientists
had anticipated as a result of using the TEAM formulae. In July 1988, the TEAM-2 project
was initiated by the AWC, AWTA Ltd and the CSIRO to investigate the applicability of the
general prediction formulae and to extend and improve those formulae with additional
consignment data (Australian Wool Corporation 1988).
The TEAM formula for Hauteur was reconstructed through the TEAM-2 project with the
inclusion of adjusted percentage of mid-breaks and vegetable matter content to account for
longer fibre length in wool top (see text box below) (Ward & Somerville 2003; Australian
Wool Testing Authority 1988). Sufficient data was available in TEAM-2 for the
publication of general formulae for the prediction of Hauteur and a coefficient of variation
in Hauteur and Romaine.
TEAM-1 Predicted Hauteur (mm) = 0.7D + 0.45L + 0.41S – 5.7
When D=Mean fibre diameter (µm), L= mean staple length (mm) and S=mean staple strength (N/ktex)
TEAM-2 Predicted Hauteur (mm) = 0.52L + 0.47S + 0.95D – 0.19M* - 0.45V – 3.5
When D=Mean fibre diameter (µm), L= mean staple length (mm) and S=mean staple strength (N/ktex),
M*=adjusted percentage of middle breaks (%), V=vegetable matter base (%)
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Prior to the commercialisation of Additional Measurements in 1986, the potential benefits
of pre-sale testing for Australian woolgrowers were estimated at 4 cents per kilogram of
greasy wool compared with testing costs of around 1.6 cents per kilogram; a predicted net
gain of 2.4 cents per kilogram of wool to Australian woolgrowers (Spinks & Lehmer 1985).
However, when AM methods were commercialised in 1986 there were no clear market
price signals for wool with AM as these tests had not been widely adopted by wool
processors. In 1986, less than one per cent of Australian combing length wool was sold at
auction with AM (see Figure 4.2). In order to increase the adoption of AM by Australian
woolgrowers, the AWC placed a fixed premium of 2 cents per kilogram of clean wool
purchased under the Wool Reserve Price Scheme (WRPS) with AM (Ward & Somerville
2003). In 1987, the fixed premium for wool purchased under the WRPS with AM was
increased to five cents per kilogram clean and the proportion of Australian wool sold with
AM at auction increased to around six per cent (see Figure 4.2).
In the final TEAM-2 report, published in 1988, it was argued that the success of AM relied
on a substantial increase in the number of lots sold with these staple tests. Therefore,
Australian woolgrowers needed to be encouraged to adopt pre-sale AM to enable complete
consignments with AM to be made up without the need for post-sale testing (Australian
Wool Corporation 1988). It was recommended in the TEAM-2 report that topmakers and
mills request AM with their wool consignments to pull demand for tested wool through the
supply chain. In 1988, the AWCorp funded a three year program (the Additional Staple
Measurements Adoption Program (ASMAP)) to transfer AM and general TEAM formulae
to processors. In its first year, 22 out of a total of 57 woollen mills participated in ASMAP
(Australian Wool Corporation 1988).
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In 1988, the fixed premium for wool sold through the WRPS with AM was increased to 10
cents per kilogram of clean wool. The proportion of wool offered at auction with AM grew
from ten per cent in 1988 to 37 per cent in 1990 (see Figure 4.2). In 1990, despite an
increase in the proportion of Australian wool with AM, it was estimated that tested wool
received an additional 3 cents per kilogram clean in the market compared with test costs of
5 cents per kilogram, resulting in a net loss of 2 cents per kilogram of clean, tested wool to
Australian woolgrowers (Stott 1990).
The AWTA, CSIRO and AWCorp developed and tested a range of new testing
technologies and methods for SXAM, including the Hunter-lab colorimeter, grab sampler
and equipment for testing SS, SL and SL variability (AWC Advisory Committee on
Objective Measurement 1980; Szemes 1981; Baumann 1981). The CSIRO developed the
Automated Tester for Length and Strength (ATLAS) instrument for testing SS and SL
(Baird 1984) and UNSW developed an alternative instrument for testing SS and SL called
PERSEUS (Kennedy 1983). The AWTA adopted ATLAS for commercial pre-sale AM
(Baird 1984; Thompson 1985). During TEAM, the AWTA developed the Mechanical Tuft
Sampling (MTS) machine to draw staple tufts from the display sample to test for
Additional Measurements (Douglas 2004). Together ATLAS and MTS technologies
enabled large-scale pre-sale AM to be conducted (Douglas 2004).
A decade after expressing support for achieving SXD in the Australian Wool Industry, the
AWCorp published a formal plan for the introduction of this proposed new selling system
(Quirk 1983). The plan, developed in consultation with the major sectors of the industry,
was for a staged introduction of SXD in the 1980s combining OM, AM and the guaranteed
subjective appraisal of residual non-measured fibre characteristics (Quirk 1983; Asimus
1987). The AWCorp estimated that the move to SXD would save the industry around 13
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cents per kilogram of greasy wool (Quirk 1983). Despite industry consultation, the SXD
plan met with mixed reactions from industry participants. Wool processors expressed
concerns about the move to SXD without improvements to the quality of clip preparation
and reduction of within bale variability (Provost 1983). While some woolgrowers and
wool selling brokers supported SXD as they believed that it would significantly reduce
wool marketing and distribution costs (Skillecorn 1983), wool buyers continued to oppose
the proposed new selling system.
The AWCorp and researchers believed the development of appropriate testing technologies
and methods was a major constraint to the achievement of SXD (Baird 1984; Asimus
1987). During the 1980s, the CSIRO, UNSW and other research organisations intensified
their efforts to develop new testing technologies and methods for unmeasured greasy wool
fibre attributes. Researchers developed methods to visually appraise VM types using
photographic standards and image analysis techniques for the measurement of greasy wool
style factors. Of these technologies the most prominent was the CSIRO Style Instrument,
which was considered to be pivotal to the successful introduction of SXD (van Schle,
Marler & Barry 1990). New technologies for the measurement of fibre diameter
distribution (FDD) were also developed in this period, including the Fibre Fineness
Distribution Analyser (FFDA), its successor Sirolan Laserscan and an alternative system
using image analysis (Fibre Image Display and Measurement (FIDAM)) developed by the
AWTA (Hemsley & Marshall 1983; Higgerson & Whiteley 1983; Foulds, Wong &
Andrews 1984; Palithorpe 1984; Anson 1985; Hansford, Emery & Teasdale 1985; Marler
& McNally 1987; Bell 1987).
Despite reports of a reduction in the number of auction sale lots and an increase in average
sale lot size at the end of the 1970s, small lots remained a problem in the auction system in
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the 1980s. The CSIRO found the adoption of OCP had been slow among woolgrowers and
that the impact of OCP had not met researchers’ expectations (Whiteley 1983).
Woolgrowers and classers continued to focus on lot uniformity and create more sale lines
than was necessary (Whiteley 1983) and many woolgrowers continued to market their
finest fleeces as a separate line on the advice of brokers and buyers (Johnston 1985;
Charlton & David 1987; McMahon 1987; Rottenbury et al. 1987). Efforts to promote the
use of bulk classing and interlotting to reduce the number of small lots was unsuccessful,
despite researchers finding that levels of variability in bulk lots and interlots did not affect
processing performance (Tucker, Teasdale & Knight 1988; Thompson et al. 1983a).
Despite the low levels of adoption of OCP among woolgrowers, researchers recommended
the adoption of improved fleece skirting practices and, in 1986, the AWC introduced a new
clip preparation code of practice to assist the adoption of SXAM (Whiteley 1983; Charlton
& McInerney 1982; Lunney 1982; Fairhead 1986).
Throughout the 1980s the AWCorp continued to operate the WRPS. The reserve price for
wool was increased from 365 cents per kilogram clean in 1980 to 870 cents per kilogram
clean in 1988. In 1989, the market price for wool dropped below the reserve price, creating
turmoil in the Australian wool industry (Richardson 2001). In 1990, the Australian
Government overruled the AWCorp and reduced the minimum reserve price for wool to
700 cents per kilogram clean. However, the stockpile of wool purchased under the WRPS
continued to grow (Richardson 2001). The AWCorp introduced a national marketing quota
for wool and subsidised the reduction of sheep number through the flock reduction scheme
in order to reduce the volume of wool in the market. The outlook for the Australian wool
industry at the start of the 1990s was grim.
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The emergence of on-farm fibre testing and the abandonment of ‘Sale by Description’
(1991-2001)
In 1991, the WRPS was abandoned by the Australian Government, leaving a stockpile of
4.7 million bales of unsold wool, debts of AUD$4 billion and woolgrowers facing volatile
market conditions without price protection (Department of Agriculture Fisheries and
Forestry 2001). The abandonment of the WRPS resulted in an overnight fall in the price
for wool from 700 cents per kilogram clean to 430 cents per kilogram clean (Richardson
2001). This crisis impelled a series of reviews and structural changes in the Australian
wool industry in the 1990s and significantly influenced the evolution of OM. The fixed
premiums for wool with AM purchased under the scheme disappeared and the proportion
of wool with pre-sale AM dropped sharply from around 38 per cent in 1991 to around 28
per cent in 1992 (see Figure 4.2). In a depressed wool market it was difficult to persuade
growers to spend money on AM without the protection of the WRPS (Douglas 2004).
After the collapse of the WRPS, wool processors began to publicly support AM and urged
woolgrowers to undertake pre-sale testing. Price premiums for tested wool gradually
emerged in the Australian wool auction system (Woolwise 2000). In 1993, Gleeson,
Lubulwa and Beare (1993) reported a price premium for wool with AM of 6 cents per
kilogram for fleece wool, and 7.3 cents per kilogram for skirtings and around 31 per cent of
Australian wool was offered at auction with AM (see Figure 4.2).
After the abandonment of the WRPS in 1991, the Australian Government appointed Sir
William Vines to head a Wool Review Committee. The Committee recommended that the
Australian wool industry introduce SXD as soon as possible to reduce marketing costs
(Hoadley 1991). However, appropriate technologies had not been developed to measure
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the style attributes of greasy wool and SXD still encountered opposition from sections of
the trade (Hoadley 1991). Wool buyers, in particular, opposed the removal of the sample
from the show floor and were constraining the adoption of SXAM on the basis that it may
lead to the introduction of SXD (Hoadley 1991; Loutit 1992; Johnston 1992; Turk 1993).
Wool buyers argued that wool lots sold by description would be discounted in the auction
system as buyers did not have confidence in new objective descriptions of all greasy wool
attributes (Quirk 1997).
In 1992 a second Committee of Enquiry into the wool industry was instituted by the
Australian Government to examine the status of the industry in the global textile market
(Garnaut 1993). However, in April 1993, before the Committee submitted its final report,
the price of wool slumped to the lowest it had been in the twentieth century. It was widely
recognised that the industry was in crisis and that substantial structural and cultural changes
were needed if it were to survive (Garnaut 1993). The Committee recommended the
industry introduce a centralised marketing system, adopt improved specification and wool
identification systems, on-farm fleece testing, industry standards for non-measured fibre
attributes and SXD (Morris 1993).
Despite the turmoil in the Australian wool industry in the early 1990s, wool metrology
researchers continued to develop new testing technologies and methods. SGS Wool
Testing Services, a private technology company, developed the Optical Fibre Diameter
Analyser (OFDA) for the measurement of fibre diameter distribution (FDD) and the
Agritest Staplebreaker for testing staple attributes on-farm (Baxter, Brims & Teasdale
1992; Vizard, Scrivener & Anderson 1994; Baxter 1996a). The CSIRO launched the Fibre
Diameter Distribution Task Force to undertake research on FDD (Charlton & David 1991)
and Sirolan Laserscan was eventually adopted by the AWTA in 2000 as the standard
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method for measuring mean fibre diameter (MFD) (Charlton 1995; Hansford 1992;
Sommerville 2000). In 1993, the AWTA Ltd introduced Woolink, an electronic wool
selling system (Douglas 1993).
The most significant technological advance in OM in this period was the development and
commercialisation of clean colour (CC) testing for greasy wool fibre. CC is an objective
measurement of the colour of wool after scouring; colour is measured in terms of brightness
and yellowness, both of which can affect the dyeing potential of greasy wool fibre
(Australian Wool Corporation 1986). Initially CC testing was included in the SXAM
initiative, yet due to technical difficulties associated with developing an appropriate testing
methodology its introduction was delayed until 1994 (Baird 1984). In 1996 around 23 per
cent of Australian wool offered at auction was CC tested (see Figure 4.2). Clean Colour
testing grew to around 30 per cent of wool sold at auction in 1999 before dropping to less
than four per cent in 2001 (see Figure 4.2).
The widespread adoption of CC testing by Australian woolgrowers was constrained by
relatively high test fees and a widely held perception that Australian wool is of good colour
and is therefore unlikely to be discounted for unscourable colour (White 2003). The
adoption of CC by Australian woolgrowers was further inhibited as the tests were rejected
by many processors. The clean colour of greasy wool fibre is only important to processors
if there is a demand for white or pastel wool yarn. In other words, demand for tested wool
is driven by fashion trends (White 2003; Ward & Somerville 2003). Erratic demand for
wool with CC measurements from processors prevented clear price premiums for tested
wool emerging in the Australian wool auction system.
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Despite the recommendations of two government appointed Committees in the 1990s for
the Australian wool industry to move towards SXD, progress was slow. The focus of wool
metrology research in the mid-1990s shifted to the on-farm use of OM and, once again, the
AWC introduced a new code of practice for clip preparation to reduce the number of small
lots in the auction system. As with previous clip preparation improvement initiatives,
evidence quickly emerged that woolgrowers and classers were not adopting the new
industry recipe (Cottle 1994).
In an effort to generate the use of OM on-farm and support new clip preparation practices,
AWTA Ltd introduced the ‘Every Sheep Tested’ on-farm fleece-testing program to guide
wool classing and clip preparation. The CSIRO, AWTA and Melbourne University
published research on the use of fibre diameter distribution (FDD) testing on-farm in
breeding, selection and animal husbandry (Mayo et al. 1994). The CSIRO also tackled the
dark fibre contamination problem from a farm perspective and introduced the Dark Fibre
Risk Scheme, a code of practice to be used on-farm to prevent dark fibre contamination in
sale lots (Rottenbury, Burbidge & McInnes 1995).
Wool metrology research in this period also addressed the needs of consumers of wool
apparel (Edmonds 1997). Researchers examined the relationship between greasy wool
fibre curvature variation and crimp frequency and fabric comfort (Fish, Mahar & Crook
1999; Crook, Nivison & Purvis 1999). Fibre crimp frequency had been subjectively
appraised as a proxy for fibre diameter in the wool selling system prior to the introduction
of OM and had largely been ignored under the new objective testing regime (Fish, Mahar &
Crook 1999; Crook, Nivison & Purvis 1999).
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Structural turmoil in the Australian wool industry continued into the late 1990s. In 1998,
woolgrowers returned a vote of ‘no confidence’ in the industry statutory body and the
Australian Government established the Wool Industry Future Directions Task Force to map
out the future of the industry. The task force made recommendations for industry
innovation initiatives to increase the demand for wool textile products and to reduce the
costs of production (Wool Industry Future Directions Task Force 1999). There was no
recommendation for the industry to achieve SXD in the Task Force’s final report.
Australian Wool Innovation Ltd (AWI) was established in 2001 as an independent
company with responsibility for the allocation of woolgrower and Government funds for
research and development and the facilitation of the commercialisation, transfer and
adoption of the results of industry research (Australian Wool Innovation 2002).
As the Australian wool industry entered the twenty-first century, consensus had congealed
around a range of pre-sale objective measurements incorporating OM, AM and CC testing
technologies and methods. The principal descriptors of greasy and scoured wool used by
AWTA Ltd in 2001 are listed in Table 4.1. However, in 2001 the CSIRO discontinued
research into the objective measurement of the style elements of wool (AWTA and The
Woolmark Company 2002). Without testing technologies and methods to replace
subjectively appraised wool fibre attributes the achievement of SXD was not possible.
After three decades of research, development and policy supporting the introduction of
SXD, the Australian wool industry finally abandoned this innovation initiative.
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Table 4.1: Technologies used to objectively measure greasy wool (source: Baxter 2002)
Wool fibre characteristic Measurement technology in common use
Woolbase (clean yield) Scouring, drying and residuals measurement
Vegetable Matter content Chemical methods
Mean fibre diameter and diameter distribution
Laserscan and OFDA for combing wool and Airflow for all other wool
Mean fibre curvature Laserscan for combing wool only
Mean length and strength and length distribution
Staple length and strength for combing wool, Length after carding for carding wool
Clean Colour Spectrophotometer
Residual grease (scoured/carbonised) Soxhlet extraction
4.4 Discussion of the findings
The case study calls into question the central premises to traditional models of agricultural
innovation that were discussed earlier in this chapter, namely:
1. Agricultural innovation is a simple, linear process in which researchers develop
new technologies that are then adopted by end users.
2. New technologies drive and determine social change.
3. Technology users play a passive role in the innovation process.
The process by which OM testing technologies were developed, introduced and adopted by
the Australian wool industry was nothing like the smooth transfer of new technologies to
passive, rational actors described in the dominant technological determinist models. The
adoption of OM was not a uniform process in which industry participants passively yielded
to normative pressures as described in innovation diffusion models (e.g. Rogers 2003).
Events revealed a political process involving conflict, coercion, compliance and consensus
among industry groups. The outcomes of the development, introduction and adoption of
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OM initiatives were negotiated by Australian wool industry participants with power and
interest in these technologies and related industry belief systems. The introduction and
adoption of OM along the wool supply chain, from the use of post-sale OM by wool buyers
and processors to pre-sale OM adopted by woolgrowers, was not a process by which
industry participants passively accepted the proposed superiority of OM over subjective
appraisal and adopted the new technologies in place of incumbent practice as a result of
normative pressures (e.g. Rogers 2003). The case study revealed a political process in
which industry participants negotiated the outcomes of OM initiatives, socially constructing
these technologies and reconstructing industry belief systems around them.
The process of the development, introduction and adoption of OM differed from that of
many other agricultural innovations because it involved participants along the entire supply
chain from stud breeders to wool processors. The development and introduction of OM
opened a ‘black box’ of subjective wool appraisal, marketing and valuation to woolgrowers
and challenged the dominance of wool buyers as information brokers in the supply chain.
The development, introduction and adoption of OM were inextricably linked to the
evolution of industry belief systems in terms of an objective product ontology, the
expansion of boundary beliefs to incorporate synthetic textiles as direct competition and the
institutionalisation of pre-sale OM and AM in wool marketing systems.
Not satisfied with the development and introduction and adoption of post-sale OM in the
1950s, wool metrology and textile researchers forced a renegotiation of the role of OM in
the Australian wool industry. This renegotiation of the role of OM eventually led to the
introduction and widespread adoption of pre-sale OM, SXS and SXAM and the proposal
for SXD. Researchers forced the renegotiation of the role of OM in the Australian wool
industry with a steady flow of research publications which evidenced the relative advantage
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of OM over subjective appraisal. This occurred against a backdrop of trade and industry
policy opposition. The renegotiation of the role of OM in the Australian wool industry
occurred simultaneously with the redefinition of greasy wool fibre as a textile product and
the expansion of the boundaries of competition for greasy wool to encompass synthetic
fibres. These changes to Australian wool industry belief systems in the 1960s underpinned
the evolution of pre-sale OM as a shared technology frame and industry recipe in the 1970s.
As researchers renegotiated the role of OM in the Australian wool industry and what
constituted industry wool marketing and selling recipes, the politics of power and interest
emerged. Answers to questions about whether OM should replace subjective appraisal
became a battle-ground of objective, scientific knowledge against the importance of
tradition, skill and craft. The ongoing struggle between objective and subjective
perspectives of greasy wool as a product can be seen in documents and research articles
published from the 1960s to the 1990s, in which researchers promoted OM and members of
the trade criticised it. Objective and subjective nomenclatures for wool fibre became
competing normative claims for Australian wool industry beliefs about product,
competition, industry recipes and reputation. The promoters of OM and subjective
appraisal competed for allies in their struggle for dominance of the wool production and
marketing system.
The notions of power and interest implicit in the adoption of OM in the Australian wool
industry are those that emphasise a participant’s ability to manage, manipulate and impose
meaning. Australian wool industry participants with power and interest in the introduction
of OM acted as sense givers as they sought to project their interpretation of technological
and environmental events onto other industry participants and to persuade them to construct
and share the same meaning. For example, the AWB and wool buyers opposed the
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introduction of pre-sale OM in the 1960s. Both the AWB and wool buyers were powerful
participants in the Australian wool industry with an interest in maintaining the use of
subjective appraisal for the valuation and marketing of greasy wool. In 1963, the AWB
rejected the recommendations of a government appointed Committee to introduce pre-sale
OM as they did not believe it was necessary to maintain the competitiveness of Australian
wool. The AWB and wool buyers dominated industry strategies for wool marketing and
selling in this period and attempted to impose their interpretations of pre-sale OM onto
other industry participants. Brokers and buyers controlled the nature and use of
information in the auction system and prevented the results of OM guidance tests
undertaken by woolgrowers from being printed in the sale catalogue. Researchers did not
have the power to introduce pre-sale OM in the 1960s and were dismissed as ‘quacks’ by
the AWB and other members of the trade.
Power and interest in the Australian wool industry in relation to OM was fluid.
Researchers at the CSIRO and UNSW became a single voice in their criticism of subjective
appraisal and their promotion of the superiority of OM. These research groups continued to
produce evidence supporting pre-sale OM until the AWB responded to pressure and
examined the feasibility of pre-sale OM in the wool marketing and selling system (despite
continuing to oppose its introduction). Trials of pre-sale OM undertaken in 1969
demonstrated considerable benefits to the industry that could not be ignored by the AWB.
The growing support for pre-sale OM amongst researchers, test houses, growers and
brokers pushed industry policy makers into supporting the introduction of pre-sale OM and
SXS. When pre-sale OM and SXS were finally introduced in 1972 they had the backing of
all major participants in the Australian wool industry, with the exception of wool buyers,
and were rapidly adopted and diffused among woolgrowers.
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As the example of the introduction of pre-sale OM demonstrates, power and interest and
the ability of industry participants to act as sense givers dramatically influenced the fate of
testing technologies in the Australian wool industry. In the case of pre-sale OM, wool
buyers were forced to comply with this initiative in the face of overwhelming industry
consensus. The example of the introduction and subsequent abandonment of SXD as an
industry policy highlights, once again, the central role power and interest have in
innovation and how conflict between industry participants can influence the innovation
process. The AWCorp and an informal coalition of Australian wool metrology and textile
researchers (CSIRO, UNSW and AWTA) believed greasy wool product could be fully
described with direct or proxy objective measurements. This objective product ontology
underpinned their beliefs that greasy wool fibre would be a highly competitive textile fibre
if the industry adopted SXD as a new industry recipe. The AWCorp and the coalition of
researchers sought to impose SXD as a new industry recipe for the preparation, sale and
marketing of greasy wool on the Australian wool industry. The AWCorp controlled the
funding of research, development and promotion, the reserve price scheme and wool fibre
testing; therefore they had the power and interest to act as industry sense givers on the
subject of SXD.
The proposal for SXD was met with a great deal of opposition from wool buyers and with
ambivalence by processors, brokers and woolgrowers. Wool buyers, who had reluctantly
complied with the introduction of pre-sale OM and SXS, vehemently opposed the removal
of their right to view a sample of the sale lot prior to purchase, as proposed under the SXD
system. The opposition of buyers to the introduction of SXD exposed a clash of industry
beliefs about product ontology, competition and industry recipes and a battle for control of
information in the auction system. Although the AWCorp and researchers were a powerful
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coalition supporting the development and introduction of SXD in the Australian wool
industry, they had no jurisdiction over the auction system and could not prevent buyers
from carrying out their threat to discount sale lots offered without samples. There was
evidence buyers would act on these threats as they had constrained the adoption of SXAM,
claiming that this was simply a step towards the removal of the sale lot sample from the
show floor. It was also unlikely the promoters of SXD would have been able to generate
demand for this proposed industry recipe among processors as these industry participants
were suspicious of the concept of wool consignments being prepared sight-unseen by wool
buyers and the ability of the proposed testing technologies to replace wool buyers’
appraisal skills. Woolgrowers were ambivalent about the introduction of SXD and required
evidence that this initiative would provide significant cost savings and market premiums
over and above the increase in testing costs.
In the example of the failed introduction of SXD, the AWCorp and researchers did not
attempt to renegotiate the role of OM in the Australian wool industry but, rather, tried to
manipulate existing industry beliefs and impose new beliefs through the development of
new testing technologies and methods for SXD. Industry discourse relating to SXD in the
1980s and 1990s was reflected in a stream of industry policy declarations and promotional
publications from the AWCorp and researchers on industry progress towards SXD. The
trade responded to these promotional publications with counter claims about the
effectiveness of the existing marketing system and called into question the predicted
benefits of SXD to growers, as well as the selling system and the accuracy and efficiency of
proposed tests for unmeasured greasy wool attributes. The conflict over SXD continued as
a war of words in the main industry journal until SXD was abandoned in 2001 as both sides
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attempted to dominate the debate over subjective and objective wool marking systems.
Therefore the following research proposition is advanced:
Research proposition 1: The development, introduction and adoption of new
agricultural technology is a political, negotiated process with outcomes
influenced by powerful interest groups and coalitions.
In the dominant technological determinist models of innovation, it is assumed, given a set
of conditions, that technological outcomes can be predicted with a level of certainty (T.
Pinch & W.E. Bijker 1987; Bijker 1999). It is also assumed the development of
technological artefacts move in a linear and predetermined fashion towards a final end state
of adoption by all members of a social system (T. Pinch & W.E. Bijker 1987; Bijker 1999).
This is known as pro-innovation bias (Rogers 2003) and is prevalent in the agricultural
innovation literature (Ruttan 1996). The case study of OM suggests the development and
introduction of new agricultural technologies does not necessarily result in the rapid and
full adoption and diffusion of the technology by all industry participants. In this case study
OM testing technologies were given different meanings by different industry participants,
resulting in a range of physical artefacts, functional characteristics and uses. The case
study suggests that new agricultural technologies can be uncertain, unstable and intertwined
with industry beliefs. Therefore the following research proposition is advanced:
Research proposition 2: New agricultural technologies are not predetermined,
stable and independent of industry beliefs.
After a little more than two decades of addressing increasing competition from synthetic
fibres with OM, Australian wool textile and metrology researchers and their supporters
succeeded in transforming industry recipes for wool marketing and selling and technology
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frames for the description, valuation and specification of greasy wool fibre. By 1980, over
90 per cent of all Australian wool was sold by sample. The successful introduction of pre-
sale OM and SXS in the Australia wool industry paved the way for new OM testing
technologies and methods to be developed and introduced during the 1980s and 1990s.
However, not all of these technologies and methods were as successful as pre-sale OM and
SXS. The history of the evolution of OM is not only one in which new technologies were
successfully introduced and widely adopted, but one of technology abandonment,
reinvention, adaptation, rejection and disadoption. Therefore, new OM technologies were
not predetermined, stable or independent of industry belief systems.
Examples of technology development resulting in the abandonment and reinvention of
testing technologies and methods in this case are plentiful. Conflict over which testing
technology emerged as the industry standard occurred when new OM initiatives were
introduced. For example, post-sale OM was introduced in Australia in 1957 when a
consensus had not been reached as to a standard technology and method for objectively
testing greasy wool for clean yield and fibre diameter. Wool metrology and textile
researchers in different jurisdictions developed and introduced a range of competing testing
technologies and methods, each vying to become the industry standard. Conflict over
which testing technology and method was the most effective and accurate continued until
the IWTO specified standards for yield and fibre diameter testing. The yield and fibre
diameter testing technologies and methods that were not accepted for certification by the
IWTO were eventually abandoned. Consensus between industry participants eventually
closed around IWTO test standards.
After the introduction of pre-sale OM and SXS in 1972, the development of new testing
technologies mushroomed. However, the physical and functional characteristics and use of
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these testing technologies were unstable. Many of these technologies were abandoned,
reinvented or adapted. For example, the sonic fineness tester, image analyser and wool
base analyser were abandoned after development as they were found to be unsuitable for
commercial use. As end users increasingly demanded accurate, rapid and inexpensive
testing methods, researchers reinvented and adapted existing testing instruments, such as
the FDA and FIDIVAN, to meet user requirements. Although these technologies did not
achieve commercial use, they were precursors to the successful Sirolan Laserscan and
FIDAM technologies.
Competition between researchers over the development of new testing technologies was
common during the SXAM and SXD initiatives. During the development of SXAM, two
competing instruments were developed to measure staple length and strength (ATLAS and
PERSEUS). ATLAS emerged as the commercial success as the AWTA adopted this
instrument for staple testing, whereas PERSEUS was confined to laboratory use. The
unstable and endogenous nature of OM technologies and industry belief systems were
highlighted by the development and reinvention of the TEAM general processing formulae.
The TEAM formulae were reinvented between the initial trials and TEAM 2 project in
response to changes in the processing performance of mills. In effect, the TEAM formulae
and industry recipe for topmaking co-evolved as a result of the interpretations and actions
of TEAM participants.
Not only were new OM testing technologies abandoned or reinvented, but the rapid and
widespread adoption and use of commercial testing technologies was not guaranteed. The
AWC introduced OCP as a voluntary selling system in 1972. OCP promoters anticipated
that the adoption of this new practice would be as rapid and extensive as pre-sale OM and
SXS as it was a complementary innovation. However, the adoption of pre-sale OM and
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SXS was not contingent on the adoption of OCP. Researchers and policy makers attempted
to extend OCP to woolgrowers through field days and demonstrations as a ‘rational’
solution to the problem of high marketing costs. They faced an uphill struggle as on-farm
change proved to be more complex than the adoption of tests for wool marketing. Brokers
and buyers continued to discount lines that were not visually uniform (i.e. those prepared
using OCP) and requested that growers prepare a separate fine line to achieve the highest
price at auction. Mixed messages and a lack of desire and ability to change traditional clip
preparation recipes influenced the widespread rejection of OCP by Australian woolgrowers
in the 1970s.
The case study also illustrated the uncertain nature of agricultural innovation as the initial
adoption of new OM technologies did not guarantee the ongoing use of these technologies.
For example, SXAM promoters misjudged the level of trade and woolgrower support for
the introduction of AM. SXAM lacked the obvious cost savings that had accrued to
brokers and growers through their adoption of SXS. There was no initial demand for wool
lots with AM from processors, therefore there were no price premiums for AM tested sale
lots or discounts for untested lots. Understanding the importance of price signals in the
adoption of OM, the AWC employed fixed price incentives under the WRPS to drive
adoption among woolgrowers. When the WRPS was abandoned in 1991, many
woolgrowers disadopted SXAM in the absence of real market demand for wool lots with
AM. It was not until a significant number of processors adopted the TEAM formulae and
started to demand sale lots with AM that price premiums for AM tested wool appeared and
woolgrowers gradually readopted SXAM.
The introduction of clean colour (CC) testing in the 1990s also highlighted the unstable and
unpredictable nature of the adoption and diffusion of agricultural innovations. Although
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CC was adopted relatively rapidly by a large proportion of Australian woolgrowers, it was
disadopted by all but a small proportion of woolgrowers after one use. The initial adoption
of CC was influenced by the AWCorp and AWTA and wool selling brokers who advised
their clients to test. However, the disadoption of CC was driven by the lack of demand for
test results among processors and the subsequent lack of price indicators by way of price
premiums and discounts for tested or untested wool. CC testing also challenged the
widespread belief among woolgrowers that Australian wool is ‘white and bright’. This well
entrenched industry belief about Australian greasy wool product supported the rejection of
CC as a component of the industry recipe for wool marketing, selling and processing.
Therefore, the following research proposition is advanced:
Research proposition 3: New agricultural technologies and industry belief
systems co-evolve through the social sensemaking of agricultural industry
participants.
As well as negotiating the attributes of the technology itself, industry participants can
renegotiate related industry beliefs by achieving plausibility and authority. Scientific
research can be used to establish plausibility and the use of arguments about market equity,
competition and control of market information can be used to attain authority in this space.
Establishing plausibility and authority are important in the social construction and
reconstruction of new agricultural technologies and industry belief systems because
industry participants who emerge as sense givers are more likely to win future battles in the
same technology space. This occurred when wool metrology and textile researchers fought
to achieve plausibility and authority in the battle to introduce pre-sale OM and SXS. In
doing so, they were able to promote SXAM and SXD from a position of strength in the OM
technology space.
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The case highlights the potential for overbearing behaviour from those who have authority
over development and introduction of new agricultural technologies. The abandonment,
rejection and disadoption of agricultural technology suggests that what were deemed to be
prudent, beneficial technologies by policy makers and researchers were sometimes seen as
an irrational choice by potential end users. Similarly, what end users considered to be
economically rational decisions (e.g. the decision to reject OCP) were seen as imprudent
and illogical by researchers and policy makers. The beliefs and responses of members of
the trade and woolgrowers were often dismissed, yet were critical when it came to
determining the adoption and use of new OM technologies and, in effect, shared technology
frames and industry belief systems. Therefore, researchers and policy makers engaged in
agricultural innovation should be wary of dismissing potential end users’ beliefs and
responses and need to recognise the role played by industry participants who have power
and interest in the technology space. It is these participants who are likely to determine the
success or failure of the agricultural innovation process.
Conclusions
The Australian wool industry OM innovation initiatives were a unique and singular case,
but that does not mean that we cannot draw lessons from it to inform the management of
agricultural innovation. As a large scale industry innovation initiative, what the
introduction of OM in the Australian wool industry has made clear is that aspects of the
agricultural innovation process are likely to occur in a more subtle manner in smaller scale
innovation initiatives. A mix of different participants in the agricultural innovation process
can result in different innovation outcomes, technologies and industry beliefs. The political
and negotiated agricultural innovation process found in this case study offers insights for
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policy makers, researchers and innovation participants who are unhappy with the pattern of
the development, introduction, adoption and diffusion of agricultural technologies. For not
only was change in the innovation process and outcomes revealed as possible, but the case
study highlighted the actions required to bring about that change.
In the following Chapter, the second empirical study of the OM innovation initiatives in the
Australian wool industry, ‘Fad, fashion, compliance or efficient choice? A study of the
diffusion of technologies in the Australian wool industry’ is presented.
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5 Fad, fashion, compliance or efficient choice? A study of the diffusion of technologies in the Australian wool
industry The Australian wool industry increasingly depends on innovation to improve the
productivity and profitability of woolgrowers. Consequently, the widespread, successful
adoption and diffusion of new technologies has become a critical element in the industry’s
competitive strategy. However, the take-up of new agricultural technologies often fails to
meet expectations. As an example, in light of the potential economic advantages expected
from woolgrowers’ adoption of Additional Measurements (AM) and Clean Colour (CC)
testing (as discussed in Chapter 4), the patterns of diffusion and the abandonment of these
technologies are striking. The diffusion of AM and CC among Australian woolgrowers
was not the smooth sigmoid-shaped diffusion curve described in the dominant models of
innovation diffusion (e.g. Rogers 2003; Griliches 1957). Figure 5.1 shows the proportion
of Australian wool offered at auction with AM and CC tests each year from 1988 to 2006.
These data highlight the uncertain and unstable nature of the adoption of AM and the
abandonment of CC by Australian woolgrowers.
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0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Year
Pro
po
rtio
n o
f A
ustr
ali
an
gre
asy w
oo
l
AM tested Clean colour tested
Figure 5.1: Average proportion of the Australian clip offered at auction with AM and
CC, 1988-2006 The study of innovation diffusion has accumulated an impressive body of theory and
empirical results over the last five decades of research (Rogers 2003). Agricultural
innovation researchers have studied the adoption and diffusion of a wide range of new
agricultural technologies, including high yielding crop varieties (Griliches 1957) and new
varieties of legumes (Marsh, Pannell & Lindner 2000). The majority of these studies
examined the adoption of new agricultural technologies from an economic perspective
(Feder & Umali 1993). Such studies assumed the decision to adopt or reject a new
technology is based on an efficient choice made to maximise the potential adopters’
expected utility. Few studies have suggested technology adoption and abandonment may
be driven by factors other than efficient-choice. However, given the political, negotiated,
uncertain and unstable nature of the agricultural innovation process described in Chapter 4,
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the examination of the diffusion of new agricultural technologies from a purely efficient-
choice perspective may be insufficient. Therefore, the present study examined how new
wool testing technologies were adopted or abandoned by Australian woolgrowers.
Drawing on the findings from the study of the co-evolution of innovation and industry
belief systems outlined in Chapter 4, it was proposed that the driving forces behind the
diffusion and abandonment of agricultural technologies are unstable and intertwined with
industry beliefs and social influence. Abrahamson (1991) suggested a typology of the
diffusion and rejection of new technologies with four perspectives (efficient-choice, forced-
selection, fad and fashion). This study attempted to build on Abrahamson’s (1991)
typology by applying these diffusion and rejection perspectives in an agricultural
innovation context. The study attempted to contribute to the agricultural innovation debate
by:
1. Seeing whether the dominant efficient-choice perspective of diffusion is sufficient
to explain the pattern of diffusion and abandonment of new technologies and
therefore the social construction of technology frames.
2. Examining empirical data on the abandonment of new agricultural technologies, an
area which has received little attention in the agricultural innovation literature, yet is
crucial to understanding the innovation process (Sultan, Farley & Lehmann 1996;
Feder & Umali 1993).
This chapter is divided into four sections. The first section describes the theoretical
background of the study. The second section outlines the empirical study of the diffusion
and abandonment of AM and CC. In this section, the data collection and analytical
methods used are described and the empirical data are presented. The third section
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discusses the results of the empirical study and offers tentative research propositions, while
the final section provides a chapter summary.
5.1 Theoretical background
A central theme of agricultural innovation research has been the mathematical modelling of
the diffusion of different types of technologies under different assumptions (Feder & Umali
1993). One general finding in such studies is the observation of a bell-shaped curve when
the frequency of adoption is plotted over time and a sigmoid or S-shaped curve when the
cumulative number of adopters is plotted over time (Ryan & Goss 1943; Griliches 1957).
The S-shaped diffusion curve, presented in Figure 5.2, shows that the rate of diffusion of a
new technology is initially low as adoption occurs among only a few members of a social
system (Grübler 1991). Over time, the rate of diffusion increases sharply when it reaches a
critical mass point, known as the ‘take-off’ period (Rogers 2003). After the ‘take-off’
period, the cumulative number of adopters continues asymptotically until the saturation
point (the maximum level of adoption) is reached (Rogers 2003).
Economists have traditionally explained S-shaped diffusion curves in terms of a shift in the
balance of supply and demand, which is a function of the adoption investment and the
profit potential of the technology (von Hippel 1988; Mansfield 1968). Economists usually
attribute the ‘take off’ period to a drop in the price of the technology. Sociologists have
explained the innovation diffusion pattern as a process of social contagion. The basic
sociological assumption underlying the S-shaped diffusion curve is that it takes different
amounts of time for information about a new technology to reach different members of a
social system. The S-shaped pattern of innovation diffusion is a normal outcome of an
increasing number of adopters within a population or social system who generate more
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information about the technology and reduce technology uncertainty for later adopters
(Rogers 2003).
Figure 5.2: S-shaped diffusion curve (source: Rogers 2003)
The majority of innovation diffusion studies have examined successful technologies (i.e.
those that have reached a high level of market saturation) (Sultan, Farley & Lehmann
1996). Such studies have been criticised for having a pro-innovation bias (Abrahamson
1991; Rogers 2003; Van de Ven 1986). This pro-innovation bias perpetuates the
assumption that individuals or organisations make an efficient choice to adopt technologies
that are beneficial and reject technologies that are not (Abrahamson 1991). This is
particularly the case in agricultural innovation research (e.g. Leathers & Smale 1991; Pitt &
Sumodiningrat 1991). The pro-innovation bias has also created a ‘blind spot’ around the
study of unsuccessful innovations (i.e. those technologies that are rejected or abandoned
over time), even though the number of new product failures out numbers the successful
diffusion of products (Sultan, Farley & Lehmann 1996; Sultan, Farley & Lehmann 1990).
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This suggests unsuccessful products should be researched to identify the early warning
signs of market failure (Sultan, Farley & Lehmann 1990). Some research has examined
rejection or abandonment in terms of the replacement pattern of products or technologies
over time (Fisher & Pry 1971; Cameron & Metcalfe 1987), and new generations of
products (Bass 2004; Easingwood 1988). However, these studies still assume an efficient
choice is made to reject or abandon new technologies.
Although some researchers have investigated the reasons for the abandonment of
agricultural innovations at a farm level (Coughenour 1961; Wilkening 1956; Neill & Lee
2001; Leuthold 1967; Deutschmann & Havens 1965). Few studies have examined the
aggregate patterns of rejection or abandonment of agricultural technologies (Feder & Umali
1993). Dinar and Yaron (1992) developed one of the first empirical models describing the
diffusion and abandonment of agricultural technologies. In an examination of the adoption
and abandonment of irrigation technologies in Israel and Gaza they estimated the life-
cycles of a range of technologies used to irrigate citrus groves, again assuming farmers
made an efficient choice to adopt and then abandon these technologies.
The efficient-choice perspective of technology diffusion and abandonment explains the
diffusion process as the aggregate of the adoption of an innovation by individuals or
organisations to close a perceived gap in performance (Abrahamson 1991). Potential
adopters of a technology are assumed to have similar interpretation frameworks and clearly
defined performance gaps that emerge as a result of technological or environmental events
(Abrahamson 1991; Grandori 1987). Members of a social system are assumed to adopt
technologies they believe will efficiently close the performance gap and reject technologies
they believe will not efficiently close the performance gap (Abrahamson 1991). The
efficient-choice approach to the study of innovation adoption and abandonment takes a
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technological determinist perspective of the innovation process. The assumption of
efficient choice in the decision to adopt, reject or abandon a new technology does not take
into account social influence, retrospection, plausibility and enactment in the agricultural
innovation process.
A social sensemaking perspective of technology diffusion and abandonment
Abrahamson (1991) argued the efficient-choice perspective does not consider technological
and adopter uncertainties that may affect their decision to adopt or reject a new technology.
Individuals and organisations are not often able to assess the technical efficiency of
innovations or do not have sufficient clarity around their goals to determine what
efficiencies matter at a point in time or in the future (Abrahamson 1991). Where there is
insufficient knowledge available to the adopter in relation to the physical and functional
characteristics, use and performance of a new technology to enable them to make an
‘efficient choice’ to adopt or reject it in the early stages of diffusion the adopter must fall
back on ‘fuzzy’ assessments of the technology (Bendoly 2007). Fuzzy assessments may
include the opinions of other adopters or actors outside of the social system. In other
words, they must struggle to make sense of the new technology in their own context, under
conditions of uncertainty. As was noted in Chapter 3, this sensemaking process may also
include the extraction and enactment of cues from existing technology frames, industry
belief systems, personal identity and social context. The social context is particularly
important in uncertain innovation environments as potential adopters may be influenced by
industry participants inside and outside the adopter group (Abrahamson 1991; DiMaggio &
Powell 1983).
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Abrahamson (1991) argued, if the decision to adopt or reject a new technology is uncertain,
the imitation of other people’s behaviour can create fads and fashions that drive diffusion
and rejection and suggested the diffusion typology shown in Figure 5.3. The typology has
two dimensions (imitation-focus and outside-influence) and suggests imitation processes
may or may not impel the diffusion and rejection of an innovation, while groups or
organisations inside or outside an adopter group may influence the diffusion and rejection
of an innovation.
Imitation-focus dimension
Imitation processes do not impel innovation diffusion
or rejection
Imitation processes impel innovation
diffusion or rejection
Organisations within a group determine the diffusion and rejection within this group
Efficient-Choice perspective
Fad Perspective
Outside-influence dimension
Organisations outside a group determine the diffusion and rejection within this group
Forced-Selection Perspective
Fashion Perspective
Figure 5.3: Typology of diffusion perspectives (source: Abrahamson 1991) The imitation-focus dimension has been suggested as a diffusion driver by scholars such as
Bass (1969) and Mansfield (1961) since the 1960s. Abrahamson (1991) argued imitation
or internal influence exists when technologies are uncertain. Therefore, the inclusion of
imitation or internal influence in the diffusion and abandonment of new technologies
challenges the efficient-choice diffusion perspective as internal influences can result in the
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imitative adoption of inefficient innovations or the imitative abandonment of efficient
innovations (Abrahamson 1991).
Organisations and individuals from outside of the adopter group can have the power to
select which technologies will diffuse and which will be rejected (DiMaggio 1987). The
external influence dimension has been examined as a diffusion driver by scholars such as
Bass (1969) and Fourt and Woodlock (1960) since the 1960s. Abrahamson (1991) argued
technologically inefficient innovations may diffuse when backed by politically powerful
organisations and technologically efficient innovations may be abandoned when rejected by
politically powerful entities (DiMaggio 1987).
The typology shown in Figure 5.3 suggests four innovation diffusion and rejection
perspectives (efficient-choice, forced selection, fad and fashion) (Abrahamson 1991). The
efficient-choice perspective explains the diffusion process as the aggregate of the adoption
of an innovation by individuals or organisations to close a clearly articulated gap in
performance (Abrahamson 1991). In Abrahamson’s (1991) typology, the efficient-choice
perspective is a result of individuals or organisations within an adopter group determining
the adoption or rejection of an innovation under conditions of low uncertainty, where
imitation does not impel diffusion or rejection. The forced selection perspective explains
the diffusion process as occurring in situations where a number of organisations or
individuals outside the adopter group have the power to determine which technologies will
be adopted and rejected (DiMaggio 1987). In this perspective there is low uncertainty
about the technology and imitation does not impel diffusion or rejection.
The fashion perspective assumes conditions of uncertainty in the technology and goals and
preferences of potential adopters (Abrahamson 1991). In this perspective, ‘fashion-setters’
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(influential organisations or individuals outside the adopter group) are imitated by members
of the adopter group (DiMaggio 1987). Abrahamson (1991) argued fashion setting can
support the diffusion of inefficient innovations and the rejection of efficient innovations as
fashion setting organisations either promote or debunk new technologies. The fad
perspective of diffusion and rejection also assumes uncertainty in the technology and goals
and preferences of the adopter. In this perspective, members of the adopter group imitate
each other in terms of technology adoption or rejection. Adopters may reject an innovation
after use because its appeal wears off or as they learn about its inefficiencies.
The present study examined the driving forces behind the diffusion and abandonment of
agricultural technologies. The driving forces behind the diffusion and abandonment of
agricultural technologies are likely to change over time as uncertainty about the technology
diminishes and the nature and extent of social influence changes. It is suggested the
fashion perspective may best explain the start of the diffusion process where there is a great
deal of uncertainty about the characteristics and use of new technologies. The fashion
perspective may also best explain the diffusion of a new agricultural technology reaching a
critical mass or ‘take-off’ point as industry ‘fashion-setters’ or ‘sense givers’ gain the
plausibility and authority to promote the technology to the potential adopter group. As
technology uncertainty and the influence of ‘fashion-setters’ or ‘sense givers’ diminish over
time and technology frames close around an accepted form, efficient-choice and imitation
among the adopter group may dominate the diffusion or abandonment of the technology.
Finally, the technology may become institutionalised in a social system over time, resulting
in the diffusion and rejection of the technology being driven by forced-selection or
compliance.
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In order to test this proposition, the patterns of diffusion and abandonment of AM and CC,
which were described in Chapter 4, are examined, along with industry discourse relating to
these technologies to see whether Australian woolgrowers were able to make economically
rational or ‘efficient’ choices to adopt or abandon AM and CC testing technologies over
time. The patterns of diffusion and abandonment of these testing technologies were then
estimated and compared. The following section provides a detailed description of the
procedure used for examining and analysing market data and the patterns of diffusion and
abandonment of the selected technologies.
5.2 Research Method
In order to see how AM and CC were diffused and abandoned by Australian woolgrowers,
an empirical study was undertaken with AM and CC use data from 1988 to 2006. The
market prices for tested and untested wool per annum were compared to see whether
Australian woolgrowers were able to make an efficient choice to test or not test wool sale
lots. Bibliographical data were also collected to capture the major trends in industry
discourse related to AM and CC. The patterns of AM and CC publications were used to
represent patterns of industry discourse about these innovation initiatives. Although the
discourse generated by participants in agricultural innovation initiatives may appear to be
chaotic, these technology stories capture industry sensemaking of a new technology that,
when systematically analysed, can reveal how participants made sense of technologies
during their development, introduction and adoption. For example, Tanaka, Juska and
Busch (1999) and Juska and Busch (1994) examined the evolution of the rapeseed sub-
sector through an analysis of the technology knowledge and market stories published in
academic journals. They found the information and stories participants shared revealed
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activities that drove the development of participants’ knowledge of the technology and the
co-evolution of the technology and the social system.
Data collection
Market Price
In order to examine the market price for tested and untested wool, the price of all wool lots
for Type 56, 21.0 micron wool sold at auction in Australia in October of each year from
1988 to 2006 was extracted from the auction database. Type 56 is a subjective
categorisation of high quality Merino fleece wool that has good top making properties, no
discounts and is a good representation of the national average Merino fleece wool sold by
Australian woolgrowers (Stanton 2007). Only those lots of Type 56 wool sold in October
were examined to control for seasonal market adjustments and 21.0 micron wool was
selected as it is the average micron of commercial wool consignments and its market
indicator price is used as an average price guide (Stanton 2007).
Table 5.1 shows the total number of Type 56, 21.0 micron lots sold at auction in Australia
in October each year from 1988 to 2006 and the proportion of these wool lots with and
without AM and CC tests. The price comparison between tested and untested wool was
limited to years with wool lots sold with tested and untested wool.
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Table 5.1: Sale lots of Type 56, 21.0 micron wool auctioned per annum, 1988-2006
Year Lot count AM (%) No AM (%) CC (%) No CC (%)
1988 138 30 70 n/a n/a 1989 129 78 22 n/a n/a 1990 119 79 21 n/a n/a 1991 60 55 45 n/a n/a 1992 90 73 27 n/a n/a 1993 77 74 26 1 99 1994 68 79 21 0 100 1995 56 89 11 25 75 1996 99 86 14 32 68 1997* 65 89 11 29 71 1998 87 99 1 25 75 1999 61 100 0 21 79 2000** 122 98 2 8 92 2001 83 98 2 1 99 2002 50 100 0 0 100 2003 53 100 0 0 100 2004 19 100 0 0 100 2005 18 100 0 0 100 2006 26 100 0 0 100
* Final year of price comparison for AM as tested wool lots reached 89% of all lots
** Final year of price comparison for Clean Colour as untested wool lots reached 92% of all lots
Diffusion Patterns
In the present study, the diffusion and rejection of AM and CC were analysed at a national
level. The adoption data used were extracted from the Australian wool auction database
compiled by the Wool Desk in the Department of Agriculture and Food, Western Australia
(DAFWA) from auction records collected by the Australian Wool Exchange Ltd (AWEX).
The Wool Desk compiles data on all sale lots offered at auction in Australia in terms of lot
size, quality, price, subjective appraisal and objective test results. The details of wool sale
lots are recorded against a Wool Statistical Area, identifying the geographical location of
production and an individual wool brand name, identifying the individual wool enterprise
that produced the wool. In order to examine the patterns of diffusion and abandonment of
AM and CC over time, the total weight of wool in kilograms with AM and CC tests per
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annum from 1988 to 2006 was extracted from the wool auction database and compared
with the total weight of Australian wool sold per annum over that period.
Industry Discourse
Interviews with four industry experts (scientists and policy advisors involved in the AM
and CC innovation initiatives) identified the print media as the main source of industry
discourse about the AM and CC innovation initiatives. This is consistent with Weick’s
(1995) emphasis on print media as a vehicle for sensemaking about products and markets.
A number of databases and journals were identified in the interviews and were searched for
AM and CC articles published between 1973 and 2006. The sources searched included
OVID Biological Abstracts, Agriculture and Natural Resources Index (ANRI),
CabAbstracts, ProQuest 5000 International, the Sheep Cooperative Research Centre
Livestock Library, the libraries of the IWTO, the CSIRO, AWI Ltd, AWTA Ltd, the
International Journal of Sheep and Wool Science (formerly Wool Technology and Sheep
Breeding), the Australian Journal of Agricultural Research, the Australian Journal of
Experimental Agriculture and the Textile Research Journal. AM and CC publications were
identified using a ‘key word’ search strategy that had been agreed with the interviewees.
The sources were searched for titles and abstracts containing a number of relevant terms
(wool strength, wool length, staple measurements, wool fibre, position of break, wool
metrology, Additional Measurements, clean colour, unscourable colour, ‘sale by/with
Additional Measurements’ and ‘sale by description’). Citations containing one or more of
these terms were identified, collected and analysed.
After checking for duplication, 304 publications were identified. Figure 5.4 shows the
number of AM and CC citations found each year from 1973 to 2006. While no claim of
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completeness can ever be made about such data, the citations should be close to a full list of
AM and CC testing technology papers published between 1973 and 2006.
0
5
10
15
20
25
30
35
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
Year
Nu
mb
er
of
pu
bli
cati
on
s
Figure 5.4: Total AM and CC publications per annum, 1973-2006
Data Analysis
Market price
Price and other economic variables have been included in a range of diffusion studies (e.g.
Griliches 1957; Willems 1980; Jensen 1982; Stoneman 1981; Nowak 1987). However, in
the present study, price was examined separately to diffusion patterns as a single, fixed
price point does not exist for greasy wool per se. A large range of wool types are produced
in Australia from Merino fleece, skirtings, lambs and weaners wool to fibre from cross-bred
sheep with a wide range of characteristics. The price of different types of wool sold at
auction is determined by a combination of highly variable factors, including the quality and
quantity of wool produced and existing market conditions (Shafron, Martin & Ashton
2002).
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In order to compare the market price of AM and CC tested and untested wool, the price of
all lots of Type 56, 21.0 micron wool sold in October from 1988 to 2006 was plotted by
year in cents per kilogram clean. These data display the extent and difference in price
ranges for tested and untested wool. The mean sale lot price for AM and CC tested and
untested wool was compared using two sample t-tests (with unequal variances assumed),
where sufficient data points were available. In addition to the price of tested and untested
wool lots sold at auction, the average Eastern Market Indicator (EMI) price for 21 micron
wool per annum from AWEX was included in the analysis to represent the main market
price indicator for the wool sale lots examined.
Diffusion patterns
The patterns of diffusion and abandonment of AM and CC were examined using a
mathematical modelling approach that allowed the rate and extent of diffusion and the
nature of imitation and outside social influence to be estimated and analysed. A large
number of diffusion models have been developed to reflect the S-shaped diffusion curve as
a process of social contagion (Sultan, Farley & Lehmann 1990). The most widely used
diffusion models in innovation research are the external influence model (Fourt &
Woodlock 1960), internal influence model (Mansfield 1961) and the mixed influence
model (Bass 1969).
The internal influence diffusion model portrays an imitation and learning dynamic among
the society of potential adopters (Mansfield 1961), whereas the external influence model
depicts a dominating driving force from outside of the organization or social system (Fourt
& Woodlock 1960). The mixed influence diffusion model introduced by Bass (1969)
estimates both internal and external influences. The parameters of the Bass diffusion model
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are a coefficient of imitation and a coefficient of innovation. These parameters are related
to the critical dimensions of the social dynamics of the diffusion process. The coefficient
of imitation describes how quickly the innovation spreads through contacts between
potential adopters in a social system (Bass 1969) and is used in this study as a proxy
measure of Abrahamson’s (1991) imitation-focus dimension. The coefficient of innovation
describes how quickly the innovation spreads through members of the adopter group who
are not influenced in their timing of adoption by members of the group who have already
adopted. The coefficient of innovation is used in this study as a proxy measure of
Abrahamson’s (1991) outside-influence dimension.
The central proposition of the majority of diffusion models based on the S-shaped diffusion
curve, is that the rate of diffusion of an innovation is proportional to the number of
potential adopters at a given time t (Mahajan & Peterson 1985; Sultan, Farley & Lehmann
1990). This can be expressed through the following equation:
Where
dN(t)/dt is the rate of diffusion at time t
N(t) is the cumulative number of adopters at time t
m is the total number of potential adopters is a social system
g(t) is coefficient of diffusion
The coefficient of diffusion g(t) can be viewed as a function of the number of previous
adopters. Different types of diffusion models can be distinguished by the articulation of the
coefficient of diffusion g(t) (Mahajan & Peterson 1985). In external influence diffusion
models, g(t) = p. In such models it is assumed there is no communication between
members of a social system and that all diffusion influences come from outside of the
dN(t) g(t)(m – N(t)) dt
=
- 157 -
social system through mass media or change agents (e.g. Fourt & Woodlock 1960; Mahajan
& Peterson 1985). The substitution of g(t) with p in the basic diffusion model yields a rate
of diffusion under external influence:
The external influence model has a constant growth pattern and is only applicable in cases
where members of a social system have no contact with each other (Mahajan & Peterson
1985).
In internal influence diffusion models, g(t) = qN(t). In such models it is assumed diffusion
occurs only through contacts among members of a social system (e.g. Mansfield 1961).
The constant q can be defined as a coefficient of imitation (Mahajan & Peterson 1985).
The substitution of g(t) in the basic diffusion model by qN(t) yields a rate of diffusion
under internal influence:
Bass (1969) presented the first theoretical model that supported an s-shaped diffusion curve
using a mixed influence model (Mahajan & Peterson 1985). The Bass diffusion model is an
empirical generalisation of Rogers (1962) Innovation Diffusion Theory. In Innovation
Diffusion Theory, forms of internal communication, such as word of mouth, and external
communication, such as mass media and change agents, are seen as driving the
dissemination of new products or technologies through a social system (Rogers 2003).
Different members of a social system rely on these two types of communication or
influence differently (Rogers 2003). The Bass diffusion model combines both external and
internal influence in modelling the diffusion of innovations. It is assumed potential
dN(t) p(m – N(t)) dt
=
dN(t) qN(t)(m – N(t)) dt
=
- 158 -
adopters are influenced by members of the social system and external influences, such as
mass media and change agents (Bass 1969). The substitution of g(t) in the basic diffusion
model by p+qN(t) yields a rate of diffusion:
The parameter p, which represents external influence, is known as the coefficient of
innovation (Bass 1969). Parameter q, which represents internal influence, is known as the
coefficient of imitation (Bass 1969). The Bass diffusion model has been applied to a wide
range of new products and technologies from consumer durables to agricultural input
chemicals (as can be seen Table 5.2).
Table 5.2: Some diffusion studies that used the Bass model
Author(s) Innovation Year
Bass Consumer durables 1969
Dowling Television 1980
Akinola Cocoa-spraying chemicals 1986
Skidas & Giovanis Electricity consumption 1997
Steffens Agricultural input chemicals 1998
Kumar, Baisya & Shankar Mobile communications technologies 2007
In a meta-analysis of the diffusion of 213 innovations Sultan, Farley and Lehmann (1990)
found the coefficient of innovation (p) is small (on average 0.03) when compared with the
coefficient of imitation (q) (on average 0.38). The coefficient of innovation was stable
under a range of conditions, whereas the coefficient of imitation was found to vary widely
with the innovation being examined (Sultan, Farley & Lehmann 1990). Lawrence (1981)
reported values of p plus q were between 0.3 and 0.07, while Jeuland (1994) found p values
were 0.01 or less and q values were between 0.3 and 0.5. These findings suggest that,
dN(t) (p +qbN(t))(m – N(t)) dt
=
- 159 -
although diffusion is driven by both internal and external influences, it is largely a process
of imitation rather than innovation (Sultan, Farley & Lehmann 1990).
In the Bass diffusion model, parameters p+q and q/p describe the shape of the diffusion
curve (Bass 1969; Chatterjee & Eliashberg 1990). The diffusion curve is S-shaped when
q>p; this curve becomes more pronounced as the q/p ratio increases (Bass 1969). Bass
(1969) found the q/p values for the eleven consumer durables he examined ranged from 9.0
(home freezers) to 82.4 (electric refrigerators). Consumer durables with relatively high q/p
ratios had a relatively slower rate of diffusion in terms of time to peak sales than did those
products with relatively low q/p ratios.
The original Bass diffusion model has been criticised for failing to incorporate economic
variables (Russell 1980), and has been extended to incorporate additional variables and
estimation procedures. For example, Dolan and Jueland (1981) added price variables and
Horsky and Simon (1983) added advertising variables to the model. However, the original
Bass diffusion model has been found to describe the empirical adoption curve without
additional decision variables and changes have been found to provide relatively small
improvements in its predictive capability (Bass, Krishnan & Jain 1994; Sultan, Farley &
Lehmann 1996).
The original Bass diffusion model has described the empirical adoption of a large number
of products and technologies well (Bass, Krishnan & Jain 1994), reviews have generally
found a good fit to actual adoption data (Mahajan, Muller & Bass 1990) and the pattern of
adoption generally looks like a relatively symmetrical bell-shaped curve. Jeuland (1994)
fitted 35 new product diffusion data sets to the original Bass diffusion model and found a
good fit to actual adoption data with R2 values greater than 0.90. Therefore, the original
- 160 -
Bass diffusion model was used in the present study as a simple, parsimonious and robust
model through which the patterns of diffusion and abandonment of AM and CC could be
estimated and compared.
The Bass diffusion model was applied to aggregate AM and CC adoption data in order to
summarise data into a small number of parameters from which insights could be drawn
about how these technologies were diffused and abandoned. Inferences were drawn from
the parameters of the Bass diffusion model as to which of Abrahamson’s types of diffusion
and rejection were more pronounced for AM and CC over time.
Industry Discourse
A review of the 304 AM and CC publications identified in the database search was also
undertaken. Initially, an exhaustive list of publication topics was generated from a review
of publication titles, abstracts and keywords and the full content of industry reports. The
topic list was refined to four key industry discourse themes that are described in Table 5.3.
Table 5.3: AM and CC discourse themes in the Australian wool industry, 1973 – 2006
Theme Theme Components Number of
citations
% of
citations
Animal science Sheep husbandry, breeding and selection, nutrition, genetics, reproduction, pests and disease
146 48
Technologies Fibre testing technologies, specifications and standards, measurements of wool fibre traits
66 22
Policy and Adoption
Industry policy, position papers, promotion and reporting on strategic initiatives, technology adoption and application and the economics of technology use
62 20
Marketing and Processing
Clip preparation, lot building, fibre processing and fabric research
30 10
Total 304 100%
- 161 -
The authors of the 304 citations identified were also examined to identify the main
participants in AM and CC industry discourse. Three-hundred and fifty-five authors from
43 organisations, including national and state research organisations, tertiary institutions,
testing houses and industry bodies, were identified as participants in AM and CC related
industry discourse and were categorised into the seven participant groups described in
Table 5.4.
Table 5.4: Australian wool industry participants in AM and CC discourse, 1973 –
2006
Industry group Role in the Australian wool industry Number of
authors
% of authors
Policy Advisor Developing and promoting industry policy, representing statutory wool industry and other government bodies
24 7
Textile Scientist Research in wool textile area, employed by the CSIRO division of textile physics
43 12
Animal Scientist (basic)
Research in wool fibre and animal production, employed by local and international universities or the CSIRO division of livestock production
124 34
Animal Scientist (applied)
Research in the application of objective wool measurements to animal production on-farm, employed by state departments of agriculture
112 32
Industry – Production Representing woolgrowers and brokers 17 5
Industry – Processing Representing wool buyers and processors 11 3
Industry – Testing Research and development in wool fibre testing technologies, employed by the AWTA Ltd
24 7
Total 355 100%
- 162 -
5.3 Research findings
Market price
The scatter plot of prices for clean wool with and without AM, which are shown in Figure
5.5, suggested a relatively close range of price points. The EMI appears to be a relatively
good indicator of average lot price, except in 1994 and 1995.
400
600
800
1000
1200
1400
1600
1800
2000
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
Year
Wo
ol
pri
ce (
cen
ts p
er
kg
cle
an
)
AM tested Not AM tested Price indicator 21 micron
Figure 5.5: Scatter plot of the price of AM tested and untested 21.0 micron wool sale
lots Table 5.5 compares the mean price per kilogram of AM tested and untested wool sold from
1988 to 1994. It is not until 1994 that the mean price for AM tested wool is significantly
higher (at the 0.05 level) than the mean price for untested wool.
- 163 -
Table 5.5: Comparison of mean sale lot prices for AM tested and untested wool
AM tested No AM test
Year
Mean
(cents per
kg) Variance obs
Mean
(cents per
kg) Variance obs t statistic p value df
1988 1505 5670 41 1506 7674 96 -0.1 0.46 87
1989 1148 1533 101 1142 1077 28 0.9 0.19 50
1990 934 820 94 939 381 25 -1.0 0.15 55
1991 613 7357 33 611 7774 27 0.1 0.46 55
1992 581 910 66 573 539 24 1.3 0.09 53
1993 540 716 57 528 938 20 1.6 0.06 30
1994 905 2115 54 881 1695 14 1.9 0.03* 22
1995 677 - 50 655 - 6 - - -
1996 646 - 85 633 - 14 - - -
1997 815 - 58 807 - 7 - - -
* Significant at 0.05 level
The price data for AM tested and untested wool showed that, in 1988 and 1989, when the
diffusion of AM took off (as can be seen in Figure 5.1) there was no significant difference
in the mean price per kilogram of Type 56 21.0 micron wool. In 1990, despite an increase
in the proportion of AM tested Australian wool, it was estimated that tested wool received
an additional 3 cents per kilogram in the market compared with test costs of 5 cents per
kilogram, resulting in a net loss of 2 cents per kilogram for tested wool to Australian
woolgrowers (Stott 1990). Australian woolgrowers abandoned AM testing in 1991 and
1992 despite the mean price for Type 56 21.0 micron wool being higher for wool with AM,
albeit the difference in means was not significant at the 0.05 level. Woolgrowers began to
readopt AM in 1993 (see Figure 5.1). In 1993 Gleeson, Lubulwa and Beare (1993)
reported a price premium for AM wool of 6 cents per kilogram for fleece wool, and 7.3
- 164 -
cents per kilogram for skirtings. However, the mean sale price for AM tested Type 56, 21.0
micron wool was found to be significantly higher than untested wool sale lots, at the 0.05
level, only in 1994.
The scatter plot of prices for clean wool with and without CC, shown in Figure 5.6,
suggests there was a large range of relatively close price points after 1994. Table 5.6 shows
the comparison of the mean price per kilogram of CC tested and untested wool sold from
1995 to 1998.
0
200
400
600
800
1000
1200
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
Year
Wo
ol
pri
ce (
cen
ts p
er
kg
cle
an
)
CC tested Not CC tested Price indicator 21 micron
Figure 5.6: Scatter plot of the price of CC tested and untested 21.0 micron wool sale
lots It was not possible to compare the mean lot price of wool with and without CC tests in
1993 and 1994 as there were insufficient data. Australian woolgrowers were not CC testing
Type 56, 21.0 micron wool in this period. The diffusion of CC took off in 1995 and 1996,
despite the mean price for wool with CC being significantly lower, at the 0.05 level, in
1996 than the mean price of lots without CC. These testing technologies were abandoned
- 165 -
after 1996, despite the mean price for tested wool being higher than untested wool in 1998
and 1999 (although the difference was not significant).
Table 5.6: Comparison of mean sale lot prices for CC tested and untested wool
No CC test CC tested
Year
Mean
(cents
per kg) Variance Obs
Mean
(cents
per kg) Variance obs t statistic p value df
1995 679 513 14 673 741 42 0.9 0.19 27
1996 649 407 32 642 403 67 1.7 0.05* 61
1997 816 462 19 813 316 46 0.7 0.25 29
1998 471 1997 22 483 1130 65 -1.1 0.14 29
1999 491 - 13 503 - 48 - - -
2000 605 - 10 605 - 112 - - -
* Significant at 0.05 level
Diffusion patterns
In order to estimate the Bass diffusion model, regression estimates of the model parameters
were calculated using annual time series data for the use of AM and CC. In order to
estimate the Bass diffusion model, regression estimates of the model parameters were
calculated using annual time series data for the use of AM and CC. As was outlined in
Section 5.2, the Bass model assumes there are innovators and imitators and that their
relative acceptance of an innovation determines the pattern of diffusion. The model can be
shown mathematically as the following equation:
Where
dN(t) g(t)(m – N(t)) dt
=
- 166 -
dN(t)/dt is the rate of diffusion at time t
N(t) is the cumulative number of adopters at time t
m is the total number of potential adopters is a social system
g(t) is coefficient of diffusion
The substitution of g(t) in the basic diffusion model by p+qN(t) yields a rate of diffusion:
The parameter p, which represents external influence, is known as the coefficient of
innovation (Bass 1969). Parameter q, which represents internal influence, is known as the
coefficient of imitation (Bass 1969).
Following the procedure suggested by Bass (1969), regression analysis was used to
estimate the various parameters and the results obtained are shown in Table 5.7, as are the
estimated Bass model parameters that can be derived from them. The Bass diffusion model
appears to fit the AM and CC use data, although the R2 values are not reported given the
small number of observations used in the regression. However, the Mean Absolute
Deviations (MAD) for each model were relatively small, suggesting the model describes
diffusion and abandonment patterns well.
Table 5.7: Regression results and Bass model parameter estimates
Technology Period a* b** c*** MAD M (’00
000 kgs
wool)
p q p+q q/p
AM 1988-2006
2280.7 0.07 -1.28E-06 499.6 55888 0.04 0.11 0.15 2.79
AM I 1988-1992
1430.2 0.9 -0.0000775
560.4 13185 0.11 1.01 1.12 9.33
AM II 1993- 2869.4 0.05 - 338 42702 0.07 0.12 0.18 1.75
dN(t) (p +qbN(t))(m – N(t)) dt
=
- 167 -
2006 0.0000015
CC 1993-2006
209.4 0.74 -0.00022 81 3598 0.06 0.78 0.86 13.75
* a = constant representing innovation, ** b = constant representing imitation, *** c = constant representing the portion of the market that
are potential adopters.
Figure 5.7 shows actual and predicted use of CC in Australia. The regression equation
describes the trend path of the diffusion and abandonment of CC very well. In addition the
regression equation provides a good estimate of the magnitude and timing of the adoption
peak for this testing technology.
0
200
400
600
800
1000
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
Year
Wo
ol
teste
d (
'00 0
00kg
s)
Predicted Observed
Figure 5.7: The predicted and observed diffusion of CC Testing
Figure 5.8 shows actual and predicted use of AM in Australia. The regression equation did
not describe the trend path of the initial diffusion of AM testing, abandonment and
subsequent diffusion well as it failed to capture the initial adoption peak in 1990 and the
subsequent abandonment of AM in 1991. The pattern of actual use of AM suggested AM
went through two separate cycles of diffusion. The first cycle of diffusion occurred from
1988 to 1992, during which time the technology was rapidly adopted and reached an
adoption peak in 1990, it was then abandoned as the price of wool collapsed and the WRPS
was abandoned in 1991. In 1993, woolgrowers began to readopt AM, diffusion
- 168 -
recommenced and the diffusion of the technology increased steadily over time. Therefore,
the modelling of the use of AM was split into two time periods: AM I (1988-1992) and AM
II (1993-2006).
0
1000
2000
3000
4000
5000
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
Year
Gre
asy w
oo
l ('00 0
00kg
s)
Predicted Observed
Figure 5.8: The predicted and observed diffusion of AM
The Bass regression equation was applied to AM use data in AM I and AM II. The trend
data for the two periods of AM diffusion are shown in Figure 5.9 and the parameter
estimates are reported in Table 5.7. The regression equation describes the trend path of
AM diffusion and abandonment in AM I and AM II well. In addition, the regression
equation provides a good estimate of the magnitude and timing of the adoption peak for this
testing technology in the two periods examined. Deviations from the trend in 2002 are
largely explained by a substantial reduction in the size of the national wool clip in that year
due to drought.
- 169 -
0
1000
2000
3000
4000
5000
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
Year
Wo
ol w
ith
AM
('0
0 0
00kg
s)
Observed AM I Observed AM II
Predicted AM I Predicted AM II
Figure 5.9: Predicted and observed diffusion of AM in Period I and Period II Parameter estimates. The parameter estimates from the regression, which are shown in
Table 5.7, seem reasonable for the Bass diffusion model. The regression estimates for
parameter c are negative for each technology and time period, as required by the model.
The coefficient of innovation (p) and coefficient of imitation (q) are both positive, with p
being lower than q, as was expected. However, the values of p and q varied from other
reported Bass diffusion model results (Sultan, Farley & Lehmann 1990; Jeuland 1994). In
the present study, estimates of p were relatively high for AM in AM I (0.11) and AM II
(0.07) and relatively high for CC (0.06), compared with Sultan’s (1990) study. In the
present study, estimates of q were relatively high for AM in AM I (1.01), relatively low for
AM in AM II (0.12) and relatively high for CC (0.78) compared with Sultan, Farley and
Lehmann’s (1990) meta-analysis of the Bass parameters. In the present study, the value of
p+q for AM was relatively high in AM I (1.12), but relatively low in AM II (0.18), while
the value of p+q for CC was relatively high (0.86), compared with Lawrence and Lawton’s
(1981) analysis of empirical diffusion studies as they found the value of p+q was generally
between 0.3 and 0.7.
- 170 -
It would seem fair to conclude that the AM (in periods I and II) and CC testing data are in
agreement with Bass’s diffusion model. The question is whether these results help our
understanding of how these technologies were diffused and abandoned.
Industry Discourse
The bibliographic data were divided into three time periods relating to major events in the
AM and CC innovation initiatives. Industry discourse in periods I, construction and
transfer (1973 to 1988) focused on the development and introduction of AM, including the
introduction of industry policy to move to ‘sale by description’, the launch of the TEAM
project and commercialisation of pre-sale AM. Industry discourse in period II, adoption
(1988 to 1999) focused on the adoption of AM along the wool supply chain, including the
reinvention of the original TEAM formulae through the TEAM-2 project, the collapse of
the Wool Reserve Price Scheme (WRPS) in 1991 and the introduction of CC testing in
1994. Industry discourse in period III, implementation (2000 to 2006) focused on the use
of AM and CC testing on-farm. Figure 5.10 shows the proportion of AM and CC
publications in each of the four key themes of industry discourse that were identified in
Table 5.8 in each of the three periods that were examined in this study. These data
suggested that the pattern of industry discourse relating to AM and CC changed over the
three time periods.
- 171 -
12%
31%
9%
48%
10%
21%
63%
7%
8%
16%
58%
17%
0%
20%
40%
60%
80%
100%
Pro
po
rtio
n o
f cit
ati
on
s
Construction &
transfer
Adoption Implementation
Marketing and processing Technology Animal science Policy & Adoption
Figure 5.10: AM and CC discourse themes by proportion of citation topics in three
time periods The data in Figure 5.11, which shows the pattern of industry participants in discourse
related to AM and CC from 1973 to 2006, suggested participation in AM and CC discourse
also changed over time.
- 172 -
0
5
10
15
20
25
30
35
40
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
Year
Num
ber of citations
Textile scientists Animal scientists - basic Animal scientists - applied
Industry - production Industry - processing Industry - testing
policy and adoption
Figure 5.11: Participants in AM and CC discourse by industry group, 1973-2006
- 173 -
In the construction and transfer period, industry policies for SXAM and SXD and new
testing technologies were heavily promoted to Australian wool industry participants.
Forty-eight per cent of all AM and CC publications in this period related to policy and
adoption. The majority of authors (78 per cent) in this period were policy advisors, wool
metrologists and textile scientists. Together these industry participants formed a powerful
coalition promoting the introduction and adoption of AM and CC.
The main themes of AM and CC discourse in this period were arguments for the Australian
wool industry to move towards SXAM and, ultimately, SXD. This reflected the
interpretation frameworks of policy makers and textile testing technologists. These
industry participants enacted their beliefs about the nature of greasy wool as a product and
their beliefs about competition from synthetic fibres through SXAM and SXD policies and
the TEAM project.
There was a considerable change in the themes of AM and CC discourse from the
construction and transfer period to the adoption period. In the adoption period, new
testing technologies for AM and CC were introduced and the use of AM and CC on-farm
was examined by animal scientists. Sixty-three per cent of all AM and CC publications in
this period related to research into the use of AM and CC on-farm and twenty-one per cent
related to testing technology research and development. Whereas the majority of authors in
the construction and transfer period were policy advisors, wool metrologists and textile
scientists, in the adoption period the majority of discourse participants were animal
scientists (77 per cent). This shift in industry discourse themes and participation suggests
that, after the introduction of AM and CC, industry participants sought evidence of the
technical efficiency of these testing technologies on-farm.
- 174 -
In the implementation period, AM and CC discourse continued to focus on the use of these
testing technologies on-farm. However, interest in industry policy and the adoption of AM
and CC also increased in this period. Fifty-eight per cent of all AM and CC publications in
this period related to research into the use of AM and CC on-farm and seventeen per cent
related to policy and adoption, specifically in terms the future of wool metrology in the
Australian wool industry. In the implementation period, seventy-seven per cent of industry
discourse participants were animal scientists, suggesting that issues relating to the use of
AM and CC on-farm had not been resolved.
In the implementation period, the beliefs of policy advisors, textile scientists and wool
metrologists were challenged as the long held industry policy of moving towards SXD was
abandoned. Wool metrologists and textile researchers reconstructed AM through TEAM
for the third time in an effort to improve the efficiency and effectiveness of existing testing
technologies. In this period, wool metrologists adopted the role of industry policy advisors
in an attempt to revive flagging industry interest in wool metrology.
5.4 Discussion of the findings
The aim of the present study was to examine how AM and CC were diffused and
abandoned by Australian woolgrowers. The innovation and imitation parameters estimated
through the Bass model were combined with Abrahamson’s (1991) diffusion and rejection
typology to see what individual and social processes were likely to have impelled the
diffusion and abandonment of AM and CC in the Australian wool industry. The data were
an aggregate of imitation and innovation effects over time and do not indicate changes in
the level of imitation and innovation at specific points in time (i.e. they do not represent
cross sectional diffusion and abandonment data). However, the parameter estimates for
- 175 -
innovation and imitation effects derived from the Bass diffusion model provided a base
from which to examine how AM and CC were diffused and abandoned.
In Figure 5.12, the parameter estimates for the diffusion and abandonment of AM and CC
were plotted on two dimensions: (1) the estimated level of imitation effect and, (2) the
estimated level of innovation effect. The imitation and innovation parameters of the Bass
diffusion model were superimposed on Abrahamson’s (1991) typology, which suggests the
individual and social processes that impel the diffusion and rejection of innovations.
Results of a meta-analysis of Bass diffusion model parameter estimates undertaken by
Jeuland (1994) and Sultan, Farley and Lehmann (1990) were also plotted in Figure 5.12 as
a point of comparison with AM and CC diffusion parameters.
Figure 5.12: Imitation and innovation parameters and types of diffusion and rejection
0 0.01
0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0 AM Period I ●
AM Period II ●
CC ●
(Jueland, 1994) ● (Sultan, Farley & Lehmann,1990) ●
Imitation (q)
Inn
ov
ati
on
(p
)
Forced Selection Fashion
Fad Efficient-Choice
- 176 -
The parameter estimates for the diffusion of AM suggest the diffusion and abandonment of
AM in AM I was a fashion-setting process. During AM I, this technology initially diffused
rapidly, peaking in 1991, before being widely abandoned by woolgrowers after the collapse
of the WRPS. The rapid rate of diffusion in AM I is supported by relatively high
coefficients of innovation (0.11) and imitation (1.01). The high estimates of external and
internal influence suggest woolgrowers were persuaded to adopt AM as a result of external
influence and imitation. External influence may have been in the form of fixed price
incentives under the WRPS and the promotion of AM by powerful industry participants.
During AM I, woolgrowers were unable to make an efficient-choice to adopt this
technology as price premiums for tested wool and discounts for untested wool had not yet
emerged in the market.
If the efficient-choice of agricultural innovations is to be assumed, woolgrowers should
have rejected AM in this period in the absence of price and cost benefits. However, given
the rapid rate of diffusion in AM I, it seems the diffusion of this technology (inefficient in
terms of market price at the time) was driven by the external influence of powerful industry
participants. This observation supports Abrahamson’s (1991, p. 594) suggestion that
“technically inefficient innovations will tend to diffuse among groups of organizations
when these innovations receive the backing of powerful organisations outside these
groups.”
In the absence of an external influence (i.e. when WRPS fixed price premiums were no
longer available), many woolgrowers abandoned AM. However, according to the price
data, the price for wool with AM was higher than the price for untested wool in 1991 and
1992 when these tests were abandoned. Although the difference in the price received for
- 177 -
tested and untested wool sale lots in 1991 and 1992 was not significant, this observation
suggests woolgrowers may have abandoned AM at a time in which adoption was becoming
economically rational. This observation does not support Abrahamson’s (1991) suggestion
that adopters will reject a technically efficient innovation when there is overwhelming
external pressure to do so as, in the case of the abandonment of AM, there was no external
pressure to abandon this technology from powerful and influential industry participants.
Therefore it can be suggested that:
Research Proposition 1: Adopters will tend to reject an innovation when it is
perceived that environmental changes will render it economically inefficient.
The parameter estimates for the diffusion of AM suggest the diffusion and abandonment in
AM II was faddish behaviour. In AM II, the rate of diffusion of this technology was slower
than in AM I. The relatively slow rate of diffusion in AM II can be seen in the relatively
low coefficients of innovation (0.07) and imitation (0.12) compared with AM I. These
parameter estimates suggest that, as in AM I, external and internal influences were
important factors in the adoption and re-adoption of AM by woolgrowers in this period. It
was only in 1994 that the price of 21 micron, Type 56 AM tested wool was significantly
higher than untested lots, suggesting price premiums for tested wool and discounts for
untested wool emerged in the market in this period. Therefore, it is likely that, although the
adoption and re-adoption of AM in 1992 and 1993 may have reflected faddish behaviour,
the decision to adopt may have emerged as an efficient choice in 1994. Given the changing
patterns of the diffusion and abandonment of AM in periods I and II (from fashion to fad to
efficient choice) it can be suggested that:
- 178 -
Research Proposition 2: Fashion-setters may drive the adoption of technically
inefficient innovations in the initial stages of the diffusion process where external
organizations have the power to drive diffusion and the interest in doing so.
Research Proposition 3: Efficient choices may drive the adoption of technically
efficient innovations in later stages of the diffusion process when the effect of
fashion-setters and imitation has diminished.
The number of sale lots offered at auction without AM declined in the 1990s as more
woolgrowers adopted the tests. The ability of growers to make an efficient choice to
undertake AM tests became unclear. The data suggested there may not have been a
significant price difference between AM tested and untested wool in the late 1990s.
Therefore, it is possible the diffusion of AM after 1994 was a process of forced selection or
compliance as woolgrowers undertook AM tests because they had become institutionalised
in the Australian wool industry, rather than because they were economically beneficial.
Therefore it can be suggested that:
Research Proposition 4: The adoption of mature innovations in the later stage of
diffusion may be driven by forced selection in contexts in which the innovation
has become institutionalised in the industry and it is no longer possible to make
an efficient choice to adopt or reject the technology.
The parameter estimates for the diffusion of CC suggest the diffusion and abandonment of
CC was a fashion-setting process. Clean Colour testing diffused relatively rapidly after its
introduction in 1994 and peaked in 1996, before being widely abandoned by woolgrowers.
The relatively rapid rate of diffusion of CC can be seen in the high coefficients of
innovation (0.06) and imitation (0.78). The relatively high values of external and internal
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influence parameters suggested a proportion of the population of woolgrowers were
persuaded to adopt CC as a result of external influence, despite its economic inefficiency.
This external influence was likely to have come from wool selling brokers who received an
incentive for testing their client’s wool and industry supporters of CC who heavily
promoted these tests. The relatively high imitation parameter suggests many woolgrowers
imitated early adopters of CC testing up to 1996.
In 1995, when the diffusion of CC took off, woolgrowers were unlikely to have been able
to make an efficient-choice to adopt these tests, as price premiums for tested wool and
discounts for untested wool had not emerged in the market. Therefore, if the efficient-
choice of agricultural innovations is to be assumed, woolgrowers should have rejected CC
in this period. However, given the rapid rate of diffusion of CC in 1995, the empirical
results suggest this was the diffusion of an inefficient technology driven by the external
influence of powerful industry participants and imitation among woolgrowers.
Clean Colour testing was abandoned by the majority of adopters between 1996 and 2001.
However, the present analysis shows that, in 1998 and 1999, the price of CC tested 21
micron, Type 56 wool was higher than the price of untested wool. In the case of CC
testing, woolgrowers may have abandoned an economically ‘efficient’ innovation. The
diffusion and abandonment of CC support Abrahamson’s (1991, pp. 596-7) suggestion that,
“over time, organizations will tend to reject technically efficient innovations promoted by
fashion-setting networks.”
The data examined in this study suggest the diffusion and abandonment of agricultural
technologies is not simply an efficient-choice made by industry participants to close an
observable performance gap. These findings further highlight the inadequacies of
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employing technological determinist approaches to the study of agricultural innovation, as
an economic rationality or efficient-choice cannot be assumed in the decision to adopt or
abandon new agricultural technologies. The adoption of inefficient innovations and the
abandonment of efficient innovations can be impelled the adopter’s social context in terms
of by powerful external influences and imitation within an adopter group. These findings
suggested that the innovation process is socially constructed and that the social context of
potential adopters is a critical element of how they make sense of new agricultural
technologies. The research propositions presented also suggest an innovation process in
which the drivers of diffusion and abandonment change over time, further supporting the
notion of evolutionary sensemaking underlying the agricultural innovation process.
Conclusions
The dominant models of diffusion used in agricultural innovation research assume an
efficient choice is made to adopt efficient technologies that will reduce performance gaps
or to reject technologies deemed to be inefficient. The analysis of the diffusion and
abandonment of AM and CC, however, highlights the social and dynamic nature of the
agricultural innovation process. In the case of AM, the forces impelling the diffusion and
abandonment of these testing technologies changed over time, from ‘fashion-setters’ or
‘sense givers’ driving initial adoption to imitation within the adopter group influencing
abandonment and re-adoption. It was observed that the initial adoption of AM occurred
when the decision to adopt was not economically rational. In the case of CC, the adoption
of these tests occurred when the innovation was economically inefficient and abandonment
occurred when the technology was entering a period of economic efficiency. These
observations supported a series of research propositions about the nature of the diffusion
and abandonment of new agricultural technologies as a social process of fashion-setting,
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faddish behaviour, efficient choice and forced selection. In addition, further research
propositions were made that reflected the social and dynamic nature of agricultural
innovation diffusion, extending Abrahamson’s (1991) work in this area.
In the following Chapter, the third and final empirical study of OM innovation initiatives in
the Australian wool industry, ‘The enactment of new technologies on-farm: A sensemaking
perspective’ is presented.
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6 The enactment of new technologies on-farm: A sensemaking perspective
The study of the diffusion and abandonment of Additional Measurements (AM) and Clean
Colour testing (CC) that was discussed in Chapter 5, highlighted the social and dynamic
nature of the agricultural innovation process. The patterns of the diffusion and
abandonment of AM and CC were found to have changed over time, reflecting the
emergence and decline of external influence, imitation, efficient choice and forced
selection. However, these aggregate data told only part of the story of the diffusion and
abandonment of AM and CC testing technologies. They did not explain how or why
woolgrowers adopted, implemented, used and abandoned these technologies on-farm.
In the agricultural innovation adoption literature, most empirical research has been aimed at
establishing the determinants of the adoption decisions made by individuals and farm
families (e.g. Rogers & Shoemaker 1971; Abolaji 1993; Nowak 1987; Findlay 1980;
Wilkening 1953; Beal & Rogers 1960; Feder 1980). These have generally been variance-
type studies that examine the covariance’s among a set of pre-determined adoption
variables (Rogers 2003). Dominant agricultural innovation adoption theories have come
from rural sociology and take a technological determinist view of the adoption decision that
make the assumption that new technologies will replace inferior technologies because of
their superiority (Rogers 2003) and that the adoption and use of new technologies is a
relatively simple process (Black 2000). This perspective is reflected in linear, staged
conceptualisations of the adoption of new technologies (e.g. Kennedy 1977; Rogers 2003;
Jones 1967).
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In such models, the decision to adopt a new technology is assumed to reflect the potential
adopters’ perception of the technologies attributes (Rogers 2003). Adopters are viewed as
passive recipients of a new technology, operating in stable environments. As already
noted,17 linear adoption decision models do not consider the dynamic and discontinuous
nature of the innovation adoption process (Simon 1977).
Researchers have studied the adoption of a wide range of new agricultural technologies. In
an Australian context, such studies have included the adoption of pasture production
technologies (Trompf & Sale 2000), formal farm management training (Murray-Prior, Hart
& Dymond 2000) and beef cattle breeding practices (Kaine & Lees 1994). As discussed in
Chapter 218, agricultural innovation researchers have highlighted the central role the
farming context (Aubry, Papy & Capillon 1998; Kaine & Lees 1994; Murray-Prior, Hart &
Dymond 2000; Frank 1995a), experiential learning (Trompf & Sale 2000; Martin &
Sherington 1997; Cameron 1999) and social learning (Foster & Rosenzweig 1995) play in
the adoption of agricultural technologies on-farm. However, these studies have focused on
the decision to adopt new technologies and do not examine the implementation, use and
possible abandonment of innovations. In this light, the objective of this study was to
broaden and strengthen our understanding of the adoption, implementation, use and
abandonment of new agricultural technologies on-farm. Indeed, while it was recognised
that progress has been made in this area, what has been developed so far is a fragmented,
narrow and static understanding of agricultural innovation on-farm. Therefore, it was
proposed that a more dynamic view of agricultural technology enactment on-farm was
needed.
17 Chapter 2, p.28. 18 Chapter 2, pp 23-24.
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The study of the enactment of new technologies on-farm is presented over two Chapters.
This Chapter describes the selection of the six case study farms and presents an analysis of
the quantitative technology enactment data for these farms. In Chapter 7, the story of the
enactment of AM and CC on-farm is presented and discussed and tentative research
propositions relating to the enactment of new agricultural technologies on-farm are
presented. This Chapter is divided into four sections. The first section presents a brief
summary of the theoretical background of the study. The second section describes the
research method, specifically the selection of case study farms and the collection and
analysis of case study data. The third section presents an analysis of the patterns of
enactment of AM and CC on-farm. The final section provides a conclusion to the chapter.
6.1 Theoretical Background
In the present study the shortcomings in the agricultural innovation research were addressed
by approaching the adoption, implementation, use and abandonment of agricultural
technologies on-farm from a sensemaking perspective. This approach questioned the
assumption that adopters are passive recipients of new technologies, which has dominated
agricultural innovation research. Instead of examining the innovation decision as a linear
process driven by a potential adopter’s perception of technology attributes, the enactment
of new agricultural technology was viewed as an evolutionary, socio-cognitive process in
which woolgrowers notice, bracket and select salient cues to create a plausible story about
the technology that guides the ongoing enactment of that technology on-farm.
In this study, agricultural innovation on-farm was examined through a multiple case study
of six individual commercial wool enterprises engaged in the adoption, implementation,
use and abandonment of Additional Measurements (AM) and Clean Colour (CC) testing
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technologies. Particular emphasis was placed on examining the enactment of cues
extracted from AM and CC technologies and environmental stimuli and the influence and
updating of woolgrower’s interpretation frameworks in respect to their personal identity
and social context.
6.2 Research Method
As already noted, this study attempted to address some gaps in agricultural innovation
literature by adopting a sensemaking perspective of the enactment of new technologies on-
farm. The study investigated whether the enactment of new agricultural technologies on-
farm reflected farmers’ individual and social sensemaking. The analytical framework
shown in Figure 3.3 guided data collection and analysis.
Rogers (2003) argued that the focus of innovation research should be on the dynamic nature
of the innovation process; emphasising the ‘how’ and ‘why’ of what occurs. As previously
discussed19, the case study method allows researchers to examine the ‘how’ and ‘why’ of
phenomena in their natural setting (Yin 1994; Sechrest, Stickle & Sidani 1996; General
Accounts Office 1987). Consequently, the present study was based on a multiple case
study approach, aimed at theory-building, which investigated the enactment of AM and CC
technologies on-farm by Australian woolgrowers.
Case Study Selection
The unit of analysis in this study was the Australian commercial wool production
enterprise. Six such enterprises were purposely selected as cases in the expectation they
would yield similar results (i.e. literal replication) or different results (i.e. theoretical
19 Chapter 3, p.83.
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replication) (Yin 1994). Eisenhardt (1995) noted there is no ideal number of cases for a
multiple, theory-building case study design, although between four and ten cases are likely
to be sufficient. With less than four cases, theory is difficult to generate and with more
than ten cases, the volume of data generated can become difficult. Therefore, it was
decided that six cases should provide sufficient data without constraining data analysis.
In the present study, the six individual enterprises were selected from the Australian wool
auction database. A cluster analysis was used to find groups of homogeneous commercial
wool production enterprises in terms of enterprise size and the adoption and use of AM, so
that similar cases could be selected from within the obtained clusters and contrasting cases
could be selected from across the clusters. As the name implies, the primary objective of
cluster analysis is to classify objects into homogeneous groups based on a set of underlying
variables (Hair et al. 1998). Thus, the objects in a given cluster should be similar in terms
of the variables used in the analysis and different from objects in the other groups.
The geographic location of this study was limited to Western Australia (WA) as access to
AM and CC data at an enterprise level was only available for enterprises operating in that
location. The enterprises were selected from the W09 Australian Wool Statistical Area
(WSA), which is a significant wool production area. Wool enterprises in W09 contribute
around 16 per cent of the Western Australian clip and four per cent of the national clip
(around 10 million kgs per annum) from around 3.5 million sheep (Australian Wool
Testing Authority 2006). Figure 6.1 shows the location of W09 in the lower Great-
Southern region of WA.
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Figure 6.1: Southern WA Wool Selling Areas (source: Australian Bureau of Statistics) A total of 6321 wool brands operating in W09 were identified in the Australian wool
auction database. The Wool Desk amalgamated correct and miss-spelt wool brands into
individual wool enterprise data, reducing the dataset to 3870 individual wool enterprises.
As the study examined the adoption, implementation and abandonment of AM and CC on
farm, only enterprises that had adopted these technologies were included in the data set,
reducing the dataset further to 2120 enterprises.
These enterprises were sorted by average clean weight of wool offered at auction per
annum. Individual enterprises that offered, on average, less than 11000 kilograms per
annum were excluded as they were defined as non-commercial wool enterprises (Shafron,
Martin & Ashton 2002). As a result, 629 enterprises were retained. Of those, 482
enterprises had complete data sets and were clustered to find farms that were similar in
terms of their size, the time of AM adoption and the proportion of their wool offered at
auction with AM and from which individual cases were selected for the study.
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Wool enterprise size was used as a cluster variable as farm size has been linked to
technology adoption. Itharat (1980) and Hill and Kau (1973) found relatively large
enterprises were more likely to adopt new technologies relatively early. Size was defined
as the average clean weight (kgs) of wool offered at auction each year. Enterprise size in
the dataset ranged from 11197 kilograms a year to 53111 kilograms a year, with a mean
size of 20427 kilograms (SD 8061 kilograms).
The relative time of adoption of AM was also included as it has been linked to adopters’
innovativeness (Rogers 2003; Midgley 1976; Midgley & Dowling 1978) and to the
effective and continued use of technologies (Rogers & Shoemaker 1971; Parthasarathy &
Bhattacherjee 1998). The time of adoption of AM testing technologies was defined as the
year in which AM testing technologies were first used by the enterprise. The time of
adoption ranged from year 1 (i.e. began AM testing in 1988) to year 14 (i.e. began testing
in 2001), with a mean of 2.9 years and a mode of year 2.
The proportion of wool offered at auction with AM was included to measure the extent of
use of the technology. The proportion of wool AM tested was defined as the average
proportion of total clean weight of wool offered at auction with AM from 1988 and 2001.
The proportion of wool with AM results ranged from two per cent to 97 per cent of total
clean wool offered at auction with a mean of 55 per cent (SD 18%).
The Clustering Procedure
Cluster analysis can be hierarchical or non-hierarchical (Hair et al. 1998). Here, Ward’s
hierarchical clustering technique was used as the clusters tend to be more coherent than
other approaches (Hair et al. 1998) and the method has outperformed many other clustering
algorithms (Punj & Stewart 1983; Harrigan 1985). As the three cluster variables selected in
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this study were measured on very different scales, they were standardised by computing z-
scores prior to undertaking the cluster analysis. The agglomeration coefficient, which is
summarised in Table 6.1, was used to select the cluster solution. Small coefficients
indicate the merging of relatively homogeneous clusters, whereas large coefficients indicate
the merging of very different clusters (Hair et al. 1998). By evaluating the change in the
agglomeration coefficient at each stage of the clustering process, an appropriate cluster
solution was determined. The agglomeration coefficient had a large increase when
combining four to three clusters (31%) compared to a relatively small changes combining
five clusters down to four (13%). Consequently, the four cluster solution was used to
selection the cases needed for further study.
Table 6.1: Cluster analysis agglomeration coefficient
Number of
clusters
Agglomeration
coefficient Difference
Change in coefficient to next
level (%)
10 313.53 34.12 10.88
9 347.66 35.05 10.08
8 382.7 41.73 10.91
7 424.45 50.77 11.96
6 475.23 68.06 14.32
5 543.29 68.72 12.65
4 612.01 189.44 30.95
3 801.46 287.33 35.85
2 1088.80 354.20 32.53
1 1443.00 ~ ~
An Examination of Group Differences
The mean scores of the four groups are shown in Table 6.2. It can be seen that enterprises
in group 1 had relatively small wool enterprises, early adoption and relatively high use of
AM. Enterprises in group 2 had relatively small wool enterprises, late adoption and low
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levels of use of AM. Enterprises in group 3 had relatively large wool enterprises, early
adoption and low levels of use of AM. Enterprises in group 4 had relatively large wool
enterprises, late adoption and high levels of use of AM.
Table 6.2: Cluster group profiles
Group Means
Group Enterprise size
(average kgs wool per
annum)
Year of AM
adoption
% of wool AM
tested
Cluster size
1 17599 2 61 221
2 15546 2.5 30 83
3 32728 2.3 50 100
4 17864 7 69 78
As cluster membership is nominally scaled, discriminant analysis is an appropriate way to
examine the clusters’ differences (Klecka 1988) and the procedure was used in the present
study. Wilks’ Lambda can be used to define an F statistic that provides an indication as to
whether the three variables differed across the groups in a univariate way. As can be seen
in Table 6.3, each of the three constructs did differ across the groups, suggesting they
should all be included in the discriminant analysis.
Table 6.3: Tests of Equality of Group Means
Cluster variable Wilks’
Lambda
F Df1 df2 Significance
Average clean weight of wool sold per annum
0.40 235.26 3 478 0.000
Year of adoption of AM 0.40 235.85 3 478 0.000
Proportion of wool AM tested
0.50 159.13 3 478 0.000
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An examination of the F statistics between the groups found that all were significantly
different from each other, which is not surprising as a cluster analysis is designed to find
groups that are maximally different. Two significant functions were found and the I squared
statistic (Peterson & Mahajan 1976) suggested these functions explained seventy five per
cent of the variation between the groups, which also suggests the cluster analysis did find
different groups of wool enterprises. The structural correlations show the relationship
between the estimated discriminant scores on each of the estimated functions and the
original variables (Soutar & McNeil 1995). These correlations were rotated to obtain
simple structure as this provides a better understanding of the obtained functions. As can
be seen from Table 6.4, the first function was related to the acceptance of AM technologies,
with higher discriminant scores implying later and more intensive use of these
technologies. The second function was related to the size of the wool growing enterprise,
with higher discriminant scores implying a larger enterprise.
Table 6.4: The Structural Correlations
Value Dimension Function 1 Function 2
Year of adoption of AM 0.85 0.04
Proportion of wool AM tested 0.52 -0.01
Average clean weight of wool sold per annum 0.02 0.99
As there are two discriminant functions, the structural correlations can be shown
diagrammatically as in Figure 6.2. In this case, the direction of the vectors shows the nature
of the relationship, while the length of the vector shows the strength of the relationship
(Soutar & Clarke 1981). As can be seen from the Figure, the horizontal function
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(discriminant function 1) can be termed AM acceptance, while the vertical function
(discriminant function 2) can be termed enterprise size.
Figure 6.2: A Discriminant Map of the Four Clusters The four clusters can also be placed into the same space by using the centroids obtained
from the discriminant analysis (Soutar & Clarke 1981), which show the relationship
between the four groups and between the groups and the various explanatory variables (in
this case, the three variables used in the cluster analysis). Group 4 members were the latest
adopters of AM, but, on average, had the highest level of use among the user groups.
Group 1 was the next highest user of AM testing and the earliest adopter group, while the
other two groups had similar rates and levels of acceptance of AM. Group 2 had the
smallest mean amount of wool sold per annum, while group 3 had the highest mean amount
of wool sold per annum. The Figure clearly shows the differences between the groups,
suggesting group 1 and group 2 are most similar, while group 3 and group 4 are the most
Proportion AM tested
Size of wool clip
* Group 1* Group 2
Group 3 *
Group 4 *
AM Acceptance
Size
Year AM Adopted
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dissimilar. Thus, choosing enterprises from these clusters should provide an opportunity
for literal and theoretical replication.
Six enterprises were selected at random from the four cluster groups for this study. Two
enterprises were selected from cluster groups one and three as these were the largest
groups. One enterprise was selected from cluster groups two and four as these were the
smallest groups. A letter, which is shown in Appendix C, was sent to the farm families
operating these wool enterprises requesting their consent to participate in the study and
each of the six agreed to participate. The key characteristics of the six farms selected as
cases in this study are summarised in Table 6.5 and described in the following sub-section.
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Table 6.5: Key characteristics of the cases studied
Cluster
Group
Wool
Enterprise
name*
Farm enterprise
structure
Farm
enterprise size
Farm enterprise
income mix
Average
annual wool
production
(kgs)
Year AM
adopted
Average % wool
AM tested per
annum
Year CC
adopted
Year CC
abandoned
1 Dorset Farm
Second generation family farm, inherited in 1999
1500 hectares
3200 sheep
35 cattle
60% sheep and cattle, 40% crops
22458 1989 73 1998 2003
1 Saxon Farm
Third generation family farm, inherited in 1994
1200 hectares
4000 sheep
45% wool, 22% sheep meat, 33% crops, >1% ram stud
30967 1990 35 Not adopted
Not adopted
2 Polwarth Farm
Second generation family farm, inherited in 1996
800 hectares
1700 sheep
30% wool, 30% sheep meat, 40% crops
25564 1990 58 1996 2002
3 Peppin Farm
Fourth generation family farm, acquired in 1990
3500 hectares
10000 sheep
33% wool, 14% sheep meat, 50 % crops
42457 1988 73 1996 2003
3 Coriedale Farm
Fifth generation family farm
7000 hectares
8000 sheep
40% ram stud, 20% wool, 20% sheep meat, 20% crop
43111 1989 49 1995 2001
4 Romney Farm
Second generation family farm, inherited in 1980
4000 hectares
9000 sheep
40% wool, 30% sheep meat, 30% crops, >1% ram stud
41618 1988 61 1996 2002
* Not the actual name of the enterprise. The real names of the farm and the farming family are disguised to maintain confidentiality
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Case Study Descriptions
The following brief descriptions of the six case study farms provide a useful context for the
analysis undertaken in this study. The six farm businesses studied represented a variety of
farm business structures, size, enterprise mix and enactment of AM and CC testing
technologies (see Table 6.5).
Dorset Farm
Dorset farm was a mixed farm business comprising commercial sheep, cattle and crop
production enterprises. The farm business was owned and operated by Mr and Mrs Dorset,
who lived at the homestead with their two pre-primary school aged children. Mr Dorset
had worked in the family farm business for more than 20 years and Mrs Dorset was
previously employed as a health professional. Mr Dorset and his brother inherited the
family farm from their parents in the late 1980s when they ceased wool production. Mr
Dorset and his brother ran the family farm business in partnership until 1998 when the
partnership ended acrimoniously. Mr and Mrs Dorset recommenced wool production in
1998 and became the sole owners and operators of Dorset farm in 1999. Despite retiring
over 15 years ago, Mr Dorset’s father continued to assist with the management of the farm
and was present during the case study interviews.
The Dorset farm animal enterprise comprised around 2000 Merino sheep, 1200 cross bred
sheep and 35 beef cattle. Between 250 to 300 hectares of canola and oats were planted
each year. The crop and animal enterprises were flexible and interchangeable and the
proportion of income obtained from each depended on the season and the comparative price
of wool, sheep meat, canola and oats.
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Saxon Farm
Saxon Farm was a commercial wool and crop production enterprise owned and operated by
Mr and Mrs Saxon. The farm business was inherited from Mr Saxon’s parents in 1994. Mr
and Mrs Saxon were the sole owners and operators of the family farm and lived at the
homestead with their two pre-primary school aged children. Both Mr and Mrs Saxon
derived a proportion of their income off-farm from the provision of farm consultancy
services.
Forty-five per cent of farm income was derived from wool production, 22 per cent from
sheep meat and 33 per cent from cropping. The remainder of farm income was derived
from a small Merino ram stud. The Saxon farm sheep enterprise comprised a 4000 sheep
flock made up predominantly of ewes (60%). Rotational and strip grazing practices and
formal feed budgeting were employed on Saxon farm to manage pasture utilisation. The
goals of the Saxon farm sheep enterprise were to reduce wool fibre diameter and produce
wool fibre with a staple strength of no less than 28 Newtons per kilotex.
Polwarth Farm
Polwarth Farm was a commercial sheep and crop enterprise, owned and operated by Mr
and Mrs Polwarth who had lived on the property with their two primary-school aged
children for seven years. Mr and Mrs Polwarth inherited the family farm from Mr
Polwarth’s parents in 1996 and operated two farming properties until they sold an
uninhabited block in 2000.
Polwarth farm income was derived from wool (30%), sheep meat (30%) and cropping
(30%). A flock of approximately 1700 sheep was grazed on 60 per cent of the property.
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The Polwarth farm sheep flock was comprised of ewes (50%) and mixed lambs (50%). The
majority of the flock were Merino breed; however, cross bred sheep were kept for meat
production. On Polwarth farm rotational and cell grazing strategies were employed to
maintain high stocking rates.
Peppin Farm
Peppin Farm was a commercial sheep and cropping enterprise with a flock of
approximately 10000 sheep. The property was originally owned and operated by Mr
Peppin’s father and brother. However, Mr Peppin acquired the family farm business in
1990 when it hit financial difficulties. Mr Peppin owned and operated Peppin Farm and his
brother Mr Peppin Jr was employed as the farm manager.
Peppin farm income was derived from wool (33%), sheep meat (14%) and cropping (50%).
The sheep flock comprised ewes (44%) hoggets (37%), wethers (18%) and rams (1%)
which produced medium-fine and super-fibre wool. The average fibre diameter of
medium-fine wool was 17.5 micron for young sheep and 19 micron for ewes. The average
diameter of super-fine wool was 16 micron for young sheep and 17 micron for ewes.
Rotational grazing practices and feed budgeting were undertaken on Peppin farm to manage
stocking rates.
Coriedale Farm
Coriedale Farm was a commercial ram stud and wool enterprise, owned and operated by
members of the Coriedale family for five generations. Mr Coriedale had worked in the
family farm business for over thirty years and his eldest son, Mr Coriedale Jr, had worked
on the farm since 1999.
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Coriedale farm income was derived from ram breeding (40%), wool (20%), sheep meat
(20%) and cropping (20%). Coriedale farm had a flock of 8000 sheep and produced, on
average, 230 bales of wool per annum. Eighty-five per cent of the sheep flock were ewes
and 15 per cent were rams. Set stocking of sheep was employed as the main grazing
strategy for the Coriedale farm flock and feed budgeting was not undertaken.
Romney Farm
Romney Farm was a 4000 hectare commercial sheep and crop enterprise, owned and
operated by Mr and Mrs Romney since 1980. Mr and Mrs Romney lived on the property
and had two high school aged children attending boarding school in Perth. Romney farm
had a flock of approximately 9000 sheep grazing 70 per cent of the property and a small
ram stud, the rest of the property was cultivated for Canola production. Farm income was
derived from wool (40%), sheep meat (30%) and crops (30%).
The Romney Farm sheep flock comprised ewes (50%), wethers (20%) and hoggets (30%).
The average fibre diameter of the Romney Farm flock was 18 micron for young sheep and
20.4 micron for ewes. Rotational and deferred grazing, feed lotting and feed budgeting
practices were undertaken to manage pasture utilisation. The majority of Romney farm
wool was sold through the Australian wool auction system with Primaries wool brokers;
however, wool was forward sold wool through E-Wool in favourable seasons.
Data collection
Yin (1994) identified six sources of qualitative evidence that can be used in case study
analysis (documents, records, interviews, direct observation, participant observation and
physical artefacts). In this study, data were collected through participant interviews and
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physical and documentary evidence. In-depth, semi-structured interviews were used to
obtain the needed data. Every person aged over eighteen engaged in the wool enterprise
was interviewed to obtain the broadest range of views and information about the enactment
of AM and CC on-farm. Snowball sampling was used to identify key members of the
social network of the commercial wool enterprise with involvement in or influence on the
enactment of AM and CC. Members of the wool enterprise were asked to identify such
people and give permission for the researcher to contact them directly and request an
interview.
Typically, four interviews were conducted within each case study. Interviews were
undertaken with family members from each selected enterprise and their wool selling
broker(s). Farm consultants and other key participants in enterprise wool marketing and
production were also interviewed where relevant. Interviews lasted, on average, 74
minutes. The researcher undertook all interviews with farm family members at the farm
property. Family members were interviewed together wherever possible to explore the
social nature of sensemaking through discussion and shared meaning. An interview guide,
in Appendix D, which was developed for the semi-structured interviews, had 33 open-
ended questions and facilitating statements. Each question and statement in the guide was
used as a point of reference for the interviews and was neither repeated verbatim nor
delivered in a set order. The researcher relied on interviewees’ responses to direct further,
probing questions. The interview schedules undertaken in each of the cases are
summarised in Table 6.6.
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Table 6.6: Case analysis interview schedule
Case study farm Interview participants Interview duration
(minutes)
Mr & Mrs Saxon (1) 120
Saxon farm Broker 85
Saxon Farm
Mr & Mrs Saxon (2) 55
Mr Dorset, Mrs Dorset & Mr Dorset’s father (1) 90
Brand D Broker 78
Dorset Farm
Mr Dorset, Mrs Dorset & Mr Dorset’s father (2) 60
Mr & Mrs Polwarth (1) 110
Polwarth farm Agronomist 45
Polwarth farm Broker 90
Polwarth Farm
Mr & Mrs Polwarth (2) 75
Mr Peppin & Mr Peppin Jr (1) 88
Peppin farm Broker 65
Mr Peppin (2) 95
Peppin Farm
Mr Peppin (3) 20
Mr Coriedale & Mr Coriedale Jr (1) 75
Coriedale farm Broker 1 40
Coreidale farm Broker 2 35
Coriedale Farm
Mr Coriedale & Mr Coriedale Jr (2) 53
Mr & Mrs Romney (1) 95
Romney farm Broker 1 83
Romney farm Broker 2 45
Romney farm consultant 70
Romney Farm
Mr & Mrs Romney (2) 125
Each interview began with a discussion of the farm business and wool enterprise. The
interviewees were asked questions about the farm business and their role in the business.
Interviewees were then asked to talk through the history of a particular aspect of their wool
enterprise. The interview questions were broad and open-ended to give interviewees an
opportunity to structure and frame their responses in ways that reflected their personal
identity frames, social context and perceptions of technology frames and industry belief
systems.
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The qualitative case study interview data was combined with quantitative wool auction data
for each farm business. Stake (1994) argued that the use of multiple data sources and
perceptions to clarify meaning and verify the repeatability of an observation or
interpretation is critical in case study research. Therefore, the wool auction data for each of
the selected case study wool brands were used to guide the interviews and to compare
interviewees’ responses about their enactment of AM and CC on-farm as a means of
clarifying and triangulating those observations and perceptions. The types of quantitative
data extracted from the Australian Wool Auction Database for each of the six case study
farms in the data triangulation process are described in Table 6.7 below. Where
descriptions and interpretations of the enactment of AM and CC on-farm differed between
the quantitative data and interview responses, additional effort was made to confirm
responses from other data sources, such as members of the farm families’ social network
and farm financial records.
Table 6.7: Quantitative case study data description
Auction code Description
Bale Description Subjective description of the wool contained in the bales as classed at shearing
Bales Number of bales of wool in the sale lot
Brand Registered wool brand name identifying the wool producer
Broker Code Code identifying the wool broker
Lot Type AWEX code identifying the type of wool in the sale lot (replaced in 2001 with the AWEX ID code)
Clean Weight Estimate of the clean weight of the sale lot
Sale Outcome Code to identify whether the sale lot was sold, traded, withdrawn or passed-in at auction
Sale Price The sale price of the wool lot per kilogram of clean wool
Staple Length (SL) Objective measurement of staple length in millimetres
Staple Strength (SS) Objective measurement of staple strength in Newtons per kilotex
Vegetable Matter (VM)
Objective measurement of the percentage of vegetable matter content in the sale lot
Diameter Objective measurement of the average fibre diameter of the sale lot in microns
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The richness and complexity of the data collected in case studies means it may be open to
different interpretations, and potential biases from researchers’ own perceptions and
construction of events (Cornford & Smithson 1996). Criticisms have been made of
researchers allowing ambiguous data or their own bias to influence the directions of their
findings and the conclusions of their research (Yin 1994). In this study, researcher bias was
addressed through consistent and rigorous accuracy-checking of data by case study
participants. Case study participants were involved from initial data collection to the
development and verification of findings and conclusions. All interviews with enterprise
and network members were recorded and fully transcribed. These transcriptions were
solely of the verbal content of the interviews. To ensure interview data were representative
of interviewees’ responses, transcripts of the interviews were drafted and returned to
participants for accuracy-checking prior to analysis. The interpretations of the research
findings were discussed with case study participants to check for accuracy after the
analysis. To further enhance the reliability of interview information, the initial interview
undertaken with members of the wool enterprise was attended by an independent observer
from the Department of Agriculture and Food Western Australia (DAFWA) and interview
transcripts and interpretations were discussed with the observer.
Data Analysis
Analysis of the data collected and generated in this study involved data reduction, data
display and conclusion drawing and verification (Miles & Huberman 1994). These
activities were undertaken concurrently.
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Data Reduction
Data reduction is the process through which data are selected, focused, simplified,
abstracted and transformed in field notes or transcriptions (Miles & Huberman 1994). In
the data reduction stage of analysis, a researcher cycles back and forth between the initial
analysis of the data and strategies for generating new data (Miles & Huberman 1994). In
the present study, field notes from direct observation and casual conversations at initial
meetings with the farm families were made to capture verbal and non-verbal
communication and context. These notes were summarised and reviewed to identify
important issues and conflicting answers. Informants were interviewed again with the
guiding data to clarify issues and to fill in gaps in the data. Reflective remarks were noted
during the interviews and expanded after the interview, which allowed data collection and
analysis to overlap and for the researcher to make adjustments to interview guides, review
additional data sources and interview new participants.
Coding of the data collected from the individual cases commenced in the early stage of data
analysis. Coding is a significant data reduction tool in qualitative studies. In this study, a
coding scheme was developed as a template for organising the case data. Initial codes were
adopted from the preliminary analytical framework shown in Figure 3.3 and other codes
were added during the data collection and analysis process.
Patterns and codes were identified and defined during the early stage of data analysis to
measure emergent themes, patterns or explanations (Miles & Huberman 1994). Pattern
coding was used to reduce the large amounts of data generated for each case into a smaller
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number of analytic units and to help to build a cognitive map of the enactment of AM and
CC on-farm.
Both within case and cross-case analyses were undertaken. Within-case analysis allows
unique patterns of cases to emerge before patterns are examined across cases (Eisenhardt
1989). The analytic technique used was explanation-building; a type of pattern matching in
which the analysis is carried out by building an explanation of the case. This strategy is
used to develop a case description as a framework for organising the study. In order to
understand how AM and CC testing technologies were enacted on-farm, a logical chain of
evidence, based on the sensemaking process, was established.
After carrying out the within-case analysis, a cross-case analysis was undertaken to identify
patterns and themes emerging from the data. Searching for cross-case patterns is an
iterative process of systematic comparison of data from the initial case to other cases by
which emerging research propositions are defined, to iterate towards a theory that fits the
data (Pare & Elam 1997). Cases that support the emerging theory enhance confidence in
validity, while cases that do not support the theory offer an opportunity to refine and extend
the model (Eisenhardt 1989).
Data analysis followed a twenty-four hour rule to ensure the accurate recall of discussions
and events. Interview tapes were transcribed in their entirety and contact summary sheets
outlining salient points and impressions were prepared by the end of the day following the
interview. Document summary sheets were prepared for quantitative data sources within
twenty-four hours of review. The semi-structured interviews were analysed using thematic
coding techniques that involved:
1. coding and splitting transcribed texts
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2. integrating the split data into themes
3. aligning and contrasting themes with the sensemaking properties identified in the
analytical framework.
A database of the case study data was created in the qualitative data analysis software QSR
N6 from the document summary sheets, transcripts of the tape-recorded interviews and
field notes from participant observations. The QSR N6 software program is a tool for
categorizing, coding, retrieving and reporting on qualitative data. Data was coded for
meaning by line. Coded data were stored at one or more nodes in the formation of a
concept tree or map that expresses a hierarchy and the connections of meanings. The tree
or map represents the categories of meaning and relationships between the various
categories generated by a researcher. The nodes and concept map initially generated
reflected the main elements of the analytical framework shown in Figure 3.3. N6 enables
researchers to ‘jump back to source’ from any data in a node, so it is possible to see any
coded piece of text in its original context. The ability to ‘jump back to source’ supported
the iterative process of data generation and analysis and was used increasingly as the
researcher’s understanding of the data grew.
Data Display
Miles and Huberman (1994) suggest data displays in qualitative research should be
designed to assemble organised information into a compact form that is easily and
immediately accessible. The main data display technique used in this study was the
arrangement of empirical evidence in tables. By looking at these tables, research themes
within the framework that agreed or disagreed across cases emerged (Miles & Huberman
1994; Sutton & Callahan 1987).
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Drawing conclusions and verification
Drawing conclusions from the data and verifying them was undertaken before, during and
after data collection. Before collecting the case study data, the researcher had developed a
preliminary analytical framework for sensemaking in agricultural innovation (shown in
Figure 3.3). As the collection and analysis of data proceeded, the sensemaking properties
in the analytical framework and emerging themes relating to these properties were tested
and refined systematically.
6.3 Research findings: Patterns of technology enactment
The presentation of the findings from the multiple case analyses begins with an overview
of the enactment of AM and CC testing technologies on-farm by the six farm enterprises
examined. These results were extracted from an analysis of quantitative wool production
data described in Table 6.7 and summarise the adoption, implementation, use and
abandonment of AM and CC on the six case study farms over time.
Patterns of enactment of Additional Measurements
The enactment of AM on the six farm businesses examined in this study varied in terms of
the timing and extent of adoption, implementation and the ongoing use of AM. Figure 6.3
shows the proportion of wool offered at auction with AM from 1988 to 2003 by the six case
study farms. Peppin and Romney farms adopted AM in 1988 and offered a proportion of
their clip at auction with AM each year. However, the proportion of the wool clip offered
with AM did not increase steadily over time on Peppin and Romney farms, but varied each
year until 1997 and 1999 respectively. In comparison, AM was adopted on Dorset and
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Coriedale farms in 1989 and abandoned the following year. Coriedale farm readopted AM
in 1992; however, Dorset farm was not active in the wool market between 1989 and 1998
and no auction data was available for that period. Saxon and Polwarth farms were the last
of the six farms to adopt AM. The Saxon farm family first used AM in 1990, abandoned
these tests in 1992 and then readopted them in 1994. On Polwarth farm, AM was adopted
in 1990, abandoned in 1991 and readopted in 1992.
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0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
Year
% A
M teste
d
Saxon Farm
Polwarth Farm
Dorset Farm
Romney Farm
Peppin Farm
Coriedale Farm
Figure 6.3: The proportion of wool offered at auction with AM by the six case study farms, 1988-2003
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The AM use data shown in Figure 6.3, revealed variation in the time of adoption and in the
extent of use of AM among the case study farms. The abandonment and readoption of AM
by four of the six case study farms in the early 1990s highlighted the discontinuous nature
of the enactment of AM on-farm. Changes in the extent of AM use over time, specifically
the increase in the proportion of wool offered at auction with AM by the six case study
farms from the mid-1990s onwards, revealed the dynamic nature of the enactment of AM
on-farm.
The evolving nature of the enactment of AM on the six case study farms was also reflected
in changes in the types of wool offered at auction with AM over time. Figure 6.4 shows the
proportion of Merino fleece wool offered at auction with AM by the six farms from 1988 to
2001. These data show a gradual increase in the proportion of Merino fleece wool offered
at auction with AM over time. Each of the six case study farms had offered over 50 per
cent of their Merino fleece at auction with AM by 1994 and five out of six of the farms
tested all of their Merino fleece wool in 2001.
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0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
Year
% M
eri
no
fle
ec
e w
ith
AM
Saxon
Polwarth
Dorset
Romney
Peppin
Coriedale
Figure 6.4: The proportion of Merino fleece wool offered at auction with AM by the six case study farms, 1988-2001
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Figure 6.5 shows the average proportion of Merino weaner wool offered at auction with
AM by the six case study farms from 1988 to 2001. These data show a gradual increase in
the proportion of weaner wool tested over time, albeit at a time lag of several years after the
increase in the proportion of Merino fleece wool offered at auction with AM. Together, the
data shown in Figures 6.4 and 6.5 revealed the enactment of AM on-farm, in terms of the
types of wool tested, to be an evolving process.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
Year
Av
era
ge %
we
an
ers
wo
ol w
ith
AM
Figure 6.5: The average proportion of Merino weaner wool with AM from the six
farms
Figure 6.6 shows the staple strength test results for wool offered at auction with AM by the
six case study farms from 1988 to 2004. These data show the farms extended their use of
AM across an increasingly broad range of sale lots in terms of staple strength over time.
From 1996 onwards, the farms began to test increasingly tender sale lots offered at auction
(less than 20 Newtons per kilotex). The staple strength data examined in this study
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suggested that the enactment of AM on the case study farms evolved over time in terms of
the strength of wool offered at auction with AM.
0
10
20
30
40
50
60
70
1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Year
Sta
ple
Str
en
gth
(N
kt)
Figure 6.6: Staple strength of case study farm sale lots
Figure 6.7 shows the Vegetable Matter (VM) content of wool offered at auction with AM
by the six case study farms from 1988 to 2002. These data show that the farms offered sale
lots at auction with AM with relatively low VM content from 1988 to 1995. From 1996
onwards the farms tested an increasingly broad range of sale lots in terms of VM content,
ranging from very low VM (0.2%) to very high VM (14.3%). These data suggest that the
farms did not discriminate between testing high and low quality lines in terms of VM
content from 1996 onwards. The Vegetable Matter content data examined in this study
exposed the enactment of AM on-farm as an evolving process in terms of the quality of
wool tested.
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0
2
4
6
8
10
12
14
16
1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Year
% V
M c
on
ten
t
Figure 6.7: VM content of case study farm sale lots with AM
Figure 6.8 shows the average fibre diameter of sale lots offered at auction with AM by the
six farms from 1988 to 2004. These data show a downward trend in average fibre diameter
across the farms and an increase in the range of the average diameter of sale lots offered at
auction with AM from 1992 onwards. These data suggest the farms began to offer an
increasingly wide range of their clip with AM in terms of fibre diameter and that the
enactment of AM on the farms evolved over time in terms of the average diameter of sale
lots offered at auction with AM.
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0
5
10
15
20
25
30
1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Year
Fib
re d
iam
ete
r (m
icro
n)
Figure 6.8: Average diameter of case study farm sale lots with AM
Patterns of enactment of Clean Colour testing
The Dorset, Polwarth, Peppin, Coriedale and Romney farms adopted CC testing. The
Saxon farm family rejected CC testing as they did not believe their clip was of poor colour.
The enactment of CC testing on these five farms varied greatly in terms of the timing and
extent of adoption, the level of use of CC and the timing of the abandonment of these tests.
Figure 6.9 shows the proportion of wool offered at auction with CC by the five farms from
1995 to 2003. The adoption and use of CC by these woolgrowers was neither a smooth,
linear process, nor a static adoption decision. Coriedale farm adopted CC in 1995 and
subsequently tested a small proportion of their clip in 1999 and 2000 (11% and 1%
respectively). Peppin, Polwarth and Romney farms adopted CC in 1996, but had
abandoned these tests by 2003. On Peppin farm, a portion of the clip was offered with CC
each year from 1996 to 2002 (between 10% and 16 % per annum) whereas, on Polwarth
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and Romney farms, CC was not used consistently after the adoption of these testing
technologies. The Dorset farm family adopted CC testing relatively late in 1998, but
abandoned these tests in 2003.
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0%
5%
10%
15%
20%
25%
30%
1995 1996 1997 1998 1999 2000 2001 2002 2003
Year
% C
C T
este
d Coriedale Farm
Peppin Farm
Romney Farm
Dorset Farm
Polwarth Farm
Figure 6.9: The proportion of wool offered at auction with CC by five case study farms, 1995-2003
- 217 -
Conclusions
The proportion, types, quality attributes and value of wool offered at auction with AM and
CC, shown in Figures 6.3 to 6.9, revealed the enactment of AM and CC on the case study
farms to be a dynamic, discontinuous and evolving process. Importantly, a comparison of
patterns of AM and CC enactment on the six farms revealed incongruities in the enactment
of these testing technologies over time between individual farms. These data suggested
factors other than perceptions of technology attributes may have influenced the enactment
of AM and CC on the six case study farms.
In the following chapter, the story of how AM and CC testing technologies were enacted on
the six case study farms is recounted in relation to four phases of technology enactment
encountered during the case study interviews. The interviews with woolgrowers and their
brokers and advisors revealed how the enactment of AM and CC on-farm mutually
influenced the interpretation frameworks, technology frames and industry belief systems of
case study participants over time. Table 6.8 lists the four phases of technology enactment
encountered in this study.
Table 6.8: Phases of technology enactment
Phase 1 The adoption of AM on-farm
Phase 2 The implementation of AM on-farm
Phase 3 The use of AM on-farm
Phase 4 The adoption and abandonment of CC on-farm
Phase 1 of the enactment of AM and CC related to the adoption of AM on-farm. In phase 1
of the case analysis, the enactment of AM by the six farms was examined in terms of the
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decision to adopt AM and the first use of these testing technologies. Phase 2 of the case
analysis examined the implementation of AM on-farm by the six farms. In this phase the
enactment of AM on-farm was examined from the period directly after the adoption of AM
to 1994, when price premiums for wool offered at auction with AM emerged in the
market20. Phase 3 of the enactment of AM and CC on-farm examined the use of AM on the
six farms from 1995 to 2003. Phase 4 of the case analysis examined the adoption and
abandonment of CC testing on the six farms from 1995 to 2003.
20 Chapter 5, p.122.
- 219 -
7 The enactment of new technologies on-farm: Case study findings and discussion
In Chapter six, six commercial wool production enterprises located in the South West of
Western Australia (WA) were selected for a multiple case study of the enactment of wool
fibre testing technologies on-farm. The quantitative data relating to the adoption,
implementation, use and abandonment of Additional Measurements (AM) and Clean
Colour (CC) testing technologies examined in Chapter 6 suggested that the enactment of
AM and CC on-farm was a dynamic, discontinuous and evolving process. These data
suggested factors other than woolgrowers’ perceptions of technology attributes may have
influenced the enactment of AM and CC on the six case study farms over time. In this
Chapter, the story of how AM and CC testing technologies were enacted on the six farms is
recounted in relation to four phases of technology enactment encountered during the case
study interviews (as is shown in Table 6.8).
This Chapter is divided into three sections. The first section presents the case study
findings as a story of the enactment of AM and CC technologies on the six case study
farms. This section is divided into the following four phases of technology enactment:
1. the adoption of AM on-farm,
2. the implementation of AM on-farm,
3. the use of AM on-farm, and
4. the adoption and abandonment of CC on-farm.
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The second section discusses the case study findings in relation to the sensemaking process
and tentative research propositions relating to the enactment of new technologies on-farm
are advanced. The final section provides a conclusion to the chapter.
7.1 Case study findings
Phase 1: The adoption of AM on-farm
Although AM was introduced in 1986, Figure 6.3 shows that the earliest adoption of AM
by the woolgrowers interviewed in this study was in 1988 (Peppin Farm and Romney
Farm) and the latest in 1990 (Saxon and Polwarth farms). Wool auction data and interview
responses relating to the adoption of AM on the six case study farms are summarised in
Table 7.1. The six farms tested between seven per cent and 36 per cent of their clip when
they adopted AM. The majority of the case study farms adopted AM to test a small
proportion of relatively high quality Merino fleece wool (see Table 7.1).
The adoption of AM by the six farms was largely influenced by interactions between
woolgrowers and their wool selling brokers. Cues extracted from interactions with wool
selling brokers about the adoption of AM were interpreted by woolgrowers through their
personal identity frames and social context in terms of social influence, the normative
beliefs and behaviour of their industry group, their experience and knowledge of objective
measurements and their existing farming practices and goals.
In the AM adoption phase, there was evidence of conflict among industry participants in
response to the replacement of the subjective appraisal of fibre strength and length with
AM. According to one broker, “each [industry participant] had different agendas. From an
exporter’s point of view it was often expedient for him not to deliver staple strength and
- 221 -
length parcels.” The lack of support for AM among wool buyers translated into a lack of
demand for wool lots offered at auction with AM in the late 1980s. The lack of demand for
AM at auction delayed and constrained the adoption of AM among the woolgrowers
interviewed as they were unable to extract meaningful cues about these testing technologies
from the auction system. The wool selling brokers interviewed advised their clients to
reject AM or to adopt these tests with great caution in order to avoid unnecessary discounts
from buyers.
The belief shared by the wool selling brokers interviewed was that Western Australian
(WA) wool was relatively tender and that AM would highlight a discount feature that could
be avoided if woolgrowers continued to use subjective appraisal. The influence of wool
selling brokers’ beliefs on the AM adoption behaviour of the case study farms was reflected
in their selection of relatively sound sale lots for testing. Table 7.1 shows that, with the
exception of Saxon Farm, the case study farms initially offered sale lots with AM with a
higher average diameter than untested sale lots. It was assumed by the woolgrowers and
their brokers that Merino fleece wool with relatively high fibre diameter would be less
likely to test tender than finer portions of the clip.
The Polwarth farm family was initially told about AM by their broker, who advised them to
approach these tests with care. They adopted AM in 1990. However, as there was little
evidence of premiums for sale lots with AM in the market at that time, they tested only 25
per cent of their clip on the advice of their broker.
It would be the wool selling company that told me about it [AM]…I think it was new, like
the new test out and I was not really sure how much notice will I ever take of it, you know
and will I get discounts for it or what? They [brokers] just started off steady. (Mr Polwarth)
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According to Mr Polwarth, their broker was reluctant for them to adopt AM across their
clip out of a concern that relatively tender sale lots would be heavily discounted. However,
there was no evidence in the Polwarth farm auction data that the price received for sale lots
with AM in 1989 was subject to discounts over and above that of untested sale lots. Table
7.1 shows the Polwarth Farm sale lots offered at auction with AM in 1990 achieved a
higher average price than did untested sale lots. A comparison of the price of Polwarth
Farm sale lots with similar quality attributes (Merino fleece wool, 21.7 µ, 1.3% VM)
offered at auction with and without AM in 1990 showed the sale lot with AM sold for 1038
cents per kilogram clean, compared to 1035 cents per kilogram clean for the untested sale
lot. The higher price achieved by the sale lot with AM did not encourage the Polwarth’s to
implement AM as the 3 cent price ‘premium’ per kilogram achieved for the tested sale lot
did not cover the cost of testing.
The Dorset farm family were persuaded to adopt AM in 1989 by their wool selling broker
who advised them that AM was a ‘good idea’ and that it would be ‘inevitable’ that growers
would test for staple strength and length. Despite endorsing AM, the Dorset farm wool
selling broker advised Mr and Mrs Dorset to test only a small proportion (23 per cent) of
their clip and to test only sound Merino fleece wool. Mr and Mrs Dorset did not test sale
lots with relatively fine fibre diameter (under 20 microns) or sale lots with VM content of
over 1.2 per cent as those lots were subject to discounts for tenderness and low yield. The
adoption of AM for relatively high quality sale lots was reflected in a higher average price
for sale lots with AM compared with untested lots (see Table 7.1).
A comparison of the price of Dorset Farm sale lots with similar quality attributes (Merino
fleece wool, 20.3 µ, 0.5% VM) offered at auction in 1989 showed that the sale lot with AM
sold for 1300 cents per kilogram clean compared with 1145 cents per kilogram clean for the
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untested sale lot. The price ‘premium’ for the sale lot with AM did not motivate the
Dorset’s to implement AM as they dropped out of the auction system in 1990 and did not
offer wool with AM at auction again until 1998.
In contrast to the brokers for Polwarth and Dorset Farms, Peppin and Romney Farm
brokers advised their clients to reject AM. Mr and Mrs Romney ignored their broker’s
advice and tested 36 per cent of their clip in 1988. Mr Romney felt that they would be able
to use AM to objectively determine that their clip was not tender and avoid unnecessary
price discounts at auction.
The brokers were selling our wool at auction then and they were saying ‘don't test for staple
strength at all’. Their philosophy was, I suppose, that a lot of tender wool was spring shorn
and if it's tender why prove it’s tender so don't test and they'll sort out whether it's tender or
not. Whereas, my tactic was more that in shearing in April the wool would be sound. So I
wanted to prove that our wool was actually sound, not leave it up to someone else’s
discretion and wear a discount on a perception that it could be tender. (Mr Romney)
Mr and Mrs Romney did not test relatively fine portions of their clip (under 21 microns) or
sale lots with VM content of over 0.9 per cent as those lots were subject to discounts for
tenderness and low yield. A comparison of the price of Romney Farm sale lots with similar
quality attributes (Merino fleece wool, 21.2 µ, 0.9% VM) offered at auction in 1988
showed that the sale lot with AM sold for 1331 cents per kilogram clean compared with
1215 cents per kilogram clean for the untested sale lot. The Romneys were encouraged by
the price ‘premium’ achieved by the sale lot with AM and continued to use AM to test
Merino fleece wool after 1988. The price difference between similar quality sale lots
offered at auction with and without AM confirmed Mr Romney’s beliefs that their clip had
been discounted for tenderness on the basis of inaccurate subjective appraisal.
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The Peppin farm family were advised by their wool selling broker to reject AM as he
believed the tests had not been properly validated. According to Mr Peppin, “the validity
of the strength test I was advised was suss. He [the broker] was saying that on re-testing
they had big variations.” As a result of reservations about the validity of AM the Peppin’s
adopted AM in 1988 but tested only seven per cent of their clip. Mr Peppin offered Merino
fleece sale lots with AM with relatively high fibre diameter and low value compared with
untested sale lots. These sale lots were offered at auction with AM to reduce the risk of
highlighting discount features of premium Merino fleece lines.
According to the auction price data analysed in Chapter 5, there was little evidence of price
premiums for sale lots with AM or discounts for sale lots without AM in the auction system
until 1994. The lack of premium and discount data for AM was confirmed by the Dorset
farm family who recollected that they were not persuaded to adopt AM by evidence of
price premiums for wool offered at auction with AM. According to Mr Dorset, "at first in
the auction system there was bugger all, no premium". However, the Saxon farm family
described their decision to adopt AM in 1990 as being influenced by observable price
premiums for wool offered at auction with AM, specifically for Merino pieces with long
staples. In contrast to the other case study farms when the Saxon farm family adopted AM
in 1990 they tested the majority of their relatively low quality and low value Merino pieces
and other fleece wool types but did not test any Merino fleece sale lots (see Table 7.1).
The pieces are the ones that I remember that they started testing [AM] quite early on. You
got a bit of a bonus in length so you'd test them for length and strength and they'd come out
longer than they'd visually assessed them, so we thought that that was worth doing. (Mr
Saxon)
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The fixed price premium for wool purchased under the WRPS with AM influenced the
decision to adopt AM by the Polwarth and Dorset farm families. For example, when the
Dorset farm family adopted AM in 1989 they sold all of their tested wool under the WRPS.
The Polwarth farm family offered 75 per cent of their sale lots with AM under the WRPS in
1989. Although Peppin and Romney farms did not sell wool with AM under the WRPS
when they adopted these tests, both farm families went on to test sale lots under the scheme
in subsequent years. However, neither Peppin nor Romney farm families recollected their
adoption and implementation of AM as being influenced by the fixed price premium under
the WRPS. None of the sale lots sold under the WRPS from Coriedale and Saxon farms
was subject to AM. Mr Coriedale and Mr Saxon believed that the fixed price premium for
tested wool would have been swallowed up by the cost of testing, resulting in no financial
benefit for offering sale lots purchased under the WRPS with AM.
The Coriedale farm family did not cite the influence of their wool selling broker or the
WRPS on their decision to adopt AM in 1989. Mr Coriedale described the adoption of AM
as a means of achieving their enterprise goal to be as objective as possible in the
measurement of the performance of their wool enterprise and ram stud. The Coriedale farm
family set this goal in the early 1980s in order to improve the reputation and quality of their
commercial wool enterprise and ram stud.
We like to be as objective as we can. We have a ram stud so when the industry introduces
new tests it is imperative that we are one of the first to adopt; so that when people come to
buy our rams, we give them measurements. (Mr Coriedale)
The Coriedale farm family adopted AM on sale lots comprised of Merino fleece and
weaners wool across a relatively broad range of fibre diameter (19.4 microns to 21.5
- 226 -
microns). However, they avoided testing sale lots with relatively high VM content as those
lots were subject to discounts for low yield (see Table 7.1).
A comparison of the price of Coriedale farm sale lots with similar quality attributes
(Merino weaner’s wool, 19.4 µ, 1.1% VM) offered at auction in 1989 showed that the sale
lot with AM sold for 1412 cents per kilogram clean compared with 1426 cents per kilogram
clean for the untested sale lot. These data highlighted a discount for relatively short and
tender sale lots of Merino weaner wool with AM. The response of the market to tested
weaners wool dissuaded the Coriedales from testing weaner wool again until 1993.
The woolgrowers interviewed who operated ram studs, namely the Coriedale, Saxon, and
Romney farm families, shared a belief that the performance and reputation of their ram stud
could be enhanced with more objective measurements of wool attributes. This shared
belief was articulated through the inclusion of staple strength in their ram breeding index
over time. As Mrs Saxon pointed out, “part of the breeding index is an emphasis on
increasing staple strength...its important enough to breed for but it’s not important enough
to manage for.” The case study farms with ram studs associated their decision to adopt AM
with their beliefs and experience that ram buyers would eventually demand AM.
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Table 7.1: Phase 1 case study data summary
21 Wool types: Merino fleece - main fleece line not including broader, short or cast fleeces. Other fleece – shorter or broader fleece line. Weaners – fleeces from sheep of either sex over 6 months of age which are being shorn for the first time. Pieces – Frib and short edges removed during skirting. Bellies – belly wool removed during skirting. Cotts – cotted fleeces or part fleece. Locks – second cuts, short crutchings and wiggings. Crutchings – removed during skirting, stained, combing or carding length. Cross bred – wool shorn from non-Merino sheep (source: AWEX). 22 Marketing activity: WRPS – sale lots purchased under the Wool Reserve Price Scheme. Sold – sale lots sold on the day offered at auction. PI – sale lots passed-in at auction. W – sale lots withdrawn at auction. T – sale lots traded between brokers or buyers at auction.
Wool Enterprise
Name
Year AM adopted % of clip
with AM
Wool Types21 Marketing
activity22
Average SL
and SS
Average
diameter
Average price
(per kg clean)
Average VM
content (%)
With AM 100% Merino fleece 100% WRPS SL 92mm SS 37 Nkt
21.7µ 1086 cents 0.96% Dorset Farm 1989 23%
Without AM 56% Merino fleece, 19% bellies, 25% weaners, pieces & cross bred
67% Sold, 33% WRPS
21.4µ
835 cents 2.35%
With AM 75% Merino pieces, 25% other fleece
100% Sold
SL 65mm SS 41 Nkt
20.4µ
909 cents
4.4%
Saxon Farm 1990 9%
Without AM 93% Merino fleece, 7% bellies, lambs & weaners
65% Sold, 22% PI, 14% WRPS
21.2µ 1038 cents 2.7%
With AM 100% Merino fleece
71% WRPS, 29% Sold
SL 88mm SS 34 Nkt
22.1µ
996 cents
1.6%
Polwarth Farm 1989 25%
Without AM 59% Merino fleece, 26% bellies & pieces, 14% weaners
53% Sold, 47% WRPS
21.8µ 827 cents 3.5%
With AM 100% Merino fleece
100% Sold
SL 92mm SS 34 Nkt
24.2µ
834 cents
0.5%
Peppin Farm 1988 7%
Without AM 64% Merino fleece, 36% pieces, skirting, cotts & lambs
95% Sold, 5% WRPS
21.8µ 979 cents 1.4%
With AM 85% Merino fleece, 15% weaners
100% Sold SL 86mm SS 31 Nkt
20.6µ
1263 cents
0.95%
Coriedale Farm 1989 29%
Without AM 24 Merino fleece, 26% weaners, 22% pieces, 28% skirtings, locks, crutchings & cross bred
47% Sold, 39% WRPS, 14% PI
20.5µ 811 cents 4%
With AM 100% Merino fleece
100% Sold
SL 91mm SS 31 Nkt
21.2µ
1351 cents
0.9% Romney Farm 1988 36%
Without AM 63% Merino fleece, 22% weaners, 15% pieces & cotts
76% Sold, 24% PI
19.9µ 1486 cents 1.5%
- 228 -
Phase 2: The implementation of AM on-farm (year of adoption to 1994)
Each of the farm families interviewed had adopted AM by 1991. However, it was
evident from the highly variable use of AM on the case study farms from 1988 to 1994
that the implementation of AM during phase 2 was discontinuous and dynamic in terms
of the proportion and types of wool tested (as shown in Figure 6.3). Wool auction data
and interview responses relating to the implementation of AM between 1988 and 1994
on the six farms are summarised in Table 7.2. The farms tested between seven per cent
and 53 per cent of their clip during Phase 2. The majority of the farms continued to test
Merino fleece lines in phase 2. However, they also began to offer sale lots comprised of
weaner wool and pieces at auction with AM. The implementation of AM across
different types of wool was reflected in the decrease in the average diameter of wool
with AM on the Polwarth, Peppin, Coriedale and Romney Farms (see Table 7.2). The
increase in types of wool offered at auction with AM was also reflected in the increase
in the average VM content of wool with AM on Coriedale, Romney and Peppin Farms.
However, the six farms continued to test relatively high quality sale lots in terms of
type, VM content and SS in phase 2 compared with untested sale lots (see Table 7.2).
The influence of wool selling brokers on the enactment of AM on-farm continued in
phase 2. Although four of the six brokers interviewed recommended that their clients
adopt AM their approach towards AM remained cautious during phase 2. The wool
selling brokers were concerned that the wool market would be distorted by
comprehensive testing.
When you introduce a new tool and you don't have penetration in the market, what you
end up with over time is another degree of discounts and an imbalanced market. That's
why initially we weren't so much against Additional Measurements but we were
strongly opposed to blanket testing. In fact that's worked, if you have a look at the
- 229 -
discounts for tender wool over the years you'll find that there were severe cut off points
and beyond those points you just fell in a hole. Over time processors have learnt to
interpret and use the information [AM] better. (Polwarth Farm broker)
The wool selling brokers’ cautious approach towards AM in phase 2 influenced the
type, proportion and quality of wool tested on the case study farms. The wool selling
brokers interviewed continued to advise woolgrowers not to test their tender wool in
order to avoid additional price discounts. According to the Coriedale farm broker, prior
to 1994 the discounting of tender wool offered at auction with AM was a fairly blunt
tool that was best avoided by relying on subjective appraisal: “you used to get one
market that was 35 Newton’s and above, 30 to 34 was another, and below 30 you could
get no price differential between 28 and 22 so why bother testing?”
The Polwarth farm family avoided testing any wool that was subjectively appraised as
tender during the AM implementation phase. According to Mr Polwarth, “the rationale
back here [before 1994] was, if it’s part tender don't test ‘cause we know its going to be
rotten and maybe they'll be a bit optimistic on the day [at auction].” In contrast, the
Romney farm family began to test relatively tender sale lots in 1988, which reflected
their adoption philosophy to apply AM to their clip in order to prove that it was sound.
The Saxon farm family were strongly advised by their broker not to test the tender
portion of their clip.
We were pretty much driven by the broker about what they reckoned was worth testing
and what wasn't in the early days. If it was obviously tender you were better off not to
test it, to just prove that it was tender. If it was obviously sound then you were better
off to test it and prove that it was sound. (Mrs Saxon)
However, the majority of the Saxon farm wool clip was relatively tender (under 35
Newtons per kilotex) as a result of their goal to increase stocking rates and shearing
- 230 -
their flock in spring time. Despite being advised against testing tender wool by their
broker, Mr Saxon explained that much of their tender wool was tested as a result of poor
in-shed classing. Mr and Mrs Saxon said they found it difficult to accurately class wool
according to staple strength during shearing in phase 2. They found lines classed as
sound returned tender test results and that lines classed as tender often returned a higher
SS than anticipated.
The types of wool offered at auction with AM changed during phase 2 on four of the six
farms. Mr Saxon described the implementation of AM on-farm as a “gradual
progression” over time. The woolgrowers interviewed expanded their use of AM from
testing mainly Merino fleece wool to Merino weaner wool and pieces during this phase.
Coriedale, Peppin, Romney and Saxon farms had all offered sale lots of Merino pieces
at auction with AM by 1995 (see Table 7.2). Coriedale farm began to test Merino
pieces in 1992 in response to normative pressures from other woolgrowers and price
signals: according to Mr Coriedale “it was just what most people did…obviously we
would have been influenced by all that information (premiums and discounts for AM
tested wool).”
On Peppin and Saxon farms, woolgrowers attributed the testing of different types of
wool during phase 2 to the advice of their wool selling broker and positive market
signals that were emerging for sale lots offered at auction with AM.
On Peppin, Romney and Saxon farms Merino weaner wool was offered at auction with
AM for the first time during phase 2 (see Table 7.2). These woolgrowers attributed the
use of AM for weaners’ wool sale lots to the advice of their broker, but also suggested
the decision to test these types of wool was influenced by their flock structure and time
of shearing. For example, Mr and Mrs Saxon tested their Merino weaner wool for the
first time in 1995. They explained that because of the time of shearing on their farm,
- 231 -
much of their Merino weaner wool tested short and was discounted heavily. Therefore,
they abandoned testing Merino weaner sale lots after 1995.
In contrast to the expansion of types of wool with AM already described, the Polwarth
farm family did not expand their use of AM across different wool types during phase 2
(see Table 7.2). The Polwarth farm family avoided testing wool types other than high
value Merino fleece lines in order to avoid what they perceived to be heavy discounts
for lower quality wool offered with AM. Mr and Mrs Polwarth continued to offer only
Merino fleece wool with AM at auction until 1997 on the advice of their broker. In
1997 the Polwarth farm family switched wool selling brokers and were advised by their
new brokers to test all types of wool offered at auction. The attitude of the new
Polwarth farm broker was different to the original broker who discouraged testing all
wool types. The new Polwarth farm broker argued that the more testing that was
undertaken on the Polwarth clip, the less chance that the wool would be subject to
unnecessary discounts as a result of inaccurate, subjective appraisal.
What you want to make sure you do when you're selling something is to make sure that
the information levels are at their highest and most relevant. If there's a problem with
your wool you don't try to hide it. You test it to demonstrate that the issue is
manageable…the more you tell a buyer the more he can interpret the information he's
got and if you spread that information then everybody's got a balance of transactional
information and it generates competition in the market. (Polwarth Farm broker)
In 1991 the price of wool collapsed and the WRPS was abandoned, along with the fixed
price premium for sale lots purchased under the scheme with AM. The collapse of the
wool price impacted the implementation of AM by the woolgrowers interviewed in this
study. On Polwarth, Coriedale, Saxon and Dorset farms, AM were abandoned during
phase 2 in response to poor market prices, albeit in different years, in order to cut what
was perceived to be non-essential wool marketing costs (see Figure 6.3). However, the
- 232 -
Peppin and Romney farm families continued to offer sale lots at auction with AM
throughout phase 2. Forty-nine per cent of the Peppin farm clip and 43 per cent of the
Romney farm clip were offered at auction with AM in 1991. According to Mr Peppin
and Mr Romney, they continued to offer sale lots with AM during this period to avoid
additional discounts that would have further reduced the price received in a tight
market.
In the early 1990s when things got tight, the whole dynamics of the industry changed
when we had massive oversupply of wool and huge infrastructural changes in the
industry and suddenly everything counted and you'd be arguing over a cent, it was a
huge issue for everybody. (Mr Peppin)
- 233 -
Table 7.2: Phase 2 case study data summary
Wool
Enterprise
Name
% of
clip with
AM
Wool Types Marketing
activity
Sale lot size SL and SS Diameter Price per kg
clean wool
VM content (%)
Dorset Farm No data No data No data No data No data No data No data No data
With AM 68% Merino fleece, 32% pieces
68% Sold, 32% PI
Mean 13 bales, range 11 to 15 bales
SS mean 82mm SL mean 33Ntk, range 29 to 37Nkt
Mean 20.5µ, range 19.4µ to 21.8µ
Mean 686 cents, range 572 to 800 cents
Mean 3.5%, range 1.3% to 7.4%
Saxon Farm 7%
Without AM 88% Merino fleece, 5% bellies & locks, 4% pieces, 3% lambs & weaners
39% Sold, 61% PI
Mean 14 bales, range 3 to 36 bales
Mean 20.1µ, range 17.9µ to 22.8µ
Mean 635 cents, range 221 to 1511 cents
Mean 2.7%, range 0.4 to 12.4%
With AM 93% Merino fleece, 7% weaners
81% Sold, 19% PI
Mean 9 bales, range 3 to 19 bales
SS mean 85mm SL mean 40Nkt, range 29 to 59 Nkt
Mean 21.5µ, range 19.4µ to 23.8µ
Mean 546 cents, range 385 to 800 cents
Mean 1.6%, range 0.8 to 2.4%
Polwarth
Farm
34%
Without AM 47% Merino fleece, 22% bellies & locks, 15% pieces, 16% weaners
96% Sold, 4% PI
Mean 7 bales, range 1 to 15 bales
Mean 20.9µ, range 18.9µ to 24.3µ
Mean 443 cents, range 201 to 814 cents
Mean 3.6%, range 0.8 to 14%
With AM 74% Merino fleece, 14% fleece other & pieces, 12% weaners
70% Sold, 5% PI, 25% WRPS
Mean 7 bales, range 1 to 30 bales
SS mean 78mm SL mean 31Nkt, range 19 to 45 Nkt
Mean 20.5µ, range 16.9µ to 24.9µ
Mean 678 cents, range 307 to1415 cents
Mean 1.5%, range 0.2 to 6.8%
Peppin Farm 53%
Without AM 35% Merino fleece, 21% fleece other, 23% bellies, 17% pieces, 4% weaners
90% Sold, 3% PI, 7% WRPS
Mean 4 bales, range 1 to 19 bales
Mean 20.6µ, range 17.4µ to 25.2µ
Mean 660 cents, range 179 to 2362 cents
Mean 3.2%, range 0.3 to 12.1%
With AM 73% Merino fleece, 23% pieces, 4% fleece other & weaners
85% Sold, 15% PI
Mean 10 bales, range 4 to 24 bales
SS mean 84mm SL mean 33Nkt, range 23 to 46Nkt
Mean 19.9µ, range 17.2µ to 23.9µ
Mean 721 cents, range 419 to 1426 cents
Mean 2.1%, range 0.6 to 8.4%
Coriedale
Farm
48%
Without AM 42% Merino fleece, 21% bellies, 10% pieces, 12% weaners, 25% fleece other
63% Sold, 12% PI, 25% WRPS
Mean 7 bales, range 1 to 25 bales
Mean 19.8µ, range 17.8µ to 23.6µ
Mean 607 cents, range 152 to 1502 cents
Mean 3%, range 0.2 to 15.2%
With AM 90% Merino fleece, 10% pieces
88% Sold, 8% PI, 4% WRPS
Mean 13 bales, range 2 to 35 bales
SS mean 90mm SL mean 35Nkt, range 22 to 50Nkt
Mean 21.2µ, range 18µ to 23.7µ
Mean 674 cents, range 277 to 1607 cents
Mean 2%, range 0.6 to 7.7%
Romney
Farm
47%
Without AM 52% Merino fleece, 13% pieces, 21% fleece other & bellies, 3% weaners
79% Sold, 6% PI, 15% WRPS
Mean 7 bales, range 1 to 29 bales
Mean 21µ, range 17.4µ to 24.5µ
Mean 622 cents, range 152 to 1917 cents
Mean 3.2%, range 0.2 to 13.3%
- 234 -
Phase 3: AM use on-farm (1995-2003)
As Australian woolgrowers began to recover from the collapse of the price of wool in
the early and mid-1990s, AM became a widely accepted marketing tool in the
Australian wool auction system. After 1994, the use of AM increased steadily among
the woolgrowers interviewed (as can be seen in Figure 6.3). According to Mrs Saxon,
the increase in the use of AM after 1994 was influenced by a consensus among industry
participants that greater transparency was needed along the wool supply chain, which
meant “we were much more willing just to put everything on the table, like with the
dark fibre thing, and make sure everything is open.” The Dorset farm family echoed
Mrs Saxon’s beliefs about AM becoming a standard requirement in the auction system.
However, Mr Dorset described the use of AM in this period as a means of avoiding
discounts for untested wool as opposed to subscribing to the notion of testing for greater
transparency in the auction system.
It's just the norm yeah, but I'm sure that if you elected not to do it and, I'm not sure
whether you have to or not, but if you elected not to do it you would suffer a huge...no
one would buy your wool, if they did it would be discounted a lot because they just
don't know what they are getting, its all about discounts not a premium. (Mr Dorset)
Wool auction data and interview responses relating to the use of AM on the farms from
1995 to 2003 is summarised in Table 7.3. The farms tested between 58 per cent and 89
per cent of their clip during Phase 3. In phase 3, the majority of the farms continued to
test sale lots comprised of Merino fleece, weaners and pieces. However, they also
began to offer lower quality wool types at auction with AM, such as bellies, locks and
crutchings. The expansion of the use of AM across the majority of wool types offered
at auction in phase 3 was reflected in the decrease in the average diameter of wool with
AM on the Saxon, Polwarth, Peppin and Romney farms (as can be seen in Table 7.3).
- 235 -
The increase in types of wool offered at auction with AM in phase 3 was also reflected
in the increase in the range of VM content of sale lots offered with AM by the Polwarth,
Coriedale, Romney and Peppin farms (as can be seen in Table 7.3). However, despite
the increase in testing lower quality wool types in phase 3, higher average prices were
received for sale lots with AM compared to untested lots by the six farms (as can be
seen in Table 7.3).
The Peppin farm family tested 92 per cent of their clip in 1996 after a dramatic
reduction in the use of AM in the previous year when poor prices forced them out of the
auction system (see Figure 6.3). The Peppin farm family tested on average 80 per cent
of clip each year from 1995 to 2003. Mr Peppin strongly believed that it was the role of
the grower to provide as much objective data on their clip as possible, despite the lack
of premiums for tested wool.
It is better for people to know, to have some idea of what the strength is rather than to
have no idea. So it wasn't a pricing issue because I don't think we've ever received a
fair price for it. It’s fine to do things visually but at some point you've got to validate
your visual judgment and I am very much in favour of everything being measured that
can be and strength is a crucial measurement of the top. Being a primary producer
you're slugged with it [the cost of testing]. Producing any commodity the buck stops
with you, you are the one who has to carry the burden of the costs, whether you grow
apples or whatever. The burden is on you to test, you're a seller, you're not actually
physically buying anyone else's produce and if you do you want it tested. I just wish
the market would recognise it that's all, which they don't seem to do. (Mr Peppin)
As AM were increasingly embraced by woolgrowers after 1994 an industry norm
emerged for testing all sale lots offered at auction that had at least four bales of wool.
The wool selling brokers interviewed began to advise their clients to test all sale lots
with a minimum of four bales in 1995 when it became clear to them that untested sale
- 236 -
lots were subject to discounts. All of the woolgrowers interviewed agreed that they had
adopted the ‘four bale minimum’ rule for AM, as advised by their brokers. According
to Mrs Dorset, “we just test all lines over four bales, they tell me, the marketing guys
tell me that that is the go. It's just the norm.” Any sale lots fewer than four bales were
placed, untested, into interlots23 by the wool selling broker.
We don't test individually for interlots. Individually tested components of inter-lots are
called objectively matched interlots. Discounts for objectively matched interlots are
greater than for subjectively matched interlots and it's a huge cost. (Coriedale Farm
broker)
The case study farm families and their wool selling brokers agreed that, since 1995, all
sale lots of a minimum size of four bales were offered at auction with AM. However,
the auction data for the six farms did not support this proposition. Although the average
sale lot offered at auction with AM was larger than untested sale lots, all of the farms
tested sale lots with less than four bales (see Table 7.3). According to the Saxon,
Romney Polwarth and Coriedale farm families, any lines under four bales that were
offered with AM were tested in error by the broker as they should have been placed in
an interlot without AM. However, the Dorset and Peppin farm families deliberately
tested sale lots classed as superfine that were offered at auction in lines of less than four
bales. Such sale lots were tested as they were of relatively high value and produced in
small quantities and would not be placed in an interlot by the wool selling broker.
Although the woolgrowers interviewed were using AM to market their wool clip by the
late 1990s, few of them were using AM test results to make production management
decisions on-farm. Overall, the woolgrowers interviewed agreed that they used AM test
results on-farm primarily to reflect on the outcomes of the previous season. For
23 An interlot is a lot of greasy wool comprising bales from different clips matched before testing by the wool selling broker (source: Australian Wool Testing Authority Ltd)
- 237 -
example, Mr Peppin explained that AM testing was an integral component of wool
marketing but that it was not used for making farm management decisions: “in many
ways it [AM] doesn't mean a great deal for what you do on the farm, except to reinforce
the genetic direction that you are taking.” Mr Romney also admitted to using AM test
results primarily to market their clip and, to a lesser extent, to reflect on production
performance, "I suppose it would be intrinsic use, I don't sit down and pour over it and
say this year we fed 100 grams of lupins and last year we fed 150 grams of lupins and
the staple strength is this and such and such and therefore this is what our strategies will
be.” Mr and Mrs Saxon used AM test results to check the quality and accuracy of their
in-shed classing: “partly it was to check our classing, to get a bit of feedback on our
classing…to make sure we're actually doing what we thought we were doing.” AM test
results were not used as a management tool on Saxon farm because staple strength was
not a major driver of farm income. However, Mr Dorset described the use of AM test
results on-farm to assess the impact of management strategies applied in the previous
season and the impact of climatic conditions.
By saying this is what happened I guess by default you're planning for the future, we do
change our management quite a lot on the data we get, as far as what we do. I'd look at
the staple strength and if they're up over the 32-35 then there's not a huge problem but if
they're down around the 25's and you're getting and they've got a 50% mid-break, that's
a huge issue. (Mr Dorset)
Mr Coriedale Jr suggested that AM test results were particularly important for
commercial woolgrowers with ram studs as a means of tracking the progress of animal
husbandry and genetics programs.
Well we're very interested in them [AM test results] because we're ram breeding and it
tells us how we're going there. We're trying to get finer all the time but without losing
fleece weight, it's a balancing act. (Mr Coriedale Jr.)
- 238 -
The notion that AM test results were an important farm management tool for ram studs
was not shared by the Saxon and Romney farm families.
- 239 -
Table 7.3: Phase 3 case study data summary
Wool
Enterprise
%
AM
Wool Types Marketing
activity
Sale lot size SL and SS Diameter Price per kg
clean wool
VM content (%)
With AM 77% Merino fleece, 10% bellies & locks, 5% weaners, 8% pieces
57% Sold, 23% PI, 20% T
Mean 7 bales, range 1 to 20 bales
SL mean 79mm SS mean 29 Nkt, range 16 to 59Nkt
Mean 19.5µ, range 15.5µ to 23.1µ
Mean 893 cents, range 301 to 2250 cents
Mean 2.5%, range 0.4% to 9%
Dorset Farm 89%
Without AM 17% Merino fleece, 25% bellies & locks, 26% weaners, 29% pieces,
63% Sold, 12% PI, 25% T
Mean 4 bales, range 1 to 13 bales
Mean 19.5µ, range 17.6µ to 24.8µ
Mean 599 cents, range 198 to 1194 cents
Mean 3.2%, range 0.6% to 7.8%
With AM 85% Merino fleece, 11% pieces, 4% weaners
63% Sold, 30% PI, 7% T
Mean 9 bales, range 2 to 21 bales
SL mean 93mm SS mean 27Nkt, range 13 to 66 Nkt
Mean 20µ, range 16.8µ to 24.5µ
Mean 834 cents, range 408 to 1791 cents
Mean 1.3%, range 0.3 to 4.6%
Saxon Farm 58%
Without AM 49% Merino fleece, 23% bellies & locks, 14% pieces, 14% fleece other and weaners
53% Sold, 37% PI, 10% T
Mean 6 bales, range 1 to 17 bales
Mean 19.2µ, range 16.7µ to 22.3µ
Mean 722 cents, range 86 to 1798 cents
Mean 2.6%, range 0.2% to 9.5%
With AM 84% Merino fleece, 8% pieces, 4% weaners, 4% bellies & locks
85% Sold, 12% PI, 7% W
Mean 10 bales, range 2 to 20 bales
SL mean 84mm SS mean 34Ntk, range 15 to 52Nkt
Mean 21.4µ, range 17.3µ to 25µ
Mean 760 cents, range 321 to 1552 cents
Mean 1.6%, range 0.2% to 6.5%
Polwarth
Farm
79%
Without AM 11% Merino fleece, 41% bellies & locks, 19% pieces, 24% weaners, 5% cross bred
60% Sold, 34% PI, 6% W
Mean 5 bales, range 2 to 11 bales
Mean 20.6µ, range 18.1µ to 30.7µ
Mean 656 cents, range 151 to 1103 cents
Mean 3.5%, range 0.2% to 10.5%
With AM 58% Merino fleece, 15% bellies & locks, 15% pieces, 12% weaners
65% Sold, 20% PI, 15% T
Mean 5 bales, range 1 to 25 bales
SL mean 79mm SS mean 31Nkt, range 15 to 59Nkt
Mean 18.8µ, range 15.9µ to 23.9µ
Mean 1079 cents, range 270 to 3149 cents
Mean 2.9%, range 0.3% to 14.3%
Peppin Farm 80%
Without AM 3% Merino fleece, 28% fleece other, 49% bellies, locks & crutchings, 7% weaners
79% Sold, 15% PI, 16% T
Mean 4 bales, range 1 to 22 bales
Mean 18.6µ, range 16µ to 22.2µ
Mean 635 cents, range 182 to 1616 cents
Mean 4.5%, range 0.2% to 12.3%
With AM 71% Merino fleece, 12% fleece other, 9% bellies & locks, 7% pieces, 1% weaners
59% Sold, 19% PI, 22% T
Mean 9 bales, range 1 to 33 bales
SL mean 84mm SS mean 32Nkt, range 15 to 49Nkt
Mean 19µ, range 15.5µ to 21.3µ
Mean 926 cents, range 175 to 2296 cents
Mean 2.2%, range 0.6% to 11.2%
Coriedale
Farm
75%
Without AM 14% Merino fleece, 19% pieces, 36% bellies & locks, 30% weaners
67% Sold, 20% PI, 13% W
Mean 6 bales, range 1 to 16 bales
Mean 19.4µ, range 17.4µ to 23.3µ
Mean 551 cents, range 195 to 985 cents
Mean 3.6%, range 0.1% to 9.8%
With AM 78% Merino fleece, 7% pieces, 6% fleece other, 4% bellies, 3% weaners, 2% oddments
72% Sold, 25% PI, 3% W
Mean 8 bales, range 1 to 21 bales
SL mean 83mm SS mean 33Nkt, range 17 to 65Nkt
Mean 19.8µ, range 16.7µ to 22.9µ
Mean 871 cents, range 424 to 1926 cents
Mean 2.1%, range 0.3% to 11.6%
Romney
Farm
88%
Without AM 4% Merino fleece, 30% fleece other, 39% bellies & locks, 11% pieces, 17% weaners
69% Sold, 26% PI, 5% T
Mean 4 bales, range 1 to 9 bales
Mean 19.7µ, range 16.9µ to 24.1µ
Mean 653 cents, range 246 to 1726 cents
Mean 2.3%, range 0.2% to 9.5%
- 240 -
Phase 4: The adoption and abandonment of CC on-farm
Wool auction data and interview responses relating to the adoption and abandonment of
CC testing on the case study farms from 1995 to 2003 is summarised in Table 7.4. The
case study farms offered between 46 and 358 bales of clean wool with CC tests during
phase 4. Unlike the gradual expansion of AM across the Australian clip from phase 1 to
3, the data relating to the adoption and abandonment of CC testing on the case study
farms did not reveal a pattern of increasing use over time. A broad range of wool types
with variety of quality attributes were offered at auction with CC by the five farm
families that adopted these tests (see Table 7.4). The CC test results showed that a
range of wool was tested in terms of clean colour or average yellowness. On Coriedale
and Romney farms only white wool was tested, whereas on Dorset and Peppin farms
wool test results ranged from white to very yellow. On Polwarth farm only very yellow
wool was tested (see Table 7.4).
The key theme running through the case study interviews in relation to the adoption and
abandonment of CC tests was a strongly held belief that wool produced in Western
Australia (WA) was white and bright and did not need to be objectively measured for
clean colour. The wool selling brokers interviewed were reluctant to advise their clients
to adopt CC testing on this basis as they believed their wool would not be subject to
colour discounts. According to the Dorset farm broker, “if you have a decent breeding
program you shouldn't have a continuous colour problem with your flock, you may have
seasonal colour problems and they are few and far between in WA.” Despite the belief
about the whiteness of WA wool held by the woolgrowers and brokers interviewed, five
of the farms adopted CC tests. According to Mr Dorset, they adopted CC testing in
1998 in response to negative media attention to the colour of Australian wool.
However, he dismissed these tests as a trend that was quickly reversed when
woolgrowers realised that their wool was of good colour.
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We were putting batches of wool together and I think there was a big stir up about
colour in the media or the brokers are going ‘buyers are being really careful about
colour’. So there was a big beat up there, so I think colour testing became quite popular
and all the colour tests come out and said there's nothing wrong with the wool and
everyone stopped doing it. You get these things, there's a trend every now and then.
(Mr Dorset)
The Polwarth farm family adopted CC testing in 1996 because it was a new objective
measurement and they wanted to assess the colour performance of their clip, "yeah, so it
was a new thing out, let’s do a colour test see where we're at.” The Romney farm
family adopted CC in 1996 and tested only three per cent of their clip as they believed
that their wool was of good colour and would not need to be tested.
I think most of our wool is good to very good in terms of visual colour, we're spring
shearing so the only colour we have is with fleece rot, we're very fastidious at removing
that during shearing as we find it. (Mr Romney)
As a well established ram stud, the Coriedale farm family pride themselves on breeding
rams that produced progeny with white and bright wool. The Coriedale farm rams and
wool brand had a strong reputation in the market for being of good colour: “colour isn't
really a problem with our sheep. It's one of their attributes because we've always, from
80 years ago, been very conscious of [wool] colour”. The Coriedale farm family
adopted CC testing in 1995 to defend the integrity of their wool brand and ram stud
when batches of their clip were classed as yellow during shearing. The CC test results
confirmed that their wool was of good colour and Coriedale abandoned testing as an
unnecessary expense in 2000.
In the late 1990s and early 2000s the wool selling brokers interviewed recommended
that their clients test only wool classed as being of poor colour. This highly selective
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approach to CC testing was in sharp contrast to AM testing, where woolgrowers were
advised to test all sale lots of four bales and over. According to the Polwarth farm
broker, “the only area that we would colour test are lines that we perceive subjectively
to be potentially discountable by a buyer, there is very little real colour discount in the
WA market.” The brokers interviewed would preferentially test lines that were
appraised as having poor visual colour in order to prove that they were of good colour.
If a classer attacks our lines of colour that we think are ridiculous then we will colour
test those lines. I’ve never seen any evidence that non colour tested wool is heavily
discounted. (Peppin farm broker)
Compared with AM, Clean Colour testing did not become an entrenched marketing
practice in the wool auction system. The woolgrowers interviewed saw little benefit
from regular testing and had abandoned CC by 2003. The Polwarth farm family
abandoned CC testing in 2001 after they received discounts on wool offered at auction
with poor CC test results. According to Mr Polwarth, “it was not particularly flash, so
what's the point of highlighting a discount feature?” Mr Polwarth argued against testing
wool with poor colour but felt that they would have tested their wool if it had been of
good colour “if we had really white wool and things were good I guess we would
[test].” The Romney farm family also abandoned CC testing in 2001 after achieving
poor clean colour test results. The Dorset farm family abandoned CC testing in 2002
because it highlighted a colour problem in their clip which was then subject to heavy
discounts: “those colour tests were showing me that I had a heap of black in it so I
wouldn't want anybody to know”. Whether CC test results showed white or yellow
wool, the woolgrowers interviewed in this study who adopted CC came to view these
tests as an additional, unnecessary marketing expense.
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Table 7.4: Phase 4 case study data summary
Case study
farm
Bales with CC CC results (YZ
yellowness
index)24
Price with CC
(cents per kg
clean)
Diameter with
CC
SL and SS with
CC
VM content
with CC
Lot size with
CC
Type with CC
Dorset Farm 82 bales -0.6 to 9 Mean 511 cents, range 198 to 960 cents
Mean 20.2µ, range 18.4µ to 24.8µ
Mean 81mm Mean 34Nkt
Mean 2.5%, range 1% to 6.8%
Mean 7 bales, range 1 to 16 bales
50% Merino fleece, 12% bellies, 37% weaners, 1% cross bred
Polwarth Farm 46 bales 8.3 to 9 Mean 639 cents, range 256 to 1124 cents
Mean 21µ, range 18.7µ to 23.2µ
Mean 81mm Mean 36Nkt
Mean 2.4%, range 0.5% to 6.9%
Mean 6 bales, range 2 to 19 bales
41% Merino fleece, 41% fleece other, 17% bellies
Peppin Farm 358 bales -0.2 to 10 Mean 669 cents, range 182 to 1290 cents
Mean 18µ, range 16µ to 20.8µ
Mean 63mm Mean 37Nkt
Mean 4.5%, range 1.2% to 10.4%
Mean 6 bales, range 1 to 17 bales
39% pieces, 7% fleece other, 53% weaners
Coriedale Farm 56 bales 0 to 1 Mean 725 cents, range 534 to 1283 cents
Mean 19µ, range 17.8µ to 20.7µ
Mean 88mm Mean 32Nkt
Mean 1.6%, range 0.9% to 1.9%
Mean 7 bales, range 1 to 15 bales
50% Merino fleece, 50% fleece other
Romney Farm 55 bales 0.2 to 1 Mean 1027 cents, range 582 to 1592 cents
Mean 18.9µ, range 16.7µ to 22.5µ
Mean 74mm Mean 36Nkt
Mean 1.6%, range 0.9% to 5.9%
Mean 6 bales, range 2 to 18 bales
33% Merino fleece, 56% weaners, 5% fleece other, 5% bellies
24 Clean colour is an objective measurement of the colour of wool after scouring which can affect the dyeing potential of the wool. The Y-Z portion of the CC test result is a measure of the yellowness of the wool. This is measured as the difference between the reflectance of a surface in the green and blue regions of the colour spectrum and expressed as the difference between the tristimulus values Y and Z.
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7.2 Discussion of the multiple case study findings
New insights into the agricultural innovation process and research propositions can be
derived from the case analysis of the enactment of AM and CC on-farm. In a general
sense, it appears these insights are consistent with a sensemaking perspective of the
enactment of new technology as its underlying logic can be explained through the
sensemaking process. In the following sub-sections, the insights and propositions
derived from the case analyses are presented in detail, along with their implications for
researchers, as well as for agricultural innovation professionals. The sensemaking
process of the adoption, implementation, use and abandonment of AM and CC on-farm
suggested by the case study data is explained in terms of the seven properties of
sensemaking proposed by Weick (1995). These properties were chosen to organise the
discussion of the case analysis as they represent a synthesis of the relevant sensemaking
literature and a comprehensive view of the key aspects of sensemaking that relate to
agricultural innovation.
Technology enactment on-farm
Enactment captures the notion that woolgrowers deliberately created the environment
for the enactment of AM and CC, rather than simply responding to it. The actions taken
by sensemakers create cues they notice and extract and use to guide further action.
Actions may take the form of creation, abandonment, postponement and adaptation, all
of which generate meaning (Weick 1995). People create their environments as their
environments create them. Therefore, enactment is an integral component of the
sensemaking process and can be understood as an input, process and outcome of
sensemaking.
The case study data suggested there were different perceptions and outcomes of the
enactment of AM and CC across the case study farms. Some woolgrowers saw their
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enactment of AM and CC on-farm as shaping industry participants’ perceptions of the
quality of their wool enterprise. These farm families took steps to shape the enactment
of AM and CC on-farm in ways that promoted the perceived quality of their wool brand
and ram stud. Others saw the enactment of AM and CC on-farm as being much more
reactive to the influence of industry participants, such as wool selling brokers and
buyers. While these woolgrowers shaped their enactment of AM and CC on-farm
through their compliance with influential members of the industry, they did not perceive
their adoption and implementation of AM and CC strictly in those terms. The case
study data suggested the enactment of AM and CC on-farm was framed by the personal
identity and social context of the case study farm families.
A number of the case study farm families, who were confident in their ability to
implement and use AM and CC on-farm, revealed that their social context and emerging
industry belief systems prevented them from doing so in the way that they would have
liked. How the woolgrower interfaced with their wool selling broker and the auction
system influenced how they perceived their ability to shape the use of AM and CC on-
farm. A recurrent theme in the data in phase 3 was the expansion of the use of AM
across a range of wool types and qualities in response to demands from buyers and
brokers, despite the potential for heavy discounts and the lack of evidence that these test
results were employed along the supply chain.
The case study data suggested the benefits that accrued to the farm business as a result
of adopting, implementing and using AM and CC were attributed to the endeavours of
the farm family. Failures in terms of attracting additional discounts for tested or
untested wool or applying AM and CC to the wrong sale lots were attributed to
mistakes made by the wool classer at shearing or the wool selling broker. Whether or
not they were accurate did not matter, these explanations were used by woolgrowers to
explain past actions and plan future actions towards AM.
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These findings suggest the sensemakers and their environment are co-created through
the enactment of new technologies on-farm. This study calls into question the
assumption that the farming context is objective and stable, as conceptualised in the
ToT approach and staged innovation decision models. These findings lend support to
Coughenour’s (2003) research, which found that individuals and groups construct and
reconstruct their environment in response to agricultural innovation. Therefore, it can
be suggested that:
Research Proposition 1: The enactment of new agricultural technologies both
constructs and is constructed by sensemakers’ farming environments.
Personal Identity and technology enactment on-farm
People are driven by a need for a sense of identity and have a general orientation to
situations that maintain the consistency of their self-conceptions (Erez & Earley 1993).
Sensemakers need to confirm their own personal identities and sensemaking is a means
to achieve this objective. Cues for personal identity construction can be extracted from
the conduct of others, but the conduct of others can be influenced by the actions of the
sensemaker. Therefore, personal identity construction is a social sensemaking process.
Sensemakers view a technology in terms of their assumptions about the functions and
use of the technology, expectations of the performance of the technology, experience
and knowledge of existing technologies and practices and current projects and goals
(Agarwal & Prasad 1999; Sproull & Hofmeister 1986; Orlikowski & Gash 1994; Weick
1995). This issue of personal identity was significant for the case study farm families
who enacted AM and CC on-farm. However, the question of whether those farm
families constructed personal identity frames as a result of adopting, implementing,
using and abandoning AM and CC was not fully resolved in this study.
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There is evidence that the personal identity of participating woolgrowers influenced
their enactment of AM and CC on-farm. For example, the identity of the Coriedale
farm family members was that they were producers of a high quality, well established
and respected wool brand and ram stud. The Coriedale farm family adopted AM to
confirm and maintain their identity as they believed a ram stud of their quality and
reputation should provide objective measurements of as many wool traits as possible.
Therefore AM was adopted on the Coriedale farm in the absence of broker influence
and market price premiums for tested sale lots in order to maintain the Coriedale farm
family identity. In the same vein, CC was adopted on the Coriedale farm to confirm
that their wool was of good colour. The Coriedale farm family strongly associated the
success of their ram stud to the production of rams with ‘white and bright’ wool. By
challenging the actions of wool classers in relation to the colour of their clip the
Coriedale farm family’s sensemaking was triggered to confirm and maintain their
personal identity frames in terms of the good colour of their wool brand.
The need to confirm personal identity through the enactment of new technologies on-
farm was also highlighted by the AM adoption behaviour of the Romney farm family.
There is evidence that Mr and Mrs Romney ignored the advice of their wool broker to
reject AM on the basis that their clip would be discounted for low staple strength. The
Romney farm family based their decision to adopt AM on their experience of producing
relatively sound wool as a result of shearing their flock in April. Based on their
knowledge and experience, Mr and Mrs Romney expected the use of AM would
confirm their belief that their clip was relatively sound.
The case study evidence suggested that farm families sought to confirm their identity
through the enactment of AM and CC on-farm. If the personal identity of woolgrowers
and farm families is to serve as a general orientation to the adoption and use of new
technologies, technology developers and extensionists need to have a sense of the
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components of personal identity frames that may influence agricultural innovation
sensemaking and whether those frames are likely to be confirmed or questioned though
the enactment of the new technology. Therefore, it is suggested that:
Research Proposition 2: People enact new agricultural technologies on-farm
to confirm their personal identity frames.
Sensemaking is, in part, controlled by people’s expectations as people seek to make
sense of experiences that deviate from their expectations (Weick 1995; Louis 1980). It
is clear from the case analysis that, as the enactment of AM and CC on-farm evolved, so
too did the expectations of woolgrowers based on their experience and knowledge.
Woolgrowers’ expectations changed as price discounts appeared in the market for
untested sale lots. This resulted in the application of AM across the majority of sale
lots. This evidence supports Orlikowski and Gash’s (1994, p. 175) proposition that
people develop particular expectations about a technology when they interact with it
and their expectations shape subsequent actions towards the technology.
The multiple case analysis clearly depicted the ongoing enactment of AM and CC on-
farm as being influenced by and, in turn, influencing the personal identity construction
of participating woolgrowers. Evidence from the cases suggested that, as woolgrowers
used AM and CC on-farm and their experience and knowledge of these testing
technologies increased, social influence decreased in importance in driving technology
enactment. For example, despite woolgrowers and brokers articulating the ‘minimum
four bale rule’ for offering sale lots with AM as the industry norm, smaller sale lots
were routinely tested on the Dorset and Peppin farms based on their experience that
superfine wool would not be interlotted or adversely discounted on the basis of the
small lot size.
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There was evidence that the enactment of AM on-farm influenced and was influenced
by the reconstruction of the personal identity frames of woolgrowers over time. For
example, Mr Peppin’s personal identity in relation to the objective measurement of
wool fibre changed over time as the use of these testing technologies increased. He
shifted from being an ‘AM sceptic’ in phase 1 to identifying with the role of
woolgrowers as providers of objective measurements including AM in phase 3.
In this study there was strong evidence of the role played by indigenous knowledge and
experience in the enactment of new technologies on-farm. In the case of the adoption
and abandonment of CC testing there was a widely held belief among wool selling
brokers and woolgrowers that wool produced in WA was generally of good colour.
This belief was based on the experience and knowledge of woolgrowers and brokers
that WA clips were not often discounted when they were appraised as being of poor
colour. This existing experience and knowledge of the market response to the colour of
WA wool influenced the enactment of CC testing on-farm in terms of the small
proportion of wool actually tested and the abandonment of these tests by 2003. These
findings supported the research of Martin and Sherington (1997) and Coughenour
(2003) as they found evidence that indigenous farming knowledge interacted with and
influenced the enactment of new agricultural technologies on-farm.
The multiple case studies found that interpretation frameworks influenced and were
influenced by the enactment of AM and CC on-farm at an individual level, a farm
family business level and an industry group level. An in-depth analysis of the personal
identity frames of the potential adopters and end users of new technologies should not
only help to develop the technology as an effective tool for sensemakers and identify
adoption, implementation and use challenges, but also determine the degree of risk and
uncertainty associated with the enactment of new agricultural technologies on-farm. An
early assessment of the personal identity of adopters and end users becomes even more
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important when it is realised that every technology enactment situation is unique, with
its own challenges. However, it is clear from the case analysis that personal identity
frames, in terms of assumptions and expectations, experience and knowledge and
projects and goals, co-evolved over time with the enactment of AM and CC on-farm.
Therefore, it is imperative for technology developers to consider the impact of such
changes over time. It is suggested that:
Research Proposition 3: The personal identity frames of sensemakers co-
evolve with the enactment of new agricultural technologies on-farm.
Retrospect and technology enactment on-farm
Conceptually, sensemaking occurs retrospectively because a person can only make
sense of what has already happened, not what is happening in the instant that it occurs
(Weick 1995). A crucial element of sensemaking is the notion that, although situations
are progressively clarified, this clarification often works in reverse. Rather than the
outcome of a situation fulfilling some prior definition, the outcome often influences the
definition of the situation retrospectively (Weick 1995). According to Weick, people
need values, priorities and clarity about their preferences to enable them to be clear
about which projects matter.
According to Weick (1995) and Gioia and Chittipeddi (1991) what is happening, in
terms of the sensemakers’ projects and goals, at the moment of retrospection influences
how they make sense of the past. There was evidence that current projects and goals
influenced the interpretation of the enactment of AM and CC on-farm. For example,
the Coriedale farm family retrospectively justified their adoption and implementation of
AM as being a means to achieve their goal to be as objective as possible in the
measurement of their wool attributes. This suggests woolgrowers’ current goals and
activities influenced the sense they made of their actions towards AM and CC.
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The degree to which past experience can be considered relevant to new agricultural
technologies is open to question. Although similar technologies had been experienced
by the case study farm families, there was no precedent for the objective measurement
of the staple length, strength and clean colour characteristics of greasy wool fibre.
Woolgrowers had to draw on previous experiences on-farm and in the auction system
that could be approximated to the adoption, implementation and use of AM and CC. In
this way the diverse experiences of the case study farm families contributed to the
different ways in which AM and CC were enacted on-farm.
Experience of using AM and CC on-farm provided salient cues that guided further
actions in respect to these testing technologies. Explanations of previous experiences
with AM and CC in terms of market response to tested and untested sale lots provided
guidance for the ongoing enactment of these testing technologies on-farm. When
reflecting on the past experiences that had influenced their use of AM and CC on-farm,
the themes that emerged from the case study interviews were relatively consistent.
Most woolgrowers reported memories of events influenced by the response of wool
buyers to tested and untested sale lots in terms of market price received and the
application of any price premiums or discounts. It is suggested that:
Research Proposition 4: The sense that people make of new agricultural
technologies is retrospective and is influenced by their past experiences and
current projects and goals.
Social context and technology enactment on-farm
People make sense of the world in relation to their peers and colleagues and the groups
to which they belong (Weick 1995). The social nature of sensemaking binds people to
actions that require social justification. This affects the saliency of cues that
sensemakers extract from new technologies and provides group norms and expectations
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against which to measure the salience of those cues (Weick 1995). The social
sensemaking context of technology enactment combines how sensemakers view
themselves and the technology in relation to their peers, their colleagues, their social
groups, the industry to which they belong, and the norms of that industry group (Weick
1995; Tanaka, Juska & Busch 1999; Biemans 1992; Fincham et al. 1995; Preece 1989;
Fleck 1994).
The role of social context in the enactment of new technologies on-farm was revealed in
this study in terms of the influence wool selling brokers had on woolgrowers’ adoption,
implementation, use and abandonment of AM and CC. The case study farms engaged
their brokers in an ongoing dialogue about the use of AM and CC as a way of making
sense of industry norms in relation to those testing technologies. For example, wool
selling brokers played a key role in advising their clients on the adoption of AM and
CC. The Polwarth, Saxon, Dorset and Peppin farm families acted on their wool selling
brokers’ advice in the absence of defined personal identity frames relating to these
testing technologies or expectations of these tests based on clear market signals.
Therefore, it is likely that, in the absence of well developed personal identity frames
relating to a new technology, social influence and industry norms may drive technology
enactment.
There was also evidence of the observation of the AM and CC use behaviour of other
woolgrowers as a means of accessing industry norms. For example, in phase 2 the
Coriedale farm family observed the testing behaviour of other farmers in respect to
offering Merino pieces at auction with AM. These findings support the research of
Foster and Rosenzweig (1995) that identified farming peers as a source of influence in
the adoption and implementation of new agricultural technologies.
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There was evidence that group belonging influenced the enactment of AM on-farm. For
example, the Coriedale, Saxon and Romney farm families identified with belonging to
the ram stud breeders’ industry group and, in part, their enactment of AM reflected their
beliefs about the norms of that industry group to provide ram buyers with as many
objective measurements of wool traits as possible.
Social interaction occurs throughout the sensemaking process as people extract cues
from the actions of others and modify their frames and actions. Social influences on
sensemaking may take the form of shared meaning, overlapping views of ambiguous
events, compromise and duress (Eisenberg 1984; Blumer 1969; Weick 1995). For
example, evidence from the case analysis suggested the dialogue between brokers and
woolgrowers varied and changed over time, ranging from conflict and consensus to
consensus and compliance. Social interactions in relation to the enactment of AM and
CC on-farm was ongoing and evolved over time, suggesting:
Research Proposition 5: The enactment of new technologies on-farm
influences and is influenced by the social context of the sensemaker.
From a research perspective, this sensemaking approach to the enactment of new
agricultural technologies on-farm suggests a different approach for studying agricultural
innovation. Indeed, rather than focusing on the technological artefact and functional
characteristics, more effort should be devoted towards understanding the nature of the
enactment of new agricultural technology on-farm by identifying and examining the
social context of the potential adopter and end user of the technology.
Ongoing technology enactment on-farm
The flow of events that people experience is continuous, which means the sensemaking
process is ongoing (Weick 1995; Louis & Sutton 1991). Within the ongoing flow of
events of which people make sense, there are events of importance that ‘crystallise
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meaning.’ Such events or interruptions are ‘bracketed’ by sensemakers, who extract
cues from them (Eccles, Nohria & Berkley 1992). The quantitative data relating to the
proportion, type, quality and value of wool offered at auction with AM and CC (see
Figures 6.3 to 6.9) suggested the enactment of these testing technologies on-farm was
ongoing. The case study findings also highlighted the impact of external events that
interrupted the enactment of AM and CC on-farm. For example, the collapse of the
wool price and abandonment of the WRPS in 1991 influenced the six case study farm
families to either abandon AM completely or reduce the proportion of wool offered at
auction with AM.
In the dominant linear technology transfer approach to agricultural innovation it is
assumed farmers are passive adopters of new technologies that are transferred to them
through extension programs (Chambers & Jiggins 1986; Biggs 1989; Lionberger &
Gwin 1991). This perspective of agricultural innovation does not anticipate challenges
occurring during or after the adoption of a new agricultural technology that may result
in the adaptation or abandonment of the technology. In this study, the evidence showed
woolgrowers were not passive actors in the innovation process. Woolgrowers
responded to changing technology and market stimuli and adapted the use of these
technologies in order to make them sensible to their own farming context. The
enactment of AM and CC on the case study farms supported Rogers’ (2003) proposition
that the adopters of new technologies are active participants in the innovation process.
The evidence from the case analysis clearly suggested that technology transfer did not
simply result in a woolgrower’s decision to adopt AM and CC and make full use of
those technologies on-farm. On the contrary, evidence from the six farms revealed a
broad range of technology actions, including the adoption, abandonment,
implementation and readoption of AM and CC over time. There was also evidence that
the use of AM and CC varied between the case study farms and was adapted over time
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in terms of the proportion, type, quality and value of the sale lots tested. This evidence
did not support the notion of the adoption, implementation and use of agricultural
technology as a simple, linear, staged uni-directional decision process (e.g. Rogers
2003; Jones 1967). Indeed, it seems that there was an ongoing enactment of new
agricultural technologies on-farm that reflected a series of sensemaking cycles,
suggesting:
Research Proposition 6: The ongoing enactment of new agricultural
technologies on-farm reflects a series of sensemaking cycles.
The evidence of the enactment of AM and CC on-farm as a series of sensemaking
cycles calls into question the dominant conceptualisations of staged, linear technology
transfer and innovation decision processes and related agricultural innovation
management practices. While much of the agricultural innovation research has equated
successful innovation and technology transfer to the adoption of new technologies by
farmers, these case study findings suggest good management of the agricultural
innovation process must also focus on the extension of support for technology
implementation and use in order to anticipate and address challenges to ongoing
technology enactment on-farm. For example, there is evidence that the adoption of AM
on Polwarth and Dorset farms was influenced by the fixed price premium available for
wool with AM purchased under the WRPS. However, both the Polwarth and Dorset
farm families abandoned AM when the WRPS fixed price premium was withdrawn and
was not replaced with other price incentives or interventions, suggesting:
Research Proposition 7: Successful agricultural innovation requires
technology developers and extensionists to identify and address enactment
challenges on-farm as they emerge over time through ongoing engagement
with technology users.
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Salient cues and technology enactment on-farm
Sensemaking is focused on and by extracted cues. Actions create cues that are then
extracted for further sensemaking. Cue extraction is the process of ‘noticing’ and
extracting what is salient and useful for mentally representing stimuli (Starbuck &
Milliken 1988). According to Smircich and Morgan (1982), cues have three important
characteristics:
1. They are received as perceptions and are therefore subjective.
2. There is no reason to assume that everyone who experiences a particular event
will extract the same cues from it, or that two people who perceive the same cue
will incorporate it in the same way into their frames.
3. Control over cues is a source of influence and power in a social system.
The case study data suggested that the noticing and extraction of salient cues is a key
issue. Elements of conflicting social influence and ambiguity in market signals
impacted on the salience of cues relating to AM and CC that were noticed by the case
study farm families. In the passage of a busy and eventful wool production season,
wool production and marketing cues relating to AM and CC often went unnoticed.
Similarly, ambiguous market signals made it difficult to judge which cues relating to
AM and CC were salient to the enactment of these technologies on-farm. A lack of
experience with the testing technologies contributed to difficulties in assessing the
saliency of market cues in particular, suggesting:
Research Proposition 8: The ability of people to extract salient cues from the
enactment of new agricultural technologies on-farm is constrained by the
complexity of their farming context and ambiguous market signals.
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In this study it was found that woolgrowers who experienced the same ‘events’ in the
technology enactment process extracted different salient cues from that ‘event,’
resulting in different outcomes in terms of technology enactment. For example, the
wool brokers advised their clients to avoid testing relatively tender portions of their clip
in order to avoid discounts for poor staple strength. Five out of six of the woolgrowers
interviewed in this study acted on these cues by avoiding testing relatively tender sale
lots in order to avoid potential discounts. However, on the Romney farm, these cues
were interpreted to mean that buyers may discount wool that was not tender if the
woolgrower did not prove that it was sound. As a result, AM was applied to Romney
farm sale lots that were subjectively appraised as tender in order to prove that they may
be sound.
The case study data suggested that industry beliefs in respect to AM and CC had
changed considerably since their introduction in 1986 and 1995 respectively. This time
period served to emphasise the market response to tested and untested sale lots. Thus,
cues for the enactment of AM and CC on-farm became more concrete and reliable over
time, making it increasingly easy for woolgrowers to act and extract salient cues from
those actions. The six case study farm families developed a body of experience and
knowledge of the use of AM and CC on which they drew to guide further actions. That
experience and knowledge made them more able to enact AM and CC in a way that was
meaningful for their farm business, suggesting:
Research Proposition 9: The ability of people to extract salient cues from the
enactment of new agricultural technologies on-farm increases with their level
of experience and knowledge of that technology.
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Plausibility and technology enactment on-farm
Sensemaking is about plausibility rather than accuracy, as it involves the embellishment
of a single extracted cue (Weick 1995). According to Fiske and Taylor (1991), most
people give meanings to situations that are ‘good enough’ to enable them to undertake
effective actions. Plausible explanations of events are created when situations are
matched to the interpretation framework of the sensemaker. The elements of the event
are imposed on the elements of the sensemakers’ interpretation framework, which
enables them to outline their expectations within a range of acceptability. If
information is missing, the sensemaker inserts default values to create a full explanation
of the event.
The ability of the case study farm families to develop plausible explanations of AM and
CC in the initial stages of enactment on-farm was compromised by a lack of experience
with these testing technologies and the absence of clear market signals related to the use
of these tests. Any plausible explanations of AM and CC were initially tested against
the explanation of events made by their wool selling brokers. A lack of trust in the
auction system and wool buyers on the part of wool broker and woolgrowers made any
congruence of plausible explanations of AM and CC suspect.
When the explanations of others were in doubt and experience with AM and CC was
lacking, woolgrowers made plausible explanations of these testing technologies based
on their assumptions, expectations and intuition. For example, the case study farm
families acted on plausible explanations of the response of the market to sale lots
offered with AM, rather than on accurate price premium and discount information in
phase 1 and 2. The woolgrowers avoided testing relatively short or tender wool in
phase 1 and 2 as they believed sale lots with those qualities would be subject to
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unnecessary discounts from buyers, despite a lack of evidence in the market to support
that assumption, suggesting:
Research Proposition 10: The ability of people to reach plausible explanations
of new agricultural technologies may be compromised by their lack of
experience and the absence of clear market signals
Conclusions
In summary, the enactment of AM and CC on-farm was an evolving, dynamic process
that changed over time as woolgrowers made sense of these technologies in relation to
their personal identity frames and social context. The primary message to agricultural
innovation researchers, technology developers, policy makers and extensionists from
this case study is that the successful enactment of agricultural technologies on-farm
requires the active, ongoing engagement of industry participants. In order to engage
technology adopters and users in the innovation process, the sensemakers’ personal
identity frames and social context and how these interpretation frameworks relate to the
new technology need to be understood. The actions of ‘sense givers’ to engage
sensemakers need to evolve to meet new challenges as they arise through the process of
enacting new technologies on-farm. In other words, this case study suggests effective
agricultural technology development, transfer and extension should start with an
understanding of the social context of potential adopters and then their personal identity
frames and perceptions of existing technology frames and industry belief systems,
rather than focusing on the development and transfer of the technology. In a similar
vein, it appears that the successful enactment of new agricultural technologies on-farm
is more likely to occur when industry participants develop and present clear signals
relating to the use and value of the technology on-farm in a way that those signals can
be extracted as salient cues in the agricultural innovation sensemaking process.
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Therefore, having funding and plans for technology development and transfer is not
enough. The ability of ‘sense givers’ to recognise the cues to which sensemakers will
respond and actively develop, articulate and manage those cues through a technology
development, introduction and implementation process is also important.
In the following Chapter the main findings from the three empirical agricultural
innovation sensemaking studies are discussed in relation to the proposed preliminary
analytical framework along with the implications of these findings for theory and
practice.
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8 Contributions and implications for theory, policy and practice
The present research focused on agricultural innovation and, specifically, on the
development, introduction and adoption of new objective wool fibre testing
technologies in the Australian wool industry. As previously mentioned25, new
agricultural technology has long been considered a silver bullet for poor profitability,
productivity and sustainability. However, empirical and anecdotal data suggests new
agricultural technologies often fail to meet expectations (e.g. Carletto, de Janvry &
Sadoulet 1996; Neill & Lee 2001; Moser & Barrett 2003; Barnett & Sneddon 2006b).
In this study it was argued that a constraint to the development of effective agricultural
innovation programs is the dominant conceptualisation of agricultural innovation as a
simple, linear, staged process of the development, transfer, extension and adoption of
new technologies. Implicit in this conceptualisation of agricultural innovation is the
assumption that reality is objective and that the scientific method, in the form of
fundamental and applied research, can be used to understand reality and translate
problems into appropriate new agricultural technologies (Röling 1996). This dominant
technological determinist perspective of agricultural innovation assumes new
technologies apply equally to all farmers and that it is in their best interests to adopt the
new technologies; thus guaranteeing successful technology transfer (Buttel, Larson &
Gillespie 1990). However, this approach to agricultural innovation is problematic
(Howden et al. 1998; Röling 1988; Black 2000). The linear, ‘Transfer of Technology’
approach has been blamed for the development and transfer of unsuitable technologies
and the rejection and over-adoption of new agricultural technologies (Vanclay 1994;
Ruttan 1996; Dunn, Gray & Phillips 2000).
25 Chapter 1, p.1.
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In this study it was proposed that agricultural innovation can be better understood by
adopting a socio-technical perspective of the development, transfer and adoption of new
technologies. Socio-technical perspectives of innovation assume technological artefacts
are socially constructed and interpreted and that there is no one single best way for a
technology to be developed and used (T. Pinch & W.E. Bijker 1987). Theory and
concepts of social sensemaking were suggested as a unifying framework for the analysis
of the agricultural innovation process. A social sensemaking framework was used to
provide a unified socio-cognitive perspective of agricultural innovation that includes
iteration, enactment, adaptation, transition, individual cognitions and social learning at
an individual, group and industry level (e.g. Weick 1995; Weick, Sutcliffe & Obstfeld
2005). The data collection and analysis in the empirical studies that were a part of the
study were guided by a preliminary analytical framework of agricultural innovation
sensemaking (as was shown in Figure 3.3). The elements and construction of this
analytical framework were inspired by the sensemaking concepts and empirical research
that were discussed in Chapter 2 and Chapter 3. A model of the theory of agricultural
innovation sensemaking and how the empirical findings from this study relate to
elements of the sensemaking process is presented in Figure 8.1.
Three empirical studies of Objective Measurements (OM) innovation initiatives in the
Australian wool industry were undertaken to examine agricultural innovation from a
social sensemaking perspective, namely:
1. An examination of the co-evolution of agricultural innovation and
Australian wool industry belief systems (which was discussed in Chapter
4).
2. An examination of the diffusion of new technologies in the Australian
wool industry (which was discussed in Chapter 5).
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3. An examination of the enactment of new technologies on-farm (which
was discussed in Chapters 6 and 7).
These studies sought to examine agricultural innovation as a social sensemaking process
and to answer the research questions posed in this study, namely:
- How did the sensemaking of Australian wool industry participants shape the use
of objective wool fibre testing technologies?
- What are the implications of how sensemaking shaped the use of
objective wool fibre testing technologies for future advances in the
management of agricultural innovation?
The results suggested agricultural innovation processes are dynamic, evolving and
reflect interactions between individual and collective sensemaking. The conclusions
drawn from the studies that showed how the sensemaking of Australian wool industry
participants shaped the use of objective wool fibre testing technologies and the
implications for future advances in the management of agricultural innovation are
presented and discussed in the following section of this Chapter. The preliminary
analytical model that was developed in Chapter 3 (as shown in Figure 3.3) was used to
frame these concluding discussions. The empirical findings and implications of the
three empirical studies are discussed in relation to the interpretation frameworks of
industry participants, the sensemaking process, technology frames, and industry belief
systems. In the second section of this chapter, contributions of the empirical studies to
theory are discussed in relation to Innovation Diffusion Theory, sensemaking and
predictive behavioural models. In the third section of this chapter, the implications of
the empirical studies for agricultural innovation policy and practice are proposed. In the
fourth section of this chapter, directions for future research are presented, along with
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some tentative research questions. The final section provides a conclusion to this
chapter.
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Figure 8.1: A model of agricultural innovation sensemaking and case study contributions
INDUSTRY PARTICIPANT
Assumptions Expectations Experience Knowledge Current projects Goals
Retrospect
Social context
Salient cues
SENSEMAKING PROCESS
PERSONAL ELEMENTS
TECHNOLOGY
Physical artefacts Functional characteristics Use Performance evaluation
INDUSTRY ELEMENTS
Reputational Ranking
Industry Recipes
Boundary Beliefs
Product Ontology
Enactment
Ongoing projects
Plausibility
Personal identity
SOCIAL ELEMENTS
Power & Influence
Group & Industry norms
TECHNOLOGY ELEMENTS
Group belonging S
ENSEMAKIN
Chapter 4 Co-evolution of
agricultural innovation and Australian wool
industry belief systems
Chapter 5 Fad, fashion, compliance
or efficient choice? A study of the diffusion of
technologies in the Australian wool industry
Chapters 6 & 7 The enactment of new technologies on farm: a
sensemaking perspective
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8.1 Concluding discussion of the empirical findings
In agricultural innovation initiatives, the successful transfer of new technologies from
developers to end users is seen as an essential activity if new technologies are to be
successfully diffused in agricultural industries. However, prior empirical studies of
such innovation initiatives have generally assumed a simple, linear, staged process,
which was not observed in the present study of OM initiatives.
The reality of the OM innovation initiatives was not a smooth, simple transfer of
technology from developers to end users, but a political, negotiated, ongoing, evolving
process. There was evidence of conflict, compliance and consensus among industry
participants and of these responses changing over time. The OM innovation initiatives
examined can be conceptualised as a series of social sensemaking cycles, through which
shared technology frames and industry belief systems were socially constructed and
reconstructed over time.
The conclusions drawn from the three empirical studies support the central proposition
of this research that agricultural innovation is an occasion for social sensemaking and
therefore is not linear, static or deterministic. In this study sensemaking concepts were
proposed as an explanation for the social and interpretive nature of the agricultural
innovation process in the absence of a unifying socio-technical framework in the
literature. Although research has been undertaken to examine agricultural innovation
from a socio-technical perspective, these studies have largely used an Actor-Network
Theory (ANT) approach (e.g. Coughenour 2003; Lockie 1997b). However, this
approach has been criticised for disregarding social structures, neutralising the role of
human actors, a lack of political analysis, and its focus on description rather than its
capacity for explanation (Walsham 1997). In contrast, sensemaking concepts provide a
comprehensive analytical framework for examining the social construction of
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agricultural innovation at the individual, group and industry levels. The conclusions
drawn from the three empirical studies in this thesis are discussed in relation to the
elements of the proposed analytical framework shown in Figure 3.3.
Findings and implications in relation to interpretation frameworks and technology
enactment
The enactment of new agricultural technologies is mediated by sensemakers’
interpretation frameworks that are constructed out of their personal identity and social
context frames. The study suggested the diffusion of new agricultural technologies is
often dynamic and heterogeneous as it is mediated by industry participants’
interpretation frameworks. These results do not support a technological determinist
view of innovation in which industry participants are passive recipients of new
technologies (T. Pinch & W.E. Bijker 1987; Bijker 1999). Rogers’ (2003) argument
about people giving meaning to a technology in their own context is more in line with
these empirical findings. However, he did not discuss the mediating role personal and
social interpretation frames play in the meaning making process or people’s ability to
reconstruct these frameworks and their environment through action. In this sense, his
idea of meaning making differs from the sensemaking process discussed in this study.
The case studies showed the mediating role woolgrowers’ interpretation frameworks
played in the enactment of OM technologies. The diffusion of Additional
Measurements (AM) was initially mediated through sensemakers’ social contexts prior
to their development of expectations, experience and knowledge of the new testing
technology. There was also evidence that, as the use of new testing technologies
became routine, the use of AM on-farm was largely mediated by social norms.
These findings imply that the nature and role of interpretation frameworks in the
enactment of new technologies are dynamic, complex and evolve over time. The
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transition from social context to personal identity frames suggests the need for a
deliberate attempt by technology developers and extensionists to improve the clarity and
relevance of market signals for new agricultural technologies. It may also require
efforts to engage and co-opt industry participants with interest in and influence over the
adoption and use of new agricultural technologies into the innovation process.
The complex and dynamic nature of interpretations frameworks does not correspond
with the commonly-held assumption that the adopters and users of new technologies are
passive actors in the agricultural innovation process. Evidence of woolgrowers’
abandonment of AM and CC calls into question the rationale for targeting ‘progressive
farmers’ who are assumed to diffuse new technologies throughout the social system. It
appears all potential technology users need to be seen as equal partners in the
development, introduction and implementation of new agricultural technologies. It is
also seems that, in addition to the assumptions about users’ needs made by technology
developers and policy makers, challenges to the ongoing use of new technologies in
industry participants’ contexts need to be seen as highly important for ongoing
technology development and extension. Woolgrowers’ attempts to address the
challenges on-farm that were created by the use of AM and CC signalled a need for new
and salient market cues and the reconstruction of the functional characteristics and use
of technologies in a way that was meaningful for woolgrowers. The articulation and
analysis of challenges to the use of new agricultural technologies could be seen as a way
to facilitate their solution and to continue to positively engage industry participants in
the innovation process.
The study suggested attempts to introduce new OM technologies into the Australian
wool industry were not negotiated with end users and other key industry participants
(i.e. wool brokers, buyers and woolgrowers). It seems that, while potential users’ needs
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and perceptions of the new technology are important, so also are their interpretation
frameworks.
Industry participants’ interpretation frameworks underpin their perceptions of a new
technology’s attributes, which are presumed to drive their adoption decision (e.g.
Rogers 2003). However, in the light of the present evidence of diverse and evolving
interpretation frameworks mediating a range of technology actions, the present findings
suggested such perceptions are socially constructed and dynamic and, therefore, may
not be useful in predicting people’s post-adoption behaviour.
Industry participants seek to confirm their personal identity frames through the use of
new agricultural technologies. The present findings suggest industry participants
actively seek to confirm their personal identity frames through the use of new
technologies in their own context. In this respect, the findings do not support the
traditional agricultural innovation view that users passively accept new technologies.
This study suggested there is a need to understand how personal identity frames are
constructed and the role these frames play in technology use. Developers may need to
engage in the proactive management of users’ interactions with new technologies to
address challenges to industry participants’ self-conceptions that are created by new
agricultural technologies.
The analysis of the introduction of OM technologies into the Australian wool industry
showed how the introduction of these technologies created a major challenge to the
personal identity frames of industry participants along the wool supply chain. In the
early stages of the development and introduction of OM, Australian wool buyers
generally rejected new testing technologies in order to maintain self-conceptions of their
role in wool valuation in the auction system. The challenges that OM technologies
presented to wool buyers’ personal identity frames resulted in them refusing to pass on
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pre-sale test results to processor clients. The conflict between technology developers,
policy makers and wool buyers over the use of OM in the auction system suggested
challenges to industry participants’ personal identity frames were activated by the
introduction of these testing technologies. Wool buyers’ personal identity frames were
constructed around their ability to value wool lots based on their subjective appraisal of
wool fibre attributes, which were in direct contradiction with the introduction and use of
OM technologies. The scientists involved in the OM innovation initiatives did not
recognise that OM use was an activity different from technology development and
transfer. These findings imply that, not only the technology, but also new personal
identity frames aligned with new patterns of action and industry recipes, need to be
constructed as a shared activity among industry participants. To create such a
collaborative approach to agricultural innovation, historically accumulated conflict
between industry participant groups needs to be resolved. Resolution of such conflict
may help the successful construction of shared technology frames through which
industry participants can confirm that their personal identity frames are aligned with the
success of the industry.
Through their use of AM and CC on-farm, woolgrowers’ personal identity frames
shifted from their passive-actor base. However, the direction of this shift was uncertain
and perceptions of woolgrowers’ role in the wool marketing system was complicated by
inconsistent and conflicting feedback about the use of wool fibre testing technologies
from brokers, wool buyers, processors, technology developers and policy makers.
These findings suggested the enactment of new agricultural technologies, from initial
trial to collectively used tools reflecting standard industry practice, cannot be
understood solely in terms of the gradual adaptation of the technology by end-users. It
seems a qualitatively deeper integration process needs to take place between a
technology and the industry participants’ interpretation frames. This involves the co-
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evolution of industry participants’ personal identity frames and the construction of
shared technology frames among those people participating in the adoption,
implementation and use of the technology. In order to manage the co-evolution of
participants’ identity frames and shared technology frames, practitioners need to
understand end users’ personal identity frames and how these frames change over time
through people’s interaction with technologies. An ethnographic approach is probably
needed to examine the role, identity, expectations, experience and knowledge of new
technologies and how these factors relate to emerging technology frames. Simple,
variance type research that focuses on potential adopters’ perceptions of technology
attributes cannot provide such information.
Although OM and AM use was widespread in the Australian wool industry, the
development, transfer and use of these technologies did not occur smoothly or
simultaneously. The present study shows a lack of trust between key industry
participant groups and how the negative influence of industry participants with power
and interest in the OM innovation initiatives constrained the use of new technologies in
the early stages of the innovation process. A need for and a sense of shared
understanding and consensus around technology frames and industry belief systems
emerged over time, although not explicitly.
The case studies suggested there are problems connecting sensemaking at an individual,
group and industry level. For instance, the AWB and wool buyers opposed the
introduction of pre-sale OM in the 1960s. Wool buyers controlled the wool auction
system and prevented the results of OM guidance tests undertaken by woolgrowers
from being accessed by processors, constraining the use of objective measurements
along the wool supply chain. Technology developers did not attempt to negotiate a
shared understanding of OM technologies with wool buyers but sought, instead, to lock
them out of the innovation process by approaching wool processors directly. These
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issues in social sensemaking call for the development of ways to collectively understand
the challenges faced by industry participants at the individual, group and industry level.
Findings and implications in relation to the sensemaking process
Industry participants’ attempts to make sense of new agricultural technologies in their
own context are often ignored by technology developers and policy makers. In this
respect, this study supports Weick (1995) and Porac, Thompson and Baden-Fuller’s
(1989) research that examined the need for people to create environments that are
sensible to them and, in that way, produce part of the competitive environment they
face. It also suggests that social sensemaking in response to agricultural innovation
initiatives needs to be seen as a purposive activity that is undertaken so industry
participants can use technology effectively. That is, the effective use of new
agricultural technologies is intrinsically linked to transitional environments, industry
belief systems and further technological advances.
The diffusion of new OM technologies in the Australian wool industry reflected the
sense industry participants made of the physical artefact, functional characteristics, use
and evaluation of the technology; in other words their perceived technology frame. For
instance, the proportion, types, quality attributes and value of wool offered at auction
with AM and CC suggested the use of these technologies on-farm was a dynamic,
discontinuous and evolving process. The heterogeneous and evolving nature of
technology use was also highlighted by the actions of industry participants, such as
brokers and buyers and technology developers, in the development, introduction and use
of new testing technologies. These findings suggest the transfer of new agricultural
technologies from development to use requires the active construction of the salient
cues required by industry participants so they can make sense of the technology in their
own contexts. They also suggest changes need to take place in technology users’
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activities and environment if they are to extract maximum value from a new technology.
It was shown in this case, however, that control of salient technology cues was not in
the hands of technology developers and that the expertise and responsibility for
generating salient cues for industry participants to make sense of OM technologies was
weak or was ignored by technology developers. In order to support the effective use of
new agricultural technologies, technology developers and extensionists need to play an
active role in the construction and dissemination of such cues.
Problems in communication were found in interactions between industry participants in
the technology adoption, implementation and use process. Misinformation occurred in
dialogue between brokers, buyers and woolgrowers in the initial stages of the AM
innovation initiative. The distinct and separate use of new testing technologies by these
industry groups hampered the social construction of shared technology frames and
industry belief systems.
The enactment of AM on-farm introduced broader problems into the OM innovation
initiatives in terms of their use in wool marketing and wool production. In the initial
stages of technology development, researchers failed to recognise that OM technologies
should have been promoted as an opportunity for improved farm management, rather
than as an additional expense. While technology developers, brokers and buyers
focused on the use of AM in the wool processing system, woolgrowers struggled to
make sense of the technologies in the marketing and production of their clip. Although
scientists attempted to address the challenge of using the testing technologies in wool
production, these attempts appeared to be too little too late, as woolgrowers were either
unable or unwilling to extract salient cues from this work.
These findings have important implications for the agricultural innovation process.
They suggest that, during the development of new technologies, the focus needs to shift
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from the technological artefact itself to an understanding as to how industry participants
make sense of the technology in their own environment. Technology developers and
extensionists need to recognise that end users are not interested in the physical artefact
or its functional characteristics per se; they are interested in the use of the technology as
a tool to help them undertake their own work. The ability of all participants in the
agricultural innovation process (e.g. developers, extensionists, change agents and
farmers) to collaborate on how a technology can be used in a way that is flexible and
open to industry participants’ sensemaking is a vital part of a successful agricultural
innovation process. As already noted, the present study did not support the notion of
the innovation-decision process as a simple, linear, staged uni-directional decision
process (e.g. Rogers 2003; Jones 1967). Indeed, the case studies suggested the
enactment of new agricultural technologies on-farm reflects ongoing cycles of social
sensemaking as industry participants made sense of the technology in their own context
and, in effect, reconstructed technology frames and their environment.
Findings and implications in relation to constructing shared Technology Frames
The present study illustrated the complexity of agricultural innovation initiatives that
involve several technologies and various industry groups and participants. It also
showed how challenging attempts to introduce a new agricultural technology into a
complex and transitional environment can be to industry participants. These findings
call into question expectations placed on technology developers to define the physical,
functional characteristics and use of a technology. The present findings are more in line
with suggestions that technology frames are subjective and flexible and evolve through
social interaction (T. Pinch & W.E. Bijker 1987; Bijker 1999; Weick 1990; Constant
1980).
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More than pointing to a simple need for technology developers and extensionists to
impose their perceived technology frames on end users, the results reveal fundamental
limitations in a static perception of technology frames. In this respect, the idea of
flexible and negotiated technology frames, in terms of the physical artefact and its
functional characteristics and use, suggested by Orlikowski and Gash (1994) comes
close to the idea of new agricultural technology frames being socially constructed by
pointing out the need for collective sensemaking around the introduction of new
agricultural technologies.
The developers of OM technologies assumed the technologies would be widely used in
the wool marketing system. They focused on developing technological artefacts with
wool processors and made little attempt to resolve issues related to the use of testing
technologies in the auction system. It was clear the technological issues requiring
resolution were questions not only about how processors would employ OM
technologies, but also about how OM would be used to value, market and sell wool lots
in the wool auction system and how they would be used on-farm. It was shown OM
testing technologies and methods were given different meanings by different industry
participants, resulting in a range of technological artefacts, functional characteristics
and uses. This implies that shared agricultural technology frames can be uncertain,
unstable and intertwined with industry beliefs. Shared sensemaking processes are
needed to facilitate collaborative technology development and implementation.
Findings and implications in relation to Industry Belief Systems
The anticipated evolution of industry belief systems in response to the development and
introduction of new technology in terms of product ontology, industry recipes,
boundary beliefs and reputational rankings is built into the innovation process. The
present study suggested the evolution of industry belief systems during the innovation
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process is often complex and reflects conflict, consensus and compliance among
industry participants. In this respect, the findings do not support the view of
agricultural innovation as a simple, linear process of “moving scientific and technical
knowledge, ideas, services, inventions and products from the origin of their
development to where they can be put into operation” (Guerin & Guerin 1994, p. 550).
Porac, Ventresca and Mishina’s (2002) argument about the evolution of industry belief
systems through industry level sensemaking is more in line with the present findings.
However, they did not discuss the impact of the development, introduction and use of
new technologies on the evolution of shared product ontology, boundary beliefs,
industry recipes and reputational rankings. In this sense, the idea of technology frames
and industry beliefs systems co-evolving through the social sensemaking of industry
participants supported the idea of reciprocity between the social construction of new
technologies and industry patterns of actions (e.g. Coughenour 2003; Tanaka, Juska &
Busch 1999; de Souza & Busch 1998).
The development and introduction of OM opened a ‘black box’ of subjective raw wool
appraisal, marketing and valuation to woolgrowers and challenged the dominance of
wool buyers as information brokers in the supply chain. Thus, the introduction and
enactment of OM and AM influenced the social reconstruction of industry belief
systems in terms of broadening the ontology of Australian wool to include objective
measurements of fibre attributes for wool offered at auction. Researchers prompted the
renegotiation of the product ontology for Australian wool through their redefinition of
raw wool fibre as a textile product and the expansion of the boundaries of competition
for raw wool to encompass synthetic fibres, despite the negative response of other
industry participants. These changes in industry beliefs in the 1960s co-evolved with
emerging technology frames for OM and underpinned the rapid adoption of pre-sale
OM as a new industry recipe in the 1970s.
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The present findings suggest the evolution of industry belief systems did not take place
simultaneously or smoothly. The case studies revealed a variety of industry participant
responses to the attempts of technology developers to reconstruct industry belief
systems around objective measurements. Multiple industry participant groups with
divergent goals, beliefs, roles and motives towards OM technologies were involved in
the construction and reconstruction of industry belief systems. A gap was found in the
innovation process in relation to the ongoing negotiation of industry belief systems as
no industry group took responsibility for facilitating dialogue around the potential
impact of OM technologies on existing industry beliefs. The ensuing conflict between
developers, buyers, brokers and woolgrowers over the introduction and use of OM in
the wool supply chain implied support for the argument that industry discourse or
rhetoric externalises internal cognitions as public interpretations of industry events or
conditions (Rosa, Judson & Porac 2005). This supports the idea that industry belief
systems are evolutionary sensemaking schemata that reflect new contingencies,
participants, artefacts and discourse.
The co-evolution of shared OM technology frames and industry belief systems found in
this study corresponds with the idea of the interpretive flexibility of industry beliefs. It
appears that OM technologies were increasingly used in the wool auction system over
time as consensus between industry participants firmed around shared technology
frames and industry belief systems. These findings lend support to the idea of product
ontology moving towards closure as ‘common sense’ evolves around the definition of a
product and its market structure (T. Pinch & W.E. Bijker 1987; Bijker 1999; Porac,
Ventresca & Mishina 2002; Petroski 1993). In the present study, the evolution of
product ontology towards closure around OM occurred through an initial conflict
between industry participants that was eventually replaced by consensus and, on the part
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of woolgrowers, compliance. In this sense, the idea of industry participants’ responses
to industry belief systems and technology frames evolving out of conflict and into
consensus and compliance between industry participants suggests common sense is not
a prerequisite for collective action (Porac, Ventresca & Mishina 2002; Lant 2002).
8.2 Contributions to theory
The study provided preliminary evidence suggesting a number of contributions to
theory. The contributions of the research are discussed in this section in relation to
Innovation Diffusion Theory, Sensemaking and predictive behavioural models.
Contributions to innovation diffusion and adoption theory
The Bass diffusion model (Bass 1969) used to estimate the diffusion of AM and CC
testing technologies in Chapter 5 explained the trend adoption and abandonment paths
very well. The findings of this study supported the work of Bass, Krishna and Jain
(1994) on the robustness of the Bass diffusion model without additional decision
variables. This study contributes to the innovation diffusion research by successfully
extending the application of the Bass diffusion model to the prediction of patterns of
adoption and abandonment of new agricultural technologies, potentially addressing
previous challenges associated with the application of this model in the agricultural
innovation context (e.g. Akinola 1986). The integration of the Bass diffusion model
(Bass 1969) with Abrahamson’s (1991) typology of the diffusion and rejection of
innovations in Chapter 5 also offers an additional dimension to the use of diffusion
parameter estimates and the interpretation of external and internal diffusion influence
parameters.
The empirical findings in this thesis regarding the sensemaking process and the
adoption, implementation, abandonment and use of new agricultural technologies lend
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support to Seligman’s (2000; 2006) adopter-centred process model which maps
sensemaking concepts across Rogers’ (2003) innovation-decision model.
Contributions to Sensemaking
This study contributes to the sensemaking literature by extending the application of
sensemaking concepts to the agricultural innovation process. Further contributions to
sensemaking theory arising from this research include the development of a
comprehensive framework for the analysis of sensemaking at the individual, group and
industry levels in the context of innovation processes (as shown in Figure 3.3). The
application of this analytical framework in the three empirical studies resulted in a
series of propositions to guide further research and theory development in the area of
agricultural innovation sensemaking.
In Chapter 3 the issue of shared understanding and common sense was discussed as an
anticipated outcome of collective sensemaking (Weick, Sutcliffe & Obstfeld 2005;
Aydin & Rice 1992; Bettis & Prahalad 1995; Brown & Eisenhardt 1997). Lant (2002)
and Porac, Ventresca and Mishina (2002) have pointed out that the question as to
whether shared beliefs are a prerequisite for collective action and, indeed, whether the
concept of collective beliefs is meaningful, remains unanswered (Weick, Sutcliffe &
Obstfeld 2005). The empirical findings of the three studies suggested that collective
beliefs evolve at the group level through social sensemaking processes and that these
processes are largely driven by consensus among group participants. However, the
notion of collective beliefs or shared meaning at the industry level evolving as a result
of consensus among industry participants was not evident in this study. For example,
industry beliefs relating to the use of AM and CC appear to have evolved out of conflict
and compliance rather than consensus among industry participants. These findings
supported the notion that individual cognitions are not stable and industry beliefs about
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new agricultural technologies emerge as a result of a political, negotiated process with
outcomes influenced by powerful interest groups and coalitions.
Porac, Ventresca and Mishina (2002) have called for further research into tensions
between industry participants in the sensemaking process and how they are externalised
through conflict, consensus and compliance. They argued that sensemaking scholars
need to assume cognitions are not stable or complete at an individual actor level and
that greater focus is need to understand how patterns of conflict, consensus and
compliance in generic and extra-subjective sensemaking evolve over time. The
empirical findings in this study offer support to the research on collective sensemaking
that identifies ambiguity, compromise and duress as legitimate responses of industry
participants (Eisenberg 1984; Blumer 1969; Weick 1995).
Contributions to predictive behavioural models
The empirical findings in this research supported the proposition that technologies are
socially constructed by industry participants over time (Weick 1990). In the case of
OM innovation initiatives in the Australian wool industry, different industry
participants enacted new agricultural technologies in their own context, resulting in the
construction, adaptation and reinvention of shared technology frames. The new
technologies examined in this study were found to have different meanings to different
industry participants (e.g. Orlikowski 1992; Orlikowski & Gash 1994). As it was found
that interpretation frameworks, technology frames and industry belief systems change as
people enact new agricultural technologies in their own context. The study calls into
question the usefulness of predictive models of rational innovation adoption and use
behaviour, such as the Theory of Reasoned Action (Fishbein & Ajzen 1975), and
Theory of Planned Behaviour (Ajzen 1985) to forecast technology use behaviour from
pre-adoption behavioural intentions.
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The current study provides preliminary evidence that the adoption and use of new
agricultural technologies are influenced by different factors. While the adoption of AM
and CC by Australian woolgrowers was influenced by the social context of
woolgrowers, the use of these technologies was influenced by a combination of personal
identity frames and socials norms. These conclusions were drawn from longitudinal,
qualitative case study data. Cross sectional studies undertaken temporally throughout
the agricultural innovation process would provide additional quantitative evidence as to
the process through which personal and social interpretation frameworks are
constructed and evolve over time.
Studies of agricultural innovation that combine aspects of behaviour prediction models
(as shown in Figures 2.4 and 2.5) with the sensemaking concepts identified in this study
(as shown in Figure 3.3) and which use a combination of longitudinal, cross-sectional,
qualitative and quantitative research methods, may help to provide more conclusive and
generalisable evidence of how and why industry participants adopt and use new
agricultural technologies. For example, the personal identity frame and social context
concepts discussed in this study could be incorporated into behaviour prediction models
such as Bagozzi’s (2000) Modified Model of Goal-directed Behaviour (MMGB). This
model suggests that desires or goals are the cause of behavioural intentions, moderating
attitudes and normative beliefs, and that social identity is a determinant of desires and
behavioural intentions. Bagozzi (2000) developed the MMGB as an extension of the
TRA and TBP (see Figures 2.4 and 2.5) and an alternative theoretical framework for
understanding goal-directed action. The structure of this model presents a useful
framework for further analysis of the sensemaking concepts discussed in this study.
Figure 8.2 shows the MMGDB with additional social context and personal identify
frame sensemaking concepts. In this proposed theoretical model, personal identity
frames influence Bagozzi’s (2000) past behaviour construct as this sensemaking
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concept identifies the persons assumptions, expectations, experiences and knowledge of
the target technology and similar technologies as well as their current projects and
goals. The past behaviour construct in the proposed theoretical model reflects how past
technology adoption and use behaviour informs future intentions and behaviour as
described in the sensemaking process. Bagozzi’s (2000) social identity construct may
be a reasonable proxy for the social context sensemaking concept as it identifies group
belonging through self-categorization and group-based self-esteem. The perceived
behavioural control construct may be a reasonable proxy for the sensemaking concept,
‘extent of power and influence’.
Figure 8.2: Proposed modified theoretical model of the Modified Model of Goal
Directed Behaviour and sensemaking concepts
Attitude toward technology
adoption or use
Desire
Perceived
control of
adoption or use
behaviour
Intention to adopt or use technology
Adoption or use behaviour in
context
Positive anticipated emotions
Negative anticipated emotions
Subjective
group and
industry norms
Social Identity
Personal
Identity
Frame
Past technology
adoption or use behaviour
Self
categorization
Affective commitment
Group-based
self-esteem
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Models such as the MMGB with additional sensemaking constructs, as shown in Figure
8.2, could be applied temporally throughout the innovation process to reflect the
adoption, implementation and use of technologies on-farm in order to attempt to capture
the cyclical nature of agricultural innovation sensemaking.
The findings of this study indicate that the relationship between beliefs about
technology adoption and use and sensemakers’ interpretation frameworks should be
more closely investigated, along with the relationship between social norms, beliefs
about technology frames and industry belief systems. The work of researchers in the
area of Information Systems adoption and implementation offers some guidance as to
how to examine these relationships. In the Information Systems innovation context,
some progress has been made towards the conceptualisation and operationalisation of a
range of constructs antecedent to beliefs and social norms that relate to the
interpretation frameworks discussed in this study. For example, prior experience has
been found to be a key determinant of intentions and actual behaviour (Ajzen &
Fishbein 1980; Fishbein & Ajzen 1975). Taylor and Todd (1995) and Mathieson (1991)
have explored the role of prior experience in technology acceptance and offer guidance
on the operationalisation of this construct as an antecedent to beliefs. Further work is
required to define and operationalise the components of interpretation frameworks and
industry beliefs discussed in this study as variables antecedent to behavioural beliefs
and social norms in the agricultural innovation context.
8.3 Implications for policy and practice
The findings of this research also have important implications for the management of
agricultural innovation initiatives. The study has provided some preliminary evidence
concerning the dynamic, evolving and socially constructed nature of the agricultural
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innovation process. These insights into the agricultural innovation process are
important when considering the design of innovation initiatives and the development
and extension of new agricultural technologies, as they suggest that greater emphasis
needs to be placed on building flexible and collaborative approaches. The linear
agricultural innovation models that have dominated the Agricultural sector and continue
to be applied in the Australian wool industry assume a stable environment and the
passive acceptance of a superior technology. However, the empirical studies in this
thesis brought the validity of those assumptions into question.
The linear, technology transfer approach to agricultural innovation was established in
the 1950s and 1960s when policymakers sought to address market failure under the
assumption that the size of farm businesses was too small to justify their own research
infrastructure, creating a need for government intervention (Niosi 1999; Bozeman
2000). It can be argued that in the Twenty-first Century, agricultural industries are
operating in fundamentally different economic, social and technological environments.
For example, the Australian wool industry has been shaped over the last few decades by
seismic shifts in market demand, a decline in the political power of the industry,
increasing environmental uncertainty, rapid technological change and negative social
influence26 (Wool Industry Future Directions Task Force 1999). In this dynamic and
transitional environment, the outcomes of research and development are more risky and
uncertain, technology obsolescence is rapid and public funding of agricultural
innovation initiatives is lower. Moreover, policymakers are demanding greater
collaboration between public research agencies and private enterprise: greater
justification of how public funding of innovation initiatives has contributed to economic
26 Primarily as a result of the People for the Ethical Treatment of Animals (PETA) campaign against mulesing and opposition to the live sheep trade (Barber, M & Smart, A 2006, AWI performance review
2003 to 2006, Australian Wool Industry Ltd.)
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growth, industry competitiveness and social welfare is required (Wool Industry Future
Directions Task Force 1999).
The empirical findings presented in this study, combined with evidence of the
transitional political, economic, social and technological landscape of agricultural
industries, presents an opportunity for change in agricultural innovation policy and
practice. In the face of the challenges similar to those described above, commercial
research and development policy and practice has evolved from linear to more flexible
and collaborative models (Niosi 1999). These flexible and collaborative innovation
models have been referred to as the ‘fourth generation’ of research and development
(R&D) management approaches (Miller & Morris 1998). Fourth generation R&D
approaches emerged in the corporate research sphere in the 1980s and incorporate
systematic links between researchers in the public and private sectors and alliances
between producers and end users. Such approaches to innovation management seek to
incorporate the knowledge of researchers, users, suppliers, producers and competitors in
an expanded and boundary-spanning innovation arena (Niosi 1999; Miller & Morris
1998).
In fourth generation R&D management approaches, it is acknowledged that innovation
occurs within a network of relationships where the outcomes of innovation initiatives
depend to a great extent on the performance of other actors involved both directly and
indirectly in the innovation process (Miller & Morris 1998). These flexible and
collaborate innovation models emphasise direct collaboration with end-users and the
rapid evaluation of the performance of new technologies in situ (Miller & Morris 1998;
Niosi 1999).
The development and management of collaborative relationships, the ability to access
and address the unmet needs of target market segments and the speed of technology
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delivery to market are critical innovation management issues in the present agricultural
innovation system. As this study shows, the way to manage innovation initiatives that
can deliver effective technologies is becoming more complex. Research organisations
find themselves needing to engage more industry participants to fill in skill and
knowledge gaps in order to address increasingly complex environments. From an
innovation management perspective, these industry participants need to be engaged in
the innovation process earlier, which requires network management expertise.
Whereas traditional models of innovation management emphasised the need to avoid
disruption or change during technology development, more flexible and collaborative
models of innovation management seek to embrace change by continuing to develop the
technology concept as the needs of the end-user emerge (Miller & Morris 1998).
Innovation program participants actively manage changes in the direction of the
research and development process without a clear definition of the end-product. They
focus instead on rapid response and make effective feedback loops between adoption
and implementation a reality.
In flexible models of innovation management, technology development, adoption and
implementation are linked together and the project team addresses challenges associated
with these phases iteratively as they cycle between development and use and
incorporate feedback into further development (Miller & Morris 1998). This cycle of
‘design-build-test’ is repeated until the development and implementation of the
technology no longer overlap. The feedback loops in flexible innovation models reflect
ongoing cycles of social sensemaking discussed in this study. Such an iterative,
dynamic and collaborative approach to innovation makes it possible for rapid response
to changes in technological or environmental events. Niosi (1999) argued that the use
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of effective fourth generation R&D innovation management practices can result in a
reduction in R&D costs, reduced risk and uncertainty, the acceleration of innovation,
less duplication, improved collaboration and greater access to target markets.
There is evidence in the literature that some agricultural innovation programs are
moving towards more flexible and collaborative management models. An increase in
the number of participatory agricultural research and extension programs in Australia
and other countries suggests that the agricultural research sector may be moving
towards practices consistent with third and fourth generation R&D management
approaches (Miller & Morris 1998). Participatory approaches to agricultural extension
have been in use since the 1980s (Chambers, Pacey & Thrupp 1989; Chambers 1983;
Buttel, Larson & Gillespie 1990). Such research and extension models seek to engage
farmers in the generation and transfer of new technologies in order to achieve a better fit
between new technologies and diverse farming contexts (Martin & Sherington 1997;
Dunn, Gray & Phillips 2000). Implicit in participatory approaches to agricultural
innovation is the belief that the successful development and transfer of new agricultural
technologies is not simply a process of identifying and removing barriers to adoption,
but that “complex human systems and problems are involved, and the environmental
and social consequences of change are unknown” (Dunn, Gray & Phillips 2000, p. 18).
In a review of extension theory and practice Black (2000) identified some thirty-two
participatory research, development and extension methodologies employed in the
agricultural sector since the 1980s. Some participatory approaches, such as Rapid Rural
Appraisal, provide researchers with a rich picture of the farming context (Black 2000).
Other approaches, such as Participatory Action Research, engage farmers in on-farm
research to identify production problems and to generate context-specific solutions. In
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the Australian wool industry, participatory programs include Bestwool 201027 and
8X528 Wool for Profit.
Figure 8.2 shows the ‘farmer first’ participatory agricultural innovation model in which
farmers work with researchers to set the direction and priorities for innovation activities
(Knight 1980). The ‘farmer first’ model prioritises local knowledge and experience and
the adaptation of technologies to the local farming context (Pretty & Chambers 1993;
Cornwall, Guijt & Welbourne 1993). This approach attempts to provide an
environment for sharing ideas (Carr 1997), understanding complex problems (Frost
1998) and encouraging participant ownership of both the problems that they encounter
and the solutions that they have a hand in developing (Marsh & Pannell 2000).
Figure 8.1: ‘Farmer first’ approach to agricultural innovation (source: Knight
1980)
27 For a description of the Bestwool 2010 program refer to AWI Ltd at http://www.woolinnovation.com.au/Education/AWI_grower_networks/page__2170.aspx 28 For a description of the 8X5 Wool for Profit program refer to AWI Ltd at http://www.woolinnovation.com.au/Education/AWI_grower_networks/page__2165.aspx
Farm problems
Solutions sought by farmers, extensionists and researchers
Solutions attempted by researchers and farmers
Practical extension messages
Adoption on farm
Extension Groups
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Many agricultural research agencies have attempted to balance traditional linear,
technology transfer models with new participatory approaches to innovation, such as the
‘farmer first’ model (Vanclay 1994). However, some researchers have argued that
participatory approaches have been used as a means of reducing the cost of extension
rather than seeking to improve outcomes for farmers (Vanclay & Lawrence 1994;
Lawrence et al. 1992). The effectiveness of participatory agricultural innovation
approaches has been questioned in the literature. For example, Martin and Sherington
(1997) pointed out that participatory research and extension is viewed with caution by
much of the research community because of farmers’ increased influence over the
research agenda. In addition, participation in such programs trends towards larger,
more influential farmers who self-select on the basis of interest and capacity, creating
similar problems of exclusion generated by targeting ‘progressive farmers’ in the ToT
approach (Martin & Sherington 1997).
Given the problems associated with participatory agricultural research and extension
approaches, the successful incorporation of more flexible and collaborative R&D
models into agricultural innovation programs is likely to have a number of implications
for innovation management. First, as new technology concepts are developed, the
simultaneous exploration of market potential and identification of potential target
markets is required. Researchers, extensionists and market researchers need to work
together to find unmet needs in the market for the proposed technology concept.
Second, complex agricultural environments require funding and management models
that enable rapid response to environmental and technological events. This may require
the structuring of smaller and potentially more responsive multi-functional research
units that operate as small business units. Third, in publicly funded agricultural
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research organisations, a culture amenable to engendering a flexible and collaborative
approach to research, development and extension is required.
Active participation of research and extension program managers is critical to effecting
cultural change in the agricultural innovation system. There are many ways for a
program manager to exercise leadership beyond simply advocating collaboration and
the adoption of new technologies. One of the most effective ways to engage and
energise researchers and to engender cultural change is to incorporate flexible and
collaborative innovation model standards in the recruitment, selection, evaluation and
promotion of program staff. In other words, program staff need to be able to
demonstrate their capacity to collaborate with others in the effective development and
implementation of new technologies. However, program staff need to be assured that
more flexible and collaborative approaches to agricultural innovation will actually
enhance their performance and not detract from it. This is likely to be most relevant to
the management of scientists, who may be wary of the perceived impact of undertaking
marketing activities on their research reputation and career. Training people engaged in
research, development and extension is a critical element of implementing flexible and
collaborative approaches to agricultural innovation. Such training should include the
development of network management and collaboration capacity, the ability to assess
market need for new technologies, technology product development skills, the capacity
to assess and manage risks associated with agricultural innovation and intellectual
property management. For example the Innovation Excellence Program (IEP) delivered
by the Business School at the University of Western Australia provides skill
development for early career researchers in each of these areas, plus strategic innovation
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management, venture financing and business planning29. However, no such capacity-
building program currently exists in the Australian agricultural sector.
The constitution of Australian Wool Innovation Ltd (AWI) requires that it ‘facilitate the
dissemination, adoption and commercialisation of the results of research and
development and innovation in relation to the wool industry’ (Australian Wool
Innovation 2003b). Given that AWI tends to fund other agencies to undertake research,
development and extension, this constitutional requirement may be better served if the
requirement for performance evaluation against the adoption, implementation and
effective use of new technologies reached into all partner organisations. Traditional
measures of innovation success, such as the number of papers published, patents filed
and the predicted benefit-cost ratio, may not be adequate to capture the impact
innovation initiatives have on the performance of agricultural industries. Instead,
performance evaluation needs to assess the impact of new technologies by using a
combination of qualitative and quantitative evaluation techniques and the results of
program evaluation must be applied back into the system for future initiatives. One
method of innovation program evaluation with potential in the agricultural sector is the
R&D value mapping case study approach described by Kingsley, Bozeman and Coker
(1996) and Bozeman and Kingsley (1997). This methodology supports the evaluation
of the type and amount of value extracted from an innovation program and the reasons
for its success or failure. The R&D value mapping case study methodology is both
qualitative and quantitative, as it combines the development of detailed cases and
performance indicators tested as explicit models using a sequential path analysis. This
evaluation method can be used for both summative and formative program evaluation
(Kingsley, Bozeman & Coker 1996; Bozeman & Kingsley 1997).
29 For a description of the IEP refer to the UWA Business School at http://www.business.uwa.edu.au/home/executive_programs#innovation
- 292 -
8.4 Limitations of the research
There are several limitations of this research which need to be considered. First, this
study focused on a unique type of agricultural technology, objective wool fibre
measurements. Although woolgrowers were the ‘economic buyer’ of these technologies
they were used primarily by buyers and early stage processors. Therefore, the adoption
and use of wool fibre testing technologies may not reflect the adoption and use of all
other on-farm technologies or new farming practices. Second, this study was
undertaken within a single industry. It would only be possible to identify the extent to
which the Australian wool industry is indicative of other agricultural industries and
wool fibre testing technologies are indicative of other agricultural technologies through
further research. Third, it was not always possible to get first hand accounts of the
development, adoption and use of the wool fibre testing technologies from industry
participants as some of these technologies were developed and introduced several
decades ago. Fourth, this research utilised mainly qualitative research methods and it is
possible that the greater use of quantitative methods to explore sensemaking concepts
and test relationships between them would add a different perspective on the
development, adoption and use of wool fibre testing technologies.
8.5 Directions for future research
Social sensemaking concepts and theories have been applied in conceptual and
empirical innovation research, such as studies of the construction and transfer of new
technologies (Choo & Johnston 2004; Faraj, Kwon & Watts 2004; Guney 2004;
Theoharakis & Wong 2002; Dougherty et al. 2000), technology adoption (Griffith 1999;
Prasad 1993; Seligman 2000; Seligman 2006) and implementation (Orlikowski & Gash
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1994; Barley 1986; Leonard-Barton 1988). However, this is one of the first studies to
apply a social sensemaking framework to the agricultural innovation process.
The agricultural innovation process offered a transitional and complex context in which
to study the co-evolution of individual interpretation frameworks, technology frames
and industry belief systems. The anticipated development of new agricultural
technologies from scientific idea to technology to successful incorporation in industry
patterns of action is built into the agricultural innovation process. The sensemaking
framework applied in this study offered comprehensive theoretical and methodological
resources for studying the agricultural innovation process. The ongoing, social and
enactive nature of sensemaking offered a new perspective of complex and dynamic
agricultural innovation processes. The challenges in the agricultural innovation process
that are faced by industry participants were viewed in the light of their interpretation
frameworks, technological frames and industry belief systems. The sensemaking
framework offered analytical resources for studying technology use in context and the
interactions between industry participants in relation to their technology actions. It
helped in the analysis of the challenges in the innovation process, as well as opening up
opportunities for collective sensemaking and further technology and industry
development that are inherent in the innovation process.
The study was an empirical case analysis of selected OM innovation initiatives in the
Australian wool industry. The study provided new insights into how industry
participants make sense of agricultural technologies in their own context and suggested
agricultural innovation is not a smooth, simple transfer of technology from developers
to end users, but a political, negotiated, ongoing, evolving process that can be
conceptualised as a series of social sensemaking cycles, through which shared
technology frames and industry belief systems are socially constructed and
reconstructed over time. The study suggested opportunities for the further systematic
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investigation of the agricultural innovation process and how people make sense of new
technologies in their own context. Further research questions could be related to
broader agricultural innovation issues and the dynamics between individual, group and
industry level sensemaking, as the problem of moving new technologies from
development to effective end use is common in agricultural innovation research and
practice. The dynamics of the construction of a ‘common sense’ closing around shared
technology frames and industry belief systems are generally not examined in detail in
agricultural innovation studies. Interesting questions for future research include the
examination of the environmental, technological or dialogical events and actions that
support or constrain the development of shared technology frames and industry belief
systems in the agricultural innovation context.
Studies of agricultural innovation have neglected end users’ (i.e. farmers’)
instrumentality in the construction and reconstruction of new technologies through on-
farm use. In the present study, woolgrowers efforts to use new technologies in a
meaningful way on-farm were documented and analysed. End users have practical
knowledge and understanding of the use and impact new agricultural technologies have
in their own farming context. This contextual knowledge and understanding is vital for
the success of agricultural innovation initiatives and should be acknowledged by
technology developers, policy makers and extensionists. An important question for
further research concerns how end users’ capacity to reconstruct new agricultural
technologies can be identified, examined and incorporated into the technology
development process. Equally important, is the issue of finding out how technology
developers’, policy makers’ and extensionists’ ability to recognise gaps between the
technology and end user context can be improved.
In the present study, various sensemaking challenges in the agricultural innovation
process were identified. The research was reflective and historical in nature and was
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not intended to be interventionist. However, the woolgrowers, wool selling brokers and
industry group representatives who were interviewed found the conversations around
OM innovation initiatives useful for exploring and challenging how they make sense of
new technologies. Therefore, finding ways to develop methods that encourage such
dialogue across industry participant groups is an area worthy of further study.
Another interesting question is whether the challenges and actions found in this study
were unique to the OM innovation initiatives and the Australian wool industry or
whether they are common to other agricultural innovation processes. The particular
technologies examined significantly impacted on the wool supply chain and auction
system. Collective actions towards OM technologies were needed at an industry level.
However, OM technologies may not be the only agricultural innovation initiative in
which these issues are important and there may be industries outside the agricultural
sector for which this process is relevant. Therefore, social sensemaking in response to
the development and introduction of new technologies needs to be studied across a
range of technologies and industries.
Although the innovation process does not appear to be the same experience for every
industry participant, the significance of these differing experiences has not been
examined closely in empirical studies of agricultural innovation. The sensemaking
framework used in this study offered a socio-technical approach that helped reveal the
social dynamics of agricultural innovation. It showed how a sensemaking perspective
of agricultural technology development, introduction, adoption and use can highlight
the opportunities and challenges faced by industry participants. The proposed
sensemaking framework can be used to examine and support agricultural innovation
processes from their inception to end use. It has the potential to be applied and further
developed as a unifying socio-technical conceptualisation of the agricultural innovation
process.
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This study demonstrates that the interpretation of and behaviour towards new
agricultural technologies is not static. As a result, cross-sectional studies of technology
adoption are unlikely to fully capture the dynamic and complex nature of technology
adoption, implementation and use. Longitudinal studies that examine how
interpretation frameworks, industry belief systems and shared technology frames co-
evolve over time are likely to provide a more rigorous test of the agricultural innovation
process. Where cross sectional approaches are used, innovation models should be
tested at a series of points in time to give an idea of the factors that influence different
phases in the agricultural innovation process.
Conclusions
To conclude, the examination of OM innovation initiatives in the Australian wool
industry provided an opportunity for developing, applying and empirically testing a new
framework for studying the agricultural innovation process. However, the study is only
an introduction to a social sensemaking perspective of agricultural innovation. The
agricultural innovation sensemaking framework needs to be further tested with other
cases in empirical studies of the agricultural innovation process.
The findings in this study are not statistically and empirically generalisable to the
population of new agricultural technologies and innovation processes. Rather, an
attempt has been made to apply sensemaking concepts and theories to the agricultural
innovation process in order to reveal its complexity and to make meaning from it. By
building on prior theoretical and empirical agricultural innovation and sensemaking
research, an attempt was made to identify and examine the most important phenomena
and relationships in the agricultural innovation process. It is hoped the study has shed
light on critical challenges and issues in the agricultural innovation process and offered
- 297 -
suggestions for further research and practice that will address the challenges and issues
that future agricultural innovation initiatives may also face.
In summary, the study provided several empirically useful new insights into the
agricultural innovation process, especially on the process through which industry
participants make sense of new technologies in their own context. The agricultural
innovation sensemaking framework presented in this study appears relevant and
applicable for examining the ongoing, dynamic and socially constructed processes of
agricultural innovation. It is especially useful for studying the enactment of new
technologies on-farm. However, the study is only an introduction to a social
sensemaking perspective to agricultural innovation. The agricultural innovation
sensemaking framework needs to be further tested with other cases in empirical studies
of the agricultural innovation process.
- 298 -
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Appendix A
Glossary of Wool Industry Terms30
Airflow technology
The dominant method of measuring mean fibre diameter of a wool sample.
Animal husbandry
The agricultural practice of breeding and raising livestock.
Appraisal A subjective estimate of the value characteristics of a parcel of wool.
Australian clip
The total amount of wool shorn from all Australian sheep flocks.
Australian wool auction database
Database owned and operated by the Department of Food and Agriculture, Western Australia (DAFWA) that comprises all subjective and objective data relating to wool sale lots offered in the Australian wool auction system.
Australian Wool Testing Authority Ltd Established in 1957 by the Australian Government as a statutory authority, to control and administer wool testing services in Australia. AWTA Ltd established as a Company Limited by Guarantee in 1982.
Breeding index (ram)
A weighted index constructed from the Estimated Breeding Values for individual animal traits used to simplify the process of ram selection.
Bulk classed lots A sale lot blended from various sources but from a single country of origin.
Clean colour The colour of wool after scouring which may be expressed by two measurements; brightness and yellowness.
Clean wool
Scoured wool less charges and loss incurred in carding, the basis on which the price of wool is set.
Clean yield
The percentage of clean wool that is expected to be retrieved from a delivery of greasy wool when processed.
30 Definitions for this glossary obtained from the following sources: (1) Australian Wool Corporation 1986, Glossary of terms for Additional Measurement, Australian Wool Corporation, Parkeville, VIC.(2) Australian Wool Testing Authority Ltd Glossary of Terms (www.awta.com.au), (3) Botkin, MP, Field, RA & Johnson, CL 1988, Sheep and wool: Science, production and management, Prentice-Hall.
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Clip preparation
Process of sorting, grading and identifying wool after shearing to separate wool from different breeds of sheep and produce lines of wool with uniform colour, length, vegetable matter and fibre diameter.
Combing wool
Wool suitable for conversion into yarn (generally wool with a staple length of 40mm or greater)
Coresample
A representative sample of greasy wool extracted from each bale in a sale lot using coring techniques.
Coretest
A series of objective measurements (Wool Base, Vegetable Matter Base and Mean Fibre Diameter) carried out on a coresample.
Cotts/Cotted wool
Wool that has become partially felted/matted on the sheep.
Crimp
A regular undulation along the length of a wool fibre, usually taken as an indicator of mean fibre diameter.
Crutchings
Wool removed from around the tail and between the rear legs of the sheep. Fleece skirting
The removal of sweat tags and other undesirable parts of the fleece from around the edges of the fleece.
Greasy wool (or raw wool)
Wool in its natural state taken directly from the sheep containing contaminants such as yolk, grease, dirt, dried sweat (suint) and moisture.
Hauteur
The mean fibre length in wool top.
Interlotting Sale lot comprising bales matched before testing from different clips.
Mean fibre diameter
Mean thickness of the wool fibre, quoted in microns (micrometers). Mechanical tuft sampling machine (MTS)
Device used to mechanically obtain representative tufts of wool staples from a grab sample.
Merino sheep
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Most numerous breed of sheep in Australia producing white, high quality and heavy cutting fleece.
Micron
Commonly used term for the unit of measurement of fibre diameter, correctly termed ‘micrometer’.
Noil The short wool fibres removed during the combing process.
Objective Measurements
The specification of the characteristics of greasy wool by measurement rather than by subjective description.
Position of break
An indication of where a staple breaks during extension.
Sale by private treaty Grower-negotiated direct sale of wool from the farm to a private buyer or processor.
Romaine
The amount of noil produced during the combing process, expressed as a percentage of the total top and noil.
Sale lot A number of bales of similar wool type and mass from the same country of origin prepared to accepted trade standards.
Sound wool Wool which is unlikely to break during the combing process in manufacturing.
Staple
A well-defined bunch of wool fibres removed from a mass of greasy wool as a unit for testing.
Staple length The length of a staple obtained by measurement without stretching the fibres.
Staple Strength The maximum force (in Newtons/Kilotex) required to rupture the staple.
Superfine Merino wool
Wool with an average fibre diameter of 17.6 to 18.5 microns.
Tender wool Wool that displays a significant proportion of staples with marked points of weakness and is likely to break during the combing process.
Top
Sliver of wool fibres that form part of the starting material for processing.
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Vegetable Matter Vegetable contaminants such as burrs, twigs, seeds, leaves and grasses present in greasy wool.
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- 330 -
Appendix B
Major events in the evolution of OM in the Australian Wool Industry31
Year Analytical Category Industry Participant
Group
Chronology of Events
The wool industry responds to increasing competition from synthetic fibres (1957-1968)
Environmental Event (Political, Technological) Technology Frame (Use) Industry Beliefs (Industry Recipe)
Australian Commonwealth Government
Australian Wool Testing Authority (AWTA) is established at the request of wool exporters, wool testing is limited to the moisture content/regain of scoured and carbonised wool (Dixie 1958; Sommerville 2002; Ward 1969).
Environmental Event (Technological), Technology Frame (Use) Industry Beliefs (Industry Recipe)
Statutory Industry Organisation The AWTA adopt existing core-testing and Airflow technologies and commence Post-sale Objective Measurement (OM) of Australian greasy wool for yield and fibre diameter (Welsman 1981; Dixie 1958).
Environmental Event (Political) Industry Beliefs (Industry Recipe)
Australian Commonwealth Government
The Wool Research Trust Fund is established to fund research and promotion with woolgrower levies (Phelp, Butterfield & Merry 1962).
Technology Frame (Physical Artefact, Functional Characteristics, Use)
Research Organisation The University of New South Wales (UNSW) improves yield testing methods with a new test for variable moisture content in greasy wool (McMahon 1957).
Industry Beliefs (Industry Recipe)
Research Organisation The Commonwealth Scientific and Industrial Research Organisation (CSIRO) find evidence that subjectively appraised Australian wool is being scoured unnecessarily for Vegetable Fault (VF) (Pressley 1957).
1957
Industry Beliefs (Product Ontology)
Wool Processors, Wool Buyers Mean Fibre Diameter (MFD) expressed in micron gradually replaces Quality Number (QN) in buyer and processor transactions and contracts (Skinner 1960).
External Cues (Political) Woolgrowers The Federal Council of Graziers Association supports increased spending on research, development and
31 The citations in Appendix A have been included in the preceding referee list.
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Industry Beliefs (Industry Recipe) promotion through woolgrower levies but argues against further Government interference in the wool industry (Falkiner 1958).
Environmental Event (Political)
Australian Commonwealth Government, Research Organisation
The CSIRO Division of Textile Physics (DTP) is established to develop new textile sampling, testing and processing technologies.
1958
Technology Frame (Physical Artefact)
Research Organisation, Statutory Industry Body
The AWTA, New Zealand Wool Testing Authority (NZWTA) and CSIRO develop and introduce new manual pressure core-testing technology (NSW Department of Technical and Further Education 1976) (Taylor 1988).
Technology Frame (Use) Industry Beliefs (Industry Recipe)
Statutory Industry Body The Wool Tariff Board recommends the use of Objective Measurements (OM) in the preparation and sale of the Australian clip (Boyer 1959).
1959
Industry Beliefs (Product Ontology, Industry Recipe)
Research Organisations The UNSW and Australian Textile Colleges commence the publication of research questioning the relationship between crimp frequency and mean fibre diameter and the accuracy of the subjective appraisal (SA) of greasy wool attributes in comparison with OM (Boyer 1959; Lang 1961; Skinner 1964; Chapman 1964).
Technology Frame (Use) Industry Beliefs (Industry Recipe)
Statutory Industry Body – Testing The AWTA commences post-sale OM for greasy wool yield, Fibre Diameter (FD), Fibre Length (FL) and Fibre Strength (FS) (Ward 1969).
Industry Beliefs (Industry Recipe) Research Organisation The Bureau of Agricultural Economics (BAE) publishes evidence supporting the use of bulk classing or interlotting to reduce the number of small wool lots offered at auction (Whan 1960).
Industry Beliefs (Industry Recipe) Research Organisation Researchers at the UNSW call for improvements in the preparation of the Australian clip (Paynter 1960).
1960
Industry Beliefs (Product Ontology)
Wool Selling Brokers The National Council of Wool Selling Brokers publish guidelines for fleece judging at agricultural shows based on subjective appraisal (National Council of Wool Selling Brokers of Australia 1960).
Industry Beliefs (Product Ontology, Industry Recipe)
Research Organisation The UNSW publish evidence that processors continue to place orders for wool by QN and top-making style and sort wool consignments using subjective appraisal of crimps per inch. They find that 45 per cent of SA’s of greasy wool underestimate clean yield (Skinner 1961).
Industry Beliefs (Product Ontology) Technology Frame (Use)
Textile Industry Organisation – testing standards, Wool Processors
The International Wool Textile Organisation (IWTO) and Bradford Spinners oppose the use of scales which relate fibre thickness to QN (Lang 1961).
1961
Technology Frame (Functional Characteristics, Use, Performance Evaluation)
Textile Industry Organisation – testing standards
The IWTO certify the standard for clean yield testing (McKenzie 1972).
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Environmental Event (Political, Economic)
Australian Commonwealth Government
The Government establish a Committee of Enquiry into Australian wool marketing in response to woolgrowers concerns about wool prices (Phelp, Butterfield & Merry 1962).
Technology Frames (Physical Artefacts)
Research Organisation The Gordon Institute identify a range of testing instruments developed to measure mean fibre diameter, including: micro projection, micrometer, Airflow, weight-length, optical inference, sedimentation of fibres in liquid medium, photometric measurement, vibroscope, electronic fibre profile scanning (Lang 1961).
Environmental Event (Political) Australian Commonwealth Government
The AWTA is abolished and replaced by a new AWTA as a statutory body operating under the Australian Wool Board.
Environmental Event (Political, Economic)
International Industry Body The International Wool Secretariat (IWS) encourage the Australian wool industry to increase the production of wool and support further investment in research and promotion in order for wool to capture a larger share of the textile market (Vines 1962).
1962
Industry Beliefs (Industry Recipe, Product Ontology)
Australian Commonwealth Government
The Government appointed Wool Marketing Committee of Enquiry criticises the existing wool marketing and selling system and recommends a central system of appraisal of Australian wool, the use of OM in the sale of the Australian clip and improvements to clip preparation (Phelp, Butterfield & Merry 1962; Merry 1963).
Environmental Event (Political) Australian Commonwealth Government
The Australian Wool Board (AWB III) is established to replace the Australian Wool Bureau.
Industry Beliefs (Product Ontology, Boundary Beliefs)
International Industry Body The IWS launch the Woolmark brand (Australian Wool Services Limited 2004; Richardson 2001).
Industry Beliefs (Industry Recipe) Technology Frames (Use)
Statutory Industry Organisation The AWB III introduce a voluntary register for wool classers, a clip inspection service and clip preparation standards in response to the Committee of Enquiry report and criticisms of the quality of Australian clip preparation from the trade (Australian Wool Board 1963).
1963
Environmental Event (Political) Industry Beliefs (Industry Recipe)
Australian Commonwealth Government
The Minister for Primary Industry supports improvements to the preparation of the Australian clip (Adermann 1963).
Industry Beliefs (Product Ontology, Industry Recipe)
Wool Processors British wool processors support improvements to the preparation of the Australian clip as long as the uniformity of wool attributes in lots is maintained (Ponting 1964).
1964
Industry Beliefs (Product Ontology)
Research Organisation The BAE identify the emergence of two distinct nomenclatures, subjective and objective, in the Australian wool industry used to describe greasy wool product (Whan 1964).
1965
Environmental Event (Political, Economic) Industry Beliefs (Industry Recipe)
Australian Commonwealth Government
The Government hold a referendum for woolgrowers to vote on the introduction of a Reserve Prices Plan proposed by the AWB, the plan is rejected by a small majority.
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Technology Frame (Physical Artefacts)
Research Organisations The UNSW and CSIRO collaborate on the development of technologies and methods for pre-sale OM (Charlton 1965; MacKay & David 1965).
1966
Industry Beliefs (Industry Recipe) Industry Statutory Body The AWB establish ad hoc committees to examine private wool selling, small lots in the auction system, bulk classing and clip preparation.
Industry Beliefs (Industry Recipe) Industry Statutory Body The Australian Wool Industry Conference recommends the equal division of levy and taxpayer funds to wool research and promotion.
Environmental Event (Political) Australian Commonwealth Government
The Government increase contributions to research and development in the Australian wool industry.
Technology Frame (Use) Industry Beliefs (Industry Recipe)
Industry Statutory Body
The AWTA reports that 23 per cent of the global supply of greasy wool is being tested for yield and FD post-sale (Ward 1969).
Technology Frame (Use) Australian Woolgrowers Some woolgrowers begin to request OM as guidance tests on their sale lots (Ward & Somerville 2003; Morgan 2003).
Industry Beliefs (Product Ontology, Industry Recipe)
Research Organisation The UNSW publish evidence that woolgrowers are being penalised in the market for producing heavier cutting fleece with fewer crimps per inch despite this not being a reliable measure of FD (Whiteley 1967).
1967
Technology Frame (Functional Characteristic, Use)
Research Organisation The CSIRO improve the accuracy of core testing to achieve the specified precision required for pre-sale OM (David 1967).
Industry Beliefs (Industry Recipe) Statutory Industry Bodies The Australian Wool Industry Conference accepts the AWB’s proposal for a non-statutory marketing organisation (Australian Wool Marketing Corporation Pty Ltd) after four meetings and conflict between various industry participant groups (Baxter 1970; Whan 1968a).
Industry Beliefs (Industry Recipe) Technology Frame (Use)
Wool Processors The use of OM in the supply of wool tops becomes common practice in the wool textile industry (MacKay 1968).
Industry Beliefs (Product Ontology, Industry Recipe) Technology Frame (Use)
Research Organisation The BAE support the introduction of pre-sale OM as the basis for classing and valuing the Australian clip (Whan 1968a).
1968
Technology Frames (Physical Artefacts, Functional Characteristics, Performance Evaluation)
Wool Testing Standards Organisations
The wool industry continues to use a range of methods of testing for clean yield (American Standards, British Wool Federation, IWTO, ATA and Japanese Clean Scoured yield testing method) (Douglas 1968).
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Technology Frame (Functional Characteristics, Use, Performance Evaluation)
Textile Industry Organisation – testing standards
The IWTO certify standards for fibre diameter measurement (Douglas 1968; McKenzie 1972).
Year Analytical Category Industry Participant
Group
Chronology of Events
Pre-sale Objective Measurements and Sale by Sample are introduced into the Australian Wool selling system (1969-1979)
Industry Beliefs (Product Ontology, Industry Recipe)
Statutory Industry Body The AWB establish two Committees to investigate pre-sale OM. The Committees include representatives from grower, broker, buyer and scientific organisations. However, the AWB continue to oppose the introduction of pre-sale OM, interfere with the Committee and are criticised for failing to provide technical support (McKenzie 1972; Ward 1969; Whan 1973).
Industry Beliefs (Product Ontology, Industry Recipe)
Statutory Industry Body – Testing The AWTA calls for greater industry cooperation to support the development, introduction and adoption of OM along the wool supply chain (Ward 1969).
Industry Beliefs (Product Ontology, Industry Recipe)
Wool Selling Brokers Brokers continue to support the existing wool marketing and selling system and express concern over the introduction of pre-sale OM and OM test results appearing in the sale catalogue (Higginson 1969).
Environmental Event (Technology) Statutory Industry Authority (NZ)
Wool Research Organisation New Zealand (WRONZ) report on the successful trial of pre-sale OM and Sale by Sample (SXS) in NZ (Fraser 1969).
Technology Frame (Physical Artefacts)
Research Organisation The CSIRO develop a new washer/dryer for clean yield testing, a sonic fineness tester and prototype core sampling machine (Downes 1969).
Industry Beliefs (Industry Recipe) Technology Frame (Use)
Research Organisation The UNSW publish evidence that well prepared wool does not attract price premiums at auction and propose that OM be used in wool classing on-farm (McMahon 1969).
1969
Technology Frame (Performance Evaluation)
Statutory Industry Body – Testing The AWTA calls for the IWTO to unify all available test methods for clean yield into a single standardised test (Ward 1969).
Environmental Event (Economic) Woolgrowers The number of sheep in Australia reaches an all time high at 180 million head (Vamplew 1987).
Environmental Event (Political) Australian Commonwealth Government
The Australian Wool Commission (AWC) is established to replace the AWB.
1970
Industry Beliefs (Industry Recipe, Product Ontology)
Statutory Industry Body, Research Organisations
The AWC launch the Australian Objective Measurement Project (AOMP) in collaboration with representatives from all sectors of the Australian wool industry. The aim of the AOMP is to investigate the technical and
- 335 -
Technology Frame (Use) organisational aspects of introducing pre-sale OM as an aid to marketing. The CSIRO is given responsibility for providing facilities for program research, (McKenzie 1972; Morgan 2003).
Environmental Event (Political, Economic) Industry Beliefs (Industry Recipe)
Australian Commonwealth Government, Statutory Industry Body
A temporary Wool Deficiency Payment Scheme is introduced (Ward 1985).
Industry Beliefs (Industry Recipe)– Woolgrowers The NSW Graziers Association supports the introduction of an incorporated Australian Wool Marketing Corporation as opposed to a statutory authority and argues that market reform is opposed by wool buyers and brokers (Baxter 1970).
Industry Beliefs (Industry Recipe) Technology Frame (Use)
Research Organisation, Woolgrowers
Economic Wool Producers (woolgrower marking co-operative) support the introduction of pre-sale OM and acknowledge that the majority of the wool industry opposes the introduction of pre-sale OM (Maple-Brown 1970). The UNSW continues to support the use of OM in clip preparation and marketing (McKinnon 1970).
Industry Beliefs (Industry Recipe) Technology Frame (Use)
Research Organisation The UNSW and BAE publish further evidence that well prepared lots do not achieve a price premium in the market and support the use of OM in clip preparation (McMahon 1970; Whan 1970; Roberts 1970).
Industry Beliefs (Product Ontology, Industry Recipe) Technology Frame (Use)
Statutory Industry Bodies, Research Organisations
Pre-sale OM and Sale-by-Sample (SXS) is trialled in the Australian wool industry.
Environmental Event (Economic) Woolgrowers A 12 year decline in wool production begins as a result of market price fluctuations (Vamplew 1987).
Technology Frames (Physical Artefacts)
Research Organisation The CSIRO develop new grab sampling technology, a wool base analyser and sonic fineness tester (Baxter 2002; James & Stearn 1971).
1971
Industry Beliefs (Product Ontology, Industry Recipe)
Research Organisation The BAE propose a three stage introduction of pre-sale OM: (1) traditional selling of wool with core tests, (2) SXS, (3) Sale by Description (SXD) (Whan 1971).
Industry Beliefs (Industry Recipe) Technology Frame (Use)
Statutory Industry Body The AWC report that forty per cent of Australian wool is post-sale tested for yield and FD (McKenzie 1972).
Industry Beliefs (Product Ontology, Industry Recipe) Technology Frame (Use)
Statutory Industry Bodies, Research Organisations
Pre-sale OM and SXS are introduced in the Australian wool industry as an optional marketing method, 0.6 per cent of the Australian clip is pre-sale tested and sold by sample in 1972. The AWTA adopt CSIRO instruments and their own designs for testing (MacKay 1973; Asimus 1987; Taylor 1988; Butcher 1983; Sommerville 2002; MacKay 1972).
1972
Technology Frame (Physical Artefact)
Research Organisation The CSIRO develop the Fibre Diameter Video Analyser (FIDIVAN) (Baxter 2002).
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Industry Beliefs (Industry Recipe) Statutory Industry Body The AWC propose the introduction of new Objective Clip Preparation (OCP) methods including new standards
and training for wool classers (Dalton 1972).
Technology Frame (Use, Physical Artefact)
Research Organisation, Statutory Industry Body
The AWTA adopt a new core test procedure developed by NZWTA. The CSIRO commence work on the development of the Almeter for measuring Staple Length (SL) and Fibre Length (FL) (MacKay 1972).
Technology Frame (Use) Woolgrowers EWP Ltd adopts pre-sale OM and OCP (Maple-Brown 1972).
Industry Beliefs (Product Ontology, Industry Recipe)
Research Organisations The BAE and UNSW publish evidence that clips prepared using OCP are being discounted by wool buyers because of visual variability in lots (McMahon 1972; Whiteley 1972).
Industry Beliefs (Industry Recipe) Technology Frame (Use)
Research Organisation The CSIRO publish research on feedback from processors which concludes that many classed lines are identical and that the number of lines produced with traditional classing can be reduced (Anon 1973).
Technology Frame (Physical Artefacts)
Research Organisation The CSIRO develop the following testing technologies: washer for core samples, method for testing fibre fineness and yellowness using radioisotope technology, methods and technologies for testing scoured wool colour using colour grading, Laser Fibre Fineness Distribution Analyser (FFDA) tester, grab and T core sampling technologies (Connell & Mackay 1973; Downes 1973; Jackson 1973; Lynch & Michie 1973; MacKay 1973).
Environmental Event (Political) Industry Beliefs (Industry Recipe)
Australian Commonwealth Government
The Australian Wool Corporation (AWC) is established to replace the Australian Wool Commission (L. White 1981; Piggott 1998).
Industry Beliefs (Industry Recipe, Product Ontology) Technology Frame (Physical Artefacts)
Statutory Industry Body, Research Organisation
The AWC report on the progress of the introduction of SXS and propose that the industry move towards SXD. The CSIRO are tasked with the development of testing technologies and methods for SXD (MacKay 1973).
Industry Beliefs (Industry Recipe, Product Ontology)
Wool Selling Brokers Brokers support pre-sale OM and SXS but oppose the introduction of SXD (Hamilton 1973).
1973
Industry Beliefs (Industry Recipe) Research Organisation The BAE introduce the concept of Radical Clip Preparation which is intended to replace OCP when the industry moves towards SXD (Whan 1973).
Industry Beliefs (Industry Recipe) Wool Buyers The Australian Council of Wool Buyers question the value of OM and urge the industry to take caution with regards to the proposed introduction of SXD (Booth 1974; James 1974).
1974
Industry Beliefs (Industry Recipe) Technology Frame (Use)
Research Organisation The BAE report on the adoption and use of OCP and traditional clip preparation standards in Australia, 40 per cent of the clip is found to be classed using OCP. BAE calls for tighter clip preparation standards and more
- 337 -
formal classer training to support trade acceptance of SXS and SXD (Jenkins 1974).
Environmental Event (Political, Economic) Industry Beliefs (Industry Recipe)
Australian Commonwealth Government, Statutory Industry Body
The AWC introduce a permanent Wool Reserve Price Scheme (WRPS) is in the Australian Wool Industry (Department of Agriculture Fisheries and Forestry 2001; Richardson 2001; Ward 1985)
1975
Industry Beliefs (Industry Recipe) Technology Frame (Use)
Research Organisation The CSIRO undertake trials of the processing performance of wool prepared with OCP to gain trade support (Rottenbury & Andrews 1975; Downes 1975).
Technology Frame (Physical Artefact)
Research Organisation, Statutory Industry Body
The CSIRO and AWTA develop and trial liquid scintillation spectrometry method for measuring MFD, and develop testing technologies for SL and Staple Strength (Rottenbury & Andrews 1975; Jackson & Downes 1975).
Technology Frame (Functional Characteristics, Use, Performance Evaluation)
Textile Industry Organisation – testing standards
The IWTO certify standards for FD measurement using Airflow technology (Baxter 2002).
Technology Frame (Use) Research Organisation Approximately 55 per cent of the Australian clip is sold by sample with OM, five per cent of clip is prepared using OCP (Welsman 1981).
1975
Industry Beliefs (Industry Recipe) Statutory Industry Authority The Australian Wool Measurements Standards Authority (AWMSA) institutes a new code of practice for handling samples for wool fibre testing (James 1975).
Industry Beliefs (Industry Recipe, Product Ontology)
Statutory Industry Body, Research Organisation
The UNSW and AWC support the move towards SXD and the development of testing technologies for SA fibre characteristics. However, there is clear evidence of opposition to SXD from wool selling brokers and buyers (Whiteley 1975; Asimus 1976).
Technology Frame (Physical Artefact)
Research Organisation The CSIRO develop the Fibre Diameter Analyser (FDA), technology for sample blending and new test methods for the SA of VF types (Buckenham & Stearn 1976; Connell 1976; Baxter 2002).
Technology Frame (Use) Wool Processors Evidence that Japanese wool processors have not widely adopted OM (Gendall & Tier 1976).
Industry Beliefs (Industry Recipe, Product Ontology)
Woolgrowers EWP Ltd supports the move towards SXD (Maple-Brown 1976).
Technology Frame (Use) Statutory Industry Body, State Departments of Agriculture, Selling Brokers
The AWC publish evidence of low levels of adoption of OCP by Australian woolgrowers. The AWC, State Departments of agriculture and Brokers undertake an extension program to increase adoption of OCP (Welsman 1976).
1976
Technology Frame (Use) Industry State Department of Agriculture The NSW Department of Agricultural introduce a fleece testing service at Trangie to promote the use of OM in
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Beliefs (Industry Recipe)
fleece measurement, breeding and selection (McGuirk 1978).
Environmental Event (Economic) Statutory Industry Body The AWC increase the minimum reserve price for wool to 284 cents/kg clean.
Technology Frame (Physical Artefact)
Research Organisation The UNSW develop the Vegemat technology for analysing vegetable matter (VM) (Wilkins & Whiteley 1977; Baxter 2002).
Environmental Event (Economic) The terms of trade in the Australian wool industry start to decline (Productivity Commission 2005).
Technology Frame (Physical Artefact)
Technology Company Computer Sciences Australia develops Woolnet, a computerised auction system (Kassel & Coleman 1977).
Technology Frame (Physical Artefact)
Wool Selling Brokers, Statutory Industry Body
Grazcos Brokers introduce Jumbo bales; the initiative is supported by the AWC (O'Donnell 1977).
1977
Industry Beliefs (Industry Recipe, Product Ontology) Technology Frame (Physical Artefacts, Use)
Statutory Industry Bodies The AWC and AWTA commence mill trials to extend pre-sale OM to processors around the world, and continue to develop new testing technologies to move the industry towards SXD (Ward 1977; Welsman 1977).
Environmental Event (Economic) Statutory Industry Body The AWC increase the minimum reserve price for wool to 298 cents/kg clean.
Technology Frame (Use) Industry Beliefs (Industry Recipe)
Statutory Industry Body The AWC trial OCP in stations in the Riverina and find cost savings from the use of OCP in place of traditional classing practices (Savage 1978).
1978
Technology Frame (Physical Artefact)
Research Organisation The UNSW develop a new, non-destructive method for testing fleece weight using infrared reflectance spectroscopy (Scott & Roberts 1978).
Environmental Event (Economic) Statutory Industry Body The AWC increase the minimum reserve price for wool to 318 cents/kg clean.
1979
Industry Beliefs (Industry Recipe) Technology Frame (Use)
Statutory Industry Organisation The AWC publish research findings that the use of OCP and SXS has reduced the number of wool lots offered at auction (increased lot size) and that this could be decreased further with greater understanding and adoption of OCP by woolgrowers (MacKenzie 1979).
Year Analytical Category Industry Participant
Group
Chronology of Events
The move towards Sale by Description and the Introduction of Sale with Additional Measurements (1980-1990)
Environmental Event (Economic) Statutory Industry Body The AWC increase the minimum reserve price for wool to 365 cents/kg clean.
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Industry Beliefs (Industry Recipe, Product Ontology) Technology Frame (Physical Artefacts, Use)
Statutory Industry Body The AWC form an Advisory Committee on Objective Measurement with the CSIRO, UNSW, AWTA, Council of wool buyers, AWMSA, brokers and farmers to investigate the feasibility of Sale with Additional Measurement (SXAM), the Committee organises trials of pre-sale tests for SS, SL and Clean Colour (CC) with growers (AWC Advisory Committee on Objective Measurement 1980).
1980
Technology Frame (Physical Artefacts, Use)
Statutory Industry Bodies, Research Organisation
The AWC, CSIRO and AWTA develop new testing equipment for SXAM: Hunter-lab colorimeter, grab sampler and SL testing instruments, new testing methods for SL variability (SLV), FD variability (FDV), resistance to compression, cotts and tip damage (AWC Advisory Committee on Objective Measurement 1980). The CSIRO develop the Sonic fineness tester for use in fleece testing for the breeding and selection of sheep (Jackson & Engel 1980).
Technology Frame (Use) Woolgrowers, Wool Selling Brokers
96 per cent of the Australian clip is sold by sample with OM (Welsman 1981).
Technology (Physical Artefacts, Use)
Research Organisation The CSIRO develop technology to test CC using NIR spectroscopy, and two portable apparatus for measuring SS for use on-farm (Szemes 1981; Baumann 1981; Baxter 2002).
Technology Frame (Use) Merino Stud Breeders Federation of Merino Performance Breeders is established for breeders using OM in breeding and selection (Anon 1981a).
Industry Beliefs (Product Ontology, Industry Recipe) Technology Frame (Use, Performance Evaluation)
Statutory Industry Bodies, Research Organisations
The AWC launches Trials Evaluating Additional Measurement (TEAM) to evaluate the potential of the pre-sale Additional Measurement of greasy wool; TEAM targets local and overseas processors. Participants in TEAM are the AWTA, CSIRO and the AWC (Anon 1981a; Douglas et al. 1985).
Environmental Event (Political) Australian Commonwealth Government
The Government Advisor to the Minister for Foreign Affairs criticises the ‘conservatism’ of the Australian wool industry and advocates the use of electronic technologies (Boyer 1981).
Industry Beliefs (Product Ontology, Industry Recipe) Technology (Use)
Research Organisation The UNSW publish research that demonstrates that ‘style’ characteristics are becoming less important in determining the price of wool at auction as OM is increasingly used in valuation. Researchers support the introduction of SXAM (Pattison 1981).
1981
Industry Beliefs (Product Ontology, Industry Recipe) Technology (Use)
Statutory Industry Body The AWC support for the introduction of SXD in the Australian wool industry within five years and propose a three stage introduction process: (1) SXS, (2) SXD, (3) major industry changes as a result of SXD (Welsman 1981).
1982
Environmental Event (Political) Australian Commonwealth Government
The Government’s Expenditure Review Committee winds up the statutory AWTA, assets are transferred to a new public company AWTA Ltd, and sectors of the Australian wool industry lobby for a company structure that ensures the independence from the selling system.
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Environmental Event (Economic) Statutory Industry Body The AWC increase the minimum reserve price for wool to 422 cents/kg clean.
Industry Beliefs (Industry Recipe) Technology Frame (Use)
Statutory Industry Body, Research Organisation
The AWC and CSIRO report on trials testing the effect of clip preparation on the processing performance of greasy wool in mills. The report recommends the use of improved fleece skirting practices in-shed to assist the adoption of SXAM. Researchers examine the impact of OCP lots versus traditionally classed lots on spinning performance in Japanese mills and find no major differences between fleece lines classed on SS (Charlton & McInerney 1982; Lunney 1982).
Technology Frame (Physical Artefacts)
Research Organisations, Testing Organisation
The CSIRO and UNSW develop new testing technologies: column extraction method of the estimation of wax an suint in greasy wool for detecting fleece rot (Hemsley & Marshall 1983), image analysis techniques for use in the measurement of style factors of greasy wool (Higgerson & Whiteley 1983), UNSW develop an alternative SS and SL testing machine PERSEUS (Kennedy 1983), CSIRO develops a mechanical tuft sampler and ATLAS for SL and SS testing, ATLAS is adopted for SXAM by the AWTA (Baird 1984).
Environmental Event (Economic) Statutory Industry Body The AWC increase the minimum reserve price for wool to 460 cents/kg clean following the devaluation of the Australian dollar.
Industry Beliefs (Industry Recipe, Product Ontology)
Statutory Industry Body The AWC plan for SXD is published after input from major sectors of the Australian wool industry, it is proposed that SXD will be implemented in the 1980s as a combination of OM, AM, and the guaranteed appraisal of residual non-measured characteristics, the AWC make conservative estimates of a benefit 13 cents/kg greasy wool and establish a full-time unit of staff dedicated to the introduction of SXD (Quirk 1983; Asimus 1987).
Industry Beliefs (Industry Recipe, Product Ontology)
Wool Processors Top makers express concerns at the proposed move towards SXD and request that good clip preparation is maintained for low within-bale variability (Provost 1983).
Industry Beliefs (Industry Recipe, Product Ontology)
Woolgrowers Grazco’s support SXD as it would reduce wool marketing and distribution costs (Skillecorn 1983).
Industry Beliefs (Industry Recipe) Technology Frame (Use)
Research Organisations The CSIRO and UNSW undertake research on the reduction of the number of small lots (less than 3 bales) in the auction system through the use of bulk classing and interlotting, and find that levels of variability in bulk and interlots would not affect processing performance (Thompson et al. 1983a; Thompson et al. 1983b).
1983
Industry Beliefs (Industry Recipe) Technology Frame (Use)
Research Organisation The CSIRO publish research that find that the adoption of OCP has been slow among woolgrowers and the impact of OCP has been below the expectations of researchers. Many wool classers continue to focus on uniformity and create too many lines. Researchers introduce radical or rational classing to support SXAM (Whiteley 1983).
Industry Beliefs (Industry Recipe) Statutory Industry Body The AWC report on progress towards achieving SXD and acknowledge that progress has been stalled by
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Technology Frame (Physical Artefacts)
problems with technology development (Baird 1984).
Industry Beliefs (Industry Recipe) Technology Frame (Use)
Testing Organisation The AWTA report on the progress of TEAM and present positive findings for the ability of AM to predict the processing performance of greasy wool (Douglas 1984).
1984
Technology Frame (Physical Artefacts)
Research Organisation The CSIRO undertake research on the OM of dark fibre count in wool lots and develop a sample illumination method of measurement and practices to reduce contamination in-shed (Foulds, Wong & Andrews 1984). The CSIRO also develop new instrumentation and methods for measuring the colour of scoured wool samples (Palithorpe 1984).
Environmental Event (Economic) Woolgrowers The number of sheep in Australia begins to decline (Productivity Commission 2005).
Industry Beliefs (Product Ontology, Industry Recipe) Technology Frame (Use, Performance Evaluation)
Statutory Industry Organisation, Research Organisations
The AWC publish the final TEAM report, and report that the three year trials demonstrate the benefits of SXAM to mills in predicting the processing performance of greasy wool (Douglas et al. 1985). SXAM is introduced as a voluntary marketing system (Asimus 1987).
Technology Frame (Physical Artefacts)
Research Organisations The AWTA and UNSW develop methods to visually appraise VM types in core samples using photographic standards and apply for IWTO approval (Anson 1985). Researchers also develop a method to measure the diameter profile of wool staples using the FDDA (Hansford, Emery & Teasdale 1985).
Industry Beliefs (Industry Recipe) Technology Frames (Use)
Wool Buyers The Australian Council of Wool Buyers raise concerns over the number of small lots in the auction system and contend that OCP has not reduced small lot numbers, buyers continue to encourage growers to separate a lead line of their best quality wool to encourage buyers to bid their best price (Johnston 1985).
Industry Beliefs (Industry Recipe)
Statutory Industry Body, Research Organisation
The CSIRO and AWC publish evidence of the over-skirting fleeces in Australian shearing sheds (Lunney 1985), and recommend the need for improvements in clip preparation to improve processing performance (Charlton, Eley & Rottenbury 1985).
1985
Technology Frame (Physical Artefact, Use)
Testing Organisation The AWTA adopt the Automated Tester for Length and Strength (ATLAS) developed by the CSIRO for commercial AM testing (Thompson & Whiteley 1985).
Technology Frame (Use) Statutory Industry Body, Research Organisations, Testing Organisation
TEAM 2 commences to formally expand the TEAM database and review prediction formulae (Douglas 1989). 1986
Technology Frame (Use) Statutory Industry Body, Research Organisation
The IWS, CSIRO, AWC undertake a joint research project to study the impact of clip preparation techniques on the value of top and noil, researchers found that fleece bellies, locks and stain removal increased fleece value for processors (Bell, Charlton & Rottenbury 1986).
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Industry Beliefs (Product Ontology, Industry Recipe) Technology Frame (Use)
Statutory Industry Body The AWC introduce a new voluntary Code of Practice for clip preparation developed to prepare wool for sale in a manner as to provide a textile fibre that processors can use with confidence (Fairhead 1986).
Technology Frame (Physical Artefact)
Research Organisation The CSIRO commercialise the SiroSPUN™ spinning process for very fine weaving yarn and ‘trans-seasonal’ garment production (Taylor 1985; Commonwealth Scientific and Industrial Research Organisation 2004).
Industry Belief (Industry Recipe) Australian Commonwealth Government
The Government hand responsibility for price setting under the WRPS over to the Australian Wool Council in consultation with the AWC.
Industry Beliefs (Product Ontology, Industry Recipe)
Research Organisation WOOLPLAN, the national performance recording scheme for stud breeders in the wool industry, is launched (Massey 1990; Lewer et al. 1985).
Industry Beliefs (Industry Recipe) Statutory Industry Body The AWC review plans for the introduction of SXD but continue to support SXD as industry policy (Asimus 1987).
Technology Frame (Physical Artefact)
Statutory Industry Body, Research Organisations
The AWC, CSIRO, AWTA and UNSW develop new technologies for the measurement of MFD: the CSIRO developed new Fibre Fineness Distribution Analyser, the AWTA develop an alternative system using image analysis (Fibre Image Display and Measurement, FIDAM) (Marler & McNally 1987; Bell 1987). Evidence of the ineffectiveness of the SA of CC is published along with the development of an objective yellowness measurement of core test samples (Thompson 1987).
Industry Beliefs (Industry Recipe) Technology Frame (Use)
Statutory Industry Body, Research Organisations
The AWC, CSIRO and UNSW publish evidence that marketing the finest fleeces in a clip separately does not improve market value of the overall clip despite brokers and buyers advising growers and classer to prepare a ‘fine line’ for market. Researchers found that there had not been widespread adoption of clip preparation COP (Charlton & David 1987) and reinforce the need for clip preparation along scientific lines using OM and AM (McMahon 1987; Rottenbury et al. 1987).
1987
Industry Beliefs (Product Ontology)
Research Organisation The CSIRO undertake research that highlights the importance of fibre crimp in late stage processing (Whiteley 1987).
Industry Beliefs (Industry Recipe, Product Ontology) Technology Frame (Use)
Statutory Industry Body, Research Organisations, Testing Organisation
The final report of the TEAM2 project is published with the following recommendations: new formulae for Hauteur, guidance formulae for CVH and noil, mills to establish their own database to customise general prediction formulae (Douglas 1989).
Environmental Event (Economic) Statutory Industry Bodies The AWC and Australian Wool Council increase the minimum price of wool to 870 cents/kg clean against the advice of AWC economists.
1988
Environmental Event (Political, Research Organisation The CSIRO Division of Wool Technology (DWT) is established to replace the DTP.
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Technological)
Technology Frame (Use, Performance Evaluation)
Australian Commonwealth Government, Research Organisation
The Australian standard for measuring the average yellowness of scoured core samples using spectrophotometer is introduced (Thompson 1988).
Industry Beliefs (Product Ontology, Industry Recipe)
Research Organisation, Wool Buyers
The UNSW and Australian Council of Wool Exporters publish research that shows that buyers discriminate against (i.e. discount) interlots because of concerns over lot variability (Tucker, Teasdale & Knight 1988).
Environmental Event (Economic) The demand for wool collapses and the market price drops below the minimum reserve price.
1989
Technology Frame (Physical Artefact, Use)
Statutory Industry Body The AWC develop and introduce the Woolamalg computer program to assist growers and classer in the preparation of clips for market using OM in response to evidence that the majority of growers do not follow the clip preparation code of practice for OCP or radical classing on skirting (O'Sullivan 1989).
Environmental Event (Economic, Political)
Australian Commonwealth Government
The Australian Government reduces the minimum reserve price for wool to 700 cents/kg clean despite opposition from the AWC.
Environmental Event (Economic, Political)
Statutory Industry Body The AWC introduce the National Marketing Quota for wool supported by the Flock Reduction Scheme which subsidises growers for costs associated with destroying sheep.
Technology Frame (Performance Evaluation, Use)
Research Organisation The UNSW publish research that argues that clear price signals in the market for wool are required before woolgrowers will adopt SXD (Cottle & Bowman 1990).
Technology Frame (Physical Artefact, Use)
Testing Organisation The AWTA continue to test FIDAM but decide not to commercialise this instrument for greasy wool testing (van Schle, Marler & Barry 1990).
1990
Industry Beliefs (Product Ontology, Industry Recipe) Technology Frame (Physical Artefact, Use)
Research Organisation The CSIRO report on the continued efforts of the organisation to develop testing technologies for SXD as researchers believe that technology development is the key barrier to the introduction of SXD, focus on imaging technology to measure style factors (van Schle, Marler & Barry 1990).
Year Analytical Category Industry Participant
Group
Chronology of Events
The collapse of the market for wool and the emergence of on-farm fibre testing (1991-2001)
Environmental Event (Political, Australian Commonwealth The WRPS is abandoned by the Australian Government with a stockpile of 4.7 million bales of wool and
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Economic) Industry Beliefs (Industry Recipe)
Government AUD$4 billion (Department of Agriculture Fisheries and Forestry 2001).
Industry Beliefs ( Product Ontology, Industry Recipe)
Australian Commonwealth Government
Sir William Vines is appointed to head a Committee of Review into the wool industry. The Wool Review Committee recommends the adoption of SXD as a major objective of the Australian wool industry to the Commonwealth Government (Hoadley 1991).
Industry Beliefs (Industry Recipe)
Australian Commonwealth Government
The AWC (II), Wool Research and Development Corporation and Australian Wool Realisation Committee are established (Garnaut 1993).
Industry Beliefs ( Product Ontology) Technology Frame (Physical Artefact)
Research Organisation The CSIRO establish the Fibre Diameter Distribution Task Force to undertake research on the measurement of fibre diameter distribution (FDD) (Charlton & David 1991).
1991
Industry Beliefs ( Product Ontology, Industry Recipe) Technology Frame (Use)
Woolgrowers, Wool Buyers The Wool Council of Australia (peak industry body for woolgrowers) accept the proposal for SXD but report that wool buyers oppose the introduction of SXD and reserve their legal right to make a subjective appraisal of greasy wool sample. Buyers oppose the removal of the sample from the show floor and are constraining the adoption of SXAM on that basis (Hoadley 1991).
Environmental Event (Political) Australian Commonwealth Government
The Garnaut Committee of Enquiry into the Australian wool industry is launched (Garnaut 1993).
Technology Frame (Physical Artefact)
Testing Organisation, Research Organisation
SGS Wool Testing Services develop the Optical Fibre Diameter Analyser (OFDA) for the rapid and precise measurement of FDD to replace the FDA instrument (Baxter, Brims & Teasdale 1992). The CSIRO develop the Sirolan Laserscan instrument to measure MFD (Hansford 1992; Charlton 1995).
Industry Beliefs ( Product Ontology, Industry Recipe) Technology Frame (Use)
Wool Buyers, Woolgrowers The Australian Council of Wool Exporters argue that SXD is not acceptable and that they require access to a wool sample prior to purchase (Booth 1992). The Australian Wool Industry Council commission a report to review the use of OM in the wool industry which recommends an improvement in staple sampling and testing precision and supports the use of OFDA (Johnston 1992). The Council support the introduction of centralised selling system to reduce the number of selling centres; however this is opposed by much of the industry (Loutit 1992).
Industry Beliefs (Industry Recipe) Wool Selling Brokers The National Council of Wool Selling Brokers propose the establishment of an independent wool exchange to represent all buyers, selling systems, QA and electronic communication (Ward 1992).
Technology Frame (Physical Artefact)
Research Organisation The CSIRO develop Sirolot-TM a clip lotting computer program which derives dark fibre risk for sale lots (Charlton 1992).
1992
Technology Frame (Use) Research Organisation The CSIRO attribute the slow adoption of SXAM to a lack of information relating measurements of SS and SL
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to wool production (Hansford 1992).
Industry Recipe (Product Ontology) Technology Frame (Physical Artefact, Use)
Research Organisation The CSIRO undertake research on testing for wool fibre prickle and fabric comfort (Naylor 1992; Phillips 1992).
Industry Beliefs (Industry Recipe) Statutory Industry Body The AWC introduce a new Code of Practice for clip preparation aimed at reducing the number of small lots and the total number of sale lots in the auction system through interlotting.
Environmental Event (Political) Industry Beliefs (Product Ontology, Industry Recipe) Technology Frame (Use)
Australian Commonwealth Government
The Garnaut Wool Industry Review Committee makes the following recommendations: centralised marketing system, adoption of improved specification and wool identification systems, on-farm fleece testing, move towards SXD, industry standards for non-measured attributes (Morris 1993).
Environmental Event (Political)
Australian Commonwealth Government
The AWC (II) is replaced with Wool International and Australian Wool Research and Promotion (AWRAP) organisation (Garnaut 1993).
Environmental Event (Political) Industry Beliefs (Industry Recipe)
Australian Commonwealth Government
The Australian Wool Exchange (AWEX) is established as an incorporated organisation (Smith 1993).
Technology Frame (Use) Research Organisation The CSIRO create a new sampling schedule for core and grab sampling (Charlton & David 1993).
Technology Frame (Physical Artefact)
Testing Organisation The AWTA introduce Woolink, electronic wool selling system (Douglas 1993).
Industry Beliefs (Industry Recipe) Technology Frame (Use)
Wool Processors Wool processors support the adoption of OM, AM and CC testing of Australian greasy wool (Turk 1993).
Industry Recipe (Product Ontology) Technology Frame (Physical Artefact)
Research Organisations Industry research into fibre curvature commences (Edmonds 1997).
1993
Technology Frame (Use) Testing Organisation Commercial pre-sale Clean Colour testing introduced by AWTA.
Technology Frame (Use) Research Organisation The UNSW publish evidence that code of practice for clip preparation introduced in 1993 is not being widely adopted by woolgrowers and classers (Cottle 1994).
1994
Environmental Event (Political) Australian Commonwealth AWRAP is merged with the IWS.
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Government
Technology Frame (Physical Artefact)
Testing Organisation SGS Wool Testing Services develop the Agritest Staplebreaker for on-farm mid-side sample testing of SS (Vizard, Scrivener & Anderson 1994).
Technology Frame (Physical Artefact, Use)
Research Organisations The CSIRO, AWTA and Melbourne University publish research on the use of FDD testing on-farm in breeding, selection and animal husbandry (Mayo et al. 1994). The AWTA introduce ‘Every Sheep Tested’ on-farm fleece testing program to guide classing (Stadler & Gillies 1994).
Environmental Event (Technology) Research Organisations, Testing Organisations, Industry Organisations
The Wool Specification Conference ‘WoolSpec’ is held In Australia.
1995
Industry Beliefs (Industry Recipe) Research Organisations The CSIRO introduce the Dark Fibre Risk Scheme, a Code of Practice to be used on-farm to prevent dark fibre contamination (Rottenbury, Burbidge & McInnes 1995).
Technology Frame (Physical Artefacts, Performance Evaluation)
Textile Industry Organisation – testing standards
In 1996 the following testing technologies and methods were awaiting IWTO approval: FDD measured by OFDA and Laserscan, SLD measured by SiroHauteur, VM components, Scoured colour measured with NIRA, style measured with CSIRO Style instrument, fibre medullation measured by NIRA and OFDA, residual grease variation measured by NIRA, regain variation measured by NIRA (Baxter 1996b).
Technology Frame (Physical Artefact)
Testing Organisation SGS Wool Testing Services develop the Agritest Staple Breaker model 2 a semi-automated instrument for testing SL, SS and Position of Break (POB) for use in fleece testing on-farm (Baxter 1996a).
Environmental Event (Technology) Research Organisations, Testing Organisations, Industry Organisations, Wool Processors
‘Top-Tech ‘96’ early stage wool processing conference was held in Geelong attracting 300 visitors from 18 countries.
1996
Technology (Physical Artefacts) Research Organisation, Testing Organisation
The AWTA and CSIRO make improvements to the method for Clean Colour testing (Lindsay 1996a; Lindsay 1996b; Mahar & Osborne 1996).
Environmental Event (Political) Australian Commonwealth Government
The Woolmark Company Pty Ltd is established as a subsidiary of AWRAP and Australian Wool Services Ltd.
Industry Beliefs (Product Ontology) Technology (Physical Artefacts)
Research Organisation WRONZ publish research on fibre curvature, demonstrating that this greasy wool attribute can be measured using OFDA and Sirolan Laserscan (Edmonds 1997).
1997
Technology Frame (Physical Artefact, Use, Performance
Research Organisation The CSIRO propose the replacement of Airflow technology with OFDA or Sirolan Laserscan to provide accurate measurement of FDD and CV of FD (Naylor 1997).
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Evaluation)
Industry Beliefs (Product Ontology, Industry Recipe)
Wool Buyers The Australian Council of Wool Exporters oppose the introduction of SXD on the basis that buyers would not have the same confidence in wool sold sight unseen as they would with sampled wool and that lots sold by description would be discounted in the market. Report that trade confidence in the AWEX-ID has not reached a level where it can be relied upon for describing non-measured greasy wool attributes (Quirk 1997).
1998
Environmental Event (Political) Woolgrowers, Australian Commonwealth Government
Growers enter a vote of no confidence in AWRAP and the Wool Industry Future Directions (McLachlan) Task Force is established (Wool Industry Future Directions Task Force 1999).
Environmental Event (Political, Technological)
Research Organisation The CSIRO Division of Textile and Fibre Technology replace the DWT.
Environmental Event (Political) Australian Commonwealth Government
Wool International is privatised and Woolstock Australia Ltd is established to complete the sale of the WRPS stockpile.
Technology Frame (Physical Artefact, Use)
Research Organisations The University of New England (UNE) and CSIRO undertake research on consumer preference for soft, comfortable clothing, examine the relationship between crimp frequency and variation in fibre curvature and fabric comfort, measure crimp frequency using the CSIRO Style Instrument (Crook, Nivison & Purvis 1999; Fish, Mahar & Crook 1999).
Technology Frame (Physical Artefact, Use)
Research Organisation The CSIRO demonstrate the use of Sirolan Laserscan, OFDA and Agritest for on-farm fibre measurement (Hansford 1999; Schlink, Clark & Murray 1999).
1999
Industry Beliefs (Industry Recipe) Research Organisation The CSIRO publish evidence that superfine wools continue to be valued by subjective appraisal despite no evidence of premiums and discount matching processing performance (Scrivener, Vizard & Hansford 1999).
Technology Frame (Physical Artefact, Use)
Testing Organisation SGS commercialise the OFDA2000 to measure OM on-farm (Baxter 2002).
Technology Frame (Physical Artefact, Use)
Testing Organisation The AWTA develop the Uniformity Index to measure the uniformity of wool characteristics in a sale lot, in order to differentiate between bulk and classed lots (Byrne, Mahar & Connors 2000).
Technology Frame (Use) Testing Organisation The AWTA adopt Sirolan Laserscan as the standard technology to measure MFD (Sommerville 2000).
2000
Technology Frame (Physical Artefact)
Testing Organisation The AWTA develop a method for measuring crimp curvature using light microscopy and image analysis (Swan & Mahar 2000).
2001
Technology Frame (Physical Artefact)
Wool Selling Brokers and Exporters
Australian Wool Handlers develop Laser Matched Interlots technology to objectively match bales using OM and SA (Hansford 2001).
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Environmental Event (Political) Industry Beliefs (Industry Recipe)
Australian Commonwealth Government
Australian Wool Services and Australian Wool Innovation are incorporated as wool industry corporations managing research and development funds (Australian Bureau of Statistics 2002).
Industry Beliefs (Product Ontology, Industry Recipe) Technology Frame (Physical Artefact)
Research Organisation The CSIRO discontinue research into objectively measuring the style element of wool and SXD is effectively abandoned by the Australian wool industry (AWTA and The Woolmark Company 2002).
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Appendix C
Case Study Interviewee Consent Letter
Wool Desk
Department of Agriculture, WA 3 Baron-Hay Court South Perth WA 6151 Tel: 08 9368 3333 [email protected]
Participants Name Participants address
Dear Sir/Madam,
The diffusion of new agricultural technologies: The case of AM in the Australian
wool industry
I am a doctoral candidate at the Graduate School of Management, University of
Western Australia under the supervision of Dr Tim Mazzarol and Associate Professor
John Stanton (DAWA). I have been engaged by the Department of Agriculture, WA to
conduct research into how wool farm family businesses adopt and implement new
technologies. Agricultural research and extension agencies would greatly benefit from
this research, which will help them to develop appropriate and effective innovation
programs to meet the needs of Australian farming businesses.
The Wool Desk at the Department of Agriculture has selected your wool brand from the
public Australian wool auction database and I seek the consent of you and members of
your farm family businesses to participate in this case study research project. Your
cooperation in participating in an in-depth interview will be vital for the completion of
this study. I anticipate that the interview will be conducted at your property, will take
no longer than two hours and will involve all members of your family, aged over 18,
who are engaged in the management of the family farm. All of your responses will be
anonymous and confidential, and data will be reported in a way that will ensure that you
will not be identified. You may withdraw your written consent at any time during the
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data collection period of this research project, which will run from October 2003 to
December 2005. The interview will be taped and transcribed to ensure that all question
responses are captured. The interview transcripts will be returned to you prior to the
case study report being written to check for accuracy and consistency.
During the interview the following topics will be discussed:
1. How you make decisions about your wool enterprise,
2. How you interact with other members of the wool industry,
3. Why and how you adopted and implemented staple strength testing,
4. Why and how you adopted and implemented clean colour testing,
Transcribed copies of the anonymous case study interviews will be kept in a database
on the computer of the researcher at the Graduate School of Management, UWA until
the thesis is submitted when all interview records will be destroyed.
If you would like to consent to participating in this research please complete and return
the attached consent form in the stamped addressed envelope enclosed with this letter.
If you have any further questions, you can contact me by phone on 08-9380-1727 or by
e-mail at [email protected]. Thank you in advance for your participation in
this study.
Yours sincerely,
Joanne Sneddon
Doctoral Candidate
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Written Consent
I/We (the participants) have read the information provided and any questions I have
asked about the research study have been answered to my satisfaction. I agree to
participate in this research study, realising that I may withdraw at any time without
reason and without prejudice.
I understand that all information provided is treated as strictly confidential and will not
be released by the researcher unless required to by law. I have been advised as to what
data are being collected, what the purpose is, and what will be done with the data upon
completion of the research.
I agree that research data gathered for the study may be published, provided my name or
other identifying information is not used.
Signature: ______________________________________________________
Name of Participant: __________________________________________________
Date: ______________________________________________________
The Human Research Ethics Committee at the University of Western Australia
requires that all participants are informed that, if they have any complaint regarding
the manner, in which a research project is conducted, it may be given to the Chief
Investigator, or alternatively, to the Secretary, Human Research Ethics Committee,
Registrar’s Office, University of Western Australia, 35 Stirling Highway, Crawley,
WA 6009 (Tel: 9380 -3703). All study participants will be provided with a copy of the
Information Sheet and Consent Form for their personal records.
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Appendix D
Preliminary Case Study Interview Guide
Tell me about your farm family business…
1. Who is involved in running the farm business? What roles do they play?
2. How long have you been farming?
3. How long have you been operating your current farm business?
4. How many hectares do you farm?
5. What proportion of your land is grazed and in crop?
6. What farming enterprises do you operate?
7. What proportion of your farm income do you derive from each enterprise?
8. How do you manage your wool enterprise in terms of grazing, flock structure,
lambing, shearing?
9. What type of wool fibre do you produce in terms of diameter, strength, length
and colour?
10. What goals do you have for your wool enterprise?
Present data showing weight and types of wool offered under their wool brand at
auction each year from 1988 (figure 1, Appendix 3)
11. Can you cast your mind back to the late 1980s and talk me through how you
have been running your wool enterprise each year?
a. What production ‘actions’ do you take to produce a wool clip each year?
b. How do you make decisions about how much and what type of wool to
produce?
c. What information do you base those decisions on?
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Present data showing the proportion of wool sold, passed-in, withdrawn and traded
under their wool brand at auction each year from 1988 (figure 2, appendix 3)
12. How do you market and sell your greasy wool fibre?
a. What marketing actions do you take?
b. How do you make decisions about the marketing actions that you take?
c. What sources and types of information do you use to inform your
marketing decisions and actions?
Present data showing the diameter of wool offered at auction each year under their
wool brand from 1988 (figure 3, appendix 3)
13. What has happened to the quality of wool produced in your enterprise since
1988?
a. How do you manage the quality of your wool fibre?
b. What management actions do you take to manage wool quality and why?
c. What information do you use to guide these wool quality management
actions?
Present data showing the staple strength, length and position of break of greasy wool
fibre since 1988 under their wool brand (figure 4 – all wool, figure 5 – staple strength,
figure 6 – staple length, figure 7 – position of break, appendix 3)
14. Cast your mind back to the launch of pre-sale measurements for staple strength,
length and position of break in the mid 1980s, why did you start testing your
wool for strength and length in 19xx?
15. How did you select the wool that you tested?
16. What information did you use to make the decision to test the staple strength and
length of your wool fibre?
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17. What did you do with your test results?
18. After your first test in 19xx, what did you think of pre-sale AM?
19. Why did you continue/discontinue testing in 19xx?
20. What were the events or information that most influenced your testing behaviour
from 19xx to 19xx?
21. You are testing over one half of your wool fibre by 19xx, how did you choose
which wool to test?
22. What information did you use to make the decision to continue to test the staple
strength and length of your wool fibre?
23. What did you do with your test results?
a. How did you use them?
b. What did they tell you about your wool enterprise?
c. What did they tell you about the market for greasy wool?
24. What did you think of pre-sale AM in this period?
25. Did you use AM test results to manage your wool enterprise? If so, how?
26. What were the events or information that most influenced your testing behaviour
after you had AM tested over half of your greasy wool fibre?
Present data showing the clean colour of greasy wool fibre since 1994 under their wool
brand (figure 4, appendix 3)
27. Can you cast your mind back to the launch of clean colour testing in the mid
1990s, why did you start testing your wool for clean colour in 19xx?
28. How did you select the wool that you tested?
29. What information did you use to make the decision test your wool fibre for to
clean colour?
30. What did you do with your test results?
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a. How did you use them?
b. What did they tell you about your wool enterprise?
c. What did they tell you about the market for greasy wool?
31. After your first test in 19xx, what did you think of pre-sale clean colour testing?
32. Why did you discontinue testing in 19xx/200x?
33. What were the events or information that most influenced your clean colour
testing behaviour from 19xx to 200x?