language of life-giving connection: the emotional …€¦ · table 11. direct quotes from positive...
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LANGUAGE OF LIFE-GIVING CONNECTION:
THE EMOTIONAL TONE OF LANGUAGE THAT FOSTERS FLOURISHING
CAMPUS SUSTAINABILITY PROGRAMS
by
LINDA ROBSON
Submitted in partial fulfillment of the requirements
for the degree of
Doctor of Philosophy
Department of Organizational Behavior
CASE WESTERN RESERVE UNIVERSITY
May 2015
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CASE WESTERN RESERVE UNIVERSITY
SCHOOL OF GRADUATE STUDIES
We hereby approve the dissertation of
LINDA ROBSON
Candidate for the degree of Doctorate of Philosophy
Committee Chair
Ronald Fry, Ph.D.
Committee Member
Mark Chupp, Ph.D.
Committee Member
David Cooperrider, Ph.D.
Committee Member
Peter Whitehouse, M.D., Ph.D.
We certify that written approval has been obtained
for any proprietary material contained therein.
TABLE OF CONTENTS
List of Tables………………………………………………………………………...…. vii
List of Figures……………………………………………………………………….….. ix
Abstract …….…………………………………………………………………………... x
CHAPTER ONE: INTRODUCTION TO THE STUDY..……………………………... 1
CHAPTER TWO: BACKGROUND..………………………………………………… 10
The Role of Higher Education………………………………………………….. 10
The Language of Fear and Negativity………………………………………….. 13
Sustainability in Higher Education…………………………………………..… 15
CHAPTER THREE: LITERATURE REVIEW………………………………..……... 19
Social Construction…………………………………………………………..… 19
Organizational Change…………………………………………………………. 22
Disconfirmation………………………………………………………….. 26
Anxieties Associated with Learning and Change……………………….. 27
Restructuring…………………………………………………………….. 28
Imitation and Positive or Defensive Identification with a Role Model….. 29
Defensive Identification………………...……………………………….. 31
Scanning: Insight or Trial and Error Learning………………………….. 31
Personal and Relational Re-Freezing (Crystallizing)……………………. 33
Positive Organizational Scholarship..………………………………...………… 36
Positive Organizational Scholarship and Language……………………………. 43
Emotion…………………………………………………………………………. 54
Research Questions and Hypotheses…………………………………………… 59
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CHAPTER FOUR: RESEARCH DESIGN AND METHODOLOGY.……………….. 65
Content Analysis………………………………..…………….………………… 65
Directed Content Analysis……………………………………………………… 67
Theoretical Issues in Content Analysis…………………………………………. 69
Assumptions……………………………………………………………………. 71
Inclusion and Exclusion Criteria……………………………………………...... 71
Confidentiality………………………………………………...………………... 76
Research Design……………………………………………..….……………… 77
Data Collection……………………………………………..…………… 77
Data Analysis…………………………………………………...……….. 83
Reliability ……………………………………………………………...... 90
CHAPTER FIVE: RESULTS……………………………………………………..….… 94
Data Analysis…………………………………………………………..………. 94
Analysis of Emotional Tone Used by Sustainability Programs…………. 94
Analysis of Language by Performance Category……………………...… 96
Analysis of Interview Data by Performance Category………………….. 97
Analysis of Group Meeting Data by Performance Category……………. 99
Analysis of Website Data by Performance Category…………………..... 101
Positive / Negative Ratios by Performance Category………………….... 104
Discourse Families Aggregated by Data Source and Performance
Category …………………………………………………………………
105
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CHAPTER SIX: DISCUSSION…………………………………………………….… 128
Overview............................................................................................................... 128
Support for Hypotheses……………………………………………..…………. 129
Additional Findings…………………………………………………………….. 139
Implications for Future Research ………………………………………………. 147
Implications for Practice………………………………………………………... 150
Contributions of the Study……………………………………………………… 152
Limitations of the Study……………………………………………………...… 153
CHAPTER SEVEN: CONCLUSION…………………………………………………. 155
Appendix A……………………………………………………………………………... 159
References………………………………………………………………………………. 161
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LIST OF TABLES
Table 1. Stages of the Change Process (Schein, 2002)
Table 2. Ohio Colleges and Universities Included in Campus Sustainability Report Card Ranking 2011
Table 3a. Table 3b.
Positive Discourse Categories from Cooperrider et al (2008) Negative Discourse Categories from Cooperrider et al (2008)
Table 4. Positive, Negative, and Neutral Labels Added to Cooperrider et al’s Positive/Negative Discourse Code (2008)
Table 5. Positive, Negative and Neutral Discourse for All Sources of Data by Performance Category
Table 6. Positive, Negative, and Neutral Discourse for Interview Data by Performance Category
Table 7. Positive, Negative, and Neutral Discourse for Group Meeting Data by Performance Category
Table 8. Positive, Negative, and Neutral Discourse for Website Data by Performance Category
Table 9. Positive / Negative Discourse Ratios by Performance Category
Table 10. Number and Frequency of Positive Discourse Labels by Performance Category: All Sources of Data
Table 11. Direct Quotes from Positive Discourse Labels with Highest Frequency: High Performers
Table 12. Direct Quotes from Positive Discourse Labels with Highest Frequency: Moderate Performers
Table 13. Direct Quotes from Positive Discourse Labels with Highest Frequency: Base Performers
Table 14. Number and Frequency of Positive Discourse Labels: Interview Data
Table 15. Number and Frequency of Positive Discourse Labels: Group Meeting Data
Table 16. Number and Frequency of Positive Discourse Labels: Website Data
Table 17. Number and Frequency of Negative Discourse Labels by Performance Category: All Sources of Data
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Table 18. Direct Quotes from Negative Discourse Labels with Highest Frequency: High Performers
Table 19. Direct Quotes from Negative Discourse Labels with Highest Frequency: Moderate Performers
Table 20. Direct Quotes from Negative Discourse Labels with Highest Frequency: Base Performers
Table 21. Number and Frequency of Negative Discourse Labels: Interview Data
Table 22. Number and Frequency of Negative Discourse Labels: Group Meeting Data
Table 23. Number and Frequency of Negative Discourse Labels: Website Data
Table 24. Number and Frequency of Neutral Discourse Labels by Performance Category: All Sources of Data
Table 25. Direct Quotes from Neutral Discourse Labels with Highest Frequency: High Performers
Table 26. Direct Quotes from Neutral Discourse Labels with Highest Frequency: Moderate Performers
Table 27. Direct Quotes from Neutral Discourse Labels with Highest Frequency: Base Performers
Table 28. Number and Frequency of Neutral Discourse Labels: Interview Data
Table 29. Number and Frequency of Neutral Discourse Labels: Group Meeting Data
Table 30. Number and Frequency of Neutral Discourse Labels: Website Data
Table 31. Support for Hypotheses
Table 32. P/N Ratios for High and Base Performance Categories, Comparing Group Meetings and Website Data
Table 33. Chi-square Analysis of All Data Sources
Table 34. Chi-square Analysis of Interview Data
Table 35. Chi-square Analysis of Group Meeting Data
Table 36. Chi-square Analysis of Website Data
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LIST OF FIGURES
Figure 1. Greenpeace 2013 Artic Protection Campaign
Figure 2. Discourse by Performance Category
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ABSTRACT
Insights from Positive Organizational Scholarship (POS) were used as a lens
through which to examine the language used by campus sustainability programs. The use
of positive, negative, and neutral language was explored in ten college and university
sustainability programs and these findings were compared to the performance ranking of
each sustainability program. Directed content analysis was used to analyze one-on-one
interviews, campus sustainability staff meetings, and campus sustainability programs
websites. Taking all sources of data together, high performing sustainability programs
demonstrate a 4:1 positive to negative (P/N) ratio, where moderate performers possessed
a 2:1 P/N ratio and base performers a 1:1 P/N ratio. These findings, associating higher
incidence of positive language with high performing programs joins other scholarship,
which connects higher levels of positive language with higher functioning individuals,
higher team performance, high relational satisfaction, and increased longevity of teams
and dyads. Heretofore, few links between the sustainability domain and POS literature
exist. This study serves as one of the first such bridges.
Keywords: language, Positive Organizational Scholarship, sustainability, flourishing,
performance, love, change, higher education
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CHAPTER ONE: INTRODUCTION TO THE STUDY
In 2009, the American Association of Sustainability in Higher Education
estimated more than 700 institutions were engaged in sustainability efforts, with almost
200 of these institutions employing a full time Sustainability Professional to lead the
initiatives (AASHE, 2009). In less than two years, almost 600 colleges and universities
signed the American College & University Presidents’ Climate Commitment, publically
stating their institution will move toward climate neutrality and committing to actions and
public reporting of progress (AASHE, 2008).
Despite the promising story these numbers suggest, and the leadership exhibited
among several schools, sustainability is a fairly recent development in the higher
education sector (Chronicle of Higher Education, 2006). Each sustainability program
represents a process of subtle demarcation, defining the field.
While technical and operational practices exist, which decrease the
environmental and financial footprints of a higher educational institution, the long-term
goals for sustainability are underpinned by changes in individual and system behavior,
and mindset within the broader campus community (Hart & Milstein, 2003; McKenzie-
Mohr & Smith, 1999). As such, the success of the campus Sustainability Professional,
and his or her program, is predicated on an ability to capture the attention of campus
constituencies and hold their attention long enough to establish new habits and thus long-
term behavior change toward sustainable practices (MacKenzie-Mohr & Smith, 1999). In
this way, sustainability programs, which have been rated as high performers have been
more successful than their counterparts at organizational change within their institutions.
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The success of Sustainability Professionals in higher educational settings is
complicated by the diversity of the incumbents’ occupational backgrounds (AASHE,
2008). The skills and knowledge required is a varied composite, which includes:
teaching and research; business; energy industry knowledge; construction practices,
materials, and processes; transportation; food utilization; waste minimization; and
environmental science (Sharp, 2005; AASHE, 2008, 2010). Embedded within this list of
explicit capabilities is the implicit expectation that Sustainability Professionals will foster
change within the complex systems of universities and colleges. Are campus
sustainability leaders equipped with the skills to transform these systems?
For insight into how to engage campus constituents, the Sustainability
Professional who is unfamiliar with the field of organizational development and change,
will often reference other college and university sustainability programs, benchmarking
what is included under the umbrella term of ‘sustainability’ and collecting data about
how other institutions go about fostering and maintaining sustainable behavior change. It
becomes clear soon after a higher educational sustainability program is launched that
changes in infrastructure alone are not enough to achieve institutional goals or to win the
attention of constituents. To capture attention and motivate campus stakeholders to adopt
sustainable behaviors, campus Sustainability Professionals borrow and mimic techniques
used at other campuses and from environmental non-governmental organizations (NGOs)
(Sharp, 2005). My own experience as a Sustainability Professional suggests that the habit
of looking to colleagues on other campuses as role models for what a sustainability
program should look like- in other words, mimetic isomorphism (DiMaggio & Powell,
1983)- offers researchers in the field of organizational change an opportunity to increase
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the use of effective sustainability communication practices for a wide audience of
campuses if the researchers are able to influence leading programs. This technique has
its shortcomings.
In the literature review contained in Chapter Three of this manuscript, a more
thorough explanation will be presented, but for now it is worth noting that mimicking
role models offers only short term gains, based on Schein’s theory of change (2002).
Schein explains that role models increase the likelihood of rapid new learning through
imitation and identification, but this approach risks that people (or institutions) will learn
things that do not really fit their context or culture. In other words, imitation and
identification provide a quick, but not necessarily lasting, solution.
The adoption and implementation of sustainability principles within an institution
of higher education is an organizational change process and, when viewed through this
lens, a host of methodologies and scholarship becomes available. Organizational change
research has the potential to create a significant and positive impact on sustainability
programs across the nation’s campuses. The central focus of this study is the compelling
discussion advocating the powerful role that language plays in directing our attention and
acting as proxy for our mindset and intentions, which comes from both positive
organizational scholarship (POS) and positive psychology (Schwartz, 1986; Cooperrider,
1997; Fredrikson, 1998; Schmidt, 2005; McKenzie-Mohr & Smith, 1997).
Our language serves as proxy for our thoughts and mindset and our words steer
our vision and that of others, shaping what we will create (Cooperrider, 1997; Gergen,
1994; Ludema et al. 1997). The scholarship of Fredrickson (2003) and Cooperrider and
Sekerka (2003) offer convincing arguments for steering organizational change with
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hopeful and compelling narratives of the system’s desired future. Of interest to this
research is diversity of narratives. The focus is not solely about content, but is equally
about the processes through which sustainability is communicated in higher education
contexts and claims are staked for attention and influence (Meppem, 2000).
The sustainability narrative coming from environmental NGOs regularly utilizes
crisis messaging, and thus draws from and perpetuates emotional responses of fear
(Spence et al, 2011; Feinberg & Willer, 2010; Luntz, 2006; Potter et al, 2006; Hoog et al,
2005; Das et al, 2003; LaTour & Rotfeld, 1997). Many climate change communication
strategies by NGOs and government agencies drew the reasonable conclusion that
because the threat of climate change was perceived as something to worry about in the
future, increasing the fear factor might be a good way of getting people to be more
concerned (Feinberg & Willer, 2010).
For example, on Earth Day 2014, StopGlobalWarming.org sent e-mails
summarizing the recent release of the Intergovernmental Panel on Climate Change
(IPCC) report. The environmental organization’s e-mail subject line was “Climate
Change Report Offers Dire Warning.” The e-mail uses phrasing to describe the report,
such as, “paints a bleak picture” and “dire warning.” When encouraging citizen action,
fear-based language is still used, “the worst is yet to come if no measures are taken,” and
“catastrophic scenarios can still be avoided” (StopGlobalWarming.org April 22, 2014).
In both of these phrases, it is the threat that impacts the reader, not a sense of hope or
possibility for the future.
Another example comes from Green Peace, whose 2013 save-the-Arctic
campaign featured a bedraggled Santa Claus, in a stained undershirt, who threatens to
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cancel Christmas because melting polar ice has decimated his workshop at the North
Pole. In the print ads and short film produced, Santa implores children to get their parents
to sign the petition to express their outrage (Greenpeace, 2013).
Figure 1. 2013 Greenpeace Artic Protection Campaign
(Greenpeace.org.uk, 2015)
An approach built around fear appeals in not completely misguided. Spence, Poortinga,
Butler, and Pidgeon (2011) found that if the ‘psychological distance’ between an
individual and the impacts of climate change is reduced through a first hand experience,
such as experiencing a flooding event which is similar to the sort of impacts climate
change will bring, they are more likely to express concern over climate change and show
a greater willingness to adopt sustainable behaviors, such as reducing energy
consumption or their carbon footprint. Additionally, acknowledging that climate change
is happening, and will cause significant problems for human and natural systems can be a
frightening prospect, however research has shown that deliberate attempts to instill fear
or guilt in people carry a considerable risk of backfiring.
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Studies on fear appeals show the potential for fear to change attitudes or verbal
expressions of concern, but often not actions or behavior (Das et al, 2003). The impact of
fear appeals is specific to both the context and the audience. For those who do not yet
realize the potentially frightening aspects of climate change, people need to first
experience themselves as vulnerable to the risks in some way in order to feel moved or
affected (Das et al, 2003; Hoog et al, 2005; Spence et al, 2011). While fear of a negative
outcome, can be an effective way of promoting behavioral changes, the link between the
threat and the behavior must be personal and direct (Hoog et al, 2005).
Typically, climate change is perceived as neither a direct nor a personal threat,
therefore, scaring or shocking people into recycling is not necessarily the most effective
idea. Fear appeals often fall prey to denial responses (Schmidt, 2005) and are
disempowering, producing feelings of helplessness, remoteness and lack of control
(O’Neill and Nicholson-Cole, 2009). For these reasons, fear is not sufficient to achieve
long-term behavior change in human systems (LaTour & Rotfeld, 1997; Hulme, 2008).
LeDoux (1998) and Goleman and collegues (2001) explain why.
When we are afraid, we are hard-wired to engage in defensive behavior: we run; we
fight; or we freeze. Fear, a response activated and controlled by the amygdala, is in a
different part of the brain than the prefrontal cortex, which supports logical, strategic, and
reasonable decision-making, (LeDoux, 1998; Goleman et al, 2001). The body and brain
react to fear in a “prepackaged” way, shaped by evolution and occurring involuntarily
(LaDoux, 1998, p. 175) and these fear responses take place before the prefrontal cortex
has the chance to start thinking about what to do (LaDoux, 1998; Goleman et al, 2001).
When we are afraid, we dream smaller dreams, we speak less freely, we identify more
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easily with perceptions of an “us,” while growing more distrustful toward anyone we
perceive as one of “them.”
Several researchers have identified the relationship between the positive
emotional affect of communicative gestures and interpersonal interactions links to higher
levels of functioning and performance (Schwartz & Gottman, 1976; Schwartz, 1986;
Gottman, 1994, 1999; Losada & Heaphy, 2004). Fredrikson (1998) echoed the power of
positive affect in fostering systems for higher performance among individuals and teams.
The impact of positive interactions was confirmed by several other scholars who
advocate for emotionally safe or “expansive” and “generative” spaces in support of high
performance, improved levels of satisfaction of organizational members, and better
organizational capacity to deal with increasingly complex environments (Losada &
Heaphy, 2004; Echeverria, 1994; Stacey, 1992, 1996). This brief snapshot of the
scholarship that grounds this study will be expanded upon in later sections of this
dissertation. However, at this point, it is worth highlighting that two decades of research
support the relationship between positive language and higher levels of success in human
systems.
If we apply these rubrics to the sustainability discourse within higher education,
would we find language that leads to higher performance? What is the language of
sustainability currently directing our attention toward? The research described here
addresses these issues by way of answering two questions. First, what is the emotional
tone of sustainability language that is used in higher educational contexts? And second,
how does the emotional tone of campus sustainability narrative relate to the performance
of sustainability programs?
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In this study, language is positioned as the unit of analysis, which is best explored
using descriptive, qualitative methods. Scholars of social issues in management support
this approach and encourage descriptive research in order to bring clarity to ‘complex
social phenomena’ (Rozin, 2001; Margolis & Walsh, 2003). I have explored the language
used by sustainability professionals on ten university and college campuses in the State of
Ohio. The study included three phases. First I conducted a survey of the sustainability
narrative in higher education contexts, collecting data from campus sustainability
websites, interviews with campus sustainability leaders, and sustainability team meetings.
The second phase of the research study included a thematic analysis of the emotional tone
of sustainability language, applying Cooperrider and colleagues’ (2008) code of positive
and negative discourse categories. The code was further developed to capture possible
new categories. In the final phase of this research, I compared results from the thematic
analysis with sustainability program performance rankings, to identify whether emotional
tone of the sustainability discourse impacts the success of higher education sustainability
programs, based on national sustainability program rankings.
Chapter Two provides the context of my inquiry, providing background on the
higher education sector, current debates in the environmental field, and the relationship
between higher education and sustainability. Chapter Three reviews pertinent literature
which represents the groundwork of the theoretical basis from which this research has
emerged, including Organizational Change, Social Construction, Positive Organizational
Scholarship, Organizational Development, and Emotion. Chapter Four details the
research design, including sample, and data collection. In this section the qualitative
methodology employed to analyze data- thematic analysis- is discussed at length as well
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as the code which was used to analyze the data. Chapter Five presents the results of the
analysis, from multiple perspectives. Findings are offered organized by type of data,
performance category, and categorized by the positive / negative discourse code.
Discussion of the findings, implications for future research, contributions to the field, and
limitations of the study make up Chapter Six and final conclusions are offered in Chapter
Seven.
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CHAPTER TWO: BACKGROUND
This chapter provides the context for my study, including factors that created the
need for such a study and the landscape within which my research exists. I begin by
discussing the higher education sector, then the language of fear and negativity, followed
by a review of sustainability in higher education. Throughout this chapter, dynamics,
which are central to this research are highlighted.
The Role of Higher Education
As attention to ecological and social challenges deepen, colleges and universities
are increasingly committed to fostering learning and service for the purpose of
developing solutions for real world problems (Pollack et al, 2009). Calls for profound
changes in higher education are becoming commonplace as both critics and visionaries
lay out a context for education’s role in creating a sustainable future (Bowers, 2001;
Bogotch, 2002; Furman & Gruenewald, 2004; Reid & Petocz, 2006; UNESCO, 2006;
Hammond & Churchman, 2008). Blaze, Corcoran, and Wals (2004) believe higher
education should play the pivotal role in turning society toward a sustainable future. Orr
(2002) agrees, stating that the higher education sector is not only best situated, but also
obliged to lead this transformation of society.
These calls to action land in a sector with structures originated in medieval times
(Hammond & Churchman, 2008). The ‘ancient’ cultures of the academe can
inadvertently stall movement toward sustainable campuses and curricula (Hammond &
Churchman, 2008). From the 1960s to the 1980’s, numerous scholars explored the nature
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of higher education’s culture (for example, Clark, 1963, 1970; Becker, 1963; Chait, 1982;
and Dill, 1982). Dill (1982) found that higher education in the U.S. shared many
essential cultural characteristics with Japanese firms, a popular topic of study for
organizational scholars at the time. He wrote, "Ironically, the organizations in Western
society, which most approximate the essential characteristics of Japanese firms are
academic institutions. They are characterized by lifetime employment, centralized
decision making, individual responsibility, infrequent promotion, and implicit, informal
evaluation" (Dill, 1982).
The holistic, systems-oriented nature of sustainability, and its programs of
change, has often bumped up against the pillars of academic disciplines, which are
reinforced by professional bodies, career structures, and criteria for promotion and
advancement (Dill, 1982; UNESCO, 2002; Patterson, 2007). Since the 1980’s, academe
has been changing, gradually moving away from cultures of inquiry toward more
corporate models, defining managerial techniques based on strategic planning, marketing
and management control in higher education settings (Tierney, 1997,1988). There is an
increasing centralization of decision-making, marginalizing academicians from decisions,
and reducing the multiplicity of institutional views (Tierney, 1997, 1988; Murray &
Dollery, 2005). Multiple, and often contradictory, forces influence academia across
several aspects of these complex systems, pitting ethos and inquiry against resource
management (Sterling, 2004). Universities are increasingly reliant on fee charging,
research performance, and links with industry or government for their survival
(Marginson, 2000; Clugston, 2004), a practice that may impede the creation and
transformation of sustainability practices.
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Understanding the barriers inherent to universities, which inhibit innovation and
whole-system engagement, is important to this study. One example is the increasing lack
of a sense of collective community in an increasingly competitive and isolated
environment (Doyle & Hind, 1998; Churchman, 2004; Hammond & Churchman, 2008).
This relates to the tendency for competing university priorities, which serve to undermine
innovative and systemic programs (Thomas, 2004; Hammond & Churchman, 2008;
Rowe, 2007). While university priorities are often established in a “top-down” manner,
organizational change requires goals to be shared by members of the community and
developed through face-to-face discourse and discussion (Senge, 1990; Meadows, 1996;
Fien, 2002).
Isolated, compartmentalized, or piecemeal sustainability reforms- in other words
“bolt-on” sustainability- will not support an adequate or effective response to the current
ecological, ethical, and social concerns that comprise sustainability (Thomas, 2004; Orr,
2006; Rowe, 2007). Yet we know that campus sustainability programs are rarely
embedded throughout institutional operations and culture in higher education, often
remaining relegated to individual initiatives such as recycling or green buildings (Orr,
2006; Rowe, 2007).
The research described throughout this manuscript was initially inspired by the
frontiers and opportunities I saw in the higher educational sustainability field, such as 1)
What are campus Sustainability Professionals missing; 2) What skills or knowledge must
they acquire; and 3) In what ways must they interact with their institutions for more
comprehensive and systemic change? When I acted as a Sustainability Professional, I
came to the field with a theoretical and practical grounding in organizational change and
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development and could see the benefits of brining this body of knowledge to
sustainability in higher education.
In particular, I was curious about what ways the domain of positive organizational
scholarship (POS) could benefit and inform campus Sustainability Professionals. My
specific interest was in the role language plays as a tool in change.
A favorite quote from Henry David Thoreau is, “All good things are wild, and
free.” Language is as such. Language and the larger narratives it builds, is inherently
democratic in its availability. No matter how vast or limited resources possessed by a
sustainability program are, the Sustainability Professional always has choices about the
language she uses to guide and direct the attention of her audiences, the conversations she
has with other members of the sustainability initiative on her campus, and in crafting the
overall narrative of the sustainability program. Language can draw us closer to one
another, establish a sense of “we” and fuels the vision of what might be. It is a powerful
change technology , evolved over millennia, capable of changing the way people think,
how they act, and what they feel (Pagel, 2011).
The Language of Fear and Negativity
Language is at the heart of my inquiry and has been of growing interest among
others working with environmental organizations, political campaigns, and organizational
change. Since the mid 1990s, environmental insiders and critics alike have questioned the
methods used by large environmental organizations (Tokar, 1997; Schellenburg &
Nordhaus, 2004, Nordhaus & Schellenburg, 2007; Luntz, 2006) and have cautioned
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environmental organizations that unless the divisive approach is adapted to keep up with
its shifting target audience, environmentalism may well make itself irrelevant. (Luntz,
2006; Werbach, 2009; Shellenberger & Nordhaus, 2004; Norhaus & Shellenberger,
2006).
Schmidt (2005) characterizes environmentalism’s mindset as being data-focused,
“us versus them”, dualistic, punitive, regulatory, and fear-based. The emphasis on
compliance, regulation, and policies, while seemingly necessary for corporate polluters
from the 1970’s and 1980’s is characteristic of a deeper mindset which holds people to be
generally untrustworthy, out for themselves, and mal-intentioned (Hart & Milstein,
2003). Using focus groups and polling research methods, Luntz (2006) reports Americans
find the fear-based and us-versus-them messaging as being divisive, which inhibits
adoption of pro-environmental behaviors.
Kegan and Lehay (2001) find virtue in complaining, whining, and other
negatively toned language. While these researchers admit negatively toned language
lacks transformative ability, it allows people to let off steam, and serves a relational,
group dynamic function, allowing people to connect around a common enemy. When
people are disappointed, they feel less alone when they find allies who share their
perspectives, and therefore negative language and negative talk among colleagues can
play an important role in communication (Kegan & Lehay, 2001). Fear-based language
is widespread, complaining exists across contexts, and it possesses something essential:
passion. “Where there is passion, there are also possibilities for transformation. People
don’t complain if they don’t care.” (Kegan & Lehay, 2001, p. 20). Baumeister and his
colleagues (2001) agree, finding negative circumstances take a more significant toll on
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our senses, compared to positive experiences, and therefore our reactions to, and the
impact of, negative life events is stronger.
Fear-based communication strategies remain central in advertising, politics, and
entertainment (Shellenberger & Nordhaus, 2004; Nordhaus & Schellenberger; 2007).
LeDoux (1998) reminds us how powerful fear is, inextricably linked to our perceptions of
survival. In organizational settings, many managers would argue that some degree of fear
is a good thing, motivating employees to follow cultural norms and perform at higher
levels than if they did not feel some insecurity about their positions (McManus, 2006;
Kotter, 1996; 2008; Pechmann & Reibling, 2006,). Marketers use the “fear appeal” to
stimulate our innate drive for status to sell everything from financial services to
moisturizers (Lawrence & Nohria, 2002; McManus, 2006; Potter et al, 2006). The
entertainment industry uses fear appeal as the basis of television programming (Potter et
al, 2006), and fear has been a tool leveraged by politicians for centuries (Machiavelli,
1513; McManus, 2006; Pechmann & Reibling, 2006; Westen, 2008). If fear is effective
at capturing attention, and influencing decision making among individuals, does it also
drive successful systemic change? This study sheds light on this very question, and in
later sections reveals what emotional tones, and in what ratio, are associated with high
performance.
Sustainability in Higher Education
In 1987, the Brundtland Commission Report popularized "ecologically sustainable
development" as a means for simultaneously dealing with economic and ecological
problems (World Commission on Environment and Development, 1987). The 1992 Earth
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Summit in Rio de Janeiro further cemented international commitment to ecologically
sustainable development through treaties for dealing with ozone depletion, global
warming, and declining biodiversity (Stern et al, 1992). Sustainability has been defined
in a variety of complimentary ways, distinct to the sectors engaging in it. Broad
agreement exists that it is “the ability to meet the needs of today, without compromising
the ability of future generations to meet their needs” (World Commission on
Environment and Development. 1987). Elkington (1994) offers a corporate-specific
definition, describing the sustainable firm as one which “contributes to sustainable
development by delivering simultaneously economic, social, and environmental
benefits—the so-called triple bottom line.'' Cortese (1993) provides a definition
particularly tailored to higher education, calling for learning environments which fosters
awareness, knowledge, skills and values to achieve a future where current and future
generations achieve good health, economic security, social fairness and stability while
restoring and sustaining the Earth's life support systems.
A definition I find particular affinity with comes from Ehrenfeld (2008) who
suggests the word ‘sustainability’ itself inspires short term thinking and development of
strategies concerned with reducing harm, but not necessarily creating or developing value
or improvement. Ehrenfeld encourages us to diverge from this ‘be less bad’ mindset and
offers a rather hopeful, future-oriented term: flourishing. Ehrenfeld offers an expanded
and generative definition for sustainability: sustainability is the possibility that humans
and all other life will flourish on Earth for all time (Ehrenfeld, 2008; Hoffman &
Ehrenfeld, 2013; Laszlo & Brown, 2014).
Many in the higher education sector believe we are in the midst of revolutionary
17
change. In just the past few years, the threat of global warming has shifted in the United
States from a distant worry to a present and intense national public conversation.
Business leaders and policy makers are responding with new processes and products and
markets are shifting dramatically. These shifts have been rapid by any measure and they
challenge American higher education to keep pace, re-design, and ultimately to lead in
the realm of the environment and sustainability. American higher education produces 30
percent of the world’s scientists and a remarkably large percentage of the world’s
business, diplomatic and government leaders (Cortese, 1993). But even institutions as
accomplished as U.S. colleges and universities change at different speeds and in different
ways.
In 2008, the Association for the Advancement of Sustainability in Higher
Education (AASHE) reported the changes on educational campuses needed to embrace
the new energy economy began long ago, however, they may actually be lagging in
higher education overall. AASHE’s findings differed for campus leadership and
operations. They found that presidents, administrators and physical plant managers value
sustainability; they speak to it, hire the staff to support it, and the campuses they lead are
steadily becoming “greener”. But at the same time, the educational programs offered to
students have not reflected the institutional values (AASHE, 2008). From 2001 to 2008
comparison of the curricular and academic dimensions of sustainability showed no
significant gains, despite the growing depth of the global warming challenge and what it
means to future professions and their related disciplines (AASHE, 2008; Coyle, 2008).
To facilitate campus cultures, which embrace sustainability embedded throughout
the institutional culture, campus sustainability programs, and the Sustainability
18
Professionals who drive them, must necessarily see the whole rather than simply the parts
of the institutional system. Sustainability efforts on campuses must take into account
additional characteristics of campus life, such as the diversity of services and functions of
a campus, to include the different meanings the campus may attribute for different
stakeholders. Chapter Three presents a review of literature from management and
psychology, which speak to these tasks, and form the theoretical basis of this study.
19
CHAPTER THREE: LITERATURE REVIEW
This chapter provides a review of the literature relevant to this study on the
language of sustainability in higher educational contexts and the impact the emotional
tone of this language has on sustainability program performance. Each section of this
chapter includes an overview of the bodies of literature, which are pertinent to my
research and includes linkages to the issues and research that are most relevant to my
study. The review begins with a discussion of social construction, followed by
organizational change, positive organizational scholarship (POS), POS and language, and
emotion.
Social Construction
Perhaps to a far greater extent than is generally acknowledged, human systems
create their realities through symbolic and mental processes (Schwartz, 1986; Shotter,
1993). Human systems mentally and verbally project expectations and images ahead of
themselves, which can serve to bring the future powerfully into the present as a
mobilizing agent (Cooperrider, 1997). Images are operative, virtually everywhere. Every
system holds conscious and subconscious self-images, images of its own potential and
desired future, as well as the potential of the others. It has been argued (Barrett &
Cooperrider, 2001) that every organization, product, or innovative service began as an
image, the spark of an idea, which was developed and nurtured into fruition. In this
sense, positive images build hope and momentum, and are essential elements in
organizational change, as powerful tools for a new narrative.
20
A central premise of social construction is that knowing is created and takes place
through interactions within and with human systems (Cooperrider et al. 2003). In other
words, human systems are the broad products of agreement of the members of the
system. As such, what can be called the culture of a system-- the patterns of actions,
production, or interactions—is not fixed, but remains a dynamic process of construction
based on dialogue, interpretations, and narratives, in iterative cycles of re-affirmation
(Barrett et al. 1995; Cooperrider et al. 2003). The actions taken within human systems
are done so within an agreed upon context, predicated on the stories, beliefs, and meaning
making of each system. The stories, beliefs, and meaning making are embedded in the
language of the system (Gergen, 1982; Ludema et al. 1997; Cooperrider et al. 2003;
Barrett et al. 1995), thus making language one of the most powerful vehicles
communities have for changing social action (Cooperrider et al. 2003).
Through this constructionist epistemological lens, the development of
vocabularies of hope becomes an essential methodological tool for crafting positive
changes within organizations. In this perspective, words are not pictures, but rather tools
and navigation devices (Barrett et al. 1995), allowing members of systems to coordinate
ongoing relations with one another. Through the combination of cognition and emotion,
generative images and vocabularies of hope become powerful catalysts for change
(Cooperrider, 1997; Ludema et al. 1997). Social construction is a useful theoretical and
methodological lens to explore the role of language in creating positive organizational
change.
21
Constructionism is based on the notion that knowledge is not a product of
empirical observation as more positivist paradigms would suggest, but rather knowledge,
like language, is a social artifact (Ludema et al. 1997). Three tenets of social
construction are particular helpful for this conversation, integrating positive imagery and
positive language (Berger & Luckmann, 1967; Gergen, 1982, 1994; Astley, 1985; Schein,
1985; Unger, 1987; Clegg, 1990; Weick, 1995; Ludema et al. 1997).
1. The pragmatics of language: Words do not gain their “truth value” by
accurately describing the world (picture theory of language); rather they
gain their power by virtue of their function within sets of relationships and
as public activity (Wittgenstein, 1968; Barrett et al. 1995). Thus,
vocabularies that offer positive images and hopeful possibilities for new
ways of relating function are powerful resources for strengthening social
and organizational systems.
2. Language, knowledge, action: Constructionist views a direct and
simultaneous link between language, knowledge, and action. All language
sustains certain kinds of knowledge, to the exclusion of others, and all
knowledge sustains certain patterns of activity, to the exclusion of others.
Thus, the more hopeful the available vocabularies, the more positive will
be the forms of social action and organizing that they support.
22
3. Methodology: Social construction encourages a generative theoretical
approach. Because constructionism is based on the belief that “words
create worlds,” methods of inquiry, which highlight and establish future
orientated and optimistic visions of social and organizational life are
sought out. Rather than critiquing “Does this method efficiently eliminate
the opposition?” the question for evaluating good method becomes, “To
what extent does this method stimulate moral dialogue about how we can
and should organize ourselves, and to what extent does it present
compelling new images and possibilities for collective action?”
These three tenets in conjunction with the positioning of dialogue, made possible
by language, is one of the most powerful resources in any system for changing social
order. Therefore, changes in linguistic practices hold profound opportunities for change
in social systems (Cooperrider et al, 2008). Through the application of social
construction, language becomes one of the most democratic and powerful tools of
organizational change, accessible by all, and powerful in the creation of positive change.
Organizational Change
A review of leading organizational development (OD) and change theories is
necessary for any discussion about the success of campus sustainability programs, which
are implicitly and explicitly comprehensive and systemic change initiatives. The
following section reviews three theories, which have informed and shaped this research:
Kotter; Schein; and POS.
23
John Kotter and Edgar Schein are two practitioners in the world of organizational
change worth mentioning in any conversation about change in complex systems, due to
their influence on the field, and- specifically relevant to this research- because of their
assessments of the role of anxiety and fear in change processes. We begin with Kotter.
One of the pertinent aspects of Kotter’s work is to eliminate the “existing
mindset,” which uses fear to build a sense of urgency as an unfreezing fulcrum in
organizational change (1996, 2008). Whereas Schein, an organizational development
theorist, proposes that fear is necessary and natural in organizational change (1995,
1999).
The emphasis of Kotter’s work is on overcoming complacency in order to achieve
excellence. As an organizational strategist, he prescribes creating a sense of urgency, not
only as the first step in any change process, but because “without belief that the status
quo is unacceptable, it is difficult to make any progress on a major change effort”
(Kotter, 1996, p.46). Kotter espouses the use of fear to motivate change, like the
examples provided in earlier sections of advertisers and environmental NGOs. Kotter
advises managers to use urgency proactively and goes so far as to suggest leaders create
fictional crises or allow for real crises to emerge, such as allowing a financial loss, to
motivate teams to achieve better performance (Kotter, 1996, 2008; Cooperrider &
Sekerka, 2006). Despite specific examples and suggestions of turning up the heat on an
organizational sense of urgency, Kotter does not describe any differentiation between
creating a sense of urgency and a sense of fear, or how much urgency is effective versus
levels of urgency which lead to anxiety, potentially stalling change efforts.
24
Kotter identifies urgency (rather than passion, learning, engagement, or meaning)
as the antidote to employee complacency. He offers that, in a complacent environment, a
leader should “…relentlessly bombard employees with information about problems”
(p.45), or expose managers to negative data and unsatisfied stakeholders will be the
disconfirming data necessary to instigate new behaviors (1996). Kotter created a guide
(2008) solely dedicated to leveraging urgency, which focused managers’ and leaders’
attentions on their organization’s need to overcome a lack of urgency and an excess of
complacency. Kotter’s view of organizations is not shaped by a belief that “everything is
a mess, but instead, that the world contains great opportunities and great hazards” (2008,
p. 129).
Despite his recurring stance advocating for the power of urgency, Kotter’s
perspective is holistic. Highly successful behavior change happens primarily by speaking
to people’s feelings (Kotter & Cohen, 2002, p. 194). In successful change efforts, people
find ways to help others see the problems or solutions in manners that influence
emotions, not just thoughts. “Fear can produce movement. It can dynamite a cement
wall, but we have yet to see great transformations launched with fear as the primary and
sustaining force.” (Kotter & Cohen, 2002, p. 28).
Organizational theorist Edgar Schein, like Kotter, is also concerned with creating
organizational excellence, however he offers a different approach to achieve this goal.
Schein counsels practitioners (1999; 1987; 1993) about designing and leading
transformational organizational change based on his early work in clinical and social
psychology, which dealt with attitude changes that had occurred in military and civilian
prisoners of the Chinese Communists during the Korean war (Schein 1956, 1961, 1968).
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Schein was inspired by Lewin's three-stage model of change (1947), which
depicted a process of un-freezing, changing, and re-freezing (or crystallizing), and this
model became the theoretical foundation upon which Schein’s own change theory was
built (Schein, 1987, 1993, 1995, 1996, 1999a, 1999b, 2002). Lewin's basic model of
change prompted Schein to develop a range of insights and new concepts that enriched
change theory and made change dynamics more understandable and manageable.
Central to Schein’s approach was to see that human change, whether at the
individual or group level, was a profound psychological process that involved unlearning,
with some degree of loss of ego identity and difficult re-learning as one cognitively
attempted to restructure one's thoughts, perceptions, feelings, and attitudes (Schein, 1993,
1995, 1999a, 1999b, 2002). Schein’s model of change, presented in Table 1, is
comprised of three stages-- un-freezing, changing, and re-freezing-- with sub-stages:
disconfirmation; induction of guilt or survival anxiety; creation of psychological safety or
overcoming learning anxiety; cognitive redefinition; imitation and positive or defensive
re-identification with a role model; scanning, insight, or trial and error learning; and
finally, personal and relational re-freezing (Schein, 1993; 1995; 1999a; 1999b, 2002).
Each of these stages is described in greater detail below Table 1.
26
Table 1. Stages of the Change Process (Schein, 2002) Stage 1. Unfreezing: Creating the motivation to change Disconfirmation Creation of survival anxiety or guilt Creation of psychological safety to overcome learning anxiety Stage 2. Changing: Learning new concepts, new meanings, and new standards Imitation of and identification with role models Scanning for solutions and trial-and-error learning Stage 3. Refreezing: Internalizing new concepts, meaning, and standards Incorporating into self-concept and identity Incorporating into ongoing relationships and groups
Note. Adapted from “Models and Tools for Stability and Change in Human Systems,” by Edgar H. Schein, 2010. Reflections, 2, p. 36. Copyright 2002 by the Society for Organizational Learning and the Massachusetts Institute of Technology.
Disconfirmation. Like Kotter (1996, 2008) Schein believes that all forms of
change (a term he uses interchangeably with learning) begin with some form of
dissatisfaction or frustration generated by data that dis-confirm previously held
perceptions of reality, expectations, or hopes (1995; 1999). This may be adaptation to
changing environment, evolving sector regulations, or shifting customer desires.
Disconfirming information can also be genuinely creative and generative learning, a
desire to know more, to be more. No matter the shape it takes, disconfirmation of some
previously held belief is the launch pad and pre-requisite for learning and change to occur
(Schein, 1995; 1999).
While disconfirmation functions as the primary driving force in learning, the
information is confronted with challenges before it is accepted. As information that
challenges our “quasi-stationary equilibria”, it is natural for individuals and large,
complex systems alike to resist disconfirming information (Schein, 1993; 1995; 1999;
1999). The information can be ignored, dismissed as irrelevant, or, as has been the case
27
with sustainability, the validity of the information is questioned or fully denied (Schein,
1999).
In order to continue through the following stages and undergo a complete change
process, the information must be accepted and connected to something the system cares
about. As such, the disconfirmation arouses "survival anxiety" or the feeling that if
change does not occur, the system will fail to meet its needs or fail to achieve its goals or
ideals ("survival guilt") (Schein 1999).
Anxieties Associated with Learning and Change. It is at this stage in the learning
process that Schein’s theory of change diverges from Kotter’s (Kotter, 1996, 2008;
Schein 1968, 1999, 1999). Schein acknowledges the natural, and often subconscious,
grief reaction that results from accepting disconfirming information and leaving a
previously held belief, habit, or mindset. While the grief reaction from disconfirmation
may be subtle, anxieties arise. In light of the disconfirming data, feelings of survival
anxiety or guilt may be present, as the goals or wellbeing of the system may now seem to
have been compromised prior to the new information, practice, or mindset. Additionally,
the sense of defensiveness that can arise when presented with disconfirming information
is learning anxiety, which is the feeling that if we allow ourselves to enter a learning or
change process, if we admit that something was wrong or imperfect, we lose our grip on
effectiveness, self-esteem, and maybe even our identity. Learning anxiety is the
fundamental restraining force, which increases in direct proportion to the amount of
disconfirmation, leading to the maintenance of the equilibrium by defensive avoidance of
the disconfirming information.
28
This process can be conceptualized in its own right as creating for the learner
some degree of "psychological safety" (Schein, 1995; 1997). Attention to this stage and
skill in decreasing learning anxiety while increasing psychological safety is the key to
producing change (Schein, 1987; 1993; 1995; 1996; 1999; 1999). Schein argues that
unless sufficient psychological safety is created, the disconfirming information will be
denied or in other ways defended against, no survival anxiety will be felt, and learning
anxiety will work to keep change at bay (Schein 1993; 1995; 1999; 1999). The key to
effective change management, in Schein’s view, becomes the ability to balance the
amount of perceived threat and anxiety produced by disconfirming data with enough
psychological safety to allow the change target to accept the information, feel the survival
anxiety, and begin moving toward change and learning.
Framed in this way, it becomes clear that in Schein’s belief system, change
management involves a delicate approach, balancing various tactics that change agents
employ to create psychological safety. Schein uses examples of breaking down the
learning process in to small steps and creating small groups or teams to address change to
address change together. Providing practice fields in which errors are embraced as
valuable learning rather than failure or the creation of parallel learning systems that allow
some relief from day to day work pressures, and use of a shared positive vision to
encourage the learner are additional ways of increasing learners’ sense of security
(Schein, 1995).
Restructuring. Cognitive restructuring, sometimes referred to as re-framing, is
the process by which we learn something new. This is the phase of learning when new
information is taken in, resulting in one or more of the following modes of redefinition:
29
1) semantic redefinition, which is learning that words can mean something different from
what we had assumed; 2) cognitive broadening is when we learn that a given concept can
be much more broadly interpreted than what we had assumed; and 3) new standards of
judgment or evaluation--we learn that the anchors we used for judgment and comparison
are not absolute, and if we use a different anchor our scale of judgment shifts (Schein,
1995).
According to Schein, the key to sustained change is about how new information is
assimilated by learners. The new information that makes any or all of these processes
possible comes into us by one of two fundamental mechanisms. The first approach—
identification with a role model—offers easier short-term gains in assimilation of the new
information but with questionable long-term results (Schein, 1995; 1999). The second
approach—learning through trial and error, or scanning—while a longer process, results
in more thorough assimilation of the new information (Schein, 1995; 1999; 1999).
Imitation and Positive or Defensive Identification with a Role Model. As
described above, cognitive re-definition occurs when the learner has become unfrozen,
has accepted disconfirming information as is motivated to change, and has, therefore
opened him or herself up to new information. The most basic mechanism of acquiring
new information that leads to cognitive restructuring is to discover in a conversational
process that the interpretation that someone else puts on a concept is different from one's
own. If one is motivated to change, like when the factors described above have been
operating, he or she may be able to "hear" or "see" something from a new perspective.
Schein’s dissertation and early body of work focused on this area of change, in his studies
of what has colloquially been labeled "brainwashing" with POWs held by Chinese
30
communists (Schein 1956, 1961, 1968). The POWs were judged "guilty" yet felt
innocent (disconfirmation). The prisoners were finally able to admit their guilt when they
could identify with their more advanced cell mates sufficiently to realize that the
concepts of "crime" and "guilt" were defined differently by the Chinese communists. One
was guilty because a crime was defined as "any action that could be harmful to the
communists" even if no harm had occurred. A postcard home, could conceivably contain
information that would help the enemy; so sending the postcard was an act of espionage,
no matter what information it contained. The sender had to learn to appreciate and
confess his or her guilt. Moreover, being born into the wrong social class was a crime
because middle class attitudes could be very harmful to the communist cause (Schein
1956; 1995). Semantic redefinition, cognitive broadening and changing standards of
judgment were all present in this process.
Only by recognizing this potential for harm, confessing one's guilt, and
acknowledging the incorrectness of one's social origins could one hope to learn how to be
a good communist or to be released from jail. Once one had accepted the new cognitive
frame of reference and learned the new definitions and standards, one could make rapid
progress in re-education and remove the heavy disconfirming pressure. The key to the
whole process, however, was to identify psychologically with other prisoners (role
models) who had already made the cognitive shift and learning to see the world through
their eyes (Schein, 1995). Outside of this painful context, there are parallels for
organizational change. In many types of organizations, identification takes the form of
mentoring programs. The mentor is often both a source of psychological safety and the
role model to facilitate cognitive redefinition (Schein, 1968; Van Maanen & Schein,
31
1979).
Defensive identification. Defensive identification is an arguably less common
process that occurs when the learner is a captive in a hostile environment in which the
most salient role models are the hostile captors, such as prison guards, or in more
common situations, authoritarian bosses. Defensive identification, or identifying with the
aggressor (Bettelheim, 1943) was described in relation to Nazi Concentration Camps
where some prisoners, in the face of survival anxiety, took on the values and beliefs of
the guards and maltreated fellow prisoners. Genuine new learning and change occurred,
but, of course, in a direction deemed undesirable by others (Schein, 1995).
While an extreme and uncomfortable example, this serves to remind practitioners
that unfreezing creates motivation to learn (adaptation, not inquiry), but does not
necessarily control or predict the direction of learning. If the only new information
available is from salient and powerful role models, learning will occur in that direction.
One of the key elements of a managed change process is, therefore, what kind of role
models one makes available to the learners once they are unfrozen. In the event that
there are no positive role models or one wants the learning to be more genuinely creative
and self-identified with by the learner, the conditions for “scanning” must be created
(Schein, 1995; 1999).
Scanning: Insight or Trial and Error Learning. A learner or “change target” can
be highly motivated to learn something, yet have neither appropriate role models nor
initial feelings for where the answer or solution might lie. The learner then searches or
scans his or her environment by reading, looking on line, traveling, talking to people,
hiring consultants, entering therapy, going back to school, to expose him or herself to a
32
variety of new information that might reveal a solution to the problem. Alternatively,
when the learner finally feels psychologically safe, he or she may spontaneously
experience an insight that spells out the solution. Practitioners and change agents count
on such insights because of the assumption that the best and most stable solution will be
one that the learner has invented for him or herself. Once some cognitive redefinition has
taken place, the new mental categories are tested with new behavior which leads to a
period of trial and error, either reinforcing assimilation of the new behaviors, new
information, new mindsets, or starts a new cycle of disconfirmation and search.
Schein notes that in the process of search, if role models are readily available,
they will most likely be used. Identification is thus an efficient and fast process, but it
may lead to solutions that do not stick because they do not fit the learner's total
personality. If one wants to avoid that, one must create learning environments that do not
display role models, thereby forcing the learner to scan and invent his or her own
solutions.
It is this dynamic, to rely on identification with a role model, that explains why so
many consultation processes go awry. The consultant, by design or unwittingly, becomes
a role model and generates solutions and cognitive categories that do not really fit into
the culture of the client organization and will therefore only be adopted temporarily. A
similar result occurs when organizations attempt to check on their own performance by
"benchmarking," or comparing themselves to a reference group of organizations and
attempting to identify "best practices." The speed and simplicity of that process is offset
by two dangers. First, it may be that none of the organizations in the reference set have
scanned for a good solution so the whole set continues to operate sub-optimally, or,
33
second, that the identified best practice works only in certain kinds of organizational
cultures and will fail in the particular organization that is trying to improve itself. In other
words, those who are modeling themselves on others can attempt to learn things that will
not survive because they do not fit the personality or culture of the learning system. For
change to remain more stable it must be re-frozen or crystallized.
Personal and Relational Re-freezing (Crystallizing). Central to the success of
learning new behaviors is the congruence with the rest of the learner’s suite of behaviors
and personality. If a new practice or mindset does not fit the broader and existing
constellation of beliefs and habits, it will launch new rounds of disconfirmation that often
lead to unlearning the very thing one has learned. Schein makes his teaching point clear
to practitioners: for a crystallization or re-freezing to occur it is best to avoid
identification with a role model and encourage scanning so that the learner will pick
solutions that fit him or her. Moreover, when an old behavior has been supported or
enabled by a group, relational re-freezing must occur, thus, it is best to train the entire
group that holds the norms (Schein, 1968; 1995; 1999).
To summarize, both Kotter and Schein have put forth change theories, which can
be characterized as disconfirming. The disconfirmation perspective is deficit-focused,
identifies dis-satisfactions, and focuses on fixing errors. From my vantage point, it is
indiscernible whether sustainability thought-leaders, framing current sustainability
discourse, are intentional in their use of disconfirming change strategies, but it is clear
that by using visual images and stories of planetary crisis, collapse, or threats to national
security in order to motivate society-wide change, we are focusing attention on global
scale, systemic problems that need solving, rather than creating new opportunities or
34
hopeful narratives to lead us forward.
Disconfirming change theories have deep roots in the academic community and
are widely applied by practitioners, however with mixed results. This may be due to the
fact these theories are not transformational, but rather reactions to dysfunction (Sekerka
& Fredrickson, 2008).
Disconfirming theories of change, and the beliefs about human systems
underpinning these approaches to change, are no longer the only game in town. What
would happen to our change practices if we began all of our research or our practical
interventions with the positive presumption—that organizations, as centers of human
relatedness, are alive with infinite constructive capacity (Cooperrider & Whitney, 2001)?
Positive Organizational Scholarship (POS) is trying to answer that question.
POS offers the OD field balance through its affirmative stances. Through the POS
lens, organizational development and change is a purposeful evolution (Sekerka &
Fredrickson, 2008). This confirming, constructionist approach to change, which is
interested in contributing to the best of organizational life, is what the research detailed in
this manuscript is aligned with.
The field of (POS) offers a theory of change and for how we view human systems
and organizations. Underpinning POS is a desire to better understand how to build
contexts that enable human flourishing (Dutton & Heaphy, 2003). POS views
organizations as being alive and hubs of human relatedness (Cooperrider & Sekerka,
2006) and considers organizations as being energized by and drawn toward desired
futures, not just away from dis-satisfaction. The POS orientation focuses on developing
strengths of the organization, including high quality relationships (Dutton & Heaphy,
35
2003) and fostering narratives and vocabularies of hope (Ludema et al, 1997; Barrett,
1990). Through scholarship and practical applications, POS addresses multiple levels of
system and employs strategies focusing on idealized images of the future (Fredrikson,
2003; Cooperrider & Sekerka, 2003).
One particular area where a confirming theory of change differs from
disconfirming counterparts is in the privilege given to fear and anxiety in the change
process. In POS and confirming change, positive emotional affect and psychological
safety are highlighted. An early example comes from Schwartz (1986), who offered an
early paper on the power of positive emotional affect among individuals, arguing a 1.7:1
ratio of positive to negative ratio of internal dialogue or self-talk was found among higher
functioning participants in his sample. Fredrickson (1998) posited that positive emotions
serve to broaden one’s momentary “thought-action repertoires,” which in turn has the
effect of building that individual's physical, intellectual, and social resources, allowing
them to think more creatively, see more options, and read their environment with more
accuracy. In later work, Fredrickson (2001) further developed this idea, advocating for
the development of more emotionally safe or “expansive” and “generative” spaces in
support of higher performance, improved levels of satisfaction of organizational
members, and better organizational capacity to deal with increasingly complex
environments. More discussion of the positive emotional affect is described throughout
the rest of this chapter.
36
Positive Organizational Scholarship (POS) & Positive Psychology
Positive psychology is the scientific study of the strengths and virtues that enable
individuals and communities to thrive (Duckworth et al, 2005; Peterson et al, 2003). POS
seeks to understand what represents the best of the human condition, emphasizing
positive deviance, exploring enablers, motivation, and effects associated with positive
interventions (Cameron et al, 2003). These complimentary fields were founded on the
belief that people want to lead meaningful and fulfilling lives, to cultivate what is best
within themselves, and to enhance their experiences of love, work, and play (Seligman,
2007).
Positive psychology has three central concerns: positive emotions, positive
individual traits, and positive institutions. Understanding positive emotions entails the
study of contentment with the past, happiness in the present, and hope for the future.
Understanding positive individual traits consists of the study of the strengths and virtues,
such as the capacity for love and work, courage, compassion, resilience, creativity,
curiosity, integrity, self-knowledge, moderation, self-control, and wisdom. Understanding
positive institutions entails the study of the strengths that foster better communities, such
as justice, responsibility, civility, parenting, nurturance, work ethic, leadership,
teamwork, purpose, and tolerance (Seligman & Csikszentmihalyi, 2000;
Csikszentmihalyi, 2000).
In an extensive review of existing literature on the influence internal dialogue has
on human systems, Schwartz (1986) suggests higher functioning systems exhibit at least
a 1.7:1 ratio, of positive to negative thoughts, whereas dysfunctional counterparts were
characterized by a 1:1 ratio, balancing positive and negative thoughts. Both Schwartz
37
(1986) and Cooperrider (1997) argue that positively biased organizational
communications and “internal dialogue” will contribute more to heliotropic movement
toward goals and future images than either neutral (characterized by in-attention) or
organizational dialogue and communications underpinned with a negative or restrictive
tone, such as a focus on problems or deficiencies (Cooperrider, 1997).
Cooperrider (1997) explores the thesis that the creation of positive imagery on a
collective basis may well be the most fruitful activity for systems to engage in if their aim
is to help bring to fruition a positive and humanly significant future. In his exploration of
the potential of positive imagery in human systems, Cooperrider (1997) offers a review of
the literature on human systems and positive images. The author stresses the consistency
of findings of research done on the relationship between positive imagery and positive
action across diverse areas of study and across levels of system, illustrating his points by
citing examples using placebo, Pygmalion studies in primary schools, positive emotion,
internal dialogue, cultural vitality, and metacognitive competence (1997). The more an
organization experiments with the affirmative mode, the more its affirmative and
heliotropic competence will grow. Citing William James, Cooperrider’s conclusion is that
when human systems identify or state an image that asserts that the future is worth living
for, it will provoke those actions that help create the fact.
Emphasizing the positive is a position that Hallsmith (2003) counsels systems
starting the process of adopting more sustainable modes of operating. Instead of focusing
on what can be defined as problems, institutions and communities are more likely to
attract participants and support to sustainability efforts when the message and images are
positive. Moreover, she argues an initial collection of the strengths and positive stories of
38
the system build a base of information about the community that can help identify
strategies of improvement (Hallsmith, 2003).
Cooperrider (1997) connected streams of research, inquiry, poetry, and
philosophy for a treatise on the conscious crafting of future images. “Human beings,” he
asserts, “create our own realities through symbolic and mental processes.” Because of
this uniquely human ability- of future-creating mental activism- consciousness evolution
of the future is a human option that should be exercised. The development and use of
positive imagery reveals itself as permeable and emergent, open to the mind’s causal
influence. “[Reality] is conditioned, reconstructed, and often profoundly created through
our anticipatory images, values, plans, intentions, beliefs, and the like.”
Of critical relevance to my research is Cooperrider’s argument that the
relationship between image and action in human systems operates in a heliotropic
fashion, meaning human systems exhibit an observable and largely automatic tendency to
evolve in the direction of positive anticipatory images of the future. Important under-
girders to the research proposed here are drawn from Heidegger (1967, 1971), Schultz
(1967) and Weick’s (1976) work, which collectively suggests that each social action
begins with an image of the future, engaging us in a forward-looking projection of
ourselves into desired future images. As such, our future images become active causal
agents in the present (Cooperrider, 1997).
In the absence of a vision, a system is likely to “muddle along with business as
usual, not satisfied with the way things are, with no clear plan that takes them to another
reality” (Hallsmith, 2003). The use of generative discourse, hopeful images and a
compelling vision of the future serves not only to inspire systems engaged in change, but
39
also to liberate these communities from tacit and unconscious beliefs and motivations
which are keeping the status quo in place (Hallsmith, 2003; Coupland et al., 2005). The
value of a shared vision emerges when the community creates a common understanding
regarding its direction and goals and when members co-author their future (Hallsmith,
2003; Shotter, 1993). This shared sense of a desired future, this shared sense of the
benefit they will move toward attaining, encourages, motivates and coordinates actions
that create transformational change in systems (Senge, 2006; Hallsmith, 2003;
Cooperrider, 1997). Using positive images and compelling visions of the future, systems
make explicit and conscious common goals, and thus the actions to achieve these,
through the use of positive images and visions of the future and by focusing on benefit
attainment, rather than simply behavioural change (Goleman, 2000; Goleman et al,
2001).
A frequently cited phenomenon illustrating the power of image is the Pygmalion
(or fulfilled prophesy) dynamic, which can be applied positively and negatively. The
Pygmalion dynamic is the power of positive images creating self-fulfilling realities.
Three phases of the positive Pygmalion dynamic (as modelled by Jussim, 1986) begin
with a positive image of others followed by affirmative thoughts and affirmative actions,
leading to a heliotropic (creating positive, image fulfilling) action (Cooperrider, 1997
citing Jussim, 1986). The initial stage of Jussim’s model describes how positive images
of the other are formed through mechanisms like reputation, projection, stereotypes,
objective measures, early performance, and the like. From this first phase, interactions
occur over time and the positive images begin to materialize, fulfilling prophecies and
expectations. Entering into the second phase, the initial positive expectations and images
40
are enhanced by additional behaviors and responses that were not included in the early
estimation. Combined, these anticipatory positive images stitch together a patchwork of
positive “interpersonal expectancy” mediated by expectancy-consistent cognition and
expectancy-consistent treatment (Cooperrider, 1997). Through positive expectancy, it is
possible that this affirmative capacity to cognitively tune into the most positive aspects of
another person. Positive expectancy refers to an attitude of optimism. It is a state of
confidence expectation and pleasurable anticipation (Rosenthal & Jacobson, 2003),
which can be applied as a cognitive and creative tool in the construction of reality and
future images. We see what our images make us capable of seeing. In the final phase of
Jussim’s model (1986), perception, memory, learning are each cued and shaped by the
positive images projected through expectancies. Through this process, human systems
fall into a cycle of affirmative and prophetic expectancies, seeing “proof” of our images
through, which further endows our positive prospects of the future. As the adage goes,
“seeing is believing,” thus our acts often take on deeper positive tone and character
depending on the strength, vitality, and force of a given image (Cooperrider, 1997).
A more recent conception of heliotropism in organizations is mirror flourishing.
Cooperrider and Fry (2013) and Cooperrider (in Laszlo & Brown, 2014), have observed
that when institutions themselves become vehicles of flourishing through corporate social
responsibility (CSR) and sustainability initiatives, the people within those institutions
begin to experience a similar phenomenon in their own lives, such as increased senses of
connectedness and thriving (Glavas & Piderit, 2009). Cooperrider and Fry call this
phenomenon mirror flourishing (2013).
41
The promise of mirror flourishing is one of intimacy and deepening connection to
the relational field surrounding, including, and within organizations. Cooperrider and Fry
define mirror flourishing as “the constant flourishing or growing together that happens
naturally and reciprocally to us when participate in, or witness, the acts that help nature
flourish, others flourish, or the world as a whole to flourish” (2012, p. 8).
The mirroring effect is not a one-way expression, nor is it simply a receiving of
good feelings. It is, writes Cooperrider (in Laszlo & Brown, 2014), a symbiotic flow,
speaking to the unified and integrative relationships between our world, our institutions,
and ourselves, further blurring boundaries and calling into question notions of “in here”
and “out there.” The implications for Sustainability Professionals are noteworthy: mirror
flourishing presents the possibility that when we help the world to flourish, we too may
flourish.
Support for the two-way manifestation of flourishing is supported in Post’s
extensive writing on altruism (2002, 2003, 2005; Post et al, 2002; Post & Neimark,
2008). Post has observed and documented the positive unintended consequences of
altruism, categorizing how when we do good for others, we thrive as a result, with
improved well-being, physical health, social connections, and longevity (Post, 2005; Post
& Neimark, 2008). Post and colleagues (2002) describe altruism as one of the purest acts
of agape or brotherly love.
Love. It is neither a new conceptual model nor is it complicated, but discussion
of love in organizational contexts is like a day on the verge of its dawn. Margaret Atwood
once remarked, “The Eskimo had fifty-two names for snow because it was important to
them. There ought to be as many for love.” It appears increasingly possible that
42
management scholars, and POS scholars in particular, have taken on this challenge and
have been exploring the forms love takes in organizational contexts.
Organizational scholars speak plainly and clearly about love. Fry (2003) and Fry
and Kriger (2009) include altruistic love in their dimensions of spiritual leadership. These
authors define love as a sense of wholeness, harmony, and well-being produced through
care, concern, and appreciation for both self and others (Fry, 2003 p. 712).
Fredrickson (2013) produced an entire book on the topic of love, describing how
ubiquitous love is, in organizational contexts and beyond. She provides possibly the
easiest definition of love, in terms of its inclusiveness: love is the micro-moments of
warmth and connection that you share with another living being.
Fredrickson refers to love as the supreme emotion. Rather than being a synonym
for care, connectedness, joy, or gratitude, it overarches these because each can be turned
in to an instance of love if experienced in close connection with another. Love is supreme
in an additional way. Referencing her earlier work (2001) on positive emotions,
Fredrickson writes, while all positive emotions provide benefits, like broadening your
mindset and building your resourcefulness, the benefits of love run far deeper. As the
supreme emotion, love makes us come most fully alive and feel most fully human. It is,
she asserts, perhaps the most essential emotional experience for thriving and health
(2013).
Maturana, a biologist and who extended his writings to organizational contexts,
and Bunnell (1999) define love as the collection of relational behaviors through which
another person, being, or thing arises as a legitimate other in coexistence with oneself (p.
59). Perhaps most aptly for the content of this research, Coombe (2011) elucidates the
43
parallels between sustainability definitions and definitions of love, noting that both come
about only after shifts from limited self-interest to a recognition of connectedness,
mutuality, and interest in the well-being of others. Both sustainability, particularly when
defined as flourishing (Laszlo & Brown, 2014) and love are reflections of a
consciousness of interdependence and connectedness. According to Coombe,
sustainability may be one of the clearest expressions of love coming from the
organizational level of system (2011).
Positive Organizational Scholarship (POS) and Language
If positive images lead to positive action, as Cooperrider (1997) asserts, then
developing future images consciously becomes a critical aspect of organizational change.
Guiding images of the future exists deep within the internal dialogue of human systems,
like organizations. The image is therefore part of the “public domain” of the system and
is neither a person-centered or position-centered phenomenon. Guiding images and the
internal dialogue, which contains them, are situational and interactional tapestries in the
organization, property and creation of the whole rather than of any single element or part
(Shotter, 1993; Stacey, 1992). An organization’s guiding image of the future stretches
deeper and is far richer than a single leader’s policy or vision statement. The guiding
image, as part of an organization’s internal dialogue, is part of the complex, cooperative
aspects of organizational life (Shotter, 1993; Schwartz, 1986).
The following section reviews scholarship from and related to the POS domain,
which describes the role of language at both team and organizational levels of system.
Because my research studied the language of the sustainability programs (individuals and
44
teams) who are change agents within their institutions, team and group level phenomena
are particularly relevant. The section also includes a review of the arguments of language
in organizational change.
In human systems, like teams, organizations, or societies, the guiding image of the
future does not, even metaphorically, exist within some individual or unified and separate
“mass of brain” (Cooperrider, 1997). The guiding image exists in a very observable and
tangible way in the language, the ongoing dialogue and narrative flowing through every
level of system, expressing itself anew at every moment (Losada & Heaphy, 2004;
Cooperrider, 1997; Schwartz, 1986; Shotter, 1993; Barrett et al. 1995). Much like a
fingerprint, a system’s language is unique and the sole property of that community,
reflecting the beliefs and the spirit of the community (Shotter, 1993; Millroy, 2001;
Hallsmith, 2003; Coupland et al, 2005), be that a married couple, a team, an organization,
or a nation.
The emotional tone of verbal and written communications is, in and of itself, a
social phenomenon, a realm of distinctiveness that, although operating on a linguistic
plane (Irvine, 2001), is proxy for other characteristics (Copland et al, 2005), like team
performance (Echeverria, 1994; Bartle & Saver, 2000, Losada & Heaphy, 2004). A
growing body of organizational and psychological scholarship has established the
importance of emotional processes in groups; the role affect plays among group members
through a number of processes (Bartel & Saavedra, 2000; Losada & Heaphy, 2004;
Barsade, 2002).
Barrett (1990) studied the relational development of a group over 5 years. Over
this period, Barrett found that changes among members’ social relationships and
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language preceded the emergence of new knowledge. In addition to development of the
group, Barrett noted the individual development occurring, supported by a group context
which legitimized talk about possibilities (Barrett, 1990). Barrett says, “People inherently
want to be released from stale scripts and frozen schemas that lock them into perception
of the world” (1990, p. 280).
Supporting the findings of Barrett’s research, Stacey (1996) found that teams in
particular, and organizations more broadly, engage in nonlinear feedback networks,
which employ ongoing processes of positive and negative feedback, constructing
organizational culture and norms (Shotter, 1993; Stacey, 1992; 1996). Underlying these
processes are conscious and unconscious communication between group members
(Stacey, 1996; Losada & Heaphy, 2004).
Teams who employed positive speech patterns and gestures and teams who
employed neutral and negative speech patterns and gestures had noteworthy differences
in team member satisfaction (Echeverria, 1994). Those teams whose language contained
encouraging utterances and positive gestures were found to have higher team member
satisfaction and identified more strategic opportunities. Moreover, teams characterized as
having a positive emotional tone also had generative or expansive emotional spaces,
which facilitated creative thinking and opportunity recognition. Those teams who used
negative or critical speech patterns and gestures showed lower team member satisfaction
and fostered restricted emotional spaces, which have been found to inhibit possibilities
for action (Echeverria, 1994).
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The language we use has emotional impact and is correlated with higher levels of
satisfaction, more successful and enduring relationships, higher team performance,
cohesiveness, and morale as reported by Donnellon (1996), Gottman (1994; 1999), and
Losada and Heaphy (2004) in particular. Donnellon (1996) found more cohesive team
language, which used encouraging statements in conjunction with “us,” “we,” and “our”,
correlated with higher functioning teams, whereas lower functioning teams used “your,”
“my,” and divisive statements.
Because they represent important building blocks to the research I have
conducted, the work of Schwartz (1986), Gottman (1979, 1994, 1999), and Losada and
Heaphy (2004) warrants a more extensive account. What this group has in common is
their exploration of positivity and negativity as it occurs in thoughts, common language
and interactions, and their contributions of positivity / negativity (P/N) ratios associated
with different measures of functioning or performance. Additionally, they raise the issue
of asymmetry between the effects of negative and positive phenomena. We begin with
the work of Schwartz (1986) and Schwartz and Gottman (1976).
In 1986 Schwartz put forth one of the early papers, summarizing the effects of
positive and negative thoughts and discussing P/N ratios. Schwartz traces the theoretical
history of positive and negative self-talk conceptually and considers the asymmetry
between positive and negative cognitions. Research, which offers the example at the
center of Schwartz’s paper is Schwartz and Gottman’s investigation (1976), originally set
out to assess patterns of positive and negative coping cognitions in a task analysis among
assertive and low assertive subjects. Schwartz later reconsidered these data, to assess P/N
ratios, inspired by work done by Hollandsworth and colleagues (1979), who conducted
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studies on text anxiety, finding low-anxious subjects reported approximately two
‘facilitative’ statements for every ‘deliberative’ statement, whereas high-anxiety subjects
reported a one to one ratio of facilitative to deliberative statements.
Calculating Mean Positive Thought Scores / Mean Negative Thought Scores from
1976, Schwartz found functional (‘assertives’) subjects yielding a 1.7:1 P/N ratio and
dysfunctional (‘low assertives’) yielding a P/N ratio of 1:1 (1986). Moreover, the 1.7:1
P/N pattern among functional subjects (based on data collected through self-report) has
been directly replicated in other studies of assertiveness, such as Bruch (1981) and
Heimberg et al. (1983), social anxiety (Glass et al, 1982), test anxiety as mentioned above
(Hollandsworth et al, 1979), and self-esteem (Vasta & Brockner, 1979). Because these
findings have been replicated using diverse methods of assessment, consistency of the
ratio cannot be attributed to an artifact of method (Schwartz, 1986). Thus, writes
Schwartz, across problem areas and methods of cognitive assessment, the functional
groups were characterized by approximately a 1.7: 1 P/N thought ratio and the
dysfunctional groups by a 1:1 P/N thought ratio. Schwartz’s second thesis point is the
asymmetry between negative and positive thoughts.
Each of the studies included in Schwartz’s review demonstrate that negative
cognitions, relative to positive, weigh more heavily in distinguishing functional versus
dysfunctional groups- in other words, bad is stronger than good (Baumeister et al, 2001).
In Schwartz and Gottman's (1976) task analysis study, although high and low assertives
differed in the frequency of both positive and negative self-statements, there was a
stronger relationship on the negative dimension. This asymmetry has been consistently
supported by subsequent studies of non-assertiveness. Bruch (1981), who replicated
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Schwartz and Gottman’s study, found a significant inverse relationship between cognitive
complexity and negative self-statements, whereas complexity and positive self-statements
were not related. Rhyne and colleagues (1983) extended the task analysis to a broader
subject population, finding that negative self-statements added significantly to a multiple
regression equation predicting actual assertive behavior, whereas positive self-statements
failed to increase the predictive power. Klass (1981) examined the relationship of
frequency and impact of two types of positive (criticism of other and self-directed
concerns) and negative (harm and responsibility) self-statements to a measure of guilt
over assertion. Looking at both frequency and impact, the negative self-statements
(relative to positive) were more strongly related to guilt. The asymmetrical effects of
negative thoughts versus positive ones extend further. Schwartz details study after study,
in which the asymmetrical pattern has also been observed, including topics such as
coping (Kendall et al, 1979), social anxiety, test anxiety (Hollandsworth et al, 1979;
Galassi et al, 1981), and coping with stressful medical procedures (Cacioppo et al, 1979).
Schwartz summarizes: it appears that negative events and cognitions are more salient
and make a greater impact than positive ones-that negative thoughts and feelings, relative
to positive, may be more central to adaptation (1986).
The work of Gottman and Gottman and colleagues (Gottman 1979, 1994, 1995,
1999; Gottman & Krokoff, 1989; Gottman & Levenson, 1986) has reported on
longitudinal studies of married couples’ interactions, communication patterns, emotion
affect, and relational satisfaction. To do this, Gottman and his colleagues videotaped
couples having conversations about an array of topics, some mundane and others more
sensitive, such as discussing specific issues in the relationship. The couples’
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conversations and behavior were coded as verbal or non-verbal, positive or negative. As
one might expect, positivity of communication is associated with high relational
satisfaction. These subjects communicate with more positive verbal behaviors (such as
agreement, expressing care, politeness, constructive problem solving) as well as non-
verbal behaviors (such as nodding, concerned tones of voice, smiling) (Gottman, 1979).
In later, related work, Gottman and Levenson (1986; Levenson & Gottman, 1983, 1985)
created the videotapes observing the couples, then shared the taped interactions with the
couples, to collect ongoing ratings of affect. Emerging from these data was the important
role of reciprocity, defined as one person expressing a similar emotion or change in
emotion right after their partner had indicated a feeling.
Like the asymmetries found in Gottman’s earlier work with Schwartz (1976),
reciprocity of negative affect was especially potent and in particular was more influential
than reciprocity of positive affect. As part of the longitudinal nature of this study, couples
were followed up with two years later. Those subject who had initially shown higher
rates of negative affect reciprocity reported greater declines in relationship satisfaction,
whereas reciprocity of positive affect had no significant effect (Levenson & Gottman,
19855). Therefore, relationships are most affects by patterns in which one person
responds negatively to the other’s negative act or feeling. In light of these findings and
the asymmetrical impact of negative and positive encounters, Gottman (1994) proposed
that for relationships to succeed, positive interactions have to outnumber negative ones
by at least five to one.
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Losada and Heaphy’s study (2004) examined the language of teams by observing
and recording meetings of sales teams in a specially designed boardroom, which was a
computer lab designed for team research. All dialogue was recorded and subsequently
coded. The sales teams participating in the study were categorized into three groups
(high performance, mixed success, and low performance) based on their performance on
distinct business indicators of customer satisfaction, profitability, and evaluations by
supervisors, peers, and direct reports.
As each team met in the mock boardroom setting, Losada and Heaphy’s team paid
attention to the manner in which team members communicated with one another (Losada
& Heaphy, 2004). Three dimensions were given particular attention in terms of team
member interactions:
(1) positive or negative utterances;
(2) self-focused or other-focused communications, and
(3) advocacy-based (driving one’s own point of view) or inquiry-based (asking
questions) language.
In addition to these key parameters, the degree to which team members influenced the
behavior of the colleagues, referred to as “connectivity,” was also observed and tracked.
Overlaying the results of the team meeting observations with sales performance, the
results were noteworthy.
Teams with the highest sales were characterized by conversations that had high
positivity ratios, with an average of six positive comments to each negative utterance (a
6:1 positivity to negativity or P/N ratio). Moreover, high performing teams were
51
characterized by asking as many, if not more, questions of their colleagues than
defending or advocating for their own point of view. Additionally, individuals in the
high performing teams were characterized as being focused outwardly (on the team as a
whole, on other members of the team, or the larger contexts of the team) as much or more
than they were self-focused and scored highest on the degree to which they were able to
influence one another—the team’s “connectivity” (Losada & Heaphy, 2004; Fredrickson,
2009).
Comparing the 6:1 positivity ratio of the highest performing sales team to the
other groups in the sample, the researchers found the low-performing teams had
remarkably low positivity ratios, well below 1:1. These teams demonstrated low levels
of connectivity, asked almost no questions of one another, and were consistently self-
focused rather than being outwardly focused (Losada & Heaphy, 2004; Fredrickson,
2009). As one might expect, the teams who fell into the “mixed-performance” category,
fell in the middle of each of Losada and Heaphy’s dimensions, with a positivity ratio of
2:1, just above the low performing teams.
Losada applied a nonlinear dynamic model to the data in order to predict to
positivity ratios, which marks the tipping point for human systems. (Losada & Heaphy,
2004; Fredrickson & Losada, 2005; Fredrickson, 2009). This mathematical model was
subsequently shown to be invalid (Brown et al., 2013), although the evidence linking
high positivity ratios with high performance remains valid (Expression of Concern, ABP,
2014).
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At first glance, these data might suggest that interactions that are peppered with
positive comments are all that are required for high functioning human systems,
implicitly suggesting we avoid negativity. But dismissing the value of negative or critical
comments, and only focusing on pleasantries misses the point of this important research,
and in fact, is not supported by Losada and Heaphy’s analysis.
High performance teams are not high performing because they have learned to
sprinkle in a target number of positive expressions. High-performing teams stood apart
from the other groups in this research for their ability, when experiencing pressure, to
stay resilient and to rebound. The higher performing teams were able to leverage group
strengths by asking questions to identify new information, new options, and ideas. They
remained open to one another and flexible due to their underlying high positivity ratio.
The study’s high performing (high positivity ratio) teams avoided getting stuck in critical
thought spirals or self-absorbed advocacy (Losada & Heaphy, 2004; Fredrickson &
Losada, 2005; Fredrickson, 2009). Challenges and critical comments are present,
necessary, and perhaps even natural in groups, however it is how the group (or in this
case the sales team) responds and keeps moving forward that is distinguishing.
Resilience and positivity go hand in hand, without positivity there is no rebound (Tugade
& Fredrickson, 2004). Fredrickson’s (1998, 2001) broaden-and-build theory of positive
emotions offers an overarching theoretical explanation by linking the cumulative
experience of momentary positive emotions to the development of resources for long-
term success and well-being. This, and other similar work, are described later.
Kegan and Lahey (2001) view language as a technology for creating
transformative learning in organizations. The places where we live and work are places
53
where certain forms of speech are promoted and other ways of talking are discouraged or
made impossible. Work settings are language communities and all leaders in
organizations are language leaders, possessing an exponentially greater access and
opportunity to shape existing language rules (Kegan & Lahey, 2001). An analogy can be
drawn to each child’s development of language. As an individual’s language develops as
part of the whole of child, it is also indicative of and in accordance with their
membership in a larger society (Millroy, 2001). It is impossible to disassociate facts of
language from the cultural values that saturate them.
Even when looking at groups or team within organizational systems, the role
language plays in system-wide change initiatives has been explored across diverse
contexts and sectors (Barrett, 1990; Donnellon, 1996; Cooperrider, 1997; Gottman, 1999;
Losada & Heaphy, 2004; Schmidt, 2005; Fredrickson & Losada, 2005; Fredrickson,
2009). All language, contends Ludema and colleagues (1997), sustains certain kinds of
knowledge, to the exclusion of other kinds of knowledge. These authors extend the idea,
asserting that all knowledge sustains certain patterns of activity, to the exclusion of
others. Thus, the more hopeful the available vocabularies, the more positive will be the
forms of future image, social action, and organization that they support. Those
vocabularies that offer positive examples of future images and hopeful possibilities will
be more powerful resources for organizational change (Gergen, 1994; Ludema et al.
1997; Cooperrider, 1990, 1997; Hallsmith, 2003). The Pygmalion dynamic is evident
when the positive image of another serves as a powerful cognitive measuring stick, which
appears to trigger in the perceiver an increased capacity to perceive the successes of the
other (Deaux & Ernswiller, 1974), recall more positive than negative aspects of the other
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(Hastie & Kumar, 1979), as well as enabling positive outcomes to ambiguous situations,
rather than expectations of negative or unsatisfactory possibilities (Darley & Gross,
1983).
Emotion
William James (1902), arguably the forefather of positive psychology, wrote,
“Emotional occasions, especially violent ones, are extremely potent in precipitating
mental rearrangements. He described the “sudden and explosive ways” in which negative
emotions such as jealousy, guilt, fear, or anger can “seize upon” each of us (p. 163). He
went on to say, “ Hope, happiness, security, resolve—emotions characteristic of
conversion, however, can be equally explosive. And emotions that come in this explosive
way seldom leave things as they found them” (James, 1902, p. 163-164). A century
later, social scientists are catching up to James’ insights. Central to many existing
theories of emotion is the concept of specific-action tendencies – the idea that emotions
prepare the body both physically and psychologically to act in particular ways
(Fredrickson, 1998).
The broaden-and-build theory of positive emotions (Fredrickson, 1998;
Fredrickson & Cohn, 2008) proposes that positive emotions are evolved adaptations that
function to build lasting resources. Unlike negative emotions, which narrow attention,
cognition, and physiology toward coping with an immediate threat or problem (Carver,
2003; Cosmides & Tooby, 2000; LeDoux, 1998), positive emotions produce unique and
broad-ranging thoughts and actions (Fredrickson, 1998). While these new thought-
action-repertoires are usually not critical to one’s immediate safety or survival, over time
55
they aggregate into consequential resources that can change people’s lives (Cohn et al,
2009; Fredrickson, 2001; 2003).
Evidence confirms that positive emotions broaden thought-action repertoires:
induced positive emotions produce wider visual search patterns, novel and creative
thoughts and actions, more inclusive social groups, and more flexible goals and mindsets
(Ashby et al, 1999; Fredrickson & Cohn, 2008). The work of Fazio, Eiser and Shook
(2004) has leant further support to the broaden-and-build theory, arguing that positive
and open mindsets produce psychological safety, allowing for exploratory and
experiential learning, and producing more accurate mental maps of the world (Fazio et al,
2004). Thus, relative to negative or even neutral emotions, positive emotions are more
likely to promote interest and curiosity among groups.
Negativity and neutrality narrow thought-action-repertoires, limiting possible
responses, increasing self-protection (Fazio et al, 2004; Fredrickson, 2009, Schmidt,
2006). LeDoux (1998) explored relationships between brain physiology and experience
of emotions. Much of his work revolves around how brain systems process fear and
related experience, like anxiety. LeDoux asserts that fear or anxiety are closely related,
the difference is that anxiety comes from within, distinct from fear because there is no
external stimulus (1998). LeDoux proposes that fear, unlike other kinds of emotions
(anger or happiness) is a conditioned result of a system in the brain. The system does not
result in the experience of fear, but rather is an evolutionary by product, which detects
danger and produces responses that maximize the probability of surviving a dangerous
situation in the most beneficial way. In other words defensive behaviors represent the
operation of brain systems that have been programmed by evolution to deal with danger
56
in routine ways (LeDoux, 1998).
In 1890, William James noted that nothing characterizes the ascendency of human
kind more clearly than the reduction of the conditions under which fear is evoked. James
was undoubtedly referring to man’s ability to establish societies in which we are not in
danger of becoming someone else’s dinner. As a species, we have been successful in
creating ways of living in which the likelihood of encountering predators is greatly
reduced, but of course, not all danger growls. In our quest for dominion over nature we
have created new forms of danger- crime, automobile accidents, financial crises, nuclear
threats, and now, climate change. “We’ve traded in the dangers of a life amongst the
wild things for other dangers that may, in the end, be far more harmful to our species than
any natural predator” (LeDoux, 1998, p.129). The dangers modern humans face are not
fewer or less significant than those of our ancestors, they are simply different.
Baumeister and colleagues (2001) serve as a linkage, highlighting the theme of
asymmetry in the effect of negative phenomena versus positive events or utterances
(Schwartz, 1986; Schwartz & Gottman, 1976; Gottman, 1979, 1995). The Baumeister
team wrote a fascinating paper, exploring the hypothesiss ‘bad is stronger than good.’
Through an extensive, and seemingly exhaustive, survey of psychological literature, the
authors delve into diverse realms of psychology scholarship including, but not limited to,
the science of first impressions, experimental exposure to odors, traumatic life events,
number of negative and positive words in the English language, learning, conceptions of
the self, childhood development, and relationships. Like Schwartz’s thorough survey
(1986) but on a vaster scale, the researchers combed through other scholars’ research,
finding study after study, across a broad range of psychological phenomena, citing over
57
and over that circumstances with a negative valence will have a greater impact than equal
events with a positive valence. This greater impact of bad was found in cognition,
motivation, inner and intrapsychic processes, interpersonal interactions, decision making,
memories, emotional responses, information processing and more (Baumeister et al,
2001). So, while there may be notable, yet singular contradictions, the Baumeister team
concluded that negative information and events produce more emotion, have bigger
requirements for adjustment, and have longer lasting effects (2001).
Reflecting on all the literature reviewed above, it is clear that there are multiple
and interrelated research lenses one can look through to explore the impact a positive
perspective, positive action, and positives words can have on supporting human systems
(Fredrickson 2001, 2003, 2009; Losada & Heaphy, 2004; Losada & Fredrickson, 2005;
Cooperrider, 1997; Cooperrider & Sekerka, 2003, Goleman et al, 2001). Our language
directs our attention and therein shapes the results of what we create (Cooperrider, 1997;
Fredrikson, 1998; Schmidt, 2005; McKenzie-Mohr & Smith, 1997). Taken together, the
literature cited point to the importance of language on increased levels of performance in
teams and organizations, improved levels of satisfaction of organizational members, and
better organizational capacity to deal with increasingly complex environments (Schein,
1999; Cooperrider, 1997; Losada & Heaphy,2004; Echeverria, 1994; Stacey,1992, 1996).
I posit that the knowledge resulting from the confirming approaches to change
and theories of positive emotional affect reviewed above is essential for Sustainability
Professionals on university and college campuses to be successful in leading their
institutions into systemic, comprehensive change. Additionally, in light of the asymmetry
of effects that negative information and events have (Schwarz, 1986; Gottman 1994;
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Baumeister et al, 2001), it becomes more clear that negativity in the sustainability
rhetoric, especially when unbalanced by a prevalence of positivity, reduces the
audience’s capacity to respond.
In the preceding sections, relevant studies and literature from management and
psychology were presented to argue that words are more than objective symbols. The
explicit language of a system and the internal dialogue of a system are both creating the
system itself, which is an ongoing and collective process (Ludema et al, 1997; Shotter,
1993; Losada & Heaphy, 2004; Kegan & Lahey, 2001). Language does not gain its
“truth value” by accurately describing the world, but rather by virtue of its function
within relationships (Ludema et al. 1997). Language is internal to the common culture
(Millroy, 2001; Irvine & Gal, 2000; Coupland et al, 2005) and is itself a socio-cultural
phenomenon (Ferguson, 1994). The conscious combination of hopeful vocabularies and
positive narratives makes it possible to vicariously experience that which is held in the
imagination of a system (Ludema et al, 1997; Cooperrider, 1997).
Based on the literature described above, I have a clear bias and theory of change. I
believe that disconfirming events and information have a powerful effect and get our
attention, but does not result in or sustain comprehensive, systemic change. Therefore, I
believe that change occurs as a confirming and evolutionary process, characterized by a
future-oriented stance, proactive in creating opportunities to further develop the system’s
strengths.
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Research Questions and Hypotheses
This research focuses on the emerging field of sustainability in higher education to
explore these assumptions. Through a multi-phased qualitative research design I will
analyze the language itself, across three different contexts: (1) language as it is used to
represent the ‘public face’ of sustainability via campus sustainability websites; (2)
language use at the individual-level derived from interviews with campus sustainability
leaders; and (3) language occurring among sustainability teams on higher education
campuses. Two research questions will be explored in my study:
1. What is the emotional tone of sustainability language used in higher educational
contexts?
2. How does the emotional tone of campus sustainability narratives relate to the
performance of sustainability programs?
These questions generate three hypotheses. Hypotheses 1a and 1b relate to the current
state of higher education sustainability program communications, and highlight two
dynamics: mimetic isomorphism (DiMaggio & Powell, 1983) and the role data-driven
arguments play in sustainability narratives (Schmidt, 2005).
In the introduction of this proposal, it is noted that the majority of sustainability
professionals in higher education come from diverse occupational backgrounds (AASHE,
2008) and while they are charged with transforming campus cultures through education,
research, management and operations, the majority of incumbents do not possess the
skills necessary to implement these changes. Additionally, professional development
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opportunities related to system wide change strategies are scant in the higher education
sustainability domain. Uncertainty is a powerful driver for imitation (DiMaggio &
Powell, 1983), therefore for insight into how to engage campus constituents, the
Sustainability Professional, unfamiliar with organizational development and change—or
unaware that organizational development is indeed what she or he is doing by way of the
sustainability program—turn to exemplars and role models for assistance.
The advantages of imitation in the “economy of human action” are considerable;
when an organization faces a problem with ambiguous causes, or unclear or un-tried
solutions, bench-marking what other institutions are doing and how they do it can yield
viable solutions with little cost in time and money (DiMaggio & Powell, 1983). While
modeling one’s program from the examples of others may serve to establish credibility
for new sustainability program, it also runs into problems. As detailed in in the literature
review, Schein (2002) believed role modeling to be the cruder of change approaches.
While faster than trial and error learning, copying role modeled behavior often does not
fit the target’s culture or context (Schein, 2002). DiMaggio and Powell (1983) also stress
that mimicking results in homogeneity, especially in small domains or sectors, which
applies to a field such as higher educational sustainability programs.
Mimicking the activities, communication, and organizational forms from
successful sustainability programs or environmental organizations helps to legitimate
fledgling sustainability programs. New Sustainability Professionals ascertain what topics
are included in typical sustainability program and what are not, what kinds of information
should be featured on the sustainability program’s website, and how to communicate and
phrase sustainability-related issues (Sharp, 2005). This is evidenced by similar definitions
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for sustainability, wide spread use of certain kinds of data in ‘making the case’ for
sustainability, common phrasing, and using sustainability websites as hubs of information
and instruction.
Hart and Milstein (2003, Schmidt (2005) and Ehrenfeld (2008) use similar
language to describe the mindset of environmental NGOs and orientation of the
sustainability movement: data-focused; regulatory; compliance-oriented; and concerned
with being less bad. In my experience of being a Sustainability Professional, a majority
of the focus was on energy conservation and recycling, both at my institution and with
the other sustainability programs with whom I interacted. As such, the messages being
communicated to campus interest groups, like students, faculty, and leadership, had to do
with sharing data and information about the campus, as well as instructions on how
individuals could support the sustainability initiatives through their behaviors, such as
Greening Your Dorm Room or Greening Your Office initiatives.
Hypothesis 1a and 1b reflect my experiences of mimetic isomorphism, and
assume this still occurs within the communication of sustainability programs, and
influences the emotional tone of sustainability communications on higher education
campuses. These hypotheses relates to the penchant for information sharing and
instruction giving and using “data” to make the case for sustainability, which would
result in narratives that are largely neutral in their emotional tone, characterized by
statistics, data and facts, and instructions. Additionally, these hypotheses served as “null
hypotheses” or default positions, in the event positive and negative emotional tone were
not represented in the data in a significant way.
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H1a: The current state of sustainability language by its proponents in higher
education, including all performance categories, will be dominated by a neutral
emotional tone.
H1b: Instruction and information sharing will comprise the majority of the
neutral discourse.
Hypothesis 2 derives from arguments made by Schwartz (1986), Gottman (1994),
and Losada and Heaphy (2004) regarding P/N ratios associated with differences in
performance and functioning. Each of these has been described in the Literature Review.
Studying intra-psychic self-talk, Schwartz (1986) and Schwartz and Gottman (1976)
found a 1.7:1 P/N ratio in functional subjects versus a 1:1 P/N ratio in dysfunctional
subjects. Gottman’s longitudinal studies of couples found high relational satisfaction in
those couples whose verbal and non-verbal interactions possessed a 5:1 P/N ratio, and
Losada and Heaphy (2004) found high-performing sales teams demonstrated a 6:1 P/N
ratio. In all of these examples, dysfunction and low performance were associated with a
1:1 P/N ratio. When taken together with Ludema and colleagues (1997), Shotter (1993),
Kegan and Lahey (2001) it is both the explicit language of a system and the internal
dialogue of a system that creates the system itself. Therefore, when language in a system
is confirming, collaborative, creation-oriented, and proactive- when the system’s
narrative is generally positive- the system will recognize higher performance. For
Hypothesis 2, I chose a ‘middle ground’ approach for predicting P/N ratios in my study
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population, from the range created by Schwartz (1986), Gottman (1994), and Losada and
Heaphy (2004).
H2: Higher education sustainability programs with at least a 3:1 ratio of positive
to negative language in their personal and public communications (written and
verbal) will be rated higher on a national campus sustainability ranking than
sustainability programs whose communications employ a lower ratio of positive
to negative communication acts.
I have described the concepts and methods of Schein, and Kotter as three
influential figures in organizational change. These thought leaders approach change from
a disconfirmation stance, believing that disconfirmation functions as the primary driver in
all change (Schein, 1996). The disconfirmation theory of change asserts that
dissatisfaction or frustration, generated by data that dis-confirm previously held
perceptions of reality, expectations, or hopes, starts the change (or learning) process
(Schein, 1995, 1996, 1999; Kotter, 1996, 2008). Based on Schein’s own description of
disconfirmation (1995, 1996, 1999) it is a reactive, problem solving orientation to
change—it is the equivalent of “coming in on the back foot.” In contrast, the
confirmation-based scholarship of Fredrickson (2003), Cooperrider and Sekerka (2003)
and Schmidt (2005) offer convincing arguments for steering organizational change with
hopeful and compelling narratives of the system’s desired future, characterizing change
as about being at the front of opportunities, being proactive, and creation-oriented.
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H3: The highest performing campuses will approach change for sustainability
through a confirmation-based approach, characterized by a future-oriented stance,
a propensity for innovation, being proactive and creating opportunities to further
develop their institution’s sustainability program.
In the next chapter details about research design and qualitative methodology of
this study are provided, including description of the study population and coding process.
Following this, results are presented in Chapter Five and the discussion and implications
of the findings are offered in Chapter Six.
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CHAPTER FOUR: RESEARCH DESIGN AND METHOD
In organizational research, the search for research methods that are able to capture
a holistic view of organizational issues has led to greater usage of descriptive and
interpretivist methods (Klein & Meyers, 1999). Describing and evaluating the emotional
tone of sustainability language at institutions of higher education requires a
methodological approach capable of rendering the underlying emotional themes held in
each campus’ patterns of communication (Grant et al., 1998; Mumby & Clair, 1997).
Qualitative methodologies have become one of the more accepted approaches for
studying such complex social phenomena (Margolis & Walsh, 2003; Miles & Huberman,
1994; Tesch, 1990).
Content Analysis
The goal of content analysis is “to provide knowledge and understanding of the
phenomenon under study” (Downe-Wamboldt, 1992, p. 314). In this study, qualitative
content analysis is defined as a research method for the interpretation of the content of
text data through the systematic classification process of coding and identifying themes
or patterns. These themes or patterns can represent either explicit communication or
inferred communication (Hseih & Shannon, 2005).
Content analysis has a long history in research (Kohlbacher, 2006; Hsieh &
Shannon, 2005; Titscher et al, 2000) with a family of methodologies, including both
qualitative and quantitative approaches (Hsieh & Shannon, 2005). Several researchers
trace use of content analysis back to the 18th century in Europe and early 20th century in
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the US (Rosengren, 1981; Hseih & Shannon, 2005; Kohlbacher, 2006; Titscher e al,
2000). Although initially qualitative or quantitative methods were used (Hseih &
Shannon, 2005) later studies, used content analysis primarily as a quantitative research
method, with text data coded into explicit categories and then described using statistics.
For example, in the fields of journalism or linguistics, content analysis is used to track
(count) usage of particular words or phrases in written documents over periods of time,
for example newspapers or annual reports, in order to assess the attention being paid to a
particular concept or phenomenon (Krippendorf, 1980).
Qualitative content analysis is one of numerous research methods used to analyze
text data. Other methods include ethnography, grounded theory, phenomenology, and
historical research (Hsieh & Shannon, 2005). Research using qualitative content analysis
focuses on the characteristics of language as communication with attention to the content
or contextual meaning of the text (Hseih & Shannon, 2005; Tesch, 1990). Text data
might be in verbal, print, or electronic form and might have been obtained from narrative
responses, open-ended survey questions, interviews, focus groups, observations, or print
media such as articles, books, or manuals (Kondracki &Wellman, 2002).
Among the diverse content analysis approaches, Hseih and Shannon (2005) group
current applications of content analysis into three distinct approaches: summative;
conventional; and directed. The three approaches share a common goal of interpreting
meaning from the content of text data. Hseih and Shannon differentiate the approaches
based on origins of codes, coding schemes, and challenges to trustworthiness. A
summative content analysis involves counting and comparisons, usually of keywords or
content, followed by the interpretation of the underlying context. In conventional content
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analysis, coding categories are derived directly from the text data. With a directed
approach, analysis starts with a theory or relevant research findings as guidance for initial
codes. The research methodology here falls into the directed content analytic approach.
Directed Content Analysis
When research is being situated within existing theory, or prior research exists
about a phenomenon that is incomplete or would benefit from further description, the
qualitative researcher might choose to use a directed approach to content analysis. Potter
and Levine-Donnerstein (1999) categorize this as a deductive use of theory based on their
distinctions on the role of theory. The goal of a directed approach to content analysis is to
validate or extend conceptually a theoretical framework or theory. Existing theory or
research can help focus the research question. It can provide predictions about the
variables of interest or about the relationships among variables, thus helping to determine
the initial coding scheme or relationships between codes. Mayring (2000) referred to this
as deductive category application.
Content analysis using a directed approach is guided by a more structured process
than is a conventional approach (Hickey & Kipping, 1996). One strategy that can be used
with directed content analysis is to begin with an existing or pre-determined code. This is
how I performed my analysis, which is detailed below. Data that cannot be coded are
identified immediately because they do not fit into any of the categories of the existing
code and are analyzed later to determine if they represent a new category or a
subcategory of the existing code.
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Coding refers to the creation of categories in relation to data; the grouping
together of different communication acts or utterances under an umbrella term that can
enable them to be regarded as of the same type (Aronson, 1994; Boyatzis, 1998).
Decisions about what counts as a category can originate from different sources, such as
theory, literature, the data itself, or the researcher’s experience. I will analyze my data by
first applying an existing code of Positive and Negative Discourse Categories, developed
by Cooperrider and colleagues (2008) (see Tables 2a & 2b). If phenomena present in my
data are not reflected in the Positive and Negative Discourse Categories, additional
categories or sub-categories will be created in order to capture un-represented themes.
The Positive and Negative Discourse Categories will be described in greater detail below.
The findings from a directed content analysis offer supporting and non-supporting
evidence for a theory. This evidence can be presented by showing codes with exemplars
and by offering descriptive evidence. The theory or prior research used will guide the
discussion of the findings. Newly identified categories could offer a contradictory view
of the phenomenon or could further refine, extend, or enrich the theory (Hsieh &
Shannon, 2005).
The main strength of a directed approach to content analysis is that existing
theory can be supported and extended. In addition, as research in an area grows, a
directed approach makes explicit the reality that researchers are likely to be working from
informed perspectives (Hseih & Shannon, 2005). Additional methodological and
practical benefits have been noted in implementing content analysis (Woodrum, 1984).
First, content analysis is a safe methodology because the coding scheme can be corrected
if flaws are detected as the study proceeds (Tallerico, 1991; Woodrum, 1984). Second,
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when content analysis is done correctly, it entails the specification of category criteria for
reliability and validity checks that fosters the creation of a replicable database (Lissack,
1998; Woodrum, 1984).
The directed approach also presents challenges. Grounding a research study in
existing theory has some inherent limitations in that researchers approach the data with
an informed but, nonetheless, strong bias. Hence, researchers might be more likely to find
evidence that is supportive rather than non-supportive of a theory. Second, researcher’s
bias may direct respondents through the wording of questions, leading or cueing
participants to answer questions in a certain way or agree. Third, an overemphasis on the
theory can blind researchers to contextual aspects of the phenomenon and miss important
findings or insights that do not fit the predetermined categories/codes.
Theoretical Issues in Content Analysis
As with any decision to choose a particular research design, there are
consequences to using content analysis. One of the central positions, associated with all
qualitative research, pertains to the idea of interpretivism or projection (Braun & Clark,
2006). This ideas suggests that I, as the researcher, will project my perspectives and
“truths” on to the data or interpret the data through my understanding and meaning
making of the actions of others, which may not be the meaning the actors intended. I am
of two minds in relation to the challenge.
First, I will be engaging additional analysts (coders of data) to establish a
consistency of coding, labeling, and interpretation (Boyatzis, 1998). See below for further
discussion on reliability. With this said, and with the seriousness that I have for
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conducting an accurate and thorough program of research, I and we inhabit cultural
worlds and engage in cultural practices that are defined by shared interpretations. While
I have done all that I could to alleviate biases, I cannot remove myself from my own
context and the shared contextual assumptions and interpretations that my sample is
embedded in.
Language is a prominent aspect of the ways in which we make sense of and order
our experiences of the world. Wittgenstein’s famous phrase ‘the limits of my language
are the limits of my world’ draws attention to the idea that language forms, in some quite
profound way, a tangible context for our actions. Exploring these contexts – their specific
features and interrelationships – is, in essence, the central project that drives management
and organizational scholarship, and is certainly the central project driving my work.
The key assumption about content analysis is that the analysis of texts lets the
researcher understand other people’s cognitive schemas (Huff, 1990; Gephart, 1993;
Woodrum, 1984). Content analysis assumes that groups of words reveal underlying
themes—both the explicit and the inferred (Hseih & Shannon, 2005)—and that, for
instance, co-occurrences of keywords can be interpreted as reflecting association between
the underlying concepts (Huff, 1990; Weber, 1990). Foremost to organizational research,
qualitative content analysis provides a replicable methodology to access deep individual
or collective structures such as values, intentions, attitudes, and cognitions (Carley, 1997;
Huff, 1990; Kabanoff, 1996). As such, content analysis is applicable to a broad range of
organizational phenomena.
My research will apply directed content analysis to words, phrases, utterances,
full sentences, multiple sentences, and images from public, archival documents, such as
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websites and promotional materials, and transcriptions of one-on-one interviews and staff
meetings. A detailed explanation of inclusion and exclusion criteria, data collection, and
analysis is presented below.
Assumptions
The research project I propose is based on two, interrelated assumptions, and is
founded on the scholarship reviewed in the previous sections:
Human systems create and communicate their realities through symbolic and
mental processes (Schwartz, 1986; Shotter, 1993; Barrett et al, 1995), such as
language and the creation of images of the future, which serve to bring those
expectations powerfully into the present as a mobilizing agent (Cooperrider,
1997).
Generative and positive narratives build hope and momentum, and are essential
elements in confirmation-based change in complex human systems (Barrett &
Cooperrider, 1990; Kegan & Lahey, 2001; Dutton, 2003).
Inclusion and Exclusion Criteria
The sample population for this study was limited to Sustainability Professionals
from institutions of higher education that (a) exist within the State of Ohio, and who are
also (b) evaluated by the Rockefeller Sustainable Endowment Institute, via the College
Sustainability Report Card (see Table 1). Limiting my sample to colleges and
universities within Ohio serves to equalize factors related to energy policies, and state-
level differences in the funding supporting sustainability initiatives.
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The Rockefeller Sustainable Endowment Institute is a nonprofit organization
engaged in research and education to advance sustainability in campus operations and
endowment practices. The College Sustainability Report Card is designed to identify
colleges and universities who are leading by example in their commitment to
sustainability. The aim is to provide accessible information to all higher educational
institutions to learn from one another's experiences, enabling them to establish more
effective sustainability policies (greenreportcard.org, 2011). The report card “grade” each
institution receives is determined by assessing performance across indicators in nine main
categories.
1. Administration: examines sustainability policies and commitments by school
administrators and trustees.
2. Climate Change & Energy: looks at energy efficiency, conservation, commitment
to emissions reductions, and use of renewable energy on campus.
3. Food & Recycling: evaluates dining services policies, including recycling and
composting programs.
4. Green Building: recognizes campus-wide green building guidelines and green
building design for new and existing buildings.
5. Student Involvement: looks at student participation in sustainability initiatives and
support for these activities by school administrators.
6. Transportation: focuses on alternative transportation for students, faculty, and
staff, as well as alternative fuel or hybrid technology for campus fleets.
7. Endowment Transparency: addresses accessibility to endowment investment
information and shareholder proxy voting records.
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8. Investment Priorities: considers prioritization of return on investment, investment
in renewable energy funds, and investment in community development loan funds.
9. Shareholder Engagement: looks at shareholder proxy voting practices, including
opportunities for student, faculty, and alumni participation.
Categories and indicators which comprise the College Sustainability Report Card
were collected based on research of best practices in sustainability in higher education,
specifically concerning campus operations and endowment policies, which speaks to the
comprehensive reach sustainability programs would ideally have. The nine topic
categories are comprised of 48 indicators, which address a broad range of policies and
programs, however exclude teaching, research, or other academic aspects concerning
sustainability.
Category grades are calculated based on the total number of points earned for the
indicators within the category. To receive an "A" in any category, a school needed to
accumulate at least 70 percent of total available points for the indicators in that category.
At least 50 percent of available points were necessary to receive a "B," 30 percent of
available points for a "C," and 10 percent of available points for a "D." No school
received a "D" or “F” in the Investment Priorities category because all schools were
awarded a minimum grade of “C” for aiming to optimize investment return. Only full
letter grades were given for individual categories (i.e., no plus or minus) of A, B, C, D,
and F were used for the individual categories (greenreportcard.org 2011). Overall grades
given to each institution evaluated are averages of the grades received for each of the
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nine categories (greenreportcard.org, 2011), which is calculated at the plus and minus
level.
In the State of Ohio, the Campus Sustainability Report Card evaluates 17 colleges
and universities annually and the results are published in a Campus Sustainability Report
Card (Table 2). Table 2 presents each college and university and the grades given in the
2011 Report Card. The downside of using this particular rubric is that its data is dated,
however there remains is no alternative ranking system, which is both as thorough as the
Report Card and includes as many institutions.
Based on the Sustainability Report Card’s grading system, I have organized the
sustainability programs into three performance categories: high performers, moderate
performers, and base performers. The grade and performance categories will be used in
the final stages of data analysis, explained in detail in the section describing Phase 3 data
analysis.
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Table 2: Ohio Colleges and Universities Included in Campus Sustainability Report Card Ranking 2011
CATEGORY RED:
High Performers
CATEGORY BLUE: Moderate Performers
CATEGORY YELLOW:
Base / Beginner Performers
INSTITUTION GRADE INSTITUTION GRADE INSTITUTION GRADE
Oberlin College
A Ohio University B Ohio Northern University
C
Case Western Reserve University
B+ University of Dayton
B Hocking College C
Denison College B+ University of Toledo
B- Youngstown State University
C-
The Ohio State University
B+ John Carroll University
B- College of Wooster
C-
University of Cincinnati
B+ Ohio Wesleyan University
B- University of Akron
D+
Miami University B-
Kenyon College C+
Invitations were sent to the sustainability leaders at each of the seventeen
institutions listed in Table 2, with the goal of recruiting at least three institutions in each
performance category. Ten of the institutions listed in Table 2 responded: four were from
the high performing sustainability programs; three from the moderately performing
sustainability programs; and three from the base performer category. Eighty percent of
the study sample was composed of universities, while the remaining 20% were colleges.
Institution size, as defined by student populations, ranged from just over 2,000 to almost
58,000. Thirty percent of my study sample was from public higher educational
institutions, 70% were private, and 40% of my sample was comprised of colleges or
universities that have a religious affiliation.
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One of the criteria for participation in this study was that each sustainability team
have at least three members; therefore, team meetings were, by definition, meetings of
three or more people. “Sustainability teams” were defined as any group collectively
working on developing sustainability initiatives on campus, and could include any
member of the campus community, such as students, staff, faculty, or other institutional
stakeholders. In other words, sustainability teams could be formally appointed staff with
sustainability in their title or job description or teams could be made up of committee
members, or students.
Confidentiality
As institutions were recruited for participation they were assigned a number /
letter label for tracking, described below. Through the transcription process, names of
individuals, institutions, and other identifiable characteristics were removed, prior to
coding. While members of the sample cannot remain anonymous due to my study design
and proposed data collection methods, confidentiality of respondents has been
maintained.
Participating institutions were categorized according to Table 2. Once assigned a
performance category color based on the letter grade received from the 2011 Campus
Sustainability Report Card (red = high performers, blue = moderate performers, yellow =
base performers), each institution in my sample was assigned a letter for tracking data
related to their institution (e.g. Red A, Blue A, or Yellow A). For data management
purposes, each phase of data collection was also assigned a number, such as RedA1 for
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website data, RedA2 for the one-on-one interviews with sustainability leaders, and
RedA3 for transcripts of sustainability team meetings.
Each participating sustainability program submitted at least one team meeting
recording. Some submitted two and one university submitted three. Similarly, for the
interviewing process, some institutions had only one person to interview, while others
had up to three sustainability leaders to interview. I will speak to the implications this had
on results in a later section, however the purpose of mentioning these differences now is
to explicate how the data tracking and respondent identifying was designed.
Research Design
Data Collection
My research treats language itself as the unit of analysis. I collected
language data from three sources at each participating institution: (1) one-on-one,
open-ended interviews; (2) sustainability team meetings; and (3) archival data in
the form of sustainability program websites. The interviews and group meetings
were audio recorded and transcribed. During the transcription process, all
identifying characteristics and references were removed to “clean” the data prior
to sharing the data with the other raters involved in coding.
(1) One-on-One Interviews with Sustainability Leaders: A total of 20 interviews
were conducted: nine interviews with high performing sustainability programs in my
sample; five with moderately performing sustainability programs; and six interviews with
individuals representing base performing sustainability programs. Two interviews were
lost to technical, recording issues. When contacted to re-interview, one respondent
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declined because he had left his position. The second respondent was non-responsive
after three attempts at contact. Therefore, 18 interviews were coded.
I conducted conduct one-on-one interviews with at least one representative
from each participating institution, affiliated with the campus’ sustainability
activities. ‘Sustainability leader’ was defined as either someone in a formal
organizational position, such as Sustainability Coordinator or Director of
Sustainability, or, for those intuitions lacking a formal position, chair of the
campus sustainability committee.
My assumption is that in their leadership role, these representatives
influence the formal or public persona of the sustainability program. Therefore,
the interview questions were open-ended and aimed at ascertaining interviewees’
perspectives on sustainability and capturing their language and framing of
sustainability. Interviews were completely voluntary and interviewees completed
an informed consent process and were aware that they may choose not to answer
any question or end the interview at any point, although none chose to do either.
The interviews were audio recorded and transcribed into Word documents,
and were cleaned of any references, which might identify the institution or
individual respondents. The blinded transcriptions were imported into ATLAS.ti
qualitative analysis software for coding (ATLAS.ti Americas, Corvallis, OR),
which will be described later in this section.
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The one-on-one interview consisted of open-ended questions, such as:
§ Is sustainability an important issue for your campus? Whether you
answer yes or no, what makes it so?
§ How and why did sustainability begin here?
§ What do you think motivates people to engage in sustainability?
§ How do you engage people to adopt sustainable behaviors or to support
sustainability on campus?
§ Has your approach to getting the campus “on board” changed since the
beginning? If so, how?
§ If you were to close your eyes, and think of the sustainability program
here, taken in its entirety, what comes to mind? How do you feel?
§ What are the current strengths of your program?
§ What skills or knowledge to you need to be better in your role?
§ What do you need to do to further develop the sustainability program?
§ What kind of developments would you like to see for your campus in
terms of sustainability, over the next five to ten years?
§ If you were to only speak from your heart / gut (no rules, institutional
guidelines, no filter) what would you say to people about sustainability?
§ Tell me about the time when you realized you had a passion for
sustainability. What were you doing, who were you working with, where
were you, what was the ah-hah that clicked for you?
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Additional, more specific, questions were also included.
§ Who is responsible for creating the content on your sustainability website
and for other communications about the sustainability program?
§ As you create the content for the website (e-mail blast, newsletter or
presentation) who is the audience you are communicating with? How do
you shape your messaging to get your audience(s) engaged with
sustainability?
§ Who else would you recommend I speak with here?
§ Does the sustainability program have its own funding?
§ Are there staff positions solely focused on sustainability? Title / position
§ Has your president signed on the ACUPCC?
§ Has your institution created a Climate Action Plan?
(2) Sustainability Team Meetings: The language of sustainability teams at each
participating institution was collected by audio recording for at least one sustainability
team meeting at each participating college or university. Assessing language at the level
of team allows for the most direct application of the language studies of Losada and
Heaphy (2004) and Donnellon (1996).
As stated above, part of the inclusion criteria was that only those colleges or
universities with a team of three or more people would be included in this study. These
teams could be comprised of sustainability specific staff, students, or an appointed
sustainability committee. To obtain a representative sample of language from each
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sustainability program in my sample, I encouraged each respondent to submit two
meeting recordings, if possible.
In order to keep my presence from influencing the content or tone of the team
discussion (Orne, 1962; Fernald et al., 2012) I asked each team to record their meetings
without me being present. This was achieved by using a free conference calling system,
which included a recording function. Using the remote recording technology of a
conference calling system meant the impact of the researcher or the awareness of being
researched was as light as possible (Orne, 1962; Fernald et al., 2012). Teams conducted
regularly scheduled meetings with a telephone (which could be a mobile phone) placed at
the center of the discussants, with the speaker function enabled, thus picking up all voices
in the room. I provided informed consent forms for everyone present at each meeting and
these were mailed back to me with signatures. Once I was aware of a sustainability
team’s meeting schedule, I provided each with a conference call dial-in telephone number
and a participant pass code. When the team dialed in, they used an additional key-stroke
to begin recording of their conversation.
Recorded conversations were automatically saved and were retrieved by me as a
voice file, after the fact. These recordings were downloaded and transcribed, with all
identifying characteristics and references being removed in the transcription process. The
group meeting files were then imported into ATLAS.ti qualitative software and coded
using the categories of Cooperrider and colleagues (2008), which will be described in
detail below (see also Table 2a & 2b).
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Sustainability team meeting recordings submitted for this study typically lasted,
on average, about 60 minutes, no matter what the performance category of the
sustainability program. I should note that there were two meetings in my sample, which
lasted less than 30 minutes and two meetings, which lasted about 80 minutes. The
discrepancies in length of meeting time resulted in varying lengths of meeting transcripts,
differences in amount of codable data and therefore coding frequencies reported in the
results section. I considered devising a rule in which all meetings were coded for the
same amount of content, as was done with website data, described below. However, in
contrast to website data, meeting data contains no clear demarcations. In this data set, the
sustainability team meetings are guided by agendas and conversation is free flowing. My
interest for including team meetings was to collect data similar to that used in by Losada
and Heaphy (2004) and Gottman (1994; 1999) in order to hear how members of the
sustainability teams talk to one another about sustainability.
(3) Website Content: A directed content analysis was performed on the
text of each participating institution’s sustainability website. Text was copied and
pasted into rich text documents and imported into ATLAS.ti for analysis. Other
management researchers have applied similar techniques when using website data
and content analysis. Jose and Lee (2007) used content analysis to analyze content
of online corporate environmental disclosure of Fortune 200 firms. Govers and
Go (2005) analyzed how websites are used to establish and promote identity and
sense of culture in the tourism industry. Kiyatkin and colleagues (in press)
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compared website text of business schools and corporations, estimating which
sector leads awareness and action in terms of social issues.
The sustainability programs in my sample had a wide range of web-based
resources. Some members of my sample had multiple pages of information,
programs, community events, maps of green attributes on campus, or links to
related centers and institutes. Other schools in my sample had far less messaging
via their websites. To assure that I obtained a representative sample of language
and a “like for like” comparison between schools, I created a template from the
school with the briefest web presence, noting what information was given or
topics addressed. I limited myself to only the topics or messaging themes on this
template when gathering data from other participants in my study. For example,
all the webpages in this study defined sustainability, discussed what and where to
recycle, and offered contact information about initiatives open to campus
involvement and key sustainability team contacts. Whether or not users accessed
the sustainability webpage or used search options from the university’s website
did not impact my research. The results obtained were, therefore, not a question of
website traffic or responses to the website content, but rather how the campus
sustainability program discussed sustainability on the website.
Data Analysis
As described in earlier sections of this chapter, data collected from
interviews, group meetings, and websites were given identifier codes that
communicated the performance category and data source, understood only by me.
I recruited three people to act as additional coders for this study. None of these
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individuals lives in NE Ohio, has ties or knowledge of any the institutions who
participated in this study, nor are any of them involved in the field of
sustainability. We applied directed content analysis to create a map capturing the
degree to which differences in emotional tone existed within sustainability
language of higher education across the three performance aggregates and taken
together as a whole. Coding for all data was conducted using Cooperrider and
associates’ (2008) Positive and Negative Discourse Categories featured in the
Appreciative Inquiry Handbook for Leaders of Change (2nd Ed. p.20-22) (see
Tables 3a & 3b).
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Table 3a: Positive Discourse Categories from Cooperrider et al (2008, p. 20)
Positive
Discourse Labels
Label Definition
Positive valuing
Any mention of positive values past or present.
Hope toward future
Any mention of hope, optimism, or positive anticipation toward the future.
Skill or competency
Any mention of skill, competency, action, or positive quality about self or others.
Openness, receptivity, learning
Any mention of receptivity in self or others accompanied by a positive outcome, also, any noticing of one’s, or another’s, learning or interests.
Active connection, effort to include, cooperation, or combination
Any noticing of efforts to include, cooperate, connect, and related that may be accompanied by at least an inferred positive outcome.
Mention of surprise, curiosity, or excitement
Any mention of curiosity, surprise, openness to fresh insights, or excitement in self or others.
Notice of facilitating action or movement toward a positive outcome
Any mention of a facilitating action or movement toward a real or imagined positive outcome or any mention of a facilitating object or circumstance. Also, noticing of any event that enhances another event, an effective state, or a person; noticing facilitative or positive cause and effect.
Effort to reframe in positive terms
Any mention of a negative emotion or action accompanied by the possibility of a positive desired outcome; also, any mention of a change in mood from negative to positive, including any mention of an obstacle that is temporary or getting over a negative static state, or reframing a negative situation into more positive terms.
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Table 3b: Negative Discourse Categories from Cooperrider et al (2008, p. 21)
Negative
Discourse Labels
Label Definitions
Negative valuing
Any mention of negative valuing; for example, fatalism, apathy, or dislike. Any description of person, a group, a circumstance, or an event as a problem or an obstacle.
Concern, worry, preoccupation, doubt
Any mention of concern, worry, or preoccupation without mention of a possible model to alleviate concern or to enhance understanding; any mention of doubt, suspicion, or lack of confidence in future outcomes.
Unfulfilled expectation
Any mention of any event, action, state, or person that does not match intention, wish, desire, goal, or other unfulfilled expectation.
Lack of receptivity, absence of connection
Any mention of a lack of receptivity in self or others, including a lack of collaboration, a lack of understanding, a failure to listen or failure to agree, or any explicit mention of an absence of connection.
Deficiency in self or others
Any mention of a sense that something is missing; for example, a deficiency in self or others or a lack of motivation, appropriate effort, skill, or competence or an absence of resources (such as time or money).
Negative effect Any mention of feelings of dissatisfaction, selfishness, sadness, defensiveness, irritation, or anger without mentioning a possible antidote or relief or effort to understand.
Withdrawal or suppression
Any mention of avoidance, ignoring, withdrawal of energy or surrender, or suppressing of self or others.
Control or domination
Any notice of effort or action to disrupt, dominate, wield control, or halt a mood or an action in self or other.
Wasted effort Any mention of excessive investment of time, resources, or energy without mention of reward or positive outcome.
Prediction, image of a negative future
Any mention of prediction, vision, image, or expectation of a negative future.
Attribution of control by others in combination with self-deprecation
Any notice of effort or action in /others to disrupt, dominate, or wield control in combination with attribution of helplessness to self or self-pity.
Negative cause and effect relation
Any explicit notice of a cause and effect relationship leading to a negative outcome
Reframing a situation in negative terms
Any mention of a positive emotion with the possibility of a negative outcome; mention of experiencing a change in mood from positive to negative or getting into a negative state, focusing on possible obstacles, or reframing a positive situation into more negative terms.
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The Positive and Negative Discourse Category labels were used to build “code families”
in the ATLAS.ti software program and were applied to highlighted portions of text—or
quotations from the data. Each label and its definition were visible for consistency in the
coding process among multiple coders.
When using a code developed by another scholar, and the objective is to replicate,
extend, or challenge their findings, the code must be used exactly as it appears in the
earlier or original research (Boyatzis, 1998). This study is neither an extension, challenge
nor replication of Cooperrider and colleague’s earlier research. During my initial process
of coding, labels were added to the positive and negative discourse families, reflecting
phenomena I was finding in the raw data that was not being accurately represented by the
existing code. These additional labels were meant as complements to the existing code,
adapting it to the different types of raw information contained in my data. This process
was conducted prior to the training of the three other coders. Two examples of this are
the addition of Embedded Sustainability to the Positive Discourse family and Bolt-On
Sustainability to the Negative Discourse family. The terms come from Laszlo and
Zhexembayeva (2011)
Embedded Sustainability implies systemic changes in thinking and practice.
Internal and external boundary lines become blurred, as notions of competitor or
customer shift to ideas of partner. Rather than defensiveness, a spirit of inquiry and
exploration become comfortable modes of operation, seeking new partnerships and
innovation. Sustainability becomes the lens through which institutions can re-discover
and re-design themselves (Laszlo & Zhexembayeva, 2011).
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Bolt-on Sustainability on the other hand, characterizes a superficial or piecemeal
response to sustainability, leaving the mainstream institution un-impacted by its presence.
Bolt-on sustainability efforts produce fragmentary wins on the fringes of an institution’s
activities (Laszlo & Zhexembayeva, 2011; Sterling, 2004). When sustainability efforts
are bolted-on, they lack integration throughout the institution’s operations or policies.
This study is concerned with emotional tone of narratives and the impact of the
emotional tone on change. Embedded Sustainability was added to the positive discourse
family because it complements and vivifies other labels included in this code family, such
as Active Connection, Effort to Include, Cooperation, or Combination and Mention of
Surprise, Curiosity, or Excitement. Bolt-on Sustainability is categorized as a negative
because it relates to labels included in the negative code family, such as Lack of
Receptivity, Absence of Connection; and Withdrawal or Suppression.
An additional discourse family of codes was created to capture neutral discourse
themes. I anticipated these labels prior to beginning the coding process, based on my
experience as a sustainability coordinator in higher education, which suggested neutral or
information-based discourse to be a major component of sustainability communications.
This includes instructions, directions, or sharing of data from the campus or experts in the
field, as indicated in Hypothesis 1a. Table 4 presents the labels added to the code.
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Table 4. Positive, Negative, and Neutral Labels Added to Cooperrider et al’s Positive/Negative Discourse
Code (2008)
Labels Added
Label Definitions
Positive Discourse Labels
Systems Thinking or Interconnectedness
Any mention of or expressed desire for interconnectedness. Includes references to how parts of the whole work together or a systems level perspective striving for better connection within a system toward a positive end.
Positive Cause & Effect Any explicit notice of a cause and effect relationship leading to a positive outcome.
Embedded Sustainability
Any mention of the need for, or presence of, sustainability as being embedded or integrated into processes or thinking, applied to one’s self, others, the institution, or society. References that sustainability is everyone’s concern, job, or opportunity and connections between sustainability and transformational change for the system.
Emphasizes the “We” Any references to being part of a group, the institution, or a larger domain (ie sustainability professionals). Any references that one’s role or department is part of the larger whole of the institution, or mention of achieving things together. Includes mention of working with others across campus toward a positive end and with an inclusive tone or campus pride, such as ‘we at [name of institution] are really good at….” Use of words like: we; our; us; team; together. This is the opposite of an “us vs. them” mindset.
Negative Discourse Labels Bolt-on Sustainability Any mention that sustainability efforts are present for PR (public relations),
marketing, school rankings, or recruitment purposes. Also includes any comments, which suggest sustainability is only pertinent to, or the concern of, one group, one position or one person, or department. This label includes any views of sustainability as transactional, or as being worthwhile in order to save money or reduce energy consumption, as well as compartmentalizing sustainability to be just about the environment or recycling, for example.
Separateness References to a lack of connectedness with other parts of the campus, the institution, other groups or departments on campus. Any emphasis on differences among groups on campus (i.e. emphasizing administration or faculty as though they are a separate class), references to silos. Also, any mention or reference to an "us vs them" perspective. Use of words such as: they; theirs; those guys.
Emphasizes the “I” A focus on one’s self as opposed to seeing one’s self as part of a team or community. Individualism, self-focused, narcissistic comments which communicate the speaker sees the achievements has being due to their efforts, or references that they don’t have to work within the typical rules. Includes language that is predominately about me, mine, my, I. References to others will only be done in a passing way, others play only a small role, big achievements are / have been achieved by me, I’m the special ingredient around here.
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Table 4 cont’d. Positive, Negative, and Neutral Labels Added to Cooperrider et al.’s Positive/Negative
Discourse Code (2008)
Neutral Discourse Labels Behavioral Instruction Any mention of recommended behaviors, which are framed as helping to
create a more sustainable campus, world, life, office, dorm room, department or the like. These are often directions, suggestions, instructions, tools, tips, or lists of behaviors one would ideally be engaging in.
Uses Expert Data Any mention of expert statistics, quotes, or exert data related to sustainability. Includes scientific and other research information. An ‘expert’ can be someone on campus with a position of authority or a particular knowledge / skill set, or someone outside the institution. Also includes quotes from famous people, writers, poets, etc.
Uses Campus Data Any mention of campus statistics or data related to the person’s own campus size, population, energy consumption, purchasing, waste, recycling, behaviors, etc. Also, may include references to increases or decreases in the above (i.e., pre or post sustainability intervention).
Information to Inspire Behavior Change
Any reference that simply sharing information, increasing awareness of statistics, data, facts, opportunities, cost savings, etc. will be effective in changing perspectives, beliefs, behaviors.
How Change Occurs Any references or conversation, which include the speaker’s theories on how behavior or organizational change occurs, such as “sustainability will gain traction once the President / students get behind it,” or “it’s gotta be personal for people or they won’t change,” “it’s gotta hit people in their pocket books before they’ll change their behavior.”
Reliability
Armstrong and colleagues (1997) observed that generally, qualitative
methodologies are not explicit about use of the concept of inter-rater reliability to
establish consistency of findings, however the concept emerges implicitly in descriptions
of procedures for carrying out the analysis. Despite this, the frequent attention paid to
qualitative analyses being better conducted as a group activity suggests results will be
improved if one view is moderated by another (Armstrong et al, 1997). Boyatzis (1998)
is more direct, arguing that the establishment of reliability is critical when applying
qualitative and interpretivist research methods. He defines reliability as a “consistency of
observation, labeling, or interpretation” (Boyatzis, 1998, p. 144). Krippendorf (2004)
generally agrees, defining reliability as the degree to which a coding process is
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reproducible with different coders, elsewhere, over time, and under conditions that should
not affect the results. While there is some disagreement about the efficacy of measures of
inter-rater reliability (see Krippendorf, 2004; Lombard et al, 2003; Pope et al, 2000;
Armstrong et al, 1997) there remains merit in involving more than one analyst in
situations where researcher bias is perceived to be present and an alternative has not yet
been identified.
For the research described here, use of multiple data analysts is essential due to
my relationship with one of the sites in my sample. Though not formally connected at the
time of this study, as one of the founders of one of the sustainability programs
participating in the study, my preferences for confirming approaches to change and
hopeful messaging have surely influenced the current emotional tone of discourse,
despite my having left this position five years ago. Although it is a vastly different
program today than when I left my role, I must be aware of possible personal and
theoretical biases. This led me to pay particular attention to inter-reter reliability in
coding the data.
Based on Boyatzis’ (1998) definition, inter-rater reliability is consistency among
multiple data analysts or viewers. Consistency is achieved when multiple people identify
the same themes in the same data. Inter-rater reliability is the “consistency of judgment
among multiple observers” (Boyatzis, 1998, p. 147). The most common way of
establishing reliability among multiple analysts is through double coding, in which coders
examine the same raw data, making judgments without interacting or seeing the results of
one another’s analysis. Observers then share and compare their results, discussing each
label or category until agreement is reached (Boyatzis, 1998).
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My coding-training protocol included reviewing the code and definitions with my
coders, and thinking of examples from shared experiences or popular culture (movies, TV
shows) that fit each label. In this way, my three coders were able to perform successful
“dry runs” using data outside my sample, which enabled them to gain skill at applying the
Positive, Negative and Neutral Discourse Categories Code (Cooperrider et al, 2008).
These exercises were then followed with short samples from my data set, which I had
already coded so the coders could see how selections from the data (quotes) can
sometimes be longer to capture the entirety of an idea or sentiment and are sometimes
shorter if the quote is more succinct or directly speaking to one of the code labels.
Moreover, providing the coders-in-training examples of coded transcripts allowed them
to see how more than one label can apply to a selected passage of text. In the next step,
each of the three coders-in-training, had to look at short, “clean” selections of text from
my data set and select the same quotations or selections of data as I had in samples from
the study data and apply the same label or labels as I did, as described by Boyatzis
(1998). We did this process together, either in person or via Skype. Through these
training exercises both the other coders and I developed skills and greater consistency in
our coding. I was able to ascertain the degree of similarity and consistency among my
coders before our collective efforts were applied to the sample. This approach also
allowed me to determine general similarities and difference in interpretation of label
names and definitions, and to determine the degree of difficulty in applying the code.
Once the coders expressed their confidence with their understanding of the code
and coding process, we then went on to establish inter-rater reliability. I provided each
coder multiple selections from interviews, group meetings, and websites to code, in order
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to establish inter-rater reliability. Boyatzis (1998) and Morse and colleagues (2002) have
presented multiple ways to establish reliability among coders. Included in both of these
papers is percentage agreement scores, which are calculated as the number of times of
observation that multiple analysts agree, divided by the number of times of total possible
observations. Percentage agreement is most appropriate when the unit of coding and unit
of analysis are the same, like language. Therefore, I employed this method to determine
an inter-rater reliability score for each of the three coders. One of the coders scored 87%,
a second scored 83%, and the third 82%, resulting in an average inter-rater reliability
score of 84%, which is considered to be reliable.
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CHAPTER FIVE: RESULTS
This chapter provides the results of my study on the exploration of the emotional
tone of the language used by sustainability programs in higher education contexts. My
study had two aims: first, to map and describe the emotional tone of language currently
used by college and university-based sustainability programs and secondly, to identify
whether differences in emotional tone correlated with campus sustainability rankings of
high, moderate, and base performance. The descriptive analysis of the data, illustrative
quotes, and interpretation of the research findings are presented in this chapter.
Data Analysis
Analysis of emotional tone used by sustainability programs
Analysis of language tone was determined by identifying discourse as positive,
negative or neutral, as described in Chapter Four. Initial analysis of language was
performed for all participating sustainability programs and results were aggregated by the
high, moderate, and base performance categories. Percentages of quotations from
discourse that were coded as positive, negative, and neutral are shown in Table 5. Across
all sources of data (one-on-one interviews, group meetings, and sustainability program
websites) and including all three of the performance categories, a total of 2,252
quotations were coded. Of the total number of coded quotations from interviews, group
meetings, and websites, 54% (1,224) were from the four high performing sustainability
programs, 22% (504) were from the three moderately performing institutions, and 23%
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(524) of the quotations coded were from the three base performing sustainability
programs.
Table 5. Positive, Negative and Neutral Discourse for All Sources of Data by Performance Category1
High
Performers
Moderate
Performers
Base
Performers
Number of Programs 4 3 3
Positive Discourse Labels 63.8% 50.2% 39.3%
Negative Discourse Labels 15.4% 22.8% 39.3%
Neutral Discourse Labels 20.8% 27% 21.4%
Number of Quotes Coded
(total N = 2,252) 1224 504 524
It should be noted that there were more than twice as many quotations from the
high performing institutions as the moderate and base performers, combined. This may be
due to the greater number of individuals interviewed from high performing sustainability
programs (N = 7 interviews) than from moderate performing programs (N = 5) and base
performing programs (N = 6). Additionally, sustainability programs making up the high
performer category submitted more group meetings, and group meetings which were
often longer in length (often longer than 60 minutes). The high performing programs
submitted a total of 9 group meeting recordings, compared to 4 group meeting recordings
1 A Chi-Square analysis was performed on data from Tables 5 – 8. Results are presented in Appendix A. To summarize, the relationships between performance categories were found to be statistically significant for all sources of data (Table 5), interview data (Table 6), and group meeting data (Table 7). Relationships presented in Table 8 for website data were not found to be significant.
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from moderate performing programs, and 3 for the base performing sustainability
programs.
Analysis of language by performance category
High performing sustainability programs, all data: When all three sources of
data were combined for high performing sustainability programs positive discourse labels
accounted for 63.8% of quotes, while negative discourse labels represented just 15.4% of
quotations. Neutral labels were applied to 20.8% of high performers’ quotes.
Calculation of the positive / negative ratio from this data, resulted in a 4:1 ratio (63.8%:
15.4%) of the top ranking institutions in my sample.
Moderate performing sustainability programs, all data: Among the moderate
performing programs, positive discourse comprised 50.2% of the coded quotations.
Labels categorized as negative discourse were applied to 22.9% of the moderate
performer’s quotes, and 27% of the quotes were coded with neutral labels. This yielded a
2:1 positive / negative ratio (50.2%: 22.8%) for the moderate performers in my sample.
Base performing sustainability programs, all data: When the language for the
base performing sustainability programs was analyzed, 39.3% of the quotes were labeled
positive, 39.4% of quotes were coded with negative discourse labels and neutral labels
were applied to 21.4% of the base performers’ quotes. The sustainability programs
falling into the base performance category were labeled with negative discourse codes 2.5
times more frequently across all sources of data than those programs in the high
performing category and had a 1:1 ratio (39.3%: 39.4%) of positive to negative discourse.
Direct quotes from the data are presented later in this chapter.
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Analysis of interview data by performance category
Thirty-eight percent of these data came from interviews with sustainability leaders
representing high performing sustainability programs (N = 7), 28% of the interview data
(N = 5) represented the moderate performers in my sample, and the remaining 33% of the
interview data (N = 6) resulted from interviews with base performing sustainability
programs. Women made up 39% of my interviews sample (N = 7) and men comprised
the remaining 61% (N = 11), ranging from 23 to 68 years of age.
Table 6 presents percentages of quotations coded as positive, neutral, and
negative discourse collected through one-on-one interviews, aggregated by the high,
moderate, and base performance categories. A total of 1,101 quotes were coded from
interview data representing all three of the performance categories. Four hundred and
ninety of these quotes were from interviews with high performers, 244 quotes from
moderate performers, and 367 quotes came from interviews of participants in base
performing sustainability programs.
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Table 6. Positive, Negative, and Neutral Discourse for Interview Data by Performance Category
High
Performers
Moderate
Performers
Base
Performers
Number of Programs 4 3 3
Positive Discourse Labels 71.2% 40.6% 42.5%
Negative Discourse Labels 21.6% 41.4% 39.2%
Neutral Discourse Labels 7.1% 18% 18.3%
Number of Quotes Coded
(total N = 1,101) 490 244 367
High performing sustainability programs, interviews: Seven, one-on-one
interviews were conducted with people identified as “sustainability leaders” at four
higher educational campuses categorized as high performing by the Sustainability Report
Card. Four hundred and ninety quotes were coded from these seven interviews, with
71.2% of the quotes coded with positive discourse labels. Negative discourse labels were
applied to 21.6% of the high performers’ interview quotes, and 7.1% were labeled as
neutral discourse. Within the interviews, the high performing sustainability programs’
ratio of positive / negative discourse was greater than 3:1.
Moderate performing sustainability programs, interviews: Two hundred and
forty-four quotes were coded from five interviews with higher educational sustainability
programs categorized as moderate performers. Of these, 40.6% were labeled as positive
discourse, 41.4% as negative discourse, and 18% as neutral discourse. Calculations from
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analysis of this interview data resulted in a positive to negative discourse ratio of 1:1 for
the moderate performing sustainability programs.
Base performing sustainability programs, interviews: Six interviews were
conducted with “sustainability leaders” of higher educational sustainability programs
categorized as base performing, resulting in 367 coded quotes. Positive discourse labels
were applied to 42.5% of the quotes, 39.2% of the quotes were labeled as negative
discourse. Neutral discourse labels were applied to the remaining 18.3% of quotes from
interviews in this category. The base performing sustainability programs in my sample
had a positive / negative discourse ratio that was slightly greater than 1:1.
Analysis of group meeting data by performance category
Sustainability team meetings were audio recorded and the transcripts were coded
with the same procedures described for one on one interviews. Fourteen group meetings
were submitted from all of the sustainability programs. One audio recording from the
moderate performer program had such poor sound quality, due to the audio device, that
the recordings could not be transcribed. The total number of meetings analyzed from the
sustainability programs was seven high performing, three moderate performing and three
base performing. From the 13 group meetings transcribed for analysis, 405 quotes were
coded. While this data source is closest to that described by Gottman (1994) and Losada
and Heaphy (2004), it has also been the most complicated, given reliance on technology
for data collection, which was not always dependable.
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Group meetings ranged in length from less than 30 minutes to more than 60
minutes, these differences are illustrated in Table 7 through the range of total number of
words transcribed for each category. All groups had some combination of narrative-rich
and narrative-thin discourse, meaning more strategic versus more operational discussions
respectively. Within the mix of discourse across performance categories, high
performing sustainability teams had more narrative-rich conversations, discussing
dilemmas, management, future visions for their programs, and strategy. Those programs
categorized as base performers demonstrated more narrative-thin conversations,
characterized by agreements about responsibilities or action lists for the day or week.
Table 7 presents these data aggregated by performance category.
Table 7. Positive, Negative, and Neutral Discourse for Group Meeting Data by Performance Category
High
Performers
Moderate
Performers
Base
Performers
Number of Programs 4 3 3
Total Number of Words 28,590 15,013 9,083
Positive Discourse Labels 52.2% 41.9% 14.3%
Negative Discourse Labels 25.7% 29% 61.2%
Neutral Discourse Labels 22.1% 29% 24.5%
Number of Quotes Coded
(total N = 405 ) 276 31 98
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High performing sustainability programs, group meetings: From the seven group
meetings analyzed from high performers, 276 quotes were coded. Labels from the
positive discourse family were applied to 52.5% of the quotes. Negative discourse labels
were applied to 25.7% of the group meeting quotes, with 22.1% of the quotes labeled as
neutral. The positive / negative communication ratio of participants discourse was
approximately 2:1.
Moderate performing sustainability programs, group meetings: From the three
group meetings analyzed from moderate performers in the sample, 31 quotes were coded.
Positive discourse labels were applied to 41.9% of these quotes, with 29% coded with
negative discourse labels and 29% coded with neutral labels. The group conversations of
moderately performing sustainability programs had a 1.4: 1 positive / negative
communication ratio.
Base performing sustainability programs, group meetings: Data analyzed from
the three base performing sustainability program group meetings, 98 quotes were coded.
Of these, 14.3% were positively labeled, whereas 61.2% were labeled as negative
discourse. Neutral discourse labels represented 24.5% of the quotes. The base performing
sustainability programs’ group meeting conversations had a 1:4 positive / negative ratio.
Analysis of website data
Content analysis was performed on the text of each participating institution’s
sustainability website analyzed with ATLAS.ti as described in Chapter Four. Ten of the
sustainability programs had websites, and all of these were analyzed. As stated earlier,
some programs had more extensive websites than others. The majority of university or
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college sustainability program websites were comprised of information about research
institutes and centers, student clubs, certification programs, and other similar areas.
However, some of the sustainability programs included in my sample offered fewer web-
based resources, resulting in less content to examine.
In an attempt to compare like with like, it was necessary to create a consistent
way of analyzing the website data. Therefore, I created a guidelines to determine which
items would be selected from each website for analysis. I reviewed the website of the
sustainability program with the least number of sections in its website, and used those
sections to create a template of sections or pages to be analyzed across all the
sustainability programs in my population. To illustrate, all of the websites contain
definitions of sustainability and why the issue is important to their campus. Additionally,
all the websites include information on recycling as well as presentation of the
institution’s sustainability activities. Once the template was created from the institution
with the least content, I then collected data from the same sections of the rest of the
institutions in my sample. The results of how these “like” sections were coded are shown
in Table 8.
It is interesting to note the similarity across the three performance categories, with
specific regard to the positivity / negativity ratios and the percentages of quotes coded
with neutral discourse labels. These points will be addressed in Chapter Six.
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Table 8. Positive, Negative, and Neutral Discourse for Website Data by Performance Category
High
Performers
Moderate
Performers
Base
Performers
Number of Programs 4 3 3
Positive Discourse Labels 62.9% 61.6% 61%
Negative Discourse Labels 2.4% 2.2% 5.1%
Neutral Discourse Labels 34.7% 36.2% 33.9%
Number of Quotes Coded
(total N = 746 ) 458 229 59
High performing sustainability programs, websites: Among the four high
performing sustainability programs in my sample, 458 quotes were coded from their
websites. Of these quotes, 62.9% were coded with labels from the positive discourse
family. Negative discourse labels were applied to just 2.4% and neutral discourse labels
were applied to 34.7% of the quotes. The websites of high performing sustainability
programs in my population had a positivity / negativity ratio of nearly 26:1.
Moderate performing sustainability programs, websites: Three websites were
analyzed from the moderate performers in my sample, with 229 quotes or passages being
coded. Positive discourse labels were applied to 61.6% of the quotes. Negative
discourse labels were applied in only 2.2% of the quotes with neutral labels being related
to 36.2% of the quotes. The ratio of positivity / negativity for the moderate performers’
websites was 28:1.
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Base performing sustainability programs, websites: Much like the other two
performance category, I found a fairly high percentage of quotes being coded with
positive discourse labels from the three websites of the base performing members of my
sample. Positive discourse comprised 61% of the quotes, 5.1% were negative, and like
the two other categories, neutral discourse labels were applied to 33.9% of the website
quotes and passages. The positivity / negativity ratio for the base performers’ websites
was approximately 12:1.
Positive / negative ratios, by performance category
The ratios of positive to negative (P/N) communication for each data source and
all sources combined, aggregated by performance groups, are shown in Table 9.
Evaluation of all sources of data combined, revealed all forms of communication of high
performing sustainability programs had twice as much positivity as the moderate
programs, and four times as much as the base performing programs. Looking solely at
interview data, high performing programs exhibited three times as much positivity in
their language than both moderate and base performing programs. Group meeting data
revealed the high performers communicated using language that was two-times more
positive as the moderate performing programs. Group meetings among the base
performing sustainability programs had four times more negativity than positivity, and
they communicated with four times more negativity than their moderate or high
performing counterparts in this sample. In contrast to the ratio differences in interview
and group meeting data, website data revealed much higher P/N ratios across all three
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performance categories. Implications of these ratio results will be discussed in further
detail in Chapter Six.
Table 9. Positive / Negative Discourse Ratios by Performance Category
Discourse families, aggregated by data source and performance category
Thus far, I have presented results of the data analysis from the perspective of
performance categories and sources of data and the percentages of these that are positive,
negative, or neutral. These results may help determine whether different performance
categories employ different emotional tone in their sustainability communication.
In the next section of this chapter, I take a more granular look at the data by
unpacking the positive, negative, and neutral discourse code. By evaluating where and
how often each label is applied- across different sources of data and performance
categories, rather than by simply looking at the code families of positive, negative, or
neutral- information about sustainability communication in higher educational contexts
can be expanded. I begin with the positive discourse family of labels.
High
Performers
Moderate
Performers
Base
Performers
Number of Programs 4 3 3
All Data Combined 4:1 2:1 1:1
Interview Data 3:1 1:1 1:1
Group Meeting Data 2:1 1:1 1:4
Website Data 26:1 28:1 12:1
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Positive Discourse: The positive discourse family of labels and the frequency of
each label’s application are shown in Tables 10 – 16. Table 10 provides an overview of
label frequencies for all sources of data combined. Tables 11– 13 present direct quotes
associated with the three labels with the highest frequencies for each performance group.
Tables 14 – 16 present the positive discourse label frequencies, organized by data
sources, beginning with interview data (Table 14), group meeting data (Table 15), and
website data (Table 16).
The frequencies of labels that comprise the positive discourse family, for all
sources of data (interviews, group meetings, and websites) for each performance category
are shown in Table 10. There are two numbers in each column, one indicates the
frequency of coded quotations for each label. The other number, presented in
parentheses, shows the frequency as a percentage of the total label applications within
that performance category for that particular source of data. Within each table, I have
bolded the three most frequently occurring positive discourse labels for each performance
category.
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Table 10. Number and Frequency of Positive Discourse Labels by Performance Category: All Sources of
Data
All Sources of Data High Performers
Moderate Performers
Base Performers
Number of Positive Coded Quotes: 781 253 206 Percent of all Positive Quotes (1,240) 63% 20% 17%
Positive Discourse Family Labels Frequency of Label & Within-Performance
Category Percentage (highest frequencies in bold) Facilitating Action, Movement Toward a Positive Outcome 155 (20%) 64 (25%) 36 (17%)
Active Connection/Effort to Include, Cooperate or Combination 114 (15%) 39 (15%) 24 (12%) Positive Valuing 67 (9%) 26 (10%) 10 (5%) Skill or Competency 56 (7%) 17 (7%) 22 (11%) Systems Thinking or Interconnectedness 57 (7%) 17 (7%) 14 (7%) Openness/Receptivity to Learning 45 (6%) 28 (11%) 14 (7%) Embedded Sustainability 58 (7%) 13 (5%) 9 (4%) Emphasizes the "We" 41 (5%) 9 (4%) 23 (11%) Positive Cause & Effect 47 (6%) 10 (4%) 13 (6%) Hope Toward Future 49 (6%) 10 (4%) 10 (5%) Envisioned Ideal 34 (4%) 11 (4%) 10 (5%) Surprise, Curiosity, Excitement 35 (4%) 1 (0%) 9 (4%) Reframing in Positive Terms 23 (3%) 8 (3%) 12 (6%)
Across all sources of data from high performers, the three most frequently applied
coding labels were: Facilitating Action, Movement Toward a Positive Outcome; Active
Connection/Effort to Include, Cooperate or Combination; and Positive Valuing. Table
11 presents examples of quotes coded with these labels from sustainability programs in
this performance category.
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Table 11. Direct Quotes from Positive Discourse Labels with Highest Frequency: High Performers
Among moderate performers, the most frequently applied labels were also
Facilitating Action, Movement Toward a Positive Outcome; Active Connection/Effort to
Include, Cooperate or Combination; and Openness or Receptivity to Learning. Table 12
presents direct quotes from moderate performer data.
Label
Quote
Facilitating Action, Movement Toward a Positive Outcome
“I needed to have those higher level administrative conversations about what our [academic integration] intentions were and how we can… paint a picture of where we're going academically in sustainability because… we really didn't have an academic, sustainability vision. Those meetings helped us create one.”
Active Connection/Effort to Include, Cooperate or Combination
“So what we have created is a process and a building energy management agreement form,. What I do is… before we initiate a setback, we get signoff from the building customers, from the automation guys, and from our maintenance organization.”
Positive Valuing
“The faculty, I think, comes from that place… I think deep down they really want us to be sustainable because they want us to be great and they want us to be here in a hundred years.”
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Table 12. Direct Quotes from Positive Discourse Labels with Highest Frequency: Moderate Performers
Label
Quote
Facilitating Action, Movement Toward a Positive Outcome
“I think that some process or project aspects of sustainability actually strike sentimental chords with some people and they go, ‘Yeah, yeah, I can do that.’”
Active Connection/Effort to Include, Cooperate or Combination
“The first thing I thought I’d do was, let’s find what our actual educational philosophy is, because we have one that’s explicit on paper, and find that adaptation and change piece of it and start to make that language connect with the goals of the sustainability program.”
Openness or Receptivity to Learning
“I've been doing a lot of soul searching on that, and I've really started trying to what I truly believe. So like I said, [trying to motivate people is] so new to me, and I've been all – in the last year I've really evolved, I think, a lot on that. And I really try to avoid reward-based things, because I don't think that is really what's motivating to people.”
The most frequently applied positive discourse labels for all sources of data for
the sustainability programs making up the base performing category were Facilitating
Action, Movement Toward a Positive Outcome; Active Connection/Effort to Include,
Cooperate or Combination; and Skill or Competency. Table 13 features direct quotes
from this performance category, for these labels.
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Table 13. Direct Quotes from Positive Discourse Labels with Highest Frequency: Base Performers
Label
Quote
Facilitating Action, Movement Toward a Positive Outcome
“I came in about 7 plus years ago, and it wasn’t really big then. It was probably about 6 years ago that we started seeing changes and that’s when the new dean came in. He really wanted to get sustainability, get it out there..”
Active Connection/Effort to Include, Cooperate or Combination
“I think a lot of [our strength] is the cooperation between faculty and staff. I think that’s a big strength we have, there’s a lot of support for ideas about how to be more sustainable. When someone does something about sustainability in our classrooms, that gets passed on to somebody else and it’s like a domino effect.”
Skill or Competency
“Yes, we have newer buildings but [our competitor institution has] this cool vibe going on, and they like to compete with us on recycling… and they beat us!”
The positive discourse labels as applied to interview data across the three
performance categories are shown in Table 14. The three predominant labels from all
interview data, which combines the three performance categories, were: Facilitating
Action, Movement Toward a Positive Outcome; Active Connection/Effort to Include,
Cooperate or Combination; and Skill or Competency. As with all sources of data
described in Table 9, there were similarities across the performance categories, but not
exact duplication.
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Table 14. Number and Frequency of Positive Discourse Labels: Interview Data
Interview Data ALL Interviews
High Performer
Moderate Performer
Base Performer
Number of Positive Coded Quotes 604 349 99 156 Percent Positive Coded Quotes 100% 58% 16% 26%
Positive Discourse Family Labels Frequency of Label & Within-Performance Category
Percentage (highest frequencies in bold) Facilitating Action, Movement Toward a Positive Outcome 85 (14%) 47 (13%) 14 (14%) 24 (15%) Active Connection/Effort to Include, Cooperate or Combination 72 (12%) 46 (13%) 8 (8%) 18 (12%) Positive Valuing 37 (6%) 26 (7%) 6 (6%) 5 (3%) Skill or Competency 72 (12%) 40 (11%) 11 (11%) 21 (13%) Systems Thinking or Interconnectedness 50 (8%) 28 (8%) 10 (10%) 12 (8%) Openness/Receptivity to Learning 75 (12%) 36 (10%) 26 (26%) 13 (8%) Embedded Sustainability 40 (7%) 31 (9%) 4 (4%) 5 (3%) Emphasizes the "We" 34 (6%) 15 (4%) 3 (3%) 16 (10%) Positive Cause & Effect 33 (5%) 19 (5%) 4 (4%) 10 (6%) Hope Toward Future 30 (5%) 20 (6%) 5 (5%) 5 (3%) Envisioned Ideal 23 (4%) 15 (4%) 1 (1%) 7 (4%) Surprise, Curiosity, Excitement 17 (3%) 7 (2%) 1 (1%) 9 (6%) Reframing in Positive Terms 36 (6%) 19 (5%) 6 (6%) 11 (7%)
Labels with the highest frequency, obtained from interview data with high
performing sustainability programs, were Facilitating Action, Movement Toward a
Positive Outcome; Openness or Receptivity to Learning; and Active Connection/Effort to
Include, Cooperate or Combination and Skill or Competency.
The labels most frequently applied to moderate performer interviews were
Facilitating Action, Movement Toward a Positive Outcome; Active Connection/Effort to
Include, Cooperate or Combination; and Skill or Competency.
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The three labels coded most frequently for base performers were the same as for
their high and moderate performing colleagues: Facilitating Action, Movement Toward a
Positive Outcome; Active Connection/Effort to Include, Cooperate or Combination; and
Skill or Competency.
The label frequencies for positive discourse coded data from all group meetings
combined and by performance category are shown in Table 15.
Table 15. Number and Frequency of Positive Discourse Labels: Group Meeting Data
Group Meeting Data ALL Group Meetings
High Performer
Moderate Performer
Base Performer
Number of Positive Coded Quotes 171 144 13 14 Percent Positive Coded Quotes 100% 84% 13% 13%
Positive Discourse Family Labels Frequency of Label & Within-Performance Category
Percentage (highest frequencies in bold) Facilitating Action, Movement Toward a Positive Outcome 41 (24%) 32 (22%) 1 (8%) 8 (57%) Active Connection/Effort to Include, Cooperate or Combination 27 (16%) 20 (14%) 4 (31%) 3 (21%) Positive Valuing 14 (8%) 14 (10%) 0 (0%) 0 (0%) Skill or Competency 5 (3%) 5 (3%) 0 (0%) 0 (0%) Systems Thinking or Interconnectedness 11 (6%) 9 (6%) 2 (15%) 0 (0%) Openness/Receptivity to Learning 6 (4%) 6 (4%) 0 (0%) 0 (0%) Embedded Sustainability 9 (5%) 9 (6%) 0 (0%) 0 (0%) Emphasizes the "We" 11 (6%) 8 (6%) 3 (23%) 0 (0%) Positive Cause & Effect 12 (7%) 8 (6%) 2 (15%) 2 (14%) Hope Toward Future 13 (8%) 11 (8%) 1 (8%) 1 (7%) Envisioned Ideal 3 (2%) 3 (2%) 0 (0%) 0 (0%) Surprise, Curiosity, Excitement 15 (9%) 15 (10%) 0 (0%) 0 (0%) Reframing in Positive Terms 4 (2%) 4 (3%) 0 (0%) 0 (0%)
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For all group meetings, the three most recurrent labels were Facilitating Action,
Movement Toward a Positive Outcome; Active Connection, Effort to Include, Cooperate
or Combination; and Surprise, Curiosity, Excitement. A direct quote, illustrating the
Surprise, Curiosity, Excitement label is:
“When I first heard about it, it was like, ‘Well, you know we're talking about this recycling program… ‘Really? Oh, interesting… ‘We're actually talking about a program?!’ Yeah, we'd like you to develop a program. ‘No kidding?!.. Ground floor?! ... Alright! I'm in!’” The label frequencies for each performance group were also determined. The
same labels that were most frequently applied for all group meetings combined were also
the most frequent for the high performer group meetings (frequency in bold). Among
moderate performers, Active Connection, Effort to Include, Cooperate or Combination
was the most frequently applied label, followed by Emphasizes the “We”. Positive Cause
and Effect and Systems Thinking or Interconnectedness occurred with the same
frequency.
Emphasizes the “We” was applied to this quote: “I think the whole point of
sustainability is the teamwork ethic. This is only going to be successful if we do it
together.”
Positive Cause and Effect was applied to this quotation: “There’s an obvious
connection between [faith] identity and mission and the values central to sustainability,
and that’s the language we’re going to be working on because I feel once we make that
connection, it will kick the snowball down the hill!” A direct quote illustrating Systems
Thinking or Interconnectedness is:
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“I keep saying this, the one thing that sustainability has taught me is about the connectedness of everything-- everything is connected to everything. You know, every action has a reaction. Well, it isn't just that every action has a reaction, every action has an impact…I’m thinking of the student this morning from the other school, it’s totally mystifying why a facilities manager would be opposed to sustainability. The people on every campus, in my mind, and I'm not saying this because I'm doing it, I mean, to me it makes no sense why, but the people.. on campus who should be leading the charge should be the facilities manager because look at what we deal with-- the entire system!”
Results from the base performer group meetings were led by Facilitating Action,
Movement Toward a Positive Outcome followed by Active Connection, Effort to Include,
Cooperate or Combination; and Positive Cause and Effect.
Frequencies of positive discourse was also determined for website data, which are
presented in Table 16. While the labels of Facilitating Action, Movement Toward a
Positive Outcome, Active Connection or Effort to Include or Combination and Positive
Valuing were the most frequent labels for all website data combined and for the high and
moderate performer categories, the base performers’ highest labels were Emphasizes the
We, Positive Valuing, and Embedded Sustainability, Hope Toward Future, and
Facilitating Action, Movement Toward a Positive Outcome.
Embedded Sustainability is shown by this selection: “[The founders] were able to
change a conversation, from [sustainability] not just being about technology and really
giving it some teeth and momentum to the idea that it’s not just about the light bulbs, that
there’s a behavior change, organizational component and that there’s this whole cultural
side of sustainability.” The label Hope Toward Future is described by this line, “I feel
like we’re headed in the right direction. I feel like we have a solid foundation.”
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Table 16. Number and Frequency of Positive Discourse Labels: Website Data
Website Data All
Website Data
High Performer
Moderate Performer
Base Performer
Number of Positive Coded Quotes 465 288 141 36 Percent Positive Coded Quotes 100% 62% 30% 8%
Positive Discourse Family Labels Frequency of Label & Within-Performance Category
Percentage (highest frequencies in bold) Facilitating Action, Movement Toward a Positive Outcome 129 (28%) 76 (26%) 49 (35%) 4 (11%) Active Connection/Effort to Include, Cooperate or Combination 78 (17%) 48 (17%) 27 (19%) 3 (8%) Positive Valuing 52 (11%) 27 (9%) 20 (14%) 5 (14%) Skill or Competency 18 (4%) 11 (4%) 6 (4%) 1 (3%) Systems Thinking or Interconnectedness 27 (6%) 20 (7%) 5 (4%) 2 (6%) Openness/Receptivity to Learning 6 (1%) 3 (1%) 2 (1%) 1 (3%) Embedded Sustainability 31 (7%) 18 (6%) 9 (6%) 4 (11%) Emphasizes the "We" 28 (6%) 18 (6%) 3 (2%) 7 (19%) Positive Cause & Effect 25 (5%) 20 (7%) 4 (3%) 1 (3%) Hope Toward Future 26 (6%) 18 (6%) 4 (3%) 4 (11%) Envisioned Ideal 29 (6%) 16 (6%) 10 (7%) 3 (8%) Surprise, Curiosity, Excitement 13 (3%) 13 (5%) 0 (0%) 0 (0%) Reframing in Positive Terms 3 (1%) 0 (0%) 2 (1%) 1 (3%)
Negative discourse: The negative discourse family of labels and the frequency of
each label’s application are shown in Tables 17 – 23. Table 17 provides an overview of
label frequencies for all sources of data combined. Tables 18 – 20 present direct quotes
associated with the three labels with the highest frequencies for each performance group.
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Table 17. Number and Frequency of Negative Discourse Labels by Performance Category: All Sources of
Data
All Sources of Data High Performers
All Sources of Data Medium Performers
All Sources of Data
Base Performe
rs Number of Negative Coded Quotes: 188 115 206 Percent of all Negative Quotes ( 37% 23% 41%
Negative Discourse Family Labels
Frequency of Label & Within-Performance Category Percentage
(highest frequency in bold) Bolt-on Sustainability 17 (9%) 33 (29%) 57 (28%)
Deficiency in Self or Others 41 (22%) 12 (10%) 14 (7%) Lack of Receptivity/Absence of Connection
18 (10%) 14 (12%) 22 (11%)
Negative Valuing 20 (11%) 7 (6%) 15 (7%) Separateness & Individualism 12 (6%) 11 (10%) 18 (9%) Negative Affect 16 (9%) 8 (7%) 14 (7%) Concern/Worry/Preoccupation/ Doubt
18 (10%) 4 (3%) 8 (4%)
Attribution of Control by Others in Combination w Self Deprecation
8 (4%) 5 (4%) 11 (5%)
Emphasizes the "I" 0 (0%) 4 (3%) 15 (7%) Negative Cause & Effect 7 (4%) 7 (6%) 5 (2%) Unfulfilled Expectation 10 (5%) 3 (3%) 5 (2%) Wasted Effort 8 (4%) 0 (0%) 8 (4%) Reframing in Negative Terms 4 (2%) 4 (3%) 5 (2%) Prediction/Image of a Negative Future
4 (2%) 2 (2%) 4 (2%)
Withdrawal/Ignoring/Avoidance/ Suppression
1 (0%) 1 (1%) 4 (2%)
Control or Domination 4 (2%) 0 (0%) 1 (0%)
Table 17 features all sources of data combined for each performance category.
While negative labels are distributed across the three performance categories fairly
evenly, it is noteworthy to see, within each performance group, differences in frequencies
of each label. For example, for moderate and base performing programs, Bolt-on
Sustainability is applied at least twice as often as the next most applied codes. Among
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high performers the negative discourse label with the highest frequency is Deficiency in
Self or Other, but Bolt-on Sustainability is not included in the three most frequently
applied codes for this performance category.
Looking only at data coming from the high performing category, Table 18
presents direct quotations for each of the highest frequency labels.
Table 18. Direct Quotes from Negative Discourse Labels with Highest Frequency: High Performers
Label
Quote
Deficiency in Self or Other
“I don’t talk about sustainability in any kind of… at a philosophical level or even a business level. I think that’s one of the areas I feel most uncomfortable, you know. You hear people talk about the ‘internal rate of return’ for the sustainability programs being higher and stuff, but I’m not comfortable with any of that, like that level of conversation.”
Negative Valuing
“They did not approach it in a professional manner for one… It was not a positive exchange.”
Concern / Worry / Preoccupation / Doubt
“It is not a great option for us because it costs a lot of money and is a big, expensive process.”
Table 19 features direct quotes representing the moderate performance category, and the
most frequently applied negative discourse labels.
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Table 19. Direct Quotes from Negative Discourse Labels with Highest Frequency: Moderate Performers
Label
Quote
Bolt-on Sustainability
“… if administration could be able to see the potential economic benefit of making sustainability an initiative-then I think they'd be far more interested. But, at this stage of the game, you are looking at a one or two or three million dollars short fall this fiscal year because of enrollment whatever state funding cuts- it's hard in the day to day fight to stay afloat, to argue for sustainability to be a priority.”
Deficiency in Self or Others
“The change resistance comes from middle management.”
Lack of Receptivity / Absence of Connection
“My perception was that they were feeling so stepped on by the administration to do more with less every single year, because of cuts from the state, that any perception of money being spent- no matter how small- they felt like they had to push back on the administration and point out the inefficiencies, or what they didn't perceive as effective use of resources.”
Direct quotations from base performer programs’ negative discourse is presented in Table
20.
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Table 20. Direct Quotes from Negative Discourse Labels with Highest Frequency: Base Performers
Label
Quote
Bolt-on Sustainability
… if administration could be able to see the potential economic benefit of making sustainability an initiative-then I think they'd be far more interested. But, at this stage of the game, you are looking at a one or two or three million dollars short fall this fiscal year because of enrollment whatever state funding cuts- it's hard in the day to day fight to stay afloat, to argue for sustainability to be a priority.
Lack of Receptivity / Absence of Connection
A lot of people just don’t understand and I've gotten this this feedback sometimes where its like, “Well, I'm not gonna go live in a cave and give up my car.” It's like, that’s not what [sustainability] means.
Separateness / Individualism
“Talking about how coordinated the campus’ sustainability program is, uh… piece-meal is a great way to put it.”
Table 21 presents results for negative discourse label frequencies for interview
data. For all performance groups combined, Bolt-on Sustainability and Deficiency in Self
or Others are the labels applied with the highest frequency. Bolt-on Sustainability is
approximately three times more frequent in moderate and base performers than in the
high performing programs.
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As in Table 18, the label with the highest frequency for high performers in
Deficiency in Self or Others. Among high performers, this label occurs twice as often as
it does among moderate sustainability programs and almost four times as often as it does
among the base performing programs.
Table 21. Number and Frequency of Negative Discourse Labels: Interview Data
All Interview
Data
High Performer
Moderate Performer
Base Performer
Number of Negative Coded Quotes: 351 30% (N = 106)
29% (N = 101)
41% (N = 144)
Negative Discourse Family Labels
Frequency of Label & Within-Performance Category Percentage (highest frequency in bold)
Bolt-on Sustainability 87 (25%) 12 (11%) 29 (29%) 46 (32%) Deficiency in Self or Others 48 (14%) 28 (26%) 12 (12%) 8 (6%) Lack of Receptivity/Absence of Connection 37 (11%) 11 (10%) 13 (13%) 13 (9%)
Negative Valuing 26 (7%) 9 (8%) 7 (7%) 10 (7%) Separateness & Individualism 30 (9%) 7 (7%) 10 (10%) 13 (9%) Negative Affect 28 (8%) 12 (11%) 7 (7%) 9 (6%) Concern/Worry/Preoccupation/ Doubt 16 (5%) 5 (5%) 4 (4%) 7 (5%)
Attribution of Control by Others in Combination w Self Deprecation 16 (5%) 6 (6%) 4 (4%) 6 (4%)
Emphasizes the "I" 16 (5%) 0 (0%) 1 (1%) 15 (10%) Negative Cause & Effect 8 (2%) 2 (2%) 5 (5%) 1 (1%) Unfulfilled Expectation 6 (2%) 2 (2%) 2 (2%) 2 (1%) Wasted Effort 11 (3%) 6 (6%) 0 (0%) 5 (3%) Reframing in Negative Terms 10 (3%) 2 (2%) 4 (4%) 4 (3%) Prediction/Image of a Negative Future 5 (1%) 0 (0%) 2 (2%) 3 (2%)
Withdrawal/Ignoring/Avoidance/ Suppression 4 (1%) 1 (1%) 1 (1%) 2 (1%)
Control or Domination 3 (1%) 3 (3%) 0 (0%) 0 (0%)
Table 22 provides the results of negative discourse label frequencies for group
meetings, organized by performance categories. Looking at all three categories it is
interesting to note the spread of frequencies across all the labels. The three most
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frequently applied negative discourse labels for high performing sustainability programs
are Deficiency in Self or Other; Concern Worry, Preoccupation or Doubt; and Negative
Valuing. Base performers’ most frequent label is Bolt-On Sustainability and Lack of
Receptivity / Absence of Connection followed by Deficiency in Self or Others, and Lack
of Receptivity/Absence of Connection; Negative Valuing; Separateness and
Individualism; and Concern/Worry/Preoccupation/Doubt.
Table 22. Number and Frequency of Negative Discourse Labels: Group Meeting Data
All Group Meeting
Data
High Performer
Group Meetings
Moderate Performer
Group Meetings
Base Performer Group Meetings
Number of Negative Coded Quotes: 139 51% (N = 71)
6% (N = 9)
42% (N = 59)
Negative Discourse Family Labels
Frequency of Label & Within-Performance Category Percentage (highest frequency in bold)
Bolt-on Sustainability 14 (10%) 5 (7%) 0 (0%) 9 (15%) Deficiency in Self or Others 17 (12%) 11 (15%) 0 (0%) 6 (10%) Lack of Receptivity/Absence of Connection 17 (12%) 7 (10%) 1 (11%) 9 (15%) Negative Valuing 14 (10%) 9 (13%) 0 (0%) 5 (8%) Separateness & Individualism 11 (8%) 5 (7%) 1 (11%) 5 (8%) Negative Affect 9 (6%) 3 (4%) 1 (11%) 5 (8%) Concern/Worry/Preoccupation/Doubt 13 (9%) 12 (17%) 0 (0%) 1 (2%) Attribution of Control by Others in Combination w Self Deprecation 8 (6%) 2 (3%) 1 (11%) 5 (8%) Emphasizes the "I" 3 (2%) 0 (0%) 3 (33%) 0 Negative Cause & Effect 8 (6%) 3 (4%) 1 (11%) 4 (7%) Unfulfilled Expectation 11 (8%) 7 (10%) 1 (11%) 3 (5%) Wasted Effort 5 (4%) 2 (3%) 0 (0%) 3 (5%) Reframing in Negative Terms 3 (2%) 2 (3%) 0 (0%) 1 (2%) Prediction/Image of a Negative Future 2 (1% 2 (3%) 0 (0%) 0 (0%) Withdrawal/Ignoring/Avoidance/ Suppression 2 (1%) 0 (0%) 0 2 (3%) Control or Domination 2 (1%) 1 (1%) 0 1 (2%)
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Table 23 sorts frequencies for the negative discourse family for website data,
aggregated by performance category. These results stand out compared to interview and
group meeting data for their relatively low incidence of negative label application. Across
all three performance groups, frequencies of negative labels are at their lowest.
Discussion of why this is and why low negativity occurs across the three performance
groups follows in Chapter Six.
Table 23. Number and Frequency of Negative Discourse Labels: Website Data
All Website
Data
High Performer Websites
Moderate Performer Websites
Base Performer Websites
Number of Negative Coded Quotes: 19 58% (N = 11)
26% (N = 5)
18% (N = 3)
Negative Discourse Family Labels
Frequency of Label & Within-Performance Category Percentage (highest frequency in bold)
Bolt-on Sustainability 6 (32%) 0 (0%) 4 (80%) 2 (67%) Deficiency in Self or Others 2 (11%) 2 (18%) 0 (0%) 0 (0%) Lack of Receptivity/Absence of Connection 0 (0%) 0 (0%) 0 (0%) 0 (0%) Negative Valuing 2 (11%) 2 (18%) 0 (0%) 0 (0%) Separateness & Individualism 0 (0%) 0 (0%) 0 (0%) 0 (0%) Negative Affect 1 (5%) 1 (9%) 0 (0%) 0 (0%) Concern/Worry/Preoccupation/Doubt 1 (5%) 1(9%) 0 (0%) 0 (0%) Attribution of Control by Others in Combination w Self Deprecation 0 (0%) 0 (0%) 0 (0%) 0 (0%) Emphasizes the "I" 0 (0%) 0 (0%) 0 (0%) 0 (0%) Negative Cause & Effect 3 (16%) 2 (18%) 1 (20%) 0 (0%) Unfulfilled Expectation 1 (5%) 1 (9%) 0 (0%) 0 (0%) Wasted Effort 0 (0%) 0 (0%) 0 (0%) 0 (0%) Reframing in Negative Terms 0 (0%) 0 (0%) 0 (0%) 0 (0%) Prediction/Image of a Negative Future 3 (16%) 2 (18%) 0 (0%) 1 (35%) Withdrawal/Ignoring/Avoidance/Suppression 0 (0%) 0 (0%) 0 (0%) 0 (0%) Control or Domination 0 (0%) 0 (0%) 0 (0%) 0 (0%)
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Neutral discourse: Tables 24 – 30 present the neutral discourse family of labels
and the frequency of each label’s application (by number of coded quotations),
aggregated by performance categories and sources of data. I begin with Table 24, which
includes all sources of data combined for each of the three performance categories.
Following, are Tables 25 – 27, which provide direct quotes for each of the performance
categories and highest frequency labels. Tables 28 – 30 are organized by the source of
data: interview data; group meeting data; and website data. Within tables 24 and 28 – 30,
I have bolded the two most frequently occurring positive discourse labels for each
performance category due to the lower number of total labels in this code family.
Table 24. Number and Frequency of Neutral Discourse Labels by Performance Category: All Sources of
Data
All Sources of Data
High Performers
All Sources of Data
Moderate Performers
All Sources of Data
Base Performers
Number of Neutral Coded Quotes 51%
(N = 255) 27%
(N = 136) 22%
(N = 112)
Neutral Discourse Family Labels
Frequency of Label & Within-Performance Category Percentage
(highest frequency in bold)
Information to Inspire Behavior Change 79 (31%) 55 (40%) 38 (34%)
How Change Occurs 67 (26%) 42 (31%) 47 (42%)
Uses Campus Data- 76 (30%) 25 (18%) 13 (12%) Behavioral Instruction 30 (12%) 11 (8%) 12 (11%) Uses Expert Data 3 (1%) 3 (2%) 2 (2%)
Table 24 presents frequency results for the sources of data, combining the three
performance groups. It offers a high level snapshot of where the neutral discourse labels
were applied most frequently across sources of data and within each column, we see
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which labels were most regularly used in coding. Table 25 features data from the high
performing data set.
Table 25. Direct Quotes from Neutral Discourse Labels with Highest Frequency: High Performers
Label
Quote
Information to Inspire Behavior Change
“It was when I began to read the books and began to understand the impact of climate change and I realized that sustainability was the one thing that everyone can do personally.”
Uses Campus Data
“When you crunch the numbers to assess how it compares to FY00 or even FY08, it’s like maybe a quarter of the usage!”
Table 26 features direct quotes representing the moderate performance category, and the
most frequently applied neutral discourse labels.
Table 26. Direct Quotes from Negative Discourse Labels with Highest Frequency: Moderate Performers
Label
Quote
Information to Inspire Behavior Change
“In reality, my method is to just make people aware of things. “
How Change Occurs
“Once students got interested in sustainability then the administration had to pay attention. On a campus like ours, student interest carries a lot of weight.”
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Direct quotations from base performer programs’ neutral discourse is presented in Table
27.
Table 27. Direct Quotes from Neutral Discourse Labels with Highest Frequency: Base Performers
Label
Quote
Information to Inspire Behavior Change
“We calculated that one year’s worth of paper consumption on this campus would cover the football field and be 8 reams of paper tall. I use this in my classes all the time, to get people’s attention.”
How Change Occurs
“If we had a president who was in to sustainability then it would be a different story around here, you know… when leadership is behind something, they find a way to make it happen.”
Table 28 presents the frequencies neutral discourse labels were applied to
interviews. In interviews, the communication of respondents from base performing
programs in my sample were coded with neutral labels more than moderate performers
and almost twice as much as interviews with representatives of high performing
sustainability programs. How Change Occurs was the most frequently applied label for
all three of the performance groups.
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Table 28. Number and Frequency of Neutral Discourse Labels: Interview Data
ALL
Interviews
High Performer Interviews
Moderate Performer Interviews
Base Performer Interviews
Number of Neutral Coded Quotes 146 24%
(N = 35) 30%
(N = 44) 46%
(N = 67)
Neutral Discourse Family Labels
Frequency of Label & Within-Performance Category Percentage
(highest frequency in bold)
Information to Inspire Behavior Change 32 (22%) 4 (11%) 8 (18%) 20 (30%)
How Change Occurs 89 (61%) 23 (66%) 31 (70%) 35 (52%)
Uses Campus Data- 20 (14%) 8 (23%) 3 (7%) 9 (13%) Behavioral Instruction 4 (3%) 0 (0%) 1 (2%) 3 (4%) Uses Expert Data 1 (1%) 0 (0%) 1 (2%) 0 (0%)
Table 29 features the frequencies with which neutral labels were applied to group
meeting data. Within all the group meeting data How Change Occurs was the most
frequently applied label. Uses Campus Data represents a higher percentage of the
conversation among high performing sustainability programs, than in the meetings of
moderate and low performing programs.
Table 29. Number and Frequency of Neutral Discourse Labels: Group Meeting Data
All Group
Meetings
High Performer
Group Meetings
Moderate Performer
Group Meetings
Base Performer
Group Meetings
Number of Neutral Coded Quotes 95 64%
(N = 61) 10%
(N = 9) 26%
(N = 25)
Neutral Discourse Family Labels
Frequency of Label & Within-Performance Category Percentage
(highest frequency in bold)
Information to Inspire Behavior Change 13 (14%) 5 (8%) 1 (11%) 7 (28%)
How Change Occurs 34 (36%) 18 (30%) 5 (56%) 11 (44%)
Uses Campus Data- 38 (40%) 33 (54)% 1 (11%) 4 (16%) Behavioral Instruction 6 (6%) 4 (7%) 1 (11%) 1 (4%) Uses Expert Data 4 (4%) 1 (2%) 1 (11%) 2 (8%)
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Table 30 provides frequency results for neutral discourse frequencies for website
data from high, moderate, and base performing sustainability programs in my sample. Of
all the sources of data, websites have the highest number of neutral label applications. It
is via sustainability program websites that definitions, instructions, and various pieces of
campus data (from building energy consumption to course lists for sustainability majors)
are most regularly and most broadly communicated to constituents of the sustainability
programs. Par for this type of data source, Information to Inspire Behavior Change is the
most frequently applied label for each of the performance groups.
Table 30. Number and Frequency of Neutral Discourse Labels: Website Data
All
Websites
High Performer Websites
Moderate Performer Websites
Base Performer Websites
Number of Neutral Coded Quotes 262 61%
(N = 159) 32%
(N = 83) 8%
(N = 20)
Neutral Discourse Family Labels
Frequency of Label & Within-Performance Category Percentage
(highest frequency in bold)
Information to Inspire Behavior Change 127 (48%) 70 (44%) 46 (55%) 11 (55%)
How Change Occurs 33 (13%) 26 (16%) 6 (7%) 1 (5%)
Uses Campus Data- 56 (21%) 35 (22%) 21 (25%) 0 (0%) Behavioral Instruction 43 (16%) 26 (16%) 9 (11%) 8 (40%) Uses Expert Data 3 (1%) 2 (1%) 1 (1%) 0 (0%)
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CHAPTER SIX: DISCUSSION
This chapter begins with a discussion of the study’s primary findings, followed by
additional findings of interest. Later in this section, limitations of the study are presented,
followed by implications for future research and the study’s contributions. I will first
offer an overview of the research questions, hypotheses, and their relationship to
findings.
Overview
This research explored two questions. First, what is the emotional tone of
sustainability language used in higher educational contexts? Second, how does the
emotional tone of campus sustainability narratives relate to the performance of
sustainability programs? Three hypotheses were associated with these questions.
H1a: The current state of sustainability language in higher education, including
all performance categories, will be dominated by a neutral emotional tone.
H1b: Instruction and information sharing will comprise the majority of the
neutral discourse.
H2: Higher education sustainability programs with at least a 3:1 ratio of positive
to negative language in their personal and public communications (written and
verbal) will be rated higher on a national campus sustainability ranking than
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sustainability programs whose communications employ a lower ratio of positive
to negative communication acts.
H3: The highest performing campuses will approach change for sustainability
through a confirmation-based approach, characterized by a future oriented stance,
propensity for innovation, being proactive and creating opportunities to further
develop their institution’s sustainability program.
Support for Hypotheses
Table 31 presents each hypothesis, whether or not it was supported by the
findings, and if there was support, those findings are outlined. Discussion of the findings
follows the table.
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Table 31. Support for Hypotheses
Hypotheses
Supported
Findings
H1a: The current state of sustainability language in higher education, including all performance categories, will be dominated by a neutral emotional tone. H1b: Instruction and information sharing will comprise the majority of the neutral discourse.
No
Yes
H1a: Based on all sources of data (Table 4), neutral discourse does not predominate across performance categories. H1b. Within the neutral discourse family of labels, the most frequently applied label was Information to Inspire Behavior Change (Table 17).
H2: Higher education sustainability programs with at least a 3:1 ratio of positive to negative language in their personal and public communications (written and verbal) will be rated higher on a national campus sustainability ranking than sustainability programs whose communications employ a lower ratio of positive to negative communication acts.
Yes
Sustainability programs in the high performer category had a 4:1 P/N ratio. Moderate performers had at 2:1 P/N ration, and base performers had a 1:1 P/N ratio (Table 8).
H3: The highest performing campuses will approach change for sustainability through a confirmation-based approach, characterized by a future oriented stance, propensity for innovation, being proactive and creating opportunities to further develop their institution’s sustainability program.
Yes
1. The positive discourse labels with the highest frequencies applied in the high performance category were Facilitating Action, Movement Toward a Positive Outcome, Active Connection/Effort to Include, Cooperate or Combination, and Positive Valuing. 2. Direct quotes from high performing interviews, group meetings, and websites, which support this hypothesis are presented in the discussion.
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Hypothesis 1a:
Hypothesis 1a anticipated neutral discourse would play a larger role in the
sustainability narratives than findings support. This expectation was not supported by the
data. As presented in Table 4, when all sources of data are combined, neutral discourse
comprises only 20.8% for high performing sustainability programs (compared to 63.8%
positive discourse), 27% for moderate performers (compared to 50.2% positive
discourse), and 21.4% for base performers (compared to 39.3% positive discourse).
Reviewing just website data (Table 8), where neutral discourse had the highest
frequencies compared to interview and group meeting data, positive discourse again
prevailed. For high performing programs, neutral discourse labels comprised 34.7% of
the total data, whereas positive discourse labels were applied to 62.9% of this category’s
website data. For the moderate performers in this sample, neutral discourse labels were
applied to 36.2% of the website data and labels reflecting the positive discourse family
were applied to 61.6%. Among base performers, website data was labeled as 33.9%
neutral and 61% positive.
Hypothesis 1a served a two-fold purpose. First, it reflected my experiences as a
Sustainability Professional and was based on observations and critiques that
environmental and sustainability rhetoric is data-driven, compliance and regulatory-
focused by Meppem (2000), Schmidt (2005), Hart and Milstein (2003), and Malka and
colleagues (2009). Second, although I was inspired by the work of Schwartz (1986),
Gottman (1994), and especially Losada and Heaphy (2004), I was not convinced that
higher educational sustainability narratives would contain clearly positive emotional tone
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to the extent that P/N ratios would be significant. In this way, Hypothesis 1a served as a
“null hypothesis.”
Hypothesis 1b:
Even though H1a was not supported by the data, there is support for H1b. The
most frequently applied neutral labels did indeed reflect a propensity for information
sharing. In the case of these data, information is shared as a means of motivating change.
Higher educational Sustainability Professionals assume inaction or disinterest in
sustainability is based in a lack of knowledge, underpinned by the belief that “if you
knew what I know, you would do what I do.”
Malka, Krosnick, and Langer (2009) explored whether or not sharing information
about global warming had an impact on Americans’ concern related to the issue. They
found that pre-existing trust in who was doing the communicating- in their study it was
‘scientists’- and pre-existing agreement or disagreement with the content- is global
warming real and is it a threat to human systems- determined levels of concern and
predicted action or inaction.
In a higher education context, the findings of Malka and colleagues indicate that
who is communicating to the campus constituency is as important as what they are
communicating. In a complex human system like a college or university, with multiple
interest groups and stakeholders, information coming from institutional leaders, faculty
members and other kinds of thought leaders each represent diverse aspects of the system.
While this is key to a systemic approach to communicating about sustainability, it is only
part of the story.
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While this argument may send a hopeful message to campus Sustainability
Professionals as they think of recruiting popular professors, student leaders, or beloved
staff members, it is incomplete for one reason. It assumes neutral discourse, like
information sharing, will evoke and sustain behavior change. A data-centric narrative
privileges particular kinds of reasoning, knowledge, and inspiration and excludes others
(Meppem, 2000) and is not an effective counterbalance to negative remarks or
information (Fredrickson, 2013).
At the turn of the 20th Century, William James wrote, “The world we see that
seems so insane is the result of a belief system that is not working. To perceive the world
differently, we must be willing to change our belief system, let the past slip away, expand
our sense of now, and dissolve the fear in our minds” (1897). Language with a positive
emotional tone opens our minds and allows us to appreciate- and see more clearly- what
is, whereas neutrality (and understandably negativity) hide what is from us, by inspiring
us to protect ourselves, pulling back from situations (Fredrickson, 2009).
Hypothesis 2:
The sustainability programs making up the high performance category in this
study are indeed characterized by more positive language than negative, with a 4:1 P/N
ratio. Comparing the high performers’ P/N ration to the other groups in the sample, it is
clear that the base performers had a low positivity ratio at 1:1. As one might expect, the
moderate performers fell into the middle, with a positivity ratio of 2:1. These findings
not only support H2 but they also join and support Gottman’s findings (1994) and those
presented in Losada and Heaphy’s research (2004), which characterize high, moderate,
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and low performing sales teams by high, moderate, and low positivity ratios in their
language.
As discussed in the literature review, Losada applied a nonlinear dynamic model
to the data in order to predict to positivity ratios, which marks the tipping point for
human systems (Losada & Heaphy, 2004; Fredrickson & Losada, 2005; Fredrickson,
2009). This mathematical model was challenged and found to be invalid by Brown and
colleagues (2013), although the evidence linking high positivity ratios with high
performance remains valid (Expression of Concern, ABP, 2014).
Figure 2 presents the findings supporting H2 by showing the downward slope of
positive discourse present in all sources of data from high to moderate to base performers
and the upward slope of the presence of negative discourse, at its lowest with high
performers and increasing for moderates and increasing further for base performing
sustainability programs.
Figure 2. Discourse by Performance Category
64%
50%
39%
15%
23%
39%
21% 27%
21%
0%
10%
20%
30%
40%
50%
60%
70%
High Performers Moderate Performers Base Performers
% o
f cod
es w
ithin
per
form
ance
cat
egor
y
Positive Discourse Labels
Negative Discourse Labels
Neutral Discourse Labels
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Hypothesis 3:
Hypothesis 3 anticipated a spirit of innovation and explicit hope, and proactively
scanning for opportunities. This hypothesis predicted that the high performing
sustainability programs would approach change through a confirmation-based
orientation. Ways in which this hypothesis is supported are described, starting with
excerpts from the raw data. I then discuss the corroboration with confirming stances to
change, and introduce additional literature that may add to clarity of these findings.
The positive discourse labels with the highest frequencies applied across all
performance categories were Facilitating Action or Movement Toward a Positive
Outcome, Active Connection/Effort to Include, Cooperate or Combination, and Positive
Valuing. As presented in the previous chapter, remarks to which these labels were
applied were twice as frequent among high performers than with moderate performers
and four times as frequent as with base performers. Examining just the high performers,
these labels were applied to numerous quotes and passages of data from all three sources
of data. A selection of direct quotes from interview data with representatives from high
performing sustainability programs, are provided below, demonstrating hope, innovation,
and a proactive approach to change.
“[Sustainability professionals'] perspectives are really wide, [whereas] most people in an academic setting’s [view is] really deep. I love the wide and I love seeing where the connections are and where they could be.”
“[The university’s vision plan] is phenomenal, so it's not just master planning. It envisions everything! It’s like our dream. It looks at the arts corridor, it looks at the river corridor, the arts district, the medical center, student life, all aspects of campus, and literally… it's got sustainability principles all through it. No new academic space, period, so we’ll either re-purpose existing space or demolish and build new for need, not for a college. Stuff like that. Pedestrianizing the core, cutting down on parking, so all the stuff you see, is part of the framework.”
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“You know what [sustainability] is for us? It's about graduating global citizens, really. I think the greatest impact we can make… For me it's about, look at how many students who graduated per year, and if each student went out there and tried to make a difference that would be the largest impact we would have in the world.”
“So we can say that we [integrated sustainability into our institutional mission] but that means that we now need to continue to do all the things that we are doing and push further. We need to continue to be in the forefront of all these things. You know, [NAME OF INSTITUTION] has this reputation for being pretty sustainable, but in order to keep that reputation we need to keep growing and exploring, we can’t just continue to be doing all the things that we’ve been doing and expect to stay at our edge.”
It is fair to say the high performing sustainability programs in this study have each
been successful at organizational change. The success of sustainability programs
included in the high performing category is demonstrated by the grade assigned to their
efforts by the Campus Sustainability Report Card and by other factors not evaluated by
the Report Card, such as content and activities discussed in interviews and contained in
website data like integration of sustainability into curriculum, development of new
interdisciplinary streams of research, changes in investment policy and practice, the
development of institutes and centers, and changes in institutional strategy from the top
levels of these campuses. However, the opportunity for organizational scholars studying
sustainability programs is that these are change processes which do not have a clear end,
they are ongoing, ever-deepening and expansive change initiatives with the potential to
impact every aspect of institutional operations, strategy, and culture.
Taken together, Facilitating Action, Movement Toward a Positive Outcome,
Active Connection/Effort to Include, Cooperate or Combination, and Positive Valuing
can be interpreted as a particular way of approaching change, aligned with confirmation-
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based theories from POS. These discourse labels describe a suite of behaviors,
specifically speaking to the way high performers are scanning their organizational
environments (Schein, 1995; 1999). The high performing sustainability programs are
looking for ways to develop, seeking connections and opportunities with a propensity not
found among the other performance categories. They see how events and people can
enhance and embolden movement toward sustainability goals within their institution and
they are active in their search of the environment and experiences for that which is
positive, that which is right, that which is good, that which works well, and what
strengths they and others possess.
I am not suggesting these behaviors (or even these specific positive discourse
labels) are unique to just the high performers in this study. What is unique, however is
the frequency with which high performers are engaging in and speaking to these
dynamics.
Moreover, it is possible that the high performing sustainability programs may be
demonstrating an observable progression from a sustainability mindset to a flourishing
mindset. Sustainability, by its very definition is static and refers to maintenance.
Ehrenfeld (2008) offers a convincing argument that the social and ecological dilemmas
we are facing at a global scale will not be addressed by a maintenance mindset, nor will
they be resolved by encouraging behaviors which are simply less harmful. Laszlo and
others (2012) offer that our language holds us back and that the time has come to shift
from language that encourages less harm toward a discourse focused on prosperity and
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flourishing. Ehrenfeld (2008), Hoffman and Ehrenfeld (2013), Laszlo and colleagues
(2012), and Laszlo and Brown (2014) encourage this shift, evolving to a broader and
deeper orientation of what sustainability could mean, and does mean when we call it
flourishing.
As described by the authors above, flourishing is the active, generative nature of
building connectedness across boundaries, fostering webs of interconnectedness, which
seeds the kind of world in which all species exist harmoniously, for all time. This, in
addition to Cooperrider and Fry’s (2013) and Cooperrider’s (2014) proposition of mirror
flourishing help us see Hypothesis 3 and the high performing members of this sample
with more dimensionality. For example, among the high performers, there exists a
richness or texture in their experiences of leading change at their colleges and universities
that does not exist as vividly with the moderate and base performers in the study. It is
possible that the generative behaviors and mindsets indicated by the most frequently
applied positive discourse labels (Facilitating Action, Movement Toward a Positive
Outcome; Active Connection/Effort to Include, Cooperate or Combination; and Positive
Valuing) and their ubiquity, are leading to far more than successful sustainability
programs measurable by third party ranking surveys or annual reports.
The possibility of mirror flourishing, especially as it blurs the boundaries of “in
here” and “out there” brings the proactivity of high performers’ collaborations and
connection seeking to life. Mirror flourishing brings positive valuing to scale, and adds
color to the high performers’ awareness that interactions and circumstances can be
generative, leading to a ripple effect of good leading to positive outcomes.
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Taking the spirit described in Hypothesis 3 together with the connected nature of
sustainability as flourishing (Ehrenfeld, 2008; Laszlo & Brown, 2014) and the boundary-
blurring phenomena of mirror flourishing (Cooperrider & Fry, 2013; Cooperrider, 2014),
it seems plausible that the high performers in this study are experiencing love in their
organizational contexts. Hypothesis 3 and the data which supports it describes shared
micro-moments of positivity (Fredrickson, 2013); the sense of agape resulting from
altruistic acts (Post et al. 2002); the sense of wholeness, harmony, and well-being
produced through care, concern, and appreciation for both self and others (Fry, 2003 p.
712); relational behaviors through which another person, being, or thing arises as a
legitimate other in coexistence with oneself (Maturana & Bunnell, 1999); and shifts from
limited self-interest to a recognition of connectedness, mutuality, and interest in the well-
being of others (Coombe, 2011).
Additional Findings
Beyond confirmation and disconfirmation of hypotheses, additional findings are
of interest. The first regards the difference in P/N ratios between interviews and group
meeting data. Specifically, interview data from the high performing category possess a
3:1 P/N ratio, however in group meeting data representing the same population the P/N
ratio drops to 2:1. Looking at the other end of the performance spectrum, base
performers’ interview data possesses a 1:1 P/N ratio, but plummets to 1:4 P/N in group
meeting data. This begs the question, what phenomena are present at group meetings,
which are not present during one on one interviews, and vice versa.
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A possible explanation comes from Goffman’s work in sociology (1959), which
uses metaphors of theater to propose that individuals act in life, using either their front
stage or back stage versions of themselves. According to Goffman’s theory, it is likely
that during one-on-one interviews, the Sustainability Professionals being interviewed are
aware they are making an impression on the interviewer (me), that I am collecting
information about them and their institutions, so they are likely to be working hard
(consciously or subconsciously) to create a desired impression. This is the front stage
self. Once in group meeting contexts, with colleagues, it is likely that impression
management is less figural. Participants are engaging in an ‘internal’ discussion, which
is, in comparison to being interviewed and recorded by someone outside the system, can
be experienced as a private place where individuals can be themselves and set aside a
sensed need to manage impressions or play a role.
Considering the apparent power of negative comments, events, and
circumstances, as put forth by Schwartz (1986), Gottman (1994), and Baumeister et al
(2001), I became curious about where negative labels were applied in high performer
group meeting data, and wondered how the high performers ‘made up for’ the
asymmetrical impact of negative utterances or quotes with positive interactions.
Considering the possibility of mirror flourishing in these systems, I was curious if it is
possible to see such a phenomenon come to life.
Looking at all the negative labels applied to high performer group meetings, two
clear patterns emerge. In about half of the instances, a negative label is an aspect within
a generally neutral or positive conversation. For example, the following direct quote from
the data illustrates two negative labels: Lack of Receptivity or Absence of Connection and
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Negative Valuing. This passage also includes Hope Toward Future, Reframing in
Positive Terms, Openness and Receptivity to Learning, and the neutral label Information
to Inspire Behavior Change. In this selection, three staff members of a high performing
sustainability program discuss opportunities to educate people about campus
sustainability, including a walking map of sustainability installations and points of
interest and the need for related campus signage for sustainability features.
Speaker A: The brochure has a green map of the campus so I’m very excited for Parents’ Weekend and things like that. This summer, we’re hosting the American Institute of Architects green building conference, so this will be especially great for them. To my knowledge there isn’t another map or brochure for people who just want to walk through campus. Speaker B: Yeah, the other piece of the location brochure is kind of on top of my priority list, and that is external signage. I got some money authorized to spend on education, so I think it is important to get a few key signs up around campus, telling people what stuff is, or what to look at. Overall, we already have one or two at the wind turbine, one pointing out the solar array, that a lot of people don’t see otherwise. We should have at least one about the pervious paving and snow melt capture. Speaker C: I would like to get a sign up at [NAME OF BUILDING] by the steam vent of the [NAME OF BUILDING], saying “Hey! We’re not wasting energy! This steam vent comes from the steam tunnels on campus to heat other things and melt the ice on these sidewalks! Blah, blah, blah!” People are always telling me we’re wasting energy and they get so worked up about it. It’s like they’re saying, ‘Ah ha!... caught you! You’re not really doing sustainability here!’ Speaker B: Yeah, I think that people have a lot of questions about why that happens. I’ve seen people go walking by it, saying stuff about why we say we’re so sustainable… they see this and think we’re wasteful and green washing. So, especially with the green building project happening right next door at the [NAME OF BUILDING] that’s a high traffic area, which means it’s a good opportunity to tell people what they’re seeing, why it’s good. We gotta get how ‘efficiency’ works— that it’s a relationship— into the conversation. Explaining what they’re seeing is really is necessary.
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In the other half of the instances, a challenging circumstance or issue is being
discussed. While that portion of the meeting agenda may have an overall negative tone,
there are multiple positive labels embedded through the selection, seemingly dissipating
the impact of the negative and quite possibly demonstrating how mirror flourishing
‘shows up.’ An illustration of this, includes one the most frequently applied negative
labels, Deficiency in Self or Other. It occurs with two positive labels: Openness and
Receptivity to Learning and Reframing in Positive Terms. In this passage, a Sustainability
Director speaks to an error that both he and the Sustainability Committee are responsible
for, however he does so reframing the situation as both a positive indication of visibility
and as a learning experience for the group’s work together.
I won’t mention the person’s name, but this is important for us in moving forward with the Sustainability Report that we did… and we can learn from this as it relates to some of the other irons we have in the fire, that we’re trying to get out. The Sustainability Report needed to go out and was ready to go, and we sort of- as a group- said yes. On my end I did not do due diligence of actually going back and making sure that the students actually did have it ready to go. … There were typographical errors and a couple of grammatical errors. …I got a call from someone on the Donor Committee who was not so nice in the feedback that was given to me. I made the changes in the document and sort of fell on the sword for everyone on this committee saying, ‘You know what, it was totally my fault.’ I wanted to raise this issue in our meeting today because I know several of you interact with the Donor Committee and I wanted you to know what the situation was in case it gets brought up, and also we need to be more diligent in reviewing this kind of stuff before it goes out, so maybe that’s a role that could rotate in the future, and not just relying on students for that. But, you know, this also tells us that our documents are getting the attention… are being read, which is cool especially by people on the Donor Committee.
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Another example of the negative topic made buoyant by positive labels is featured here.
This conversation is between the Sustainability Director (SD), the Vice President (VP) he
reports to, occurs within a larger Sustainability Committee meeting. The discussion
concerns a member of the custodial staff who is suspected of taking items, which staff
and faculty members have set out for recycling.
The negative labels applied are Concern, Worry, Preoccupation, or Doubt, and
Negative Valuing. The positive labels also applied to this segment are Reframing in
Positive Terms, Facilitating Action, Movement Toward a Positive Outcome, and Systems
Thinking or Interconnectedness.
VP: You know there were rumors at one point that people, that when they pick up recycling that some people purposefully fill the recycling bags with trash and were stealing. Do you know whether that could have happened? SD: I supposed that could have happened…. There are rumors that a custodian may have taken something from the end of year “Dump & Run” event at the dorms. VP: Yeah? Well, I know that the person who cleans my building and I know they are not inviting that kind of thing. SD: Okay, good! But in another… well, the rumor is that this has occurred in the past, and no one in Facilities will deny that it hasn’t, but I don’t think it’s common place anymore. Some of the carts that the cleaners have, have two big barrels and but because we collect trash one day and recycling the next usually one of the barrels should always be empty… VP: (interjects / cross talk) But that doesn’t mean it isn’t happening now… I know, okay… SD: … it’s possible that she may have taken something, but I don’t know for sure and I don’t know if it’s a pattern or is happening consistently now. They’re not supposed keep anything. When I talk to other custodians, they tell me that they don’t take anything and they don’t know if [NAME OF CUSTODIAN] does, but then, I hear from others that [NAME OF CUSTODIAN] is a good person….so let it go.
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VP: This is one of those small kinds of things that could get her into big trouble unfortunately. SD: Well, depending on how we deal with it. VP: Yeah, exactly. SD: There’s enough of a reason to address it in some way with all the custodians, we’re meeting with them in a couple weeks. We can demonstrate we take this seriously, that taking things—even from recycling—is considered stealing here, but we can address it to the group so we’re not accusing one person in particular. VP: But, honestly, if she’s taking something that was put out for recycling, to take home and use… I mean, maybe we need to re-visit those parts of the rules…. She’s actually re-using things. We can change the clause and name it after her!.. (laughter around the table). SD: Maybe this is another opportunity for us to consider our campus surplus differently. I mean, maybe we should have some kind of an annual thing where members of the campus can come and get stuff that otherwise be sent off campus.
An additional finding concerns website data, and its general uniformity across
performance categories. While there were subtle differences between high, moderate, and
base sustainability programs, data collected from websites follows a clear pattern of
being comprised of positive discourse and neutral discourse.
This finding evokes two further questions. First, why is there such similarity
across all three performance categories? As described in early sections of this thesis,
mimetic isomorphism, or the habit of mimicking what others are doing to increase
perceptions or appearances of a system’s credibility- is common among higher
educational sustainability programs, particularly in relation to website data, which is
easily accessible and replicable. Looking at what esteemed sustainability programs
include on their websites and mimicking this is a common practice and is often suggested
as a way to begin designing a new sustainability program or website.
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The second question these findings stimulate is about the credibility of data
collected from archival sources, such as websites and annual reports. While Goffman’s
work focused on individuals, the theory of a front stage and back stage presentation of
self is an apt metaphor for organizations (1959). In this data set, the websites (being
public documents) are quintessentially ‘front stage,’ managing impressions and crafting
narratives. Group meetings on the other hand are equally ‘back stage,’ featuring behind
the scenes conversations. Table 32 compares the P/N ratio for high performers and base
performers for group meeting and website data, resulting in an extreme snapshot of
narrative emotional-tone.
Table 32. P/N Ratios for High and Base Performance Categories, Comparing Group Meetings and
Website Data
High
Performance
Base
Performance
Group Meeting Data P/N 2:1 1:4
Website Data P/N 26:1 12:1
Related to the above discussion, the importance of multiple sources of data is
important for creating a rich and more accurate picture of the narratives of higher
educational sustainability programs. Collecting only one of the three sources of data
would have resulted in a different and limited sense of what makes up sustainability
discourse and the emotional tone of that discourse.
Despite the ‘front stage’ nature of website data across all ten sustainability
programs in this study, the data did provide evidence not captured by the Campus
Sustainability Report Card grades used to create the performance categories each school
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was categorized in. The assessments of website data presented a challenge, as described
in the methodology discussion, because of the vast differences in amount of content
existing between institutions. To control for these differences, I created a template based
on the content and topic headings addressed by the programs with the least content. The
result is that portions of website data relating to faculty research, interdisciplinary centers
and institutes, curriculum development, and other activities which were present at high
performing schools was not included in the data because these topics were not included
by the schools with the least website content.
Carl Sagan once said, “An absence of evidence is not evidence of absence.”
While that may be true about our knowledge of the universe, or the fossil record, an
absence of website evidence for campus sustainability program is evidence of absence of
sustainability in diverse realms of the institution. Thus, website data provides the most
clear demonstrations of what bolt-on approaches to sustainability look like versus
embedded sustainability. Among base performers, website content addressed recycling,
energy conservation, and student activities, like student clubs or sustainability events, like
end of year ‘dump and run’ donation events for residence halls.
Among high performers’ websites, we also see information on recycling, energy
conservation, and student activities, but this is in addition to published research by
faculty, discussion forums and speaker series, research institutes applying sustainable
design, boundary-spanning partnerships between the institution, government, industry,
and the corporate sector to elevate best practices of sustainability and flourishing, to
name just a few examples.
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Implications for future research
The findings from this research have the potential to germinate several follow up
threads of inquiry. First, future research is needed to test and validate the code, with
specific regard to the positive and negative labels added during the coding process and
the addition of the neutral discourse category. Further development and validation of this
code would enable expansion and critique of this research. Related to data analysis, a
possible follow-up study differentiating specific units of language (words, phrases,
conversations, whole narratives) could reveal the differing impact among them.
Research employing experimental designs can test efficacy of language used in
sustainability communications, such as measuring usage of recycling bins which are
labeled with phrasing using different emotional tones, which would represent an
expansion on the field studies of community-based social marketing (McKenzie-Mohr &
Smith, 1999). Similar experimental designs including environmental NGOs and other
large climate or sustainability organizations could test these findings in fundraising
communication using focus groups and other methodologies, to determine which ratios of
emotional tone result in highest donations.
Further exploration is needed to identify causality and define the role of language
in change processes, specifically exploring the order of what must change first, thoughts,
language, or behavior. Such research, would investigate the sequence of influence,
perhaps through a longitudinal study of interventions which change language and testing
whether and how these interventions result in sustainability program performance over
time. Moreover, with results about the emotional tone of sustainability language,
additional research should explore the impact on targets of these communications.
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Through such a study, one could explore how attitudes are constructed and drawn upon to
be persuasive, thereby illustrating and making explicit the narratives created for each
campus’ community (Potter, 1996).
Related to these ideas, the opportunities that the P/N ratios provide for additional
research are rich. Systems theorists suggest the communication and the essence of the
sustainability programs are holographic, a fractal of the larger system in which they are
embedded, therefore what occurs at one level of system is present at all levels of system
(Perey, 2014). The boundaries of my study were drawn around sustainability programs
within institutions of higher education, so I did not attempt to include assessment of each
institution’s general, or universal, narrative emotional tone. The current study could be
expanded, comparing P/N ratios for intra-organizational sustainability programs and the
organizations within which they are embedded. Specific to my data set, a study exploring
each college and university’s general narrative, sampling broadly from campus data
sources, to compare how the P/N ratios of the sustainability programs relate to the
system’s entire discourse would shed light on nuances such as mutual causality. Research
questions exploring how the emotional tone of the organizational narrative influences the
narrative of the sustainability program, and vice versa, would shed light on how change
in complex systems occurs. Staying with the same study population, an expansive study
could group sustainability programs by the size of their institutions to explore the
emotional tone of narratives of change in small, moderate, and vast systems.
Additional research possibilities exist for both narrower and broader study
populations. A deeper dive into only high performing sustainability programs could
foster more detailed knowledge about narrative and its links to performance. Moreover, a
149
longitudinal study following base performers and their development over time would
offer rich insights into how programs develop and change over time, with specific regard
to observing and tracking their language and narratives over time. Such a study could
track both the sustainability program’s narrative and university-wide narrative over time,
identifying whether moving to higher performance in the sustainability program
correlates with a shift to a higher P/N ratio in both narratives.
Follow-up research, using the same population of schools, could be conducted,
looking at a broader group of campus thought leaders, including the role faculty members
play in creating and disseminating the sustainability narrative, student voices in
influencing their campuses with regard to sustainability, and exploration of campus /
student newspaper coverage of sustainability. Additionally, studying a broader segment
of population of systems, beyond just the higher education context could elucidate further
understanding of the influence of narrative emotional tone on change.
Data from the high performance category supports the idea that sustainability is
an activity of connection, looking for and speaking to strengths and assets, and using the
generative momentum of events and relationships to create more flourishing. Future
research taking a deeper look at the human experience inside these kinds of environments
and exploring the development mirror flourishing, and themes of love, is highly
warranted. Models and theory building depicting how mirror flourishing begins and
under what conditions would offer POS an additional avenue for understanding how
context and behaviors work together to create flourishing across boundaries.
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An aspect of organizational change not addressed by this study regards the role of
formal and informal leadership at each of the campuses included in my sample. Future
research could return to each institution and perform in-depth case studies focusing on
the development over time of the sustainability programs, with specific regard to the
actors who initiated and nurtured each program, for clues about how types of leadership
correlate with performance.
Implications for practice
By conducting a survey of the language used by higher educational sustainability
programs, a first map of the landscape has been created. This initial sketch invites
expansion, increased specificity and nuance, and critique. Cooperrider and Fry (2012)
wrote, “Patterns for the future can be found in the texture of the actual,” (p. 6) and
through this study’s categorization and coding of the language for emotional tone, such
patterns have emerged, which should not only inform Sustainability Professionals but
also indicate paths leading forward, leading from sustainability to flourishing.
In a very practical way, this research puts forth the idea that higher performance is
consistently associated with higher frequencies of positivity in discourse. It appears that
high, moderate, and base performers are saying the same things, but the differentiator is
how often. The positive language used most often by high performers also points to the
development of a spirit of the sustainability program, in which connectedness and
positive valuing are at the center.
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We do not yet know if flourishing among members of the sustainability program
begins with high marks from the Campus Sustainability Report Card or if the good
grades are an outcome of flourishing relationships in the institution. What is clear
however is that when our teams and organizations are helping others to thrive, we too
begin to thrive. This gives Sustainability Professionals choices about where to intervene
in the development of their programs: acting “out there” through the programs and
activities of the sustainability program or building connectedness, hope, collaboration,
and expressing positive valuing “in here.”
Based on the findings of this study, universities and colleges with sustainability
programs might consider a review of their formal and informal communications noting
the presence and context of positive and negative remarks, striving for a higher positive
to negative ratio as found in this research (4:1) and others (Losada & Heaphy, 2004;
Gottman,1994; Schwartz, 1986). This means using negative remarks or crisis messages
wisely and purposefully, balancing these with a higher number of positive remarks,
which focus the audiences attention on what is desired in terms of solutions, behaviors, or
a future vision. In contexts when negativity arises, like self-deprecating remarks or
reflection on a lack of connection or engagement in sustainability team meetings, address
it. Negative conversation or perspectives can be an indication support or encouragement
is needed, or that thought partnership is needed to collectively address a challenge.
Sustainability programs characterized as moderate or base performers should
consider increasing the frequency of their positive remarks and increase the overall P/N
ratio of their formal and informal communication. Applying the findings of this study, in
a presentation in which a negative or “crisis” message is necessary, these kinds of
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remarks should be used wisely, with four or five remarks, which are aspirational, solution
or hope oriented, and which bring the audience’s mind to the flourishing future you wish
to create.
Moderate and base performers could benefit from how their choice of language
focuses the attention of the audience and therefore choose to elevate awareness about the
prevalence of cooperation, collaborative relationships, strengths, and connectedness. Ask
questions of the sustainability team, such as: how is this sustainability program here
already building flourishing natural and human systems? In which areas of my life do I
experience flourishing? Where and with who are our collaborative relationships at their
strongest and how can we multiply these bonds? What are the unique strengths of our
program and the people who are part of it? What do I love about my role in the
sustainability program?
Contributions of this study
This research has specifically contributed to a growing field of scholarship and
practice exploring the effects of the emotional tone of language in human systems
(Schwartz, 1986; Gottman, 1994; Losada & Heaphy, 2004; Fredrikson, 1998, 2003,
2009; Fredrikson & Losada, 2005; Fredrikson & Cohn, 2008; Donnellon, 1996; Barrett &
Cooperrider, 1990; Cooperrider, 1997; Barrett et al. 1995). My work has grounded these
theories in a field study, which broadens the context of application, adding breadth to
existing knowledge about the role language plays in human systems. While this research
did not attempt to establish tipping points of P/N ratios in language, it did support the
more general findings of Schwartz (1986), Gottman (1994), and Losada and Heaphy
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(2004), linking positive discourse with high performance. With specific regard to Losada
and Heaphy’s work (2004), it is unfortunate that the controversy over the mathematical
modeling has taken attention away from underpinning idea, which is that positivity, in the
form of language, narratives, and mindset benefit human systems.
Additionally, there has not yet existed much of a connection between the promise
of POS’ scholarship and practice with applied sustainability. This study serves as one of
the first, linking two verdant fields, which have much to gain and contribute from further
exploration and collaboration.
Limitations of this study
There are four main limitations to this study: sample size; vulnerability to
technological issues; single-sector study population; and a limiting research design. I
begin with the study’s sample size.
With performance categories comprised of four (high), three (moderate), and
three (moderate) sustainability programs it is hard to make generalizations beyond the
study population. Related to sample size, the small population meant that the data was
limited and vulnerable to inconsistency, like technological issues. Problems with quality
of audio recordings for example derail such a small sample size and complications in
analysis arise when different study participants provide different amounts of data. The
third limitation is that this study only included organizations in the higher education
sector. While there are likely similarities with other sectors, colleges and universities are
unique in their power structures, which greatly influence change narratives.
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The fourth limitation is one of research design. In its current design this study cannot
engage in conversation attributing causation to either performance or emotional tone of
language. It is not clear whether positive narratives lead to high performance, or if high
performance leads to more positive narratives. Moreover, related to causality, this study
did not control for amount of financial resources available to the sustainability programs
in the sample or for high-level institutional commitment, such as engagement and support
from board of trustees or the president’s office.
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CHAPTER SEVEN: CONCLUSIONS
Evolutionary biologist Mark Pagel describes language as our species’ most
important social technology and the most powerful trait that natural selection devised.
Investigation of narrative offers a general contribution to the literature of organizational
change. What would formerly be regarded as only part of the way to understanding an
issue – language – has been treated as central by this study, a phenomenon worthy of
attention and investigation in and of itself. (Oswick et al, 2000). This study expands the
central premises of social construction theory, drawing from positive psychology and
positive organizational scholarship, by focusing attention on the language needed for
successful sustainability programs.
Language- be it through metaphor, stories, or the longer arc of narrative- is one of
organizational change’s most powerful tools. Language is alchemical. It is through
language that the mundane transforms to meaningful and the illusions of un-questioned
mental models are not only revealed, but can be re-designed to support flourishing human
systems. Every narrative is a story, all of which holds a certain poetry about experience
in human systems. Our language reflects identity, emotion, and boundaries; we can let it
carry us toward new regions of knowing, feeling, and experiencing. Historian and
memoirist Rebecca Solnit wrote, “Stories are both compasses and architecture. We
navigate by our stories, and we build both our sanctuaries and prisons with our stories”
(2014, p. 3). Applying the metaphors of navigation and architecture to human systems
sheds light on the importance of the tone, content, and process of organizational
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discourse. Attention paid to narratives reveals the direction and architecture of our
language, are we steering ourselves toward cages or are we steadily building cathedrals?
What does our language reveal about us? The language themes and patterns of the
high performers in this study tell a story of connectedness, to each other and to positive
things, events, and interactions all around them. The findings of this research are clear.
Positive discourse, and plenty of it, is a key characteristic separating high performance
sustainability programs from sustainability programs at the moderate and base
performance levels. Positive language, when it outnumbers negative language by 4:1, is
essential, not just for positivity’s sake, but because of how a positive emotional tone
counteracts the effects of negative remarks and interactions (Gottman, 1994) and how
positive emotions effect how we think, what and how much information we are able to
take in, and how we act (Fredrickson, 2009).
The kind of positive discourse that has been found in high performing
sustainability programs in this study tells a story of active connection and cooperation,
and the power people and events have on enhancing the momentum toward a positive
goal. Positive discourse sees the good in the system, in individuals, and in what is
possible. Cooperrider and Fry (2013) suggest these dynamics are not at all surprising,
occurring with regularity across sectors in organizations pursuing environmental and
social flourishing. Mirror flourishing (when people on the inside of the institution begin
to flourish when involved or are witness to their institution’s efforts to help others and the
planet flourish) may be the x-factor of the high performing sustainability programs
studied here and the reason why positive discourse makes up more of their conversations
than neutral and negative discourse combined. One characteristic of mirror flourishing is
157
that previously held beliefs about division, like ‘out there,’ or ‘them’ no longer seem to
accurately capture the new experiences of connectedness. In this way, there is only
‘here’ and there is only ‘us.’
If there is only an ‘us’ than we have all we need, we are all we need. Language
that reflects and invites connectedness, is language expressing love. Language that
encourages flourishing natural and social systems is language of love. The narratives of
the high performers are all about love, even when it seems like they are not.
When the conversation is about how to handle an issue with a custodian who is
taking things home from recycling bins, the conversation is about love. When the
conversation is about helping people learn and understand more about their
interconnectedness to the planet and their places, the conversation is about love. When
the conversation is about how a team can work better together, the conversation is about
love.
Sustainability on higher educational campuses is devised of policies, practices,
and behaviors, which express care and whose goal is development of flourishing natural
and social systems. Sustainability is one example of what love looks like when expressed
at the organizational level (Coombe, 2011). Sustainability offers us direct access to the
opportunities for expressing and experiencing love prevalent through our organizational
interactions and beyond.
The best way to love anyone, is to love everyone. Adjust your gaze to see the
prevalence of love all around you. Love is evidenced everywhere. We express and
receive love through the smallest of interactions and in our enduring relationships with
colleagues, family, and friends. We express and receive love when we do good on behalf
158
of others and the planet. We express and receive love through our inquiries and our
narratives. Love is our language and our language is love.
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APPENDIX A
A Chi-square test was performed to examine the relation between high and base
performance categories and positive and negative discourse categories for: all sources of
data; interview data; group meeting data; and website data. Apart from the website data,
the relationship between these variables was shown to be significant. Tables 33 – 36
present these analyses.
Table 33. Chi-square Analysis for All Sources of Data
Positive Discourse Negative Discourse Marginal Row Totals High Performers 64 (51.83) [2.86] 15 (27.17) [5.45] 79 Base Performers 39 (51.17) [2.9] 39 (26.83) [5.52] 78 Marginal Column Totals 103 54 157 (Grand Total)
The Chi-square statistic is 16.7289. The P value is 4.3E-05. This result is significant at p < 0.01.
Table 34. Chi-square Analysis for Interview Data Positive Discourse Negative Discourse Marginal Row Totals High Performers 71 (60.58) [1.79] 22 (32.42) [3.35] 93 Base Performers 43 (53.42) [2.03] 39 (28.58) [3.8] 82 Marginal Column Totals 114 61 175 (Grand Total) The Chi-square statistic is 10.9668. The P value is 0.000928. This result is significant at p < 0.01. Table 35. Chi-square Analysis for Group Meeting Data Positive Discourse Negative Discourse Marginal Row Totals High Performers 52 (33.65) [10.0] 26 (44.35) [7.59] 78 Base Performers 14 (32.35) [10.41] 61 (42.65) [7.9] 75 Marginal Column Totals 66 87 153 (Grand Total) The Chi-square statistic is 35.9142. The P value is 0. This result is significant at p < 0.01.
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Table 36. Chi-square Analysis for Website Data Positive Discourse Negative Discourse Marginal Row Totals High Performers 63 (61.53) [0.04] 2 (3.47) [0.62] 65 Base Performers 61 (62.47) [0.03] 5 (3.53) [0.62] 66 Marginal Column Totals 124 7 131 (Grand Total) The Chi-square statistic is 1.3104. The P value is 0.25232. This result is not significant at p < 0.01.
161
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