firm size and innovation: the influencing effects of
TRANSCRIPT
Firm Size and Innovation: The influencing effects of
Organizational Slack and Structural Inertia
Student: Emiel de Greef / Student No 11420685
Date of submission: June 22nd 2018 (Final version)
MSc. in Business Administration - Strategy track
University of Amsterdam, Faculty of Economics and Business
Supervisor: M. Stienstra
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Statement of originality
This document is written by Student Emiel de Greef who declares to take full responsibility
for the contents of this document.
I declare that the text and the work presented in this document is original and that no sources
other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of
completion of the work, not for the contents
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Table of contents
Abstract ...................................................................................................................................... 4
1. Introduction ............................................................................................................................ 5
1.1 Topic ................................................................................................................................. 5
1.2 Theoretical gap ................................................................................................................. 8
1.3 Contributions .................................................................................................................... 9
1.4 Thesis structure ............................................................................................................... 10
2. Literature Review ................................................................................................................. 11
2.1 Innovation: Defining the domain .................................................................................... 11
2.2 The size-innovation relationship .................................................................................... 14
2.3 The size-innovation relationship from a behavioral theory of the firm perspective....... 14
2.4 The size-innovation relationship from a population ecology perspective ...................... 18
2.5 Conceptual model ........................................................................................................... 22
3. Methodology ........................................................................................................................ 30
3.1 Research Design ............................................................................................................. 30
3.2 Sampling Strategy ........................................................................................................... 30
3.3 Data collection ................................................................................................................ 31
3.4 Measures ......................................................................................................................... 31
3.5 Reliability / validity ........................................................................................................ 35
3.6 Statistical analyses .......................................................................................................... 36
4. Results .................................................................................................................................. 37
4.1 Univariate analysis ......................................................................................................... 37
4.2 Bivariate analysis ............................................................................................................ 39
4.3 Mediating analysis of organizational slack on the size – innovation relationship ......... 40
4.4 Mediating analysis of structural inertia on the size – innovation relationship ............... 42
4.5 Inverted U-shape analysis ............................................................................................... 44
4.4 Hypothesis Testing ......................................................................................................... 44
5. Discussion & Conclusion ..................................................................................................... 46
5.1 Discussion of major findings .......................................................................................... 46
5.2 Contributions .................................................................................................................. 48
5.3 Limitations and future research ...................................................................................... 49
5.4 Conclusion ...................................................................................................................... 50
Reference List .......................................................................................................................... 52
Appendices ............................................................................................................................... 60
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Abstract
This paper aims to contribute to the ongoing debate whether organizational size is positively
or negatively related to innovation by examining the influence of organizational slack and
structural inertia both separately and in combination. Current literature often mentions the role
of organizational slack and structural inertia on the size-innovation relationship but is not in a
consensus about the potential mediating effect of both.. Additionally, by combining both
constructs, this study intends to explore an inverted U-shape relation between size and
innovation. By bringing organizational slack and structural inertia into a broader perspective,
both the voluntaristic view of a behavioral theory of the firm and the deterministic view of the
population ecology theory are incorporated in this paper, which may shed light on an
enhancement or an inhibition of innovation. Data from publicly listed US manufacturing firms
is used for several regression analyses in order to find support for the proposed hypotheses.
The findings suggest that organizational slack mediates the relation between organizational
size and innovation. Additionally, strong inertial pressures inhibit organizations to be
innovative. This research provides more insights into the influence of organizational slack and
structural inertia and therefore strengthens arguments with regard to innovation within the
behavioral theory of the firm and the population ecology theory. Future research can improve
the findings by incorporating non-financial measurements, considering innovation from
several dimensions and setting up a longitudinal research approach.
Key words: Strategic renewal, Organizational size, Innovation, Organizational Slack,
Behavioral Theory of the Firm Structural Inertia, Population Ecology
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1. Introduction
‘’Size is perhaps the most powerful explanatory organizational covariate in strategic
analysis’’ – Stanislav D. Dobrev & Glenn R. Carroll (2003, p. 541). This quote reflects the fact
that organizational size has been on the research agenda for several decades now. A
considerable amount of research has been conducted associated with the relation between firm
size and organizational outcomes. As such, according to the studies of Volberda, Baden-Fuller
& van den Bosch (2001) and Damanpour (1992), organizational size may be an important
determinant of innovation. Nowadays large multi-unit firms are operating in a world that
requires both stability (exploitation) and flexibility (exploration) (Volberda et al., 2001).
Especially the ability to adapt an organization for tomorrow might be a real challenge (Volberda
et al., 2001). Additionally, organizations are operating within environments in where rapid
changes are common due to technological developments and globalization.
1.1 Topic
Current literature recognizes the importance of organizational size as antecedent of
innovation outcomes (Camisón-Zornoza, Lapiedra-Alcamí, Segarra-Ciprés, & Boronat-
Navarro, 2004; Damanpour, 1992; Hadjimanolis, 2000; Josefy, Kuban, Ireland, & Hitt, 2015;
Wolfe, 1994). Several meta-analyses as well as review studies have been conducted in order to
examine a clear outcome of the size-innovation relationship. (Camisón-Zornoza et al., 2004;
Damanpour, 1992; Josefy et al., 2015). These studies predominantly proved that there still is an
ongoing debate whether size is positively (Aiken & Hage, 1971; Dewar & Dutton, 1986; Ettlie,
Bridges, & O’Keefe, 1984) or negatively (Aldrich & Auster, 1986; Ettlie et al., 1984; Hage,
1980; Kelly & Amburgey, 1991) related to innovation.
Studies concerning the positive relationship clarify their results by mentioning that large
firms might have access to more diverse & complex resources (Nord & Tucker, 1987; Sirmon,
Hitt, Arregle, & Campbell, 2010). As such, these firms seem to have an advantage to possess
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organizational slack that they can use in order to experiment with product development. The
negative relationship, in turn, can be explained, because large firms might not be able to operate
as flexible as small and medium-sized firms (SMEs). In general, large firms seem to be
structural inert which results in more formalization and bureaucracy. Therefore these
organizations may face constraints regarding innovation (Hitt, Hoskisson, & Ireland, 1990).
Thus far, the literature devoted much attention to prove either a positive or negative
relationship between organizational size and innovation. A possible reason for these mixed
results is that scholars seem to incorporate one underlying theory to explain the relationship
between organizational size and innovation. Additionally, researchers tend to emphasize
diverse elements of either an organization or an environment (Zajac & Kraatz, 1993). For
instance, studies that advocate organizational slack seem to put its focus mostly on factors that
an organization can use in order to adapt to its environment, whereas studies related to structural
inertia tend to emphasize factors that put pressure on an organizations’ ability to innovate (Zajac
& Kraatz, 1993).
Elaborating further on the mixed results regarding the size-innovation relationship, one can
divide the arguments for either a positive or a negative relationship into a theoretical perspective
and a methodological perspective. From a theoretical perspective, scholars that show a positive
relationship seem to make arguments from the perspective of the behavioral theory of the firm
(Aiken & Hage, 1971; Dewar & Dutton, 1986; Kraatz & Zajac, 2001). An example from this
perspective is given by Lewin & Volberda (1999) in that the degree of innovation can be
determined by the amount of organizational slack and whether this slack is used for innovation.
In contrast, motives for a negative relationship between size and innovation appear to be based
on the population ecology theory (Aldrich & Auster, 1986; Haveman, 1993; Kelly &
Amburgey, 1991). Regarding this theory, one can reason that large organizations seem to show
an inability to innovate due to a variety of inertial pressures (Lewin & Volberda, 1999).
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From a methodological perspective, the lack of understanding among scholars related to
the size-innovation relationship can be explained by that researchers do not appear to use a
common metric for both organizational size and innovation. In their review study, Josefy et al.
(2015) make a contribution to the discussion about what organizational size actually is. The
authors propose several definitions of organizational size related to different theories within
the field of strategic management (e.g. theory of the firm, transaction costs economics,
resource-based view, knowledge-based view, and stakeholder theory; see Appendix 1 for a
comprehensive overview). Josefy et al. (2015) extend the work of Kimberly (1976) who
argued that both the way size is conceptualized as well as the measurement used might have
an effect on the relationship between size and other organizational outcomes. Overall, Josefy
et al. (2015) argued that organizational size can be measured ideally as revenue, amount of
resources / assets, number of employees, or capacity of an organization.
According to the meta-analyses of Damanpour (1992) and Camisón-Zornoza et al. (2004),
it seems that there is a lack of a common measurement for innovation as well. These studies
demonstrate that innovations are measured along several dimensions, for example technical
versus administrative innovations, product versus process innovations, or radical versus
incremental innovations. In addition, there are contradictory results due to the level of
analysis, which can be divided into industry, organization or subunits. The stage of innovation
(generation vs adaptation) and the scope of innovation (one vs multiple innovations) appear to
be causing varied findings as well. Altogether, the mixed results can be explained by
measurements from one specific underlying theory (e.g. behavioral theory of the firm, and
population ecology). Additionally, the use of different measurements of organizational size
and innovation explained the mixed results as well. Yet, the focus of this paper will be on the
theoretical contradiction, which will be discussed in the next section.
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1.2 Theoretical gap
Current literature demonstrates a weakness regarding mediating roles within the size-
innovation relationship for organizational slack (for positive relationships) as well as structural
inertia (for negative relationships). Both are mentioned -as single forces- within a considerable
amount of research as a possible explanation for the relationship between size and innovation
(see Table 1). However, to my knowledge, their potential mediating effects regarding the size-
innovation relationship are not yet scrutinized. By incorporating organizational slack and
structural inertia as mediator, this study aims to deepen the insights for either the positive (in
the case of organizational slack) or the negative relationship (in the case of structural inertia).
As such, this study aims to strengthen the cause-effect explanation between organizational size
and innovation.
Related to the mediating effects of organizational slack and structural inertia, this study also
aims to combine these forces in order to explore a curvilinear relationship between
organizational size and innovation. This may lead to an examination of both a positive and a
negative relationship between size and innovation. Within current literature there are a few
studies that demonstrate a curvilinear relationship. First, Kelm, Narayanan & Pinches (1995)
found a curvilinear relationship between size and innovation. However, this study argued only
from the population ecologist perspective. The positive relationship is explained through
economies of scale and specialization, while the negative relationship emphasizes commitment
to a firm’s existing technology and an increase in formalization (Kelm et al., 1995). Second,
Nohria & Gulati (1996) conducted research on organizational slack and innovation and they
found a curvilinear relationship between these variables. However, this research did not
incorporate organizational size as an independent variable (Nohria & Gulati, 1996). In addition,
the emphasis is only on the behavioral theory of the firm, whereby the positive relation can be
explained through experimentation and slack search, whereas the negative relation occurs as a
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result of inefficient use of resources and the likeliness of managerial self-interest (Nohria &
Gulati, 1996). Lastly, Leiblein & Madsen (2009) found a curvilinear relationship between size
and innovation as well. However, this study argues from the perspective of the theory of the
firm in combination with the resource based view (Leiblein & Madsen, 2009). More
specifically, the authors emphasize operating experience. As such, a positive relationship can
be explained due to an increase in available operating experience, whereas a negative
relationship is declared through a decrease in operating experience (Leiblein & Madsen, 2009).
Overall, one can argue that the current literature lacks the integration of organizational slack
and structural inertia separately (as mediating effects) as well as in combination. Therefore, this
paper aims to answer the following question:
How do organizational slack and structural inertia, both separately and in combination,
influence the relation between organizational size and innovation of firms?
1.3 Contributions
This paper purposes to make two theoretical contributions to the current literature. First, by
incorporating either organizational slack (which is rooted in the behavioral theory of the firm)
or structural inertia (which is rooted in the population ecology) as mediating variables in order
to discover whether the relation between organizational size and innovation can be declared by
these constructs. As such, the potential role of organizational slack and structural inertia
mentioned in current studies is empirically tested and this may increase the understanding of
both concepts on the size-innovation relationship. Second, this paper contributes to the existing
literature through the integration of both organizational slack and structural inertia. As such, an
inverted U-shape relationship between organizational size and innovation can be demonstrated.
Josefy et al. (2015) suggested this curvilinear relationship as a path of future research in their
study of the effect of organizational size on a considerable number of organizational outcomes,
including innovation.
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A practical contribution is made in that managers can get deeper insights regarding the
effect of organizational slack and structural inertia on innovation. More specifically, managers
may get an explanation why particular organizations might be able to be more innovative in
comparison with others or why some firms may face difficulties regarding innovative activities.
1.4 Thesis structure
The structure of this paper will be as follows. After the introduction the current literature
regarding innovation, organizational slack, and structural inertia will be reviewed. After that,
the conceptual model will be described, followed by the methodology section. Then, the results
will be presented. Lastly, the discussion section will contain a summary of this paper and
elaborates further on the contributions and limitations of this study.
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2. Literature Review
This section contains a review of the current size-innovation literature. First, the domain
regarding innovation will be presented followed by an overview of the ongoing debate
concerning the size-innovation relationship. Subsequently, the influence of the behavioral
theory of the firm and the population ecology perspective on the size-innovation relationship
will be discussed. Lastly, the theoretical framework of this research will be explained.
2.1 Innovation: Defining the domain
Within the current literature, innovation appears to be studied from a variety of perspectives
such as administrative versus technical innovations (Daft, 1978; Kimberly & Evanisko, 1981),
radical versus incremental innovations (Dewar & Dutton, 1986; Ettlie et al., 1984; Nord &
Tucker, 1987), and the initiation versus the implementation phase of an innovation (Marino,
1982; Zmud, 1982). In order to avoid reasoning from a particular perspective, this study uses
the following overarching definition of innovation; ‘’the adoption of an internally generated or
purchased device, system, policy, program, process, product or service that is new to the
adopting organization’’, provided by Damanpour (1991, p. 556) and based on the work of Daft
(1982) and Damanpour and Evan (1984). This definition incorporates several perspectives, for
example different parts of an organization, several parts of an innovation operation and the
several types of innovations (Damanpour, 1992). Additionally, the adoption of an innovation is
proposed to contribute either to an organizations’ performance or effectiveness (Damanpour,
1992).
Taking this definition of innovation in to a broader perspective, one can argue that
innovation may be an answer to both internal as well as external changes within the
environment of an organization. As such, innovation can be seen as a form of strategic renewal,
which is, following Volberda et al. (2001, p. 160), defined in this research as ‘’the activities a
firm undertakes to alter its path dependence’’. According to the study of Volberda et al. (2001),
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strategic renewal can be either selective or adaptive. Within a selective renewal, organizations
are constrained by insufficient resources as well as structural inertia (Volberda et al., 2001).
Organizations that strategically renew themselves from an adaptive perspective might be able
to learn to act differently (as opposed to competitors) and therefore explore new / different
competencies (Volberda et al., 2001).
Building further on this, innovations that are developed or purchased in order to respond to
internal / external changes may be part of strategic change. Following Rajagopalan & Spreitzer
(1997, p. 49), strategic change is defined as follows within this study: ‘’a difference in the form,
quality, or state over time in an organizations’ alignment with its external environment.’’,
whereby an organizations’ alignment is defined as ‘’the fundamental pattern of present and
planned resource deployments and environmental interactions that indicates how the
organization will achieve its objectives’’ (Hofer & Schendel, 1978, p. 25; in Rajagopalan &
Spreitzer, 1997, p. 49). Based on the above mentioned definition of strategic change, one can
state that both the selective (deterministic) and the adaptive (voluntaristic) perspectives appear
here as well (Müller & Kunisch, 2017) (see Table 1).
Table 1 Single lens perspectives; size-innovation relationship
Authors Perspective Main antecedent Outcome
Ginsberg &
Buchholtz (1990)
Population Ecology
(deterministic)
Formalization,
bureaucratization, complex
structures
Lower conversion time
(time between a particular
event within an
environment and the
response of an organization
Kelly & Amburgey
(1991)
Population Ecology
(deterministic)
Formalization,
bureaucratization, complex
structures
Lower probability of a
change within the core of an
organization
Haveman (1993) Population Ecology
(deterministic)
Political constraints,
rigidity, bureaucratization
Slower response towards
dynamics within the
environment of an
organization
Barker & Duhaime
(1997)
Behavioral Theory of
the Firm
(voluntaristic)
Financial slack resources Greater extent of changes of
an organizations’ strategy
Kraatz & Zajac
(2001)
Behavioral Theory of
the Firm
(voluntaristic)
Human resources, financial
assets
Greater extent of strategic
change
Dawley, Hoffman &
Lamont (2002)
Behavioral Theory of
the Firm
(voluntaristic)
Absorbed slack, unabsorbed
slack
More adequate responses
towards environmental
dynamics after a bankruptcy
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As a result of external changes, organizations can be constrained to follow their external
environment and thus react from a selective perspective. In turn, the presentation and planning
of resource deployments may give organizations the ability to experiment with new
competencies and therefore transform its current competitive landscape.
Elaborating further on the nature of strategic renewal, Volberda et al. (2001) argue that the
basis for selective and adaptive strategic renewal can be found within several underlying
theories. Herein, population ecology, institutional theory, evolutionary theory, and resource-
based theory are mainly selective, whereas the dynamic capability theory, behavioral theory of
the firm, learning theories, and strategic choice theories are primarily adaptive (see Table 2).
With regard to this study, especially the behavioral theory of the firm and population
ecology seem interesting because these theories have received much attention within current
literature about innovation (e.g. Dewar & Dutton, 1986; Ginsberg & Buchholtz, 1990; Graves
& Langowitz, 1993; Müller & Kunisch, 2017; Nord & Tucker, 1987; Rajagopalan & Spreitzer,
1997).
Table 1: Theories on Journeys of Strategic Renewal (Volberda et al., 2001, p. 162)
Mainly Selection Journeys Mainly Adaptation Journeys
- ‘’Population Ecology: Renewal journeys are
based on and limited to accumulation of
structural and procedural baggage through
retention processes (Aldrich & Pfeffer, 1976;
Hannan & Freeman, 1977, 1984)’’
- ‘’Dynamic capability theory: Renewal journeys
are promoted by firms’ latent abilities to renew,
augment, and adapt its core competence over time
(Teece, Pisano, & Shuen, 1997)’’
- ‘’Institutional theory: Renewal journeys result
from coercive, normative, and mimetic
isomorphism. Renewal is achieved through
maintaining congruence with shifting industry
norms and shared logics (DiMaggio & Powell,
1983; Greenwood & Hinings, 1996)’’
- ‘’Behavioral theory of the firm: Renewal
journeys are determined primarily by the
availability and control of organization slack and
by the strategic intent to allocate slack to
innovation (Cyert & March, 1963)’’
- ‘’Evolutionary theory: Renewal journeys are
based on proliferation of routines and
reinforce incremental improvements (Nelson &
Winter, 1982)’’
- ‘’Learning theories: Renewal journeys as a
process of alignment of firm and environment
based on unique skills for learning, unlearning, or
relearning (Argyris & Schön, 1997; Huber,
1991)’’
- ‘’Resource-based theory: Renewal journeys
are converging trajectories of exploitation of
unique core competencies (Penrose, 1959;
Wernerfelt, 1984)’’
- ‘’Strategic choice theories: Renewal journeys as a
dynamic process subject to managerial action and
environmental forces (Child, 1972; Miles & Snow,
1978)’’
Table 2: Theories on journeys of strategic renewal (Volberda et al., 2001, p.162)
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2.2 The size-innovation relationship
According to several conducted meta-analyses (e.g. Camisón-Zornoza et al., 2004;
Damanpour, 1992; Josefy et al., 2015), the direction and the strength of the size-innovation
relationship still is an ongoing debate within the current literature (see Table 3)
Table 3: Overview ongoing debate within size-innovation relationship
Study Outcome Size-
Innovation
relationship
Independent
Variable
Dependent Variable Method
Kimberly & Evanisko (1981) Positive relationship Number of employees
and log number of
employees
Technical /
administrative
innovations
Database Research &
Survey
Ettlie et al. (1984) Positive relation Log of number of
year-round employees
Adoption of radical /
incremental product or
process innovations
Mail survey &
interviews
Dewar & Dutton (1986) Positive relation Log of the number of
employees
Radical / incremental
innovation
Interviews & Database
Graves & Langowitz (1993) Negative relation Number of employees R&D intensity Database Research
Haveman (1993) Inverted U-shape Scale of operations &
total assets
Investments in new
products / client
markets
Database Research
Hadjimanolis (2000) No significant relation
Number of employees Innovation activities
(new products and
markets)
Case Study
Ahuja and Katila (2001) Positive relation Log of the number of
employees
Patents Database Research
Leiblein & Madsen (2009) Inverted U-shape Log of revenue New adopted process
technologies
Database Research
By reviewing the current studies concerning the size-innovation relationship, it seems
that the underlying theory of a particular research determines whether the relationship is
positive or negative. The positive relationship seems to be based on the behavioral theory of
the firm, whereas the negative relationship tends to be based on the population ecology theory.
2.3 The size-innovation relationship from a behavioral theory of the firm perspective
Cyert and March (1963) were one of the first authors who tried to develop an overarching
theory regarding behavior and decision making within organizations. They wrote their book in
order to open the black box of firms (Argote & Greve, 2007). By opening this black box they
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tried to find an explanation regarding the internal systems within organizations, especially
focusing on behavior and decision making patterns (Argote & Greve, 2007). To point out the
impact and the value of a behavioral theory of the firm, Cyert and March (1963) started their
work by giving four commitments. These commitments were as follows: ‘’focusing on a small
number of key economic decisions, develop process-oriented models of the firm, link models of
the firm as closely as possible to empirical observations and develop a theory with generality
beyond the specific firms studied’’ (Cyert & March, 1963, p. 2). With these commitments and
previous research (e.g. March & Simon, 1958; Simon, 1957) regarding behavior and decision
making in mind, Cyert & March (1963) elaborated further on concepts like ‘’bounded
rationality, problemistic search, dominant coalition, standard operating systems, and slack
search’’ (Argote & Greve, 2007, p. 339).
Regarding the relationship between organizational size and innovation, in particular slack
search can be seen as interesting part of the behavioral theory of the firm, because it may be a
driver of innovation. Within the current literature organizational slack is defined in many ways.
Originally, Cyert and March (1963, p. 36) defined it as ‘’the disparity between the resources
available to the organization and the payments required to maintain the coalition.’’ Regarding
the maintenance of coalition, Cyert and March (1963) argued that organizations consist of
several subgroups (e.g. sales, production and finance) that all want to defend their own interests
or goals. Yet, this may lead to conflicts between groups, which might jeopardize the internal
organization. However, due to the availability of slack, it seems possible to solve these goal
conflicts and therefore bring the members of an organization back to a unity instead of loosely
coupled groups (Cyert & March, 1963; in Nohria & Gulati, 1996). The reasoning behind this is
that the presence of sufficient slack may allow organizations to distribute choice opportunities
to all of its members (Moch & Pondy, 1977). This function of slack is mentioned as conflict
resolution by Bourgeois (1981).
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For the purpose of this study the definition of Geiger & Cashen (2002, p. 69) is used, which
is stated as follows: ‘’the resources in or available to an organization that are in excess of the
minimum necessary to produce a given level of organizational output.’’ This definition is
comprehensive in that both current and potential slack resources are incorporated, which may
give organizational slack a multidimensional character (Geiger & Cashen, 2002). As such, both
internally generated (e.g. people or profits) and externally generated (e.g. debt financing)
organizational slack seems incorporated within the provided definition by Geiger & Cashen
(2002). Both might influence the amount of organizational slack and therefore the
innovativeness of an organization. Additionally, this definition goes beyond keeping together
the coalition by incorporating workflow buffers as another function of organizational slack
(Bourgeois, 1981).
Elaborating further on current and potential resources of an organization, one can state that
there are different types of organizational slack. Organizational slack can be specified as
absorbed or unabsorbed (Bourgeois & Singh, 1983; Singh, 1986). Absorbed slack consists of
resources that are already incorporated within the operations of an organization, for example
skilled employees, overhead costs and assembled inventory (Bourgeois & Singh, 1983;
Sharfman, Wolf, Chase, & Tansik, 1988). Unabsorbed slack, in turn, entails resources which
tend to be much more redeployable, such as for instance cash, credit lines, and raw material
inventory (Bourgeois & Singh, 1983; Sharfman et al., 1988; Singh, 1986). In line with the
distinction between absorbed and unabsorbed slack is the degree of discretion of certain slack
resources (Sharfman et al, 1988). Higher discretion resources might not be restricted to
particular situations within an organization and seem therefore more freely deployable. Slack
resources with a lesser level of discretion tend to be, in general, ‘’fixed’’ to particular situations
and thus more difficult to use in a variety of situations (Sharfman et al., 1988). As such, it can
be assumed that high discretion slack resources are comparable to unabsorbed resources,
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whereas low discretion slack resources seem similar to absorbed resources. In addition, current
studies regarding slack tend to make a specification in terms of the ease of recovery or the
extent of future employability (Sharfman et al., 1988), Thereby, cash resources might be -in
general- easy to recover, whereas resources (for example skilled labor or processed inventory)
are much more difficult to recover. Again one can argue that there is an overlap with unabsorbed
(high discretion) and absorbed (low discretion) resources in that unabsorbed resources are easy
to recover in comparison to absorbed ones (Nohria & Gulati, 1996). This study focuses on
unabsorbed, high discrete, and easy recoverable slack resources, because such resources seem
in general more applicable in order to stimulate innovation within an organization as compared
to absorbed, ‘’fixed’’ slack resources (Nohria & Gulati, 1996).
Regarding the part ‘’in excess of’’ within the chosen definition of organizational slack, slack
can be seen in surplus from an input as well as an output perspective (Nohria & Gulati, 1996).
From the input perspective slack resources contains an excess of employees, capacity or
redundant capital expenses compared to the minimum required level. Regarding the output
perspective, slack resources may exist in excess of the current way of doing business. As such,
new opportunities can be exploited, possibly resulting in the growth of margins or revenues
because of having more customers or high-tech innovations (Nohria & Gulati, 1996). Besides
the availability of slack resources within an organization, it seems also crucial to deploy these
resources in order to take advantage of it. Considering the ways in which slack resources can
be deployed, one can argue that they can be used as response to weak performance (Kamin &
Ronen, 1978), shocks within the environment of an organization (Meyer, 1982) or for
experimenting (Greve, 2003; Levinthal & March, 1981). Altogether, organizational slack has
an adaptive or voluntaristic character, whereby managers of an organization can influence their
strategic decisions as well as their environment or structure (Müller & Kunisch, 2017). In other
18
words, managers may have the capability to shape and learn about an organizations’
environment (Miles & Snow, 1978).
However, organizational slack may also constrain organizations due to the use of slack
resources in a value-destroying and inefficient way (Jensen & Meckling, 1976; Leibenstein,
1969). Arguments regarding the negative influence of slack resources on innovation find its
origin in the scholar of organizational economists (Nohria & Gulati, 1996; Vanacker,
Collewaert, & Zahra, 2017). In general, these economists (e.g. Leibenstein, 1969; Williamson,
1963) agree with Cyert and March (1963) that organizations consist of several coalitions
including its competing interests. However, these conflicting interests are a consequence of the
principal-agent relationship and therefore agents within an organization might follow their own
interests rather than the organizations’ interest (which is also called the agency problem)
(Eisenhardt, 1989). An obvious principal-agent relationship within an organization is the
relation between middle and top managers. According to organizational economists, structured
incentives can solve the principal-agent problem more effective than organizational slack
(Nohria & Gulati, 1996). Therefore, organizational slack can provide companies with
unnecessary costs and thus value-destroying inefficiency (Nohria & Gulati, 1996). In addition,
organizations that have a considerable amount of slack may invest this into uncertain (and
unrelated) innovation projects (Jensen, 1993). As such, slack may not generate the intended
innovation
2.4 The size-innovation relationship from a population ecology perspective
Hannan & Freeman (1977, 1984) can be seen as one of the first authors that tried to
establish the theory of population ecology of organizations. They wrote these papers to offer a
different view concerning the relation between an organization and its environment and
thereby they extend the work of Burns and Stalker (1961). Until that moment, the adaptive
view, whereby an organization creates its own environment, dominates the literature
19
regarding the organization-environment relation (Hannan & Freeman, 1977, 1984). However,
Hannan & Freeman (1977) argued that there are some inertial pressures at play that can
influence the structure of an organization and therefore the power of an organization over its
environment. When organizations face a strong level of these pressures, their adaptability or
flexibility can diminish in comparison to organizations with a weak level of inertial pressures
(Hannan & Freeman, 1977, 1984). As a result of strong pressures, organizations might act
reactive towards the actions within an environment rather than establishing the environment
by themselves (Aldrich & Pfeffer, 1976; Hannan & Freeman, 1977, 1984).
Elaborating further on the inertial pressures on organizational structure one can divide
them into internal (particularly structural elements) and external (environmental) pressures.
Concerning internal pressures, investments of an organization into nontransferable assets (e.g.
plants, specialized personnel, and equipment) can generate sunk costs that can constrain
adaptation (Hannan & Freeman, 1977, 1984). Second, information flows may pressure the
adaptability of an organization. As a result of several hierarchical levels, leaders of an
organization may not obtain complete information regarding the organizations’ activities
(Hannan & Freeman, 1977, 1984). Third, political constraints may play a considerable role
when the structure of an organization is transformed (Hannan & Freeman, 1977, 1984).
Therefore, resources might be reallocated across business units and this may create conflicts
within an organization. As such, some subunits can resist a restructuring and this may lead to
short-term costs. Because of these costs, leaders may decide to not alter the structure of the
organization (Downs, 1967; Hannan & Freeman, 1977, 1984). Lastly, an organization may
face inertial pressures as a result of its own history (Hannan & Freeman, 1977, 1984). Hereby,
standardization of procedures and task and the allocation of authority might play an essential
role. Such activities may increase resistance to change as well as constrain alternative
organization structures (Hannan & Freeman, 1977, 1984).
20
Considering the external pressures, legal and fiscal barriers may limit the entry and exit
decisions of an organization and thus its ability to adapt (Hannan & Freeman, 1977, 1984).
Second, external information flows (similar to internal information flows) pressure
organizations to change due to the high costs of obtaining crucial information of a relevant
environment (Hannan & Freeman, 1977, 1984). Finally, legitimacy violates adaptation as well
as problems with generating a collective rationality (Hannan & Freeman, 1977, 1984). This
study focuses particularly on the internal inertial pressures.
With these pressures in mind, the question raises why these inertial pressures exist.
According to Hannan & Freeman (1984) formal organizations tend to have the ability to act
reliable and seem rationally accountable for their actions. Regarding the reliability,
organizations want to deliver its products or services on time and at a particular quality level
(Hannan & Freeman, 1984; Kelly & Amburgey, 1991). An organizations’ accountability
refers to the specific use of resources and particular decisions / rules behind organizational
outcomes. (Hannan & Freeman, 1984; Kelly & Amburgey, 1991). To accomplish these
abilities, formal organizations can be structured around hierarchical levels and formal,
standardized procedures that are repeatable and steady over time (Hannan & Freeman, 1984;
Nelson & Winter, 1982).
Elaborating further on hierarchical organizations, every activity is localized within
subunits. Therefore specific commands, information and resources are used only in these
organizational silos (Simon, 1962). As such, changes within subunits may not influence other
subunits (Hannan & Freeman, 1984). A possible explanation for structuring along hierarchies
is that stable subunits seem to be able to resist possible shocks within the environment of the
organizations and therefore provide organizations with the assurance that their production will
be completed without interruptions (Šiljak, 1975; Simon, 1962). However, due to
organizational silos, complex relationships between employees and organizational subunits
21
may arise (Hannan, Polos, & Carroll, 2002), for example due to an increase in geographic or
product diversification (Josefy et al., 2015). As such, complex organizations might face
significant costs beyond its administrative costs (Josefy et al., 2015). First, complexity can
lead to disagreement among an organizations’ top management team concerning strategic
issues, which seems to be particularly driven by a lack of coordination and integration among
top executives (Iaquinto & Fredrickson, 1997). As such, due to the siloed organizational
subunits, executives may not be able to make unanimous strategic decisions. This ineffective
way of decision making may potentially make an organization more vulnerable compared to,
for example, competitors’ actions regarding innovation (Josefy et al., 2015). As such, these
complex organizations might suffer to change rapidly within highly fast-changing
environments. Second, complexity seems to demand more information processing capabilities
of the top management team (Henderson & Fredrickson, 1996). This may be difficult when an
organization is organized around hierarchical levels, because information will be restricted to
a particular subunit.
Regarding the execution of formal and standardized procedures, bureaucratization can
occur, whereby the influence of managers on decision making changed into the application of
institutionalized rules (Chen & Hambrick, 1995; Nelson & Winter, 1982). The elements of
bureaucracy are ‘’differentiation, specialization, administration and routinization’’ (Sørensen,
2007, p. 389). Bureaucracy can facilitate organizations with structures in order to manage its
employees effectively (Haveman, 1993; Sutton & Dobbin, 1996), as well as enable
organizations to standardize its decision making process (Baker & Cullen, 1993). As a result
of formalized processes, particular responsibilities (e.g. operational decisions or the
positioning of a business unit) can be delegated towards lower management levels within an
organization (Josefy et al., 2015). As such, the managers of these units seem to receive a
specific amount of resources, which they are accountable for. In addition, they might be
22
obliged to report the financial results to the top management. That way, top executives
focuses on administrative oversight rather instead of regulating all subunits separately (Josefy
et al., 2015). However, due to this administrative oversight, top executives might be, to a
greater extent, focus on variations in performances (particularly short term) among
organizational units instead of searching for new opportunities (particularly long term) within
the environment of an organization (Josefy et al., 2015). That way, senior executives tend to
act in a reactive rather than an active way, which might constrain these organizations to
respond adequately towards environmental changes (Josefy et al., 2015).
2.5 Conceptual model
One can state that organizational size has a positive influence on the degree of innovation
of an organization. Larger firms may, in general, have access to more diverse and complex
facilities compared to smaller firms, for example research capabilities, knowledgeable workers,
experience with regard to product or process development, and marketing / sales competencies
(Haunschild & Beckman, 1998; Nord & Tucker, 1987; Sirmon et al., 2010). In addition, larger
firms seem to possess more financial resources that can be used to fund innovation projects.
These larger firms can exert their facilities in order to enhance their innovation. Smaller firms,
in turn, may not have these advantages. A possible explanation for this is that smaller firms
may not have access to financial resources in order to obtain technical or human resources.
Lastly, as organizations grow in size, they can become less vulnerable for constraints related to
resource allocation, for instance resources allocated towards exploitation or exploration (Lin,
Yang, & Demirkan, 2007). In other words, larger firms seem to have the ability to exploit more
resources in order to realize innovation. Hence,
Hypothesis 1: There is a positive relationship between organizational size and innovation.
As opposed to smaller firms, larger firms might be able to possess more slack resources,
which can be a result of its greater amount of financial and physical capacity (Sharfman et al.,
23
1988). This capacity may give these large firms a considerable amount of excess resources
(organizational slack) compared to smaller organizations. Regarding the financial capacity,
larger firms might be able to hold more cash and financial instruments. Therefore they can attain
a higher amount of unabsorbed slack (Greve, 2003). A possible explanation for this is that larger
firms can, in comparison to smaller firms, accumulate (financial) resources beyond the
minimum level that is required in order to run an organization. This seems a result of a greater
amount of input or output volume that these large companies can generate (Damanpour, 1992).
In addition, large, diversified firms might be able to obtain economies of scale (Barney, 2002
in Josefy et al., 2015). As such, larger firms, may receive higher margins, which in turn can
positively influence their financial capacity and thus their amount of organizational slack.
Smaller firms may not have, in general, the opportunity to achieve either high efficiency
advantages or economies of scope. Hence,
Hypothesis 2: There is a positive relationship between organizational size and
organizational slack.
Organizational slack may enhance innovation for two reasons. Firstly, Organizational slack
may lead to a reduction of controls within organizations and it may provide companies with a
fund that they can use in times of uncertainty (Nohria & Gulati, 1996). Secondly, organizational
slack seems to offer companies the opportunity to conduct innovative projects. That way, slack
resources seem to provide organizations with protection regarding the possible uncertain
outcomes of such projects. As such, an experimentation culture might be established
(Bourgeois, 1981). This culture can allow organizations to try new strategies (e.g. new products
or market) (Hambrick & Snow, 1977) and may be a driver of innovation. Additionally, slack
search can enhance innovation as well (Greve, 2003). Slack search may lead to the execution
of innovation projects in which high potential, but uncertain inventions might appear (Levinthal
& March, 1981). Hereby, the role of slack might be that these resources may influence
24
decisions whether to continue an innovation project or not (Greve, 2003). In general, the
possession of more slack resources can lead to a reduced amount of performance monitoring
(Greve, 2003). Performance monitoring may occur when firms might not have the experience
to determine whether innovation projects will result in an improvement of their performance
(Lounamaa & March, 1987). As such, more organizational slack might positively influence
innovation. Hence,
Hypothesis 3: There is positive relationship between organizational slack and innovation.
Altogether, one can state that the degree of organizational slack can explain the positive
relationship between organizational size and innovation. As a result of a considerable amount
of slack resources, larger firms can afford to hire more knowledgeable, professional workers,
which may give these organizations an advantage over smaller firms with regard to technical
competencies. Technical competencies might be essential in order to conduct innovative
projects. In addition, these technical employees might be able to collaborate with other
knowledgeable, professional workers and therefore they seem to have the opportunity to
develop their capabilities even further, for example through the accessibility of new information
(Haunschild & Beckman, 1998). As a result of synergy and the existence of knowledge pools,
smaller firms can fall behind regarding innovation compared to larger firms. Furthermore,
larger firms seem to invest more in innovation due to the availability of extra unabsorbed slack
resources. As such, these firms might sell more products, due to greater marketing and sales
efforts. Therefore, larger firms can earn back research and development costs earlier relative to
smaller firms (Cohen & Klepper, 1996). Lastly, as a result of the availability of unabsorbed
slack resources, larger firms might bear potential losses related to innovation as well as
decreasing the risk of failure that may be related to experimentation (Haveman, 1993; Hitt et
al., 1990). Therefore,
25
Hypothesis 4: The degree of organizational slack mediates the positive relationship between
organizational size and innovation.
+ +
+ Figure 1: Conceptual model regarding the mediating effect of organizational slack
Compared to smaller firms, larger firms may face considerable inertial pressures as a result
of growing complexity and bureaucracy (Child, 1972; Josefy et al., 2015). As organizations
grow in size, more employees, strategic business units and decision making might appear.
Therefore, in order to keep the organization manageable, larger organizations can be structured
along hierarchical levels (Hannan et al., 2002) as a result of product or geographic
differentiation. However, Mintzberg (1979) argues that innovation requires collaboration
between different parts of an organization that seems to be difficult for larger firms to establish
as a result of divisionalization. In general, collaboration between organizational parts can be
achieved more easily in smaller organizations as compared to larger organizations (Haveman,
1993; Nord & Tucker, 1987). In addition, hierarchy might cause organizational silos, which in
turn may lead to a diversity of opinions within an organization (Iaquinto & Fredrickson, 1997).
This diversity of opinions may result into disagreement among senior executives concerning
strategic decisions. Consequently, this disagreement can result in a political conflict, which is
one of the inertial pressures according to Hannan & Freeman (1977, 1984). In addition,
organizational subunits might constrain the flow of information within an organization
(Henderson & Fredrickson, 1996). Again, this can enhance the inertial pressures of larger
Organizational
Size
Innovation
Organizational
Slack
26
organizations. Furthermore, in order to regulate the developments related to a growth in firm
size, larger organizations may compose rules and regulations, which in turn can result in
bureaucratization (Chen & Hambrick, 1995; Nelson & Winter, 1982). Lastly, in order to hold
a larger organization competitive, economies of scale can be pursued. To accomplish this, large
investments seem to be made into fixed assets as well as into the hiring of specialized personnel
(Josefy et al., 2015). However, fixed assets and specialized personnel can generate sunk costs
and therefore inertial pressures may appear (Hannan & Freeman, 1977, 1984; Nickerson &
Silverman, 2003). Altogether, larger firms, compared to smaller organizations, might adjust
their way of organizing in order to keep the organization manageable by incorporating hierarchy
and formalized processes, which in turn may enhance inertial pressures. Hence,
Hypothesis 5: There is a positive relationship between organizational size and structural
inertia.
Structural inertia may inhibit innovation for several reasons. First, the disagreement among
top executives caused by structuring along hierarchy and formalized processes may lead to slow
and ineffective decision making (Iaquinto & Fredrickson, 1997). As such, organizations with
high inertial pressures seem not be able to respond quickly to developments within their
environments (Josefy et al., 2015). Additionally, top executives might serve as supervisors
within an organization due to the decentralization of activities towards lower level managers
(Josefy et al., 2015). However, these executives seem to focus primarily on the current
(financial) situation and therefore they might act reactive. In order to be innovative, executives
may anticipate on changes within the environment of an organization. Therefore, as a result of
their reactive attitude, they may not be able to respond adequately to market opportunities.
Furthermore, hierarchy can create a considerable distance between executives and operational
staff (Dougherty & Hardy, 1996). As such, executives may not be able to obtain the right
information in order to make their decisions, which in turn may result in ineffective decisions
27
(Henderson & Fredrickson, 1996). In sum, structural inertia may generate rigidity or
inflexibility with regard to changes within the environment of organizations and thus can inhibit
the rate of innovation (Delacroix & Swaminathan, 1991; Haveman, 1993). Firms with a lesser
extent of inertial pressures might have the ability to respond more adequate to its environment
due to the absence of control and coordination mechanisms (e.g. hierarchy and standardized
processes). Hence,
Hypothesis 6: There is a negative relationship between structural inertia and innovation.
Altogether, one can argue that the negative relationship between organizational size and
innovation can be explained through the effect of structural inertia. As a result of an increase in
firm size, the amount of hierarchical levels tends to grow (Hannan et al., 2002). This may give
a rise in complexity, because these firms may have to hire more employees and might involve
in executing different (sometimes incoherent) strategic business units. As such, the increasing
amount of employees and strategic business units can demand more information processing
from executives (Henderson & Fredrickson, 1996), which in turn may lead to structural distance
between the operational part of the organization (e.g. R&D personnel ) and the senior executives
(Dougherty & Hardy, 1996). This structural distance seems to lead to conflicting interests
between top executives and operational personnel (agency problem) (Vanacker et al., 2017).
Top executives may therefore follow their own interests rather than the interest of their
organization, which in turn seems to result in using organizational resources in a value-
destroying manner (Nohria & Gulati, 1996). For example, the usage of slack resources into
unrelated innovation projects (Jensen, 1993). Additionally, due to an increase in hierarchical
levels, disagreement among senior executives tends to grow, which in turn inhibits an
adequately response towards fast-changing environments (Iaquinto & Fredrickson, 1997).
Lastly, the standardization of processes (as consequence of an increase in size) might enhance
decentralized decision making by lower level managers, which gives top executives the
28
opportunity to have an overview of an organization. However, this overview seems to have a
reactive character as it focuses only on fluctuation within performances of business units
(Josefy et al., 2015). This may result in ineffective responses towards environments as well.
Hence,
Hypothesis 7: The degree of structural inertia mediates the negative relationship between
structural inertia and innovation.
+ -
+ Figure 2: Conceptual model regarding the mediating effect of structural inertia
By combining both organizational slack and structural inertia one can make arguments
for an inverted U-shape relationship between organizational size and innovation. When
organizations are relatively small, they may need all their resources in order to survive.
However, as firms increase in size, they can obtain more resources than they actually need in
order to survive, for example better research capabilities, experience related to products and
markets, knowledgeable workers, and sales / marketing competencies (Haunschild & Beckman,
1998; Nord & Tucker, 1987; Sirmon et al., 2010). As such, organizations can increase further
in size, which in turn may have a positive influence on their slack resources.
However, it may be possible that, at a certain point, organizations might possess too
many slack resources that their senior executives are not able to use these resources most
effectively, which in turn might be a consequence of their pursuit towards self-interest (Jensen,
1993; Nohria & Gulati, 1996). Additionally, in order to control and coordinate larger firms,
Organizational
Size
Innovation
Structural
Inertia
29
hierarchy and standardized processes tend to be necessary, which in turn may cause inertial
pressures for an organization (Haveman, 1993). As such, hierarchical levels and standardized
processes may constrain the adaptability of organizations towards their environment and
therefore might inhibit the degree of innovation of organizations. Theoretically, as a firm
reaches a particular size, the advantages of organizational slack resources may be outweighed
by the disadvantages of structural inertia. On the contrary, as a firm stays small, it can take
advantage of slack resources in order to use them for innovation, while it also take advantage
of its flexible structure. Hence,
Hypothesis 8: There is an inverted U-shape relationship between organizational size
and innovation.
∩
Figure 3: Conceptual model regarding the curvilinear size-innovation relationship
Organizational
Size
Innovation
30
3. Methodology
This chapter contains the methodology that will be used for this study. First, the research
design will be explained. Subsequently, the sample and operationalization of the dependent,
independent, mediating and control variables will be described. Lastly, the reliability and
validity will be assessed followed by the justification of the models that will be used in order
to analyze the results.
3.1 Research Design
This research is conducted from a deductive perspective, whereby the influence of existing
theories (behavioral theory of the firm and population ecology) on the relationship between size
and innovation is empirically tested. As such, the theory concerning these concepts may be
further developed (Saunders, Lewis, & Thornhill, 2016). These empirical tests have a
quantitative character, whereby a database with several numerical organizational and financial
data is used. Lastly, the time horizon of this paper is cross-sectional as the research is conducted
at a fixed time (Saunders et al., 2016).
3.2 Sampling Strategy
The sample of this research consists of publicly listed US manufacturing firms (SIC code
2000 – 3999) covering the years 2000 to 2016. Particularly these firms are selected because this
is an industry in where innovations are essential in order to stay competitive. On the one hand,
manufacturing firms might strive to most efficient processes whereby exploitative (process)
innovations might appear. On the other hand, with a view on the long term, these firms have to
invent new products, which might have an explorative character. Furthermore, this sample is
chosen because it enables to compare this research with previous studies in where
manufacturing firms are used. (e.g. Dewar & Dutton, 1986; Leiblein & Madsen, 2009).
31
3.3 Data collection
In this study secondary data is used, which is collected through the CRSP – Compustat
Merged Database, accessible via Wharton Research Data Services (WRDS). This database
consists of all Standard & Poors’ 500 companies listed on the New York Stock Exchange or
NASDAQ and encompasses loads of data with regard to annual and quarterly fundamentals as
well as daily and monthly security figures and historical segments. The advantage of the
merged database is that CRSP gives access to market and corporate data of companies (e.g.
stock & bond prices), whereas Compustat offers fundamental data (e.g. sales, number of
employees, assets). First, the separate figures are gathered from the CRSP – Compustat
Merged database and subsequently the several ratios are calculated. Initially, the dataset
contains 12.467 firm-year observations. Following the data-preparation method of Kim &
Bettis (2014) and Villalonga (2004), firm-year observations (1) with missing data concerning
key variables, and (2) with an R&D intensity (R&D expenditures dived by net sales) higher
than 1 are excluded. As a result, the final sample consists of 6858 firm-year observations.
3.4 Measures
Innovation. Within this study, the dependent variable is innovation. The proxy that is used
is research & development expenditures dived by the amount of sales of a company (Net Sales).
Subsequently, this ratio is divided by the average R&D industry intensity.
𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛 = (
𝑅𝑒𝑠𝑒𝑎𝑟𝑐ℎ & 𝐷𝑒𝑣𝑒𝑙𝑜𝑝𝑚𝑒𝑛𝑡 𝐸𝑥𝑝𝑒𝑛𝑠𝑒𝑠𝑁𝑒𝑡 𝑠𝑎𝑙𝑒𝑠
)
𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑅&𝐷 𝑖𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦
This way of measuring innovation is consistent with a considerable amount of studies (e.g.
Camisón-Zornoza et al., 2004; Hitt, Hoskisson, Ireland, & Harrison, 1991; Kim & Bettis, 2014).
By using this measure, it is possible to have a relative measure of innovation that can reduce
possible scale advantages of firms that are of larger size. Additionally, such a broad way of
measuring innovation makes it possible to include all sorts of innovation (e.g. process & product
32
innovation). Furthermore, by correcting for the R&D industry intensity it can be possible to
eliminate industry effects, because some industries might be more R&D intensive than others.
Organizational size. The independent variable for this research is organizational size,
which is proxied as the number of employees of organizations. Josefy et al. (2015) provided a
list of ideal measurements which contains revenue, amount of resources / assets, number of
employees, or capacity of an organization. Often the way of measuring is dependent on the
underlying theory chosen for the firm size measurement (see appendix 1). Overall, the number
of employees is mentioned as a most robust and direct measurement (Josefy et al., 2015),
detached from any particular theory or framework. The number of employees will be logged
in order to better capture its real effect on innovation (Dewar & Dutton, 1986; Ettlie et al.,
1984).
𝑂𝑟𝑔𝑎𝑛𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑎𝑙 𝑆𝑖𝑧𝑒 = log(𝐹𝑖𝑟𝑚 𝑆𝑖𝑧𝑒 𝐸𝑚𝑝𝑙𝑜𝑦𝑒𝑒𝑠)
The use of this proxy is consistent with a considerable amount of prior studies (e.g. Dewar
& Dutton, 1986; Ettlie et al., 1984; Kimberly, 1976).
Organizational slack. Organizational slack is the first mediator variable. It might be
difficult to measure this with one generic proxy because it appears in many forms within an
organization. Current literature has made a lot of efforts to determine how slack may be
measured in a most comprehensive way (Bourgeois, 1981; Singh, 1986). However, it remains
difficult to generate a widespread proxy, especially because slack may exist, for example, as
knowledge, facilities or human resources as well as financial buffers (Nohria & Gulati, 1996).
Therefore, with an eye on the research method chosen for this paper, organizational slack is
proxied as the cash & short investments.
𝑂𝑟𝑔𝑎𝑛𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑎𝑙 𝑆𝑙𝑎𝑐𝑘 = 𝐶𝑎𝑠ℎ + 𝑆ℎ𝑜𝑟𝑡 𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡𝑠
33
This proxy is adapted from the study of Kim & Bettis (2014) and is closely related to the
definition that is used in this study. It is important to mention that this proxy of organizational
slack only covers the cash stock of an organization. However, a change exists that a company
does have a lot of absorbed slack resources, but that it lacks financial slack resources. In order
to address the above stated notion, a financial proxy for the dependent variable is chosen as
well. Therefore, only the influence of financial buffers on the degree of R&D intensity of an
organization is included in this study.
Structural inertia. Structural inertia is the second mediator variable in this research.
Because this study uses secondary data through a database, it is complex to find an
appropriate proxy in order to determine structural inertia. Previous studies measured structural
inertia only through a survey or interviews or they used variables which are not applicable
within the research setting of this study (e.g. Ginsberg & Buchholtz, 1990; Haveman, 1993;
Kelly & Amburgey, 1991). In order to find a proxy that can be applied in this research setting,
the inertial pressures provided by the articles of Hannan & Freeman (1977, 1984) are
revisited. Especially the internal inertial pressure with regard to the possession of or
investments in fixed assets like plants, equipment and specialized personnel seems interesting
with an eye on the chosen research design. These kind of assets may cause sunk costs for
organizations and therefore, for example, inflexibility (Hannan & Freeman, 1977). These sunk
costs can be quantified in terms of financial figures. Therefore, structural inertia is proxied as
the ratio between total current assets and fixed assets (total property, plant, equipment).
𝑆𝑡𝑟𝑢𝑐𝑡𝑢𝑟𝑎𝑙 𝑖𝑛𝑡𝑒𝑟𝑡𝑖𝑎 = 𝑇𝑜𝑡𝑎𝑙 𝐶𝑢𝑟𝑟𝑒𝑛𝑡 𝐴𝑠𝑠𝑒𝑡𝑠
𝑇𝑜𝑡𝑎𝑙 𝑃𝑟𝑜𝑝𝑒𝑟𝑡𝑦, 𝑃𝑙𝑎𝑛𝑡 𝑎𝑛𝑑 𝐸𝑞𝑢𝑖𝑝𝑚𝑒𝑛𝑡
Again, by using this proxy it is possible to compare the influence of the two financial
proxies (structural inertia and innovation) with each other. However, caution is needed,
because structural inertia is only measured related to one of the eight inertial pressures.
34
Therefore, it may happen that one organization is classified as structural inert based on fixed
assets, whereas another organization, which may be very bureaucratic but do not possess a lot
of fixed assets, is not mentioned as inflexible. This issue can be tackled by mentioning that all
pressures are somewhat intertwined to each other, meaning that larger firms with a great
amount of fixed assets may be, in general, diversified along product or geographic dimensions
and therefore they might need a bureaucratic approach in order to keep these firms
controllable (Hannan et al., 2002; Haveman, 1993).
Past performance. This research controls for past performance, because it tends to
influence the relationship between size and innovation. Firms with a strong performance may
be able to bear potential losses of innovation, whereas this can be more difficult for firms with
weak firm performance. (Hitt et al., 1990). Therefore, this study incorporates lagged return on
assets (t-1) as a proxy for past performance covering the years 1999 to 2015. This way of
measuring is consistent with the studies of Chen & Miller (2007) and Vanacker et al. (2017).
𝑃𝑎𝑠𝑡 𝑝𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 (𝑡 − 1) = 𝑁𝑒𝑡 𝐼𝑛𝑐𝑜𝑚𝑒
𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠
Time. In order to control for a time-effect, the years 2000 to 2016 are incorporated as
dummy variables within this study. It may be possible that organizations adjust their
innovation expenditures as a result of an economic recession for example. In addition, it may
happen that organizations plan some large R&D expenditures a few years ahead, which can
result in fluctuations within their innovation intensity. Lastly, an explosive growth in demand
caused by market forces in a particular year may lead to an increase in innovation in order to
keep up with the market situation.
Industry profitability. To incorporate a possible industry effect, this study controls for
industry profitability. The industry profitability serves as a proxy to the average return on
assets of firms that are active in the same industry (based on the four-digit SIC code). As
35
such, it is possible to compare the profitability of an industry with the profitability of single
firms. This way of measuring industry profitability is consistent with the study of Vanacker et
al. (2017).
3.5 Reliability / validity
Regarding potential reliability issues, the database used in this research (Wharton CRSP –
Compustat Merged Database) is accessible through the Wharton University of Pennsylvania
and includes company data from Standard & Poor’s. Additionally, this methodology is used
in prior studies as well (e.g. Kim & Bettis, 2014; Villalonga, 2004). In order to use the
gathered data in a reliable manner, the data-analyzing method of this research is based on the
studies empirical studies of Kim & Bettis (2014) and Villalonga (2004). These studies
examined the role of intangible and cash resources on competitive strategy and firm
performance and are published in the Strategic Management Journal and the Journal of
Economic Behavior & Organization.
To conduct a valid research, the internal, construct, and external validity are taken into
consideration as well. First, to guarantee the internal validity, several control variables are
added (time, past performance and industry profitability). These control variables are
incorporated in order to examine possible influences of them on the dependent variable. As
such, the results of this paper might be more accurate. Second, the construct validity is
assured by using or adapting proxies that has been used in a considerable amount of prior
studies (e.g. Dewar & Dutton, 1986; Hitt et al., 1991; Kim & Bettis, 2014; Kimberly, 1976;
Vanacker et al., 2017). Lastly, regarding the external validity, the use of a sample that covers
the whole publicly listed US manufacturing population may mean that the results are
generalizable to all publicly listed US manufacturing firms, but not to manufacturing firms
outside the US, firms active in other industries (e.g. agriculture, retail, or services) or not
publicly listed firms.
36
3.6 Statistical analyses
To test both a possible mediating effect of organizational slack and structural inertia and
an inverted U-shape relationship between firm size and innovation, three ordinary least
squares (OLS) regression analyses are executed in the SPSS statistical software package
(version 25). As such, it is possible to gather an understanding of the effect of the independent
variable (firm size) on the dependent variable (innovation) as well as to determine possible
effects on the predictive value of size on innovation caused by a mediator (Field, 2013).
To explore a possible mediating effect, the analysis method of Baron & Kenny (1986) and
Wu & Zumbo (2008) is used (see appendix 2). This method entails four steps in order to
determine a possible mediating effect. This includes a direct effect between the independent
and dependent variable as well as an indirect effect via the independent, mediator, and
dependent variables. If one of the four steps cannot be demonstrated, there is no evidence for
a mediating effect.
To examine an inverted U-shape relationship between organizational size and innovation
the initial organizational size variable is recoded into a mean-centered variable (Kelm et al.,
1995; Nohria & Gulati, 1996). Subsequently, the mean-centered variable is squared in order
to assess a possible inverted U-shape relationship (non-linear effect). Following the method
used in the studies of Nohria & Gulati (1996) and Kelm et al. (1995), both the mean centered
size variable and the squared variable are incorporated in an ordinary least squares (OLS)
regression. To demonstrate an inverted U-shape relationship both the size and the squared size
variable have to be positive and significant. Additionally, the incorporation of the squared size
variable has to add extra explanatory power (significant R2 change) to innovation.
37
4. Results
This section reports the results of this study. First, in order to get a good overview of the
data, the descriptive statistics will be presented. Then, a bivariate analysis of the correlations
between the several variables will be conducted. Lastly, three multiple OLS regressions will
be executed in order to test the proposed hypotheses.
4.1 Univariate analysis
Before running the analyses of this study, missing data is excluded from the data set. In
addition, outliers for all variables are deleted to limit possible biases (Field, 2013). Then, the
number of employees is recoded into a logarithmic variable. Lastly, normality is checked for
the dependent variable, which is required in order to run a reliable statistical analysis. After
the excluding of outliers, the distribution of innovation seems normally from a visual
perspective (see Figure 4). Additionally, the values of skewness (0.457) and kurtosis (-0.558)
are between the -1 and 1, which suggest that the distribution seems relatively normal (Field,
2013).
Figure 4: Distribution of dependent variable
38
Table 4 presents an overview of the observations based on the two-digit SIC code
(e.g. 20xx). The largest number of observations (18.35%) within this dataset are active in the
Chemicals and Allied Products industry (SIC Code 28) followed by the Electronic,
Electronical Equipment & Components (SIC Code 36, 18.05%), Measure, Analyze, and
Control Instruments (SIC Code 38, 16.52%) and the Industrial and Commercial Machinery
and Computer Equipment (SIC Code 35, 15.56%)
Table 4: Number of observations per industry
Two-digit
SIC Code
Industry
Number of
observations
20 Food & Kindred Products 257
21 Tobacco Products 25
22 Textile Mill Products 51
23 Apparel, Finished Products from Fabrics & Similar Materials 9
24 Lumber and Wood Products, except Furniture 31
25 Furniture and Fixtures 126
26 Paper and Allied Products 150
27 Printing, Publishing and Allied Industries 51
28 Chemicals and Allied Products 1259
29 Petroleum Refining and Related Industries 84
30 Rubber and Miscellaneous Plastic Products 146
31 Leather and Leather Products 9
32 Stone, Clay, Glass, and Concrete Products 107
33 Primary Metal Industries 162
34 Fabricated Metal Products, except Machinery & Transport Equipment 340
35 Industrial and Commercial Machinery and Computer Equipment 1067
36 Electronic, Electronical Equipment & Components, except Computer
Equipment
1238
37 Transportation Equipment 454
38 Measure, Analyze, and Control Instruments 1133
39 Miscellaneous Manufacturing Industries 159
Table 5: Descriptive statistics
N Mean S.D. Min Max
Innovation 6858 0.7769 0.4555 0.0098 1.9990
Firm Size (log) 6858 0.4996 0.7285 -1.1805 1.7868
Org. Slack 6858 383.9926 1059.8362 0.001 20071
Struct. Inertia 6858 1.1035 0.6274 0.0630 3.4913
Past Performance 6858 0.0298 0.1436 -3.5389 0.9852
Industry Profitability 6858 0.0298 0.0403 -0.2165 0.1244
39
The descriptive statistics (univariate analysis) of the variables of in this study,
including the sample size, minimum value, maximum value, mean and standard deviation are
presented in Table 5. The final sample consists of 6858 firm-year observations from 2000 to
2016.
4.2 Bivariate analysis
Table 6 provides an overview of the bivariate analysis (correlations) of this study.
These correlations are calculated based on both the Pearson and Spearman correlation
coefficients. This method is chosen, because certain variables are normally distributed
(innovation, firm size, and structural inertia), whereas others are not normally distributed
(organizational slack, past performance, and industry profitability). The correlation between
two normal variables is calculated based on the Pearson method (Field, 2013). The Spearman
method is used for the correlations between a normally and a non-normally distributed or
between two non-normal variables (Field, 2013).
Table 6: Bivariate analysis
Variables N 1 2 3 4 5
1. Innovation 6858
2. Firm Size (log) 6858 0.134**
3. Org. Slack 6858 0.193** 0.787**
4. Struct. Inertia 6858 -0.048** -0.128** 0.059**
5. Past Performance 6858 -0.017 0.184** 0.208** 0.129**
6. Industry Profitability 6858 0.077** 0.193** 0.125** -0.016 0.325**
Correlation is significant at the 0.01 level (2-tailed)
A few important findings are demonstrated by the bivariate analysis. First, the
correlation between innovation (dependent variable) and firm size (independent variable) is
positive (r = 0.134, p<0.01). Second, the correlation between organizational slack and firm
size is positive (ρ = 0.787, p<0.01) as well as the correlation between organizational slack and
innovation (ρ = 0.193, p<0.01). Third, the correlations between structural inertia and both
firm size and innovation are negative (respectively r = -0.048, p<0.01 and r = -0.128, p<0.01).
40
4.3 Mediating analysis of organizational slack on the size – innovation relationship
The results with regard to the mediating effect of organizational slack on the
relationship between organizational size and innovation are presented in Table 7. In order to
demonstrate a mediating effect, model 1 only contains the control variables and their effect on
innovation. Firm size is added in model 2. Subsequently, in model 3 only the mediator
variables (either organizational slack or structural inertia) are incorporated. Lastly, model 4
consists of both firm size and the mediator variables. This way of executing the analyses
allows the use of the same analysis method as used by Baron & Kenny (1986) and Wu &
Zumbo (2008). Hereby, a full mediation effect exists when the independent variable will be
insignificant (compared to a significant direct effect), while the mediator variable is
significant (Baron & Kenny, 1986; Wu & Zumbo, 2008). A partial mediating effect occurs
when the independent variable is still significant, but with a lower beta (Baron & Kenny,
1986; Wu & Zumbo, 2008). If one of the four steps cannot be demonstrated, there is no
evidence for a mediating effect. With regard to the conceptual model, as depicted in Figure 1,
and the method of analyzing used in this study, four hypotheses involving organizational
slack are tested. First, considering model 2, there is a positive correlation of firm size on
innovation (B = 0.089, t = 11.411, p<0.01), which indicates a weak positive relation. As such,
hypothesis 1 is supported. Then, there is a positive correlation between firm size and
organizational slack (ρ = 0.787, p<0.01) (see Table 6), indicating that larger firms possess
greater amounts of slack resources. Hence, hypothesis 2 is supported as well. Third,
organizational slack is positively related to innovation (B = 0.076, t = 6.283, p<0.01). As
such, organizations that possess a lot of slack resources are able to conduct more innovation
projects. Thus, hypothesis 3 is supported as well. Lastly, with regard to the mediating effect,
the effect of firm size on innovation decreases (B = 0.082, t = 9.727, p<0.001) compared to
the direct effect. In addition, the effect of organizational slack on innovation is significant as
41
well (B = 0.027, t = 2.085, p<0.05). However, this correlation (beta) is lower than the single
effect of organizational slack on innovation. As such, a partial mediating effect is
demonstrated and thus there is evidence to support hypothesis 4.
Table 7: Mediating effect of organizational slack on innovation
Model 1 Model 2 Model 3 Model 4
Variables Beta (SE) Sig. Beta Sig. Beta Sig. Beta Sig.
Constant 0.727***
(0.019)
0.000 0.703***
(0.019)
0.000 0.722***
(0.019)
0.000 0.703***
(0.019)
0.000
Year 2001 0.045*
(0.027)
0.095 0.047*
(0.027)
0.083 0.045*
(0.027)
0.097 0.046*
(0.027)
0.084
Year 2002 0.049*
(0.028)
0.080 0.047*
(0.028)
0.090 0.047*
(0.028)
0.093 0.046*
(0.028)
0.094
Year 2003 0.047*
(0.028)
0.094 0.043
(0.028)
0.121 0.043
(0.028)
0.123 0.042
(0.028)
0.130
Year 2004 0.029
(0.028)
0.303 0.020
(0.028)
0.477 0.022
(0.028)
0.432 0.018
(0.028)
0.517
Year 2005 0.006
(0.028)
0.829 -0.004
(0.028)
0.901 0.000
(0.028)
0.996 -0.005
(0.028)
0.859
Year 2006 -0.003
(0.029)
0.904 -0.016
(0.029)
0.580 -0.011
(0.029)
0.708 -0.018
(0.029)
0.541
Year 2007 0.010
(0.030)
0.743 -0.002
(0.029)
0.947 0.003
(0.030)
0.926 -0.004
(0.029)
0.904
Year 2008 0.026
(0.030)
0.381 0.015
(0.030)
0.622 0.018
(0.030)
0.549 0.013
(0.030)
0.673
Year 2009 0.054*
(0.030)
0.074 0.040
(0.030)
0.185 0.045
(0.030)
0.143 0.038
(0.030)
0.213
Year 2010 0.027
(0.031)
0.385 0.011
(0.031)
0.727 0.016
(0.031)
0.606 0.008
(0.031)
0.794
Year 2011 0.039
(0.031)
0.212 0.025
(0.031)
0.416 0.029
(0.031)
0.359 0.023
(0.031)
0.467
Year 2012 0.037
(0.032)
0.247 0.020
(0.032)
0.534 0.028
(0.032)
0.381 0.018
(0.032)
0.574
Year 2013 0.043
(0.032)
0.175 0.024
(0.032)
0.443 0.032
(0.032)
0.309 0.022
(0.032)
0.490
Year 2014 0.029
(0.032)
0.359 0.009
(0.032)
0.779 0.021
(0.032)
0.505 0.008
(0.032)
0.811
Year 2015 0.031
(0.032)
0.332 0.011
(0.032)
0.735 0.023
(0.032)
0.475 0.009
(0.032)
0.769
Year 2016 0.038
(0.032)
0.236 0.015
(0.032)
0.649 0.028
(0.032)
0.381 0.013
(0.032)
0.690
Past
performance
-0.181***
(0.040)
0.000 -0.254***
(0.040)
0.000 -0.203***
(0.040)
0.000 -0.256***
(0.040)
0.000
Industry
profitability
0.876***
(0.142)
0.000 0.631***
(0.142)
0.000 0.854***
(0.142)
0.000 0.642***
(0.142)
0.000
Firm Size 0.089***
(0.008)
0.000 0.082***
(0.008)
0.000
Org. Slack 0.076***
(0.009)
0.000 0.027**
(0.012)
0.037
Model F 3.301*** 0.000 10.040*** 0.000 5.222*** 0.000 9.760*** 0.000
R2 0.009 0.027 0.014 0.028
N 6858 6858 6858 6858
*** Correlation is significant at the 0.01 level (2-tailed)
** Correlation is significant at the 0.05 level (2-tailed)
* Correlation is significant at the 0.10 level (2-tailed)
42
4.4 Mediating analysis of structural inertia on the size – innovation relationship
To examine a mediating effect of structural inertia, the same structuring of the
ordinary least squares regression is used as for the examination of the mediating effect of
organizational slack, consistent with the method of Baron & Kenny (1986) and Wu & Zumbo
(2008). The results are presented in Table 8 (for the conceptual model see Figure 2). First,
firm size is positively related to innovation (B = 0.089, t = 11.411, p<0.01). Hence, hypothesis
1 is supported. Subsequently, firm size is negatively correlated to structural inertia (r = -
0.128, p<0.01) (see Table 6), whereas hypothesis 5 proposes a positive effect. Hence, there is
evidence that hypothesis 5 is not supported. Then, considering model 3, structural inertia is
negatively related to innovation (B = -0.030, t = -3.411, p<0.01), indicating that firms with
strong inertial pressures face constraints towards innovation. Hence, hypothesis 6 is
supported. Lastly, the relationship between firm size (B = 0.087, t = 11.034, p<0.01) and
innovation declines (in comparison to the direct effect of firm size on innovation) when
structural inertia is added in the same model. Furthermore, the effect of structural inertia on
innovation remains significant (B = -0.016, t = -1.823, p<0.1). However, due to that one step
could not be demonstrated (the positive relationship between organizational size and
innovation) there is no evidence to support a mediating effect of structural inertia. Thus,
hypothesis 7 is not supported.
43
Table 8: Mediating effect of structural inertia on innovation
Model 1 Model 2 Model 3 Model 4
Variables Beta Sig. Beta Sig. Beta Sig. Beta Sig.
Constant 0.727***
(0.019)
0.000 0.703***
(0.019)
0.000 0.762***
(0.022)
0.000 0.722***
(0.022)
0.000
Year 2001 0.045*
(0.027)
0.095 0.047*
(0.027)
0.083 0.044
(0.027)
0.106 0.046*
(0.028)
0.088
Year 2002 0.049*
(0.028)
0.080 0.047*
(0.028)
0.090 0.046*
(0.028)
0.098 0.046*
(0.028)
0.100
Year 2003 0.047*
(0.028)
0.094 0.043
(0.028)
0.121 0.045
(0.028)
0.111 0.042
(0.028)
0.130
Year 2004 0.029
(0.028)
0.303 0.020
(0.028)
0.477 0.028
(0.028)
0.323 0.019
(0.028)
0.486
Year 2005 0.006
(0.028)
0.829 -0.004
(0.028)
0.901 0.005
(0.028)
0.856 -0.004
(0.028)
0.892
Year 2006 -0.003
(0.029)
0.904 -0.016
(0.029)
0.580 -0.003
(0.029)
0.914 -0.015
(0.029)
0.592
Year 2007 0.010
(0.030)
0.743 -0.002
(0.029)
0.947 0.010
(0.030)
0.729 -0.001
(0.029)
0.962
Year 2008 0.026
(0.030)
0.381 0.015
(0.030)
0.622 0.026
(0.030)
0.389 0.015
(0.030)
0.621
Year 2009 0.054*
(0.030)
0.074 0.040
(0.030)
0.185 0.053*
(0.030)
0.081 0.040
(0.030)
0.189
Year 2010 0.027
(0.031)
0.385 0.011
(0.031)
0.727 0.027
(0.031)
0.387 0.011
(0.031)
0.720
Year 2011 0.039
(0.031)
0.212 0.025
(0.031)
0.416 0.039
(0.031)
0.211 0.026
(0.031)
0.409
Year 2012 0.037
(0.032)
0.247 0.020
(0.032)
0.534 0.036
(0.032)
0.264 0.019
(0.032)
0.540
Year 2013 0.043
(0.032)
0.175 0.024
(0.032)
0.443 0.044
(0.032)
0.171 0.025
(0.032)
0.431
Year 2014 0.029
(0.032)
0.359 0.009
(0.032)
0.779 0.028
(0.032)
0.376 0.009
(0.032)
0.781
Year 2015 0.031
(0.032)
0.332 0.011
(0.032)
0.735 0.029
(0.032)
0.361 0.010
(0.032)
0.747
Year 2016 0.038
(0.032)
0.236 0.015
(0.032)
0.649 0.036
(0.032)
0.260 0.014
(0.032)
0.660
Past
performance
-0.181***
(0.040)
0.000 -0.254***
(0.040)
0.000 -0.171***
(0.040)
0.000 -0.247***
(0.040)
0.000
Industry
profitability
0.876***
(0.142)
0.000 0.631***
(0.142)
0.000 0.856***
(0.142)
0.000 0.626***
(0.142)
0.000
Firm Size 0.089***
(0.008)
0.000 0.087***
(0.008)
0.000
Structural
Inertia
-0.030***
(0.009)
0.001 -0.016*
(0.009)
0.068
Model F 3.301*** 0.000 10.040*** 0.000 3.744*** 0.000 9.707*** 0.000
R2 0.009 0.027 0.010 0.028
N 6858 6858 6858 6858
*** Correlation is significant at the 0.01 level (2-tailed)
** Correlation is significant at the 0.05 level (2-tailed)
* Correlation is significant at the 0.10 level (2-tailed)
44
4.5 Inverted U-shape analysis
To examine an inverted U-shape relationship between firm size and innovation (see
Figure 3) another ordinary least squares regression analysis is set up as follows. Again, model
1 only consists of the control variables. Firm size is added in model 2. Lastly, the squared
firm size variable is incorporated in model 3 in order to demonstrate a curvilinear effect.
By considering the results of the regression analysis regarding the inverted U-shape
relationship (see Table 10), firm size is positively related to innovation in model 2 (B = 0.089,
t = 11.411, p<0.01). Additionally, the R2 value of this model is 0.026, indicating that 2,6% of
the variance within innovation can be explained through firm size. By examining model 3, in
where the squared value of firm size is added, the correlation of the initial firm size variable
remains the same compared to model 2 (B = 0.089, t = 11.411, p<0.01). Furthermore, the
relation between firm size squared and innovation is positive, but not significant (B = 0.001,
t = 0.103, p>0.10). However, in order to demonstrate an inverted U-shape relationship, both
the beta of the initial size variable and the squared size variable have to be positive and
significant. In addition, the R2 value of model 3 is 0.026 which is exactly the same as in
model 2. As such, the R2 change is 0.000, which indicates that there is no additional
explanatory power by adding the squared organizational size variable. Hence, the results
generated in this analysis do not provide evidence in order to support hypothesis 8.
4.4 Hypothesis Testing
Table 9: Overview hypotheses of this research
Hypothesis Results
H1: Positive relationship organizational size and innovation Supported
H2: Positive relationship organizational size and organizational slack Supported
H3: Positive relationship organizational slack and innovation Supported
H4: Mediating effect of organizational slack on the size – innovation relationship Supported
H5: Positive relationship organizational size and structural inertia Not supported
H6: Negative relationship structural inertia and innovation Supported
H7: Mediating effect of structural inertia on the size – innovation relationship Not supported
H8: Inverted U-shape relationship organizational size and innovation Not supported
45
Table 10: Curvilinear effect of firm size on innovation
Model 1 Model 2 Model 3
Variables Beta Sig. Beta Sig. Beta Sig.
Constant 0.727***
(0.019)
0.000 0.739***
(0.019)
0.000 0.738***
(0.020)
0.000
Year 2001 0.045*
(0.027)
0.095 0.047*
(0.027)
0.083 0.047*
(0.027)
0.083
Year 2002 0.049*
(0.028)
0.080 0.047*
(0.028)
0.090 0.047*
(0.028)
0.090
Year 2003 0.047*
(0.028)
0.094 0.043
(0.028)
0.121 0.043
(0.028)
0.121
Year 2004 0.029
(0.028)
0.303 0.020
(0.028)
0.477 0.020
(0.028)
0.477
Year 2005 0.006
(0.028)
0.829 -0.004
(0.028)
0.901 -0.003
(0.028)
0.901
Year 2006 -0.003
(0.029)
0.904 -0.016
(0.029)
0.580 -0.016
(0.029)
0.580
Year 2007 0.010
(0.030)
0.743 -0.002
(0.029)
0.947 -0.002
(0.029)
0.947
Year 2008 0.026
(0.030)
0.381 0.015
(0.030)
0.622 0.015
(0.030)
0.622
Year 2009 0.054*
(0.030)
0.074 0.040
(0.030)
0.185 0.040
(0.030)
0.185
Year 2010 0.027
(0.031)
0.385 0.011
(0.031)
0.727 0.011
(0.031)
0.727
Year 2011 0.039
(0.031)
0.212 0.025
(0.031)
0.416 0.025
(0.031)
0.416
Year 2012 0.037
(0.032)
0.247 0.020
(0.032)
0.534 0.020
(0.032)
0.534
Year 2013 0.043
(0.032)
0.175 0.024
(0.032)
0.443 0.024
(0.032)
0.443
Year 2014 0.029
(0.032)
0.359 0.009
(0.032)
0.779 0.009
(0.032)
0.779
Year 2015 0.031
(0.032)
0.332 0.011
(0.032)
0.735 0.011
(0.032)
0.735
Year 2016 0.038
(0.032)
0.236 0.015
(0.032)
0.649 0.015
(0.032)
0.649
Past
performance
-0.181***
(0.040)
0.000 -0.254***
(0.040)
0.000 -0.253***
(0.040)
0.000
Industry
profitability
0.876***
(0.142)
0.000 0.631***
(0.142)
0.000 0.631
(0.142)
0.000
Firm Size 0.089***
(0.008)
0.000 0.089***
(0.008)
0.000
Firm Size
Squared
0.001
(0.010)
0.918
Model F 3.301*** 0.000 10.040*** 0.000 9.537*** 0.000
R2 0.009 0.027 0.027
R2 Change 0.009 0.019 0.000
N 6858 6858 6858
*** Correlation is significant at the 0.01 level (2-tailed)
** Correlation is significant at the 0.05 level (2-tailed)
* Correlation is significant at the 0.10 level (2-tailed)
46
5. Discussion & Conclusion
This discussion section contains an overview of the major findings. Subsequently, the
theoretical and practical contributions are discussed. Lastly, the limitations and future
research recommendations are provided.
5.1 Discussion of major findings
With regard to the current literature, there still is an ongoing debate on the type of
relationship between organizational size and innovation. On the one hand, there are studies
that demonstrate a positive relationship between size and innovation (Aiken & Hage, 1971;
Dewar & Dutton, 1986; Ettlie et al., 1984), while on the other hand studies provide evidence
for a negative relationship (Aldrich & Auster, 1986; Hage, 1980; Kelly & Amburgey, 1991).
This study contributes to this ongoing debate by suggesting that organizational size is, in
general, positively related to innovation.
Regarding the influence of organizational slack, the results are as hypothesized. As
such, firm size is positively related to organizational slack, which is consistent with the study
of Sharfman et al. (1988). Subsequently, there is a positive correlation between organizational
slack and innovation, which is consistent with the studies of Nohria & Gulati (1996) and
Greve (2003). Altogether, the mediating effect is demonstrated as the correlation between size
an innovation declines when organizational slack is added. In other words, larger firms may
be able to possess more slack resources, which in turn lead to more innovation. Thus,
organizational slack partially mediates the relation between organizational size and
innovation. With regard to the negative relationship between structural inertia and innovation,
the results are in line with the proposed hypothesis as well. This is consistent with prior
studies (e.g. Iaquinto & Fredrickson, 1997; Josefy et al., 2015). Therefore, companies with
strong inertial pressures might not be able to respond adequately to changes within the
environment compared to companies with weaker inertial pressures (Josefy et al., 2015).
47
Furthermore, the results demonstrate that organizational size is negatively related to
structural inertia, whereas hypothesis 5 proposed a positive relationship. This can be
explained in two ways. First, from a theoretical perspective, one can state that large
organizations may not be organized along hierarchical levels, but within networks. The
appearance of networks can be ascribed to the theory of strategic choice (see Table 2),
whereby strategic renewal is a combination of managerial and environmental influences
(Volberda et al., 2001). Particularly, the rise of technology and globalization (environmental
influences) forced organizations to operate in a flexible manner. Managers (and thus
organizations) can respond to these challenges by structuring their organizations along
networks (managerial actions). Hereby, information technology and the associated ease of
communication may enable organizations to structure themselves within networks (Baker,
Nohria, & Eccles, 1992; Barthélemy & Adsit, 2003; Child & McGrath, 2001). A network may
consist of one leading company that collaborates with several other companies in order to
deliver value to customers (Baker et al., 1992; Child & McGrath, 2001). By bundling their
strengths, these companies may be able to create more value than if each firm does all
activities on its own. (Child & McGrath, 2001). Additionally, a network enables organizations
to distribute power among its members, act flexible and achieve economies of scale and scope
along horizontal relationships (Child & McGrath, 2001). That way, larger organizations may
be able to avoid the inertial pressures that are associated with vertical organization structures,
political conflicts and the possession of a considerable amount of fixed assets. Second, from a
methodological perspective and closely related to the theoretical explanation, one can argue
that larger organizations can outsource particular activities to other companies. Predominantly
publicly listed manufacturing firms seem to outsource particular activities towards low-cost
countries in order to stay competitive as well as to create more value for their shareholders.
As a results of outsourcing, larger organizations may need lesser fixed assets (Barthélemy &
48
Adsit, 2003). Due to the inclusion of fixed assets within the formula of structural inertia, it
may be possible that larger organizations are less structural inert.
Concerning the proposed curvilinear effect, the generated results of this study do not
provide evidence for an inverted U-shape relationship (non-linear relationship). A possible
explanation for this conclusion is that the relationship between organizational size and
innovation might be totally linear. Whereas the hypothesis proposes that inertial pressures
may diminish the advantages of firm size on the degree of innovation, it may be possible that
other theories (as shown in Table 2) enhance these advantages and thus extend the linear
relation between organizational size and innovation. First, according to the evolutionary
theory for example, organizations may incorporate routines that enhance incremental
(exploitative) innovations of large organizations (Nelson & Winter, 1982). Second, from the
perspective of the institutional theory, organizations find ways to maintain the relation with
their environment and therefore they might be able to constantly respond to particular
environmental forces in an adequate manner (DiMaggio & Powell, 1983). Third, as
organizations become larger, they might be able to obtain more experience, which can result
in an advantage with regard to knowledge about an organizations’ environment (Huber, 1991;
Leiblein & Madsen, 2009). This is in line with the renewal journey based on learning theories
(Volberda et al., 2001).
5.2 Contributions
From a theoretical perspective, this study provides more insights into the influence of
organizational slack and structural inertia on the size – innovation relationship through testing
these constructs both separately and in combination. That way, this paper aimed to contribute
to the ongoing debate whether firm size is positively or negatively related to innovation. The
main result is that firm size is positively related to innovation. A few additional insights are
gathered as well. First, larger firms may have access to more slack resources, which in turn
49
can enhance innovation. As such, organizational slack mediates the relation between
organizational size and innovation. This insight is in line with arguments from the behavioral
theory of the firm and thus strengthens the reasoning within this theory regarding innovation.
Second, structural inertia is related to innovation as hypothesized as well. As such,
organizations with strong inertial pressures may face difficulties in order to conduct
innovation projects, which corresponds with the arguments made within the population
ecology theory. Altogether, there is evidence for both a voluntaristic and a deterministic
determinant of innovation. Third, by incorporating both the behavioral theory of the firm and
the population ecology theory, a curvilinear relationship between size and innovation cannot
be demonstrated in this study. As such, the contribution of this analysis is that organizational
size and innovation may have a linear relationship.
With regard to the practical contributions, this study might offer managers a better
understanding why particular organizational conditions (e.g. amount of organizational slack
and level of inertial pressures) can enhance or inhibit innovation within organizations.
Additionally, they get insights with regard to the influence of firm size on organizational slack
and innovation and possible explanations of these relations.
5.3 Limitations and future research
Here the limitations of this research are presented that might be useful in order to
determine possible opportunities for future research. First, this research only examined the
effect of particular intra-organizational determinants on the size – innovation relationship.
However, future research might include environmental determinants as well in order to make
the findings more robust. Second, most measures used in this study are particularly financial
in nature. As such, constructs that have a non-financial part as well (e.g. organizational slack,
structural inertia, and innovation) are measured in a financial way. To strengthen possible
effects of organizational slack and structural inertia on innovation, future research can
50
incorporate other ways of measuring these variables (e.g. organizational resources other than
money, political constraints, information flows or number of patents). Third, this research has
a cross-sectional character, whereby the study is conducted at a fixed time. To increase the
causality and the related internal validity, further research can conduct a longitudinal research.
Fourth, this study uses a sample that only contains publicly listed US manufacturing firms
within the timeframe of 2000 to 2016. As such it is not possible to generalize these results to
manufacturing firms outside the US as well as industries other than the manufacturing
industry. In addition, the sample only includes publicly listed companies (PLC’s). This might
lead to a bias, because these companies are, in general, focused on short term results (Jensen,
2002). Innovation focuses, in general, more on the long term, because it seems unknown
whether innovation will deliver shareholder value in the future. Therefore, future research can
examine other industries or scrutinize industries in combination in order to achieve
generalization as well as incorporate other forms of organizations such as family firms or
start-ups. Fifth, a definition of innovation is used whereby all kinds of innovations are
incorporated. However, it might be possible that the consideration of particular dimensions of
innovation can lead to different findings (e.g. technical versus administrative, product versus
process, radical versus incremental). Additionally, the stage of innovation (generation as
opposed to adaptation) or the scope of innovation (one versus multiple innovation) might lead
to other findings.
5.4 Conclusion
This study tried to answer the following research question: ‘’How do organizational
slack and structural inertia, both separately and in combination, influence the relation between
organizational size and innovation of firms?’’ Innovation can be a real challenge for
organizations nowadays, due to, for example, rapid technological changes within the
environment of organizations or the rise of globalization. In order to gain a better
51
understanding of innovation, the effects of organizational slack and structural inertia on
innovation are examined. Considering the results, organizational slack mediates the size –
innovation relationship as larger firms seem to possess a greater amount of slack resources,
which in turn may enhance innovation. Furthermore, structural inertia is negatively related to
innovation. Combining both organizational slack and structural inertia does not lead to an
inverted U-shape relationship between organizational size and innovation, which may indicate
that the relationship between them is linear. This study extends the understanding of possible
drivers of innovation. Additionally, this paper strengthens the argument made in the literature
of the behavioral theory of the firm (voluntaristic view) and the population ecology
(deterministic view) with regard to the relationship between size and innovation. Hence,
organizational size can be seen as a strong explanatory variable within the strategic field as
stated by Dobrev and Carroll (2003).
52
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Appendix 1: Theoretical Frameworks and Size Definitions
(Josefy et al, 2015, p. 737)
61
Appendix 2: Graphical depiction mediation effect
(Wu & Zumbo, 2008, p. 370)