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

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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

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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)