SME OPEN INNOVATION 1 Open Innovation Strategies for Overcoming Competitive Challenges Facing Small and Mid-Sized Enterprises Brent T. Cornell A… [606418]

SME OPEN INNOVATION 1

Open Innovation Strategies for Overcoming Competitive Challenges Facing
Small and Mid-Sized Enterprises

Brent T. Cornell

A Thesis
Submitted to the
Graduate Faculty
of
University of Maryland University College
in Partial Fulfillment of the
Requirements for the Degree
of
Doctor of Management

Doctoral Committee: Dr. Dennis Winters
Dr. Sharon Hadary
Date Submitted: 7 December 2012

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SME OPEN INNOVATION 2
Table of Contents
Dedication …………………………………………………………………………………………………………………….. 6
Abstract ………………………………………………………………………………………………………………………… 7
Chapter 1: Introduction ………………………………………………………………………………………………… 8
Approach ……………………………………………………………………………………………………………………. 8
Background/Context of this Dissertation ………………………………………………………………………. 10
Value of this Dissertation to the Field of Management …………………………………………………… 19
Research Questions ……………………………………………………………………………………………………. 20
Chapter 2: Literature Review ………………………………………………………………………………………. 21
Structure of the Literature Review ……………………………………………………………………………….. 21
Holistic Model of Innovation ………………………………………………………………………………………. 25
Innovation Exploration ………………………………………………………………………………………………. 27
Innovation Exploitation ……………………………………………………………………………………………… 33
Collaborations in the Open Innovation Ecosystem …………………………………………………………. 39
Summary of SME Open Innovation Strategies ………………………………………………………………. 43
SME Competitive Challenges ……………………………………………………………………………………… 46
Chapter 3: Conceptual Framework ……………………………………………………………………………… 59
SME Competitive Challenges Model …………………………………………………………………………… 59
SME Open Innovation Strategies Model ………………………………………………………………………. 61
Chapter 4: Methods …………………………………………………………………………………………………….. 68
Evidence-Based Research …………………………………………………………………………………………… 68
Overview of Dissertation’s Evidence -Based Methods ……………………………………………………. 71
Benefits of Conducting an Eviden ce-Based Systematic Review ………………………………………. 72

SME OPEN INNOVATION 3
Steps for Conducting this Systematic Review ……………………………………………………………….. 77
Synthesizing Using the Meta-Synthesis Technique ………………………………………………………… 86
Protocol for Statistical Meta-Analysis ………………………………………………………………………….. 87
Expert Practitioner Review Panel ………………………………………………………………………………… 91
Chapter 5: Analysis and Discussion ……………………………………………………………………………… 93
Proposition 1 (SME Challenges) Findings ……………………………………………………………………. 93
Proposition 1 (SME Challenges) Conclusion ………………………………………………………………… 98
Proposition 2 (Open Innovation Collaboration) Findings ……………………………………………….. 99
Proposition 2 (Open Innovation Collaboration) Meta-Analysis ……………………………………… 110
Proposition 2 (Open Innovation Collaboration) Conclusion ………………………………………….. 112
Proposition 2a (Vertical Collaboration) Findings …………………………………………………………. 113
Proposition 2a (Vertical Collaboration) Meta-Analysis ………………………………………………… 115
Proposition 2a (Vertical Collaboration) Conclusion……………………………………………………… 117
Proposition 2b (Horizontal Collaboration) Findings …………………………………………………….. 117
Proposition 2b (Horizontal Collaboration) Meta-Analysis …………………………………………….. 120
Proposition 2b (Horizontal Collaboration) Conclusion …………………………………………………. 122
Proposition 2c (Knowledge-Intensive Collaboration) Findings ……………………………………… 123
Proposition 2c (Knowledge-Intensive Collaboration) Meta-Analysis ……………………………… 124
Proposition 2c (Knowledge-Intensive Collaboration) Conclusion ………………………………….. 126
Proposition 3 (Outward Open Innovation) Findings …………………………………………………….. 126
Proposition 3 (Outward Open Innovation) Case Study …………………………………………………. 129
Proposition 3 (Outward Open Innovation) Meta-Analysis …………………………………………….. 132
Proposition 3 (Outward Open Innovation) Conclusion …………………………………………………. 133

SME OPEN INNOVATION 4
Proposition 4 (Inward Open Innovation) Findings ……………………………………………………….. 134
Proposition 4 (Inward Open Innovation) Meta-Analysis……………………………………………….. 137
Proposition 4 (Inward Open Innovation) Conclusion ……………………………………………………. 138
Proposition 5 (Combined Strategies) Findings …………………………………………………………….. 139
Proposition 5 (Combined Strategies) Meta-Analysis ……………………………………………………. 141
Proposition 5 (Combined Strategies) Conclusion …………………………………………………………. 144
Chapter 5 Summary …………………………………………………………………………………………………. 144
Chapter 6: Conclusions and Implications …………………………………………………………………… 147
Summary of Conclusions ………………………………………………………………………………………….. 147
Implications for SMEs ……………………………………………………………………………………………… 150
Additional Implications for Global SME Open Innovation ……………………………………………. 175
Limitations and Areas for Future Research …………………………………………………………………. 186
Dissertation Summary ………………………………………………………………………………………………. 188
Appendix A: Statistical Meta-Analysis Results ……………………………………………………………. 194
References …………………………………………………………………………………………………………………. 203

SME OPEN INNOVATION 5
Table of Figures
Figure 1. Dissertation Approach ………………………………………………………………………………….. 10
Figure 2. Closed and Open Innovation …………………………………………………………………………. 13
Figure 3. Literature Review Topics ………………………………………………………………………………. 21
Figure 4. Holistic Model of Innovation …………………………………………………………………………. 26
Figure 5. Summary of SME Open Innovation Strategic Alternatives …………………………….. 44
Figure 6. Summary of SME Challenges Found in the Literature …………………………………… 47
Figure 7. Main Groupings of SME Challenges ……………………………………………………………… 48
Figure 8. SME Competitive Challenges Model ……………………………………………………………… 59
Figure 9. SME Open Innovation Strategies Model ………………………………………………………… 62
Figure 10. Dissertation’s Research Methods …………………………………………………………………. 71
Figure 11. Levels of Evidence ………………………………………………………………………………………. 80
Figure 12. Sources of Evidence …………………………………………………………………………………….. 83
Figure 13. Studies Included in this Evaluation ……………………………………………………………… 85
Figure 14. Geographic Coverage of SMEs Evaluated ……………………………………………………. 86
Figure 15. Summary of Meta-Analysis Findings …………………………………………………………. 143
Figure 16. Continuum of Innovation Openness …………………………………………………………… 151
Figure 17. SME Open Innovation Footprint Example …………………………………………………. 154
Figure 18. Key Determinants of SME Innovation Openness Impacting the Probabi lity of an
SME Benefiting from Open Innovation ………………………………………………………………………. 174
Figure 19. SME Global Open Innovation ……………………………………………………………………. 177

SME OPEN INNOVATION 6
Dedication
This dissertation is dedicated to my loving and supportive wife Dana. I would like to
thank my dissertation advisors, Dr. Dennis Winters and Dr. Sharon Hadary, who devoted
significant time providing me w ith guidance and direction. Additionally, I would like to thank
the members of my expert practitioner review panel, Dr. Francesco Calabrese, Dr . John Corso,
and Dr. Dorothea (Dolly) Greenwood Mastrangelo, who provided valuable feedback.

SME OPEN INNOVATION 7
Abstract
The purpose of this dissertation is to explore how small and mid-sized enterprises
(SMEs) can leverage open innovation to increase their economic viability and success i n this
modern, globalized post-industrial society marked by constant change and intense c ompetition.
To date, most open innovation research has focused exclusively on large companies, while
neglecting the specific competitive challenges and strategies of SME s. This dissertation
evaluates the open innovation landscape from the vantage point of SMEs because these firm s
play a significant role in economies around the globe. Innovation is a crucial driver in t heir
ability to survive, compete, and prosper. The dissertation author created three new models to
explore the research topic. The first, the Holistic Model of Innovation , is useful to more fully
understand the entire innovation landscape (both closed and open innovation as well as product
and non-product innovations). This dissertation also presents the author’s SME Competitive
Challenges Model , which identifies the main size-related competitive hurdles that SMEs face
(i.e., challenges related to a lack of resources, limited dynamic capabilit ies, and excessive risk
exposure). Additionally, the author presents his SME Open Innovation Strategies Model , which
evaluates various open innovation strategies for overcoming these competitive c hallenges. This
dissertation presents a series of propositions based on these new conceptual models and tests
them by conducting a systematic review, several meta-syntheses, a case study, and multiple
statistical meta-analyses with data from 34,676 SMEs across dozens of industrie s in 27 countries.

Keywords: Small and Mid-Sized Enterprises (SMEs), Small Business Strategy, Gl obal Open
Innovation, Closed Innovation, Size- Related Competitive Challenges

SME OPEN INNOVATION: CHAPTER 1 8
Chapter 1: Introduction
The purpose of this dissertation is to explore how small and mid-sized enterprises
(SMEs) can leverage open innovation to increase their economic performance in this modern,
globalized post-industrial society marked by constant change and intense competi tion. To date,
most open innovation research has focused exclusively on large companies, while neglect ing the
specific competitive challenges and strategies of SMEs. This dissertation evaluates the open
innovation landscape from the vantage point of SMEs. This topic is important to the field of
management because SMEs play a significant role in economies around the globe a nd innovation
is a crucial driver in their ability to survive, compete, and prosper.
This d issertation’s main line of argument is as follows: (1) SMEs encounter size-related
competitive challenges (i.e., lack of resources, limited dynamic capabilitie s, and high risk
exposure); (2) Open innovation can increase an SME’s exposure to others’ c omplementary
resources and capabilities, increase its innovation outputs, increase financial returns, and lower
an SME’s risk exposure; (3) Therefore, open innovation can assist SMEs with overcoming their
size-related competitive challenges; (4) However, various contextual mode rators can enhance (or
hinder) the effectiveness of SME open innovation strategies in different situations.
Approach
This dissertation begins by providing the background and context of SME open
innovation and the research questions in Chapter 1. Chapter 2 presents a literature review and the
dissertation author’s holistic model of what the open innovation landscape consists of and how
various types of innovations relate to one another ( Holistic Model of Innovation ). Next, based on
the literature review and this Holistic Model of Innovation (which depicts the integration and
relationships between both open and closed forms of innovation as well as product and non-

SME OPEN INNOVATION: CHAPTER 1 9
product innovations ) the dissertation evaluates the specific size-related competitive chal lenges
that SMEs face in today’s turbulent, ultra -competitive environment compared to larger firms.
Chapter 3 presents these challenges in the author’s SME Competitive Challenges Model . Then,
the dissertation builds on these two models to develop the author’s third model of the various
strategies that SMEs can utilize to overcome these challenges ( SME Open Innovation Strategies
Model ). This model provides a framework to guide SMEs toward becoming more economically
competitive. These three models build on one another and are all needed to fully examine the
research questions presented at the end of Chapter 1. The author’s Holistic Model of Innovation
(Chapter 2) is critical to fully comprehend the entire innovation landscape; the author’s SME
Competitive Challenges Model (Chapter 3) is required to cognize the size-related challenges that
SMEs face; and the author’s SME Open Innovation Strategies Model (Chapter 3) is needed to
understand how SMEs overcome those competitive challenges. This disser tation’s author create d
these three models since no such frameworks were found to exist in the literature a nd the models
are needed to fully explore the dissertation’s research questions that are presented at the end of
Chapter 1.
Chapter 4 presents the evidence-based research approach used to evaluate the va lidity of
this dissertation ’s theoretical models and the associated Research Propositions 1 through 5.
Chapter 5 provides a detailed account of the analysis and presents the researc h findings. Then,
Chapter 6 summarizes the conclusions and discusses the implications for practi tioners.
The overall dissertation approach described above is illustrated in Figure 1.

SME OPEN INNOVATION: CHAPTER 1 10

Figure 1. Dissertation Approach
Background/Context of this Dissertation
Significant advancements in technology in the U.S. during the 20th century led to the
Post-Industrial Revolution. Innovation has always been a vital economic driver, but the modern
globalized post-industrial society in particular relies even more heavily on intell ectual capital and
is primarily focused on processing knowledge rather than on simply manufacturing goods as was
the case during the Industrial Revolution (Bell, 1976). Contemporary organizations must now
adapt and innovate much more quickly than they have ever had to in order to remain
competitive. Peter Vaill (1996) explains that the modern business environment in the post-
industrial society is in a turbulent state of “permanent white water,” where companies face
constant uncertainty and discontinuity. Drucker (1985) stresses the importance of continual
innovation as an essential ingredient for organizations to survive and compete in this era of
constant change.

SME OPEN INNOVATION: CHAPTER 1 11
In advanced knowledge- based economies, a large portion of a firm’s market v alue is
often determined by its intellectual property; companies rely heavily on thes e intangible assets
for economic competition and growth (Kaplan & Norton, 2004). Innovation is a critical s uccess
factor for a company’s endurance in today’s ultra -competitive global landscape (Dervitsiotis,
2010). To remain competitive in the 21st century, companies need to not only create new
innovative products and services, but they must also continually innovate their business m odels
to respond to constant changes in the environment (Hamel, 2007). They must also refine their
business models in response to their own recent innovations (Kim & Mauborgne, 2005; Druck er,
1985).
The Organization for Economic Cooperation and Development (OECD) found that
innovation is the primary factor t hat determines a country’s long -term economic growth and
increases in productivity, and that innovation is even more important to an economy than eithe r
capital or labor resources alone (OECD, 2008). Many societies around the world recog nize the
importance of innovation and most countries and economic regions have introduced legal
intellectual property rights such as patents, copyrights, and trademark protections (Leiponen &
Byma, 2009).
Competition can accelerate the pace of innovation across entire industries as firms
compete aggressively with one another to lower their costs and to improve the functionali ties and
features of their products and enhance their services (Lafley & Charan, 2008). Ceter is paribus
(all else being equal), innovative firms are commonly rewarded with increas ed profits derived
from innovations in their products, services, and business models (Hamel, 2007 ). In the long run,
the most innovative companies tend to have the largest market shares, are generally the most
profitable, and are usually the most capable of surviving during economic downturns (At un,

SME OPEN INNOVATION: CHAPTER 1 12
Harvey & Wild, 2007). Chapter 5 of this dissertation quantifies the benefits that SMEs have
received using different types of open innovation strategies in various industries a nd countries.
There are certainly many other factors besides innovation that affect a n SME’s success
(e.g., marketing effectiveness, operational efficiency, and product quality). This dissertation’s
author does not view innovation by itself as a panacea that will guarantee organizat ional success.
For example, a perfectly crafted innovation strategy can be useless if a company improperly
manages its resources, makes incompetent managerial decisions, or if a company goes bankrupt
from lawsuits resulting from unethical behavior. However, this dissertation’s scope is narrowly
focused on the role that open innovation strategies can play in assisting SMEs wi th overcoming
their size-related competitive challenges and increasing their economi c performance. Other
management concerns are important to organizational success. However, these other non-
innovation management topics are outside of the scope of this particular evaluation.
Paradigm Shift from Closed to Open Innovation
Historically, firms primarily utilized what is now referred to as the closed innovation
approach where they develop ed innovations internally by using only their in-house resources and
technologies and then commercialized those innovations on their own. Closed innovation is still
widely practiced by many firms (at least for some of their products) because of the belief that it
is strategically advantageous to closely guard internal ideas and technologies from others, even
when those ideas and technologies are not being utilized by the firm. However, Henry
Chesbrough noticed that more companies are now looking outside of their organizational
boundaries for innovations and for assistance with both knowledge generation and
commercialization efforts. He coined the term open innovation to describe this new
phenomenon, and defined open innovation as the “purposive inflows and outflows of knowledge

SME OPEN INNOVATION: CHAPTER 1 13
to accelerate innovation, and to expand the markets for external use of innovation, respectively”
(Chesbrough, 2006, p. 1). Figure 2 is a reproduction of Chesbrough’s (2003) visual depiction of
the differences between closed and open innovation.
Closed Innovation Open Innovation
Source: Chesbrough ( 2003)
Figure 2. Closed and Open Innovation
Chesbrough’ s (2003) closed innovation model, depicted on the left side in Figure 2,
illustrates that in a closed environment all the research and development (R&D) funct ions of
projects are conducted within the boundary of an organization. The open innovation model on
the right side in Figure 2 uses the same logic as the open systems theory i n that it views the
boundaries of a firm as porous. Porous boundaries allow for intentional inflows and outflows of
knowledge with selected external partners in the overall competitive environme nt (Huang &
Rice, 2009). Morgan (1986) compares organizations to organisms that are comprised of various
sub-systems that co-exist and interact with their external environment and with other organisms
(organizations). He argues that organizations can form symbiotic relationships w ith other firms
in which both entities benefit from their mutual relationship. The open innovation model applies
this mindset specifically to the topic of innovation.

SME OPEN INNOVATION: CHAPTER 1 14
External collaboration with other firms is not a new concept and these activities ha ve
been ongoing since humans first began innovating. However, firms are now going even further
by “leveraging external sources of knowledge to drive internal growth” and are se lling/leasing
their unused intellectual assets to other firms, sometimes even to their direct com petitors
(Grönlund, Sjödin, & Frishammar, 2010, p.108). This approach now gives companies access to
additional technologies and knowledge that they would not otherwise have access to and this
enables companies to leverage creative talent from outside of their limited i nternal R&D
resource pools (Dahlander & Gann, 2010). Additionally, this benefits firms by reducing their
risks with developing/obtaining intellectual property, since it can reduce their fixe d development
costs (e.g., R&D staff overhead) in exchange for paying higher variable cost s associated with
individual intellectual property purchases or patent licensing fees (Grönlund et al., 2010 ).
Thomas Kuhn introduced the concept of paradigm shifts and argues that shifts occur in
research and scholarship when enough convincing empirical evidence mounts to overturn the
existing conventional thought in a field of study (Kuhn, 1962; Fuller, 2004). In the case of open
innovation, conventional Porterian strategy and resource-based views of competition dea l with
intellectual property as a differentiating competitive asset that should be closely guarded from
other firms. Open innovation theorists, however, have argued that there is a trend of compani es
increasingly engaging in open innovation activities (evidence that challenges the prevailing
closed innovation paradigm), which seems contrary to what the closed innovation model and
traditional competitive strategy models would have recommended for these compani es (Viskari,
Salmi, & Torkkeli, 2007 ).
Research has also shown that firms’ increased adoption of op en innovation strategies has
varied by industry and changes in adoption rates have occurred as a series of unpredictable

SME OPEN INNOVATION: CHAPTER 1 15
sudden increases rather than as a continual gradual increase in open innovation activity ( Poot,
Faems, & Vanhaverbeke, 2009). Studies also indicate that participation by firms i n open
innovation is still fairly low, and that many more firms could potentially benefit from a t least
limited participation in these strategies (Keupp & Gassmann, 2009). About 5% to 20% of SM Es
in OECD countries engage in open innovation, depending on the country. But, roughly 20% to
60% of large firms in these countries participate in open innovation activities (De Ba cker,
Lopex-Bassols, & Martinez, 2008). However, studies to date are more exploratory in nature
rather than explanatory, so they have not provided evidence to explain why more SME are not
currently engaging in open innovation activities.
Nevertheless, there will still always be strategic needs and reasons to rely on closed
innovation in certain circumstances and for certain innovation projects (Lichtenthaler , 2009).
Therefore, the conceptual Holistic Model of Innovation presented in Chapter 2 of this dissertation
includes both closed and open innovation channels.
Chesbrough (n.d.) claims that a major implication of more firms participating in the ope n
innovation landscape is that knowledge monopolies throughout economies will likely fall a s
companies increasingly seek to transfer and leverage one another’s technology and ideas. In
other words, intellectual property will be utilized more efficiently because it will be transferred
from those who are not utilizing it or who are underutilizing it to those who could fully use it.
Chesbrough argues that this will “cause the innovation landscape to become fla tter and more
distribute d” because more companies, especially SMEs, w ill now have access to more new
technologies and knowledge-sharing partnerships (Chesbrough, n.d., p. 1 ).
Historically, in a closed innovation environment large firms have outcompeted SM Es,
especially in R&D-intensive industries (Van de Vrande et al., 2009). This is primaril y because

SME OPEN INNOVATION: CHAPTER 1 16
SMEs have fewer resources and capabilities than large firms so they are at a competitive
disadvantage in a closed environment because they must rely solely on their firm’s limited
internal resources and capabilities (Habaradas, 2009 ; Bianchi et al., 2010). Additionally, SMEs
have also been found to have a higher systematic and non-systematic risk exposure compar ed to
larger companies (Gnyawali & Park, 2009 ; Madrid-Guijarro, 2009; Plehn-Dujowich, 2009).
Systematic risks are those potentially impacting entire industries or economies, whereas non-
systematic risks are those potentially impacting an individual company or project.
This dissertation refers to these competitive challenges as “size -related” (i.e., fewer
resources, less-established dynamic capabilities, and higher risk exposure) bec ause the literature
attributes these impediments specifically to SMEs when comparing them to la rger firms. A
company’s size d oes not cause it to have these challenges and does not cause a company to be
more (or less) successful with innovation. But rather, because an SME is smaller tha n a large
company (by definition) it is likely to face various size-related obstacles th at are more common
among SMEs than among larger firms. Some large firms may experience similar cha llenges. It is
also possible that large firms could face the same impediments when competing agai nst even
larger rival firms. However, according to the findings from the literature review i n Chapter 2,
these challenges are generally considered to be much more common among SMEs tha n among
large firms.
While there are certainly many other challenges that SMEs could potentially face (e.g.,
ineffective leadership or unmotivated workers ) this dissertation’s scope focuses on the size-
related challenges that are common among most SMEs. There are other obstacles that individual
SMEs could encounter. For example, a small firm may suffer from operational ineffi ciencies or
the c ompany’s business model could be ine ffective. However, these are challenges that any

SME OPEN INNOVATION: CHAPTER 1 17
company could face regardless of its size. This dissertation does not address all of t he unique
challenges that individual SMEs could potentially face. Rather, the scope of this di ssertation is
focused on evaluating challenges that the majority of SMEs in general typically face (as
identified in the literature) and the open innovation strategies that SMEs can us e to overcome
these challenges (see the SME Open Innovation Strategies Model in Chapter 3).
Role of SMEs in the Open Innovation Landscape
The U.S. Small Business Administration (SBA) defines SMEs as all businesses that have
fewer than 500 employees (SBA, 2010). These companies play a vital role in economies a round
the globe. Worldwide, 99% of businesses are SMEs. They collectively generate be tween 40%
and 50% of worldwide Gross Domestic Product (GDP) (Wurzer & DiGiammarino, 2008). In the
U.S., SMEs employ half of the private sector workforce and they pay 44% of the total U.S.
payroll (SBA, 2010).
SME R&D expenditures have grown 10 times faster than those of large firms over the
past 24 years (Chesbrough, 2010) and small firms now produce 13 times more patents per
employee than large firms (SBA, 2010). However, despite this increasing rate of R&D spending
and higher patent productivity per employee, SMEs face major barriers in actually profi ting from
these innovations because of significant challenges attributed to their relatively small size. These
impediments include fewer resources and capabilities (Bianchi et al., 2010; Enke l, Gassmann, &
Chesbrough, 2009; Habaradas, 2009) and a higher risk exposure compared to larger companies
(Gnyawali & Park, 2009 ; Bianchi et al., 2010). These challenges represent higher innovation
exploration and exploitation barriers for SMEs (Lee et al., 2010). The main challenges SMEs
face with creating innovations and profiting from them are presented in detail in t he literature
review (Chapter 2) and in the SME Competitive Challenges Model (Chapter 3). These challenges

SME OPEN INNOVATION: CHAPTER 1 18
could partially explain why large firms still account for roughly 70% of all new pa tent
applications in the U.S. and large firms still often outcompete and outperform SMEs
(Parchomovsky & Wagner, 2005; Wurzer & DiGiammarino, 2008).
As mentioned earlier, this dissertation is not arguing that firm size causes a compa ny to
be more (or less) successful with innovation. Nor is this dissertation arguing that t he SME
Competitive Challenges Model (Chapter 3) captures every conceivable challenge that an SME
could encounter. There are certainly other obstacles that individual SMEs could face. Fo r
example, a small firm may suffer from poor leadership or a company’s business model could be
ineffective. However, these are challenges that any company could encounter regardl ess of size.
The scope of this dissertation is focused on the challenges that the majority of SM Es typically
face and the open innovation strategies that SMEs can use to overcome those challen ges.
Understanding what the specific size-related competitive challenges SME s face are and
how SMEs can overcome them is not only an important management consideration, but it also
has significant implications for national economies across the globe. Developing countri es in
particular have expressed an urgent need to better understand SME competitive cha llenges and
strategies for overcoming them so they can construct governmental programs to ass ist SMEs
with increasing their participation in the global economy (Habaradas, 2009).
Despite the challenges SMEs face, they still play a vital role in the open innovation
ecosystem and they have the potential to increase participation in open innovation activities.
SMEs have historically relied on collaboration with other firms to exploit their innovat ions (Van
de Vrande et al., 2009), but since a greater number of large firms are now utilizing an ope n
innovation approach, SMEs have more opportunities than ever to join innovation networks (Lee
et al., 2010). Additionally, many SMEs have found ways of overcoming the challenges

SME OPEN INNOVATION: CHAPTER 1 19
associated with their smallness (Rahman & Ramos, 2010 ) and some of their innovations are
changing the competitive landscapes of various industries. There are even some ins tances of
large incumbents being forced to increase their innovation activities to remain c ompetitive
against SME innovators entering their markets (Leiponen & Byma, 2009). This disse rtation
examines how SMEs have been able to, and can continue to, overcome their size-related
competitive challenges by employing open innovation strategies.
Value of this Dissertation to the Field of Management
The concept of open innovation was only introduced in management literature in 2003, so
research in this area is still in its infancy. In their evaluation of the current stat e of open
innovation literature, seminal open innovation theorists such as Gassman, Enkel, and
Chesbrough have identified several vital research gaps. This dissertation addre sses two of these
important gaps. First, there is a critical need for a “holistic model of open innovation”
(Gassmann, Enkel, & Chesbrough, 2010, p. 219). Other theorists have also indicated that a
holistic model is needed that can illustrate strategic choices that firms can make in the open
innovation environment (Hobady, 2005; Schneid er et al., 2008). In response to this need, the
dissertation author created the Holistic Model of Innovation , which includes both open and
closed innovation channels as well as both product and non-product innovations. The model is
presented in Chapter 2.
Additionally, leading open innovation theorists Gassmann, Enkel, and Chesbrough
(2010) have identified an essential need for more exploration in to the area of SMEs’ size -related
competitive challenges and into the open innovation strategies that SMEs ca n leverage to
overcome these challenges. Most research currently focuses primarily on open innovati on in
large companies (Van de Vrande et al., 2008). Other literature reviews have identifi ed this same

SME OPEN INNOVATION: CHAPTER 1 20
research gap and have stress ed the critical need to evaluate SME challenges and strategies in the
open innovation landscape (Bianchi et al., 2010; Lee et al., 2010; Huang & Rice, 2009; Madrid-
Guijarro, Garcia, & Van Auken, 2009). To address this research gap, Chapter 3 presents a
conceptual model of SME size-related competitive challenges ( SME Competitive Challenges
Model ) and a model of open innovation strategies that can enable SMEs to succeed despite the se
obstacles ( SME Open Innovation Strategies Model ). These models are tested with an evidence-
based management research approach that is described in Chapter 4. The results are pre sented
and discussed in Chapter 5.
Research Questions
The primary research questions of this dissertation are:
 What does the overall innovation landscape look like? How do various types of innovations
and innovation channels relate to one another? [ Holistic Model of Innovation : Chapter 2]
 What competitive challenges do SMEs face compared to larger firms? How can SM Es
overcome these size-related challenges in a turbulent, competitive environment?
o How do these different types of SME challenges relate to and exacerbate one
another? [ SME Competitive Challenges Model & Proposition 1 : Chapter 3]
o Which open innovation strategies can SMEs utilize to overcome size-related
competitive challenges? [ SME Open Innovation Strategies Model & Proposition s 2
through 5: Chapter 3]

SME OPEN INNOVATION: CHAPTER 2 21
Chapter 2: Literature Review
This chapter conducts a literature review with the objective of creating a Holistic Model
of Innovation mapping the anatomy of open and closed forms of innovation. The literature
review also informs the creation of two conceptual models presented in Chapter 3. The first i s
the SME Competitive Challenges Model that presents the challenges that SMEs face due to their
small size. The second is the SME Open Innovation Strategies Model that presents the various
open innovation strategies that different types of SMEs utilize or could utilize to overc ome these
competitive challenges.
Structure of the Literature Review
This literature review includes the following topic areas indicated in Figure 3.

Figure 3. Literature Review Topics
Open and Closed Innovation
This literature review begins by evaluating the topics on the left side of the dia gram in
Figure 3. Almirall and Casadesus-Masanell (2010) identify the following four mai n potential
drawbacks of open innovation:

SME OPEN INNOVATION: CHAPTER 2 22
 The first is that open innovation is less effective in instances where partnering firms have
divergent goals. Goals are not always perfectly aligned because firms have competing self-
interests to maximize their own profits.
 Secondly, open innovation can compromis e a firm’s intellectual property rights if they are
not properly protected through legal means.
 The third potential drawback is that it could impose constraints that limit the l ead
developer’s ability to control a project’s technological direction when jointly innovating
with other companies.
 Lastly, the expected synergy gains of the open relationship need to be high enough to
outweigh any additional efficiency losses and costs associated with the addi tional levels of
coordination and collaboration with partners.
However, these last two drawbacks are only applicable to firms that jointly develop a
technology with others, but are irrelevant when one firm purchas es (sells) the intellectual
property from (to) another firm.
Some proponents of the closed innovation model also claim that knowledge must be
developed internally within the firm (rather than acquired) for the firm to gain a competi tive
advantage from it (Bogner & Bansal, 2007). Their reasoning is that a firm needs to devel op and
inventory knowledge in order to create capabilities for being able to develo p additional
knowledge in the future.
Despite the potential drawbacks of open innovation discussed above, other management
theorists provide support for the open innovation model. For example, Chesbrough (2006 )
identifies the following three main benefits that open innovation can have over closed innova tion
in some situations:

SME OPEN INNOVATION: CHAPTER 2 23
 The first advantage is that open innovation can be more effective because at times it can
lead to additional discovery and product diversity by aggregating knowledge and creati ng
synergies between participants leading to discoveries that would not otherwise have
occurred within a single organization.
 A second advantage of open innovation is that it can increase competition between intern al
and external resources. This can drive a firm’s people to increase their productivity and the
quality of their work products.
 The third advantage is that open innovation has the potential of reducing delays in supply
and value chains. For example, open innovation could alert suppliers ahead of time to start
preparing for new requirements and it could also enable a company to communicate
intentions to other firms providing complementary products so that they can proactivel y
make any needed changes on their end in anticipation of a new product launch.
Chesbrough and Garman (2009) describe additional advantages that open innovation can
have for companies, especially during difficult economic times. One strategy t hat some
companies (e.g., Lucent) have successfully used is to spin off non-core development ef forts
during difficult economic times to focus on core businesses, while maintaining an ownership
stake to benefit from the potential success of the spin-off in the future. Companies can al so
generate a steady stream of income from licensing technologies. For example , IBM earns over $1
billion annually from licensing patents to other companies (Viskari et al., 2007). Thi s innovation
strategy is termed outward , inside-out , or outbound open innovation . This strategy involv es
profiting from other organizations that utilize the intellectual capital that the firm developed, as
opposed to outside-in (inward) open innovation , which involves profiting from innovations

SME OPEN INNOVATION: CHAPTER 2 24
generated by others (Lichtenthaler, 2009). These and other types of open innovation are
discussed in more detail later in this chapter.
Another potential benefit of open innovation is that it can help combat groupthink. Janis
(1971) argues that groupthink can stifle innovation because proposing creative breakthrough
ideas can be perceived as too risky by some people in certain group settings. Groupthink ca n also
discourage people from voicing dis senting opinions about the group’s ideas or decisions.
Groupthink behavior has been described as “creative abrasion” since it can repress creative
problem-solving and innovation (Leonard & Straus, 1997). This tradeoff for efficiency and a
comfort of confirmatory response may block a company from viewing better approaches for
meeting customer requirements. However, in an open innovation environment a company is
exposed to ideas from many groups (internal and external to the company) rather than from a
single internal group of people. This enables a company to view the collective wisdom of the
marketplace to identify the various proposed approaches for meeting customer needs. For
example, Procter and Gamble strayed from its traditional toothbrush designs by buying the
SpinBrush product line for $474 million from a small invention-development company. This
innovation quickly became the best-selling toothbrush in the U.S. Procter and Gamble would not
have been able to profit from this novel design if it had relied solely on its internal R&D
resources as it had traditionally done in the past (Nambisan & Sawhney, 2007).
Almirall and Casadesus-Masanell (2010) argue that the relative advantages of open
versus closed innovation present tradeoffs that should be carefully considered. They conducte d a
micro-simulation experiment that concluded that closed innovation can be more advanta geous in
the beginning stages of a project when the technological complexity is high and that at a certain

SME OPEN INNOVATION: CHAPTER 2 25
point in the exploitation process open innovation becomes more advantageous as the
technological uncertainty diminishes.
Is open innovation superior to closed innovation? It depends on the situation. It is
important to not commit an either-or logical fallacy (Toulmin, 2003) by claiming that companies
should be solely engaged in either open innovation or closed innovation. It is possible that a
company could benefit from open innovation for certain innovation projects, while
simultaneously utilizing a closed innovation strategy for others. It is also pos sible that in some
competitive environments companies will almost exclusively rely on closed innova tion,
especially in the development of classified military technologies. Additionall y, firms in any
industry should not always remain entirely open, because even with adequate intel lectual
property protections some of their proprietary knowledge and trade secrets are suscepti ble to
unintentional spill-over to competitors (Kolk & Püümann, 2008). Therefore, companies should
not limit themselves to just closed or open innovation ; both can be useful strategies, depending
on the circumstances. It is, therefore, advantageous to view open innovation as a conti nuum of
openness that can vary in degrees of breadth (scope) and depth (intensity). Chapter 6 discus ses
this concept of varying levels of innovation openness in much more detail.
Holistic Model of Innovation
In their evaluation of the current state of the open innovation literature, the seminal
theorists in the field identified a critical need for a “holistic model of open innovation”
(Gassmann, Enkel, & Chesbrough, 2010, p. 219). Others have indicated that a holistic model is
needed that can illustrate strategic choices that firms can make in the open innovat ion
environment (Hobady, 2005; Schneider et al., 2008). The Holistic Model of Innovation in Figure
4 conceptualizes a high-level version of such a model. Rather than simply being a model of open

SME OPEN INNOVATION: CHAPTER 2 26
innovation, this model is truly holistic in that it also includes closed innovation channels, as well
as both product and non-product innovations. This provides a more realistic representation of
reality compared to a model that examines open innovation in isolation from other forms of
innovation. Most of the concepts and decision alternatives presented in this model are derive d
from the writings of the management theorists presented throughout this liter ature review and are
cited as appropriate.

Figure 4. Holistic Model of Innovation
This Holistic Model of Innovation provides a conceptual illustration of the anatomy of
open innovation and depicts how open innovation relates to closed innovation and to non-
product innovations, such as business model innovations. This model could be used to level-set
the field’s understanding of the innovation ecosystem and it provides a useful foundation for
examining specific strategies that SMEs can utilize in the various segme nts of the overall open

SME OPEN INNOVATION: CHAPTER 2 27
innovation environment to combat their size-related competitive challenges. These c hallenges
and strategies are presented in Chapter 3 in the SME Competitive Challenges Model and SME
Open Innovation Strategies Model . The following sections discuss each portion of the Holistic
Model of Innovation .
Innovation Exploration
[Left Side of the Holistic Model of Innovation]
The Holistic Model of Innovation in Figure 4 bifurcates the innovation ecosystem into
two areas : innovation exploration and innovation exploitation. Innovation exploration is the
knowledge creation and ideation stage that generates tacit (unwritten and unspoken) knowledge
and explicit (codified) knowledge. Innovation exploitation is the transformation of that
knowledge into goal-driven outcomes such as increasing profits and/or public good (Van de
Vrande et al., 2009).
A firm has strategic decisions to make with regard to how it wants to develop and/or
acquire knowledge. In the innovation exploration phase a firm can develop knowledge internally ,
obtain it from external sources, or it can collaborate with others to jointly develop knowledge
(co-sourcing) (Dahlander & Gann, 2010). Technology push and market pull forces direct
innovation generation efforts. Technology push occurs when an R&D group invents a new
technology and the company “pushes” the idea onto consu mers. This is effective for meeting
latent customer demands that customers do not even realize that they have. Market pull forces
occur when customers and/or suppliers provide the company with suggestions for new or
improved products and/or services. Effective innovators simultaneously utilize both m arket push
and market pull forces for generating innovations (Lafley & Charan, 2008).

SME OPEN INNOVATION: CHAPTER 2 28
The following three sub-sections describe the innovation exploration alternatives in the
Holistic Model of Innovation : (1) Internally developed knowledge; (2) Externally developed
knowledge; and (3) Co-sourcing.
Internally Developed Knowledge
Developing knowledge internally i s a closed innovation approach. Companies can rely on
their current employees and the company’s knowledg e base (its codified and institutional
knowledge) to create innovations. This can include only R&D personnel, but is more effective if
it also includes employees from across the company in various areas such as marke ting and
operations (Lafley & Charan, 2008). Many companies benefit from soliciting ideas fr om their
non-R&D employees, and some theorists classify this as an open innovation approach (V an de
Vrande et al., 2009). However, since the knowledge is developed and maintained within the
organizational boundaries of the firm, the Holistic Model of Innovation classifies it as a closed
innovation approach.
Externally Developed Knowledge
Leveraging externally developed knowledge is also referred to as inward open innovation
or inbound open innovation and it primarily involves make-or-buy decisions concerning
knowledge generation (Grönlund et al., 2010). This innovation approach involves obtaining
knowledge and innovations from outside of the firm’s boundaries.
A firm may choose to buy or lease intellectual property from others or it could deci de to
acquire another company and absorb that company’s knowledge (Jing, Dhanaraj, & Shockley,
2008). A company could also intentionally or unintentionally infringe on another organization’s
intellectual property rights or imitate competitors by using similar tec hnologies and techniques.
Firms can also obtain some intellectual property without paying royalties , such as using open

SME OPEN INNOVATION: CHAPTER 2 29
source software or public domain knowledge (Lafley & Charan, 2008). This can also include
patents, copyrights, and other kinds of intellectual property that have expired (Parchomovs ky &
Wagner, 2005). Another approach is to hire new employees or consultants to fill knowledge,
skill, and capability gaps.
Nambisan and Sawhney (2007) evaluated the innovation sourcing (exploration) phase
and the various strategic options that firms have for acquiring external innovations along the
external sourcing continuum from raw ideas, to market-ready ideas, to market-ready prod ucts.
Their analysis concludes that there is no single best strategy for externa lly sourcing innovations
and companies should balance the costs and benefits of utilizing different sourcing strat egies.
However, with the rising popularity of open innovation, many companies have begun
collaborating with external innovators to create inventions to meet specific needs . For example,
InnoCentive, NineSigma, and yet2.com are online communities that match individual i nventors
with companies seeking solutions to specific innovation challenges. Inventors compe te to find
innovative ways of helping the various companies to overcome technical and scientific
challenges and they are rewarded financially for their breakthrough ideas. Ma ny companies also
have contests where customers submit their ideas (Nambisan & Sawhney, 2007).
Open innovation has led to the expansion of the overall innovation market. Linder,
Jarvenpaa, and Davenport (2003) conducted a study that finds that there is a trend of more
companies profiting now from externally-developed innovations than they have in the past, a nd
that firms have moved from ad hoc transaction-based external innovation sourcing strategie s to a
more holistic utilization of open innovation. The se authors identify various innovation channels
that companies utilize to source external innovations. These sourcing strategi es include buying
innovations (via sponsored research, innovation for hire, or strategic procurement), investing i n

SME OPEN INNOVATION: CHAPTER 2 30
innovators (through venture capital or equity plays), co-sourcing (with innovation sectors or
across innovation sectors), community sourcing (via open collaboration forums), and resourcing
(contracting for external resources). The specific attributes, costs, and benefits of each
innovation channel differ as well as the organizational processes and structures that ar e needed to
execute each sourcing strategy.
Co-Sourcing
Co-sourcing is also called coupling or open innovation collaboration (Chesbrough,
2006). However, the terms coupling and collaboration can refer to partnering with other
companies during both the innovation exploration and exploitation phases. Therefore, the te rm
co-sourcing is used in the Holistic Model of Innovation because it refers specifically to
partnering only within the innovation exploration phase. Co-sourcing is an open innovation
exploration approach of working with others to jointly develop innovations. There are numerous
forms of collaboration agreements that firms can make with one another, ranging from i nformal
agreements to legal partnerships (such as joint ventures). Asymmetries of p roduct technologies
and knowledge can lead to a greater desire for firms to join into these types of st rategic alliances
(Grant, 1996).
Companies sometimes even form strategic partnerships with their direct compet itors to
jointly co-develop innovations (Casals, 2010). Some companies also form knowledge-sha ring
agreements with research institutions, non-profits, and universities. Additionall y, firms
sometimes choose to develop innovations in close collaboration with their suppliers or custom ers
(Van de Vrande et al., 2009). Interacting closely and regularly with customers ca n increase the
ability of an SME to discover innovation needs more effectively and more quickly. It can als o

SME OPEN INNOVATION: CHAPTER 2 31
enable an SME to anticipate changes in the market before competitors are able t o do so
(McAdam et al., 2008 ).
An example of a company that has successfully leveraged close customer relati onships in
its innovation efforts is Procter & Gamble. The company implemented its “connect and develop”
initiative to source innovations from close customer interactions. This enabled the compa ny to
increase its R&D productivity by 60% and assisted the company with attaining i ts goal of
obtaining more than half of its innovations from outside of the organization (Lafley & Char an,
2008).
Some companies have even utilized a crowdsourcing strategy of co-developing
knowledge with large groups of people, such as the general public or retired experts in a giv en
field. For example, Threadless is a t-shirt company that does not design its own s hirts. Customers
submit t-shirt designs and other customers vote for the top designs. T-shirts with w inning designs
are then sold to customers on their website (McKay, 2010).
Innovation exploration activities are uncertain and involve risks. An advantage of joint
ventures is that they distribute the risks and rewards among multiple parties. Joint ve ntures also
create what are called real options for firms. Real Options Theory literature defines a real option
as the right to take a certain action in the future, providing strategic and tactical flexibility to the
decision maker (Schneider et al., 2008). These options decrease risks because a f irm could
maintain the option to continue participation in an innovation co-development effort (which has
a high degree of uncertainty at first with uncertainty reducing as the project evol ves) or it could
choose to exit the partnership entirely. By remaining a partner in a joint venture a firm c ould also
maintain an option to continue jointly exploiting innovations or it could potentially acqui re a
partnering firm or its equity stake if the innovation becomes a profitable commerci alization

SME OPEN INNOVATION: CHAPTER 2 32
opportunity at some point in the future. However, even failed projects can generate stra tegic
options and knowledge that can lead to the development of other future innovations (e.g., 3M’s
Post- It-Notes resulted from an earlier project failure that had attempted to create a new type of
glue) (Johnson, 2007).
The value of an option is directly related to the value of the project plus the degree o f
strategic flexibility that the option provides (Jing, Dhanaraj, & Shockley, 2008). Therefor e, real
options provide strategic and operational flexibility to members of joint ventures. This t ype of
partnering approach also lets a firm invest relatively smaller amounts of res ources in a number of
innovation exploration projects rather than in a single project, which reduces the innovation
portfolio’s risk profile. Joint ventures can also enable firms to invest less in projects upfront and
allows for the possibility of early exit if projects appear less attrac tive in the future
(Vanhaverbeke, Van de Vrande, & Chesbrough, 2008).
A firm may also choose to form a joint venture rather than acquire a company if it wants
to reduce its exposure to the risks of innovation project failure (Johnson, 2007). Therefore, this
can be a useful strategy for SMEs to reduce their non-systematic risk exposure. If t wo firms
partner in a joint venture and they both agree on the value of the investment, the partnership is
likely to continue. However, if a divergence of opinions of an investment’s value develops, the
joint venture becomes more likely to dissolve through either acquisition or divestment (Jing,
Dhanaraj, & Shockley, 2008).
Lee et al. (2012) finds that large firms tend to focus their open innovation efforts more on
the innovation exploration phase of innovation, whereas SMEs tend to utilize open innovation
strategies more in the innovation exploitation phase. This is because SMEs typic ally lack
commercialization capabilities since they often do not have manufacturing faci lities or robust

SME OPEN INNOVATION: CHAPTER 2 33
marketing or distribution channels. SMEs also tend to rely more on internally-develope d and
publically available information than larger companies in the innovation exploration pha se.
There is also typically less need for SMEs to partner with others in exploration project s for
products that are less technologically complex because these types of products requi re fewer
R&D capabilities (Gnyawali & Park, 2009).
Innovation Exploitation
[Right Side of the Holistic Model of Innovation]
The previous sub-sections described the characteristics of the innovation exploration
(value creation) phase of the Holistic Model of Innovation in Figure 4. This section explains the
innovation exploitation (value capture) phase of the model (the right side of the model).
Innovation exploitation is the transformation of knowledge from the innovation
exploration phase into new or improved products, services, processes, and business models (Va n
de Vrande et al., 2009). For simplicity purposes, the Holistic Model of Innovation focuses on
product innovations, and creates a category for other non-product innovations. However, a
services firm would likely want to modify the model to place emphasis on servic e innovations by
using service innovations and non-service innovations as the two main categories instea d of the
current categories of product and non-product innovations.
Non-Product Innovations
As indicated earlier, there are other types of innovations besides product innovations.
Dervitsiotis (2010) proposes an innovation model which he calls full-spectrum innovation . His
model argues that a company should seek innovations of both products and services and th at it
should also periodically make innovative improvements to its business model and make
enhancements to its culture and decision making processes. The full-spectrum innovation model

SME OPEN INNOVATION: CHAPTER 2 34
consists of utilizing horizontal and vertical innovations. Horizontal innovations are innovations
along the value chain that result in improvements such as new products and services, f aster
customer response times, productivity enhancements, and the utilization of new m aterials or
techniques that generate greater yields. In contrast, vertical innovations include improvements
such as enhancing an organization’s management practices, improv ing communications and
decision making processes, and developing a more learning-adaptive organi zation. These
horizontal and vertical innovations are complementary and collectively enable a firm to enhance
its competitive standing. It is important to note that while they are similar term s, horizontal and
vertical innovations are different from the concepts of horizontal and vertical open innovat ion
(innovation collaboration strategies) that are discussed throughout this dissertation.
Business models can also enable innovations because they generally guide the st rategic
direction of new product development. However, technological or product breakthroughs can
sometimes lead to new strategic options for altering a firm’s business model. For example, a firm
may invent a radical technology that could lead the company to alter its business model to focus
on developing an entirely new industry or industry segment. Therefore, business models sh ould
be seen as dynamic rather than static (Grönlund et al., 2010; Teece, 2007). That is w hy the
Holistic Model of Innovation in Figure 4 shows arrows connecting these types of non-product
innovations with product innovations since changes in one can impact the other.
After Chesbrough originally introduced the concept of open innovation, he later proposed
that new strategies and business models are needed to utilize open innovation. Chesbrough and
Appleyard (2007) argue that a paradigm shift has occurred and that open innovation has changed
the way the field thinks about traditional business strategy, and that a new model of s trategy
(open strategy) and new business models (open business models) are now needed to support the

SME OPEN INNOVATION: CHAPTER 2 35
implementation of open innovation. They provide examples of how Linux, IBM, Google, Merck,
and many other companies have evolved their business models and corporate strategies t o
successfully profit from open innovation efforts. Chesbrough and Appleyard conclude that new
strategies and b usiness models are needed to effectively balance and coordinate the firm’s value
creation (exploration) efforts with its value capture (exploitation) efforts.
Hamel (2007 ) claims that an inferior new product commercialized with a superior
business model will likely be more valuable to a company than a breakthrough product
innovation that is commercialized using a weak business model. It has also been obs erved that
product innovation is generally ineffective at generating substantial returns w hen a company
lacks an effective business model, strong leadership, and proper governance (Teece , 2007).
Business model and management innovations are just as important, if not more so, than
product and service innovations (Kolk & Püümann, 2008). Empirical findings show that larger
firms invest more in management and process innovations and SMEs invest more on product
R&D (in absolute terms and as a percentage of firm expenditures). Large firms al so tend to
derive higher profits from their process innovations and SME s profit more from their product
innovations. However, product innovations are easier to imitate and are copied more frequent ly
by competitors than process innovations (Plehn-Dujowich, 2009).
Johnson, Christensen, and Kagermann (2008) argue that business model innovations can
be much more powerful for creating a competitive advantage than innovating products and
services. They provide the example of Apple, which did not introduce the first mp3 player, but
became the market leader through its innovative business model of making downloading and
storing music very simple and easily accessible for customers. Furthermore, thei r research finds
that 11 of the 27 new companies (less than 25 years old) that grew large enough to become part

SME OPEN INNOVATION: CHAPTER 2 36
of the Fortune 500 within the last 10 years were able to do so primarily through business model
innovations. However, they warn that on average it typically took these companies about four
attempts at innovating their business models until they eventually found success. T hey also warn
that unless the business model change is truly innovative enough to alter the dyna mics of an
industry, that the change will likely be a waste of resources and will ulti mately be unprofitable.
Kim and Mauborgne (2005) refer to breakthrough business model innovations as blue
ocean strategies . They argue that many highly competitive industries operate in a red ocean tha t
suffers from declining profit margins and intense rivalry for market share (the oce an is red
because it is saturated with blood from excessive competition). They argue that tra ditional
Porterian business strategy should be utilized in red oceans. However, they propose that with
business model innovations, or value innovations as they call them, a firm can create a blue
ocean market that did not exist before. One example that they provide is Cirque du Soleil, w hich
was able to create a profitable blue ocean market with its sophisticated circus that focuses on
artful theatrical presentations of acrobatics and other entertainment that appeals t o an older,
higher-income customer segment, enabling the company to charge much highe r prices than
traditional circuses. Cirque de Soleil has grown into a profitable franchise, while at the same
time the traditional circus industry has experienced a steady decline.
It can be difficult for technologists to recognize when business model changes are needed
to accompany breakthrough product innovations (Schoen, Mason, Kline, & Bunch, 2005).
Therefore, it is vital for management to proactively evaluate the firm’s innovation portfolio to
see if business model innovations or other process or management innovations are needed rather
than just delegating this responsibility to R&D managers.

SME OPEN INNOVATION: CHAPTER 2 37
Innovation Exploitation Decisions
As in the innovation exploration (value creation) phase of the Holistic Model of
Innovation , firms have strategic decisions to make in the innovation exploitation (value capture)
phase. Firms must decide how to exploit (extract value from) innovation opportunities. Real
options theory has mostly focused on innovation exploration activities and has neglec ted focus
on exploitation activities (Johnson, 2007). However, Schneider et al. (2008) argues that
companies should assess their strategic options at all stages of innovation, not just exploration.
Motohashi (2008) contends that firms have the strategic option to either sell/licens e their
innovations to other organizations or to commercialize the innovations themselves. This is why
the Holistic Model of Innovation emphasizes the need to make innovation exploitation decisions.
A firm has value-capture options such as commercializing an innovation on its own, goi ng to
market with partners, or transferring its intellectual property to others.
The traditional closed innovation approaches of exploitation include companies
commercializing innovations alone or intentionally leaving the intellectual pr operty dormant,
which is generally done as a defensive strategy (Parchomovsky & Wagner, 2005). A n open
innovation exploitation approach is to commercialize an innovation with other companies b y
some form of collaboration (Chiaroni, Chiesa, & Frattini, 2011). During the innovation
exploitation phase, SMEs tend to partner with larger companies in a supplier-custome r type of
relationship or form strategic alliances with other SMEs (Lee et al., 2010).
Transferring innovations to others is considered outward open innovation or outbound
open innovation (Chesbrough, 2006). Outward open innovation involves selling or leasing
intellectual property or providing it to others without financial remuneration. Lice nsing out
intellectual property involves making a keep-or-sell decision. A firm must eval uate its strategic

SME OPEN INNOVATION: CHAPTER 2 38
options to determine if selling/licensing will provide the most benefits compared to the other
alternatives such as commercializing the innovation on its own or with partners ( Lichtenthaler,
2009).
SMEs often lack commercialization capabilities and are typically much more li kely than
large companies to license their innovations to others (Motohashi, 2008). SMEs can benefit from
out-licensing their intellectual property because this can generate financia l returns while
avoiding high costs and risks with building or acquiring downstream commercialization
capabilities. Out-licensing can also generate funds to enable increased investments in other
innovation projects (Bianchi et al., 2010). This could be a useful strategy for SMEs with fewe r
resources.
The Holistic Model of Innovation also indicates that companies can spin off an
innovation project into a separate organization (Grönlund et al., 2010). For example, Xerox spun
off 11 research projects into separate companies. The value of these organizations n ow exceeds
the value of Xerox by a factor of two (Van de Vrande et al., 2009).
A company can also sell itself along with all of its intellectual property, wh ich can be an
attractive option for SMEs seeking immediate financial returns. Alternati vely, firms can trade
and barter with their intellectual property. This can enable a company to gain ac cess to another
firm’s patents in return for granting access to its patents. This is known as cross-licensing
(Parchomovsky & Wagner, 2005). Altruistic motives can also lead firms to make thei r
intellectual property publicly available if they choose to do so (e.g., some open source software
or research findings).
The Holistic Model of Innovation indicates that a firm with unique and strong expertise in
a particular area can also derive profits from providing consulting services to other firms

SME OPEN INNOVATION: CHAPTER 2 39
(Chesbrough, 2006). This can include providing consulting services in support of a product
offering (such as Oracle providing consulting services at an additional cost to its software
customers).
Collaborations in the Open Innovation Ecosystem
The previous sections describe the elements of the Holistic Model of Innovation . This
section evaluates the types of collaborations that occur in the open innovation ecosyste m. These
collaboration options impact a firm’s strategic exploration and exploitation decisi ons (the
decisions presented in the Holistic Model of Innovation ).
There are six main types of open innovation strategies that SMEs can implement:
 Inward open innovation (transferring others’ innovations into the firm);
 Outward open innovation (transferring the firm’s innovations out to others);
 Upstream vertical collaboration (collaborating with upstream suppliers);
 Downstream vertical collaboration (collaborating with downstream partners and
customers );
 Horizontal collaboration (collaborating with competitors); and
 Knowledge-intensive collaboration (collaborating with universities, non-profits,
consultants, and research institutions) (Poot et al., 2009).
SMEs tend to specialize more than larger companies and have been shown to utilize
collaborative networks and alliances more frequently for innovation than large comp anies (Lee et
al., 2010). These collaborations can enable SMEs to remain specialized rather than ha ving to
develop or acquire all of the necessary capabilities to invent, create, and comme rcialize
innovations on their own.

SME OPEN INNOVATION: CHAPTER 2 40
Short term, temporary collaborative interactions consist of buying and selling inte llectual
property (inward and outward open innovation). Longer term collaboration consists of various
types of partnerships such as joint ventures. Inward and outward open innovation activities are
more transactional in nature, whereas open innovation collaboration usually involves short -term
and/or long-term ongoing relationships (Grant & Baden-Fuller, 2004). Successful c ollaborations
require mutual trust, effective governance, and the ability to effectively and eff iciently transfer
and learn knowledge. These relationships also need to provide adequate resources and
capabilities for the firms to accomplish their joint objectives (Lee et al., 2010 ). Governance is the
means to manage a relationship with regard to defining roles and responsibilities , establishing a
command structure, implementing control mechanisms, establishing communication channe ls,
specifying profit and cost sharing agreements, and creating the needed rules a nd contracts to
govern behaviors (Bolisani & Scarso, 2003).
SMEs are often attracted to collaboration since they tend to lack the resources a nd
capabilities needed to manage the entire end- to-end exploration- to-exploitation innovation
process by themselves ( Rahman & Ramos, 2010). Studies show that there is a high correlation
between firms needing additional access to resources and/or market expertise and those firms
actually entering into external innovation partnerships (Madris-Guijarro et al., 2009). Partnering
increases an SME’s access to resourc es and capabilities (Van de Vrande et al., 2009), can
increase economies of scale and scope (Habaradas, 2009), can significantly increa se innovation
outputs (Huang & Rice, 2009), can enable SMEs to share risks and rewards (Terziovski, 2010;
Huang & Rice, 2009), and can assist with overcoming market entry barriers (Clarke & Turner,
2003).

SME OPEN INNOVATION: CHAPTER 2 41
A study of Australian SMEs found a strong positive correlation between high networking
practices and high firm performance. Close collaboration with customers and suppli ers was
found to be correlated with high performance (Terziovski, 2003). Several researchers have
determined that the ability to effectively utilize external networks is a critical success factor that
distinguishes successful from unsuccessful SME innovation efforts, and that networking is a key
determinant for why some innovative SMEs have been able to be more successful than their
larger competitors in some instances (Lee et al., 2010).
Partnering can increase the diversity of a firm’s capabilities and domain knowledge of
various industries, which can be essential for determining alternate uses of a te chnology in other
industries (Bianchi et al., 2010). Additionally, collaboration amongst partners faci litates inter-
organizational learning, which enables each firm to develop new capabilities (T eece & Pisano,
2004).
Partnering with other organizations can also significantly decrease the tim e-to-market for
innovations (Lee et al., 2010). In some situations (mostly with non-research oriented fi rms),
SMEs may even value speed to market more than their ability to legally prot ect their intellectual
property with patents (Leiponen & Byma, 2009).
Collaboration can also involve the use of intermediaries. Chesbrough (2003) describes
various types of intermediaries in the open innovation environment along three segments of the
innovation value chain: innovation funding, innovation generation, and innovation
commercialization. The first segment participates in open innovation during the innovati on
funding portion of the value chain. These include companies that act as innovation investors
(companies that invest in external innovation efforts undertaken by other organizations ). Moving
up the value chain are companies that are involved in generating innovations. These com panies

SME OPEN INNOVATION: CHAPTER 2 42
include innovation merchants (companies that innovate for the sole purpose of leasing or sell ing
their innovations) and innovation architects (companies that act as project managers t o manage
the overall large-scale innovation effort of many companies that are working jointl y on the same
project). At the end of the value chain are companies that commercialize innovati ons. These are
innovation marketers (companies that profit from marketing their own and others’ i nnovations)
and innovation one-stop centers (companies that deliver products and services to customers
based on the best innovations, whether they were discovered internally or externally). Na mbisan
and Sawhney (2007) identify several other types of intermediaries, such as invention ca pitalists,
idea scouts, electronic R&D marketplaces, patent brokers, licensing agents, vent ure capitalists,
and others.
Collaborative inter-organizational joint innovation development and commercializat ion
efforts rely on effective network structures to be successful (Gelatt, 2005). Harry son (2008)
claims that firms involved in open innovation rely heavily on their network system of partners
and that this network often changes over time as a project matures from exploration to
exploitation. He argues that the most effective network for innovation exploration (value
creation) efforts is usually a relatively open system with multiple weak, orga nic relationships
between members. As an innovation moves into the exploitation (value capture) phase, it
becomes increasingly preferable for the network to become more closed with fewer pa rtners, so
these participants develop closer ties with one another. This generally increa ses production
efficiencies and speeds up the commercialization process. A change in leaders hip can be very
useful in assisting a network with transiting from the more loosely-connected open netw ork to
the closer-knit closed network (Harryson, 2008).

SME OPEN INNOVATION: CHAPTER 2 43
The complexity of the innovation being developed is an important factor to consider in
the network structure design. For example, knowledge sharing relationships typically need to be
stronger the more complex the innovation (Harryson, 2008). Another aspect of network structure
is the flexibility of how easily companies can enter into and exit relationships. A lmirall and
Casadesus-Masanell (2010) conducted a simulation study that found that open networks were
more effective when firms form flexible rather than fixed relationships with one another because
flexible relationships create competition for the optimal pairing of innovation-developm ent
partnerships that have the highest degrees of synergy.
Summary of SME Open Innovation Strategies
Figure 5 provides a high-level summary of the SME open innovation strategies disc ussed
in Chapter 2 (i.e., inward, outward, and collaboration open innovation activities). These comprise
the high-level categories of strategic open innovation alternatives a vailable to SMEs.

SME OPEN INNOVATION: CHAPTER 2 44

Figure 5. Summary of SME Open Innovation Strategic Alternatives
On the left side of the figure, outward open innovation involves transferring innovations
to others. This can occur as one or multiple separate transactions (Schneider et al., 2008) . The
SME transfers a specific innovation to another organization and then the transaction is complete.
These organizations do not necessarily need to maintain an ongoing relationship. A s described
earlier, SMEs can benefit from out-licensing their technology and other intelle ctual capital
because this can generate financial returns, while avoiding high costs and risks as sociated with
developing or acquiring downstream commercialization capabilities. Out-licensi ng innovations
can also generate funds to enable increased investments in other innovation projects (Bi anchi et
al., 2010). Some SMEs may also have altruistic motives to make their innovations publicl y
available if they choose to do so (e.g., open source software).

SME OPEN INNOVATION: CHAPTER 2 45
The middle of the diagram depicts open innovation collaboration , which involves the co-
creation and/or the co-utilization of shared knowledge and innovations. As explained earl ier, this
networking can include vertical collaborations (with upstream suppliers and downstream
partners and customers), horizontal collaborations (with competitors), and knowledge-intensive
collaborations (with universities, non-profits, consultants, and research institutions) (Poot et al.,
2009). Rather than being transactional like buying/selling intellectual prope rty, this type of
innovation transfer often occurs in a more ongoing, continuous manner (Grant & Baden-Fuller,
2004). Therefore, this requires the organizations to actively manage their relati onships with one
another more so than if they were conducting a single innovation transfer transaction. As
explained in the previous section, this involves establishing mutual trust, effective governance,
and an appropriate network structure.
On the right side of the diagram, inward open innovation involves an SME obtaining
innovations from others. This is also transactional because it can occur as one or multi ple
separate transactions (Schneider et al., 2008). This type of innovation transfer primaril y involves
make-or-buy decisions concerning innovation generation (Grönlund et al., 2010). The advant age
to an acquiring organization (innovation transferee) is that this provides access to innova tions
that others have created and potentially patented first. It enables the a cquiring organization to
have access to technology/innovations/knowledge that is more advanced than the organizati on
could have developed internally.
All of these open innovation strategies can involve financial compensation (pecuniary
transfers) or occur without financial compensation (non-pecuniary transfers) (Dahlander &
Gann, 2010). The financial (or non-financial) nature of these exchanges depends on the
organizations’ motives and business models. Additionally, the innovations created and

SME OPEN INNOVATION: CHAPTER 2 46
transferred through these open innovation efforts can potentially be leveraged to create de rivative
innovations that are follow-on innovations derived from the new discoveries (Keupp &
Gassmann, 2009 ).
As discussed later in Chapter 3, research Propositions 2 through 5 of this dissertation
relate to the effectiveness (or ineffectiveness) of these open innovation strateg ies to enable SMEs
to overcome their size-related competitive challenges and increase their i nnovation performance
(defined as innovation outputs and financial gains resulting from those innovation s). However,
before discussing open innovation strategies further, the remaining sections of Chapt er 2
evaluate the size-related competitive challenges facing SMEs.
SME Competitive Challenges
A literature review of SME competitive challenges identified a number of obstac les that
SMEs face compared to larger firms. Figure 6 categorizes and summarizes the main types of
challenges discussed in the literature.

SME OPEN INNOVATION: CHAPTER 2 47
SME Challenges Sources
SMEs Often Lack Resources
Less access and availability of money, people,
relationships, legal resources, information, and
other resources Lee et al., 2010; Bianchi et al., 2010; McAdam et al., 2 008;
Terziovski 2003 & 2010; Parchomovsky & Wagner, 2005;
Clarke & Turner, 2003; Van de Vrande et al., 2009;
Chesbrough, 2010; Madrid-Guijarro, 2009; Habaradas, 20 09;
Motohashi, 2008; Keupp & Gassmann, 2009; Gnyawali &
Park, 2009; Poot et al., 2009; Rahman & Ramos, 2010;
Plehn-Dujowich, 2009
Lower economies of scale and scope and higher
variable costs Habaradas, 2009; Gnyawali & Park, 2009; Plehn-Dujowic h,
2009
Fewer dedicated resources for environmental
scanning, R&D, business development, and
other business functions Van de Vrande et al., 2009; Enkel et al., 2009; Chesb rough,
2010; Madrid-Guijarro, 2009
Smaller customer base and less robust supply
chain Lee et al., 2010; Keupp & Gassmann, 20 09
Less established reputation and brands and lack
of ability to find partners Chesbrough, 2010; Madrid-Guijarro, 2009
SMEs Often Lack Capabilities
Lack of commercialization capabilities such as:
lack of manufacturing facilities, marketing
channels, supply chains, and lack of ability to
produce and distribute products to customers Lee et al., 2010; Keupp & Gassmann, 2009; Rahman &
Ramos, 2010, Chesbrough, 2010; Bianchi et al., 2010; Enk el,
Gassmann & Chesbrough, 2009
Lack of absorptive capabilities Chesbrough, 2010; Huang & Rice, 2009; Poot et al., 2009
Difficulty dealing with growth surges Madrid-Guijarro, 2009; Habaradas, 2009
Difficulty with recruiting/retaining talent and
other resources Lee et al., 2010; Van de Vrande et al., 2009; Madrid-
Guijarro, 2009; Habaradas, 2009
Non-standardized, less efficient processes McAdam et al., 20 08; Terziovski, 2010; Van de Vrande et
al., 2009; Chesbrough, 2010
Capabilities tend to be more specialized so
SMEs typically have capability gaps Lee et al., 2010; Bianchi et al., 2010; Motohashi, 2008 ;
Gnyawali & Park, 2009
SMEs Often Experience Higher Vulnerability to Risks
More difficult to survive and prosper when
systematic risks materialize (economic
downturns, etc.) Igartua et al., 2010; Habaradas, 2009; Keupp & Gassman n,
2009; Gnyawali & Park, 2009; Rahman & Ramos, 2010
Higher non-systematic risks: specialized, non-
diversified innovation portfolio Lee et al., 2010; Bianchi et al., 2010; Parchomovsky &
Wagner, 2005; Van de Vrande et al., 2009; Madrid-Guij arro,
2009; Plehn-Dujowich, 2009
Threats of imitation and theft (accompanied with
risk of high legal fees) Lee et al., 2010; Bianchi et al., 2010; Parchomovsky &
Wagner, 2005; Enkel et al., 2009; Chesbrough, 2010;
Motohashi, 2008; Leiponen & Byma, 2009
Risk of innovation project / product failure Lee et al., 2010; Madrid-Guijarro, 2009; Keupp &
Gassmann, 2009; Plehn-Dujowich, 2009
High impacts of employee turnover Lee et al., 2010; Madrid-Guijarro, 2009; Habaradas, 20 09
Higher impacts of cash flow risks such as late
customer payments Lee et al., 2010; Rahman & Ramos, 2010
Figure 6. Summary of SME Challenges Found in the Literature
The three main categories of SME challenges identified in this literature re view include
an overall lack of resources, lack of dynamic capabilities, and high risk exposure. F igure 7
illustrates the relationship between the se three main categories of SME challenges and the

SME OPEN INNOVATION: CHAPTER 2 48
theoretical underpinnings that need to be evaluated to better understand these challenges . The
resource-related challenges (inputs) can be better understood by evaluating the resourc e-based
view theory. The firm capability-related challenges (transforming these inputs into outputs) can
be better conceptualized by examining the dynamic capabilities theor y. The risk-related
challenges (related to the uncertainty of innovation project outcomes) can be more effectively
comprehended by assessing portfolio theory. Figure 7 illustrates how the se three types of SME
challenges impact the ultimate performance goals of SMEs, whether they are financial and/or
philanthropic in nature.

Figure 7. Main Groupings of SME Challenges
The remaining sections of this chapter discuss these theoretical underpinnings: resource-
based view theory, dynamic capabilities theory, and portfolio theory.
Resource-Based View Theory
The resource-based view is one of the dominant theories in strategic management
literature (Gruber, Heinemann, Brettel, & Hungeling, 2010; Barney, 1991). The theory argues
that certain resources with the right characteristics can enable a firm to obtain a competitive
advantage. A firm can sustain a competitive advantage based on its unique set of resourc es
relative to the resources of its competitors (Clarke & Turner, 2003; Black & Boal , 1994). A

SME OPEN INNOVATION: CHAPTER 2 49
firm’s resources consist of its physical capital resources (e.g., money, land, and equipment),
human capital resources (e.g., labor and knowledge), and organizational capital resources (e.g.,
firm capabilities, culture, routines, policies, firm reputation, and other facto rs) (Barney, 1991).
The field of knowledge management has proposed a knowledge-based view of the firm.
This theory views knowledge generation, transfer, and utilization rather than other non-
knowledge resources as the primary drivers of competitive advantage (Grant, 1996; Reus, Ra nft,
Lamont, & Adams, 2009; Bogner & Bansal, 2007; Clarke & Turner, 2003). Some knowledge-
based view theorists such as Bogner and Bansal (2007) believe that knowledge must actua lly be
created and not acquired for a firm to develop a competitive advantage. However, there is no
convincing evidence to support this claim. In fact, there are examples of companies such as
Google that have developed a competitive advantage in several areas primarily throug h
knowledge acquisitions (Chesbrough & Appleyard, 2007). Due to this limitation of the
knowledge-based view and the fact that the resource-based view of the firm includes knowl edge
as a type of intangible resource, the resource-based view of the firm is a more useful theory with
which to evaluate SME open innovation challenges.
The SME open innovation literature specifically references the resource-based view since
the main challenge that SMEs face compared to their larger competitors is an overa ll lack of
resources and capabilities (Madrid-Guijarro, Garcia & Van Auken, 2009; Gnyawali & P ark,
2009; Grant & Baden-Fuller, 2004). The prevailing consensus in the literature is that SMEs are
at a strategic disadvantage compared to larger firms because of their overall lack of r esources
(Lee et al., 2010; Bianchi et al., 2010; McAdam et al., 2008; Terziovski, 2003 & 2010;
Parchomovsky & Wagner, 2005; Clarke & Turner, 2003; Van de Vrande et al., 2009;
Chesbrough, 2010; Motohashi, 2008; Keupp & Gassmann, 2009; Poot et al., 2009; Rahman &

SME OPEN INNOVATION: CHAPTER 2 50
Ramos, 2010). Grant and Baden-Fuller (2004) also cite the resource-based view as a theory to
explain why there has been an increasing trend of businesses around the world increas ing their
collaboration and other partnering activities (so they can increase access to additi onal resources).
Since SMEs typically have fewer resources, they generally have lower econom ies of
scale and scope (Habaradas, 2009; Gnyawali & Park, 2009; Plehn-Dujowich, 2009) and fewer
dedicated resources for environmental scanning, R&D, business development, and other
functions (Van de Vrande et al., 2009; Enkel et al., 2009). SMEs generally also have smalle r
customer bases and less robust supply chains (Lee et al., 2010; Keupp & Gassmann, 2009) and
have less-established company reputations (Chesbrough, 2010; Madrid-Guijarro, 2009).
To enable a firm to sustain a competitive advantage, its resources need to be: valuable (t o
enable the firm to exploit opportunities and mitigate threats and increase organiza tional
efficiency and/or effectiveness), rare (not possessed by its competitors), difficult and costly for
competitors to imitate, and imperfectly substitutable (so that no other resource c ould achieve the
same strategic objectives) (Terziovski, 2010; Barney, 1991). The true value of a f irm’s resources
is in the actual combination and configuration of its resources and capabilities and the stra tegic
fit of these with the firm’s external environment (Gruber et al., 2010; Black & Boal, 1994). It is
more challenging and costly for competitors to replicate a firm’s specific combination of uniqu e
resources and capabilities than it is to replicate individual resources and capabi lities.
Non-rare resources such as money can also impact a firm’s performance even if it does
not create a competitive advantage. For example, McAdam et al. (2008) conducte d an empirical
study of SMEs in the United Kingdom and found that grant aid is positively correlated with S ME
innovation performance and overall firm performance.

SME OPEN INNOVATION: CHAPTER 2 51
Dynamic Capabilities Theory
The second theoretical underpinning in the model in Figure 7 is the dynamic capabilities
theory. The dynamic capabilities theory is a strategic management framework that builds on the
resource-based view, which is more static in nature. The dynamic capabilities mode l argues that
although a firm can maintain a competitive advantage in the short run with sufficient resou rces
and core competencies, it will not sustain a competitive advantage in the long run unle ss it is able
to dynamically evolve its capabilities over ti me in response to changes in the firm’s internal and
external environments (Kolk & Püümann, 2008; Teece, 2007). To sustain a competitive
advantage based on the firm’s dynamic capabilities, those capabilities must be unique and
difficult to replicate or imitate (Teece & Pisano, 2004; Grant, 1996 ).
In the resource-based view, a firm’s core capabilities are its skills, expertise, and
knowledge that a firm relies on to be successful. Dynamic capabilities are a further extension of
the core capabilities concept. The term dynamic is used to emphasize the importance that a
firm’s capabilities should not be static, but should continually evolve and adapt to change s in the
environment (Grönlund et al., 2010; Kolk & Püümann, 2008). Companies that do not make
changes to their business model and their capabilities in a fluid, constantly c hanging environment
are apt to suffer from core rigidities that make them less resilient (Grönlund e t al., 2010; Teece,
2007). Capabilities should be dynamic because radical new product innovations and the
constantly changing external environment may require the firm to develop or ac quire new
capabilities over time.
Teece (2007) defined dynamic capabilities as the ability of an organization: “(1) to sense
and shape opportunities and threats, (2) to seize opportunities, and (3) to maintain
competitiveness through enhancing, combining, protecting, and when necessary, reconfi guring

SME OPEN INNOVATION: CHAPTER 2 52
the business enterprise’s intangible and tangible assets.” The superior ability to conduct these
activities relative to competitors can create a competitive advantage (p. 1319).
While the resource-based view is concerned with both tangible and intangible assets , the
dynamic capabilities theory focuses on intangible assets and the abilit y of the firm to create,
deploy, and protect them from competitors (Teece, 2007). Therefore, the resource-bas ed view
and the dynamic capabilities theory are both needed to evaluate SME open innovation chal lenges
and strategies.
A key distinction exists between dynamic capabilities and other intang ible assets such as
patents. Patents are a codified form of explicit knowledge that can be easily bought a nd sold.
However, capabilities are a type of tacit knowledge and they are not easily transfera ble.
Capabilities are transferred between organizations by either learning from people in other
organizations or by moving people from one organization into another (by hiring them,
contracting with them, or by buying a company) (Teece & Pisano, 2004; Grant, 1996; G rant &
Baden-Fuller, 2004 ).
The dynamic capabilities model argues that the possession of unique resources alone will
not generate a competitive advantage, but that strong dynamic capabilities are also needed to
effectively transform those resources into profitable outcomes. Without dynamic cap abilities, an
SME will not be able to successfully leverage or expand on innovations from its internal a nd
external environments (Kolk & Püümann, 2008; Teece & Pisano, 2004 ). Successful innovators
also integrate their unique capabilities. For example, the degree of collabor ation between
marketing and R&D functions of a company has been directly linked to the success of innovat ion
projects (Igartua, Garrigós, & Hervas-Oliver, 2010).

SME OPEN INNOVATION: CHAPTER 2 53
Empirical evidence has demonstrated a strong link between the resources and capa bilities
a firm has and the firm’s performance. A firm is also more likely to succeed with both abundant
resources and strong capabilities, but will not necessarily perform well wit h either one on its own
(Gruber et al., 2010). Access to resources does not benefit SMEs if they lack the capabil ities to
effectively utilize those resources. For example, evidence from SME innovators i n developing
countries shows that access to financial resources via international and government grants does
not translate into firm success in many of these firms because they lack critic al capabilities to
effectively utilize those additional resources (Habaradas, 2009).
SMEs are typically very specialized and, therefore, generally lack the het erogeneity of
capabilities that larger firms have (Bianchi et al., 2010; Motohashi, 2008; Gnyawal i & Park,
2009). An empirical analysis of open innovation challenges among Korean SMEs found that the
largest capability-related challenge that SMEs have is an overall lac k of commercialization
capabilities (Lee et al., 2010). This is consistent with the conclusions of a study of S MEs
engaged in open innovation in the Netherlands (Van de Vrande et al., 2009). SMEs also typicall y
lack the capabilities of dealing with sudden growth surges (Madrid-Guijarro, 2009; Habar adas,
2009) and have difficulty in general with recruiting and retaining employees (L ee et al., 2010 ;
Van de Madrid-Guijarro, 2009; Habaradas, 2009). While SMEs are often more nimble and
flexible, they generally have less efficient processes because they are t ypically not standardized
or formalized (McAdam et al., 2008; Terziovski, 2010; Van de Vrande et al., 2009; Chesbrough,
2010 ).
The literature identifies dynamic capabilities as a key driver of succ ess for SMEs that
engage in open innovation, because these firms need to continually evolve their collaborati on
and partnering capabilities as well as their absorptive capabilities (Kolk & P üümann, 2008).

SME OPEN INNOVATION: CHAPTER 2 54
Absorptive capabilities are an essential type of dynamic capability that is linked to a firm’s
success with utilizing open innovation (Igartua et al., 2010; Huang & Rice, 2009). Absorptive
capabilities are a firm’s ability to successfully assimilate, learn , and utilize knowledge from
outside of its organizational boundaries (Reus et al., 2004). This requires an open innovation
culture and enough internal expertise to fully understand external innovations. Some theor ists
argue that over-reliance on buying innovations from others can limit the long-term absorpt ive
capabilities of a firm if the firm gradually losses internal innovation capabil ities over time by
focusing progressively less attention on internal R&D efforts. Therefore, a firm s hould
continually invest in strengthening its absorptive capacities if it chooses to prim arily acquire
externally-developed innovations (Huang & Rice, 2009).
SMEs tend to have much lower absorptive capacity than larger firms, which explains wh y
SMEs primarily focus on transferring innovations to others rather than on acquiring them from
the external environment (Chesbrough, 2010). Huang and Rice (2009) find that SMEs with
higher absorptive capacities are more effective at implementing and benefiting f rom open
innovation networking strategies. The networking variable alone did not show signific ant impact
on SME performance. However a strong correlation was established once the networkin g
variable was evaluated in conjunction with the absorptive capacity variable.
SMEs often enter into collaborative partnerships such as strategic alliances because of an
overall lack of resources and capabilities (Clarke & Turner, 2003). However, a si gnificant lack of
resources and capabilities can impair networking abilities of SMEs (Huang & Rice , 2009).
Additionally, collaboration with partners becomes exponentially more difficult and cos tly the
larger the network becomes (Keupp & Gassmann, 2009; Lee et al., 2010). SMEs also ex perience
more difficulty with forming partnerships because they are less attractive as pot ential partners

SME OPEN INNOVATION: CHAPTER 2 55
than larger firms due to their overall lack of resources and capabilities, as well as their less
established company reputations. However, firms with unique expertise in a spe cialized area and
firms that have valuable resources such as patents are more likely to overcome the se barriers
because they become more attractive as potential partners (Chesbrough, 2010).
Portfolio Theory
The third theoretical underpinning in the model in Figure 7 is portfolio theory . There is a
precedent with leveraging and adapting portfolio theory from the finance literatur e and applying
it to non-finance areas of management that are concerned with mitigating ris k exposure. For
example, portfolio theory has been applied to the field of marketing to develop models to a ttempt
to maximize returns of a firm’s marketing projects given certain risks (Ryals, Dias, & Berger,
2007). These authors argued that risks need to be accounted for in any investment portfolio, not
just financial portfolios. Risks are variances in project returns and certain risks c an be reduced
through diversification by pursuing multiple projects simultaneously (whether compl ementary or
in entirely different areas) rather than concentrating all of a firm’s efforts on a single project.
Returns may potentially be higher when pursuing only a single project , but the firm’s risk
exposure would also be higher as a result (Ryals et al., 2007).
Modern portfolio theory has also led to the development of patent portfolio theory
(Parchomovsky & Wagner, 2005). According to patent portfolio theory , the portfolio valu e
increases and risks decrease when the scale and diversity of a patent portfolio inc reases. Larger
firms typically have advantages over SMEs since their patent portfolios are much l arger and
more diverse, sometimes covering technologies in multiple industries. Therefore, S MEs
experience higher non-systematic risks (Parchomovsky & Wagner, 2005). Non-systematic risks
or unsystematic risks are those that impact individual firms. They include risks of project failures

SME OPEN INNOVATION: CHAPTER 2 56
and risks of poor company performance. Systematic risks involve macroeconomic, regulatory,
and other forces that can impact all firms within an industry (Schneider et al., 2008; Lubatkin &
Chatterjee, 1994).
Innovation portfolios are different from financial investment portfolios in that most
strategic investment decisions are illiquid and somewhat irreversible. The values a nd returns of
individual innovations can be enhanced as other complementary innovations enter the portfolio.
This is unlike financial portfolios, which are concerned more with diversifying ris ks by obtaining
balance (Teece, 2007). Unrelated innovations increase a portfolio’s diversification and de crease
non-systematic risks, but some firms innovate within very specific technology areas which can
substantially increase returns when related innovations complement and enhance on e another.
Firms often diversify fairly narrowly to maximize benefits derived from comple mentary
innovations (Lubatkin & Chatterjee, 1994; Teece, 2007). Studies indicate that the collec tive
value of a patent portfolio is higher than the summed value of individual patents if those patents
are complementary and/or if they collectively increase the defensive position of the firm
(Parchomovsky & Wagner, 2005).
Firm performance will be higher if a risk is properly mitigated rather than if the ris k
actually materializes. Managing an innovation portfolio involves continually ev aluating the
various innovation processes and intellectual assets that the company has and is developing ,
while seeking to find a reasonable balance between risks and rewards (Igartua et al ., 2010).
However, excessive risk aversion can stifle innovation, especially radical innovations .
Conversely, excessive optimism can also cause a firm to take unnecessarily rec kless risks or to
not prepare its elf for the potential realization of certain risks, which could result in the firm’s
failure (Teece, 2007).

SME OPEN INNOVATION: CHAPTER 2 57
R&D diversification in innovation portfolios has been shown to lower innovation risk
exposure to firms because the success of some projects offset the negative returns of f ailed
projects (Parchomovsky & Wagner, 2005). Additionally, Lubatkin and Chatterjee (1994 )
empirically find that decreasing non-systematic R&D project risks ca n increase a firm’s
competitive position within an industry. They also find a strong negative correl ation between
R&D intensity and risk, because innovations can create bar riers for a firm’s competitors . In other
words, firms with high levels of R&D activity tend to have lower risk exposure because the y are
better positioned strategically within their industry since they created hi gher barriers for their
competitors. However, they also find that SMEs typically have more difficult y competing against
larger competitors because SMEs often focus attention on only generating patent s that will lead
to actual innovations. SME s typically devote less, if any, resources to defensive patenting, which
could erect competitive barriers and prevent others from patenting-around their i nnovations
(Motohashi, 2008).
Since SMEs typically have fewer resources than larger firms, they generally have a
higher exposure to systematic risks such as economic downturns because they have fe wer
resources to endure sudden economic shocks (Igartua et al., 2010; Habaradas, 2009; Keupp &
Gassmann, 2009; Gnyawali & Park, 2009). Non-systematic risks are also higher for S MEs due to
their overall lack of resources. For example, since SMEs often have fewer custom ers and fewer
liquid assets available they tend to be more vulnerable to risks of late customer payme nts
(Rahman & Ramos, 2010). Additionally, SMEs face higher non-systematic risk impacts of
employee turnover compared to larger firms (Lee et al., 2010; Madrid-Guijarro, 2009;
Habaradas, 2009). For example, if a single employee leaves a 10-person firm then t his equates to
an attrition rate of 10%. However, if the same person leaves a 10,000-person company then thi s

SME OPEN INNOVATION: CHAPTER 2 58
would equate to only 0.01% turnover. There are also high search costs and risks with
establishing new open innovation strategic partnerships, especially when establ ishing
relationships with firms in other industries. To mitigate these risks SMEs typi cally build fewer,
deeper, and longer lasting relationships with their partners (Lee et al., 2010).
There are various types of risks and uncertainties related to patenting. A signific ant
business risk is that a patent will never translate into financial gains and that ne w product
launches may fail. There are also risks that patents will not be granted, their values may change
unexpectedly if other firms create patents for similar innovations, the patents may be
unenforceable, or the enforcement legal fees may be too high even if the patents are enforceable
(Huang, 2009).
SMEs face relatively higher litigation risks since the average costs of li tigation are
roughly $500,000 if the case does not go to court and $1.5 million for cases that go to trial and
go through the appeals process. Since larger firms have more resources they are bette r able to
absorb high litigation costs than SMEs. Also, larger firms are statistically less likely to have to
litigate than SMEs because SMEs generally have less defensive patenti ng leverage. Small paten t
portfolios decrease SME bargaining power, decrease the attractiveness of SM Es as potential
business partners, and decrease SME access to investment capital (Parchomovsk y & Wagner,
2005).

SME OPEN INNOVATION: CHAPTER 3 59
Chapter 3: Conceptual Framework
This chapter presents two conceptual models derived from the theoretical underpinnings
of the literature review and the Holistic Model of Innovation from Chapter 2. The first is the SME
Competitive Challenges Model , which illustrates the relationships and interactions between the
main categories of size-related competitive challenges facing S MEs (i.e., lack of resources,
limited dynamic capabilities, and high risk exposure). The second conceptual model is the SME
Open Innovation Strategies Model , which depicts the various strategies that different types of
SMEs are likely to utilize to successfully overcome those size-related chal lenges.
SME Competitive Challenges Model
Figure 8 presents the SME Competitive Challenges Model . This model depicts the
relationships and interactions between the categories of resource, dynamic capabi lity, and risk-
related challenges that SMEs face.

Figure 8. SME Competitive Challenges Model
This model is a refinement of the value chain depiction presented in Figure 7. T he
framework presented in Figure 7 is useful for understanding the groupings of SME cha llenges

SME OPEN INNOVATION: CHAPTER 3 60
and the related theoretical underpinning, but it is limited in that it is linear and does not s how the
dynamic interactions between the elements as does the model in Figure 8.
The SME Competitive Challenges Model is a causal loop diagram model showing the
interactive relationships between the various categories of size-relat ed competitive challenges
that SMEs encounter (i.e., lack of resources, limited dynamic capabilities, and hi gh risk
exposure). For example, higher (lower) dynamic capabilities of an SME can lower ( raise) the
firm’s non-systematic risks and heighten (degrade) its ability to respond to systematic ri sks. No
similar model exists, but Madrid-Guijarro et al. (2009) find a strong positive rel ationship
between a lack of financial resources and excessive risk exposure among Spanish S MEs
involved in open innovation. Lenders are also less willing to lend to SMEs with higher risk
profiles, limiting access to vital financial resources. Additionally, SMEs have higher risk
exposure compared to large firms due to less established reputations and fewer res ources, which
result in higher interest rates on loans, which further constrains financial resources (Ha baradas,
2009). This also results in difficulty attracting and retaining talented emplo yees, impacting both
a firm’s resources and its dynamic capabilities (Van de Vrande et al., 2009; Lee et al., 2010).
Additionally, Keupp and Gassmann (2009) claim that obtaining additional resources and
capabilities leads to a reduction in the firm’s risk exposure. The interactions between a firm’s
resources, dynamic capabilities, and risks depicted in the SME Competitive Challenges Model
leads to this di ssertation’s first proposition:
 Proposition 1 : There is: (a) a positive relationship between increasing an SME ’s resources
and enhancing its dynamic capabilities; (b) a negative relationship between an SME ’s risk
exposure and its ability to obtain resources; and (c) a negative relationship between an
SME’s dynamic cap abilities and its risk exposure.

SME OPEN INNOVATION: CHAPTER 3 61
This SME Competitive Challenges Model depicts the firm’s dynamic cap abilities,
resources, and risks as having porous borders because in an open innovation environment
resources and capabilities can flow into and out of the firm and risks can be shared by partnering
with other firms. This depiction suggests that successful utilization of open innovation st rategies
can potentially enhance the firm’s dynamic capabilities and increase its a ccess to resources, both
of which lower the firm’s risk exposure and thus increase the probability of an innovation
project’s success, which could lead to high er firm performance overall. Propositions for these
claims are presented below in the next section.
SME Open Innovation Strategies Model
Figure 9 below presents the SME Open Innovation Strategies Model . This model depicts
the various strategies that different types of SMEs are likely to utilize to inc rease firm
performance (increase innovation outputs and financial performance) and to successfull y
overcome the main types of size-related competitive challenges that a re presented in the SME
Competitive Challenges Model above (i.e., lack of resources, limited dynamic capabilities, and
excessive risk exposure ). The below model assumes that an SME’s knowledge exploration
(value creation) and exploitation (value capture) strengths help determine which open innova tion
strategic options it could opportunistically pursue. It also assumes that if an SME’s strengths
change over time it could potentially adopt other open innovation strategies in the future.

SME OPEN INNOVATION: CHAPTER 3 62

Figure 9. SME Open Innovation Strategies Model
This model makes an implicit assumption that SMEs must also focus on critical
management factors such as strong leadership, strengthening absorptive and desorptive
capacities, nurturing an entrepreneurial culture, motivating employees properl y, utilizing
effective organizational structures, ensuring that there is strong managerial and e mployee
commitment, and many other factors. This model does not ignore the need for these and other
management considerations; it just focuses on the high-level types of open innovation st rategies
that are being (or could be) pursued by SMEs with different competitive strengths (i. e.,
exploration and exploitation capabilities).
The dissertation author created this model to evaluate various SME strategies for
competing in the open innovation environment given an SME’s relative innovation exploration
and exploitation strengths. There is nearly an indefinite number and combination of specific
SME open innovation strategies and sub-strategies. For example, there are numerous ways of
conducting inward open innovation including strategies such as buying or leasing a patent ,

SME OPEN INNOVATION: CHAPTER 3 63
issuing stock in exchange for a patent, buying into a pool of jointly-shared patents, buy ing a
business, or outsourcing R&D functions. Additionally, some unique strategies combine ot her
strategies such as cross-licensing, which is a form of both inward and outward open innovation
occurring simultaneously. For simplicity and ease-of-understanding, this model hi ghlights the
three main categories of open innovation strategies (inward, outward, and collaboration
strategies) and matches them to the types of SMEs that are most likely to uti lize (and benefit
from) them. In Chapter 5 of this dissertation, each strategy is empirically eva luated in terms of:
(1) whether it increases SME innovation outputs and financial performance and (2) how e ach
strategy mitigates or exacerbates the SME challenges in each of the t hree SME challenge
categories (i.e., lack of resources, limited dynamic capabilities, and exce ssive risk exposure).
This approach enables the evaluation of the effectiveness of the open innovation inte rventions in
the SME Open Innovation Strategies Model and assesses the SME Competitive Challenges
Model ’s validity because this dissertation directly links the reduction of challenges i n each area
to the actual performance outcomes of SMEs.
The SME Open Innovation Strategy Model is intentionally general and high-level because
this allows the model to be applicable to all industries and product types. An SME’s exploration
and exploitation strengths are determined by the firm’s unique quantity, quality, and
configuration of its resources and dynamic capabilities. An SME’s exploration and exploitation
strengths likely vary by product type. For example, an advanced research-foc used SME in the
microprocessor industry may not necessarily have high knowledge exploration capabilit ies in the
hard drive or mobile devise industries. Additionally, it is also possible for an S ME’s P roduct A
to be in the Inventive SME quadrant, while its newer Product B may be in the Collaborative SME
quadrant if the firm lacks exploration capabilities related to Product B.

SME OPEN INNOVATION: CHAPTER 3 64
The name of each quadrant is meant to describe the archetypical strategic orient ation of
SMEs in that quadrant. For example, SMEs in the upper left quadrant are likely more focused on
commercialization activities, whereas SMEs in the lower right quadrant are more likely to be
focused on producing new knowledge (inventing). The name of each quadrant could describe a ll
SMEs, but these names are meant to invoke a more memorable generalization. For exa mple, all
SMEs could be collaborative, but the SMEs in the lower left quadrant are more likely to empl oy
only collaborative open innovation strategies since they lack the exploration (value crea tion)
competencies to be considered “inventive,” the exploitative (value capture) capabilities to be
“commercializing,” and both of these capabilities together to be considered “versatile.” This is
not to imply that SMEs i n the other quadrants could not also be “collaborative.” In fact,
collaborative open innovation strategies are available strategic options for SMEs in every
quadrant.
Collaborative SMEs Quadrant
The lower left quadrant in the SME Open Innovation Strategies Model is the
Collaborative SMEs quadrant. These are SMEs that lack strength in both innovation exploration
and exploitation capabilities. While not having value creation or value capture streng ths, these
SME still have the strategic option to implement various open innovation collaboration
strategies, which could allow them to leverage the explorative and exploitative strengths of
others. In addition to internal collaboration with employees (closed innovation approach ), the
three main types of open innovation collaboration include: vertical collaboration (with upstream
suppliers and/or downstream customers and partners), horizontal collaboration (with
competitors), and knowledge-intensive collaboration (with universities, non-profits, consultants,
and research institutions) (Poot et al., 2009). It is worth re-iterating that while colla boration is the

SME OPEN INNOVATION: CHAPTER 3 65
main open innovation strategy available to this group of SMEs, collaboration is not unique to
only the Collaborative SMEs quadrant because SMEs in the other quadrants could choose to
implement collaboration-related strategies. This leads to the following pr opositions that apply to
all SMEs in general regardless of what quadrant they are in:
 Proposition 2: Collaboration-related open innovation strategies can increase SME
innovation outputs and financial performance.
 Proposition 2a: Vertical collaboration can increase SME innovation outputs and financial
performance.
 Proposition 2b: Horizontal collaboration can increase SME innovation outputs and
financial performance.
 Proposition 2c: Knowledge-intensive collaboration can increase SME innovation outputs
and financial performance.
Propositions 2a, 2b, and 2c relate to specific types of collaboration in isolation, whereas
Proposition 2 applies to all types of coll aboration collectively/undifferentiated. Many studies
evaluate collaboration in general as the level- of-analysis, rather than segmenting their analysis
by collaboration type (i.e., vertical, horizontal, or knowledge-intensive). Therefore, Proposi tion 2
evaluates open innovation at this aggregate-level of collaboration overall (all for ms collectively)
and how this collaboration impacts SME innovation performance (defined as innovation outputs
and the resultant financial performance attributed to those innovations).
Inventive SMEs Quadrant
The lower right quadrant of the SME Open Innovation Strategies Model is the Inventive
SMEs quadrant. This model refers to these SMEs as “inventive SMEs ” because they have strong
knowledge creation (exploration) abilities (likely based on the ir unique configuration of

SME OPEN INNOVATION: CHAPTER 3 66
resources and capabilities), but they lack commercialization (exploitati on) capabilities.
Numerous SMEs likely fall into this category since many SMEs have difficult y with exploitation
activities (Lee et al., 2010). Additionally, Motohashi (2008) finds that SMEs with fe wer
commercialization capabilities are more likely to license out their innovatio ns to others. This
leads to the following proposition:
 Proposition 3: Outward open innovation can increase SME innovation outputs and
financial performance.
Commercializing S MEs Quadrant
The upper left quadrant of the SME Open Innovation Strategies Model is the
Commercializing SMEs quadrant. These firms have strong commercialization (exploitation)
capabilities with less robust knowledge creation (exploration) capabilities. T hese companies
have strengths focused on the commercialization process of converting external ideas into new
products. There are likely fewer SMEs in this category than in the Inventive SMEs or
Collaborative SMEs quadrants because many SMEs lack commercialization capabilities (Keupp
& Gassmann, 2009). These Commercializing SMEs would likely include manufacturers and
niche firms with specialized distribution channels. While SMEs in any quadrant could potent ially
benefit from acquiring intellectual property, the Commercializing SMEs are well-positioned to
consider inward open innovation as a viable strategic option given their competitive st rengths
with bringing products to market. Additionally, collaboration strategies are a lso available
strategic options for these SMEs. This leads to the following proposition:
 Proposition 4 : Inward open innovation can increase SME innovation outputs and financial
performance.

SME OPEN INNOVATION: CHAPTER 3 67
Versatile SMEs Quadrant
The upper right quadrant of the SME Open Innovation Strategies Model is the Versatile
SMEs quadrant. These firms are competitively the strongest SMEs. This cluster is likely to
include the more established mid-sized and small companies that are older a nd/or deeply
entrenched in niche markets. These firms have strong knowledge creation and exploit ation
capabilities. These SMEs could utilize a more closed innovation approach if they choo se (since
they do not need to rely on others for exploration or exploitation assistance), but they could
potentially benefit from adopting one or more open innovation strategies (i.e., inward, outw ard,
and/or collaboration open innovation strategies). Therefore, these SMEs could adopt an
opportunistic approach of using different open innovation strategies, sub-strategies, and
combinations thereof in various situations when doing so could enhance their firm performan ce
and competitive standing. These firms could seek to approach the dynamically cha nging
optimum equilibrium of depth (intensity) and breadth (scope) of open innovation activities at the
point where the marginal benefits of the configuration of these activities equals the marginal
costs of engaging in these efforts. Therefore, this leads to the final propositi on:
 Proposition 5 : The combination of open innovation strategies can have a greater positive
impact on SME innovation outputs and financial performance than the utilization of a
single open innovation strategy.

SME OPEN INNOVATION: CHAPTER 4 68
Chapter 4: Methods
Chapter 4 describes the evidence-based approach that the author used to evaluate the
dissertation’s research propositions. This chapter begins by describing the evidenc e-based
management approach of systematic review and discussing the benefits of using t his research
method. Then, the author describes the four steps taken for conducting this evidence-based
evaluation. Next, the author discloses the meta-analysis protocol used in the evaluat ion presented
in Chapter 5. Finally, the chapter describes the use of a panel of expert practitioner re viewers
who provided feedback to inform this evaluation.
Evidence-Based Research
Saunders, Lewis, and Thornhill (2009) define research as “something that people
undertake in order to find out things in a systematic way, thereby increasing their knowl edge” (p.
5). Research consists of different forms of knowledge discovery including primary and
secondary research methods. This dissertation does not conduct primary research, but rat her it
uses an evidence-based research approach that integrates and synthesizes findi ngs from a diverse
set of qualitative, quantitative, and mixed-method sources. This dissertation relies on a
combination of these various forms of evidence to evaluate the research propositions in a
systematic and pragmatic manner. The evidence-based approach was first used in the field of
medicine and in the past several decades there has been growing use of this approac h as a
credible means of knowledge creation in the fields of management, public policy, education, a nd
the social sciences (Bethel & Bernard, 2010).
The process of basing managerial decisions on all available research findings is call ed
evidence-based management (Saunders et al., 2009). Evidence-based management is the subset
of evidence-based research that is applicable specifically to the field of manageme nt. Evidence-

SME OPEN INNOVATION: CHAPTER 4 69
based management is the systematic and judicious use of all available evidence in m aking
management decisions (Pfeffer & Sutton, 2007). This type of research can assist individual
decision makers, groups of decision makers, entire organizations, and even entire industri es or
governments. The approach is appropriate for evaluating this dissertation’s propositions be cause
it generates insights that can advise SME practitioners to assist them with ma king their strategic
innovation-related decisions.
Systematic reviews are the cornerstone of evidence-based management (Briner, Den yer,
& Rousseau, 2009). A systematic review is a comprehensive evaluation and synthesis of all
currently existing evidence in a field of study (Hansen & Rieper, 2009) . “Systematic reviews
have become fundamental to evidence-based practice and represent a key methodology for
locating, appraising, synthesizing, and reporting best evidence” (Briner et al., 2009, p. 24). Some
researchers argue that systematic reviews even provide more compelling evi dence than
individual studies because single primary research studies are less compreh ensive and are more
prone to producing erroneous conclusions because of their smaller and less-representative
samples (Hunter & Schmidt, 2004; Reay, Berta, & Kohn, 2009).
While primary empirical studies are vital to advancing knowledge in the managem ent
field, systematic reviews provide more comprehensive and generalizable evidenc e of the
effectiveness of various interventions (e.g., different open innovation strategies). Syste matic
reviews are vital in the field of management since they help explore the complex re lationships
among and between variables of management-focused research questions (Tranfield, D enyer, &
Smart, 2003).
There is currently a debate among researchers in different fields as to the adequa cy of
systematic reviews as a means of advancing knowledge. Some scholars belie ve that researchers

SME OPEN INNOVATION: CHAPTER 4 70
can only advance knowledge through primary studies rather than through systematic re views
(Hansen & Rieper, 2009). However, there are a growing number of researchers who belie ve that
both primary studies and systematic reviews are vital to advancing knowledge. S ystematic
reviews have informed decision makers in many fields including management, medicine ,
nursing, criminal justice, public policy, housing policy, education, government policy, non-prof it
management, and the social sciences (Tranfield et al., 2003). However, the strength ening support
of systematic reviews has not diminished support for primary empirical studies . Primary studies
have been, and will always be, essential to advancing knowledge and systematic re views rely
heavily on them as vital sources of evidence. Systematic reviews are not substi tutes for
individual studies, but rather they assist with synthesizing available knowledge and e vidence
regardless of the type or source of evidence (Pettricrew & Roberts, 2006).
Systematic reviews combine quantitative and qualitative studies into a single evaluation
that provides the benefits of both types of studies, while mitigating the limitations of e ach type.
These reviews enable researchers to triangulate results from various studies (even if they employ
different research methods) to confirm or disprove results (Harden & Thomas, 2005).
Triangulation is a powerful technique of using logic and available evidence to generate stronger
and more generalizable conclusions than simply evaluating individual studies in isola tion.
Because systematic reviews provide a more thorough and complete evaluation of a re search
question, Tranfield et al. (2003) consider systematic reviews as the highest pos sible form of
evidence, followed by randomized controlled trials, empirical experiments, other types of
quantitative studies, and then qualitative studies. However, Tranfield et al. recogni ze that each
type of study serves a purpose with answering different kinds of research questions and they

SME OPEN INNOVATION: CHAPTER 4 71
advocate the benefits of combining all available forms of evidence by employing sys tematic
reviews.
Overview of Diss ertation’s Evidence -Based Methods
This dissertation utilizes an evidence-based management approach for evaluating t he
research propositions as illustrated below in Figure 10. The overall evidence-based approa ch
consists of a systematic review and feedback from an expert panel of practitioner re viewers (the
panel is discussed at the end of this chapter). The specific systematic review s ynthesis techniques
used for evaluating each proposition are shown in Figure 10. The author conducted meta-
syntheses for all the propositions. In addition to these meta-syntheses, meta-ana lyses were also
conducted for Propositions 2 through 5 (since there was sufficient quantitative data availabl e to
conduct these statistical analyses), and a short case study was prepared to provide a dditional
evidence related to Proposition 3. The author defines these terms and describes the procedure s
for using these techniques later in this chapter. However, the author first discusses t he systematic
review approach in more detail to put this later content into context.

Figure 10. Dissertation’s Research Methods

SME OPEN INNOVATION: CHAPTER 4 72
Benefits of Conducting an Evidence-Based Systematic Review
There are six main benefits for conducting a systematic review in this dissert ation.
Benefit 1: Systematic reviews help evaluate large amounts of information
Systematic reviews enable researchers to evaluate and make sense of a lar ge body of
literature. This assists other researchers and practitioners since most do not have t ime to read and
critically evaluate the potentially thousands of sources of evidence related to a re search question.
Decision makers, policy makers, and practitioners greatly benefit from reading sys tematic
reviews because these reviews evaluate and synthesize the larger body of know ledge related to a
research question rather than focusing narrowly on the findings of a single study (Cordra y &
Morphy, 2009).
Individual studies can provide results that conflict with one another (as will be
demonstrated in the systematic review in Chapter 5). One can gain a better pers pective of the
state of literature by evaluating a large number of studies simultaneously. The re is also often a
high degree of heterogeneity between individual studies in terms of their specific i ntervention
types, contexts, methods, research designs, samples, time periods, and outcomes. Syste matic
reviews help evaluate all available evidence regardless of these differenc es (Harden & Thomas,
2005). Additionally, systematic reviews enable one to explore the similarities a nd differences
between the methods, results, and conclusions of the individual studies. Researchers can often
glean useful insights from synthesizing evidence at this higher level (Pettric rew & Roberts,
2006). The approach helps explore the complex relationships between and among the variables
of a research question. Systematic reviews are more effective at accompli shing this objective
than individual studies because the reviews have the added benefit of evaluating all av ailable
evidence, not just evidenc e from a single study’s limited data set (Hunter & Schmidt, 2004).

SME OPEN INNOVATION: CHAPTER 4 73
Benefit 2: Systematic reviews are well suited for answering certain rese arch questions
According to Pettricrew and Roberts (2006), systematic reviews are more capa ble than
individual studies at answering certain kinds of research questions. For example, these s cholars
claim that systematic reviews are well suited for investigating w hether particular interventions
are effective. Propositions 2 through 5 of this dissertation are concerned with the effecti veness of
various open innovation strategies/interventions (in terms of whether they increase SME
innovation outputs and financial performance). Pettricrew and Roberts (2006) also argue that
systematic reviews can effectively evaluate associations betwee n risk factors and outcomes. This
relates to Proposition 1, which is concerned with how various size-related competitive cha llenges
impact SMEs. These scholars argue that systematic reviews are bett er at answering these types of
research questions because a review’s results are much more generalizable than the results of any
individual study.
Benefit 3: Systematic review findings are more generalizable than primary s tudy findings
Individual studies can be misleading because they base their conclusions on stat istical
data from relatively small and narrowly-focused samples (Pettricrew & Roberts, 2006) . This
limits individual studies’ ability to generalize results for answering a res earch question. For
example, the results of a study of innovation behaviors of Japanese companies may not apply t o
American, Swiss, or Egyptian companies since firms in these countries operate in differe nt
political and socio-economic climates. Therefore, the “general consensus across all fields
interested in evidence-based practice is that a synthesis of evidence from multiple studies is
better than evidence from a single study” (Briner et al., 2009, p. 24).
Results of individual studies often conflict with one another because of the external
validity problems of generalizing with their non-representative samples. Syst ematic reviews

SME OPEN INNOVATION: CHAPTER 4 74
overcome this limitation by combining data from multiple studies/sources. Even source s that
differ in their methods and design can be triangulated to increase the generalizabi lity of
conclusions (Harden & Thomas, 2005). Systematic reviews search for all availabl e evidence
(whether it appears in scholarly literature or in the “grey literature”). Grey literature (or gray
literature) consists of potentially useful sources of evidence that are not in peer-reviewed
journals such as unpublished papers, conference proceedings, industry reports, white papers, and
other non-scholarly publications (Petticrew & Roberts, 2006).
Benefit 4: Systematic reviews enable meta-analysis
Meta-analysis is a technique of combining statistical data from multiple studies in a way
that enables the researcher to make statistical inferences about the average effect size of an
intervention along with providing a confidence interval of the estimate (Petticrew & Roberts,
2006). In other words, it is a statistical technique for combining statistical dat a from multiple
studies. This dissertation conducted several meta-analyses and the protocol for c onducting these
analyses is presented at the end of this chapter.
Meta-analysis was first used in 1904 to synthesis the results of multiple studie s on the
effectiveness of a typhoid vaccine (Cordray & Morphy, 2009). The technique has since bee n
used in other fields such as management to evaluate many studies simultaneous ly to calculate
average effect sizes and confidence intervals based on statistical data provided in indivi dual
studies. Meta-analysis enables a researcher to obtain a higher level of reliabil ity than any single
study can provide. Therefore, systematic review and in particular meta-analysi s can be
considered “fundamental scientific activity” (Tranfield et al., 2003, p. 209).
Schmidt (1996) argues that when systematic reviews conduct meta-analyses the
evaluations can produce more conclusive statistical results than those provided in individua l

SME OPEN INNOVATION: CHAPTER 4 75
studies. He claims that individual studies can also produce false confidence in their res ults
because large samples in individual studies may not necessarily be representa tive of their
population. He argues that compared to systematic reviews, primary studies t hat rely on
statistical significance testing are not as effective at producing conclusive results. He claims that
the effect sizes in meta-analyses are statistically more powerful t han isolated point estimates of
individual studies because effect sizes are more generalizable since they us e data from multiple
samples rather than from a single sample.
Schmidt (1996) contends that knowledge is continually being lost because scholars often
disregard studies that have little statistical power or that have sample sizes that are too small or
limited to make meaningful statistical inferences. Journals do not often publish these st udies.
Schmidt argues that this is a disappointing because meta-analyses can someti mes enable
researchers to calculate effect sizes by combining data from these less powerful studies with
other studies (after conducting a sensitivity analysis to ensure that the lower quali ty studies do
not significantly change the results). Therefore, systematic review s contribute to the field of
management by providing another avenue for these less conclusive studies to contribut e to the
knowledge base.
Benefit 5: Systematic reviews identify research gaps
Because systematic reviews conduct a thorough search of all available knowledge related
to a research question, these reviews help to identify gaps in research. These gaps repres ent
future lines of research. In addition to identifying areas where research is nonexi stent, these
reviews identify areas where the available research is lower quality or otherwis e unconvincing.
Also, systematic reviews discover gaps when they identify conflicting re sults between similar
studies. This heterogeneity indicates that additional research is needed on those s tudies’

SME OPEN INNOVATION: CHAPTER 4 76
particular research question in order to resolve the conflict (Pettricrew & Roberts, 2006). Chapter
6 of this dissertation presents the research gaps identified by this evaluation.
Benefit 6: Systematic reviews can reduce the impacts of quality and bias is sues
Systematic reviews help decrease the probability of relying on the flawed r esults of an
individual lower-quality study (Harden & Thomas, 2005). Because systematic revie ws use
explicit inclusion and exclusion criteria, these reviews are able to exclude low -quality studies.
Low quality studies are those with low theoretical or methodological rigor, wea k internal or
external validity, low reliability, and those that suffer from a high level of bias (Pe ttricrew &
Roberts, 2006). These low quality studies offer little evidence to further the field’s understanding
of a research topic because their results and conclusions are suspect and, ther efore,
unconvincing. For example, one quality problem could be funding bias (Cordray & Morphy,
2009). An example of this could be if a case study subject (company being evaluated) p aid the
researchers to conduct the evaluation. The findings would be questionable because there may
have been pressure on the researchers to exaggerate positive findings and to ignore nega tive
ones.
Systematic reviews also enable researchers to assign varying weights of re levance to
different sources based on their quality. Researchers can use sensitivity analy sis to determine
whether or not to exclude lower-quality sources altogether (Gough, 2007). As Schmidt (1996)
argues, there may be an appropriate use for some lower quality studies. For example, a
researcher could conduct a sensitivity analysis to determine if a lower quality stud y could be
added to a meta-analysis without significantly altering the findings (which coul d bolster the
generalizability of the effect size calculations by including it).

SME OPEN INNOVATION: CHAPTER 4 77
Steps for Conducting this Systematic Review
According to Hansen and Rieper (2009) there are four steps for conducting a systematic
review: (1) Formulate the review questions; (2) Systematically search the literature; (3) Critically
evaluate the sources; and (4) Synthesize and summarize the results. This dissert ation followed
these four steps as described below.
Step 1: Formulate the Review Questions
This dissertation presents several general research questions in Chapter 1. The author
discussed these questions throughout Chapters 2 and 3 and presented a series of specific
propositions in Chapter 3. These propositions established the scope of the systematic review .
Tranfield et al. (2003) argue that crafting effective research propositions is a critical part of the
initial phase of preparing a systematic review. These propositions guided the syst ematic search
for sources of evidence that either support or discredit them.
Step 2: Systematically Review the Literature
The dissertation author began this systematic review by creating an initial l ist of 921
sources to review based on various database searches, follow-on searches for key references in
sources, and recommendations from the program dissertation advisors and members of the expert
practitioner review panel. This systematic review used the following data bases from the UMUC
library and the Internet to search for sources: Academic Source Complete, Busine ss Source
Complete, ABI/INFORM Complete, Emerald Fulltext and Management Review, Di ssertations
and Theses (ProQuest), Management Science, and Google Scholar (to search for grey literature).
As discussed earlier, grey literature consists of potentially useful sour ces that are not in peer-
reviewed journals such as unpublished papers, conference proceedings, industry reports, and
white papers (Petticrew & Roberts, 2006). The review also searched for books in the Fa irfax and

SME OPEN INNOVATION: CHAPTER 4 78
Loudoun County Public Library Systems. The searches included keyword combinations and
synonyms for the following terms: SME, small business, open innovation, intellectual prope rty,
challenge, risk, strategy, collaboration, and licensing. The review included Engli sh texts and
foreign sources that could be electronically translated into English. However, the searc h was not
limited to the field of management, but included literature from various other fields s uch as
economics and law.
The dissertation author reviewed the abstracts of the initial 921 sources to determine
which to include in the evaluation. The author narrowed the list of sources down to 47 based on
the following exclusion criteria:
 Sources needed to specifically relate to at least one of the research propositions.
 Duplicative sources were eliminated (e.g., unpublished papers that were later publi shed and
duplicate listings among databases ).
 Sources were excluded that focused exclusively on large companies.
 Sources needed to be obtained for free (or subscription fees needed to have been covered
by UMUC or another library system).
 Sources were excluded that did not meet at least a minimum level of quality in terms of
limited bias and high internal validity, external validity, reliability, and over all theoretical
and methodological soundness. This was a subjective quality review process. Each s ource
either met or did not meet an acceptable level of quality for inclusion in the evaluation.
However, even if a source met the minimal quality criteria for inclusion, some still have
minor quality issues and these concerns are disclosed as appropriate in the analys is in
Chapter 5.

SME OPEN INNOVATION: CHAPTER 4 79
Step 3: Critically Evaluate the Sources
The dissertation author critically evaluated each source to determine its ability to support
or disprove the research propositions. The author classified each source according to the
proposition(s) it relates to, its research method, and the level of evidence it provides. T hese
classification criteria are discussed below and Figure 12 provides a crosswalk of the
classification attributes of each source.
Pettricrew and Roberts (2006) argue that there is no definitive approach to organizing
literature in a systematic review that works best in all situations. A review er needs to use an
organizational schema that categorizes and presents the literature in the m ost meaningful way
depending on the situation and the attributes of the sources in the particular area. The main
organizational approach that Pettricrew and Roberts advocate is to categorize the literature into
meaningful categories based on their respective research questions. This disserta tion grouped
sources by the proposition(s) they relate to, which identified the amount of research tha t exists
for each research proposition and helped identify areas needing further investigation.
The dissertation author also categorized the studies by the research method emplo yed
(i.e., quantitative, qualitative, or mixed method). This made it easier to identify opportunit ies for
conducting meta-analyses in situations where multiple quantitative studies tes ted the
effectiveness of the same open innovation intervention(s) (Bethel & Bernard, 2010).
Reay, Berta, and Kohn (2009) propose identifying each source in a systematic revie w as
belonging to one of six levels of evidence. Figure 11 is a reproduction of their classifi cation
schema. This is a method of categorizing evidence by its degree of strength, wit h level 1 being
the strongest evidence and level 6 being the least convincing form of evidence. Note, this
dissertation is considered level 1 evidence because it conducts several meta-anal yses.

SME OPEN INNOVATION: CHAPTER 4 80
Level of Evidence Management Research
Level 1 evidence is generated
through… Randomized controlled trials or meta-analysis.
Level 2 evidence emerges from… (a) A high-quality literature review that is replicable
and comprehensive and provides a synthesis and
actionable recommendations predicated on the
synthesis or (b) a systematic literature review.
Level 3 evidence is garnered
through… Comparative, multisite case studies or large-sample
quantitative studies involving data collected from
more than one site (organization).
Level 4 evidence is gathered
through… Small-sample, single-site qualitative or quantitative
studies. These studies are theoretically motivated
and are completed by trained (management)
researchers who have (at most) an arm’ s-length
relationship with the organization under study; the
“voice” of these studies is objective.
Level 5 evidence is generated
through… Descriptive studies and/or self- report stories.
These studies generally include observations,
admonitions, and recommendations of import to
managers. Early papers important to the then “new”
area of evidence-based management offered nascent
theory bolstered by Level 5 evidence.
Level 6 evidence is b ased on… The opinion of respected authorities or expert
committees without additional data. Some papers
offer anecdotal evidence as a means of supporting
expressed opinions. This is the weakest type of
evidence.
Source: Reay et al. (2009)
Figure 11. Levels of Evidence
As mentioned above, the dissertation author classified each source according to the
proposition(s) it relates to, its research method, and the level of evidence it provides . Figure 12
below provides a listing of the sources included in this evaluation as well as the classification
attributes of each.
Source Description Method Relates to
Proposition(s) Level of
Evidence
Almirall &
Casadesus-
Masanell,
2010 Simulation study evaluating impacts of various
types of partnerships on innovation success Qualitative 2 5

SME OPEN INNOVATION: CHAPTER 4 81
Source Description Method Relates to
Proposition(s) Level of
Evidence
Bell, 2010 Case studies of five global SMEs that have
successfully incorporated open innovation as part of
their business models Qualitative 2 3
Bolisani &
Scarso, 2003 Case studies of four global open innovation
collaboration networks Qualitative 2 3
Casals, 2010 Literature review of SME challenges and
collaboration strategies Qualitative 1, 2 2
Chadwich et
al., 2011 Case study of a global SME that has benefited from
open innovation Qualitative 2, 3 4
CHI Research,
2003 Study of patenting behaviors of 1,071 U.S. firms
(SMEs and large firms) between 1996 and 2000 Quantitative 4 4
Christensen,
Olesen, &
Kjaer, 2005 Case studies of 25 firms (SMEs and large firms) in
the consumer electronics industry to evaluate the
effectiveness of open innovation collaborations Qualitative 2 3
Dahlander &
Gann, 2010 Literature review of open innovation interventions Qualitative 2, 3, 5 2
Ebersberger et
al., 2010 Study of 3,688 firms from Austria, Belgium,
Denmark, and Norway to evaluate success of
various open innovation strategies Quantitative 2, 2a, 2b, 2c,
4, 5 3
Evens, 2009 Three case studies of SMEs' experiences with open
innovation Qualitative 2 3
Fatur, Likar,
& Ropret,
2010 Longitudinal open innovation study from 2004 to
2006 of 2,503 Slovenian companies Quantitative 2, 4 3
Gnyawali &
Park, 2009 Literature review of SME open innovation Qualitative 1 6
Gruber et al.,
2010 Study of 230 German technology-focused SMEs Quantitative 1 4
Habaradas,
2009 Study of 749 SMEs in Malaysia, Thailand, and the
Philippines Quantitative 1 3
Harryson,
2008 Case study of a global SME that has benefited from
open innovation Qualitative 2, 3 4
Huang &
Rice, 2009 Survey of 292 Australian SMEs to evaluate impacts
of networking and SME capabilities on innovation
performance Quantitative 1, 2, 4, 5 3
Huang, 2011 Comparative case studies of SME innovation in
companies in Finland and China Qualitative 2, 4 3
Huggins &
Johnston,
2009 Study of 49 SMEs in the UK Quantitative 2 4
Igartua,
Garrigos, &
Hervas-Oliver,
2012 Case study of a Spanish SME that has had success
with open innovation collaboration Qualitative 1, 2 4
Jemala, 2010 Literature review of open innovation challenges and
strategies of SMEs and large firms Qualitative 1, 2 6

SME OPEN INNOVATION: CHAPTER 4 82
Source Description Method Relates to
Proposition(s) Level of
Evidence
Karaev, Koh,
& Szamosi,
2007 Literature review of studies focused on SME
participation in open innovation networks Qualitative 1, 2 2
Keeble et al.,
1999 Study of 50 technology-intensive SMEs in the UK Qualitative 2, 2a, 2b, 2c 4
Kuehnle &
Wagenhaus,
2007 Three case studies of SMEs in the European Union Qualitative 2 5
Kumar, 20 10 Comparative study of innovation activities of 62
Italian companies (SMEs and large companies) Quantitative 2, 4 4
Laursen &
Salter, 2004 Study of 2,655 UK manufacturing firms (SMEs and
large companies) and their participation in
knowledge-intensive open innovation with
universities Quantitative 2c 3
Lee et al.,
2010 Study of 2,414 Korean SMEs and a case study
evaluating the impacts of various types of SME
innovation partnerships on innovation effectiveness
and financial performance Mixed 1, 2, 4 3
Leiponen &
Byman, 2009 Study of 504 SMEs in Finland evaluating SME
strategies for maximizing return on innovation
investments Quantitative 1, 2, 2a, 2b, 2c 3
Lichtenhaler,
2009 Study of 136 SMEs in Germany, Austria, and
Switzerland on effects of outbound open innovation
strategies on firm performance Quantitative 3 3
Madrid-
Guijarro,
Garcia, & Van
Auken, 2009 Study of 294 SMEs in Spain evaluating barriers of
SME open innovation and the link between lack of
resources and excessive risk exposure, and the
impacts of these factors on innovation efforts Quantitative 1 3
Matthews &
Sawang, 2010 Study of 449 Australian SMEs to determine the
impact on external collaboration on innovation
performance Quantitative 2 4
McAdam et
al., 2008 Study of 2,086 SMEs in the UK to evaluate various
determinants of SME innovation success Quantitative 1, 2a, 2c 3
Mesquita &
Lazzarini,
2008 Study of vertical and horizontal innovation
collaboration activities of 232 Argentine SMEs Quantitative 2a, 2b 3
Motohashi,
2008 Study on Japanese Patent Office data on all
licensing activity in Japan to analyze large firm a nd
SME patent licensing behaviors and strategies Quantitative 3 4
Nkongolo-
Bakenda et al.,
2010 Study of 81 Canadian SMEs in the agricultural
equipment industry Quantitative 2 4
Parida,
Westerberg, &
Frishammar,
2011 Study of open innovation practices of 252 Swedish
SMEs Quantitative 2a, 2b, 4 3
Poot et al.,
2009 Study of 7,671 Dutch companies evaluating their
adoption of various open innovation strategies Quantitative 2, 2a, 2b, 2c 3

SME OPEN INNOVATION: CHAPTER 4 83
Source Description Method Relates to
Proposition(s) Level of
Evidence
Rahman &
Ramos, 2010 Literature review of SME open innovation
challenges Qualitative 1, 5 6
Rammer &
Schmiele,
2009 Study of 1,000 German SMEs from 2002 – 2007 Quantitative 2b 3
Rosenbusch,
Brinckmann,
& Bausch,
2011 Meta-analysis of relationship between innovation
and performance in SMEs Quantitative 2 1
Schillo &
Walter, 2010 Study of 85 SMEs (spin-off companies from
German Universities) Quantitative 2 4
Squicciarini,
2009 Study of innovation performance of 252 firms in
Finnish science parks over a 33-year period Quantitative 2b, 2c 3
Su, Wu, &
Vanhaverbeke,
2010 Four case studies of Taiwanese biotechnology
SMEs Qualitative 2 3
Subrahmanya,
2009 Comparative case studies of product innovation
success of SMEs in Japan and India Qualitative 2 3
Terziovski,
2003 Study of 115 Australian SMEs evaluating the
relationship between SME innovation networking
practices and business performance Quantitative 2 3
Terziovski,
2010 Study of 600 Australian SMEs that tests several
SME innovation strategies and their impacts on
firm success Quantitative 1, 2 3
Van de
Vrande et al.,
2009 Study of 605 SMEs in the Netherlands and their use
of various open innovation strategies Quantitative 2, 3, 4 3
Zahra,
Ucbasaran, &
Newey, 2009 Study of 384 US-based SMEs that conduct business
internationally Quantitative 2, 3 3
Figure 12. Sources of Evidence
This dissertation categorized the sources according to the six levels of evidence , but all
evidence was considered regardless of its level. Pettricrew and Roberts (2006) explain that many
researchers (such as Raey et al., 2009) hold epistemological views that assign hig her weights of
importance to different types of studies. For example, some researchers believe t hat quantitative
studies are more important than qualitative studies for advancing knowledge. Additional ly, some
fields consider double-blind randomized controlled trials to be the golden standard of empirical
studies. This is especially true in the physical sciences. However, Tranfield et al. (2003) argue
that the field of management does not conduct these types of studies because social interve ntions

SME OPEN INNOVATION: CHAPTER 4 84
are too complex to evaluate in a controlled setting. These researchers disagree with R aey et al.
and argue that in the field of management the highest forms of evidence are empirical
experiments and quasi-experiments rather than randomized control trials.
Miller and Tsang (2010) identify three main challenges that prevent the field of
management from conducting randomized control trials. First, organizations are very com plex,
diverse, ever changing with multiple levels of analysis, and they have numerous inter -dependent
causal processes. Secondly, testing is difficult because humans have free wi ll to choose what
actions they take and they may not always behave rationally or according to es tablished
procedures or conventions. In strategic management for example, managers may inte ntionally
behave unpredictably to deceive their competitors. Thirdly, the act of being observed and the
researchers’ other interventions may cause subjects to change their beliefs and behaviors.
Locke, Silverman, and Spirduso (2010) present an alternate viewpoint and argue that
quantitative studies and qualitative studies are complementary and both are important. T hey
contend that the most appropriate method depends on what the research question is. While
rigorous experiments and quasi-experiments are essential for evaluating wh ether certain
interventions work, qualitative studies can provide more in-depth explanation of why and how
those interventions have worked in various contexts. For this reason, this dissertation eva luates
both qualitative and quantitative forms of evidence. However, since many empirical posi tivists in
the field of management share the views of Raey et al. (2009), this dissertation cons iders the
relative weights of importance that each level of evidence provides. Therefore, this di ssertation
considers all available evidence, but assigns more weight to the higher “levels of e vidence” as
defined by Raey et al. in Figure 11.

SME OPEN INNOVATION: CHAPTER 4 85
Step 4: Synthesize the Results
Based on this level of evidence classification schema, 1 of the sources is classi fied as
level 1 (2%), 3 (6%) are level 2, 26 (55%) are level 3, 12 (26%) are level 4, 2 (4%) are level 5,
and 3 (6%) are level 6. Of these studies, 19 (40%) are qualitative, 27 (57 %) are quantitative, and
1 (2%) uses a mixed methods approach. Note, the percentages do not add to 100% because of
roundin g. Figure 13 below provides a graphical representation of these studies.

Figure 13. Studies Included in this Evaluation
These studies include data from 34,676 SMEs across dozens of industries in 27 countries.
Figure 14 provides a geospatial depiction of these SMEs’ headquarters/home countries (shaded
in blue).

SME OPEN INNOVATION: CHAPTER 4 86

Figure 14. Geographic Coverage of SMEs Evaluated
Scholars have identified a need for further research on SMEs in the open innovation
literature (Bianchi et al., 2010; Chesbrough, 2010; Garcia & Van Auken, 2009) and this
systematic review confirms this research gap. Chapter 5 of this dissertatio n presents the
synthesized findings related to each proposition. Chapter 6 discusses the implications for
management (SME practitioners) and identifies needs for future research.
Synthesizing Using the Meta-Synthesis Technique
For each proposition, the dissertation author synthesized and triangulated the evidence
presented in Figure 12. Gough (2007) defines synthesis as “aggregation, integration and
interpretation of all the evidence considered to ans wer the review question” (p. 219). Systematic
reviews rely on different methods of synthesizing evidence. Of the various available t ypes of
synthesis techniques (e.g., meta-synthesis, thematic analysis, grounded theo ry, vote counting,
meta-ethnography, etc.) this dissertation utilizes the meta-synthesi s methodology. This is the
approach appropriate for simultaneously synthesizing research from quantitative , qualitative, and
mixed-method studies (Bethel & Bernard, 2010). This synthesis methodology identifies ,

SME OPEN INNOVATION: CHAPTER 4 87
compares, and contrasts key themes and patterns among the sources of evidence. This i nvolves
the translation of themes from one study to the next (Walsh & Downe, 2005). Translating refers
to the process of recognizing similar concepts between studies regardless of the spec ific
terminology or words used. Meta-synthesis also involves extracting lines of argument among the
sources, which involves “pulling corroborating concepts together and, crucially, going beyond
the content of the original studies” (Harde n & Thomas, 2008, p. 3). This may evince a heretofore
unnoticed construct that brings two or more relationships together in a novel, insightful ma nner.
Additionally, “meta -synthesis enables the researcher to represent and account for
differences in concept ualizations and measurement of the research problem” between the sources
(Bethel & Bernard, 2010, p. 245). Therefore, meta-syntheses can create novel findings that go
beyond the observations of the original sources. Thus, the methodology enables this disse rtation
to contribute knowledge to the field of management. The synthesized findings are present ed in
Chapter 5.
Protocol for Statistical Meta-Analysis
In addition to conducting meta-syntheses for each proposition, the dissertation author
conducted meta-analyses to explore Propositions 2 through 5. This was possible because there i s
sufficient quantitative statistical data available in existing studies to c onduct meta-analyses for
these propositions.
The dissertation author conducted a meta-analysis in instances where all the follow ing
conditions were met: (1) there were multiple quantitative studies related to the sa me proposition;
(2) these studies tested the effectiveness of the same intervention; (3) the stati stical analyses of
the studies were methodologically sound; (4) the variables were sufficiently simi lar; and (5)
adequate statistical data were provided to calculate effect sizes and confide nce intervals. The

SME OPEN INNOVATION: CHAPTER 4 88
meta-analyses were conducted using Microsoft Excel 2010. The results are pre sented and
discussed in Chapter 5. Appendix A contains all of the data and statistical outputs. T he specific
meta-analysis steps performed in this evaluation are presented below.
Meta-Analysis: Step 1
The dissertation author collected pertinent statistical data from relevant studi es (i.e.,
Pearson’s r correlation coefficients, regression beta coefficients, and Chi-Square results). As
described above, a study’s statistical results had to be relevant to this dissertation. The
independent variable(s) had to be the implementation of one or more of the open innovation
strategies covered by this dissertation’s research propositions . Additionally, the dependent
variable(s) of the study had to measure either innovation outputs or financial returns deri ved
from those innovation outputs.
The dissertation author included multiple effect size point estimates from a single study
when the study:
 Included multiple independent variables for different types of open innovation strategie s
(e.g., studies that evaluated both inward and outward open innovation);
 Included multiple independent variables to measure different aspects of the sam e open
innovation strategy (e.g., collaboration depth versus collaboration breadth);
 Used multiple dependent variables to represent discrete aspects of innovation performan ce
(e.g., number of new innovations introduced, changes in revenue, or some other measure );
 Used multiple models to generate statistical outputs that directly relate t o at least one of this
dissertation’s propositions; or
 Evaluated different sub-samples separately (e.g., separate measurements for SMEs in
different countries).

SME OPEN INNOVATION: CHAPTER 4 89
Meta-Analysis: Step 2
In the second step, the dissertation author calculated the effect size ( ES) estimate for each
data point (the statistical measurement reported in a study). To make like compa risons, the
author normalized the data by recording only Pearson’s r correlation coefficients to represent
effect sizes and by converting all other data points into Pearson’s r correlation coefficients. For
example, the author converted regression coefficients and Chi- Square results into Pearson’s r
correlation coefficients so that all the effect size estimates would be in the s ame unit of measure.
The author used the following protocol for determining the individual effect sizes:
 Results reported in a study as Pearson’s r correlation coefficients were already presented as
r-values, so those values became the effect sizes because r ES (Lipsey & Wilson, 2000).
 Results reported as regression beta coefficients were converted into Pearson’s r correlation
coefficients by first converting them to the standardized mean differences (Cohen’s d). This
calculation was conducted using an online conversion calculator (Campbell Collaboration,
2012). Multiple calculators were used to confirm the validity of this calculator’s accuracy.
Then, the dissertation author converted the d-values into r-values (Pearson’s r correlation
coefficient) using the following formula:
)/ ) 2 ( 4 (2N N ddr
 , where N = combined
sample sizes of the studies in the analysis (Lyons, 2003).
 Results reported as Chi-Square data were also converted into Pearson’s r correlation
coefficients by first converting them to standardized mean differences (Cohen’s d). This
calculation was conducted using the same online conversion calculator (Campbell
Collaboration, 2012). Then, the d-values were converted into r-values using the following

SME OPEN INNOVATION: CHAPTER 4 90
formula:
)/ ) 2 ( 4 (2N N ddr
 , where N = combined sample sizes of the studies in the
analysis (Lyons, 2003).
 When a study failed to report a standard error ( SE), the dissertation author calculated this
value from the standard deviation (if reported ) using the following formula:nSE ,
where  = standard deviation and n = number of observations in the study. When the
standard deviation was not reported by the study, the dissertation author calculated the
standard error using the following formula:31
nSE (Lipsey & Wilson, 2000).
Meta-Analysis: Step 3
After all of the effect size point estimates were determined/calculat ed from the studies in
Step 2, the dissertation author calculated the weight ( w) for each of these effect sizes by using the
following formula to determine the inverse variance of each data point : 21
SEw (Lipsey &
Wilson, 2000). According to Lipsey and Wilson, this is a more precise means of weighting
observations than simply using the number of observations in a study ( n) because the SE is a
direct index of ES precision. This method of weighting observations produces a more accurate
mean effect size (ES) in Step 4.
Meta-Analysis: Step 4
The dissertation author then calculated the following meta-analysis output stati stics using
formulas provided by Lipsey and Wilson (2000):
 Mean Effect Size: wESwES) (

SME OPEN INNOVATION: CHAPTER 4 91
 Standard Error of the Mean Effect Size: wseES1
 Z-test for the Mean Effect Size:
ESseESZ
 95% Confidence Interval:
) (96. 1) (96. 1
ESES
se ES Lowerse ES Upper


Meta-Analysis: Step 5
In the final step of each meta-analysis, the dissertation author calculated th e Q-test of
homogeneity to test the validity of the assumption that all of the effect sizes ar e estimating the
same population mean. The Q-value statistic was calculated with the following
formula:  wESwESw Q2
2) ( .
Q-values are distributed along the Chi-Square distribution. The degrees of freedom ( df)
was calculated as the number of ESs minus 1. The dissertation author looked up the critical value
for Chi- Square (at α=0.05) using a Chi-Square distribution table and compared the value to the
critical Q-value (calculated above ) to test the null hypothesis of homogeneity (Lipsey & Wilson,
2000). If the null hypothesis for homogeneity is rejected, it indicates that the variabili ty across
effect sizes exceeds what would be expected based solely on sampling error. This would suggest
that the observations are not measuring the same population of SMEs.
The meta-analysis data and statistical outputs from steps 1 through 5 above are present ed
in Appendix A. The dissertation author conducted separate meta-analyses to test Proposit ions 2,
2a, 2b, 2c, 3, 4, and 5. The results of these analyses are discussed in Chapter 5.
Expert Practitioner Review Panel
In addition to the systematic review, the dissertation author established a pane l of three
practitioner experts to review the initial high-level research design and concept ual models. These

SME OPEN INNOVATION: CHAPTER 4 92
reviewers are independent of the dissertation committee that reviewed and approved t his
dissertation. All of the panel members hold doctoral degrees and have professional backg rounds
that have provided them with expertise with this dissertation’s research topic . Evidence-based
research incorporates all forms of evidence that relate to the research questions . Therefore,
gaining input from experts in the field (both academics and practitioners) is an es sential
component of evidence-based research (Briner et al., 2009). Expert practitioner reviewers are
able to provide knowledgeable insights, identify any missing evidence/sources for t he review,
verify that there is a need for the research, and confirm that the topic has relevancy to the field
(Petticrew & Roberts, 2006). Therefore, the panel provided significant value to this invest igation.

SME OPEN INNOVATION: CHAPTER 5 93
Chapter 5: Analysis and Discussion
In this chapter, each proposition is evaluated using available secondary qualitative and
quantitative evidence from the literature and by conducting a case study and a s eries of meta-
analyses. The chapter assesses Proposition 1 by evaluating evidence from the li terature related to
the relationship between the SME challenge categories (i.e., limited resource s, few dynamic
capabilities, and high risk exposure). This chapter evaluates Propositions 2 through 5 in terms of
whether each open innovation strategy: (1) increases SME innovation performance (i.e .,
innovation outputs and resultant financial gains) and (2) how each strategy mitigates or
exacerbates SME challenges in each of the three SME challenge categori es (i.e., limited
resources, few dynamic capabilities, and high risk exposure). This approach enables the
evaluation of the effectiveness of the open innovation interventions in the SME Open Innovation
Strategies Model using secondary research evidence and, using this same body of evidence,
assesses the validity of th e SME Competitive Challenges Model . This assessment of both models
is necessary to evaluate the dissertation’s research questions and to test the l ine of argument that
links an increase in SME innovation performance with an increased ability to overcome size-
related competitive challenges facing SMEs.
Proposition 1 (SME Challenges) Findings
Proposition 1 : There is: (a) a positive relationship between increasing an SME ’s resources and
enhancing its dynamic capabilities; (b) a negative relationship between an SME ’s risk exposure
and its ability to obtain resources; and (c) a negative relationship between an SME’s dynamic
capabilities and its risk exposure.
This section evaluates the relationships between the factors that comprise t he SME
Competitive Challenges Model.

SME OPEN INNOVATION: CHAPTER 5 94
Proposition 1(a): Resources and Dynamic Capabilities (Positive Relationship)
Studies indicate that there is a positive relationship between these variables . For example,
Gruber et al. (2010) conducted a study of 230 German technology-focused SMEs and determ ine
that an SME’s innovation performance is highly correlated with both its resources and
capabilities. They also find a strong positive correlation between resources and cap abilities,
providing evidence that these factors positively reinforce one another and are complem entary.
Huang and Rice (2009) conducted a study of 292 Australian SMEs and the results
indicate that a lack of resources (e.g., knowledge) can negatively impact a firm’s dynamic
capabilities (e.g., networking capabilities). They also conclude that a n SME’s dynamic
capabilities assist it with acquiring new information (knowledge resources ), which the firm can
use to create more innovations (intellectual property), which can lead to higher revenue s
(financial resources) (Huang & Rice, 2009). Therefore, SMEs can leverage their d ynamic
capabilities to create additional resources.
Bell et al. (2003) provide case study evidence that finds that superior networking
capabilities can enable SMEs to strengthen their knowledge resources by le arning from external
experts. Additionally, absorptive capabilities (discussed in Chapter 2) enable an SM E to learn
and assimilate this new information gained from networking , strengthening the firm’s knowledge
resource base (Gnyawali & Park, 2009). This increased resource base enhances the SME’s
ability to attract capital investments and to recruit and retain more highly-ski lled employees
(Huang & Rice, 2009).
In a study of 294 Spanish SMEs, Madrid-Guijarro et al. (2009) also find that firms wi th
higher capabilities and resources are better able to attract employees wit h advanced skillsets.
They argue that these employees serve to further increase the SME’s knowledge base and

SME OPEN INNOVATION: CHAPTER 5 95
corporate capabilities, eventually leading to an increase in financial resourc es as these employees
enhance the firm’s ability to increase revenue. This creates a positive feedback virtuous cycle,
strengthenin g the SME’s competitiveness . Furthermore, the researchers find a link between a
lack of capabilities (e.g., poorly trained employees or fewer collaboration capabil ities) with
lower firm resources because these weaknesses negatively impact SME revenue e arning
potential. Additionally, SMEs with specialized capabilities are more likely to be admitted into
innovation networks. These inter-firm collaborations increase SME access to compl ementary
resources and lead to cost savings because SMEs can leverage the network’s economies of scale
(Gnyawali & Park, 2009). Likewise, an SME with unique resources such as speciali zed
knowledge is also more likely to be accepted into those networks, enabling the SME to l earn new
capabilities from its partners (Madrid-Guijarro et al., 2009).
In a study of 739 SMEs, Habaradas (2009) finds that SMEs have fewer resources and
capabilities than large firms, which puts SMEs at a competitive disadvantage. SMEs are typically
very specialized and generally lack the heterogeneity of capabilities that larger firms have
(Bianchi et al., 2010; Motohashi, 2008). The evaluations of Propositions 2 through 5 later in
Chapter 5, describe how various open innovation strategies can enable SMEs to increase the ir
resources and dynamic capabilities. This assists these firms with overcom ing their size-related
challenges, enabling them to become more competitive.
Proposition 1(b): Resources and Risk Exposure (Negative Relationship)
Studies indicate that there is a negative relationship between resources and risk exposure.
This negative relationship expands into a variety of areas integrally important to the f irm’s
survival. For example, Madrid-Guijarro et al. (2009) conducted a study of 294 Spanish SM Es
and they find a strong relationship between high risk profiles and low firm resources (negat ive

SME OPEN INNOVATION: CHAPTER 5 96
relationship). They argue that because of an overall lack of resources and capabilitie s, SMEs
have been found to have a higher systematic and non-systematic risk exposure compared to
larger companies. If risks of project failure or economic recession materi alize, it could result in
sudden decreases in cash flow, which could deplete the SME of vital resources, potenti ally
leading the firm to bankruptcy (Rahman & Ramos, 2010).
Leiponen and Byma (2009) argue that SMEs that lack sufficient funds face higher ris ks
because they are less able to defend their legal intellectual property rights. Fewer resources make
it more difficult for SMEs to patent an innovation, which raises risks of intellectual prope rty theft
by competitors. Additionally, it can be difficult for SMEs to afford high litigation c osts if another
firm infringes on their property. Therefore, limited SME resources can increase risk ex posure.
Habaradas (2009) conducted a study of 749 SMEs in Malaysia, Thailand, and the
Philippines. He finds that compared to large firms, SMEs pay higher interest rates and face more
borrowing restrictions and rules due to their overall lack of resources to secure financi ng.
Lenders view firms with fewer resources to be higher-risk investments. Being labe led as high-
risk decreases SME resources because these firms must make higher interest pa yments.
Therefore, their borrowing costs are higher than larger, more financially establishe d enterprises.
Some governments guarantee SME loans to reduce lending risks, which increases SME ac cess to
resources and lowers their borrowing costs.
Fatur, Likar, and Ropret (2010) conducted a study of 2,503 Slovenian companies and find
that open innovation is positively correlated with return on sales, return on equity, return on
assets, and revenue growth. They conclude that this increased financial performa nce makes it
easier and less expensive (i.e., lower borrowing costs) for these SMEs to raise a dditional
investment funding. Therefore, the higher resources that open innovation produces increase the

SME OPEN INNOVATION: CHAPTER 5 97
ability for SMEs to obtain additional funds, which lowers the risk of insolvency for these firms.
The sections of Chapter 5 that evaluate Propositions 2 through 5 provide further evidence that
open innovation can assist SMEs with increasing their resources, while reducing t heir risk
exposure.
Proposition 1(c): Dynamic Capabilities and Risk Exposure (Negative Relationsh ip)
It stands to reason that if resources and risks are negatively related, and resources a nd
dynamic capabilities are positively related, that dynamic capabilit ies and risks would be
negatively related. Studies confirm that a negative relationship exists betw een dynamic
capabilities and risk exposure. For example, Schillo and Walter (2010) conducted a study of 85
German SMEs and find that firms with stronger coordination capabilities are able to reduce ri sks
through their collaborations. Likewise, they find that SMEs with lower coordination c apabilities
have a higher incidence of experiencing negative returns from their networking activiti es (higher
risks). They also claim that higher market uncertainty (systematic risk) incr eases the correlation
strength between coordination capabilities and sales growth. In other words, dynami c capabilities
become even more important to an SME in high er-risk situations.
Huang and Rice (2009) conducted a study of 292 Australian SMEs and find that SMEs
with higher absorptive capabilities (type of dynamic capability) face lower risks with innovation
networking and these firms are more successful with benefiting from open innovation
collaboration. Additionally, increased SME cultural intelligence (another type of dynamic
capability) can increase global open innovation success and lowers collaboration risks wi th
international partners (Zahra, Ucbasaran, & Newey, 2009). Therefore, strong dynami c
capabilities can lower an SME’s risk exp osure.

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In a study of 232 Argentine SMEs, Mesquita and Lazzarini (2008) find evidence that
governance capabilities increase the effectiveness of innovation collaboration netw orks. They
argue that these dynamic capabilities reduce the risks of intellectual propert y theft among
members. They also note that higher risks of project failure, due to limited firm resource s and
poor infrastructure, encourage firms to enter into open innovation collaborative relationships .
SMEs that strengthen their collaboration capabilities are more likely to be able to s uccessfully
mitigate the negative impacts of the potential realization of risks because the se risks are shared
among multiple parties. This is consonant with the research findings of other studies t hat also
provide evidence that there is a strong correlation between a lack of collaboration c apabilities
and excessive risk exposure (Madrid-Guijarro et al., 2009).
The sections of Chapter 5 that examine Propositions 2 through 5 provide further evidence
that open innovation assists SMEs with strengthening dynamic capabilities and re ducing risk
exposure.
Proposition 1 (SME Challenges) Conclusion
When the findings from the case studies, literature reviews, and empirical studie s are
triangulated, one can reasonably conclude that the assertions of Proposition 1 are suffici ently
supported by evidence. Therefore, Proposition 1 is largely confirmed. This supports the validity
of the relationships between the various elements described in the author’s SME Competitive
Challenges Model . Therefore, any evidence that open innovation increases an SME’s resources
(evidence derived from testing Propositions 2 through 5) can also be considered evidence that
open innovation can potentially indirectly strengthen the firm’s dynamic capabiliti es and reduce
its risk exposure (assuming the SME is able to adequately protect its intellectua l property).
Secondly , any evidence demonstrating open innovation’s ability to strengthen an SME’s

SME OPEN INNOVATION: CHAPTER 5 99
dynamic capabilities (evidence derived from testing Propositions 2 through 5) is also evidence
that open innovation can potentially indirectly increase an SME’s resources and reduce i ts risk
exposure (assuming the SME can adequately protect its intellectual capital). Thirdly, any
evidence that demonstrates the ability of open innovation to potentially lower an SME’s risk
exposure (evidence derived from testing Propositions 2 through 5) is also evidence that open
innovation can potentially indirectly increase the firm’s resources and strengthen i ts dynamic
capabilities.
The following sections of Chapter 5 critically evaluate Propositions 2 through 5.
Synthesized evidence is provided indicating that, in some situations, open innovation can a ssist
SMEs with overcoming their size-related competitive challenges by: (1) incre asing SME
resources; (2) enhancing SME dynamic capabilities; and (3) reducing SME ri sk exposure.
Therefore, the remaining sections of Chapter 5 provide evidence that open innovation can
increase the competitiveness of SMEs in some situations. Chapter 6 descri bes the situational
factors that impact the probability of SMEs benefiting from the execution of open innovation
strategies.
Proposition 2 (Open Innovation Collaboration) Findings
Proposition 2: Collaboration-related open innovation strategies can increase SME innovation
outputs and financial performance.
There are three main types of open innovation collaboration: vertical collaboration (with
upstream suppliers and/or downstream customers and partners), horizontal collaboration (with
competitors), and knowledge-intensive collaboration (with universities, non-profits, consultants,
and research institutions) (Poot et al., 2009). These individual types of open innovation
collaboration are evaluated separately in Propositions 2a, 2b, and 2c respectively. Propos ition 2

SME OPEN INNOVATION: CHAPTER 5 100
evaluates open innovation collaboration overall (all forms of collaboration
collectively/undifferentiated). Many studies evaluate collaboration in gene ral as the level- of-
analysis, rather than segmenting the analysis by collaboration type (i.e., vertical, horizontal, or
knowledge-intensive). Therefore, Proposition 2 evaluates open innovation at this aggregate-l evel
of collaboration overall (all forms collectively) and how this collaboration impac ts SME
innovation performance (defined as innovation outputs and the resultant financial performanc e
attributed to those innovations).
Recent studies have evaluated this research question. For example, Terziovski (2003)
conducted a study of 115 Australian SMEs and finds a significant and positive relationship
between external networking and increased business excellence, which he defines in t erms of the
number of new ideas adopted, success of new products, reduction in waste of resources,
increased market opportunities, and increased product quality. The study finds that c ollaboration
provides SMEs with access to critical resources such as knowledge, benchmarking da ta, and
custom capabilities. Innovation collaboration has also been found to assist SMEs w ith
overcoming competitive challenges attributed to their typical lack of resource s and these
networks can assist SMEs with lowering costs because they help SMEs achieve higher levels of
economies of scale and scope (Karaev, Koh, & Szamosi, 2007).
Poot et al. (2009) conducted a study of 7,671 Dutch companies and find that there has
been increasing rates of vertical, horizontal, and knowledge-intensive collaboration over t he last
decade. They claim that there is consensus in the literature that collaboration ca n strengthen
many SMEs’ ability to create and pr ofit from innovations. For example, Kumar (2010) finds in a
study of 592 Italian companies that SMEs that had joined cooperative relationships wit h other

SME OPEN INNOVATION: CHAPTER 5 101
firms were able to introduce more novel products and had more sales revenue than SMEs that di d
not collaborate.
In a study of 2,414 Korean SMEs, Lee et al. (2010) find that SMEs participate in
networks and alliances more frequently for innovation than large companies, and SMEs tend t o
be more efficient with utilizing these networks for their gain. SMEs consider the pot ential
synergies gained by combining their resources and capabilities with those of other organizations
to accomplish a shared goal. SMEs also consider what the potential relationship coul d provide
them in terms of additional access to: financial resources, more efficient supply c hains,
commercialization capabilities, legal resources, technical expertise, anot her organization’s
reputation and brand strength, and other assets and capabilities (Teng & Das, 2000). In a study of
294 Spanish SMEs, Madrid-Guijarro et al. (2009) also find a high correlation between SM Es
needing additional access to resources and those firms actually entering into ext ernal innovation
partnerships.
Ebersberger et al. (2010) conducted an analysis of 3,688 SMEs and large firms from
Austria, Belgium, Denmark, and Norway. They find that SMEs exhibit a lower propensity to
collaborate than large firms, without identifying a rational for this behavior. Thi s conflicts with
the Lee et al. (2010) finding since Korean SMEs tend to collaborate more intensively t han larger
Korean firms. However, like Lee et al., Ebersberger et al. conclude that partner ing can enhance
the ability of SMEs to produce novel innovations because it increases SMEs’ exposure to a more
diverse group of problem solvers. They also find evidence that interactions with these external
actors can typically increase the quantity and quality of SME innovation outputs (Ebersberge r et
al., 2010).

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Su, Wu, and Vanhaverbeke (2010) conducted four case studies of Taiwanese
biotechnology SMEs. Their findings are consistent with other studies and conclude t hat
innovation collaboration networks assist firms with increasing access to resources a nd
capabilities, resulting in increased innovation outputs. The case studies indicate th at SMEs are
more successful if they are able to continually evolve their internal capabilities as innovations
progress through the stages of development. Jiang, Tan, and Thursby (2010) came to the same
conclusion in their study of 75 small and large semiconductor firms in the consumer el ectronics
industry. Their findings indicate that firms that produce the most successful innovations are the
ones that participate in larger innovation collaboration networks.
Subrahmanya (2009) conducted comparative case studies of SMEs across Japan and
India and concludes that innovation collaboration for these firms has been instrumental in
introducing technological innovations. However, the researcher warns that these relationshi ps
should be viewed only as complementary to internal innovation activities rather than a
replacement of them.
Matthews and Sawang (2010) conducted a study of 449 Australian SMEs. They find that
external collaboration has a positive impact on SME innovation performance. They conclude that
SMEs create more innovations as a result of these relationships because they provide S MEs
access to complementary resources and capabilities. These collaborations low er risks by enabling
firms to abandon innovation efforts with less impact than if they worked on these projects al one.
These researchers find that external collaboration assists SMEs with coping with innovation
abandonments (project failures). They explain that SMEs in collaboration networks face a hi gher
probability of introducing new future products after project failures compared to SMEs that a re
not in collaboration relationships. Therefore, collaboration minimizes the risk of project fai lure

SME OPEN INNOVATION: CHAPTER 5 103
and enables failures to have less impact on future innovation performance. Additionally,
collaborations can create options for SMEs to take certain actions in the future (Sc hneider et al.,
2008). Options decrease risks because an SME could decide in the future to continue
participation in an innovation partnership (which has a high degree of uncertainty at first wi th
uncertainty reducing as the project evolves) or the SME could choose to exit the relations hip
entirely. However, even failed projects can generate knowledge that can lead t o the development
of other future innovations (Johnson, 2007).
Increased R&D collaboration also enhances the quantity and quality of industry
innovation outputs and results in more products being introduced into the marketplace
(Ebersberger et al., 2010). For example, Baba (1998) conducted a study of small and large
Japanese electronics companies. He argues that the consumer electronics indust ry is scale-
intensive, which encourages companies to form collaborative R&D partnerships to leverag e
economies of scale. His data show that electronics companies have been growing thei r
collaboration networks and have increased the ir rate of innovations as a result. He claims that
during the 1980’s, Japanese companies dominated the electronics industry because of their
participation in R&D collaboration networks. These networks increased cost effici encies and
innovation productivity. He also argues that diversity among partners in networks can l ead to
greater advances in technological innovations. Other studies provide additional evide nce to
support the claim that increased R&D collaboration enables firms to benefit from cost savi ngs by
leveraging lower-cost partner resources (Han, Chung, & Sohn, 2009).
Mesquita and Lazzarini (2008) conducted a study of 232 Argentine SMEs. They find that
SMEs are able to increase their collective efficiencies by collaborat ing, which increases their
access to resources and capabilities, which in turn increases their access to gl obal markets. This

SME OPEN INNOVATION: CHAPTER 5 104
helped the SMEs in their sample overcome self- reported barriers of their country’s relatively
weak infrastructure and poor institutional environment. The researchers also find that S MEs
benefit from the use of strong governance mechanisms, including contractual commitment s,
formal and informal rules, and codes of conduct to guide behaviors.
Jemala (2010) conducted an extensive literature review of open innovation and concludes
that there is a curvilinear relationship between SME innovation performance and partic ipation in
an open innovation network. SMEs are able to benefit up to a point by gaining access to
capabilities and resources and by reducing risk exposure. However, excessive c ollaboration can
increase costs that could potentially outweigh the benefits of these relationshi ps. For example, it
becomes exceedingly more difficult and costly to collaborate with larger n etworks because there
are more bi-directional and multi-directional connections between members. It ca n become
increasingly more challenging to manage a growing number of emerging relat ionships because it
requires more time and higher resource commitments. Most firms eventually en counter a point
where the search, management, knowledge-transfer, and logistical costs exceed the marginal
benefits of collaborating with one additional partner. This “tipping point” is entirely situational
and varies by firm, so it cannot be universally quantified.
Schillo and Walter (2010) conducted a study of 85 SMEs (spin-off companies from
German Universities). They find that SMEs benefit from open innovation when they rate
themselves as having strong coordination capabilities, whereas SMEs with lowe r coordination
capabilities in their study experienced lower sales growth as a result of these r elationships. They
also observe a curvilinear effect where innovation collaboration only benefits firms up to a point
and then produces negat ive results, depending on the SME’s ability to manage a larger number
of partners.

SME OPEN INNOVATION: CHAPTER 5 105
In a study of 292 Argentine SMEs, Huang and Rice (2009) find a statistically signi ficant
positive correlation between innovation networking and SME innovation performance. They al so
find that SMEs with higher absorptive capacity benefit even more, but only up to a point. They
conclude that an over-focus on accumulating knowledge compared to creating it can eventuall y
lead to lower innovation outputs in the long run. They, therefore, conclude that there is a
curvilinear relationship between open innovation collaboration and SME innovation
performance.
Evens (2009) conducted several case studies of Flemish SMEs and finds that different
firms experience different results with open innovation collaboration. For example, one S ME
initially suffered from intellectual property theft, but then later benefited from c ollaborations
once it changed partners. Another SME in the evaluation benefited greatly from collaborat ing
with universities and a different SME benefited more from networking with other businesse s.
Kuehnle and Wagenhause (2007) conducted several case studies of SMEs in the European Union
and they have similar conclusions. One of the SMEs they evaluated was unable to effec tively
collaborate with a larger partner, which eventually bought the smaller firm. The other two SMEs
under evaluation were able to create successful new products as a result of their open innova tion
collaboration activities. The researchers argue that SMEs can benefit from ope n innovation, but
that success is not guaranteed because various contextual factors can impact t he probability of
success. These factors are discussed in Chapter 6 of this dissertation.
Igartua, Garrigos, and Hervas-Oliver (2010) conducted a case study of a Spanish SME
that has had experience with employing an open innovation collaboration strategy. The firm has
successfully leveraged innovation management tools, techniques, and methodologies to enha nce

SME OPEN INNOVATION: CHAPTER 5 106
its ability to benefit from these relationships. As the SME has implemented thes e efforts, it has
experienced annual innovation portfolio growth of over 20%.
Bell et al. (2003) conducted case studies of five global SMEs that have successfully
implemented open innovation as part of their business models. He finds that these SMEs w ere
able to leverage information and communications technology (ICT) to increase the e ffectiveness
and efficiency of their collaboration efforts. The SMEs created commercially succ essful products
and became better at anticipating market needs through these collaborative relat ionships. This is
consistent with the findings of Bolisani and Scarso (2003), who conducted case studies of four
SME innovation networks. They find that successful networks employ ICT and knowledge
management tools to facilitate the flow of information between members. They conclude that
SMEs benefit from these networks, but warn that larger firms often establish the rule s and
governance mechanisms to guide the relationships. This often does not hamper SMEs as long a s
there is mutual trust and all parties share the same strategic goals.
Almirall and Casadesus-Masanell (2010) conducted a simulation study on the impacts of
various types of partnerships on innovation success. They find that smaller innovation network s
can be more advantageous to firms in the earlier stages of a project when the technologi cal
complexity is high. After a certain point in the exploration process, larger networks become more
advantageous as the technological complexity diminishes. Studies have also s hown that SMEs
experience more success with open innovation collaboration if their partners have
complementary strategies, mutual trust, intellectual capital protection, l earning capacity, and an
open innovation culture (Casals, 2010).
Christensen, Olesen, and Kjaer (2005) conducted case studies of 25 firms in the
consumer electronics industry and find that smaller firms experienced lower risks when

SME OPEN INNOVATION: CHAPTER 5 107
collaborating with other small firms rather than large ones. They contend that when SME s
partner with other SMEs they benefit by having similar bargaining power. Partneri ng with other
SMEs can also reduce opportunistic behavior because an SME is less able to defend against
opportunism from larger companies. However, the researchers conclude that regardless of
partner sizes, SMEs typically benefit from open innovation collaboration.
Rosenbusch, Brinckmann, and Bausch (2011) conducted a meta-analysis and find that
internal innovation correlates with high SME performance, but that external colla boration has
minimal impact on SME performance. They note contradictory findings in various s tudies and
conclude that one must account for various contextual moderators (e.g., country or industry).
They explain that some SMEs benefit from the complementary resources and capa bilities, while
others incur collaboration and absorption/desorption costs that exceed those benefits. They a lso
claim that some SMEs have limited experience with managing these types of re lationships and
can, therefore, appropriate fewer returns because of inefficiencies and a limited abi lity to learn,
assimilate, and manage new knowledge. Additionally, they argue that in some ne tworks larger
firms may be more (or less) likely to violate agreements with little or no repercus sions from
small firms. The researchers also contend that their evaluation did not differentiate be tween the
types of innovations (e.g. technically complex versus simple innovations or radical versus
incremental innovations, which may be more or less amenable to development in collabor ative
relationships).
Several studies focus on SMEs engaging in open innovation collaboration on a global
scale. Grant and Baden-Fuller (2004) explain that there is a growing trend of SME s around the
world increasing global collaboration activities. They argue that SMEs col laborate to gain access
to additional resources. They cite recent research confirming that some SMEs have been able to

SME OPEN INNOVATION: CHAPTER 5 108
successfully increase their access to resources through international alli ances. Research also
indicates that globally-oriented SMEs tend to focus on innovative products, while those t hat are
not globally-oriented tend to focus on broader, lower margin product lines (Nkongolo-Bakenda
et al., 2010). Additionally, SMEs with lower absorptive capabilities tend to collaborat e only with
other local organizations, whereas SMEs with robust absorptive capabilities seem t o benefit from
joining global innovation networks (Huggins & Johnston, 2009).
Nkongolo-Bakenda et al. (2010) conducted a study of 81 Canadian SMEs. They find that
increased globalization activities increases SME financial performance . They also conclude that
forming global networks helps provide SMEs access to additional resources, opportunities , and
capabilities, thereby increasing their probability of success. The rese archers stressed the
importance of having a strong cultural awareness and found that prior international experie nce
significantly helps SMEs reduce risks of interacting with foreign firms because i t lowers cultural
barriers that could impede progress in pursuing mutual goals. Additionally, Bolis ani and Scarso
(2003) argue that effective governance such as contracts, rules, and procedures can help
strengthen mutual trust in SME international alliances, enabling SMEs to maxi mize the benefits
of these relationships.
SMEs often enter into international collaborative partnerships because of an overall lac k
of resources and capabilities (Clarke & Turner, 2003). SMEs that join international i nnovation
alliances can also reduce their overall market risk exposure because the se networks provide them
presence in multiple national economies and marketplaces (Zahra, Ucbasaran, & N ewey, 2009).
However, research indicates that medium-sized firms tend to achieve proportionally hi gher cost
savings from their international collaborative relationships compared to smaller firms. This is

SME OPEN INNOVATION: CHAPTER 5 109
likely a result of medium-sized firms being more effective than small firms at managing their
international alliances (Sen & Haq, 2010).
Rammer and Schmiele (2009) conducted a study of the drivers and effects of
internationalizing open innovation activities by 2,937 German SMEs. Only 26.5% of the SMEs
in their sample engage in any form of international business activity. However, they fi nd a
positive relationship between SMEs collaborating with international innovation partners and an
increased number of new SME products. They also find a positive correlation with membership
in these relationships and increased SME sales of new products. The researchers conclude tha t
SMEs that conduct international open innovation collaboration activities are financially stronger
than firms that do not. Additionally, internationalizing SMEs benefit from the learning and
knowledge transfer from their partners, and they apply this knowledge to their domestic R&D
operations. The researchers claim that prior experience with exporting and with protecti ng
intellectual property contributes to an SME’s success with its innovation activiti es. The
researchers conclude that SMEs are less internationalized than larger firms becaus e of various
barriers that impact SMEs more than large firms. These size-related com petitive barriers include
fewer resources and capabilities; difficulty contending with high fixed cost s for establishing
subsidiaries; le ss-established reputations; and difficulty dealing with unfamiliar foreign politic al,
economic, and cultural environments. Contrary to the findings of Nkongolo-Bakenda et al .
(2010), Rammer and Schmiele (2009) find that fierce competition in a home market interferes
with SME international innovation collaboration efforts rather than helping to drive those
activities. The data also indicate that a shortage of skilled workers in a domestic environment is a
factor that leads SMEs to partner with foreign innovation partners (Rammer & Schmiele, 2009).

SME OPEN INNOVATION: CHAPTER 5 110
Zahra, Ucbasaran, and Newey (2009) conducted a study 384 U.S.-based SMEs that
conduct business internationally. They find that collaborating in joint innovation efforts wi th
foreign companies benefits SMEs financially, increases the ir accumulation of knowledge and
capabilities, and improves their ability to innovate . The researchers’ data provide empirical
evidence that these relationships increase organizational learning by expos ing the firms to
diverse cultures and ideas. This increases know ledge accumulation and heightens SMEs’ ability
to problem-solve and innovate. The study also concludes that access to additional global m arkets
helps SMEs diversify risks and increase potential financial rewards. They n ote that there are a
number of different types of alliances (e.g., networks with higher controls and involveme nt
versus loose, informal relationships) and that SMEs benefit differently from them depending on
what type of alliance is best suited for a particular proj ect. They also find that SMEs with higher
absorptive capabilities, collaboration competencies, and cultural intelligence benefit more from
these international innovation alliances in terms of higher innovation outputs and stronger
financial performance.
Proposition 2 (Open Innovation Collaboration) Meta-Analysis
The dissertation author conducted a meta-analysis to empirically evaluate Propositi on 2.
The meta-analysis protocol is provided in Chapter 4. Appendix A provides the data and
calculated statistics for this meta-analysis. There are nine quantitati ve studies that contain usable
data applicable to Proposition 2. The analysis contains data from 9,302 SMEs from more than a
dozen countries.
The reported effect sizes from these studies are both positive and negative. This ca n
indicate differences arising from disparate research methods, sampling errors, and/or genuine

SME OPEN INNOVATION: CHAPTER 5 111
differences in the ability of distinct groups of SMEs to benefit (or experience negat ive impacts )
from open innovation collaboration.
The dissertation author calculated the mean effect size to be 0.061. This indicates a sma ll
positive effect of open innovation collaboration on SME innovation performance. A correlati on
effect size of approximately 0.10 is considered small, 0.25 is considered a medium effect, and
0.40 is considered a large effect (Lipsey & Wilson, 2000). The 95% confidence interval ranges
from 0.050 to 0.072. This indicates that the mean effect size is statistically sig nificant since the
range contains only positive values. A value of 0 would indicate a statistical likel ihood of no
effect and negative values would indicate the statistical likelihood of negative effect s. Therefore,
it is reasonable to accept Proposition 2 with the caveat that not all SMEs will benefi t (or be
negatively impacted) by open innovation collaboration to the same degree. It appears t hat on
average, SMEs may be slightly more likely to benefit from these collaborations beca use of the
positive mean effect size. However, it would be imprudent to use this data to attempt to predict
the outcome of an SME employing this strategy since some SMEs experienced negat ive effects
from open innovation collaboration. This is consistent with findings from the literature disc ussed
previously.
The author also calculated the Q-test for homogeneity. The null hypothesis for
homogeneity is rejected, indicating that the variability across effect sizes exceeds what would be
expected based on sampling error. This suggests that the observations are not measuring th e
same population mean (the same population of SMEs). This is consistent with findings from the
literature because it has been well cited that there is significant variabili ty in the degree to which
individual SMEs benefit (or are harmed by) open innovation collaboration. These impacts appear
to be moderated by a number of contextual factors such as industry, country, product-t ype, firm

SME OPEN INNOVATION: CHAPTER 5 112
characteristics, and other situational variables. These factors are discuss ed more fully in Chapter
6.
Proposition 2 (Open Innovation Collaboration) Conclusion
The qualitative and quantitative findings provide an abundance of evidence to accept
Proposition 2 because it has been shown that many SMEs across the world in different contex ts
have benefited from open innovation collaboration. However, there is also sufficient evidenc e
that indicates that these activities can potentially have negative effects on some SMEs in some
situations. Additionally, there is evidence indicating that open innovation collaborati on is
curvilinearly related to innovation performance, where SMEs can potentially bene fit only up to a
point before experiencing negative returns. There also appear to be significant potential be nefits
to engaging in innovation collaboration internationally, but there are additional challen ges and
costs associated with global collaboration that may make this approach less practical for some
SMEs.
The findings of this analysis also indicate that SMEs can potentially utilize ope n
innovation collaboration to overcome their size-related competitive challenges b ecause a number
of studies cite the ability of collaboration to increase an SME’s resources, streng then its dynamic
capabilities, and reduce its risk exposure (assuming that the SME can adequately prot ect its
intellectual capital).
The me ta-analysis shows a small, but statistically significant positive effect o f innovation
networking on SME innovation performance (in terms of innovation outputs and financial gains
from these innovations). However, opposing evidence indicates that this innovation strategy can
potentially negatively impact some SMEs in some situational contexts. Chapter 6 m ore fully

SME OPEN INNOVATION: CHAPTER 5 113
discusses the specific contextual factors impacting the success of SME open innova tion
strategies.
Proposition 2a (Vertical Collaboration) Findin gs
Proposition 2a: Vertical collaboration can increase SME innovation outputs and financial
performance.
Proposition 2 relates to all forms of open innovation collaboration collectively and
undifferentiated. Proposition 2a pertains specifically to vertical collaboration. There a re several
studies that evaluate the effectiveness of this SME open innovation strategy. For ex ample,
Terziovski (2003) conducted an evaluation of 115 Australian SMEs and finds that close
collaboration with customers and suppliers (vertical collaboration) is highly corre lated with
enhanced firm performance, and this collaboration increases SMEs’ ability to innovate. He notes
that these SMEs gain crucial knowledge from these external sources and tha t vertical
collaboration helps SMEs to become more successful with developing and introducing new
products. A study of 504 SMEs in Finland, also finds a positive correlation between vertica l
open innovation and higher levels of SME product innovations (Leiponen & Byma, 2009).
Additionally, another study of 600 Australian SME (by the above researcher) also finds a
positive correlation between stronger relationships with customers and suppliers a nd higher SME
innovation outputs and financial performance (Terziovski, 2010).
SMEs (as well as large companies) have been found to be more likely to engage in
vertical collaboration than in knowledge-intensive collaboration with universities and
government institutions (De Backer, Lopex-Bassols, & Martinez, 2008). This indicates that there
are lower barriers to executing a vertical collaboration strategy. For exampl e, it requires fewer
capabilities to solicit feedback from customers and suppliers compared to enterin g into more

SME OPEN INNOVATION: CHAPTER 5 114
formal relationships with universities or research institutions. These vertical rel ationships can
also provide SMEs with access to additional resources. For example, in a study of 2,414 Korean
SMEs, Lee et al. (2010) find that especially during the knowledge exploitation (val ue capture)
stage, SMEs benefit from entering into supply chain relationships with larger firms be cause it
enables SMEs to leverage larger firms’ more robust commercialization capabilit ies.
Huggins and Johnston (2009) conducted a study of 49 SMEs in the United Kingdom
(UK). They find a positive correlation between vertical collaboration with custome rs and SME
innovation performance. They also find a small negative correlation between suppler
collaboration and SME innovation performance, which is contradictory to the findings of relat ed
studies. They note that the majority of SME collaboration in these firms occur s vertically along
their supply chains and through knowledge-intensive collaborations with universities rather than
through horizontal collaboration with competitors.
Mesquita and Lazzarini (2008) conducted a study of 232 Argentine SMEs and find that
vertical collaboration increases innovation outputs, productivity, and efficiencies in s upply
chains. They also find that increases in relational governance in supply-chain rel ationships are
positively associated with increased product innovations of the network. Additionally , they note
that a global setting provides more vertical, horizontal, and knowledge-intensive colla boration
opportunities for SMEs than what their domestic environments can provide.
McAdam et al. (2010) conducted a study of 395 Irish SMEs. They find a positive
correlation between the involvement of customers during new product development efforts and a
higher number of new products being introduced during the following three-year period. The y
claim that the firm’s leadership, culture, business processes, quality managem ent, and knowledge

SME OPEN INNOVATION: CHAPTER 5 115
management activities can assist (or hinder) SMEs with being able to succes sfully benefit from
these vertical open innovation activities.
Arakji and Lang (2007) studied company-customer innovation collaboration in the video
gaming segment of the electronics industry. Their findings indicate that el ectronics companies
that actively involve consumer networks of avid gamers in the development of new products are
able to accelerate the production of more profitable games and gaming systems. These
researchers warn that engaging consumers in every aspect of R&D can risk inte llectual property
spillover to competitors, but that moderate engagement with user communities for innovat ion
activities correlates with more successful and profitable product launches. A study of 252
Swedish SME also finds that involving customers early and often in the R&D process ca n lead to
more successful product launches and significantly reduce innovation project risks (Pa rida,
Westerberg, & Frishammar, 2011).
Ebersberger et al. (2010) conducted an analysis of 3,688 European firms and conclude
that the interaction of international orientation and vertical collaboration variables positively
impact innovation performance of SMEs. They also find that international vertical collabor ation
has a higher positive impact on innovation performance than domestic vertical collaborati on.
Additionally, they find that vertical collaboration can benefit SMEs up to a point, after w hich
there are declining marginal returns due to increasing costs and inefficiencies wi th maintaining a
larger number of relationships and assimilating larger quantities of information (curvil inear
association between vertical collaboration and SME innovation performance).
Proposition 2a (Vertical Collaboration) Meta-Analysis
The dissertation author conducted a meta-analysis to empirically evaluate Propositi on 2a.

SME OPEN INNOVATION: CHAPTER 5 116
The meta-analysis protocol is provided in Chapter 4. Appendix A provides the data and
calculated statistics for this meta-analysis. There are six quantita tive studies that contain usable
data applicable to Proposition 2a. The analysis contains data from 5,118 SMEs.
The reported effect sizes from these studies are both positive and negative. This ca n
indicate either differences arising from disparate research methods, sampli ng errors, and/or
genuine differences in the ability of diverse groups of SMEs to benefit (or experience negat ive
returns) from open innovation vertical collaboration.
The dissertation author calculated the mean effect size to be 0.132. This indicates a sm all
positive effect of vertical open innovation collaboration on SME innovation performance. A
correlation effect size of approximately 0.10 is considered small, 0.25 is considered a m edium
effect, and 0.40 is considered a large effect (Lipsey & Wilson, 2000). The 95% confidence
interval ranges from 0.113 to 0.152. This indicates that the mean effect size is statis tically
significant since the range contains only positive values. A value of 0 would indicate a s tatistical
likelihood of no effect and negative values would indicate the statistical likelihood of nega tive
effects. Therefore, it is reasonable to accept Proposition 2a with the caveat that not all SMEs will
benefit (or be negatively impacted) by vertical collaboration to the same degre e. It appears that
on average SMEs may be slightly more likely to benefit from these collaborations. Howe ver, not
all SMEs will benefit from these activities as indicated by the presence of s ome negative effect
size values of the source data.
The dissertation author also calculated the Q-test for homogeneity. The null hypothe sis
for homogeneity is rejected, indicating that the variability across effect s izes exceeds what would
be expected based on sampling error. This suggests that the observations are not measuring the
same population mean. This is consistent with findings from the literature because it has been

SME OPEN INNOVATION: CHAPTER 5 117
well cited that there is significant variability in the degree to which individua l SMEs benefit (or
are harmed by) vertical collaboration open innovation activities because of a number of
contextual factors such as industry, country, product-type, firm characteristic s, and other
situational variables. These factors are discussed more fully in Chapter 6.
Proposition 2a (Vertical Collaboration) Conclusion
The triangulation of the qualitative and quantitative findings shows that there is suffic ient
evidence to accept Proposition 2a. However, there is also evidence that indicates that thes e
activities can potentially have negative effects on some SMEs in different situa tions and that
vertical innovation is curvilinearly related to SME innovation performance. The finding s of this
analysis also indicate that SMEs can potentially utilize vertical open innovat ion collaboration to
overcome their size-related competitive challenges, since a number of studies ci te the ability of
vertical collaboration to increase an SME’s resources and reduce product failure ris ks.
The meta-analysis shows a small, but statistically significant positive effect of vertical
collaboration on SME innovation performance (in terms of innovation outputs and financial
gains from those innovations). However, some evidence suggests that this innovation strateg y
can potentially negatively impact some SMEs in some situations. Chapter 6 discuss es the
specific contextual factors impacting the success of SME open innovation strategie s in more
detail.
Proposition 2b (Horizontal Collaboration) Findings
Proposition 2b: Horizontal collaboration can increase SME innovation outputs and financial
performance.
There are several studies that specifically measure the impact of horizontal c ollaboration
on SME innovation performance. For example, in a study of 252 Swedish SMEs, Parida,

SME OPEN INNOVATION: CHAPTER 5 118
Westerberg, and Frishammar (2011) find that horizontal open innovation with other companies
increases incremental innovation outputs of SMEs. However, their data did not produce
statistically significant results as to whether these interactions als o increase the production of
radical innovations. Although, they conclude that horizontal collaboration increases acc ess to
resources and capabilities and enables SMEs to distribute risks among its partne rs.
In a study of 504 SMEs in Finland, Leiponen and Byma (2009) find a positive correlation
between horizontal open innovation and higher levels of SME product innovation. They also
claim that SMEs have different preferences for patenting, secrecy, and speed to market
depending on what industry they are in. Patents are the preference in the sciences and for capital-
financed SMEs. These SMEs are more likely to collaborate than those valuing secrecy (e .g.,
those in service industries). SMEs who desire speed to market are able to obtain faster produ ct
launches through horizontal collaborations. They also conclude that SMEs are more likely to
collaborate in environments with strong legal intellectual property protections.
Keeble et al. (1998) conducted a study of 50 technology-focused SMEs in the Cambridge
region of the UK. The Cambridge region has a rapidly growing concentration of high-technology
firms, which is why the researchers focus on this particular region. They find that the se SMEs
collaborate with other firms in one of three ways: with firms only within their region (14.4% ),
with other UK firms outside of that region (48%), or with other firms outside of the UK (17.8%).
The SMEs indicate that their main reason for global horizontal collaboration is to inc rease their
firms’ knowledge, technologie s, and innovativeness. The SMEs also rate the importance of
collaboration with firms outside of their region and outside of the UK as having a higher impact
on their innovation activities compared to their collaborations with other technology-focuse d
firms within the Cambridge region. They conclude that SMEs collaborate to gain access t o new

SME OPEN INNOVATION: CHAPTER 5 119
sources of knowledge and to enhance innovation capabilities because SMEs in their eva luation
collaborate with firms outside of their region and country despite the higher transact ion costs of
these collaborations.
Huggins and Johnston (2009) conducted a study of 49 knowledge-intensive SMEs in
Yorkshire and Humberside in the UK. They find that in economically uncompetitive regions
(e.g., Yorkshire and Humberside) innovative SMEs prefer to form knowledge collaboration
networks that include firms outside of their region. These innovative SMEs seek to partne r with
firms that are located in geographic knowledge clusters around the world becaus e their
economically uncompetitive region is not one of those knowledge clusters. The resear chers claim
that although geographic distance can impede knowledge flows, SMEs with the highes t
innovation performance are more likely to have a balanced network of relationships of fi rms both
within and outside of their region (including international alliances). The researchers also find
that innovative SMEs are more likely to source tacit knowledge from firms outside of thei r
region, whereas less-innovative firms are more likely to seek explicit (codified) knowledge from
network partners within their region. This implies that SMEs with larger geographic ally-
dispersed networks have more sophisticated capabilities because they seek hi gher-quality
knowledge from their partners rather than just procedural, step- by-step guidance. Additionally,
the researchers find that the SMEs that regularly change their partnering relat ionships in a
dynamic manner have been more likely to enter into networks outside of their geographic re gion.
Ebersberger et al. (2010) conducted a study of 3,688 Austrian, Belgian, Danish, and
Norwegian firms. Their results show positive correlations between domestic horiz ontal open
innovation and innovation outputs. However, they find both positive and negative correlations
between international horizontal collaboration and innovation outputs. This indicates tha t SMEs

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experience more challenges with international collaboration. The results al so vary depending on
the country of origin of the firms. Therefore, this is evidence that situational context modera tes
the effectiveness of open innovation interventions.
Mesquita and Lazzarini (2008) conducted a study of 232 Argentine SMEs and finds that
horizontal collaboration with competitors provides these SMEs with access to additional
resources and capabilities, which increase and enhance their innovation activiti es. They note that
the firms in their sample have a relatively weak infrastructure and that the coope ration enables
them to access a stronger collective resource base, which strengthens t heir ability to create and
develop innovations. They note that formal contracts and governance benefits SMEs in
managing these relationships. They also find that relational governance in horizont al
relationships is positively associated with increased product innovations of the network.
However, these researchers claim that the benefits of horizontal collaboration are cu rvilinear
because managing relationships can become too costly if networks grow too large a nd innovation
can eventually be stifled d ue to “over embeddedness” of long -term relationships if organizations
do not continually expose themselves to new perspectives and mindsets that other new pa rtners
could provide. Another study of 2,414 SMEs also finds that negative returns can eventually se t in
if there are excessive sources of knowledge and too many collaboration partners (Le e et al.,
2010). This indicates that there is curvilinear relationship between horizontal open innovati on
and SME innovation performance.
Proposition 2b (Horizontal Collaboration) Meta-Analysis
The dissertation author conducted a meta-analysis to empirically evaluate Propositi on 2b.

SME OPEN INNOVATION: CHAPTER 5 121
The meta-analysis protocol is provided in Chapter 4. Appendix A provides the data and
calculated statistics for this meta-analysis. There are five quant itative studies that contain usable
data applicable to Proposition 2b. This analysis contains data from 4,723 SMEs.
The reported effect sizes from these studies are both positive and negative. This ca n
indicate either differences arising fro m disparate research methods, sampling errors, and/or
genuine differences in the ability of different groups of SMEs to benefit (or experience negat ive
effects) from horizontal open innovation collaboration. For example, firms of different countri es
of origin were shown to experience different levels of effectiveness at appropriating val ue from
horizontal collaboration activities.
The dissertation author calculated the mean effect size to be 0.080. This indicates a sm all
positive effect of horizontal open innovation on SME innovation performance. A correlation
effect size of approximately 0.10 is considered small, 0.25 is considered a medium effect, and
0.40 is considered a large effect (Lipsey & Wilson, 2000). The 95% confidence interval ranges
from 0.060 to 0.100. This indicates that the mean effect size is statistically s ignificant since the
range contains only positive values. A value of 0 would indicate a statistical likel ihood of no
effect and negative values would indicate the statistical likelihood of negative effec ts. Therefore,
it is reasonable to accept Proposition 2b with the caveat that not all SMEs will bene fit (or be
negatively impacted) by horizontal innovation collaboration to the same degree. It appears that
on average SMEs are slightly more likely to benefit from these collaborations. However, not all
SMEs will benefit from these activities as indicated by the presence of some neg ative effect size
values in the source data.
The author also calculated the Q-test for homogeneity. The null hypothesis for
homogeneity is rejected, indicating that the variability across effect s izes exceeds what would be

SME OPEN INNOVATION: CHAPTER 5 122
expected based on sampling error. This suggests that the observations are not measuring th e
same population mean. This is consistent with findings from the literature because it has been
well cited that there is significant variability in the degree to which individua l SMEs benefit (or
are harmed by) horizontal open innovation because of a number of contextual factors such as
industry, country, product-type, firm characteristics, and other situational variabl es. These
factors are discussed more fully in Chapter 6.
Proposition 2b (Horizontal Collaboration) Conclusion
The qualitative and quantitative findings are congruent and provide sufficient evidence to
accept Proposition 2b. However, there is also evidence that indicates that these activit ies can
potentially have negative effects on some SMEs in different situations and that the re is a
curvilinear relationship between horizontal collaboration and SME innovation performan ce.
The findings of this analysis also indicate that SMEs can potentially utilize hori zontal
open innovation collaboration to overcome their size-related competitive challenges. T hese
activities can increase innovation outputs and financial returns (increase resourc es), which
according to Proposition 1, indirectly enables SMEs to enhance their dynamic capabili ties and
reduce their risk exposure.
The meta-analysis revealed a small, but statistically significant pos itive effect of
horizontal innovation networking on SME innovation performance (in terms of innovation
outputs and financial gains from those innovations). However, a curvilinear relationship wa s
identified and findings indicate that this innovation strategy can potentially negativel y impact
some SMEs in some situational contexts. Chapter 6 discusses the specific contex tual factors
impacting the success of SME open innovation strategies.

SME OPEN INNOVATION: CHAPTER 5 123
Proposition 2c (Knowledge-Intensive Collaboration) Findings
Proposition 2c: Knowledge-intensive collaboration can increase SME innovation outputs and
financial performance.
There are several studies that evaluate the impact of knowledge-intensive colla boration
on SME innovation performance. For example, in a study of 504 SMEs in Finland, Leiponen and
Byma (2009) find a positive correlation between knowledge-intensive open innovation with
universities and higher outputs of SME product innovations. This is consistent with other
empirical findings. McAdam et al. (2010) conducted a study of 395 Irish SMEs and similarl y
finds a positive correlation between knowledge-intensive collaboration and a higher num ber of
new products that SME introduce to the market within three years of engaging in the
collaboration.
In a study of 3,688 European SMEs, Ebersberger et al. (2010) finds that different groups
of SMEs benefit differently from knowledge-intensive collaboration. They find that most S MEs
typically benefit from these collaborative relationships, but there is significant variability in the
outcomes that SMEs experience in different countries. This indicates that individual si tuations
and circumstances influence the degree to which SMEs benefit from (or are negatively impacted
by) knowledge-intensive open innovation activities. Chapter 6 describes the various si tuational
factors that impact the probability of an SME benefiting from knowledge-intensive open
innovation.
In an evaluation of 2,412 Korean SMEs, Lee et al. (2010) finds that innovative SMEs are
more likely to collaborate with universities and research institutions than less-i nnovative SMEs.
They conclude that SMEs prefer knowledge-intensive collaboration to horizontal collabora tion
because these relationships present a lower likelihood of unintentionally transferring secretive

SME OPEN INNOVATION: CHAPTER 5 124
competitive knowledge to rival firms, which is a legitimate concern that some SME s have with
horizontal collaboration.
Laursen and Salter (2004) studied 2,655 UK firms and find that SMEs are less likely than
larger firms to collaborate with universities; although they contend that this finding is contrary to
other studies. However, their results are only statistically significant at the .10 level and not at
the .05 level of significance, so one could consider their results inconclusive. They also find a
correlation between knowledge-intensive collaboration and R&D outputs of participatin g firms.
SMEs tend to collaborate more with universities in knowledge-intensive industries such a s
biotechnology than in other industries such as textiles. They also claim that an SME’s leve l of
absorptive capabilities influence the success of these relationships.
Squicciarini (2009) conducted a study of 252 Finnish firms in science parks and found
that involvement in collaboration with universities, research organizations, and other firms in
these science parks increased the number of patents that SMEs filed. They also observe that the
larger SMEs derived slightly more benefits from participation than smaller SME s. They note that
SMEs benefit from knowledge spillover from other participants and that each additional year
SMEs remain in science parks their likelihood of patenting increases by about 14%. They a lso
note that participation can produce diminishing marginal returns (curvilinear relati onship) as a
growing number of collaboration partners can increase the collaboration cost- to-benefit ratio.
Proposition 2c (Knowledge-Intensive Collaboration) Meta-Analysis
The dissertation author conducted a meta-analysis to empirically evaluate Propositi on 2c.
The meta-analysis protocol is provided in Chapter 4. Appendix A provides the data and
calculated statistics for this meta-analysis. There are four quantita tive studies that contain usable
data applicable to Proposition 2c. The analysis contains data from 4,634 SMEs.

SME OPEN INNOVATION: CHAPTER 5 125
The reported effect sizes from these studies are both positive and negative. This c an
indicate either differences arising from disparate research methods, sampli ng errors, and/or
genuine differences in the ability of different groups of SMEs to benefit (or experience negat ive
effects) from knowledge-intensive collaboration. For example, firms of different countr ies of
origin experienced different levels of success with implementing knowledge-intens ive
collaboration.
The dissertation author calculated the mean effect size to be 0.053. This indicates a sm all
positive effect of knowledge-intensive open innovation on SME innovation performance. A
correlation effect size of approximately 0.10 is considered small, 0.25 is considered a me dium
effect, and 0.40 is considered a large effect (Lipsey & Wilson, 2000). The 95% confidence
interval ranges from 0.033 to 0.073. This indicates that the mean effect size is statis tically
significant since the range contains only positive values. A value of 0 would indicate a st atistical
likelihood of no effect and negative values would indicate the statistical likelihood of nega tive
effects. Therefore, it is reasonable to accept Proposition 2c with the caveat that not all SMEs will
benefit (or be negatively impacted) by knowledge-intensive innovation collaborati on to the same
degree. It appears that on average SMEs may be slightly more likely to benefit from t hese
collaborations. However, not all SMEs will benefit from these activities as indi cated by the
presence of some negative effect size values of the source data.
The author also calculated the Q-test for homogeneity. The null hypothesis for
homogeneity is rejected, indicating that the variability across effect s izes exceeds what would be
expected based on sampling error. This suggests that the observations are not measuring t he
same population mean. This is consistent with findings from the literature because it has been
well cited that there is significant variability in the degree to which individua l SMEs benefit (or

SME OPEN INNOVATION: CHAPTER 5 126
are harmed by) an knowledge-intensive open innovation strategy because of a number of
contextual factors such as industry, country, product-type, firm characteristic s, and other
situational variables. These factors are discussed more fully in Chapter 6.
Proposition 2c (Knowledge-Intensive Collaboration) Conclusion
This analysis synthesized an adequate amount of evidence to accept Proposition 2c.
However, there is also evidence that indicates that these activities can poten tially have negative
effects on some SMEs in different situations and that there is a curvilinear relati onship between
knowledge-intensive collaboration and innovation performance.
The findings of this analysis indicate that SMEs can potentially utilize knowledge-
intensive open innovation collaboration to overcome their size-related challenges. This
innovation strategy assists SMEs with obtaining more resources (additional innovat ion outputs
and financial gains), which according to Proposition 1, indirectly enables SMEs to also enhanc e
their dynamic capabilities and reduce risk exposure.
The meta-analysis shows a small, but statistically significant positive effect of innovation
networking on SME’s innovation performance (in terms of innovation outputs and financial
gains from those innovations). However, opposing evidence indicates that this innovation
strategy can potentially negatively impact some SMEs in some situational c ontexts and it can be
curvilinearly related to SME innovation performance. Chapter 6 discusses the specific c ontextual
factors impacting the success of SME open innovation strategies more fully.
Proposition 3 (Outward Open Innovation) Findings
Proposition 3: Outward open innovation can increase SME innovation outputs and financial
performance.

SME OPEN INNOVATION: CHAPTER 5 127
Several studies indicate that SMEs can benefit from out-licensing their intelle ctual
property (outward open innovation) because it can generate financial returns, while avoidi ng
high costs and risks of building or acquiring downstream commercialization capabilitie s. Out-
licensing can also generate funds to enable increased investments in other innovation proje cts.
This increases an SME’s incentive and ability to increase innovation outputs (Bianchi et al.,
2010). It can create a virtuous cycle where the SME is incentivized by profit motive ( licensing
revenue) and the licensing activity provides the SME with additional resources that can be
leveraged to create additional innovations (leading to additional revenue-producing licens ing
opportunities).
SMEs engage in outward open innovation with greater frequency than inward open
innovation (Chesbrough, 2010). SMEs tend to have much lower absorptive capacity than larger
firms, which explains why SMEs primarily focus on outward open innovation rather than on
acquiring innovations from the external environment (Huang & Rice, 2009). Also, SMEs ofte n
lack commercialization capabilities and are, therefore, more likely than large c ompanies to
license their innovations to other organizations (Motohashi, 2008).
Dahlander and Gann (2010) argue that SMEs can benefit from selling intellectual
property, but that it could also potentially harm these companies if they lack the abilit y to protect
their property and appropriate returns from it.
Lichtenthaler (2009) conducted a study of 136 SMEs in Germany, Austria, and
Switzerland. He finds that outward open innovation strategies have a positive effect on SME
innovation activity and financial performance. He also finds that some SMEs benefit mor e from
these innovation strategies in situations of high er degrees of technological turbulence, higher
transaction rates (shorter product cycles), and higher competitive intensity. He arg ues that SMEs

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have different degrees of success with licensing based on their management capabilit ies and
previous experiences. His study provides evidence that open innovation can provide varying
degrees of benefit (or disadvantage) based on different unique situational contexts.
Van de Vrande et al. (2009) conducted a study of 605 SMEs in the Netherlands and they
find that outward innovation strategies can benefit SMEs. They argue that SMEs are att racted to
outward open innovation because these firms typically lack commercialization capa bilities.
However, they warn that while SMEs can gain licensing revenue, licensing can al so make it
more difficult to compete against the licensees in the same market. For exampl e, if an SME sells
automobile parts it would not necessarily be wise to license the patents related t o its products to
its direct competitors that also sell automobile parts. It would be more strategica lly advantageous
to only license unused intellectual property that the SME does not intend to commerci alize on its
own.
Motohashi (2008) conducted a study o f Japanese Patent Office data and finds that SME
patenting patterns and the impact on financial performance varies by industry. For exa mple,
SMEs appropriate more returns from patents in high-technology industries compared to pa tents
for less-technologically complex products. He also finds that SMEs out-license m ore than larger
companies and rely less on defensive patents (unlicensed and unused patents for products t hat the
firm does not intend to commercialize).
Zahra, Ucbasaran, and Newey (2009) conducted a study of 384 U.S.-based SMEs that
conduct business internationally. They find that licensing patents international ly correlates
positively with SME product innovations. The study finds that licensing to internationa l partners
has been shown to be a successful strategy for opening up new revenue streams, increasing

SME OPEN INNOVATION: CHAPTER 5 129
revenue growth, and reducing risk by diversifying the geographic locations where the p roduct is
sold.
Proposition 3 (Outward Open Innovation) Case Study
This is a brief case study that the dissertation author created to provide an exam ple of an
SME that has been able to successfully utilize outward open innovation strategies (al ong with
other types of open innovation) to become a profitable global enterprise.
Anoto is a Swedish 80-employee electronics company with a global allianc e network of
collaboration partners on all continents in more than 50 countries. The company has more than
360 patents with 200 more patents pending. Anoto’s core technologies relate to the capture,
storage, and transmission of data. The company generates revenue by licensing it s patents to
companies that produce a variety of electronics products, including personal productivity tools,
educational tools, learning toys, and visual communications equipment. Anoto’s electronic s
products sell in various industries, including consumer goods, healthcare, banking and finance,
education, transportation and logistics, and facility management. Anoto works with its
collaboration partners to create new innovations and its partners leverage Anoto’s patents to
create additional electronics innovations (Anoto, 2010).
Anoto was formed in 1999 when the founder created a technological breakthrough for a
new digital pen that could scan and store images. He approached the Swedish company Erickson
Mobile and that company provided him with financial support to develop the idea further. He
then began working closely with Swedish universities and graduate students to devel op
additional related innovations. The company at that time had a very open network with vari ous
weak ties. As Anoto began to grow, it transitioned its innovation network from primarily
academic exploration alliances to a network of global exploitation partners tha t helped Anoto

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commercialize its products. Today, Anoto’s network structure is now more closed and
mechanistic with more controls. It also consists of stronger, more formalized ties be tween
partners. Anoto executives advocate the importance of trust and integrity in their network
relationships. The company emphasizes the importance of mutual trust because its part ners are
often direct competitors and there is the potential for companies to use their ins ide knowledge of
one another in ways that would be counterproductive to their relationship (Harryson, 2008).
Within the first year, Anoto had submitted 40 patent applications and it had already
begun licensing innovations to global partners such as 3M. The company noted that its patent
protection was essential to protecting its intellectual capital. After 18 months , Anoto had 300
patent applications and it had expanded its global network to include other international
companies such as Sony-Ericsson, Nokia, Hitachi, HP, and Logitech. This enabled Anot o to
secure investment funding (with large investments from Hitachi and Logitech in pa rticular). This
allowed the company to expand and to create additional innovations, enabling the firm to
continually grow revenues (Harryson, 2008).
Anoto’s core competencies are its proprietary technologies and its human capital . The
company focuses on creating new technologies and it outsources its non-core functi ons such as
marketing, manufacturing, and distribution. The company uses a business- to-business model
rather than a business- to-consumer framework. The firm has never sold anything directly to
customers, but rather it has relied on its extensive partner network of electronics com panies to
commercialize its innovations (Anoto, 2010). The company leverages advancements in IC T to
more effectively communicate and better manage its relationships with its coll aboration partners.
This collaboration creates convergences of technologies, leading to more te chnologically

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complex innovations (e.g., the convergence of its digital pen technology with electronic
children’s books) (Anoto, 2010).
Anoto seeks partners with complementary resources, capabilities, business strate gies, and
mutual goals. Over ti me, the company has been able to increase its own skills and capabilities by
absorbing knowledge from its partners. Additionally, the firm considers its partners’ geographic
locations when deciding whether or not to add them to their network, since Anoto continuall y
seeks to gain access to new markets. Anoto demonstrates a strong commitment to continual ly
strengthening its level of cultural intelligence to enable it to more effect ively collaborate with its
growing network of international alliance partners (Chadwich et al., 2011).
Anoto formalizes its alliances with partnership agreements that define the s cope and
terms of each relationship. The company usually enters into non-equity agreements to ke ep its
network structure more loose and flexible. It also divides its partners into four tier s of
commitment: platinum, gold, silver, and bronze. This partner program is a means to manage and
control its relationships. Higher levels of commitment require higher levels of mutual trus t. The
commitment level is dete rmined by the partner’s experience and expertise as well as the partner’s
previous commercial success (sales) of Anoto’s products. The only equity sharing relati onship
Anoto currently has is a joint venture with Hitachi Maxell, which Anoto entered into in orde r to
have an ownership stake and more control over sales in Japan (Chadwich et al., 2011).
Anoto provides its partners with tools to assist them with developing, marketing, and
selling its products. Anoto also communicates regularly with its partners. The compan y sends out
a newsletter every three weeks and it hosts special events and annual conferences where partners
from around the globe meet in Sweden to discuss strategy and new products offerings. Anoto
responds to feedback from its partners during these meetings and it shares its upd ated business

SME OPEN INNOVATION: CHAPTER 5 132
plan and other strategy artifacts with its partners to gain cooperation and to build trus t (Chadwich
et al., 2011). In 2010, Anoto established a Partner Advisory Board that has regular meetings w ith
partners to provide interactive feedback and to discuss market trends, challenges, and
opportunities (Anoto 2010 ). This governance has enabled the company to extract meaningful
knowledge of its competitive environment.
Anoto has demonstrated that an SME can not only successfully utilize open innovation
strategies, but that an SME can create an entire business model around open innovation. The
company continues to increase innovation outputs and has been very financially successful as a
result. Anoto’s success can be directly attributed to its utilization of outward open innovation and
other open innovation collaboration activities. Chapter 6 provides an overview of various “best
practices” that Anoto and other successful international SMEs have employed in thei r global
open innovation activities.
Proposition 3 (Outward Open Innovation) Meta-Analysis
In addition to the above meta-synthesis and case study, the dissertation author conducte d
a meta-analysis to empirically evaluate Proposition 3. The meta-analysi s protocol is provided in
Chapter 4. Appendix A provides the data and calculated statistics for this meta-anal ysis. There
are two quantitative studies that contain usable data applicable to Proposition 3. The anal ysis
contains data from 520 SMEs.
The calculated effect sizes of all observations are positive, indicating that o utward open
innovation is positively correlated with innovation performance (innovation outputs and sale s
derived from those new products). The author calculated the mean effect size to be 0.127. This
indicates a small positive effect of outward open innovation on SME innovation performance. A
correlation effect size of approximately 0.10 is considered small, 0.25 is considered a me dium

SME OPEN INNOVATION: CHAPTER 5 133
effect, and 0.40 is considered a large effect (Lipsey & Wilson, 2000). The 95% confidence
interval ranges from 0.079 to 0.176. This indicates that the mean effect size is statisti cally
significant since the range contains only positive values. A value of 0 would indicate a st atistical
likelihood of no effect and negative values would indicate the likelihood of negative effects .
Therefore, it is reasonable to accept Proposition 3 with the caveat that not all SMEs wil l benefi t
(or be negatively impacted) from outward innovation to the same degree.
The author also calculated the Q-test for homogeneity. The null hypothesis for
homogeneity is rejected, indicating that the variability across effect s izes exceeds what would be
expected based on sampling error. This suggests that the observations are not measuring th e
same population mean. This is consistent with findings from the literature because it has been
well cited that there is significant variability in the degree to which individua l SMEs benefit (or
are harmed by) an outward open innovation strategy because of a number of contextual factors
such as industry, country, product-type, firm characteristics, and other situationa l variables.
These factors are discussed more fully in Chapter 6.
Proposition 3 (Outward Open Innovation) Conclusion
The literature review, Anoto case study, other qualitative evidence, quantitati ve studies,
and meta-analysis collectively indicate that outward open innovation can benefit SM Es by
increasing innovation outputs and financial returns. Therefore, Proposition 3 is accepted.
SMEs can also potentially utilize outward open innovation to overcome their size-re lated
challenges. According to Proposition 1, increasing resources (innovation outputs and financi al
gains) can indirectly lead to an increase in dynamic capabilities and a reduct ion in risk exposure
(assuming an SME can adequately protect its intellectual capital). This increa ses SME
competitiveness.

SME OPEN INNOVATION: CHAPTER 5 134
The meta-analysis shows a small, statistically significant positive e ffect of outward
innovation on SME innovation performance. However, the evidence does not indicate that this
innovation strategy will always benefit every SME in every situation. Chapter 6 discusses in
detail the specific contextual moderators impacting the probability of an SME benefiting from
open innovation strategies.
Proposition 4 (Inward Open Innovation) Findings
Proposition 4: Inward open innovation can increase SME innovation outputs and financial
performance.
Several studies evaluate the effectiveness of inward open innovation strategies on SME
innovation performance (defined as innovation outputs and resultant financial gains). For
example, Fatur, Likar, and Ropret (2010) conducted a study of 2,503 Slovenian SMEs and large
firms. They find that externally sourcing knowledge and ideas (inward open innovation) has a
positive and statistically significant correlation with innovation performance as indicated by
increases in innovations, return on sales, return on equity, return on assets, and revenue growth.
They conclude that the increased financial performance also makes investment funding more
accessible and less expensive for these firms. They also find that large compa nies in the sample
produced 50% more innovations than mid-sized firms and three times more than small
companies. They argue that this difference arises because SMEs have fewer re sources to acquire,
create, and commercialize innovations. However, their data show that SMEs that engage i n
inward open innovation produce more innovation outputs and higher financial returns than SMEs
abstaining from these strategies.
SME patents on average have more references to prior art (i.e., existing inventions and
knowledge) than patents of large firms. SME patents also have a smaller percentag e of self-

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citations (i.e., citing a firm’s own previous patents) . These two facts indicate that SMEs are more
reliant on externally-developed knowledge (technology from other companies) than lar ge firms
(CHI Research, 2003). This indicates that many SMEs often rely on inward open innovation t o
create new patents.
Sen and Haq (2010) conducted a survey of 100 American SMEs. They find that SME
outsourcing of R&D activities (inward open innovation) has remained relatively low a nd stabl e
over the last decade. However, SMEs have increasingly been outsourcing non-innovative
activities, such as manufacturing, distribution, and marketing activities. Addit ionally, the
researchers conclude that mid-sized firms engage in higher levels of inward open innovation
compared to small firms. They argue that this is because mid-sized fir ms have more resources
available to acquire externally-developed innovations.
In a study of 2,742 Korean firms, large firms are found to be more likely to engage in
inward open innovation than SMEs because SMEs have fewer resources for acquiring externa lly-
developed knowledge. SMEs also tend to rely more on internally-developed and publically
available information rather than on purchasing innovations from others (Lee et al., 2010). The ir
data show that SMEs benefit from increased depth and breadth of inward open innovation
activities. These activities result in the SMEs being able to produce additional de rivative major
and minor product innovations, as well as resulting in higher levels of both service a nd process
innovations. Similarly, in a study of 592 Italian companies, Kumar (2010) finds that SMEs that
had acquired proprietary rights or patents from other firms were the SMEs that had the most
successful product launches in terms of product novelty and sales revenue.
In another study, one involving 252 Swedish SMEs, Parida, Westerberg, and Frishammar
(2011) find that inward open innovation benefits these firms by increasing the number of

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derivative radical and incremental innovations that they produce as a result. However, thes e
researchers warn that the effects of these activities differed among SMEs, indic ating that some
SMEs are more (or less) suited to using this open innovation strategy. For example, SME s
require strong absorptive capabilities to benefit from inward open innovation.
Zahra, Ucbasaran, and Newey (2009) conducted a study of 384 U.S.-based SMEs that
conduct business internationally. Their investigation finds that SME foreign knowledge
acquisitions (inward open innovation activities) are slightly negatively correlate d wit h a firm’s
product innovations, which they define as the number of product innovations for the three-year
period following the acquisition. The researchers speculate that this may be be cause the SMEs in
their sample lack the required absorptive capabilities to adequately capture the know ledge from
their global partners. They also note that an SME must have higher absorptive capabili ties to
acquire knowledge from international partners than from domestic ones because of factors suc h
as language barriers, cultural differences, and logistical challenges.
Inward open innovation enables SMEs to absorb needed resources and capabilities from
outside of their organizational boundaries. Several studies indicate that SMEs can be nefit from
this approach up to a point until the search and acquisition costs exceed the benefits of i ngesting
external knowledge. Therefore, inward open innovation is curvilinearly related to innovation
performance (Dahlander & Gann, 2010).
In a study of 292 Australian SMEs, Huang and Rice (2009) find that SMEs with li mited
absorptive capacity are negatively impacted by inward open innovation, but that SMEs with
higher absorptive capabilities benefit from those acquisitions. However, they find that t here is a
curvilinear relationship between inward open innovation and SME innovation performance,
where SMEs benefit only up to a point, after which that benefit diminishes. They argue tha t an

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overreliance on this strategy can eventually lead to lower innovation outputs in the long r un as
SMEs gradually lose knowledge-generation capabilities.
Ebersberger et al. (2010) conducted a study of 3,688 Austrian, Belgian, Danish, and
Norwegian firms. Their evaluation produced inconsistent findings for inward open innovation’s
ability to increase an SME’s quantity and quality of innovation outputs. The majority of their
data indicate a positive relationship between inward open innovation and SME innovation
performance, which they define as innovation outputs and resultant increases in sales a ttributed
to the new products. For example, the Belgian firms in their analysis experie nced negative
effects from their inward open innovation activities, while the Norwegian firms experienc ed
gains in innovation performance from innovation acquisitions. Data variances are also evide nt
based on other factors such as the breadth versus depth of inward open innovation activities. The
researchers cite these inconsistencies as evidence that situational context has a significant impact
on an SME’s ability to benefit from open innovation.
Proposition 4 (Inward Open Innovation) Meta-Analysis
The dissertation author conducted a meta-analysis to empirically evaluate Propositi on 4.
The meta-analysis protocol is provided in Chapter 4. Appendix A provides the data and
calculated statistics for this meta-analysis. There are four quantitat ive studies that contain usable
data applicable to Proposition 4. The analysis contains data from 6,644 SMEs.
The reported effect sizes from these studies are both positive and negative. This ca n
indicate either differences arising from disparate research methods, sampl ing errors, and/or
genuine differences in the ability of different SMEs to benefit from inward open innovation.
The dissertation author calculated the mean effect size to be 0.051. This indicates a sma ll
positive effect of inward open innovation on SME innovation performance. A correlation ef fect

SME OPEN INNOVATION: CHAPTER 5 138
size of approximately 0.10 is considered small, 0.25 is considered a medium effect, and 0.40 is
considered a large effect (Lipsey & Wilson, 2000). The 95% confidence interval range s from
0.040 to 0.061. This indicates that the mean effect size is statistically significa nt because the
range contains only positive values. A value of 0 would indicate a statistical likel ihood of no
effect and negative values would indicate the likelihood of negative effects. Therefore, it is
reasonable to accept Proposition 4 with the caveat that not all SMEs will benefit (or b e
negatively impacted) by inward innovation to the same degree. It appears that on avera ge SMEs
may be slightly more likely to benefit from inward open innovation.
The dissertation author also calculated the Q-test for homogeneity. The null hypothe sis
for homogeneity is rejected, indicating that the variability across effect s izes exceeds what would
be expected based on sampling error. This suggests that the observations are not measuring th e
same population mean. This is consistent with findings from the literature because it has been
well cited that there is significant variability in the degree to which individua l SMEs benefit (or
are harmed by) an inward open innovation strategy because of a number of contextual factors
such as industry, country, product-type, firm characteristics, and other situationa l variables.
These factors are discussed more fully in Chapter 6.
Proposition 4 (Inward Open Innovation) Conclusion
The qualitative and quantitative findings provide adequate evidence to accept Proposition
4 because it has been shown that many SMEs have benefited from inward open innovation.
However, there is sufficient evidence that indicates that these activities ca n potentially have
negative effects on some SMEs (depending on context, such as their level of absorptive
capabilities). There is also evidence indicating that inward open innovation is curvi linearly

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related to innovation performance, where SMEs can potentially benefit up to a point before
experiencing negative returns.
The findings also indicate that SMEs can potentially utilize inward open innovation to
overcome their size-related competitive challenges because Proposition 1 conclude s that an
increase in resources (innovation outputs and financial gains) can also indirectly l ead to an
increase in dynamic capabilities and reduce risk exposure (assuming that an SM E can adequately
protect its intellectual capital). Therefore, this open innovation strategy can e nable some SMEs
to increase their competitiveness.
The meta-analysis reveals a small but statistically significant posi tive effect of inward
innovation on SME innovation performance. However, opposing evidence indicates that this
innovation strategy can potentially negatively impact some SMEs. Chapter 6 dis cusses more
fully the specific contextual factors impacting the probability of an SME benefiting from the
implementation of open innovation strategies.
Proposition 5 (Combined Strategies) Findings
Proposition 5 : The combination of open innovation strategies can have a greater positive impact
on SME innovation outputs and financial performance than the utilization of a single open
innovation strategy.
Dahlander and Gann (2010) systematically evaluated open innovation literature and find
that current studies mostly investigate the effectiveness of individual open innovat ion strategies
separately rather than assessing the effects of combining multiple strategie s. They identify this as
a research gap. They propose that firms could benefit from combining different open innovation
strategies, but that additional research is needed to confirm the hypothesis. See the di ssertation

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author’s Anoto case study presented earlier i n this chapter for an example of how an SME can
benefit from combining multiple open innovation strategies.
There are several studies that investigate this research proposition. For exampl e,
Leiponen and Byma (2009) conducted a study of 504 SMEs in Finland and find that the most
successful cluster of SMEs in their sample are firms that implement multiple open innovation
strategies simultaneously. They argue that the application of multiple strat egies concurrently
enables SMEs to exploit a wider array of opportunities.
Ebersberger et al. (2010) conducted a study of 3,688 European firms and find that a
holistic implementation of multiple open innovation strategies has a greater positi ve impact on
innovation outputs and financial performance than the utilization of individual open innovation
strategies in isolation. They conclude that the most successful firms find their bes t-suited unique
combination of depth and breadth of open innovation activities to maximize the collective
returns of these strategies. They find that the effectiveness of individual strategies can vary
considerably among SMEs in different countries, between domestic and international
collaborations, and depending on other contextual factors such as the robustness of an SME’s
absorptive capabilities. The researchers caution that there is a curvilinea r relationship between
employing open innovation strategies and innovation performance. They contend that an over-
reliance on these strategies can eventually result in negative returns in some s ituations. The se
implications are discussed in depth in Chapter 6.
In a study of 1,000 German SMEs, Rahman and Ramos (2010) find that the most
successful SMEs often implement a collection of open innovation strategies in varying degrees
of scale and scope. However, they find that SMEs with the ability to implement multiple open
innovation strategies simultaneously are more likely to have advanced capabil ities and more

SME OPEN INNOVATION: CHAPTER 5 141
resources than other SMEs. They also warn that an SME would not likely benefit from a holist ic
implantation of multiple open innovation strategies if those strategies are not appropri ate for the
SME’s particular situation. For example, an SME with few resources may be less a ttracted to
inward open innovation.
Huang (2011) conducted comparative case studies of SME innovation in companies in
Finland and China. He finds that SMEs in these countries have become more competitive
globally as they have expanded the scope and intensity of their open innovation activit ies. He
contends that SMEs have been able to combat size-related competitive disadvanta ges b y
engaging in open innovation to increase their access to resources and capabilitie s, while reducing
potential impacts of the materialization of project failure risks. Additi onally, Huang argues that
the combination of open innovation strategies can be much more impactful than the
implementation of any individual strategy on its own.
Proposition 5 (Combined Strategies) Meta-Analysis
The dissertation author conducted a meta-analysis to empirically evaluate Propositi on 5.
The meta-analysis protocol is provided in Chapter 4. Appendix A provides the data and
calculated statistics for this meta-analysis. There are only two quantit ative studies that contain
usable data applicable to Proposition 5. This analysis contains data from 3,735 SMEs.
The reported effect sizes from these studies are all positive. This indicates t hat a
comprehensive open innovation approach involving the employment of multiple strategies i s
positively correlated with SME innovation performance (measured as innovati on outputs and
sales derived from those new products).
The dissertation author calculated the mean effect size to be 0.082. This indicates that
there is a small positive effect of a multi-pronged open innovation approach on SME i nnovation

SME OPEN INNOVATION: CHAPTER 5 142
performance. A correlation effect size of approximately 0.10 is considered small, 0.25 is
considered a medium effect, and 0.40 is considered a large effect (Lipsey & Wilson, 2000). The
95% confidence interval ranges from 0.063 to 0.100. This indicates that the mean effect size is
statistically significant since the range contains only positive values. A v alue of 0 would indicate
a statistical likelihood of no effect and negative values would indicate the likelihood of nega tive
effects.
Figure 15 compares the effect sizes for each proposition. All of the mean effec t sizes of
the meta-analyses are considered small, but statistically significa nt. The 0.082 mean effect size
for Proposition 5 is only statistically larger (without overlapping confidence int ervals) than the
mean effect size for Proposition 4 – inward open innovation (mean effect size: 0.051; lower CI :
0.040; upper CI: 0.061). Proposition 5’s mean effect size is not statistically larger than the other
mean effect sizes. In fact, the mean effect size for vertical open innovation (Propos ition 2a) is
statistically larger (mean effect size: 0.132; lower CI: 0.113; upper CI: 0.152). Therefor e, the
meta-analysis results do not support the notion that combining open innovation strategies will
necessarily benefit SMEs more than the implementation of an individual open innovation
strategy. However, the combination of open innovation strategies can (but will not always)
produce more desirable results as evidenced by Proposition 5’s slightly larger mean effect size
compared to the effect size of inward open innovation (Proposition 4). Therefore, one could
reasonably accept Proposition 5 as it is worded with the caveat that in some situat ions combining
open innovation strategies can provide less benefit than implementing a single op en innovation
strategy.

SME OPEN INNOVATION: CHAPTER 5 143
95% Confidence Interval
Meta-Analysis: Open Innovation Strategies Mean
Effect Size Lower Upper
Proposition 2: Collaboration (all types) 0.061 0.050 0.072
Proposition 2a: Vertical Collaboration 0.132 0.113 0.152
Proposition 2b: Horizontal Collaboration 0.080 0.060 0.100
Proposition 2c: Knowledge-Intensive Collaboration 0.053 0.033 0.073
Proposition 3: Outward Open Innovation 0.127 0.079 0.176
Proposition 4: Inward Open Innovation 0.051 0.040 0.061
Proposition 5: Open Innovation (all types) 0.082 0.063 0.100
Figure 15. Summary of Meta-Analysis Findings
A limitation of this analysis is that there are no assurances that the SMEs in the
Proposition 2 through 4 studies do not also implement other open innovation strategies
simultaneously. For example, an SME evaluated in an outward open innovation study may al so
engage in vertical collaboration or other forms of open innovation, but the researchers may not
have captured that detail. However, the studies pertinent to Proposition 5 were able to isolate t he
impacts of implementing individual open innovation strategies and compared those effects to t he
results of collectively implementing multiple open innovation interventions simultaneous ly.
Those studies find that a holistic implementation of a collection of open innovation strat egies
provides more benefit to SMEs than the implementation of an individual strategy in isolati on.
Additionally, this meta-analysis provides statistical evidence that it is po ssible to derive more
benefits by pursuing multiple strategies (at least in some situations).
The dissertation author also calculated the Q-test for homogeneity for the Proposition 5
meta-analysis. The null hypothesis for homogeneity is rejected, indicating t hat the variability
across effect sizes exceeds what would be expected based on sampling error. This sugg ests that
the observations are not measuring the same population mean. This is consistent wit h findings
from the literature because it has been well cited that there is significant va riability in the degree

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to which individual SMEs benefit (or are negatively impacted by) open innovation st rategies
because of a number of contextual factors such as industry, country, product-type, firm
characteristics, and other situational variables. These situational fac tors are discussed more fully
in Chapter 6.
Proposition 5 (Combined Strategies) Conclusion
The available evidence indicates that the combination of open innovation strategies can
benefit SMEs by increasing their innovation outputs and strengthening their financial
performance. The meta-analysis shows a small statistically signific ant effect of open innovation
on SME performance and provides evidence that combining strategies can, in some sit uations,
provide more benefit to an SME than the implementation of a single strategy in isolati on.
However, the evidence also indicates that in some situations the implementati on of individual
open innovation strategies in isolation can provide more benefit than combining strategies.
While the majority of evidence presented in this dissertation indicates that open
innovation benefits SMEs, there are a number of examples of negative findings where some
SMEs experience lower performance (less innovation and/or lower financial returns) a s a result
of open innovation. Evidence indicates that for many open innovation strategies (all but outward
open innovation) there is a curvilinear relationship between the strategy type and SME
innovation performance. Therefore, one could reasonably conclude that context is vital to
understanding open innovation. Chapter 6 discusses the specific contextual factors modera ting
the success of open innovation strategies more fully.
Chapter 5 Summary
Chapter 5 provides an in-depth evaluation of Propositions 1 through 5. The analysis
concludes that Proposition 1 (SME challenges) is accepted based on sufficient qualit ative and

SME OPEN INNOVATION: CHAPTER 5 145
quantitative evidence. This validates the dissertation author’s SME Competitive Challenges
Model (Chapter 3) and the underlying relationships among the SME competitive challenge
categories (i.e., fewer resources, limited dynamic capabilities, and hi gher risk exposure).
Additionally, Propositions 2 through 5 are also supported with sufficient findings. The
acceptance of Propositions 2 through 5 provides evidence to support the value of the open
innovation strategies presented in the dissertation author’s SME Open Innovation Strategies
Model (Chapter 3).
However, there is evidence of curvilinear relationships between these strategi es (all but
outward open innovation) and SME innovation performance (innovation outputs and resultant
financial gains from those outputs). This indicates that SMEs’ acquisition and collaboration costs
(and inefficiencies) can potentially exceed the marginal benefits of open innovation aft er a
certain point. For example, it becomes more difficult and costly to collaborate with l arger
networks because there are more bi-directional and multi-directional connections bet ween
members as new participants join a network. It can become increasingly more cha llenging to
manage a greater number of emerging relationships because it requires more tim e and higher
resource commitments.
The evidence presented in this chapter shows that most SMEs eventually encounter a
point where the ratio of marginal costs to marginal benefits begins increasi ng because of
increased managerial complexity, logistical challenges, stretching of res ources, and other factors.
The actual “tipping point” where the marginal costs exceed the marginal benefits of adding a
new partner is entirely situational and varies by firm. It cannot be universally qua ntified as a
particular absolute point that applies to all firms in all situations. For example, firm s with greater

SME OPEN INNOVATION: CHAPTER 5 146
resources and capabilities can typically devote more to their collaboration functions (and/or be
more productive with what they have committed).
Additionally, this dissertation’s analysis exposes the existence of some extraneous
negative findings indicating that these open innovation strategies do not uniformly ben efit every
SME to the same degree in every situation. Various contextual factors and situationa l moderators
appear to influence the ability of SMEs to universally benefit from these strategies . Chapter 6
describes the specific contextual factors impacting the probability of an SM E benefiting from
open innovation.
The dissertation author’s meta-analyses find that each open innovation strategy has a
small, statistically significant positive effect on SME innovation performance (defined as
innovation outputs and resulting financial outcomes). Therefore, according to Proposition 1,
because these strategies result in higher resources, they can also either dir ectly or indirectly
increase an SME’s dynamic capabilities and reduce its risk exposure (assuming the firm can
adequately protect its intellectual capital). This provides evidence to support the as sertion that
open innovation strategies can assist SMEs with overcoming size-related competi tive challenges
(i.e., lack of resources, limited dynamic capabilities, and excessive risk expos ure). Therefore,
one can reasonably conclude that open innovation can enable SMEs to become more
competitive. Various contextual factors that moderate the success of SME open innovat ion are
discussed in Chapter 6.

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Chapter 6: Conclusions and Implications
This chapter provides a summary of the dissertation’s conclusions. It also discusses
implications that these conclusions have for practitioners. These implications include viewing
open innovation as a continuum of openness rather than viewing innovation dichotomously as
being either open or closed. Additionally, the implications section discusses the si tuational
contextual moderators that impact an SME’s level of innovation openness (its open innovation
footprint). These moderators serve to either enhance (or hinder) the probability of an SME
benefiting from the employment of open innovation strategies. Next, this chapter disc usses
additional implications that pertain specifically to SMEs engaging in global open innovation. It
then presents the dissertation’s limitations and proposes areas for future research. L astly, it
provides a summary of the dissertation.
Summary of Conclusions
This section summarizes the dissertation’s main conclusions. Chapter 5 pr ovides a more
detailed in-depth discussion of specific conclusions pertaining to each research proposition.
This d issertation’s main line of argument is as follows: (1) SMEs encounter size-related
competitive challenges (i.e., lack of resources, limited dynamic capabilitie s, and high risk
exposure); (2) Open innovation can increase an SME’s exposure to others’ complementary
resources and capabilities, increase its innovation outputs, increase financial returns, and lower
an SME’s risk exposure; (3) Therefore, open innovation can assist SMEs with overcoming their
size-related competitive challenges; (4) However, various contextual moder ators can enhance (or
hinder) the effectiveness of SME open innovation strategies in different situations.
This dissertation employed an evidence-based management research approac h consisting
of a systematic review, meta -syntheses, a case study, statistical meta-analyses, and an expert

SME OPEN INNOVATION: CHAPTER 6 148
practitioner review panel. The evaluation leveraged and produced a mix of qualitative a nd
quantitative evidence. This is a mixed-methods approach where the qualitative e vidence
complements the quant itative findings and provides additional depth of exploration resulting in
more comprehensive insights.
The evaluation concludes that Proposition 1is accepted based on the triangulation of
sufficient qualitative and quantitative evidence. This analysis validates the dissertation author’s
SME Competitive Challenges Model (Chapter 3) and the underlying relationships among and
between the SME competitive challenge categories (i.e., lack of resources, l imited dynamic
capabilities, and high risk exposure). Additionally, Propositions 2 through 5 (pertaining to the
effectiveness of open innovation strategies) were also supported with sufficient qual itative and
quantitative evidence. The acceptance of Propositions 2 through 5 provides evidence supporti ng
the value of the open innovation strategies presented in the dissertation author’s SME Open
Innovation Strategies Model (Chapter 3).
The dissertation author’s meta-analyses indicate that each open innovation strategy has a
small, statistically significant positive effect on SME innovation outputs and f inancial outcomes.
These analyses contain data from 34,676 SMEs in dozens of industries in 27 countries.
Therefore, one could reasonably conclude that these results are fairly generaliza ble to the wider
population of SMEs.
Additionally, according to Proposition 1, because these strategies result in higher
resources (innovation outputs and financial gains), they can also either directly or indir ectly
increase an SME’s dynamic capabilities and reduce its risk exposure (assuming t he SME can
adequately protect its intellectual capital). This provides evidence to support the dis sertation
author’s assertion that open inno vation strategies can assist SMEs with overcoming all three of

SME OPEN INNOVATION: CHAPTER 6 149
their main size-related competitive challenges (i.e., lack of resources, limite d dynamic
capabilities, and excessive risk exposure). Therefore, one can reasonably conclude that open
innovation (at least in some situations) can enable SMEs to become more competitive.
This is a significant finding because 99% of all businesses in the world are SME s and
they play a significant role in the global economy (Wurzer & DiGiammarino, 2008). The refore,
this dissertation’s findings have far -reaching implications because the conclusions pertain to
most businesses in existence. These smaller firms face size-related compe titive challenges and
this dissertation has shown that open innovation strategies can assist some of these S MEs with
overcoming these competitive impediments. As a result, many SMEs could become more
profitable.
However, there is a mix of positive and negative findings related to the ability of SMEs
to benefit from open innovation. The existence of negative findings indicates that these ope n
innovation strategies do not uniformly benefit every SME to the same degree in every s ituation.
Various situational factors appear to influence the ability of SMEs to universally benefit from
these strategies. The implications section of this chapter evaluat es the specific contextual
variables that impact the probability of an SME benefiting from open innovation strateg ies.
Additionally, a curvilinear relationship is observed between SME innovation
performan ce (defined as innovation outputs and resultant financial gains) and all forms of open
innovation, except outward innovation. This indicates that SMEs’ acquisition and collaboration
costs (and inefficiencies) can potentially exceed the marginal benefits of op en innovation after a
certain point. For example, it becomes more challenging and costly to collaborat e with larger
networks because there are additional bi-directional and multi-directional connections bet ween
members as new participants join. This can lead to higher collaboration costs beca use of

SME OPEN INNOVATION: CHAPTER 6 150
increased managerial complexity, logistical challenges, and other factors. As described in
Chapter 5, the actual “tipping point” where the marginal costs of open innovation exceed the
marginal benefits is entirely situational and varies by firm and by context.
Because the depth and breadth of open innovation activities can impact an SME’s ability
to benefit from these strategies (due to the curvilinear relationships describe d above and
contextual factors discussed in the next section), it is useful to explicitly e valuate open
innovation as a continuum of varying degrees of scale and intensity rather than dichotomously a s
being either open or closed. This enables one to more-readily assess the appropriate ness of
varying levels of open innovation in different situations. This is discussed further in the next
section.
Implications for SMEs
This section describes the implications that the dissertation’s findings have for
practitioners (SME management and their advisors). The dissertation concludes that open
innovation benefits some SMEs while negatively impacting others depending on situational
context. The dissertation also finds that open innovation can potentially negatively i mpact some
SMEs if these strategies are over-utilized to the point where the marginal costs of open
innovation exceed the marginal benefits of these activities (due to curvilinear as sociations).
Therefore, as described previously this indicates that SMEs should be conscious of their degree
of innovation openness (scope and intensity) given their dynamically changing unique si tuational
context.
This section first describes how it is advantageous to view open innovation as a
continuum of openness that can vary in degrees of breadth (scope) and depth (intensity). The
degree of innovation openness can be illustrated by mapping what this dissertation author terms

SME OPEN INNOVATION: CHAPTER 6 151
the open innovation footprint . Next, this section describes the main situational variables (i.e.,
product characteristics, internal factors, and external factors) that impact the confi guration of an
SME’s open innovation footprint. These contextual variables impact the SME’s open innovation
footprint because they moderate the probability that an SME will be able to benefit fr om open
innovation. These are the contextual factors that can explain the existence of the contradi ctory
positive and negative findings discovered in the evaluation of Propositions 2 through 5
(propositions related to the effectiveness of each SME open innovation strategy). This helps
explain why open innovation benefits SMEs in some situations but negatively impacts other
SMEs in different contexts. The dissertation author recommends that SMEs actively m anage
their open innovation footprint, while being mindful of these various contextual moderators that
can impact the probability of benefiting from open innovation activities. These moder ators are
discussed later in this chapter.
Viewing Innovation as a Continuum of Openness
As described in Chapter 2, much of the literature defines innovation as being either open
or closed (Christensen, Olesen, & Kjaer, 2005). It is an either-or logi cal fallacy (Toulmin, 2003)
to claim that organizations can engage in either open innovation or closed innovation. This
dissertation argues that innovation is not a binary classification of either open or cl osed, but
rather, a continuum of varying degrees of openness. Figure 16 below illustrates the conti nuum of
innovation openness.

Figure 16. Continuum of Innovation Openness

SME OPEN INNOVATION: CHAPTER 6 152
In some competitive environments, an SME may rely on more closed forms of
innovation, such as in the development of classified military technologies (Lichtent haler, 2009).
Additionally, SMEs rarely remain entirely open because they generally ne ed additional secrecy
to provide added protection to some of their knowledge and ideas from potential theft
(Chesbrough, 2006; Kolk & Püümann, 2008). If an SME is too open it could lose control over
decision making and become too dependent on other organizations. However, being too closed
can limit innovation opportunities and lengthen the time it takes to bring new products t o market
(Enkel, Gassmann, & Chesbrough, 2009). Therefore, it is logical for SMEs to seek the
appropriate degree of innovation openness that is suitable for the ir unique situational context.
An organization can also be more open in some areas (on certain innovation projects),
while more closed in other areas (Dahlander & Gann, 2010). For example, Procter and Gamble
(P&G) has strongly embraced an open innovation ap proach in its “connect and develop” strategy
(i.e., developing products in close collaboration with customers) and in procuring intellectua l
property from other firms (e.g., Spin-Brush acquisition). However, P&G still uses a more clos ed
approach for making incremental scientific improvements to its formulation of Tide laundry
detergent (Lafley & Charan, 2008).
Ideally, management should dynamically and proactively change the degre e of
innovation openness to continually seek to maximize the ratio of relative benefits to cos ts/risks
of open innovation activities. As opportunities and threats change over time, an SME should
respond by finding profitable ways of leveraging its competitive strengths, while mi nimizing the
impacts of its competitive weaknesses. This can involve transferring and/or co-developing
innovations with other organizations, which requires innovation openness.

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Open Innovation Footprint: How Open is an SME?
As described in earlier chapters, there are six main types of open innovation st rategies
that SMEs can implement:
 Inward open innovation (transferring others’ innovations into the firm);
 Outward open innovation (transferring the firm’s innovations out to others );
 Upstream vertical collaboration (collaborating with upstream suppliers);
 Downstream vertical collaboration (collaborating with downstream partners and
customers );
 Horizontal collaboration (collaborating with competitors); and
 Knowledge-intensive collaboration (collaborating with universities, non-profits,
consultants, and research institutions) (Poot et al., 2009).
These innovation activities are depicted in Figure 17. Organizations can vary in the scope
(breadth) of open innovation activities as well as the level of intensity (depth) for each of t hese
activities (Laursen & Salter, 2004; Keupp & Gassmann, 2009). Organizations can also va ry in
the open innovation strategies they use for different innovation projects. For example, an SME
could use a more closed approach for a certain project, whereas it may actively col laborate with
its competitors for developing a separate product. The following diagram in Figure 17 s hows an
example open innovation footprint for a fictitious SME. Each innovation project has its own
open innovation footprint, which could be aggregated into a single fo otprint for the SME’s entire
portfolio of innovation projects. The model could also be used to illustrate the aggregated depth
and breadth of innovation openness of an entire industry.

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Figure 17. SME Open Innovation Footprint Example
The shaded area in Figure 17 represents an example of what an open innovation footprint
for an SME could resemble. There would be no shaded area for an SME that is fully closed and
the entire diagram would be shaded if the SME was fully open with respect to its innovati on
activities. In this example, the SME participates in a fairly wide breadth of ope n innovation
activities (utilizing five of the six available open innovation strategies). How ever, the depth of
activity in each area varies. This example SME does not engage in inward open innova tion, but it
engages extensively in outward open innovation activities. Additionally, this footprint s hows that
the fictitious SME also engages in more knowledge-intensive collaboration than horiz ontal
collaboration.
The units of measure for each axis could be calculated as the function of multiple
attributes, such as the number of transactions within each dimension and the degree of inte nsity
of each transaction. This model is a theoretical representation of differing degrees of innovation

SME OPEN INNOVATION: CHAPTER 6 155
openness. Future research could attempt to quantity these different degrees of openness fo r
different organizations or industries.
Changes to an SME’s Open Innovation Footprint
The level of innovation openness can change over time as an innovation project matures
from conception to commercialization (Christensen et al., 2005) . Therefore, an SME’s open
innovation footprint also likely changes dynamically over time as its projects mat ure and as the
firm initiates new innovation projects.
There is a global trend of SMEs increasingly engaging in open innovation acti vities
(Lichtenthaler, 2009). Studies have shown that the adoption of open innovation strategies has va ried by
industry, and changes in adoption rates have occurred as a series of unpredictable s udden increases
rather than as a continual gradual increase in open innovation activity (Poot et al. , 2009). Studies also
indicate that SME participation in open innovation is still fairly low and that man y more SMEs could
potentially benefit from at least limited participation in the open innovation environment ( Keupp &
Gassmann, 2009). To date, studies have been more exploratory in nature rather than explanat ory, so they
have not provided much evidence to explain why more SMEs are not currently engaging in ope n
innovation activities.
Over time, open innovation has become more prevalent because of the increased mobility of
workforces, globalization has allowed for an increase in the division of labor, improvements ha ve been
made to worldwide intellectual property protection regimes and venture capital market s, and technical
advancements have made long-distance collaboration more efficient and effective (Da hlander & Gann,
2010). These are moderators that have facilitated and enabled increased innovation openness in
industries and societies around the world.

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There are other specific determinant contextual factors that impact manageria l decisions
on how open an organization is for different innovation projects and that impact the potential for
SMEs to benefit from, or be negatively impacted by, open innovation activities. These key
determinants are grouped into the following three categories: (a) product charac teristics; (b)
internal factors; and (c) external factors.
Product Characteristics Affecting Degrees and Effectiveness of SME I nnovation Openness
These contextual factors relate to product characteristics that can impact an SME’s
degree of innovation openness. These factors can either enhance (or hinder) the probability of an
SME benefiting from open innovation activities. These determinants include product complexi ty,
product cycle lengths, and product maturity levels. These product characteristi c factors are
described below.
Product Characteristic Factor 1: Product Complexity
Open innovation has been found to be more prevalent in industries with complex
products (De Backer, Lopez-Bassols, & Martinez, 2008). Products that are more tec hnologically
complex are more likely to require additional collaboration with innovation partners.
Technologically complex products are often more expensive to create and take lon ger to develop.
Therefore, SMEs with fewer resources are more likely to seek innovation partners for these t ypes
of innovation projects (Harryson, 2008). For example, an SME would not likely be able to
develop an innovative new commercial airliner to compete with Boeing and Airbus. How ever, an
SME could potentially collaborate with these companies on individual components of an aircraft
such as working with them to develop onboard electrical equipment innovations or other
innovations related to certain parts of the aircraft.

SME OPEN INNOVATION: CHAPTER 6 157
The risk of project failure is higher for technologically complex products. Open
innovation collaboration enables firms to spread these risks amongst multiple partners, e nabling
the partners to invest less money and other resources in the project than if a firm pursued the
opportunity on its own (Jemala, 2010). Additionally, technologically complex products expose a
firm to a higher risk of inadvertently infringing on others’ intell ectual property because complex
products often require the inclusion of many convergent innovations. This can encourage firms
to collaborate to gain access to one another’s intellectual property. For example, IBM and its
innovation partners (SMEs and large firms) reduced this risk by creating “IP -free zones” in parts
of their value chains (which is a non-pecuniary horizontal collaboration open innovation
strategy). The open source operating system Linux was violating over 250 of IBM and othe r
firms’ patents , so IBM and the other firms agreed to not enforce these affected patents for Linux
users who pursued only open-source purposes. As a result, IBM was able to gain acce ss to
others’ patents . The company leveraged that intellectual property to increase its innovations.
Sales of its Linux-based products subsequently increased due to these innovations (A lexy &
Reitzig, 2012).
Product Characteristic Factor 2: Product Cycle Lengths
Open innovation has been found to be more prevalent in industries with shorter product
cycles (De Backer et al., 2008 ; Jemala, 2010 ). Shorter product cycles often encourage SMEs to
collaborate with other firms that specialize in different parts of the value chain to bring products
to market faster. Partnering with other organizations can significantly decrease t he time- to-
market for innovations (Lee et al., 2010). This may be extremely important in some industrie s
that have very short product cycles, such as the consumer electronics industry . In some situations
(mostly with non-research oriented firms), some companies may even value speed to m arket

SME OPEN INNOVATION: CHAPTER 6 158
more than their ability to legally protect their intellectual property with patents ( Leiponen &
Byma, 2009).
Innovation openness can also potentially reduce delays in supply and value chains. For
example, open innovation could alert suppliers ahead of time to start preparing for new
requirements. Open innovation can enable a company to communicate intentions to other fi rms
producing complementary products so that they can make any needed changes on thei r end
proactively in anticipation of a new product launch (Chesbrough, 2006).
Product Characteristic Factor 3: Product Maturity Levels
Christensen et al. (2005) conducted a study of technology-intensive SME startups in the
electronics industry. They find that many of the se companies begin in open research
collaboration relationships with universities. These startups then generally beg in closing their
technology base as their innovations matu re and became more commercially viable. Then, these
companies typically begin opening up for partnering with firms with complementary resource s
and capabilities in order to commercialize their innovations. Therefore, innovation ope nness can
be cyclical rather than a linear progression. These startups are most open during the earliest and
latest stages of product maturity.
Christensen et al. (2005) also studied established firms in the electronics indus try that
engage in inward open innovation. These innovation acquirers tend to shift from an inward open
innovation strategy to employing a more closed innovation approach as they develop deri vative
innovations from their acquired innovations. These firms then begin to open up again at some
point to outsource and find external partners as their innovations reach maturity and are
commercialized. In other words, like the startups, these firms are also more ope n during the
earliest and latest stages of a product’s maturity.

SME OPEN INNOVATION: CHAPTER 6 159
Internal Factors Affecting Degrees and Effectiveness of SME Innovati on Openness
These factors are related to an SME’s internal environment that can impact the degree
and effectiveness of innovation openness. These determinants include the SME ’s business
model, its competitive strengths and weaknesses, project failure risks, organ izational culture, and
the SME’s past experiences with open innovation. These internal factors are described below.
Internal Factor 1: Business Model
An SME ’s business model is a major determinate of the firm’s level of innovation
openness because a business model represents management’s intentions and strategic preferences
for how the firm seeks to generate profits (Laursen & Salter, 2004). The degree of innovat ion
openness depends on how much value an SME wants to appropriate from the different parts of
the value chain (Christensen et al., 2005). For example, a small company of engineers m ay have
a business model focused on creating patents and licensing them to larger firms. Howe ver, a
consumer goods conglomerate may seek to create some of its products by purchasing and
commercializing externally-developed intellectual property. Therefore, a firm’s business model
influences its management’s innovation strategy decisions.
SMEs also attach different values to certain innovations that they develop. Some fi rms
have business models that lead them to selectively make some of their innovations publical ly
available without financial compensation (non-pecuniary transfers). These firms a re motivated
by the desire to elicit collaboration by making these innovations available to others . In turn, these
firms hope that others will make future derivative innovations publically available. T he firm
could have altruistic motives or it may desire to leverage the future derivative i nnovations of
others (Henkel, 2006).

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Chesbrough and Appleyard (2007) argue that open innovation requires a more open
business model that is receptive to exchanging knowledge with the external environme nt to
increase value creation and capture in the innovation value chain. Also, a firm’s business model
could accommodate both open and closed forms of innovation simultaneously. For example,
Apple uses a more closed innovation approach for developing its iPhone. However, the company
uses an open innovation approach for its Apple App Store, giving programmers open access to
develop and sell applications (providing Apple with 30% of the application sales revenue)
(Hazlett, 2012).
Internal Factor 2: Competitive Strengths and Weaknesses
An SME ’s unique configuration of resources and capabilities impact its ability to create
and commercialize innovations on its own. A firm with fewer resources and capabilities m ay
seek assistance from innovation partners. Some SMEs are attracted to more innovati on openness
if they lack the resources and capabilities needed to manage the entire end- to-end exploration to
exploitation innovation process by themselves (Lee et al., 2010). Studies show that there i s a
high correlation between firms needing additional access to resources and those fir ms actually
entering into external innovation partnerships (Madrid-Guijarro et al., 2009). Partnerin g can
increase an SME ’s access to those innovation-dependent resources and capabilities (Van de
Vrande et al., 2009). However, an overreliance on other organizations for knowledge can in
some cases gradually dec rease a firm’s ability to generate its own knowledge internally (Bogner
& Bansal, 2007).
SMEs consider what each potential open innovation relationship could provide them in
terms of additional access to: financial resources, more efficient supply chains,
commercialization capabilities, legal resources, technical expertise, anot her firm’s reputation and

SME OPEN INNOVATION: CHAPTER 6 161
brand strength, and other assets and capabilities (Teng & Das, 2008 ). Studies find that firms with
fewer resources and capabilities have a higher tendency to engage in open innovation (K eupp &
Gassmann, 2009). Some of the critical elements that enable effective use of open innova tion
strategies include: absorptive capabilities, search capabilities, cultural intelligence, an effective
organizational structure, and strong leadership. These elements are discussed below.
Absorptive capabilities are an essential type of dynamic capability that is required for an
SME to successfully engage in open innovation (Huang & Rice, 2009). Absorptive capabilities
are a firm’s ability to successfully learn, synthesize, and utilize knowledge from the externa l
environment. This requires an open innovation culture and enough internal expertise to fully
understand external innovations (Reus, 2009). An SME requires absorptive capabilities to
successfully ingest the knowledge/innovations of its innovation partners. Additionally, firms
with lower absorptive capabilities tend to only collaborate with other firms locall y, whereas
firms with robust absorptive capabilities are more likely to join global innovation c ollaboration
networks (Drejer & Lund Vinding, 2007; cited in Huggins & Johnston, 2009).
Search capabilities include the ability to effectively scan the environment to identify
open innovation opportunities. Because of cognitive limits and capacity constraints , all SMEs are
limited in their ability to search for all existing externally-developed know ledge. Studies indicate
that there is a curvilinear relationship between searching for new innovations and a f irm’s
innovation performance (Katila & Ahuja, 2002). Up to a point, an SME can benefit from
searching and acquiring externally-generated innovations and searching for addi tional
collaboration partners. However, after a certain point, the costs and efforts expended begin
outweighing the benefits of these search activities. Therefore, it is possible to over-search for
open innovation opportunities.

SME OPEN INNOVATION: CHAPTER 6 162
Cultural intelligence is the ability for an SME to function and collaborate effectively with
members of another culture and within its own culture. It is vital for a firm to have a high level
of cultural intelligence for it to form a successful working relationship with a for eign
organization or with a domestic company that has a culturally diverse workforce (Ang & Inkpen,
2008). Research suggests that a high level of internal and external cultural inte lligence can
increase an SME’s success with interacting with foreign collaboration partners and that a high
level of cultural adaptation is required to avoid cultural clashes and misunderstanding s (Crowne,
2008). Gupta and Govindarajan (2002) argue that not only does cultural intelligence increase a
company’s innovativeness by being receptive to a diversity of new ideas, but that culturally
intelligent managers also tend to adopt new ideas more quickly because they are l ess hindered by
cognitive barriers. This can help the firm develop an early-mover advantage, which coul d
strategically position the company to be more competitive.
SMEs that successfully employ open innovation strategies generally also have
organizational structures designed to support these strategies, such as appointing champions to
lead these efforts and gatekeepers to manage the firm’s interactions with other orga nizations
(Chiaroni, Chiesa, & Frattini, 2011). Additionally, organic organizational structures are known
for enhancing innovative capabilities because they encourage bottom-up innovation from lower
levels within the organization and encourage more fluid knowledge exchanges with othe r
companies. However, even though an organic structure can be advantageous during innovation
exploration, it is less efficient during the exploitation process of leveraging the innova tion to
generate profits (via commercialization or sale of the intellectual property). T his is because
organic structures lack the formalization and standardized procedures that a more mec hanistic
structure provides (Harryson, 2008). Daft (2008) proposes taking an ambidextrous approach of

SME OPEN INNOVATION: CHAPTER 6 163
leveraging an organic structure to develop ideas and then switching to a mechanistic s tructure or
transferring the innovations to another group within the company or to another organization tha t
has a mechanistic structure to actually utilize/exploit the innovation.
Strong leadership can influence creativity and innovation across an organization by
managing the following aspects of the work environment: resources, management practi ces, and
organizational motivation (Amabile, 1996). An SME can manage the configuration and
allocation of its various resources, such as financial and human capital, knowledge, and
processes. Leaders can also employ management practices, such as planning, c ontrol,
communication, and providing continuous performance feedback. Additionally, a leader can
impact the success of innovation efforts by using effective motivation schemas to provide
employees with a desirable mix of intrinsic and extrinsic rewards. Taken toget her, managing
these three aspects of the work environ ment can enhance an SME’s ability to engage in and
benefit from higher levels of innovation openness.
Internal Factor 3: Project Failure Risks
Excessive risk aversion can stifle innovation, especially radical innovations. Conversel y,
excessive optimism can cause an SME to take unnecessarily reckless risks or to not pre pare itself
for the po tential realization of certain risks, which could result in the firm’s failure (Tee ce,
2007). There is a difference between cultural risk aversion (being afraid of suggesting “bad”
ideas and being overly focused on avoiding project failures) and actively mitigati ng risks in a
systematic manner. An optimal approach is to take calculated risks, while act ively mitigating any
potential realization of those risks (Schneider et al., 2008).
Studies find that SMEs have an increased tendency to engage in open innovation for their
higher-risk innovation projects (Keupp & Gassmann, 2009). An advantage of open innovation

SME OPEN INNOVATION: CHAPTER 6 164
collaboration is that it distributes the new product development risks and rewards among
multiple parties (Schneider et al., 2008). Firms with smaller innovation project portfolios may be
more incentivized to enter into innovation partnerships with other organizations to mitigate
impacts of potential project failures.
Research and development (R&D) diversification in innovation portfolios has been
shown to lower SME innovation risk exposure. Additionally, it has been empirically shown that
decreasing non-systematic R&D project risks can increase a n SME’s competitive position within
an industry. There is also a strong negative correlation between R&D intensity a nd risk, since
innovations can create barriers for a firm’s competitors (Lubatkin & Chatterjee , 1994).
Unrelated innovations increase an R&D portfolio’s diversification and decrease non-
systematic risks, but some firms innovate within very specific technology areas whi ch can
substantially increase returns when related innovations complement and enhance one a nother
(Teece, 2007). Firms often diversify fairly narrowly to maximize the benefi ts derived from
complementary innovations (Lubatkin & Chatt erjee, 1994).
Open innovation collaboration can enable an SME to invest relatively smaller amounts of
resources in a number of innovation projects rather than in a single project, which reduces the
innovation portfolio’s risk p rofile. Joint ventures can enable SMEs to invest less in projects
upfront and allows for the possibility of early exit if projects appear less attrac tive at some point
in the future (Vanhaverbeke, Van de Vrande, & Chesbrough, 2008). A firm may also choose to
form a joint venture rather than acquire a company if it wants to reduce its exposure to the ri sks
of an innovation project failing (Jing, Dhanaraj, & Shockley, 2008).
SMEs can also avoid high risks associated with building or acquiring downstream
commercialization capabilities by licensing their intellectual property to firms specializing in

SME OPEN INNOVATION: CHAPTER 6 165
production and distribution (Bianchi et al., 2010). Additionally, firms that cross-license
intellectual property can reduce their infringement risks. This can also create a competitive
barrier for firms outside of the cross-licensing network. This gives these network members a
competitive advantage by increasing infringement risks for non-members (Motoha shi, 2008).
Internal Factor 4: Organizational Culture
Innovation openness requires an open innovation culture (Drejer & Lund Vinding, 2007;
cited in Huggins & Johnston, 2009; Reus et al., 2009; Huang & Rice, 2009). This type of culture
is more tolerant of transferring innovations to others, acquiring innovations from others, a nd
utilizing externally-developed knowledge. This culture also encourages the S ME to consider
alternative uses of its intellectual property, such as potentially selling it to other organizations or
partnering with others to further develop and commercialize its innovations. Since orga nizational
culture can change over time so can the SME ’s degree of openness with respect to innovation
activities. Dervitsiotis (2010) argues that if an organization wants to effectively leverage open
innovation strategies, it should continually make improvements to its open innovation culture
over time (by developing a learning-adaptive and collaborative culture that is more c onducive to
innovation openness).
An enemy of an open innovation culture is the “not invented here” mentality. This is
where employees have an attitude of superiority, believing that their internall y-generated
knowledge is always better than innovations created outside of their organization. This ide ology
limits innovation openness, whereas an open innovation culture pragmatically assesses the worth
of an innovation regardless of its origin (Hargadon & Sutton, 2000).
Non-innovative firm cultures can create core rigidities that result in organiza tional inertia,
preventing the SME from deviating from its current strategic trajectory (Hobda y, 2005). This can

SME OPEN INNOVATION: CHAPTER 6 166
be the case when a culture is resistant to change . This culture would suffer from the “business as
usual” effect of believing that “if it works don’t fix it.” This type of cultural impediment to
innovation can l imit an SME ’s level of innovation openness and hinder its ability to successfully
appropriate benefits from open innovation strategies (Keupp & Gassmann, 2009).
Internal Factor 5: SME ’s Past Experiences
Rammer and Schmiele (2009) conducted a study that finds that prior experiences with
open innovation impact the degree of a firm’s participation in future open innovation activities.
Leaders modify an SME ’s strategy based on what they learn from previous experiences. They
may even form cognitive biases against certain open innovation activities if t hose strategies do
not generate the results that the leaders expect. A poorly executed open innovation st rategy could
cause an SME to abandon future attempts at implementing similar strategies. Conve rsely,
successful open innovation activities are likely to encourage the SME to increase open
innovation activities in the future (Reus et al., 2009).
External Factors Affecting Degrees and Effectiveness of SME Innovation O penness
These are factors related to an SME ’s external environment that can impact its degree of
innovation openness and the effectiveness of executing these strategies. These c ontextual
determinants include industry characteristics, network configurations, collaborat ion costs and
risks, legal and regulatory environments, and government interventions. These external factors
are described below.
External Factor 1: Industry Characteristics
An industry’s characteristics and competitive dynamics can impact an SME’s degree of
innovation openness. For example, open innovation partnering can increase the economies of
scale and scope for network members, which can be valuable in scale-intensive industri es

SME OPEN INNOVATION: CHAPTER 6 167
(Habaradas, 2009). Baba (1989) conducted a study of Japanese electronics companies. He argues
that the consumer electronics industry is scale-intensive, which encourages compa nies to form
collaborative R&D partnerships to leverage economies of scale. He claims that during the
1980’s, Japanese companies dominated the electronics industry because of their participa tion in
R&D collaboration networks. These networks increased cost efficiencies (beca use of economies
of scale) and innovation productivity for participating SMEs and large companies.
Open innovation networks can also enable SMEs to overcome market entry barriers in
scale-intensive industries because many firms may not have the resources or capabilitie s to
operate on such a large scale by themselves (Clarke & Turner, 2003). Open innovation
collaboration also enables these firms to share R&D risks and rewards (Terziovski , 2010) and it
increases the industry’s overall innovation outputs (Huang & Rice, 2009).
Different open innovation activities are adopted more widely in different industries. For
example, outward open innovation tends to be more prevalent in high-technology industries and
inward open innovation is more prevalent in low-technology industries (Chiaroni et al., 2011).
Keupp and Gassmann (2009) conducted a study that finds that firms in high-technology
industries tend to participate in a greater breadth of open innovation activities compar ed to firms
in less knowledge-intensive industries. They also find that industries with shorter product cycles
often have more open innovation depth because closer inter-firm relationships are often requi red
to bring innovations to market more quickly. Additionally, other researchers find that open
innovation (knowledge-intensive collaboration with universities in particular) tends to be mor e
prevalent in R&D-intensive industries such as biotechnology, chemicals, and electroni cs
compared to less R&D-intensive industries such as textiles, paper, and printing (La ursen &
Salter, 2004). These researchers also find that as SMEs become larger and as they increase R&D

SME OPEN INNOVATION: CHAPTER 6 168
expenditures in research-focused industries, they are more likely to engage in and bene fit from
knowledge-intensive open innovation.
In some industries such as prepared foods, the dominant innovation strategy is secrecy.
Therefore, open innovation strategies would be less appropriate in these industries because
secrecy requires more closed forms of innovation (Atun, Harvey, & Wild, 2007). Other
industries such as military-grade weaponry are inherently more closed due to nat ional security
concerns and legal restrictions surrounding R&D for these types of products.
An innovation may also be introduced to multiple industries. For example, as mentioned
in the case study in Chapter 5, Anoto’s digital pen technologies are licensed and made into
different products for several industries, including consumer goods, healthcare, banking and
finance, education, transportation and logistics, and facility management (Anoto, 2010). The
dynamics of these industries can impact the level of innovation openness and the effective ness of
the open innovation strategies differently in each of these industries and can impact decis ions for
entering into additional industries.
External Factor 2: Network Configurations
Inter-organizational joint innovation development and commercialization efforts rely on
effective network structures to be successful (Gelatt, 2005). These network configu rations reflect
and can impact the degree of innovation openness for an innovation project and can moderate the
success of these partnerships.
Harryson (2008) argues that firms involved in open innovation rely heavily on their
network system of partners and that this network often changes over time as a project matures
from exploration (research) to exploitation (development). He claims that the most e ffective
network for innovation exploration efforts is usually a relatively open system with mul tiple

SME OPEN INNOVATION: CHAPTER 6 169
weak, organic relationships between various research participants. As an innovation trans itions
into the exploitation phase, it becomes increasingly preferable for the network to bec ome more
closed with fewer partners, where these participants develop closer ties with one anothe r. This
generally increases production efficiencies and speeds up the commercializa tion process
(Harryson, 2008).
Mesquita and Lazzarini (2008) warn of the possible risk of “over -embeddedness” of
organizations in long-term relationships, which could lead to network inertia where the me mbers
become less engaged, avoid new thinking, and resist expanding their network to include new
partners. Over time, this could lead to a decline in innovativeness of the network if the membe rs
are not exposed to new ideas.
SMEs may also be more open with certain parties of their network than others. F or
example, Anoto (case study firm in Chapter 5) divides its collaboration partners int o four tiers of
commitment: platinum, gold, silver, and bronze. Higher levels of commitment require higher
levels of mutual trust and more intensive collaboration. The se commitment levels are determined
by the partners ’ resources, capabilities, experience, expertise, and trustworthiness (Chadwick et
al., 2001).
External Factor 3: Collaboration Costs
If the ratio of open innovation costs- to-benefits increases, an SME would have an
incentive to become less open. For example, the costs of collaboration can vary depending on the
size and configuration of a network. As explained earlier, open innovation can potentially lead to
cost savings. However, collaboration with partners becomes exponentially more diffic ult to
manage and costlier the larger a network becomes (Keupp & Gassmann, 2009). Collabo ration
costs increase significantly as the number of network members and the scope and depth of

SME OPEN INNOVATION: CHAPTER 6 170
coordination activities increase. Therefore, SMEs generally only benefit from innova tion
collaboration up to a point. Then, as networks grow larger there are diminishing returns be cause
of the increasing collaboration inefficiencies and costs of growing networks (A lmirall &
Casadesus-Masanell, 2010). This explains the curvilinear relationship between open i nnovation
collaboration and SME innovation performance (defined as innovation outputs and financial
returns from those outputs) that was identified in the research findings in Chapter 5.
There are also differences in domestic and international collaboration costs. Some SMEs
seek to collaborate in innovation efforts in a global setting due to intense domestic competition,
to serve new markets, to gain access to new knowledge, to leverage other organization s’
resources and capabilities, and to seek R&D efficiency gains and cost savings (Rammer &
Schmiele, 2009). However, international collaboration can also increase SMEs ’ collaboration
costs in some situations because of internationalization barriers such as diss imilar languages,
cultural differences, and logistical challenges (Cao, Hartung, Forrest, & She n, 2011). SME s are
often more comfortable serving markets that are most similar to what they are used to in t erms of
industry characteristics, legal environments, and customer preferences (Carpente r & Dunung,
2011). These more similar markets present lower costs to SMEs, which increase s the ratio of
relative benefits to costs for engaging in open innovation. This makes it more likely t hat SMEs
can successfully utilize open innovation strategies in these markets compared to other
international markets.
External Factor 4: Collaboration Risks
Despite the benefits of forming open innovation relationships with other organizations,
SMEs weigh these benefits against the risks of entering into these partnerships . For example,
some potential partners may be less financially stable, increasing the li kelihood of their

SME OPEN INNOVATION: CHAPTER 6 171
insolvency. Furthermore , an SME faces opportunism risks in its relationships. Collaboration
partners could steal the SME ’s intellectual property, violate cost or profit sharing agreements,
reduce their level of commitment over time, or act in a way that could negatively impa ct the
SME ’s reputation (Teng & Das, 2008).
Moreover, collaborating with a company located in another county could potentially
present a number of additional challenges, such as increasing the amount of time and money
required to complete projects, managerial difficulties with resolving culture clashes , and reduced
levels of trust because of being separated by national borders and geographic di stance (Boschma,
2005). When selecting an overseas collaboration partner, SMEs consider factors such as:
language barriers ; the country’s economic, p olitical, and legal environments; local R&D-related
tax breaks; government grants; robustness of infrastructure; local resource costs and labor pool
characteris tics; compatibility of national cultures; and the strength of the country’s intel lectual
property rights protection regime (Ang & Inkpen, 2008; Subrahmanya, 2009). However, SME s
that join international open innovation alliances can also reduce their overall market risk
exposure because these networks provide them presence in multiple national economies (Zahra,
Ucbasaran, & Newey, 2009).
Collaborating with customers can also present certain risks. Some researchers warn that
engaging consumers in every aspect of R&D can risk intellectual property spillover to
competitors, but that moderate engagement with customers for R&D activities correlates with
more successful and profitable product launches (Arakji & Lang, 2007). This helps expla in the
curvilinear relationship between vertical open innovation collaboration and SME innovation
performance (innovation outputs and financial returns) that was identified in the resea rch
findings in Chapter 5.

SME OPEN INNOVATION: CHAPTER 6 172
External Factor 5: Legal and Regulatory Environments
International legal and regulatory environments differ. These environments and changes
to them can impact an SME’s degree of innovation openness and its ability to successfully
benefit from the implementation of open innovation strategies. A lack of legal and regulatory
stability can impede and discourage open innovation activities (Jemala, 2010). Al so, some
countries limit the ability to perform certain activities on their soil and may pre sent other legal
restrictions (e.g., stem cell research restrictions). Strong intellectual propert y protection regimes
help to stimulate open innovation activity by enabling SMEs to appropriate returns from their
R&D activities. Therefore, SME s consider the degree of regulatory interference and the strength
of intellectual property protection that a country offers before engaging in open innova tion with
organizations in that country (Rammer & Schmiele, 2009).
An SME can be limited in its innovation activities by the patents, trademarks, cop yrights,
and other legally-protected intellectual property of other firms. Many firms a re attracted to open
innovation because it can provide them access to other organizations’ intellectual capital
(Motohashi, 2008 ). However, an intellectual property owner may be unwilling to sell/lease/share
intellectual assets to an SME or collaborate with the SME in the co-creatio n of derivative
innovations. In other words, open innovation decisions are not typically unilateral (unless an
organization unilaterally makes its intellectual property freely availa ble to others or if it
unilaterally steals intellectual property from other organizations). Therefore, an SM E needs more
than just a willingness to engage in open innovation. The SME would need to also find a willing
open innovation partner.
Additionally, legal agreements such as inter-firm exclusivity and non-compete
agreements can limit the ability of firms to increase innovation networks. Also, in oli gopolies a

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government may forbid firms from participating in certain collaborative activit ies with one
another (Atun, Harvey, & Wild, 2007).
External Factor 6: Government Interventions
Government involvement and support (or interference) can influence decisions to engage
in open innovation. For example, a country may offer tax incentives for foreign firms to conduct
R&D on their soil in partnership with their country’s companies . Studies show that government
support and national pro-R&D policies and programs can act to increase open innovation
activities (Jemala, 2010). Governments may also restrict open innovation in some cases . For
example, the U.S. and other governments prohibit the exportation of certain military
technologies and knowledge.
Many OECD (Organization for Economic Cooperation and Development) countries
actively fund initiatives to increase knowledge- intensive open innovation between academia and
industry, which has spurred innovation and economic activity (Laursen & Salter, 2004). Fo r
example, the European Union (EU) places conditions on accessing EU grant funding such as
making some recipient companies enter into the European network of universities to e ncourage
knowledge-intensive collaboration (Chiaroni et al., 2011).
Evidence from innovators in developing countries shows that access to financial
resources through government grants does not necessarily translate into firm suc cess because
some SMEs lack critical capabilities to effectively utilize the additiona l resources (Habaradas,
2009). Thus, government support for innovation does not necessarily equate to increased
innovation activities in all cases, such as when an SME lacks strong leadership to effe ctively
utilize those resources. Therefore, this and other contextual considerations (discussed e arlier in
this chapter) indicate that in order to determine an SME’s optimal open innovation footprint, one

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must consider the collective impact of all product characteristics, internal fac tors, and external
factors.
Summary of Key Contextual Determinants of SME Innovation Openness
The above discussion identifies specific key situational factors that impact ma nagerial
decisions on how open an SME is for different innovation projects (the SME’s open innovation
footprint). These are contextual factors that can impact the success (or failure) of an SME’s
employment of open innovation strategies in different situations. These key determina nts are
grouped into the following three categories: (a) product characteristics; (b) interna l factors; and
(c) external factors. Figure 18 summarizes these main determinants.
Key Contextual Determinants of SME Innovation Openness
Product
Characteristics↑ Product complexity , ↓ Product cycle length, ↑ In the earliest and late st stages
of product maturity (becoming more closed in between)
Internal Factors↑ Openness of business model, ↑ Need to access others’ complementary
resources and capabilities, ↑ Firm's open innovation capabilities (absor ptive
capacity, search capabilities, cultural intelligence, conduciven ess of
organizational structure, strong leadership), ↑ Need to share project f ailure
risks, ↑ Strength of open innovation culture, ↑ Success with previous open
innovation activities
External Factors↑ Scale intensiveness of an industry , ↑ Conduciveness of the network
structure, ↑ High-technology and knowledge-intensive an industry , ↓ Degree of
industry reliance on secrecy , ↑ Ease of entry into current networks, ↓
Collaboration costs (impacted by network size, geographic diversity, and
language and cultural barriers), ↓ Collaboration risks (intellectu al property
spillover, cost/profit sharing violations, partner insolvency , country risks), ↑
Legal and regulatory stability and intellectual property protections (domestic
and international), ↓ Legal interference, ↑ Governmental support a nd funding
Figure 18. Key Determinants of SME Innovation Openness Impacting the Probability of an
SME Benefiting from Open Innovation
Managers ultimately decide what open innovation activities to pursue and the level of
intensity for each of those activities. However, the various contextual determina nts presented in
Figure 18 moderate these innovation openness decisions because these variables impact the

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SME’s ability to benefit from open innovation. This is not an exhaustive or mutually exclusive
listing of every conceivable factor; but rather, it represents a synthesis of the ke y determinants
identified in the literature.
Additional Implications for Global SME Open Innovation
The previous section describes the overall implications that this dissertation’s findi ngs
have for practitioners. However, there are additional implications that specifical ly pertain to
SMEs that engage in global open innovation with international partners. This dissertat ion’s
systematic review finds that despite size-related challenges (i.e., lac k of resources, fewer
dynamic capabilities, and higher risk exposure) many SMEs successfully parti cipate in and
benefit from open innovation in an international setting. However, global open innovation
presents additional challenges that these SMEs must overcome to successfully c ompete.
SME Global Open Innovation Participation
De Backer, Lopex-Bassols, and Martinez (2008) observe that SMEs are less active in
international open innovation than larger companies. They note that even large multinational
firms tend to concentrate a majority of their R&D collaboration activities wi thin their
headquarters’ home countries. They indicate that it can be more expensive for firms to deve lop
and maintain international collaborative relationships than domestic ones. This makes S MEs less
likely to have global partners because they typically have fewer resources tha n larger companies.
However, these researchers did not quantify the level of SME involvement in international
collaboration. They identified this as an area needing additional research.
Many SMEs seek to operate in a global setting because of intense domestic compe tition,
to serve new markets, to gain access to new knowledge, to leverage other firms’ resource s and
capabilities, and to seek efficiency gains and cost savings (Rammer & Schmie le, 2009). Research

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also indicates that globally-oriented SMEs tend to focus on innovative products, while thos e that
are not globally-oriented tend to focus on broader, lower margin product lines (Nkongolo-
Bakenda et al., 2010). Additionally, SMEs with lower absorptive capabilities tend to col laborate
only with other local organizations, whereas SMEs with robust absorptive capabilitie s seem to
benefit from joining global innovation networks (Huggins & Johnston, 2009).
To better understand and explain SME global open innovation, the dissertation author
created the Model of SME Global Open Innovation (shown in Figure 19). This is an extension of
the author’s Open Innovation Footprint Model presented previously in Figure 17. This
theoretical framework depicts the various types of domestic and international ope n innovation
channels that are available to SMEs.

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Figure 19. SME Global Open Innovation
The SME is depicted as the center point of the diagram. The dotted circle around the
SME represents a porous organizational border allowing potential, intentional inflows and
outflows of knowledge/innovations (represented by the arrows). The SME can open and close
the border with each or all of the open innovation channels. For example, an SME would have a
solid border if it only engaged in internal, closed innovation. The border could be open to an
individual channel, such as outward open innovation, while simultaneously being closed to other
channels. This would be the case for an SME that only engages in licensing its innova tions to

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other firms, while abstaining from other forms of open innovation. The SME’s organizational
border also represents a barrier for participating in the open innovation ecosystem. This ba rrier
represents potential inhibitors preventing participation in a channel(s), including: psyc hological
impediments (e.g., fear); lack of opportunities; other obstacles; or simply a desire to not e ngage
in open innovation.
Each segment of the model is characterized by the unique, dynamically changin g
environmental conditions and the actions of the SME and other actors. The interior ring of the
model represents domestic open innovation activities in the six open innovation channels
depicted (e.g., inward open innovation). The outside ring (lightest shaded area) represents ope n
innovation interactions with foreign organizations. There is a porous division (dashed line)
separating the domestic and international open innovation rings, which symbolizes the SME ’s
national border and various barriers of engaging in global open innovation. These barriers
include a lack of resources and capabilities to partner internationally, global l ogistical and
distance challenges, differences in language and culture, psychological impedim ents, challenges
with interacting in dissimilar socio-economic and legal environments, and other hurd les. An
SME may also simply not desire to engage in international open innovation. Portions of this
dotted line would be solid if an SME refrains from one or multiple international open innovation
channels.
This model could be customized for a particular SME and its unique situation. For
example, one could customize the model to differentiate between different types of inte rnational
partners, such as organizations within and those outside of a particular regional trading bloc.
Alternatively, one could modify the model to differentiate between partners in devel oped versus
developing countries, those in culturally similar versus dissimilar countries, or any other

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divisional schema where the rings in the model with the lowest SME participation barr iers are
closest to the central SME circle, while the higher-barrier (i.e., higher cost and risk) rings move
progressively further from the central SME circle.
The next sub-sections describe some of the dynamics of the international outer ri ng of the
SME Global Open Innovation Model . Specifically, the author discusses how SMEs select foreign
open innovation partners and how successful SMEs manage these international relationships .
How Successful SMEs Select Foreign Open Innovation Partners
SMEs weigh the relative benefits and costs/risks of doing business with various forei gn
organizations (i.e., firms, non-profits, and research institutes). SMEs consider the pote ntial
synergies gained by combining their complementary resources and capabilit ies with those of
other organizations to accomplish a shared goal. SMEs also consider what the potentia l
relationship could provide them in terms of additional access to: financial resources, more
efficient supply chains, commercialization capabilities, legal resource s, technical expertise,
another organization’s reputation and brand strength, and other assets and capabilities (Gra nt &
Baden-Fuller, 2004; Nkongolo-Bakenda et al., 2010).
Despite the benefits of forming open innovation relationships with foreign companies,
SMEs weigh these benefits with the risks of entering into global relationships. For example,
some potential international partners may be less financially stable, increa sing the likelihood of
their insolvency. Additionally, an SME faces opportunism risks. Its potential partner s could steal
the SME’s intellectual property, violate cost or profit sharing agreements, reduce their level of
commitment over time, or act in a way that could hurt the SME’s reputa tion (Teng & Das, 2008).
Conducting business with an organization located in another county could potentially
present a number of challenges. These challenges include increasing the amount of time and

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money required to complete projects, managerial diff iculties with resolving culture clashes, and
reduced levels of trust because of being separated by national borders (Boschma, 2005). Cao et
al. (2011) conducted a study of the barriers that Chinese SMEs encounter when expanding into
global markets. They find that the three main barriers facing these internat ionalizing SMEs are
dissimilar languages, culture differences, and logistical challenges.
The amount of risk associated with doing business in each country also varies. When
selecting an overseas partner, SMEs must consider factors such as: the country’s economic,
political, and legal environments; language barriers; local R&D-related tax bre aks and grants;
strength of a country’s infrastructure; local resource costs; compatibility of national cultures; and
the strength of the country’s intellectual property rights protection regime (Ang & Inkpen, 2008;
Subrahmanya, 2009). According to the Country Similarity Theory , SMEs are more likely to
conduct business with companies in countries that are similar to theirs. Firms often feel more
comfortable serving markets that are most similar to what they are used to in term s of industry
characteristics, legal environments, and customer preferences (Carpenter & Dunung, 2011 ).
SMEs compare the advantages of conducting business with a foreign organization to the
risks and challenges of doing so. This sub-section described the various factors SM Es consider
when deciding to engage in global open innovation (i.e., benefits versus costs/risks). The next
sub-section discusses how successful SMEs actually manage their interna tional open innovation
relationships.
How Successful SMEs Manage Their Global Open I nnovation Relationships
SMEs often enter into international collaborative partnerships (e.g., strategic allia nces)
because of an overall lack of resources and capabilities (Clarke & Turner, 2003). Howe ver, a
significant lack of resources and capabilities can also impair SME networking a bilities (Huang &

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Rice, 2009). Additionally, collaboration with partners becomes increasing ly more difficult and
costly the larger a network becomes (Keupp & Gassmann, 2009). SMEs experience more
difficulty with forming partnerships than larger firms because SMEs are often less a ttractive as
potential partners. This is due to SMEs’ overall lack of resources and capabilities and their less
established company reputations. However, SMEs with unique expertise in a special ized area
and firms that have valuable resources (e.g., patents) are more likely to overcome these barriers
because they are seen as more attractive potential partners to other organizat ions (Chesbrough,
2010).
Successful international collaborative alliances require members to effective ly and
efficiently learn and transfer knowledge. Furthermore, these relationships need to provi de
adequate resources and capabilities for the partners to accomplish their shared obje ctives (Lee et
al., 2009). Additionally, these SME global collaborations require:
 Strengthening cultural intelligence;
 Establishing mutual trust with global partners;
 Utilizing effective governance;
 Leveraging information and communications technology (ICT);
 Aligning complementary organizational goals; and
 Establishing an effective global network structure.
These factors are discussed below.
Strengthening Cultural Intelligence
Cultural intelligence is the ability for an SME to function and collaborate effec tively with
members of another culture. It is vital for two firms from different countries to have a high level
of cultural intelligence for them to form a successful working relationship (Ang & Inkpen, 2008).

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Research suggests that a high level of cultural intelligence can increase an int ernational firm’s
success; and a high level of cultural adaptation is required to avoid cultural clashes and
misunderstandings (Crowne, 2008). Crowne suggests that an SME could increase its employees’
cultural intelligence through education, international internships, overseas ass ignments, and
cultural training.
Cultural intelligence involves having a global mindset, which requires being hig hly open
to and knowledgeable of various cultures. It also entails having the ability to integr ate knowledge
from different cultures (Gupta & Govindarajan, 2002). Gupta and Govindarajan argue that not
only does a global mindset increase the company’s i nnovativeness by being open to a diversity
of new ideas, but that culturally intelligent managers tend to adopt these new ideas m ore quickly
because they are less hindered by cognitive barriers.
Zahra, Ucbasaran, and Newey (2009) conducted a study of Ameri can SMEs that conduct
business internationally. The researchers conclude that SMEs with high levels of c ultural
intelligence have an enhanced ability to benefit from their international relations hips. This
enables these SMEs to produce higher levels of product innovations with international part ners
compared to less-culturally intelligent SMEs. They also find that higher levels of SME cultural
intelligence decrease their risks associated with foreign interactions.
Establishing Mutual Trust with Global Partners
Trust is vital to any relationship. It is especially important in international a lliances
because it provides stability and predictability. Trust also lowers the perceived l evel of risk of
companies working together (Lewicki, McAllister, & Bies, 1998).
Trust can be much lower among team members in a virtual working environment (e.g.,
international alliances where people are separated by great geographic dist ances) as opposed to

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co-located teams who work together face- to-face (Kayworth & Leidner, 2000). Regular
communication and occasional face- to-face communication can help build trust quicker and can
re-enforce and strengthen trust. In-person meetings strengthen rapport between part nering
company representatives, which strengthens the quality of inter-firm relat ionships. Effective
governance (e.g., contracts, rules, and procedures) can also help strengthen trust in SME
international alliances (Bolisani & Scarso, 2003).
Utilizing Adequate Governance
Governance provides the means for managing a relationship by defining roles and
responsibilities. Governance also helps establish a command structure, control mechanisms ,
communication channels, and profit/cost sharing agreements. Additionally, effec tive governance
implements the needed contracts and rules to govern behaviors. It also defines an appropriati on
regime and it establishes the processes for managing projects and resolving conflic ts (Teng &
Das, 2008).
An effective governance structure is required to moderate global inter-firm relati onships
(Van de Vrande et al., 2009). Cao et al. (2011) advocate the use of written contracts and process
formality to regulate interactions between international network members. Additiona lly, SMEs
are generally more effective in an international setting when they protect the ir intellectual
property with strong governance mechanisms (Rammer & Schmiele, 2009). Bolisani and Sca rso
(2003) also argue that effective governance can help strengthen mutual trust in internat ional
alliances, allowing SMEs to maximize the benefits of these relationships.
Leveraging Information and Communications Technology (ICT)
While SMEs rely on effective governance to manage knowledge flows in their global
network, ICT assists with the actual transfer of knowledge between organizations (Boli sani &

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Scarso, 2003). ICT helps facilitate the maturation of global research, production, and dist ribution
systems. ICT makes it easier for firms to collaborate in joint R&D activit ies because it facilitates
the flow of knowledge between collaborators. ICT impact s the quality of a firm’s resource base
because it strengthens the firm’s technological infrastructure, which can potenti ally lower costs
and increase productivity in the product development value chain. Additionally, ICT increa ses
the potential pool of open innovation partners because it provides network access to firms that
otherwise would not have the ability to meet or collaborate with international part ners. For
example, Alibaba created an online network of 63 million companies that form internat ional
business relationships with other firms around the world through Alibaba’s website and with the
company’s other ICT tools (Cao et al., 2011). A majority of the firms using Alibaba’s service s
are SMEs. This demonstrates how ICT can create new international partnering oppor tunities for
SMEs.
Some governments are intervening to help their societies benefit from the use of IC T. For
example, the Chinese government (Beijing Academy of Science and Technology) buil t the
country’s largest commercial cloud -computing platform to serve SMEs in government-supported
industries (e.g., knowledge-intensive manufacturing) (Koda, 2012).
The digital revolution has been rapidly advancing over the past 25 years. ICT blurs
national and industry boundaries by lowering barriers of transferring knowledge over large
geographic distances. ICT is changing the way businesses interact with the ir employees,
consumers, and other companies. The trend of continually evolving ICT advancements is
projected to continue for the next several decades (Ernst & Young, 2010).
According to Moore’s law, technology doubles every 12 months. However, more recent
data have revised the law to state that information technology doubles every 18 months

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(Brynjolfsson & McAfee, 2012). Therefore, this trend still has significant momentum and it is
expected to continue in the coming decades (Economic Intelligence Unit, 2006). SMEs should
seek opportunities to leverage these new ICT advancements in their global innovati on networks.
Aligning Complementary Organizational Goals
International alliances, partnerships, and joint ventures often have low success rates
because of contrasting cultural preferences for different organizational goals (Hofs tede et al.,
2002). Hofstede et al. conducted an extensive study of thousands of business leaders in 15
countries to determine how leaders from various cultures rate the relative importance of certain
business goals. They identify many differences between goal preferences and priorities among
various national cultures. For example, they find that U.S. managers typically focus on t he
current year’s profits, whereas Chinese leaders tend to focus on longer-term profits. This
suggests a difference in the long-term orientation cultural dimension. This could crea te conflict if
companies from both of these cultures had to jointly establish shared organizational goal s. The
American firm would likely be focused on short-term performance, while the Chinese compan y
may tolerate modest short-term performance in exchange for better long-term performance.
Because managers from different cultures can have different top priorities and al ternate
viewpoints, the researchers advocate that partnering organizations should be cognizant of t heir
cultural preferences when developing their shared goals.
Sharing mutual goals and communicating continuous performance feedback are
important elements that contribute to the success of geographically dispersed allia nce networks
(Kayworth & Leidner, 2000). International networks are also generally more eff ective if all the
firms in the network have complementary strategies and business models (Bolisa ni & Scarso,
2003).

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Establishing an Effective Global Network Structure
According to network theory , global inter-organizational joint R&D efforts rely on
effective network structures to be successful (Gelatt, 2005). Harryson (2008) finds that firms
involved in open innovation rely heavily on their global network of partners. These networks
often change over time as a project matures from knowledge creation to innovation exploi tation
(commercialization). He claims that the most effective network for innovation explora tion efforts
is usually a relatively open system with multiple unstructured relationships. As an innovation
moves into the commercialization phase, it becomes increasingly preferable for t he network to
become more closed with fewer partners. These participants develop closer ties w ith one another.
This generally increases production efficiencies and speeds up the commercial ization process. A
change in leadership can be useful for assisting a global network to transition from the m ore
loosely-connected open network to a closer-knit closed network (Harryson, 2008).
The complexity of the innovation being developed is also a factor to consider in the
global network structure design. For example, knowledge sharing relationships t ypically need to
be stronger the more complex the innovation (Harryson, 2008). Another important aspect of
network structure is the flexibility of how easily domestic and international c ompanies can enter
into and exit relationships. Almirall and Casadesus-Masanell (2010) argue tha t global open
networks are more effective when firms form flexible rather than fixed relationshi ps with one
another. This is because flexible relationships create dynamic competition f or the optimal pairing
of innovation-development partnerships that have the highest degrees of synergy.
Limitations and Areas for Future Research
The previous sections of Chapter 6 summarize the dissertation’s main conclusions and
discuss the practical implications of these findings for SMEs and additional impli cations for

SME OPEN INNOVATION: CHAPTER 6 187
SMEs engaging in global open innovation. This section of Chapter 6 discusses the main
limitations of this dissertation’s evaluation and identifies areas for future rese arch.
The main limitation of this dissertation’s analysis is that the evidence -based research
approach relies on data from other studies. Research has been relatively limited i n the area of
SME open innovation, which creates limitations on the availability of observations. Howeve r,
this dissertation’s evaluation developed findings based on data from 47 studies of 34,676 SMEs
in dozens of industries in 27 countries. While this systematic review produced fairly
generalizable findings that are more generalizable than the findings of any individual study, the
aggregated sample is still a relatively small portion of SMEs in the world. Additiona lly, the data
are unrepresentative of SMEs in Africa, the Middle East, much of Eastern Europe, and the
majority of South America.
There is a limited amount of research that conducts comparative analyses to explain the
differences in varying levels of SME success with employing open innovation in diffe rent
contexts. For example, additional research is needed to evaluate the various conte xtual variables
discussed in Chapter 6 (i.e., product characteristics, internal factors, and external fa ctors) that
impact the degree of SME innovation openness and SME effectiveness with implementing open
innovation strategies. Additional research is also needed to more fully understand how SM Es can
increase their success with both domestic and global open innovation activities.
Further research is needed to gain a better understanding of the benefits and
disadvantages of combining multiple open innovation strategies. Findings related to P roposition
5 indicate that synergistic gains are possible in some situations. However, there is a lack of
primary research focused on isolating and measuring individual open innovation variables and
then measuring the cumulative effects of combining strategies. It is difficul t to utilize a meta-

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synthesis approach to evaluate this phenomenon because most studies do not explicitl y isolate
individual open innovations strategies while controlling for the effects of the paralle l
employment of other open innovation strategies. For example, a study evaluating vertic al open
innovation may not control for instances where these SMEs concurrently execute other ope n
innovation strategies, such as outward open innovation activities.
Future research could also expand on this dissertation’s evaluation by focusing on much
more specific types of open innovation strategies. For example, downstream vertical ope n
innovation with customers could be subdivided into strategies related to different types of
customer collaborations (e.g., compensation-driven customer involvement, non-compensati on
involvement, innovation contests, crowdsourcing, marketing research not known to customers,
along with other types of specific strategies and combinations thereof).
Dissertation Summary
The purpose of this dissertation was to evaluate the current open innovation landscape
from the vantage point of SMEs. The analysis focused on SME utilization of various open
innovation strategies and the ability/inability of these strategies to assi st SMEs with overcoming
their main size-related competitive challenges (i.e., lack of resources, limit ed dynamic
capabilities, and high risk exposure). This topic is of significance to the field of manageme nt
because SMEs play a significant role in economies around the globe since they compris e 99% of
all businesses. Gaining a better understanding of how these firms can overcome thei r size-related
competitive challenges is important because it can assist these firms wit h becoming more
economically viable and prosperous.

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To date, most research on open innovation has focused exclusively on large companies,
while neglecting the specific competitive challenges and strategies of SM Es. This dissertation
addressed the following research gaps:
 Need for a holistic model that can illustrate strategic choices that firms can m ake in the
open innovation environment. The dissertation author created the Holistic Model of
Innovation in response to this need.
 Need for more exploration i n the area of SMEs’ size -related competitive challenges and the
open innovation strategies SMEs can leverage to overcome those challenges. The
dissertation author created the SME Competitive Challenges Model and the SME Open
Innovation Strategies Model in response to th is need.
The dissertation proffered the following research questions:
 What does the overall innovation landscape look like? How do various types of innovations
and innovation channels relate to one another? [ Holistic Model of Innovation ]
 What competitive challenges do SMEs face compared to larger firms? How can SME s
overcome these size-related challenges in a turbulent, competitive environment?
o How do these different types of SME challenges relate to and exacerbate one
another? [ SME Competitive Challenges Model & Proposition 1]
o Which open innovation strategies can SMEs utilize to overcome size-related
competitive challenges? [ SME Open Innovation Strategies Model & Propositions 2
through 5]
These research questions resulted in the creation of the following research pr opositions:
 Proposition 1: There is: (a) a positive relationship between increasing an SME ’s
resources and enhancing its dynamic capabilities; (b) a negative relationship between an

SME OPEN INNOVATION: CHAPTER 6 190
SME ’s risk exposure and its ability to obtain resources; and (c) a negative relationship
between an SME’s dynamic cap abilities and its risk exposure. [Accepted]
 Proposition 2: Collaboration-related open innovation strategies can increase SME
innovation outputs and financial performance. [Accepted]
o Proposition 2a: Vertical collaboration can increase SME innovation outputs and
financial performance. [Accepted]
o Proposition 2b: Horizontal collaboration can increase SME innovation outputs and
financial performance. [Accepted]
o Proposition 2c: Knowledge-intensive collaboration can increase SME innovation
outputs and financial performance. [Accepted]
 Proposition 3: Outward open innovation can increase SME innovation outputs and
financial performance. [Accepted]
 Proposition 4: Inward open innovation can increase SME innovation outputs and financial
performance. [Accepted]
 Proposition 5: The combination of open innovation strategies can have a greater positive
impact on SME innovation outputs and financial performance than the utilization of a
single open innovation strategy. [Accepted]
This dissertation employed an evidence-based management research approach c onsisting
of a systematic review, meta-syntheses, a case study, statistical meta-analyses, and an expert
panel of practitioner reviewers. All of the propositions and conceptual models are support ed with
sufficient qualitative and quantitative evidence. A meta-synthesis confir ms relationships between
variables in Proposition 1 (relating to the SME Competitive Challenges Model ). Additionally, the
author’s meta-synthe ses and statistical meta-analyses provide support for Propositions 2 through

SME OPEN INNOVATION: CHAPTER 6 191
5 (relating to the SME Open Innovation Strategies Model ). The dissertation author’ s Anoto case
study also provides additional evidence to support Proposition 3.
The meta-analyses produced results that indicate small statistica lly significant positive
impacts of each open innovation strategy (and combinations of them) on SME innovation
performance (defined as innovation outputs and resultant financial gains from those out puts).
These analyses contain data from 34,676 SMEs across dozens of industries in 27 countries.
Therefore, all forms of open innovation can potentially help some SMEs overcome their si ze-
related competitive challenges and can lead to increases in innovation outputs and financi al
gains.
However, there is a mix of positive and negative findings, showing that while many
SMEs benefit from these activities, some SMEs can be negatively impacted by them. This
indicates the existence of contextual moderators governing the adoption and effectivene ss of
open innovation interventions. The dissertation author grouped these moderators into the
following three categories: product characteristics; internal factors; and e xternal factors. These
situational variables (presented in Figure 18) appear to impact the probability of an SM E
benefiting from open innovation strategies.
A curvilinear relationship is also observed between SME innovation performance and all
forms of open innovation, except outward open innovation. This indicates that SMEs’ acquisition
and collaboration costs (and inefficiencies) can potentially exceed the margina l benefits of open
innovation after a certain point. The actual “tipping point” is entirely situational, depending on
the dynamically changing point where the marginal costs exceed marginal benefits for a
particular SME. This point is different for each SME and can potentially be different for each

SME OPEN INNOVATION: CHAPTER 6 192
individual innovation project because projects may involve different innovation partners and the
relative open innovation costs, risks, and benefits can differ among projects.
This dissertation also concludes that it is not sufficient to define innovation
dichotomously as being either open or closed, but rather one should view innovation as a
continuum consisting of varying degrees of breadth (scope) and depth (intensity) of innova tion
openness. The author proposes the SME Open Innovation Footprint Model to depict the se
varying degrees of innovation openness. The dissertation author also recommends that:
 An SME should explicitly determine its open innovation footprint (the scope and scale of
its innovation openness).
 An SME should evaluate strategic options for employing different degrees of innovation
openness given the SME’s unique situational context (while att empting to maximize the
ratio of marginal benefits to marginal costs of each alternative open innovation strat egy
deployment configuration, given certain risk tolerance levels). Figure 18 ( the key
determinants table) summarizes the situational moderators impacting the se strategic
decisions. These contextual variables moderate innovation openness and they serve to
either enhance (or hinder) the probability of an SME benefiting from the employment of
open innovation strategies in different situations.
 An SME should execute the desired innovation openness configuration and then
continually adjust its degree of innovation openness (depth and breadth) to maximize the
benefits of open innovation, while minimizing potential negative impacts of these strateg ies
(costs and risks).
This dissertation’s findings also present several implications for SMEs engagin g in global
open innovation with international partners. The author’s SME Global Open Innovation Model

SME OPEN INNOVATION: CHAPTER 6 193
illustrates the various domestic and international open innovation channels available to SM Es.
The following practices can increase the probability of SMEs successfully e ngaging in and
benefiting from global open innovation:
 Strengthening cultural intelligence;
 Establishing mutual trust with global partners;
 Utilizing effective governance;
 Leveraging information and communications technology (ICT);
 Aligning complementary organizational goals; and
 Establishing an effective global network structure.
This dissertation conducted a study that provides evidence that SMEs can utilize open
innovation strategies to overcome their size-related competitive challenges (i .e., lack of
resources, limited dynamic capabilities, and high risk exposure). SMEs play a vital role in
economies around the world and this dissertation explored ways to make these organi zations
more successful and profitable by utilizing open innovation strategies. The findings a nd
implications presented herein can inform SME management, thereby assisting them w ith
overcoming their size-related competitive challenges. Open innovation can increase an SME’s
access to others’ complementary resource s and capabilities, increase innovation outputs,
strengthen financial returns, and low er risk exposure. While open innovation has been shown to
be disadvantageous in some situations (based on the situational contextual moderators dis cussed
in Chapter 6), large numbers of SMEs across the globe have benefited from leveraging open
innovation strategies. Therefore, SMEs should consider the strategic options and the potentia l
benefits that open innovation can provide.

SME OPEN INNOVATION: APPENDIX A 194
Appendix A: Statistical Meta-Analysis Results
This appendix contains the meta-analysis data, calculations, and results.

SME OPEN INNOVATION: APPENDIX A 195
Table 1 – Proposition 2: All Forms of Open Innovati on Collaboration
Source Data ES n w w*E S w*E S^2 type se sd d DV
Ebersberger et al., 2010 0.091 0.091 296 293.0 26.7 710.9 r 0.058 1.005 n/a Innovation outputs
Ebersberger et al., 2010 0.085 0.085 296 293.0 24.9 620.3 r 0.058 1.005 n/a Innovation outputs
Ebersberger et al., 2010 0.243 0.243 1023 1020.0 247.9 61434.6 r 0.031 1.001 n/a Innovation outputs
Ebersberger et al., 2010 0.217 0.217 1023 1020.0 221.3 48991.4 r 0.031 1.001 n/a Innovation outputs
Ebersberger et al., 2010 0.237 0.237 859 856.0 202.9 41157.0 r 0.034 1.002 n/a Innovation outputs
Ebersberger et al., 2010 0.197 0.197 859 856.0 168.6 28436.8 r 0.034 1.002 n/a Innovation outputs
Ebersberger et al., 2010 0.117 0.117 1508 1505.0 176.1 31005.9 r 0.026 1.001 n/a Innovation outputs
Ebersberger et al., 2010 0.101 0.101 1508 1505.0 152.0 23105.5 r 0.026 1.001 n/a Innovation outputs
Ebersberger et al., 2010 -0.031 -0.015 296 293.0 -4.5 20.5 beta 0.058 1.005 -0.031 Innovation outputs
Ebersberger et al., 2010 -0.118 -0.059 296 293.0 -17.2 296.4 beta 0.058 1.005 -0.118 Innovation outpu ts
Ebersberger et al., 2010 0.051 0.025 1023 1020.0 26.0 676.2 beta 0.031 1.001 0.051 Innovation output s
Ebersberger et al., 2010 -0.014 -0.007 1023 1020.0 -7.1 51.0 beta 0.031 1.001 -0.014 Innovation output s
Ebersberger et al., 2010 0.028 0.014 859 856.0 12.0 143.6 beta 0.034 1.002 0.028 Innovation outputs
Ebersberger et al., 2010 -0.022 -0.011 859 856.0 -9.4 88.7 beta 0.034 1.002 -0.022 Innovation outputs
Ebersberger et al., 2010 0.034 0.017 1508 1505.0 25.6 654.5 beta 0.026 1.001 0.034 Innovation output s
Ebersberger et al., 2010 0.01 0.005 1508 1505.0 7.5 56.6 beta 0.026 1.001 0.010 Innovation outputs
Ebersberger et al., 2010 0.038 0.019 296 293.0 5.5 30.7 beta 0.058 1.005 0.038 Sales growth
Ebersberger et al., 2010 -0.002 -0.001 296 293.0 -0.3 0.1 beta 0.058 1.005 -0.002 Sales growth
Ebersberger et al., 2010 -0.043 -0.021 1023 1020.0 -21.9 480.8 beta 0.031 1.001 -0.043 Sales growth
Ebersberger et al., 2010 0.041 0.020 1023 1020.0 20.9 437.1 beta 0.031 1.001 0.041 Sales growth
Ebersberger et al., 2010 0.029 0.015 859 856.0 12.4 154.1 beta 0.034 1.002 0.029 Sales growth
Ebersberger et al., 2010 -0.023 -0.012 859 856.0 -9.8 96.9 beta 0.034 1.002 -0.023 Sales growth
Ebersberger et al., 2010 0.089 0.044 1508 1505.0 66.9 4477.4 beta 0.026 1.001 0.089 Sales growth
Ebersberger et al., 2010 -0.048 -0.024 1508 1505.0 -36.1 1304.2 beta 0.026 1.001 -0.048 Sales growth
Huang & Rice, 2009 0.004 0.002 292 289.0 0.6 0.3 beta 0.059 1.005 0.004 Innovation outputs
Huang & Rice, 2009 0.58 0.289 292 289.0 83.5 6967.3 beta 0.059 1.005 0.603 Innovation output s
Matthews & Sawang, 2010 5.57 0.111 449 446.0 49.7 2469.2 x20.047 1.003 0.224 New products
Matthews & Sawang, 2010 3.93 0.094 449 446.0 41.7 1740.8 x20.047 1.003 0.188 New products
Mesquita & Lazzarini, 2008 0.05 0.05 232 229.0 11.5 131.1 r 0.066 1.007 n/a New products % sales
Mesquita & Lazzarini, 2008 0.01 0.01 232 229.0 2.3 5.2 r 0.066 1.007 n/a New products % sales
Mesquita & Lazzarini, 2008 0.02 0.02 232 229.0 4.6 21.0 r 0.066 1.007 n/a New products % sales
Rammer & Schmiele, 2009 0.027 0.014 1000 997.0 13.5 181.2 beta 0.032 1.002 0.027 New products
Rammer & Schmiele, 2009 0.155 0.077 1000 997.0 77.1 5951.1 beta 0.032 1.002 0.155 Sales growth
Rosenbusch et al., 2011 0.05 0.05 1300 1297.0 64.9 4205.5 r 0.028 1.001 n/a Financial growth
Rosenbusch et al., 2011 0.27 0.27 90 87.0 23.5 551.8 r 0.107 1.017 n/a Financial growth
Rosenbusch et al., 2011 -0.06 -0.06 52 49.0 -2.9 8.6 r 0.143 1.030 n/a Financial growth
Rosenbusch et al., 2011 0.13 0.13 319 316.0 41.1 1687.6 r 0.056 1.005 n/a Financial growth
Rosenbusch et al., 2011 0.11 0.11 137 134.0 14.7 217.3 r 0.086 1.011 n/a Financial growth
Rosenbusch et al., 2011 0.18 0.18 184 181.0 32.6 1061.5 r 0.074 1.008 n/a Financial growth
Rosenbusch et al., 2011 -0.33 -0.33 210 207.0 -68.3 4666.3 r 0.070 1.007 n/a Financial growth
Rosenbusch et al., 2011 -0.21 -0.21 651 648.0 -136.1 18517.8 r 0.039 1.002 n/a Financial growth
Rosenbusch et al., 2011 0.28 0.28 116 113.0 31.6 1001.1 r 0.094 1.013 n/a Financial growth
Schillo & Walter, 2010 -0.2 -0.2 85 82.0 -16.4 269.0 r 0.110 1.018 n/a Sales growth
Schillo & Walter, 2010 0.08 0.040 85 82.0 3.2 10.6 beta 0.110 1.018 0.079 Sales growth
Terziovski, 2003 0.18 0.18 115 112.0 20.2 406.4 r 0.094 1.013 n/a Business excellence
Terziovski, 2003 0.21 0.21 115 112.0 23.5 553.2 r 0.094 1.013 n/a Business excellence
Terziovski, 2003 0.14 0.14 115 112.0 15.7 245.9 r 0.094 1.013 n/a Business excellence
Terziovski, 2003 0.12 0.12 115 112.0 13.4 180.6 r 0.094 1.013 n/a Business excellence
Terziovski, 2003 0.01 0.01 115 112.0 1.1 1.3 r 0.094 1.013 n/a Business excellence
Terziovski, 2003 0.268 0.133 115 112.0 14.8 220.5 beta 0.094 1.013 0.268 Business excellenc e
Zahra et al., 2009 0.22 0.22 384 152.4 33.5 1124.4 r 0.081 1.587 n/a New products
Zahra et al., 2009 0.26 0.082 384 152.4 12.5 156.1 beta 0.081 1.587 0.165 New products
Zahra et al., 2009 0.26 0.214 384 1040.6 223.0 49739.1 beta 0.031 0.607 0.439 New products
31408.4 1920.9 346722.7
Mean E S 0.061
SE Mean 0.006
Z 10.839
Lower CI 0.050
Upper CI 0.072
Q 346605.2
df 52
Critical Value 67.5
N 9302

SME OPEN INNOVATION: APPENDIX A 196
Table 2 – Proposition 2a: Vertical Open Innovation
Source Data ES n w w*E S w*E S^2 type se sd d DV
Huggins & Johnston, 2009 0.12 0.12 49 46.0 5.52 30.47 r 0.147 1.032 n/a Innovation outputs
Huggins & Johnston, 2009 -0.05 -0.05 49 46.0 -2.30 5.29 r 0.147 1.032 n/a Innovation outputs
Parida et al., 2012 0.32 0.32 252 249.0 79.68 6348.90 r 0.063 1.006 n/a Innovation outputs
Parida et al., 2012 0.35 0.35 252 249.0 87.15 7595.12 r 0.063 1.006 n/a Innovation outputs
Parida et al., 2012 0.152 0.076 252 249.0 18.84 354.80 beta 0.063 1.006 0.152 Innovation outpu ts
Parida et al., 2012 0.088 0.044 252 249.0 10.90 118.76 beta 0.063 1.006 0.088 Innovation outpu ts
Ebersberger et al., 2010 -0.27 -0.134 296 293.0 -39.39 1551.75 beta 0.058 1.005 -0.271 Innovation ou tputs
Ebersberger et al., 2010 -0.027 -0.014 1023 1020.0 -13.77 189.65 beta 0.031 1.001 -0.027 Innovation ou tputs
Ebersberger et al., 2010 0.072 0.036 859 856.0 30.76 946.14 beta 0.034 1.002 0.072 Innovation outpu ts
Ebersberger et al., 2010 0.283 0.141 1508 1505.0 212.80 45282.33 beta 0.026 1.001 0.286 Innovation o utputs
Ebersberger et al., 2010 0.666 0.332 296 293.0 97.18 9443.73 beta 0.058 1.005 0.703 Innovation outp uts
Ebersberger et al., 2010 0.415 0.207 1023 1020.0 211.53 44745.09 beta 0.031 1.001 0.424 Innovation o utputs
Ebersberger et al., 2010 0.344 0.172 859 856.0 147.01 21612.72 beta 0.034 1.002 0.349 Innovation ou tputs
Ebersberger et al., 2010 0.183 0.091 1508 1505.0 137.61 18935.73 beta 0.026 1.001 0.184 Innovation o utputs
McAdam et al., 2008 0.22 0.22 395 392.0 86.24 7437.34 r 0.051 1.004 n/a Innovation outputs
Leiponen & Byman, 2009 0.376 0.376 504 501.0 188.38 35485.52 r 0.045 1.003 n/a Innovation outputs
Mesquita & Lazzarini, 2008 0.04 0.04 232 229.0 9.16 83.91 r 0.066 1.007 n/a New products % sales
Mesquita & Lazzarini, 2008 0.16 0.16 232 229.0 36.64 1342.49 r 0.066 1.007 n/a New products % sales
Mesquita & Lazzarini, 2008 0.06 0.06 232 229.0 13.74 188.79 r 0.066 1.007 n/a New products % sales
9924.0 1314.44 201662.78
Mean E S 0.132
SE Mean 0.010
Z 13.195
Lower CI 0.113
Upper CI 0.152
Q 201488.7
df 18
Critical Value 28.87
N 5118

SME OPEN INNOVATION: APPENDIX A 197
Table 3 – Proposition 2b: Horizontal Open Innovatio n
Source Data ES n w w*E S w*E S^2 type se sd d DV
Parida et al., 2012 0.23 0.23 252 249.0 57.270 3279.85 r 0.063 1.006 n/a Innovation outputs
Parida et al., 2012 0.33 0.33 252 249.0 82.170 6751.91 r 0.063 1.006 n/a Innovation outputs
Parida et al., 2012 -0.002 -0.001 252 249.0 -0.249 0.06 beta 0.063 1.006 -0.002 Innovation outpu ts
Parida et al., 2012 0.138 0.069 252 249.0 17.094 292.22 beta 0.063 1.006 0.138 Innovation outp uts
Ebersberger et al., 2010 0.371 0.185 296 293.0 54.133 2930.36 beta 0.058 1.005 0.376 Innovation out puts
Ebersberger et al., 2010 0.107 0.053 1023 1020.0 54.554 2976.18 beta 0.031 1.001 0.107 Innovation ou tputs
Ebersberger et al., 2010 0.239 0.119 859 856.0 102.135 10431.62 beta 0.034 1.002 0.240 Innovation o utputs
Ebersberger et al., 2010 0.008 0.004 1508 1505.0 6.021 36.26 beta 0.026 1.001 0.008 Innovation outpu ts
Ebersberger et al., 2010 -0.649 -0.323 296 293.0 -94.708 8969.64 beta 0.058 1.005 -0.683 Innovation o utputs
Ebersberger et al., 2010 0.287 0.143 1023 1020.0 146.301 21403.92 beta 0.031 1.001 0.290 Innovation outputs
Ebersberger et al., 2010 -0.087 -0.043 859 856.0 -37.166 1381.31 beta 0.034 1.002 -0.087 Innovation o utputs
Ebersberger et al., 2010 0.095 0.047 1508 1505.0 71.422 5101.11 beta 0.026 1.001 0.095 Innovation ou tputs
Huggins & Johnston, 2009 0.02 0.02 49 46.0 0.920 0.85 r 0.147 1.032 n/a Innovation outputs
Leiponen & Byman, 2009 0.308 0.308 504 501.0 154.308 23810.96 r 0.045 1.003 n/a Innovation output s
Mesquita & Lazzarini, 2008 0.2 0.2 232 229.0 45.800 2097.64 r 0.066 1.007 n/a New products % sale s
Mesquita & Lazzarini, 2008 0.22 0.22 232 229.0 50.380 2538.14 r 0.066 1.007 n/a New products % sale s
Mesquita & Lazzarini, 2008 0.26 0.26 232 229.0 59.540 3545.01 r 0.066 1.007 n/a New products % sale s
9578.0 769.926 95547.04
Mean E S 0.080
SE Mean 0.010
Z 7.867
Lower CI 0.060
Upper CI 0.100
Q 95485.1
df 16
Critical Value 26.3
N 4723

SME OPEN INNOVATION: APPENDIX A 198
Table 4 – Proposition 2c: Knowledge-Intensive Open Innovation
Source Data ES n w w*E S w*E S^2 type se sd d DV
Ebersberger et al., 2010 -0.395 -0.197 296 293.0 -57.640 3322.34 beta 0.058 1.005 -0.401 Innovation o utputs
Ebersberger et al., 2010 0.071 0.035 1023 1020.0 36.195 1310.08 beta 0.031 1.001 0.071 Innovation ou tputs
Ebersberger et al., 2010 0.062 0.031 859 856.0 26.486 701.52 beta 0.034 1.002 0.062 Innovation outp uts
Ebersberger et al., 2010 0.137 0.068 1508 1505.0 103.023 10613.75 beta 0.026 1.001 0.137 Innovation outputs
Ebersberger et al., 2010 0.394 0.196 296 293.0 57.488 3304.84 beta 0.058 1.005 0.400 Innovation out puts
Ebersberger et al., 2010 0.207 0.103 1023 1020.0 105.533 11137.31 beta 0.031 1.001 0.208 Innovation outputs
Ebersberger et al., 2010 -0.031 -0.016 859 856.0 -13.269 176.07 beta 0.034 1.002 -0.031 Innovation ou tputs
Ebersberger et al., 2010 -0.018 -0.009 1508 1505.0 -13.547 183.53 beta 0.026 1.001 -0.018 Innovation o utputs
Huggins & Johnston, 2009 0.08 0.08 49 46.0 3.680 13.54 r 0.147 1.032 n/a Innovation outputs
Huggins & Johnston, 2009 -0.17 -0.17 49 46.0 -7.820 61.15 r 0.147 1.032 n/a Innovation outputs
Leiponen & Byman, 2009 0.254 0.254 504 501.0 127.254 16193.58 r 0.045 1.003 n/a Innovation output s
McAdam et al., 2008 0.14 0.14 395 392.0 54.880 3011.81 r 0.051 1.004 n/a Innovation outputs
McAdam et al., 2008 0.03 0.03 395 392.0 11.760 138.30 r 0.051 1.004 n/a Innovation outputs
McAdam et al., 2008 0.09 0.09 395 392.0 35.280 1244.68 r 0.051 1.004 n/a Innovation outputs
McAdam et al., 2008 0.09 0.09 395 392.0 35.280 1244.68 r 0.051 1.004 n/a Innovation outputs
9509.0 504.583 52657.19
Mean E S 0.053
SE Mean 0.010
Z 5.174
Lower CI 0.033
Upper CI 0.073
Q 52630.4
df 14
Critical Value 23.68
N 4634

SME OPEN INNOVATION: APPENDIX A 199
Table 5 – Proposition 3: Outward Open Innovation
Source Data ES n w w*ES w*ES^2 type se sd d DV
Lichtenhaler, 2009 0.15 0.15 136 133.000 19.950 398.003 r 0.087 1.011 n/a Return on sales
Lichtenhaler, 2009 1.49 0.062 136 0.943 0.059 0.003 beta 1.030 12.012 0.125 Return on sales
Lichtenhaler, 2009 1.43 0.060 136 0.943 0.056 0.003 beta 1.030 12.012 0.120 Return on sales
Lichtenhaler, 2009 1.49 0.062 136 0.943 0.059 0.003 beta 1.030 12.012 0.125 Return on sales
Lichtenhaler, 2009 1.49 0.062 136 0.943 0.059 0.003 beta 1.030 12.012 0.125 Return on sales
Lichtenhaler, 2009 1.38 0.057 136 0.907 0.051 0.003 beta 1.050 12.245 0.113 Return on sales
Lichtenhaler, 2009 0.71 0.033 136 1.156 0.038 0.001 beta 0.930 10.846 0.066 Return on sales
Lichtenhaler, 2009 0.86 0.038 136 1.041 0.040 0.002 beta 0.980 11.429 0.076 Return on sales
Lichtenhaler, 2009 0.76 0.035 136 1.108 0.038 0.001 beta 0.950 11.079 0.069 Return on sales
Lichtenhaler, 2009 0.91 0.054 136 1.877 0.100 0.010 beta 0.730 8.513 0.107 Return on sales
Zahra et al., 2009 0.22 0.22 384 381.000 83.820 7025.792 beta 0.051 1.004 n/a New products
Zahra et al., 2009 0.15 0.091 384 566.893 51.754 2678.452 beta 0.042 0.823 0.183 New product s
Zahra et al., 2009 0.16 0.095 384 540.833 51.514 2653.679 beta 0.043 0.843 0.191 New product s
1631.586 207.538 12755.956
Mean ES 0.127
SE Mean 0.025
Z 5.138
Lower CI 0.079
Upper CI 0.176
Q 12729.558
df 12
Critical Value 21.03
N 520

SME OPEN INNOVATION: APPENDIX A 200
Table 6 – Proposition 4: Inward Open Innovation
Source Data ES n w w*E S w*E S^2 type se sd d DV
Huang & Rice, 2009 -0.25 -0.124 292 289.0 -35.98 1294.45 beta 0.059 1.005 -0.251 Innovation ou tputs
Huang & Rice, 2009 1.83 0.911 292 289.0 263.35 69354.15 beta 0.059 1.005 4.425 Innovation ou tputs
Parida et al., 2012 0.33 0.33 252 249.0 82.17 6751.91 r 0.063 1.006 n/a Innovation outputs
Parida et al., 2012 0.36 0.36 252 249.0 89.64 8035.33 r 0.063 1.006 n/a Innovation outputs
Parida et al., 2012 0.375 0.187 252 249.0 46.46 2158.68 beta 0.063 1.006 0.380 Innovation outp uts
Parida et al., 2012 0.158 0.079 252 249.0 19.58 383.21 beta 0.063 1.006 0.158 Innovation outpu ts
Lee et al., 2010 0.065 0.065 2414 2411.0 156.72 24559.59 r 0.020 1.001 n/a Innovation output s
Lee et al., 2010 0.063 0.063 2414 2411.0 151.89 23071.48 r 0.020 1.001 n/a Innovation output s
Lee et al., 2010 0.091 0.091 2414 2411.0 219.40 48136.80 r 0.020 1.001 n/a Innovation output s
Lee et al., 2010 0.106 0.106 2414 2411.0 255.57 65313.98 r 0.020 1.001 n/a Innovation output s
Ebersberger et al., 2010 0.028 0.028 296 293.0 8.20 67.31 r 0.058 1.005 n/a Innovation outputs
Ebersberger et al., 2010 -0.029 -0.029 296 293.0 -8.50 72.20 r 0.058 1.005 n/a Innovation outputs
Ebersberger et al., 2010 0.072 0.072 1023 1020.0 73.44 5393.43 r 0.031 1.001 n/a Innovation outputs
Ebersberger et al., 2010 0.025 0.025 1023 1020.0 25.50 650.25 r 0.031 1.001 n/a Innovation outputs
Ebersberger et al., 2010 0.011 0.011 859 856.0 9.42 88.66 r 0.034 1.002 n/a Innovation outputs
Ebersberger et al., 2010 0.057 0.057 859 856.0 48.79 2380.66 r 0.034 1.002 n/a Innovation outputs
Ebersberger et al., 2010 0.057 0.057 1508 1505.0 85.79 7359.07 r 0.026 1.001 n/a Innovation outputs
Ebersberger et al., 2010 0.061 0.061 1508 1505.0 91.81 8428.16 r 0.026 1.001 n/a Innovation outputs
Ebersberger et al., 2010 0.035 0.017 296 293.0 5.11 26.14 beta 0.058 1.005 0.035 Innovation outputs
Ebersberger et al., 2010 -0.062 -0.031 296 293.0 -9.05 81.92 beta 0.058 1.005 -0.062 Innovation outpu ts
Ebersberger et al., 2010 -0.003 -0.002 1023 1020.0 -1.53 2.34 beta 0.031 1.001 -0.003 Innovation outpu ts
Ebersberger et al., 2010 -0.058 -0.029 1023 1020.0 -29.57 874.50 beta 0.031 1.001 -0.058 Innovation ou tputs
Ebersberger et al., 2010 0.048 0.024 859 856.0 20.50 420.18 beta 0.034 1.002 0.048 Innovation outpu ts
Ebersberger et al., 2010 0.008 0.004 859 856.0 3.42 11.73 beta 0.034 1.002 0.008 Innovation outputs
Ebersberger et al., 2010 0.071 0.035 1508 1505.0 53.40 2851.76 beta 0.026 1.001 0.071 Innovation out puts
Ebersberger et al., 2010 0.01 0.005 1508 1505.0 7.53 56.64 beta 0.026 1.001 0.010 Innovation output s
Ebersberger et al., 2010 0.076 0.038 296 293.0 11.08 122.85 beta 0.058 1.005 0.076 Sales
Ebersberger et al., 2010 -0.045 -0.022 296 293.0 -6.56 43.07 beta 0.058 1.005 -0.045 Sales
Ebersberger et al., 2010 -0.069 -0.034 1023 1020.0 -35.17 1237.24 beta 0.031 1.001 -0.069 Sales
Ebersberger et al., 2010 -0.019 -0.010 1023 1020.0 -9.69 93.92 beta 0.031 1.001 -0.019 Sales
Ebersberger et al., 2010 0.02 0.010 859 856.0 8.56 73.29 beta 0.034 1.002 0.020 Sales
Ebersberger et al., 2010 0 0.000 859 856.0 0.00 0.00 beta 0.034 1.002 0.000 Sales
Ebersberger et al., 2010 0.089 0.044 1508 1505.0 66.92 4477.80 beta 0.026 1.001 0.089 Sales
Ebersberger et al., 2010 0.026 0.013 1508 1505.0 19.57 382.84 beta 0.026 1.001 0.026 Sales
33262.0 1687.75 284255.53
Mean E S 0.051
SE Mean 0.005
Z 9.254
Lower CI 0.040
Upper CI 0.061
Q 284169.9
df 33
Critical Value 47.4
N 6644

SME OPEN INNOVATION: APPENDIX A 201
Table 7 – Proposition 5: Open Innovation (All Types )
Source Data ES n w w*E S w*E S^2 type se sd d DV
Ebersberger et al., 2010 0.033 0.016 296 293.0 4.82 23.24 beta 0.058 1.005 0.033 Innovation outputs
Ebersberger et al., 2010 0.188 0.094 1023 1020.0 95.84 9184.72 beta 0.031 1.001 0.189 Innovation out puts
Ebersberger et al., 2010 0.214 0.107 859 856.0 91.48 8367.68 beta 0.034 1.002 0.215 Innovation outp uts
Ebersberger et al., 2010 0.189 0.094 1508 1505.0 142.15 20206.38 beta 0.026 1.001 0.190 Innovation o utputs
Ebersberger et al., 2010 0.222 0.111 296 293.0 32.39 1049.15 beta 0.058 1.005 0.222 Innovation outp uts
Ebersberger et al., 2010 0.217 0.108 1023 1020.0 110.60 12233.41 beta 0.031 1.001 0.218 Innovation o utputs
Ebersberger et al., 2010 0.229 0.114 859 856.0 97.86 9577.22 beta 0.034 1.002 0.230 Innovation outp uts
Ebersberger et al., 2010 0.124 0.062 1508 1505.0 93.23 8691.99 beta 0.026 1.001 0.124 Innovation out puts
Ebersberger et al., 2010 0.289 0.144 296 293.0 42.17 1778.30 beta 0.058 1.005 0.291 Sales
Ebersberger et al., 2010 0.153 0.076 1023 1020.0 77.98 6080.12 beta 0.031 1.001 0.153 Sales
Ebersberger et al., 2010 0.1 0.050 859 856.0 42.76 1828.25 beta 0.034 1.002 0.100 Sales
Ebersberger et al., 2010 0.093 0.046 1508 1505.0 69.93 4889.60 beta 0.026 1.001 0.093 Sales
Huggins & Johnston, 2009 0.07 0.07 49 46.0 3.22 10.37 r 0.1474 1.032 n/a Innovation outputs
11068.0 904.42 83920.42
Mean E S 0.082
SE Mean 0.010
Z 8.597
Lower CI 0.063
Upper CI 0.100
Q 83846.5
df 12
Critical Value 21.03
N 3735

SME OPEN INNOVATION: APPENDIX A 202

Meta-Analysis: Open Innovation Strategies Mean E ffec t Size Lower Upper
Proposition 2: Collaboration (all types) 0.061 0.050 0 .072
Proposition 2a: Vertical Collaboration 0.132 0.113 0.1 52
Proposition 2b: Horizontal Collaboration 0.080 0.060 0 .100
Proposition 2c: Knowledge-Intensive Collaboration 0. 053 0.033 0.073
Proposition 3: Outward Open Innovation 0.127 0.079 0.1 76
Proposition 4: Inward Open Innovation 0.051 0.040 0.06 1
Proposition 5: Open Innovation (all types) 0.082 0.06 3 0.100Table 8 – Summary 95% Confidence Interval

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