reviewing open innovation: structure, content and future ...€¦ · open innovation research...
TRANSCRIPT
Paper to be presented at the
DRUID Society Conference 2014, CBS, Copenhagen, June 16-18
Reviewing Open Innovation: Structure, Content and Future Research
AvenuesKrithika Randhawa
University of Technology SydneyUTS Business School
Ralf WildenUniversity of Newcastle
Newcastle Business [email protected]
Jan Hohberger
University of Technology SydneyUTS Business School
AbstractWe present a systematic review of the literature on open innovation to uncover the theoretical foundations and keythemes underlying the paradigm. To achieve this, we combine the complementary bibliometric methods of co-citationanalysis and text mining. Results show that although open innovation research has drawn from a variety of establishedschools of scholarship, there is opportunity to better integrate concepts from these research fields into open innovationliterature. Open innovation research covers three broad themes: (1) Technology, (2) Business models and valueappropriation, and (3) Users and communities. The technology theme has so far received the most research attention.Significant gaps in the literature emerge that present avenues for future research: 1) Develop a more comprehensiveperspective of open innovation by including diverse levels of analysis (users, networks and communities); 2) Directincreased attention to open innovation business models and value capture; and 3) Enhance service focus andconceptualize ?open service innovation?.
Jelcodes:M10,-
1
Reviewing Open Innovation:
Structure, Content and Future Research Avenues
Abstract
We present a systematic review of the literature on open innovation to uncover the theoretical
foundations and key themes underlying the paradigm. To achieve this, we combine the
complementary bibliometric methods of co-citation analysis and text mining. Results show that
although open innovation research has drawn from a variety of established schools of
scholarship, there is opportunity to better integrate concepts from these research fields into open
innovation literature. Open innovation research covers three broad themes: (1) Technology, (2)
Business models and value appropriation, and (3) Users and communities. The technology theme
has so far received the most research attention. Significant gaps in the literature emerge that
present avenues for future research: 1) Develop a more comprehensive perspective of open
innovation by including diverse levels of analysis (users, networks and communities); 2) Direct
increased attention to open innovation business models and value capture; and 3) Enhance
service focus and conceptualize ‘open service innovation’.
Keywords: Open Innovation; Citation Analysis; Leximancer; Literature Review
2
INTRODUCTION
Our goal in this paper is to extend existing reviews on open innovation through a more objective
and systematic review of previous literature. Our unique contribution lies in our novel and
comprehensive empirical analyses of the structure and content of the OI field to uncover
the theoretical foundations and key themes that lie at the core of the paradigm. We achieve
this by combining two bibliometric methods of network based co-citation analysis and text
mining (unstructured ontological discovery) of 299 core articles published on OI. The
application of these complementary bibliometric methods enables a more robust, structured and
consolidated overview of this rapidly expanding field, reducing bias often associated with expert
surveys and traditional literature reviews. We use co-citation analyses to provide a robust
illustration of the structure and theoretical core of the field. By using forward-citations of OI
articles, we further uncover extant theories and schools of scholarship that are used together with
OI research. Text mining helps provide detailed conceptual insights by shifting the level of
analysis from authors and their citations to the actual text/words used by the authors for a
content-driven review of the literature. This method differs from co-citation analysis in that it
systematically discovers key concepts and constructs within the OI paradigm, identifies past and
persistent themes, as well as emerging themes in OI research. Our findings thus provide a clearer
understanding of the key concepts and intellectual streams that constitute OI, and pave the way
for a more unified and theoretically grounded framework for the paradigm. We also identify
research gaps and provide directions for future research. Thus, this study provides a robust
foundation to integrate and advance research on OI.
This paper is structured as follows. First, we provide a brief summary of the history of OI
research and contextualize our contribution amidst the existing literature reviews that focus on
3
the past, current and future state of the field. We then introduce the research methodology used
to review extant research within the field, and subsequently present the results of our analysis. In
concluding the paper, we derive relevant insights from our findings, identify research gaps and
lay out an agenda for future research on OI.
PREVIOUS OPEN INNOVATION RESEARCH
According to the OI paradigm, organizational boundaries are permeable rather than closed, and
the locus of innovation is moved from a location internal to the organization to a relational
system comprising the organization and its external partners (Bogers & West, 2012; Chesbrough,
2006a, 2006b; Vanhaverbeke et al., 2008). OI in organizations can thus occur through three
processes: (i) outside-in or inbound process, which involves the inflow and acquisition of
knowledge from external sources; (ii) inside-out or outbound process, which involves the
outflow and commercialization of knowledge; and (iii) coupled process, which combines the
inbound and outbound processes to result in a continuous co-creation of knowledge (Enkel et al.,
2009; Gassmann & Enkel, 2006). OI business models enable organizations to integrate and
commercialize complementary resources and capabilities to capture value and maximize profits
from innovation (e.g., Chesbrough & Crowther, 2006; Laursen & Salter, 2006).
Following Chesbrough’s (2003c) seminal work, the field of OI has attracted significant
research attention as evident from the steady increase in published papers (e.g., Christensen et
al., 2005; Dahlander & Gann, 2010) and books (e.g., Chesbrough, 2011b; Chesbrough et al.,
2006). Journal special issues (e.g., R&D Management, Technovation) and conferences on this
topic have also significantly contributed to our knowledge. Both interest (number of articles
4
directly related to OI) in the field and its influence (number of articles that cite OI articles) has
been burgeoning over the last decade (Figure 1).1
--------------------------
Insert Figure 1 here
--------------------------
Literature on OI is becoming increasingly diverse and scattered across multiple
disciplines (Huizingh, 2011; Van De Vrande et al., 2010). Further, disparate definitions and
ambiguous theorizations hamper progress in this field (Gianiodis et al., 2010; West & Bogers,
2013) and hinder the development of an integrated conceptual framework and robust empirical
investigations (Dahlander & Gann, 2010; Lichtenthaler, 2011). The notion of OI has also been
criticized as simply being ‘old wine in new bottles’ (Trott & Hartmann, 2009) that fails to “bring
anything new to the table” (Remneland-Wikhamn & Wikhamn, 2013, p. 179). Scholars have
recognized the need to gather a consolidated understanding of the field, and have started to
review and synthesize the literature. However, patterns within existing literature can be hard to
uncover when a research field is complex, in its early stages of inquiry, and rapidly evolving (Di
Stefano et al., 2010). The relative immaturity of OI as a research domain, the multitude of
definitions and conceptualizations, and the steep increase in publications in the field add to the
task. Yet, literature reviews have contributed valuable insights on different aspects of OI
research. For example, West & Bogers (2013) review research on inbound (and coupled)
processes of OI to uncover how firms leverage external sources of innovation, and Dahlander &
Gann (2010)) define and clarify the ‘openness’ construct in OI research. Previous reviews also
vary in the methods adopted to analyse the literature. Huizingh (2011) takes a qualitative
approach to discuss OI research along the dimensions of context, content and process and
provides research directions for the field. Remneland-Wikhamn & Wikhamn (2013) empirically
1 This is based on a search for papers in the Scopus database for publications that include the term ‘open innovation’
in its title, abstract and/or keywords.
5
classify OI literature into the firm-perspective and ecosystem perspective and relate it to the
wider innovation context. To complement these reviews, we adopt a novel, more comprehensive
and systematic approach to analyze the literature in this rapidly expanding research domain. To
our knowledge, we are the first to combine the two sophisticated and complementary
bibliometric methods of co-citation analyses and text mining (unstructured ontological
discovery) to develop a more consolidated and robust understanding of the structure, concepts
and theoretical foundations of OI.
METHOD
We identified 299 focal articles published in leading management and innovation journals that
were published between 2003 and 2013 (November) and included the term ‘open innovation’ in
their title, abstract and/or keywords. This serves as an indication that the topic area of OI is one
of the main foci of the selected papers. Second, we created a list of 2140 articles that cited our
focal articles.2 We first use co-citation approaches to analyze empirically the structure and
theoretical foundations of OI research. Co-citation analysis is based on the idea that citations are
manifestations of otherwise often invisible relationships between authors, ideas and communities
(Garfield et al., 1983; Small, 1973). First, we conduct co-citation analysis publication level
rather than at the author level (Gmür, 2003), as it allows us to relate different contributions by
one author to distinct schools of thought, which is particularly important in order to distinguish
and connect ideas and theories within one research domain. Second, the proximity scores from
the co-citation analysis are visualized through two complementary methods to provide a richer
and more detailed representation of the connection between publications. In a first step, we use
2We used the Scopus, the largest citation database of peer-reviewed literature, to select articles and reviews in leading journals based on impact factors, belonging to the field of Business and Economics, and that include ‘open innovation’ in its title, abstract and/or keywords. We opted for this approach to make the sample as transparent as possible. Please contact the authors if you would like to receive a full list of articles included in this study.
6
the proximity scores from the co-citation analysis to create a network graph (Gephi software).
Thereby, the connections between publications are based on the number of co-citations, the
distances between any publications are approximated by the path length and the size of the
publication bubble reflects the number of citation for underlying publication. Then, we use a
grouping algorithm, based on the network structure, to identify clusters of related publications
(Blondel et al., 2008). Third, like most other co-citation studies we perform the analysis at the
level of the references to examine the theoretical foundations and research streams within OI
literature. However, we also apply the co-citation logic at the level of the references of the
articles that cite our focal OI publications. This approach allows analyzing how OI research is
diffused within the wider management literature. This is a particularly important question as OI
is still a relatively young area of research.
Text mining provides detailed conceptual insights by shifting the level of analysis from
authors and their citations to the actual words used by authors to provide a systematic, unbiased
and content-driven review of the literature. To do so, we use the textual data mining software
Leximancer 4.0 which is a valuable tool for narrative inquiry of a research area (Clandinin &
Connelly, 2000; Sowa, 2000).3 Leximancer applies empirically validated Bayesian algorithms to
identify: (1) the most frequently used concepts within a body of text; and, more importantly, (2)
the relationships between these concepts. Thus, this approach differs from co-citation analysis in
that it systematically discovers key concepts and constructs within the OI paradigm that emerge
from the text (thematic analysis) and how they are linked with each other (semantic analysis)
based on the co-occurrence of words within their textual contexts. Leximancer extends beyond
simple coding as it bootstraps an expanded list of related terms that signify a concept from the
3 For a more detailed description of the underlying algorithm and the process that Leximancer follows, please see
Liesch et al. (2011) and Smith & Humphreys (2006) .
7
text data. This is similar to manual coding, which, however, can be biased by the coder’s
interpretation of meaning and implicit underlying assumptions. Not only does the presence of a
concept (i.e. its frequent occurrence in the text) carry meaning, but also its absence. That is, it is
potentially indicative of OI research if an important concept does not occur often enough and is
not associated with other concepts (Liesch et al., 2011). Summing up, this technique is designed
to decipher and visualize the structure of complex textual data of the type used in scholarly
research. Thus, it appropriately complements citation-based analysis in fields where there are a
limited number of potentially citable sources and there is a lack of consensus as to what underlies
the domain under investigation.
FINDINGS
Citation and co-citation analysis
When reviewing the Top 25 journal outlets of our focal publications (Table 1), we find that our
focal papers have predominantly been published in innovation journals, with the practice-based
California Management Review being the only non-innovation-centric journal in the Top 10.
Although researchers have used a few select management journals (e.g., Organization Science)
to disseminate their findings, marketing and engineering journals only feature further down the
list (e.g., Industrial Marketing Management at 15 and IEEE Transactions on Engineering
Management at 17). Practitioner journals (e.g., Harvard Business Review) are popular outlets
denoting the relevance of the OI concept to general management practice. The pattern is not too
different when we look at citing publications, which mainly comprise innovation and
management journals, but the breadth of fields is slightly broader (e.g., European Planning
Studies). This indicates that the influence of OI concept spans several fields.
8
--------------------------
Insert Tables 1 & 2 here
--------------------------
Table 2 shows the most influential publications in the field of OI (left column) and also
its historical roots by listing the most-cited references of the focal publications (right column).
These references of our focal articles also form the basis of the network-based co-citation
analysis presented in Figure 2. Figure 2 shows the co-citation network of the references of the
focal publications. The size of the nodes represents the citations received by the references and
the thickness of the links between the nodes signifies the number of co-citations. The network
shows the importance of Chesbrough’s (2003c) seminal book which occupies a very central and
dominant position. In fact, the dominance of this article and the homogeneity between the co-
citations is so high that the cluster algorithm could not clearly identify meaningful clusters.4 It is
also interesting to note that some important contributions in OI research centered on technology
exploration and exploitation (Chesbrough, 2007; Fosfuri, 2006; Rivette & Kline, 2000), and in
the field of user innovation and OSS (Lakhani & von Hippel, 2003; von Hippel & von Krogh,
2003) appear distant and detached from the core of the network. This indicates that these lines of
work are not as well integrated with mainstream OI concepts.5
In the next step, we looked at how the focal OI publications have been applied and
diffused in later research; that is, alongside which other theories have the focal OI papers been
cited (Figure 3).6 Similar to the network of the focal references depicted in Figure 2, the work of
Chesbrough (2003c) plays a central role. Although OI literature is still nascent, we see that
4To increase the readability we only show publications with more than 20 citations, a degree range >3 and a co-citation strength of >10. 5 We also see that the case study methodological contributions (Yin, 2003; Eisenhardt, 1989) also appear separate to the rest of the network. 6 To increase the readability we only show publications with more than 75 citations, a degree range >3 and a co-citation strength of >20.
9
several mature research streams lie adjacent to this field. The algorithm identified nine research
clusters in all, eight of which are established fields that are coupled with the OI cluster:
1. Open innovation - technology integration & business models (Chesbrough, 2003c;
Chesbrough & Crowther, 2006; Chesbrough et al., 2006; Dodgson et al., 2006; West &
Gallagher, 2006)
2. Absorptive capacity (Cohen & Levinthal, 1990; Katila & Ahuja, 2002; Zahra & George,
2002)
3. Knowledge-based view (e.g. Kogut & Zander, 1992; Nonaka, 1994; Nonaka & Takeuchi,
1995)
4. Exploration & exploitation of knowledge and technology (Arora et al., 2001; Chesbrough,
2007; March, 1991; Rivette & Kline, 2000)
5. Resource-based view & dynamic capabilities (Barney, 1991; Eisenhardt & Martin, 2000;
Penrose, 1959; Teece et al., 1997; Teece, 2007; Wernerfelt, 1984)
6. Value appropriability & complementary assets (Arrow, 1962; Teece, 1986; Williamson, 1985)
7. Networks and collaboration (Ahuja, 2000; Burt, 1992; Granovetter, 1973; Powell, 1990;
Powell et al., 1996; Uzzi, 1997).
8. User innovation & OSS communities (Henkel, 2006; Lakhani & von Hippel, 2003; Lerner &
Tirole, 2002; von Hippel, 1986, 1988; von Hippel & von Krogh, 2003).
9. Qualitative methodology (Eisenhardt, 1989; Miles & Huberman, 1994; Yin, 2003).
--------------------------
Insert Figures 2 & 3 here
--------------------------
OI research is closely related to the absorptive capacity literature (Figure 3). The work of
Cohen & Levinthal (1990) is as central, and nearly as important as the seminal work from
Chesbrough (2003c). The OI cluster encompasses research focused on technology development,
10
transfer and integration (e.g., Chesbrough, 2006b; Chesbrough & Crowther, 2006; Dodgson et
al., 2006; Gassmann, 2006) as well as OI business models (e.g., Chesbrough et al., 2006; Enkel
et al., 2009). The resource-based view & dynamic capabilities cluster (e.g., Penrose, 1959; Teece
et al., 1997; Wernerfelt, 1984) has stronger linkages with the work on knowledge-based view
(e.g., Kogut & Zander, 1992; Nelson & Winter, 1982) and absorptive capacity (e.g., Cohen &
Levinthal, 1990; Zahra & George, 2002).
Similar to the co-citation network (Figure 2), user innovation & OSS communities cluster
is separate and less connected to other clusters. Although the early works of von Hippel (1986,
1988) on lead user innovation are close to the core of the network, more recent research in the
field (e.g., Henkel, 2006; Lakhani & von Hippel, 2003; von Hippel & von Krogh, 2003) seem
more distant, indicating that this research stream has moved farther away from core OI research.
Very few contributions (e.g., West & Gallagher, 2006) seem to be connecting OI researchers
with scholars investigating user and community aspects of OI. Powell et al. (1996) play a key
role in bridging the network and collaboration literature with OI research; yet this cluster seems
fairly distant within the co-citation network meaning there is scope to better integrate network
theories with OI research. It is, however, interesting to note that the network and collaboration
cluster shows stronger linkages to the absorptive capacity literature (Cohen & Levinthal, 1990).
Leximancer analysis
The next set of figures demonstrates the results from text mining. The software first generates
concept seeds and then identifies relationships between concepts, which get aggregated into
themes7. In the maps that follow, circles represent themes with pertinent concepts situated within
7 We deleted words such as ‘authors’, ‘example’, ‘use’ etc. from the text so as to not bias the creation of concepts
and themes.
11
each theme. The importance of themes is shown through the color of the circles (darker circles
are more important). The size of the circle only indicates how many concepts have been
clustered together to form a given theme. The distance between concepts on the ‘maps of
meaning’ show how closely the concepts are related; therefore concepts that are weakly related
semantically will be positioned far apart on the map (Rooney, 2005).
Complete sample
In a first step, we analyzed the complete sample of papers (Figure 4). The results indicate that OI
research essentially takes a firm-centric perspective. This is evident from the concept ‘business’
taking a central position on the map, and being closely linked with ‘firms’ and ‘management’. As
initial conceptualizations of OI centered around how firms can expand their boundaries and
collaborate with external entities for technology transfer and knowledge exchange (Chesbrough,
2003c, 2006a; Gassmann & Enkel, 2006), research has mainly taken a firm-level approach to
investigate how the focal firm can organize and implement OI (Dahlander & Piezunka, 2013;
Laursen & Salter, 2006; van de Vrande et al., 2009).
Based on the concept map, we identify three distinct areas of OI research: Technology,
Business models and value appropriation, and Users and communities. The technology area
(Area A), which addresses the technology and R&D-oriented aspects of OI, also covers the
themes firms, development and patents. The semantically closely related themes business,
management, firms and value, combine to form the second research area which is focused on the
business models and value appropriation of OI (Area B). Finally, the users and communities
research stream (Area C), made of the participants and solutions themes, revolves around the
role of individual users and communities as participants in the OI process.
12
The firm-centric perspective investigating the role of technology and R&D in OI has
received the most research attention (red color). Research in this area includes technology
sourcing and integration as well as technology development and out-licensing for OI (e.g.,
Chesbrough & Crowther, 2006; Parida et al., 2012). Many studies draw on the notion of
absorptive capacity to investigate how firms can best develop R&D resources and relational
capabilities for OI (e.g., Cassiman & Veugelers, 2006; Hughes & Wareham, 2010). The focus
here is on the knowledge exploration and exploitation processes for collaborative development
with value chain partners (suppliers, customer, partners) through R&D alliances, corporate
ventures, spinouts, IP and patents (e.g., Seldon, 2011; Vanhaverbeke et al., 2008).
Business models and value appropriation is a theme that has attracted limited research
focus (green color). Some studies have focused on business models that actively seek
complementary assets in partners and appropriability regimes for commercializing technology
(e.g., Bogers & West, 2012; Chesbrough, 2007; Laursen & Salter, 2006). Such open business
models are critical for both creating and capturing value (Chesbrough & Appleyard, 2007). Yet,
researchers have so far devoted lesser attention to business models and integrated value capture
as compared to technology-related aspects of OI. Focus of research is also on the management
aspects of OI including managing collaborative networks and corporate ventures (e.g.,
Chiaromonte, 2006; Han et al., 2012) and the ensuing social relationships between partners (e.g.,
Chesbrough & Schwartz, 2007; Huggins, 2010).
Users and communities as a research area has received relatively little attention (blue
color), despite being regarded as topical (Baldwin & von Hippel, 2011; Bogers & West, 2012).
While there is significant discussion on collaboration with value chain partners, very less focus
has been paid on individual users as innovators in the field of OI, barring a few exceptions (e.g.,
13
Füller et al., 2012; Füller et al., 2009). We also note that there is little focus on the role of
communities in OI, although some researchers (e.g., Ebner et al., 2009; Füller et al., 2008)
exclusively focus in this space. A significant amount of studies in the context of community-
based innovation is on OSS (e.g., Dahlander & Wallin, 2006; Gruber & Henkel, 2006; Lakhani
& von Hippel, 2003; Lerner & Tirole, 2002). Several researchers focus on profit appropriation
and benefit accrual despite ‘free-revealing’ of ideas through, for instance, a private-collective
model (Von Hippel & Von Krogh, 2006) and selective-revealing strategies (Henkel, 2006).
--------------------------
Insert Figure 4 here
--------------------------
Differences between early and current research
In the next step, we separated the data set in two time periods: 2003-2008 (48 papers) and 2009-
2012 (251 papers) to investigate how research foci have evolved over time. In the earlier time
period (Figure 5), OI research had a strong focus on technology (red color). The concepts within
this theme reveal that technology sourcing and licensing as means of facilitating knowledge
inflow and outflow processes for OI (e.g., Chesbrough et al., 2006; Gassmann & Enkel, 2006)
have received attention. A firm-centric perspective has been applied to study the creation of
internal and external resources and capabilities for effective exploration and exploitation of
knowledge and technology (Dittrich & Duysters, 2007; Lichtenthaler, 2007). The focus in this
research area is also on R&D alliances, networks, partnerships, IP and patenting for
implementing OI (e.g., Vanhaverbeke et al., 2008; West & Gallagher, 2006).
While there is some focus on the role of information, knowledge (and other resource)
exploitation and transfer across networks, there appears to be no attention to organizational
learning processes (note the absence of learning as a concept). There is some focus (albeit not
dominant) on customers for new product ideas, design and development (e.g. Piller & Walcher,
14
2006); yet users as innovators have received relatively less attention within the OI domain.
Community-based innovation had not yet emerged as a mainstream practice, and studies have
rarely investigated the role of OI communities and the ensuing community member relationships
(blue color). The little research in this space was also mainly centered on OSS communities (e.g.,
Henkel, 2006; Lakhani & von Hippel, 2003; Von Hippel & Von Krogh, 2006).
--------------------------
Insert Figures 5 & 6 here
--------------------------
Of the total 299 papers in our sample, 251 papers (84%) belonged to the later time period
(2009-2013), confirming that research on OI has burgeoned over the last five years. Figure 6
shows that research remains focused on technology and R&D (e.g., Veugelers et al., 2010), but
with explicit attention to the external perspective (note that external is a new theme). Research
has investigated the role of partnerships and collaboration with external stakeholders across the
value chain (suppliers, customers, partners) and integration of external knowledge and resources
as ways for firms to leverage external sources of innovation (e.g., Clausen, 2013), and in doing
so, continue to use the absorptive capacity lens.
Management emerges as a new theme, indicating increased focus on managing OI
through the creation of open business models centered on corporate venturing, strategic alliances,
patent and IP portfolio management (e.g., Rohrbeck, 2010). With network appearing as a new
theme, the focus of OI research appears to be expanding from the firm-level to the network-level
(e.g., Rampersad et al., 2010). Moreover, the role of institutional networks, public sector,
national and regional systems of innovation and government policy-making is gaining
importance in the field of OI (note that the policy and public theme and ‘government’ concept
appears newly) (e.g., Bodas Freitas et al., 2013). A key difference between the two time periods
is the decline in focus on customers and the service aspects of OI in later research (note that
15
customer is no longer a core theme as was the case in Fig 5). OI in services and the role of
customer centrality in the service context is not a prominent topic in recent research.
Studying the performance effects of OI emerges as a research topic (note that
‘performance’ appears as a concept here) (e.g., Kim & Park, 2010); yet focus on measurement of
value capture through OI still has scope to improve (West & Bogers, 2013). With ‘learning’
occurring as a concept, the emergent attention to organizational learning processes involved in
OI is evident, albeit with potential to improve. While research on users and communities seemed
more scattered and fragmented in the earlier time period, this is evolving into a more coherent
research space. There appears to be relatively more focus on the role of participant interactions
and behavior (e.g., Fichter, 2009), although there is scope for more research in this area (blue
color). Exclusive focus on IP and patents as well as software no longer exists; software is now
discussed more in the context of OSS.
DISCUSSION
The results of our text mining suggest that three distinct themes can be identified in OI research-
to-date: (1) Technology, (2) Business models and value appropriation, and (3) Users and
communities. We see that these areas align well with the eight clusters identified through co-
citation analysis. The Technology research area, with concepts such as knowledge, resources and
capacity, aligns with the research clusters: 1. Absorptive Capacity; 2. Knowledge-based view; 3.
Exploration and exploitation of knowledge and technology; 4. OI – Technology integration; and
5. Resource-based view and dynamic capabilities. Business models and value appropriation,
containing the concepts of business, management, networks, knowledge and resource, aligns
with: 4. OI - Business models 6. Value appropriability and complementary assets; and 7.
Networks and collaboration. The Users and communities research area, with concepts like
16
individuals, users, participants, community and OSS, aligns with: 8. User Innovation and OSS
communities. Of these three research areas, findings from both co-citation analysis and text
mining reveal that the technology area, which encompasses the technology and R&D-oriented
aspects of OI, has received the most research attention (note that the ‘OI - Technology
integration’ cluster is closely linked with the core of the co-citation networks in Figures 2 & 3,
and the technology research area A in Figure 4 contains themes in red color). Previous studies
have investigated technology exploration and exploitation for OI through R&D alliances,
licensing, IP and patents (e.g., Seldon, 2011; Vanhaverbeke et al., 2008; West & Gallagher,
2006). The focus here is primarily on how organizations can exploit knowledge to commercialize
technology (for instance, through out-licensing) or explore knowledge to acquire external
technology (for instance, through in-licensing) for OI (e.g., Chesbrough & Crowther, 2006;
Dittrich & Duysters, 2007), with a particular focus on technology transfer and new product
development through partnerships with external stakeholders across the value chain (suppliers,
customers, partners). Thus, this research stream bridges R&D and technology management
literature with innovation and new product development literature.
Our analysis has also uncovered several research gaps that serve as directions for future
investigation. Based on this, we identify four fertile research avenues.
1. Develop a more comprehensive perspective of OI by including diverse levels of analysis
The firm perspective – The results of our review suggest that research has predominantly adopted
a firm-centric perspective to investigate OI. From the beginning, OI has been conceptualized as a
way for firms to open up their boundaries to leverage inflows and outflows of knowledge, to
boost internal innovation, and to expand markets for external exploitation of innovation
(Chesbrough, 2003c; Chesbrough et al., 2006; Gassmann & Enkel, 2006). Researchers have
17
since had the tendency to adopt a firm-driven approach to investigate how the focal firm adopts
OI, conditions that enable this adoption, and how OI impacts performance (e.g., Dahlander &
Piezunka, 2013; Mortara & Minshall, 2011; van de Vrande et al., 2009).
The network perspective – Research on networks also predominantly focuses on how firms can
leverage external partners across the organizational value network through processes of
collaboration and alliancing (e.g., Chesbrough & Schwartz, 2007; Harryson, 2008; Huggins,
2010). Limited research appears to bring a systemic, network level focus to studying and
managing OI, where the entire collaborative network forms the level of analysis. We notice that
expansion of focus from the firm-level to the network-level is only beginning to emerge in OI
research over the last five years (e.g., Rampersad et al., 2010), and there is scope for more work
in this space. An interesting avenue for more research is in the inclusion of network learning.
The results of our textual analysis show that little research attention was given to organizational
learning processes (see Figure 5) in early OI research, despite some focus on the role of
information, knowledge (and other resource) exploitation and transfer across networks. More
recent research has revealed emerging attention to organizational learning processes (e.g.,
Chatenier et al., 2009); yet there is potential for a better understanding of the management of
these open and iterative learning processes across OI networks, and the bearing these may have
on OI performance. Our analysis further reveals that OI research strongly draws on absorptive
capacity literature to investigate knowledge inflows and outflows across networks (see Figure 2,
3 and 4). However, there seems to be little integration of OI research with network learning and
social network theories (note that the cluster of network scholars is distant within the co-citation
network in Figures 2 & 3, and the concept ‘social’ occurs in the periphery and is not strongly
connected to other concept/themes in Figure 4. A better integration of (social) network theories
18
with mainstream OI research may lead to a better understanding of the role of social ties,
embeddedness of learning and innovation in collaborative networks.
The user perspective – Findings from the textual analysis indicate that little attention has been
paid on users as innovators in OI research. This is consistent with the results of the co-citation
analyses which showed that the cluster of user innovation scholars is distant and detached from
the core of the co-citation network, indicating that this research stream has not been fully
integrated with the mainstream OI research (see Figure 2 and 3). Very few scholars (e.g., West &
Gallagher, 2006) have made the conscious effort to connect OI research with aspects of user
innovation. Although interest in this area seems to have increased over the last five years (see
Figure 6), there is scope for more research on the role of users as innovators and the management
of the ensuing B2C relationships between the innovating firm and its users. This is consistent
with the suggestions of key user innovation scholars (e.g., Baldwin & von Hippel, 2011). It is
important to shift the level of analysis from the firm to the user, in order to understand the users’
motivation and behavior while co-innovating with firms, and to gain insights into the processes
of OI from the users’ perspective (e.g., Füller et al., 2009). A better integration of user
innovation concepts into OI literature can aid the creation of a more coherent body of knowledge
on how users can be leveraged and managed as external sources of innovation.
The community perspective – Our results also reveal that the role of communities in OI has
received relatively little research attention. This may be because it is mainly the user innovation
scholars who have have been interested in communities (e.g., Henkel, 2006; Lakhani & von
Hippel, 2003). Since our co-citation networks (Figure 2 and 3) reveal that the user innovation
research cluster is separate and less connected to the other clusters, the integration of research on
community-based innovation into mainstream OI research is also weak. The emerging interest in
19
this space in the recent past is also mainly centered on OSS communities (see Figure 6) (e.g.,
Von Hippel & Von Krogh, 2006; Dahlander & Wallin, 2006). There is scope for further
investigation on how to engage other user communities and how to manage firm-hosted
communities (e.g., Ebner et al., 2009; Füller et al., 2008) to sustain community-based innovation
outcomes. In line with West & Lakhani (2008) and Fichter (2009), we see the need for defining
more clearly what an OI community is, identifying community-level constructs and looking at
communities (rather than firm/network) as the unit/level of analysis. Investigating the
phenomenon of OI from the perspective of non-firm actors such as communities serves to extend
the hitherto firm-centric approach to OI research. With an understanding of OI from the
perspective of communities, firms can better manage the OI process by aligning their practices
with those of their communities. This requires research to go beyond the traditional focus on
dyadic interactions between firms to study the one-to-many relationships between firms and
community participants. Investigating the many-to-many C2C relationships, iterative participant
interactions and behavior, and how these shape the learning and socio-cultural dynamics in OI
communities are also interesting areas for future research. Sociological and organizational
behavior theories such as ‘communities of practice’ (Brown & Duguid, 1991; Lave & Wenger,
1991) may be drawn to explore these aspects of community innovation (West & Lakhani, 2008).
2. Direct increased attention to open innovation business models and value capture
Our analyses reveal the need for more research on OI business models and value capture (note
the Business models and value appropriation area is in green color in Figure 4). This is
particularly important given Chesbrough’s (2003a, 2006a) emphasis on the centrality of both
business models and value capture to the concept of OI. Chesbrough & Appleyard (2007) has
also pointed out the importance of developing an open business strategy so as to derive full value
20
from OI models. Recent research shows an increase in focus on studying the performance effects
of OI; yet focus on measurement of value capture through OI still has scope to improve (see
Figure 6). This is in sync with the findings of West & Bogers (2013) that research on business
models of OI is still nascent, and has hitherto focussed more on outbound as compared to
inbound OI, and value creation rather than value capture. As suggested by (Enkel et al., 2009),
there is a need for research to adopt a balanced focus on investigating value captured from
outside-in, inside-out and coupled processes of OI, as well as on creating customized business
models for each situation.
3. Enhance service focus and conceptualize ‘open service innovation’
There appears to be a lack of discussion of OI in the service context and the role of customer
centrality in OI in services. While collaborating with customers for new product ideas, design
and development was a core theme of research in early OI research, the focus on customers
seems to have waning in more recent research (see Figures 5 & 6). Moreover, OI in services is
not a particularly dominant topic of research in both time periods. With the increasing
importance of services in today’s economy, there is a need for more research in this space. This
echoes the recent notion of open service innovation (Chesbrough, 2011a, 2011b) that reinforces
that even manufacturing businesses within an increasingly commoditized marketplace need to
apply a service-oriented logic to innovation by collaborating with customers at all stages of the
innovation process, other than partnering with other value network entities. In this context, there
is scope to draw on service marketing theories, and in particular incorporate co-creation concepts
from the emerging service-dominant logic of marketing (SDL) (Lusch & Vargo, 2006; Vargo &
Lusch, 2004) for a better conceptualization and theorization of this concept within OI research.
21
Integrating existing SDL literature into OI research can also benefit the proposed focus on B2C
and C2C research and address some of the issues raised.
CONCLUSION
In this study, we combined complementary bibliometric methods of network-based co-citation
analyses and text mining of 299 core articles published on OI to present a novel, systematic and
holistic review of the field. Through combining co-citation analysis and text mining, we are
better positioned to clearly ground the literature in theories and concepts as a step towards
developing a robust and integrated framework for the OI paradigm. Our results suggest that OI
research is connected with several mature fields ranging from absorptive capacity (Cohen &
Levinthal, 1990) to lead user innovation (von Hippel, 1988), lending it its diverse intellectual
roots. Yet, Chesbrough’s (2003c) seminal work forms the core foundation to the research
domain, indicating that it draws more from within than from across fields. There appears more
scope for better integration of other related theories with OI research. Our findings also reveal
three themes of OI research: Technology, Business models and Value Appropriation, and Users
and communities. Research-to-date has predominantly focused on the technology theme. Future
research opportunities include: integrating user and community perspectives to the hitherto firm-
centric approach of OI; and understanding OI in the context of services.
Our study is subject to limitations. We used the Scopus database to select publications
based on three search criteria: 1. Articles published in leading innovation and management
journals based on impact factors; 2. Articles that belong to the field of Business and Economics;
and 3. Articles that include the term ‘open innovation’ in its title, abstract and/or keywords. We
opted for this approach to make the sample as transparent as possible. However, our sample may
lack some contributions to OI research, including books such as Chesbrough’s (2003c) seminal
22
book, papers published in journals outside our list, in other fields, and conference proceedings.
Finally, by focusing on the term ‘open innovation’, we leave out research on related constructs
such as ‘openness’. However, the focus of our research was to investigate the core concepts and
theoretical underpinnings of OI research in specific; articles that did not explicitly include this
search term may not want to be linked to this research domain.
23
LITERATURE
Ahuja, G 2000. The Duality of Collaboration: Inducements and Opportunities in the Formation of Interfirm Linkages, Strategic management journal 21 (3): 317-343.
Alexy, O, George, G & Salter, AJ 2013. Cui Bono? The Selective Revealing of Knowledge and Its Implications for Innovative Activity, Academy of Management Review 38 (2): 270-291.
Arora, A, Fosfuri, A & Gambardella, A 2001. Markets for Technology and Their Implications for Corporate Strategy, Industrial and corporate change 10 (2): 419-451.
Arrow, K 1962. 'Economic Welfare and the Allocation of Resources for Invention'. In The Rate and Direction of Inventive Activity: Economic and Social Factors. Nber: 609-626.
Aslesen, HW & Freel, M 2012. Industrial Knowledge Bases as Drivers of Open Innovation?, Industry and Innovation 19 (7): 563-584.
Baldwin, C & von Hippel, E 2011. Modeling a Paradigm Shift: From Producer Innovation to User and Open Collaborative Innovation, Organization Science 22 (6): 1399-1417.
Barney, J 1991. Firm Resources and Sustained Competitive Advantage, Journal of management 17 (1): 99-120.
Blondel, VD, Guillaume, JL, Lambiotte, R & Lefebvre, E 2008. Fast Unfolding of Communities in Large Networks., Journal of Statistical Mechanics: Theory and Experiment 28 (10): P10008.
Bodas Freitas, IM, Geuna, A & Rossi, F 2013. Finding the Right Partners: Institutional and Personal Modes of Governance of University-Industry Interactions, Research Policy 42 (1): 50-62.
Bogers, M & Lhuillery, S 2011. A Functional Perspective on Learning and Innovation: Investigating the Organization of Absorptive Capacity, Industry and Innovation 18 (6): 581-610.
Bogers, M & West, J 2012. Managing Distributed Innovation: Strategic Utilization of Open and User Innovation, Creativity and Innovation Management 21 (1): 61-75.
Boyack, KW & Klavans, R 2010. Co̺Citation Analysis, Bibliographic Coupling, and Direct Citation: Which Citation Approach Represents the Research Front Most Accurately? , Journal of the American Society for Information Science and Technology 61 (12): 2389-2404.
Brown, JS & Duguid, P 1991. Organizational Learning and Communities-of-Practice: Toward a Unified View of Working, Learning, and Innovation, Organization science 2 (1): 40-57.
Burt, RS 1992. Structural Holes: The Social Structure of Competition. Harvard University Press: Cambridge, MA.
Cassiman, B & Veugelers, R 2006. In Search of Complementarity in Innovation Strategy: Internal R&D and External Knowledge Acquisition, Management science 52 (1): 68-82.
Chatenier, ED, Verstegen, JAAM, Biemans, HJA, Mulder, M & Omta, O 2009. The Challenges of Collaborative Knowledge Creation in Open Innovation Teams, Human Resource Development Review 8 (3): 350-381.
Chesbrough, H 2003a. The Era of Open Innovation, MIT Sloan Management Review 44 (3): 35–41.
Chesbrough, H 2003b. The Logic of Open Innovation: Managing Intellectual Property, California Management Review 45 (3): 33-58.
Chesbrough, H 2003c. Open Innovation: The New Imperative for Creating and Profiting from Technology. Harvard Business School Press: Boston, MA.
24
Chesbrough, H 2006a. 'New Puzzles and New Findings.'. In Open Innovation: Researching a New Paradigm, H. Chesbrough, Vanhaverbeke, W., West, J. (ed.). Oxford University Press: Oxford; 1–12.
Chesbrough, H 2006b. Open Business Models: How to Thrive in the New Innovation Landscape. Harvard Business School Press: Boston.
Chesbrough, H 2011a. Bringing Open Innovation to Services, MIT Sloan Management Review 52 (2): 85-90.
Chesbrough, H 2011b. Open Services Innovation: Rethinking Your Business to Grow and Compete in a New Era. Jossey-Bass: San Francisco.
Chesbrough, H & Crowther, AK 2006. Beyond High Tech: Early Adopters of Open Innovation in Other Industries, R and D Management 36 (3): 229-236.
Chesbrough, H & Schwartz, K 2007. Innovating Business Models with Co-Development Partnerships, Research Technology Management 50 (1): 55-59.
Chesbrough, H, Vanhaverbeke, W & West, J 2006. Open Innovation: Researching a New Paradigm. Oxford University Press: Oxford, UK.
Chesbrough, HW 2007. Why Companies Should Have Open Business Models, MIT Sloan Management Review 48 (2): 22-28+91.
Chesbrough, HW & Appleyard, MM 2007. Open Innovation and Strategy, California Management Review 50 (1): 57-76+53-54.
Chiaromonte, F 2006. Open Innovation through Alliances and Partnership: Theory and Practice, International Journal of Technology Management 33 (2-3): 111-114.
Christensen, JF, Olesen, MH & Kjær, JS 2005. The Industrial Dynamics of Open Innovation - Evidence from the Transformation of Consumer Electronics, Research Policy 34 (10): 1533-1549.
Clandinin, DJ & Connelly, FM 2000. Narrative Inquiry: Experience and Story in Qualitative Research. Jossey-Bass: San Francisco.
Clausen, TH 2013. External Knowledge Sourcing from Innovation Cooperation and the Role of Absorptive Capacity: Empirical Evidence from Norway and Sweden, Technology Analysis and Strategic Management 25 (1): 57-70.
Cohen, WM & Levinthal, DA 1990. Absorptive Capacity: A New Perspective on Learning and Innovation, Administrative Science Quarterly 35 (1): 128–152.
Crespin-Mazet, F, Goglio-Primard, K & Scheid, F 2013. Open Innovation Processes within Clusters - the Role of Tertius Iugens, Management Decision 51 (8): 1701-1715.
Dahlander, L & Gann, DM 2010. How Open Is Innovation?, Research Policy 39 (6): 699-709. Dahlander, L & Piezunka, H 2013. Open to Suggestions: How Organizations Elicit Suggestions
through Proactive and Reactive Attention, Research Policy. Dahlander, L & Wallin, MW 2006. A Man on the Inside: Unlocking Communities as
Complementary Assets, Research Policy 35 (8 SPEC. ISS.): 1243-1259. Di Stefano, G, Peteraf, M & Verona, G 2010. Dynamic Capabilities Deconstructed: A
Bibliographic Investigation into the Origins, Development, and Future Directions of the Research Domain, Industrial and Corporate Change 19 (4): 1187-1204.
Dittrich, K & Duysters, G 2007. Networking as a Means to Strategy Change: The Case of Open Innovation in Mobile Telephony, Journal of Product Innovation Management 24 (6): 510-521.
Dodgson, M, Gann, D & Salter, A 2006. The Role of Technology in the Shift Towards Open Innovation: The Case of Procter & Gamble, R and D Management 36 (3): 333-346.
25
Ebner, W, Leimeister, JM & Krcmar, H 2009. Community Engineering for Innovations: The Ideas Competition as a Method to Nurture a Virtual Community for Innovations, R and D Management 39 (4): 342-356.
Eisenhardt, KM 1989. Building Theories from Case Study Research, Academy of management review 14 (4): 532-550.
Eisenhardt, KM & Martin, JA 2000. Dynamic Capabilities: What Are They?, Strategic management journal 21 (10-11): 1105-1121.
Enkel, E, Gassmann, O & Chesbrough, H 2009. Open R&D and Open Innovation: Exploring the Phenomenon, R and D Management 39 (4): 311-316.
Fichter, K 2009. Innovation Communities: The Role of Networks of Promotors in Open Innovation, R and D Management 39 (4): 357-371.
Fosfuri, A 2006. The Licensing Dilemma: Understanding the Determinants of the Rate of Technology Licensing., Strategic Management Journal 27 (12): 1141–1158.
Füller, J, Matzler, K & Hoppe, M 2008. Brand Community Members as a Source of Innovation, Journal of Product Innovation Management 25 (6): 608-619.
Füller, J, Matzler, K, Hutter, K & Hautz, J 2012. Consumers' Creative Talent: Which Characteristics Qualify Consumers for Open Innovation Projects? An Exploration of Asymmetrical Effects, Creativity and Innovation Management 21 (3): 247-262.
Füller, J, Mühlbacher, H, Matzler, K & Jawecki, G 2009. Consumer Empowerment through Internet-Based Co-Creation, Journal of Management Information Systems 26 (3): 71-102.
Garfield, E, Malin, MV & Small, H 1983. Citation Data as Science Indicators. Gassmann, O 2006. Opening up the Innovation Process: Towards an Agenda, R&D Management
36 (3): 223-228. Gassmann, O & Enkel, E 2006. Constituents of Open Innovation: Three Core Process
Archetypes, , R&D Management. Gassmann, O, Sandmeier, P & Wecht, CH 2006. Extreme Customer Innovation in the Front-End:
Learning from a New Software Paradigm, International Journal of Technology Management 33 (1): 46-66.
Gianiodis, PT, Ellis, SC & Secchi, E 2010. Advancing a Typology of Open Innovation, International Journal of Innovation Management 14 (4): 531–572.
Gmür, M 2003. Co-Citation Analysis and the Search for Invisible Colleges: A Methodological Evaluation, Scientometrics 57 (1): 27-57.
Granovetter, M 1973. The Strength of Weak Ties, American Journal of Sociology 78 (6): 1360–1380.
Gruber, M & Henkel, J 2006. New Ventures Based on Open Innovation - an Empirical Analysis of Start-up Firms in Embedded Linux, International Journal of Technology Management 33 (4): 356-372.
Han, K, Oh, W, Im, KS, Chang, RM, Oh, H & Pinsonneault, A 2012. Valuecocreationandwealthspilloverinopeninnovationalliances, MIS Quarterly: Management Information Systems 36 (1): 291-316.
Harryson, SJ 2008. Entrepreneurship through Relationships - Navigating from Creativity to Commercialisation, R and D Management 38 (3): 290-310.
Henkel, J 2006. Selective Revealing in Open Innovation Processes: The Case of Embedded Linux, Research Policy 35 (7): 953-969.
Huggins, R 2010. Forms of Network Resource: Knowledge Access and the Role of Inter-Firm Networks, International Journal of Management Reviews 12 (3): 335-352.
26
Hughes, B & Wareham, J 2010. Knowledge Arbitrage in Global Pharma: A Synthetic View of Absorptive Capacity and Open Innovation, R and D Management 40 (3): 324-343.
Huizingh, EKRE 2011. Open Innovation: State of the Art and Future Perspectives, Technovation 31 (1): 2-9.
Huston, L & Sakkab, N 2006. Connect and Develop inside Procter & Gamble's New Model for Innovation, Harvard Business Review 84 (3): 58-67.
Katila, R & Ahuja, G 2002. Something Old, Something New: A Longitudinal Study of Search Behavior and New Product Introduction, Academy of management journal 45 (6): 1183-1194.
Katz, R & Allen, TJ 1982. Investigating the Not Invented Here (Nih) Syndrome: A Look at the Performance, Tenure, and Communication Patterns of 50 R & D Project Groups, R&D Management 12 (1): 7-20.
Kim, H & Park, Y 2010. The Effects of Open Innovation Activity on Performance of Smes: The Case of Korea, International Journal of Technology Management 52 (3-4): 236-256.
Kogut, B & Zander, U 1992. Knowledge of the Firm, Combinative Capabilities, and the Replication of Technology, Organization science 3 (3): 383-397.
Lakhani, KR & von Hippel, E 2003. How Open Source Software Works: “Free” User-to-User Assistance, Research Policy 32 (6): 923–943.
Laursen, K & Salter, A 2006. Open for Innovation: The Role of Openness in Explaining Innovation Performance among U.K. Manufacturing Firms, Strategic Management Journal 27 (2): 131–150.
Lave, J & Wenger, E 1991. Situated Learning: Legitimate Peripheral Participation. Cambridge university press.
Lerner, J & Tirole, J 2002. Some Simple Economics of Open Source, The journal of industrial economics 50 (2): 197-234.
Lichtenthaler, U 2007. The Drivers of Technology Licensing: An Industry Comparison, California Management Review 49 (4): 67-89.
Lichtenthaler, U 2011. Open Innovation: Past Research, Current Debates, and Future Directions, Academy of Management Perspectives 25 (1): 75-93.
Lichtenthaler, U & Lichtenthaler, E 2009. A Capability-Based Framework for Open Innovation: Complementing Absorptive Capacity, Journal of Management Studies 46 (8): 1315-1338.
Liesch, PW, Håkanson, L, McGaughey, SL, Middleton, S & Cretchley, J 2011. The Evolution of the International Business Field: A Scientometric Investigation of Articles Published in Its Premier Journal, Scientometrics 88 (1): 17-42.
Lusch, RF & Vargo, SL 2006. Service-Dominant Logic: Reactions, Reflections and Refinements, Marketing theory 6 (3): 281-288.
March, J 1991. Exploration and Exploitation in Organizational Learning, Organization Science 2 (1): 71–87.
Miles, MB & Huberman, AM 1994. Qualitative Data Analysis: An Expanded Sourcebook. Sage. Mortara, L & Minshall, T 2011. How Do Large Multinational Companies Implement Open
Innovation?, Technovation 31 (10-11): 586-597. Munsch, K 2009. Open Model Innovation: Culture, Contract and Competition Embrace the
Practical Issues That R&D Leaders Need to Consider, Research Technology Management 52 (3): 48-52.
Nelson, RR & Winter, SG 1982. The Schumpeterian Tradeoff Revisited, The American Economic Review 72 (1): 114-132.
27
Obal, M & Lancioni, RA 2013. Maximizing Buyer-Supplier Relationships in the Digital Era: Concept and Research Agenda, Industrial Marketing Management 42 (6): 851-854.
Ordanini, A & Maglio, PP 2009. Market Orientation, Internal Process, and External Network: A Qualitative Comparative Analysis of Key Decisional Alternatives in the New Service Development, Decision Sciences 40 (3): 601-625.
Osareh, F 1996. Bibliometrics, Citation Analysis and Co-Citation Analysis: A Review of Literature I, Libri 46 (3): 149-158.
Parida, V, Westerberg, M & Frishammar, J 2012. Inbound Open Innovation Activities in High-Tech Smes: The Impact on Innovation Performance, Journal of Small Business Management 50 (2): 283-309.
Penrose, ET 1959. The Theory of the Growth of the Firm. Wiley: New York. Piller, FT & Walcher, D 2006. Toolkits for Idea Competitions: A Novel Method to Integrate
Users in New Product Development, R and D Management 36 (3): 307-318. Powell, WW (ed.) 1990, Neither Market nor Hierarchy: Network Forms of Organization, JAI
Press, Greenwich, Conn. Powell, WW, Koput, K & Smith-Doerr, L 1996. Interorganizational Collaboration and the Locus
of Innovation: Networks of Learning in Biotechnology, Administrative Science Quarterly 41 116–145.
Rampersad, G, Quester, P & Troshani, I 2010. Managing Innovation Networks: Exploratory Evidence from Ict, Biotechnology and Nanotechnology Networks, Industrial Marketing Management 39 (5): 793-805.
Remneland-Wikhamn, B & Wikhamn, W 2013. Structuring of the Open Innovation Field, Journal of Technolology Management and Innovation 8 (3): 173-185.
Rivette, KG & Kline, D 2000. Rembrandts in the Attic: Unlocking the Hidden Value of Patents. Harvard Business School Press.: Boston.
Rohrbeck, R 2010. Harnessing a Network of Experts for Competitive Advantage: Technology Scouting in the Ict Industry, R and D Management 40 (2): 169-180.
Rooney, D 2005. Knowledge, Economy, Technology and Society: The Politics of Discourse, Telematics and Informatics 22 (4): 405-422.
Schiele, H 2010. Early Supplier Integration: The Dual Role of Purchasing in New Product Development, R and D Management 40 (2): 138-153.
Seldon, T 2011. Beyond Patents: Effective Intellectual Property Strategy in Biotechnology, Innovation: Management, Policy and Practice 13 (1): 55-61.
Small, H 1973. Co̺ Citation in the Scientific Literature: A New Measure of the Relationship between Two Documents., Journal of the American Society for information Science 24 (4): 265-269.
Smith, AE & Humphreys, MS 2006. Evaluation of Unsupervised Semantic Mapping of Natural Language with Leximancer Concept Mapping, Behavior Research Methods 38 (2): 262-279.
Sowa, JF 2000. Knowledge Representation: Logical, Philosophical, and Computational Foundations. vol. 13, Brooks Cole: Pacific Grove.
Spithoven, A 2013. Open Innovation Practices and Innovative Performances: An International Comparative Perspective, International Journal of Technology Management 62 (1): 1-34.
Teece, D, Pisano, G & Shuen, A 1997. Dynamic Capabilities and Strategic Management., Strategic Management Journal 18 (7): 509-533.
28
Teece, DJ 1986. Profiting from Technological Innovation: Implications for Integration, Collaboration, Licensing and Public Policy, Research Policy 15 285–305.
Teece, DJ 2007. Explicating Dynamic Capabilities: The Nature and Microfoundations of (Sustainable) Enterprise Performance, Strategic management journal 28 (13): 1319-1350.
Trott, P & Hartmann, D 2009. Why “Open Innovation” Is Old Wine in New Bottles, International Journal of Innovation Management 13 (4): 715–736.
Uzzi, B 1997. Social Structure and Competition in Interfirm Networks: The Paradox of Embeddedness, Administrative science quarterly 35-67.
van de Vrande, V, de Jong, JPJ, Vanhaverbeke, W & de Rochemont, M 2009. Open Innovation in Smes: Trends, Motives and Management Challenges, Technovation 29 (6-7): 423-437.
Van De Vrande, V, Vanhaverbeke, W & Gassmann, O 2010. Broadening the Scope of Open Innovation: Past Research, Current State and Future Directions, International Journal of Technology Management 52 (3-4): 231-235.
Vanhaverbeke, W, Van de Vrande, V & Chesbrough, H 2008. Understanding the Advantages of Open Innovation Practices in Corporate Venturing in Terms of Real Options, Creativity and Innovation Management 17 (4): 251–258.
Vargo, SL & Lusch, RF 2004. Evolving to a New Dominant Logic for Marketing, Journal of marketing 1-17.
Veugelers, M, Bury, J & Viaene, S 2010. Linking Technology Intelligence to Open Innovation, Technological Forecasting and Social Change 77 (2): 335-343.
von Hippel, E 1986. Lead Users: A Source of Novel Product Concepts, Management science 32 (7): 791-805.
von Hippel, E 1988. The Sources of Innovation. Oxford University Press: New York. von Hippel, E & von Krogh, G 2003. Open Source Software and the Private-Collective
Innovation Model: Issues for Organization Science, Organization Science 14 (2): 209–223.
Von Hippel, E & Von Krogh, G 2006. Free Revealing and the Private-Collective Model for Innovation Incentives, R and D Management 36 (3): 295-306.
Wernerfelt, B 1984. A Resource̺Based View of the Firm, Strategic management journal 5 (2): 171-180.
West, J & Bogers, B 2013. Leveraging External Sources of Innovation: A Review of Research on Open Innovation, Journal of Product Innovation Management 31 (4).
West, J & Gallagher, S 2006. Challenges of Open Innovation: The Paradox of Firm Investment in Open-Source Software, R and D Management 36 (3): 319-331.
West, J & Lakhani, KR 2008. Getting Clear About Communities in Open Innovation, Industry and Innovation 15 (2): 223-231.
Westergren, UH & Holmström, J 2012. Exploring Preconditions for Open Innovation: Value Networks in Industrial Firms, Information and Organization 22 (4): 209-226.
White, HD & Griffith, BC 1981. Author Cocitation: A Literature Measure of Intellectual Structure, Journal of the American Society for information Science 32 (3): 163-171.
Williamson, OE 1985. The Economic Institutions of Capitalism. Simon and Schuster. Yin, RK 2003. Case Study Research: Design and Methods. vol. 5, sage. Zahra, SA & George, G 2002. Absorptive Capacity: A Review, Reconceptualization, and
Extension, Academy of Management Review 27 (2): 185–203.
29
Table 1 Overview of journals (N = number of publications)
Rank N Outlets of focal publications N Outlets of references
1 40 Research Technology Mgt 88 R and D Mgt 2 30 R and D Mgt 87 Technovation 3 29 Int J of Technology Mgt 71 Research Policy 4 29 Research Policy 58 Int J of Technology Mgt
5 23 Technovation 41 Industrial Marketing Mgt
6 15 Tech Forecasting and Social Change 41 Technological Forecasting and Social Change
7 12 Technology Analysis and Strategic Mgt 40 J of Product Innovation Mgt
8 11 J of Product Innovation Mgt 37 Int J of Innovation Mgt 9 9 Innovation: Mgt, Policy and Practice 36 Technology Analysis and Strategic Mgt 10 8 California Mgt Review 35 European J of Innovation Mgt
11 8 Mgt Decision 34 Organization Science
12 7 MIT Sloan Mgt Review 33 Research Technology Mgt
13 6 Organization Science 28 Innovation: Mgt, Policy and Practice 14 5 Int Small Business J 28 J of Business Research 15 4 Industrial Marketing Mgt 26 J of Technology Mgt and Innovation
16 4 J of Business Research 25 Service Industries J 17 4 IEEE Transactions on Engineering Mgt 23 Int J of Technology Intelligence and Planning 18 4 European Mgt J 22 California Mgt Review 19 4 Creativity and Innovation Mgt 21 J of Engineering and Technology Mgt 20 3 Int J of Mgt Reviews 21 Industrial and Corporate Change 21 3 Strategic Mgt J 20 European Planning Studies 22 3 Harvard Business Review 18 Mgt Decision 23 3 J of Engineering and Technology Mgt 18 Long Range Planning 24 3 Mgt Science 18 Int J of Business Innovation and Research 25 3 Industry and Innovation 18 Int J of Entrepreneurship and Innovation Mgt
30
Table 2 Publication statistics
Rank Citations Focal publications Rank Citations References
1 485 Chesbrough H, 2003a 1 175 Chesbrough H, 2003c
2 234 Huston L., Sakkab N., 2006 2 107 Cohen W. Levinthal D, 1990
3 218 Chesbrough H., Crowther A.K. 2006 3 96 Laursen K, Salter A, 2006
4 154 West J., Gallagher S. 2006 4 69 Chesbrough H, 2006
5 151 Perkmann M., Walsh K. 2007 5 57 Lichtenthaler U, 2008
6 147 Enkel et al.2009 6 56 Chesbrough H, Crowther A, 2006
7 146 Dodgson et al.2006 7 55 Chesbrough H, 2003a
8 142 Piller F.T., Walcher D. 2006 8 51 March J, 1991
9 134 Dahlander L., Gann D.M. 2010 9 49 Teece D, 1986
10 134 van de Vrande et al. 2009 10 49 von Hippel E, 1988
11 134 Chesbrough H.W., Appleyard M.M. 2007 11 48 Gassmann O, 2006
12 133 Henkel J. 2006 12 46 Chesbrough Vanhaverbeke W West J, 2006
13 127 Christensen et al.2005 13 42 Huston L, Sakkab N, 2006
14 119 Lichtenthaler U. 2008 14 41 Arora et al.2001
15 114 Chesbrough H.W. 2007 15 40 von Hippel E, 2005
16 102 Cooke P. 2005 16 39 Dodgson et al.2006
17 101 Fleming L., Waguespack D.M. 2007 17 39 West J, Gallagher S, 2006
18 99 Dittrich K., Duysters G. 2007 18 36 Teece et al.1997
19 98 Chesbrough H. 2003b 19 34 Zahra S, George G, 2002
20 93 Lichtenthaler U., Lichtenthaler E. 2009 20 30 Powell et al.1996
21 93 Chesbrough H. 2004 21 29 Chesbrough H, 2007
22 81 Jacobides M.G., Billinger S. 2006 22 29 West et al.2006
23 76 Leimeister et al. 2009 23 29 Yin R, 2003
24 73 Huizingh 2011 24 29 Enkel et al.2009
25 72 Chesbrough H., Schwartz K. 2007 25 28 Rivette K, Kline D, 2000
26 71 Dahlander L., Wallin M.W. 2006 26 28 Dahlander L, Gann DM, 2010
27 65 Kohler T., Matzler K., Fuller J. 2009 27 27 Christensen et al.2005
28 65 Terwiesch C., Xu Y. 2008 28 26 Nelson R, Winter S, 1982
29 65 Von Hippel E., Von Krogh G. 2006 29 26 Barney J, 1991
30 65 Kirschbaum R. 2005 30 26 Cassiman B, Veugelers R, 2006
31 61 Jeppesen L.B., Lakhani K.R. 2010 31 26 Eisenhardt K, 1989
31
Figure 1 Growth in publications on open innovation
0
100
200
300
400
500
600
0
10
20
30
40
50
60
70
80
Cit
ing
Pu
bli
ca
tio
n
Fo
ca
l P
ub
lic
ati
on
Focal Publication Citing Publication
32
Figure 2 Co-citation network
Note: To increase the readability we only show publications with more than 20 citations, a degree range >3 and a co-citation strength of >10. Publication size indicates number of citations received, connection between publications are co-citations linkages, and the darkness of these connections denotes the number of co-citations (darker = more co-citations).
33
Figure 3
Dispersion of Open Innovation concepts
Note: To increase the readability we only show publications with more than 75 citations, a degree range >3 and a co-citation strength of >20. Publication size indicates number of citations received, connection between publications are co-citations linkages, and the darkness of these connections denotes the number of co-citations (darker = more co-citations)
34
Figure 4: Complete sample
35
Figure 5: Time period--2003-2008 (48)
Figure 6: Time period--2009-2013 (251)
36
Figure 7: Conceptual papers (91)
Figure 8: Qualitative papers (93)
37
Figure 9: Quantitative papers (127)