Success factors of innovation ecosystems -

CO-CREATE 2013 Success factors of innovation ecosystems - Initial insights from a literature review* 1 Susanne Durst1 and Petro Poutanen2 1 Aalto University School of Business, susanne.durst@aalto.fi and 2 University of Helsinki, petro.poutanen@helsinki.fi ABSTRACT The aim of this paper is to review research on innovation ecosystems to derive success factors supporting the implementation of them. The reviewed studies highlight different factors for the successful implementation of innovation ecosystems which can be assigned to the areas of resources, governance, strategy and leadership, organizational culture, human resources management, people, partners, technology and clustering. Based on the findings a number of future research directions are proposed which may stimulate more research in this new field of study. KEYWORDS Innovation ecosystems, Innovation, Success factors, Literature review INTRODUCTION Scholars as well as practitioners increasingly identify the usefulness of the concept of innovation ecosystems for explaining cooperative innovative activities. Yawson (2009) argues that one of the reasons behind the emergence of ecosystem analogy is the inability of traditional innovation models to identify successful policy strategies that drive innovations at national levels. It is believed that the evidence-based platform for science and innovation policy needs to be extended beyond input-output correlations, such as R&D investments and patent counts (Yawson 2009). Ecosystem thinking combines various perspectives from open innovation, * This is an author’s copy of the paper published in Durst, S., & Poutanen, P. (2013). Success factors of innovation ecosystems: A literature review. In R. Smeds & O. Irrmann (eds.) CO-CREATE 2013: The Boundary-Crossing Conference on CoDesign in Innovation (pp. 27-38). Aalto University Publication series SCIENCE + TECHNOLOGY 15/2013. 1 CO-CREATE 2013 crowdsourcing, strategic management, economics, structural theories etc. to the biological and evolutionary analogies and metaphors. The fundamental hope behind ecosystems thinking is to expand the capabilities of one actor beyond its own boundaries and transfer knowledge into innovation in collaboration with others (e.g. Adner 2006). To make innovation happen a suitable innovation ecosystem must meet different conditions. These conditions may address natural, structural, organizational and cultural factors. Taking this path, the aim of this paper is to review empirical research on innovation ecosystems to identify factors that support a successful implementation of it. Accordingly, our research question is the following: What are the success factors of innovation ecosystems as derived from the empirical research literature? Innovation ecosystem is a fairly new concept; consequently it is likely to assume that a research field on its own has been not developed yet. Therefore, our motivation is also to contribute to the academic discussion. Concurrently, we hope our review would pinpoint relevant areas for future research helping to further develop the concept of innovation ecosystem. The paper is organised as follows: In the next section the literature and concepts related to the research aim are briefly discussed. Then the research method employed to answer the research problem is described. Thereafter, the results are presented, and in the final section, the conclusion and implications of the study are laid out. THEORETICAL BACKGROUND Innovations and ecosystems In academic literature, innovations are often defined as new ideas, improvements or solutions that are implemented and transferred into useful outcomes (e.g. Bessant & Tidd 2011); thereby acknowledging that not all creative ideas become innovations, but only if they are implemented and adopted in a beneficial way. Innovations are generally discussed positively (Jalonen 2012) and are seen as beneficial both for companies and for nations in order to survive and develop in a market environment, “create value”, and enhance competitiveness. “Ecosystem” is a term combining the words “eco” and “system”. The former has its origin in ecology and refers to the relation of living things to their environment. The latter originates from Greek and stands for an organized whole or body. Ecosystem as a scientific concept derives from the study of natural ecological systems. In a biological sense, an “ecosystem is a set of CO-CREATE 2013 organisms interacting with one another and with their environment of nonliving matter and energy within a defined area or volume” (Miller & Spoolman 2009, p. 7). Thinking innovations through ecosystems Applying ecological concepts to management and organizational literature have long traditions (e.g. Penrose 1952). From an ecological point of view, human organizations have been studied either as populations of one branch or as communities of populations competing and/or cooperating to obtain resources from community environments (Monge et al. 2011). The ecological perspective emphasizes environmental resource niches and adaptation as fundamental driving forces of the community and dynamic evolutionary processes, such as variation, selection, and retention (Monge et al. 2008). The study of “innovation ecosystems” can be seen as a continuation of the line of research using ecological analogies and perspective. Innovation ecosystems have been described in multiple ways. According to Adner (2006), innovation ecosystems can be defined as “the collaborative arrangements through which firms combine their individual offerings into a coherent, customer-facing solution” (p. 98). Mercan & Göktaş (2011) specify that an “innovation ecosystem consists of economic agents and economic relations as well as the non-economic parts such as technology, institutions, sociological interactions and the culture” (p. 102), suggesting that an innovation ecosystem is a hybrid of different networks or systems. The collaborative arrangements, as highlighted above, might be based on local concentration of industrial specifications, such as Porter's (1998) clusters, but the ecosystem model has expanded the idea of local clustering, to encompass global, networked economy and various interdependent actors (Rubens et al. 2011). Additionally, the idea of open innovation expands the scope of potential participants of the innovation process from internal actors of the R&D function to the numerous possible co-creators and co-innovators outside an organization. In this sense, ecosystem thinking comes close to what is called an open innovation. In open innovation, actors purposively tap into the inflows and outflows of knowledge by opening up the innovation process, thus accelerating internal innovations and expanding markets for external use of it (Chesbrough 2003). CO-CREATE 2013 Using the ecosystem analogy, innovation ecosystems are not a matter of single actors, but of interacting populations of actors residing in a certain environment. Rubens et al. (2011) refer to this idea as “creation nets” that provide a mechanisms for “(a) goal-focused creation of new goods and services tailored to rapidly evolving market needs, (b) with multiple institutions and dispersed individuals, (c) for parallel innovation” (p. 1743). These creation nets come close to what Wang (2009) refers to as “innovation communities”. Innovations communities are “a set of organizations and people with interests in producing and/or using a specific innovation” (Wang 2009, p. 8). According to Wang, such communities emerge and evolve around innovation orchestrating activities and dissolve once the collective attention disappears. The innovation ecosystem is thus, what constitutes a complex set of innovations and communities, their producers and developers and interactions between them (Wang, 2009). Behind the rationale for coming together to innovate is, according to Adner (2006), the fact that innovations rarely succeed in isolation but are dependent on many types of complementary innovations. Therefore, an ecosystem allows firms to create value that no single firm could make alone. Ecosystem approach extends the cooperation beyond bargaining over the value capture of each actor and includes considerations of challenges that different actors need to overcome to make sure that the value is created in the first place (Adner & Kapoor 2010). Ecosystems thinking can also been seen as a means to combine the idea of collaborative “business ecosystems”, as coined by Moore (1993). Ecosystem thinking has also been applied at national level (Carayannis & Campbell 2012; Jackson 2011; Metcalfe & Ramlogan 2008; Yawson 2009). Theories on innovation systems, such as national (Lundvall 1992), and regional (Cooke et al. 1997) system of innovations have emphasized the idea of innovations as an open and interactive, i.e. “systemic”, processes by their very nature. However, for example, Yawson (2009) sees as one of the reasons behind the introduction of the ecosystem framework traditional innovations models’ inability to identify the successful policy strategies that drive innovations at national level. In a similar way, Metcalfe and Ramlogan (2008) redefine the traditional innovation systems models by their ecological analogy. In innovation ecologies “the principal actors are usually for-profit firms, universities and other public and private specialist research organisations and knowledge-based consultancies” (Metcalfe & Ramlogan 2008, p. 441). According to Papaioannou et al. (2007), the main difference between traditional innovation system thinking and ecosystem thinking is the stronger incorporation of market mechanism with the latter, CO-CREATE 2013 whereas the traditional approach highlights the role of non-market institutions and historically formed relationships. As indicated above, ecosystems are discussed under different labels such as platform leadership, keystone strategies, open innovation, value networks, and hyperlinked organizations (Adner 2006); consequently a unified and clear distinction has not emerged yet. Critical thinking about the concept of innovation ecosystem Papaioannou et al. (2007) ask whether the ecosystem analogy can be used to describe socially dynamic environments of innovations and whether the biological metaphor is plausible and consistent with the Schumpeterian tradition of thought, according to which innovation is essentially understood as a discontinuous and uneven historical process evolving under the influence of complex economic, social and political factors. Indeed, Papaioannou et al. (2007) argue that “eco-thinking … does not adequately capture the distinction between innovation events and structures, going beyond them to integrate innovation activity in companies and organisations” (p. 5). In addition, Papaioannou et al. (2007) claim, referring to Powell et al. (1996), that despite the abstract similarities between biological and innovation ecologies, “the latter includes complex social interrelations and networks … which are historically developed” (Papaioannou et al. 2007, p. 5). Therefore, division of labour and environment of knowledge and innovations are not biological and adaptive but social and historical processes with contradictory and uneven relations of power. Wallner & Menrad (2011) claim that the perspective adopted by Adner and Kapoor (2010) is rather linear and deterministic. According to Wallner & Menrad (2011), the linear view is focused on input factors that are supposed to influence innovation capacity, although “ecosystem is not a trivial machine, with defined input-output ratio” (p. 2). Judy Estrin (2009) provides an alternative view on innovation ecosystem at the national level. She suggests that “innovation ecosystems are made up of communities of people with different types of expertise and skill sets” and that the most important communities are research, development, and application (p. 37– 38). According to Estrin, in order for ecosystems to be innovative, there must be a constant and balanced cross-pollination of ideas, questions, knowledge and technology between the most important communities. Each community must receive “nutrients” through different supportive structures, such as leadership, funding, policy, education, and culture. As CO-CREATE 2013 Wallner & Menrad (2011) also note, cross-pollination is apparently, at least partly, a cultural aspect calling for communication, and willingness and trust to share and receive information. Some of the remaining challenges concerning ecosystem thinking are associated with its plausibility as an analogy, that is, whether the biological analogy stands as a reasonable fundament for explaining human activity and the social context. How to enable, for example, cultural values to encourage knowledge sharing or other innovation fostering behaviour? When using analogies, one must be aware that ultimately it is a matter of innovation theories and empirical research and – in general – theories of human behaviour, whether or not biological heuristics are plausible (cf. Cohen 1994; Stewart 2001). METHODOLOGY OF LITERATURE REVIEW In the review process, the authors adopted the principles of a systematic review as recommended by Jesson et al. (2011). First, a research plan was developed comprising the research questions of interest, the keywords, and a set of inclusion and exclusion criteria. The paper‘s aim was to determine the current status of research on innovation ecosystems to identify success factors facilitating the process. To help answer the research question inclusion and exclusion criteria were specified. The inclusion criteria were: peer reviewed journals, English language. Grey literature such as reports and non-academic research, other languages than English represented exclusion criteria. Additionally, an excel data sheet was produced consisting of key aspects related to the research aim. In the given case these were: name of author(s), year of publication, research aim / objectives, theoretical perspective / framework, method, main findings, and name of the journal. Once all the relevant issues had been specified, the databases Web of Science, Proquest ABI/ INFORM and EBSCO were accessed and searched for materials, using the keyword set. As keyword “innovation ecosystem” was used. The databases were searched for articles that had explicitly “innovation ecosystem” in the abstract or title. This proceeding led to 7 hits with Web of Science, 6 hits with ABI/INFORM and 4 hits with EBSCO. The search took place in November 2012 and again in March 2013. Next, one of the authors scanned the articles’ titles, abstracts and, if relevant, more parts, beginning with the conclusion section, to make sure that they actually fell within the scope of interest. Nine papers fulfilled the criteria set and thus formed the basis of analysis. In the next stage, the authors discussed the findings, which helped them to clarify what is known about success CO-CREATE 2013 factors related to innovation ecosystems. The final stage of the review process comprised the writing up of findings. PRESENTATION OF FINDINGS Studies involved The nine papers that formed the basis for our analysis are summarised in Table 1. The oldest publications are from 2006 and the most recent one is from 2012. Carayannis & Campbell 2009 Rohrbeck, Hölzle & Gemünden 2009 Tassey 2010 Samila & Sorenson 2010 2011 To explain the effects and magnitude of effects of components above on innovation making based on Global Innovation Index dataset. 2012 Considers the manner in which new business ecosystems develop to support innovation and strategic choice Mercan & Göktas Mezzourh & Nakara innovation systems approach, its evolution and innovation ecosystems Briefly summarises the steps from systems to ecosystems of innovation The interaction between universities and for profit industries accelerates innovation making. The findings showed a positive but Data from Global insignificant relationship between level of innovation culture and Innovation Index (20092010 database), regressions innovation output. Case study of Cytale (French start-up) Harvard Business Review Int. J. Technology Management R&D Management J Technol Transf Research Policy International Research Journal of Finance and Economics Introduces the concept of “keystone innovations” and discuss how they The Business affect business ecosystems Review technology and clustering. 2008 organizational culture, human resources management, people, partners, Iyer & Davenport Harvard Business Review following dimensions: resources, governance, strategy and leadership, 2006 Journal of Services Research reported in the papers reviewed. The factors can be grouped based on the Adner Journal Table 2 shows the factors seemingly facilitating innovation ecosystems as 2006 Research aim/objectives Factors facilitating the open innovation process Watanabe & Fukuda Year Table 1 Overview of empirical papers involved in the literature review Method (empirical / theoretical) Main findings The authors highlights the following policies: Technology policy should endeavor to generate innovation in a way to constructing a coevolution between innovation development cycle and advancement of the institutional system. Given the systems efficiency in constructing Reviews mutually the above coevolutional dynamism, potential resources in innovation inspiring cycle between should be effectively explored and utilized in a systems perspectives. Japan and the US and Comparative empirical Provided that seamless, all actors participation and on demand its consequence to the analysis of the development institutions requirements characterized by a ubiquitous society, To analyze the significance of a systems National Innovation concept of coevolutionary dynamism trajectories in the US and multilayer mutual inspiring cycle should be constructed in a global Ecosystem involving in an ecosystem. Japan context. Main conclusion: If managers learn to assess ecosystem risks holistically and systematically, they will be able to establish more To highlight the significance of having a realistic expectations, develop a more refined set of environmental systematic approach for analyzing the risks contingencies, and arrive at a more robust innovation strategy. N/A N/A in an ecosystem The key attributes of Google´s success are: strategic patience, infrastructure built to support innovation, architectural control, To present and discuss Google´s innovation innovation built into job description, cultivated taste for failure and ecosystem chaos, use of data to vet inspiration N/A N/A Mode 3, in combination with the broadened perspective of the Quadruple Helix, emphasises an Innovation Ecosystem that To provide a better conceptual framework encourages the co-evolution of different knowledge and innovation (Mode 3) for understanding knowledgemodes as well as balances non-linear innovation modes in the context based and knowledge-driven events and Mainly discuss the of multi-level innovation systems. Hybrid innovation networks and processes in the economy, and hence reveal underlying assumptions knowledge clusters tie together universities, commercial firms and opportunities for optimising public sector of the model to be academic firms policies and private sector practices. presented N/A Brief review of the Single case study design changes experienced in involving 15 in-depth To analyse to what extent the open Deutsche Telekom uses most of the the industry and the interviews with Deutsche innovation paradigm has been embraced benefits of open innovation without betting ist survival on an open research on open Telekom members and inside the Deutsche Telekom innovation future. innovation partners The paper is based on the assumption that the neoclassical view is inaccurate and that a new innovation model is required to guide economic growth policy. Having this in mind he provides rationales for this assumption. N/A Secondary data Proposes a new manufacturing policy model Unbalanced panel data set of all 328 Metropolitan To explore the extent to which the local Statistical Areas in the U.S. Show the relevance of the availability of venture capital to explain availability of venture capital might act as a Literature on venture from 1993-2002. capital of the differences in metropolitan statistical areas catalyst to commercialization Review Author(s) CO-CREATE 2013 Theoretical perspective / framework CO-CREATE 2013 Factors supporting innov ation ecosy stem s Studies Resources Resource management Watanabe & Fukuda (2006) Resource allocation Adner (2006) Resource av ailability Tassey (201 0) Av ailability of different funding possibilities (priv ate and public) Tassey (201 0); Samila & Sorenson (201 0) Governance Continuous inv estments in infrastructure Iy er & Dav enport (2006); Tassey (201 0) Architectural control Iy er & Dav enport (2006) Rigorous decision making facilitated by data Iy er & Dav enport (2006) Timing referring to all partners inv olv ed Adner (2006); Watanabe & Fukuda (2006) Sy stematic risk assessment Adner (2006) Demogracy Caray annis & Campbell (2009) Own organizational structure Rohrbeck et al. (2009) Use of internet platforms to support and foster interaction between partners Rohrbeck et al. (2009) Flex ible sy stem that allows integration and ex pansion Rohrbeck et al. (2009) Clear role assignment Tassey (201 0) Strategy and Leadership Adner (2006); Tassey (201 0) Patience Iy er & Dav enport (2006) Clarity of purpose and attention to detail Iy er & Dav enport (2006) Distant and distanced v iew on innov ation Mezzourh & Nakara (201 2) Organizational culture Caray annis & Campbell (2009) Open to failure and chaos Iy er & Dav enport (2006) Innov ation culture Mercan & Göktas (201 1 ) Human resources management Innov ation as integral part of job descriptions Iy er & Dav enport (2006) People Caray annis & Campbell (2009) Inv olv ing post-doctoral researchers to get access to worldwide R&D community Rohrbeck et al. (2009) Technology Caray annis & Campbell (2009) Partners Pluralism of a div ersity of agents, actors and organisations Caray annis & Campbell (2009) Use of a v ariety of partners Rohrbeck et al. (2009) Univ ersity - industry collaboration Mercan & Göktas (201 1 ) Clustering Foster interactions Mercan & Göktas (201 1 ) Table 2 Overview of success factors faciliating innovation ecosystems The table indicates that especially the governance dimension plays a central role in innovation ecosystems which is easily comprehensible given the different actors and thus communication challenges that need to be coped with in such a system. Thereby the factor addresses areas such as control, structural and technological aspects, data management, data analysis and data processing. Moreover, issues related to flexibility as well as the form of governance are highlighted. Additionally, strategy and leadership, organizational culture and partners are viewed as critical aspects that need to be carefully handled to increase the success of innovation ecosystems. These dimensions, too, are understandable recalling the concept of innovation ecosystems as presented in section 2. These dimensions are closely conntected to the dimension of governance as well. The remaining factors represent more or less individual entries and take account of the particular settings under investigation. CONCLUSIONS CO-CREATE 2013 This paper has reviewed existing articles that examined innovation ecosystems. More precisely, the interest was to identify factors that enable a successful implementation of innovation ecosystems. Given the assumed relevance of innovation ecosystems to innovative activities an understanding of those factors supporting its implementation is of utmost relevance. In addition, as the study of innovation ecosystems is still in its infancy the success factors identified may serve as a basis for future research directions. Based on a literature review the authors identified nine studies which fulfilled the a priori set selection criteria. The small number of papers identified clearly underlines our limited body of knowledge regarding the topic. Current research in this area seems to be primarily driven by some researchers´ personal interests. It can be thus concluded that the existing literature provides only rather fragmented insights into innovation ecosystems and their implementation in reality. Given the assumed importance of innovation ecosystems there is a need for more intense research activities. This would at the same time help to underpin the legitimacy of open innovation as a research field. The review of the papers suggests that factors for the successful implementation of innovation ecosystems can be found in the areas of resources, governance, strategy and leadership, organizational culture, human resources management, people, partners, technology and clustering. These areas clarify that well-known aspects need to be addressed, thus the individuals in charge can to a certain degree built upon previous experience and existing knowledge, respectively, when setting up innovation ecosystems. Considering the dearth of understanding, the authors see particularly four issues that need more attention and development: 1) The evaluation of innovation ecosystems. The actors concerned need to have measures at hand to better control and allocate their resources regarding different business operations. Given the scope of innovation ecosystems, these measures need to go beyond organization boundaries and to address all actors involved and their concerns. In addition, funding parties will be interested in measures as well in order to better assess the return of their investments. 2) The role of people in innovation ecosystems. Innovation ecosystems comprise different actors with different goals, expectations and attitudes, so the authors of this paper call for more research on that topic as a deeper CO-CREATE 2013 understanding of any supporting and hampering factors concerning the implementation of innovation ecosystems from a people-perspective. 3) The application of a variety of research designs and methods. Longitudinal studies would enable researchers to study innovation ecosystems as they actually enfold. In addition, longitudinal studies provides the opportunity to observe whether and how innovation ecosystems change over time as they mature or face new challenges, respectively. Using mixed methods research approached would also help to obtain a more holistic understanding of the subject of innovation ecosystems than is possible using mono-methods approaches. 4) Country-comparisons. Our understanding would also benefit from studies that discuss innovation ecosystems taking country differences into consideration. Is it plausible to assume that innovation ecosystems will vary from country to country (even region to region), reflecting each country´s culture, individual systems and institutions. Therefore, comparative settings would clarify what factors are likely to remain constant under different conditions and what would change. Moreover, based on our analysis of the definitions of innovation ecosystems, a better conceptual understanding will be essential in order to fully benefit from the analogy. For example, better conceptual linking is needed between innovation and ecosystem literatures. What are meant by different biological concepts in the context of innovations and human interaction? Is the concept of innovation ecosystem to be understood as a loose metaphor of co-operation beyond sectors or cluster borders or does it represent a comprehensive shift in mindset? The present study is not without limitations. Complete coverage of all the articles considering innovation ecosystems may not have been achieved, given the search proceeding chosen. So it may have left out papers that also addressed innovation ecosystems but used different language. Finally, the success factors derived from the small numbers of papers need to be treated with caution. LIST OF REFERENCES Adner, R. 2006. Match your innovation strategy to your innovation ecosystem, Harvard Business Review, Vol. 84, pp. 98–110. Adner, R. & Kapoor, R. 2010. 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