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Article

Samer Faraj and Takumi Shimizu

Online communities (OCs) are emerging as effective spaces for knowledge collaboration and innovation. As a new form of organizing, they offer possibilities for collaboration that extend beyond what is feasible in the traditional hierarchy. OC participants generate new ideas, talk about knowledge, and remix and build on each other’s contributions on a massive scale. OCs are characterized by fluidity in the resources that they draw upon, and they need to manage these tensions in order to sustain knowledge collaboration generatively. OCs sustain knowledge collaboration by facilitating both tacit and explicit knowledge flows. Further, OCs play a key role in supporting and sustaining the knowledge collaboration process that is necessary for open and user innovation. As collective spaces of knowledge flows, OCs are mutually constituted by digital technologies and participants. The future is bright for OC research adopting the knowledge perspective and focusing on how to sustain their knowledge flow.

Article

Erik E. Lehmann and Julian Schenkenhofer

The pursuit of economic growth stands out as one of the main imperatives within modern economies. Nevertheless, economies differ considerably in their competitiveness. Theories on the endogeneity of growth agree on the value of knowledge creation and innovativeness to determine a country’s capability to achieve a sustained performance and to adapt to the dynamics of changing environments and faster information flows. To this effect, national institutional regimes shape nation-specific contexts and embed individuals and firms. The resulting incentive structures shape the attitudes and behavior of individuals and firms alike, whose interactions contribute to the accumulation and flow of knowledge among the nodes of their networks. National systems of innovation (NSIs) therefore embody a concept that aims to analyze the national innovation performance of economies. It rests its rationale in the variation of national institutions that shape the diffusion of technologies through the process of shared knowledge creation and the development of learning routines. Both public and private institutions are thought to interact in a given nation-specific institutional context that essentially affects incentive schemes and resource allocation of the involved economic agents in creating, sharing, distributing, absorbing, and commercializing knowledge. To this effect, public policy plays a key role in the NSI through building bridges between these actors, reducing information asymmetries, and providing them with resources from others within the system. The different actors contributing to the creation and diffusion of knowledge within the system are needed to exchange information and provide the engine for sustained economic growth. Universities, research institutes, companies and the individual entrepreneur are in charge of shaping their economic system in a way that resource and skill complementarities are exploited to the mutual benefit.

Article

Internationalization of R&D facilitates knowledge sourcing of multinational corporations (MNCs) on a global scale. As MNCs internationalize R&D, they not only engage in domestic-driven R&D but are actively involved in overseas-driven R&D. And accordingly, the role of overseas R&D laboratories often evolves, from applying the HQ-generated innovation to local market, to innovating locally and contributing to the parent company. Within an MNC boundary, knowledge flows have become multidirectional: on top of the most typical knowledge flows from headquarters (HQ) to a subsidiary, reverse knowledge flows from a subsidiary to HQ as well as horizontal knowledge flows among overseas subsidiaries have become more salient. In addition to knowledge flows within a firm, increasing attention has been paid to external knowledge sourcing, i.e., knowledge sourcing from foreign locations outside the firm. MNCs commonly engage in local knowledge sourcing, i.e., sourcing knowledge from an overseas local environment, to tap into local hotbeds of innovation. But MNCs are also increasingly conducting global knowledge sourcing, i.e., sourcing knowledge from around the world, to practise global open innovation. Theoretically, knowledge sourcing in international R&D has often been examined from the capability and embeddedness perspectives. The effect of capability has been discussed in connection with motivation, autonomy, and mandates of subsidiaries. The effect of embeddedness has been discussed in connection with complementarity between external and internal embeddedness. As future research agenda, the following are suggested. First, cross-fertilization among the research fields of international R&D, global innovation, and open innovation deserves further attention. Second, greater research focus can be placed on managerial processes of global knowledge sourcing. Third, further research can be advanced on global knowledge sourcing at the team level. Fourth, the association between corporate governance and global knowledge sourcing can be investigated further. Fifth, much more attention needs to be paid to microfoundations of global knowledge sourcing. And lastly, further evolving patterns of global knowledge sourcing by advanced country multinationals (AMNCs) and emerging economies multinationals (EMNCs) continue to be relevant.

Article

Llewellyn D. W. Thomas and Erkko Autio

The concept of an “ecosystem” is increasingly used in management and business to describe collectives of heterogeneous, yet complementary organizations who jointly create some kind of system-level output, analogous to an “ecosystem service” delivered by natural ecosystems, which extends beyond the outputs and activities of any individual participant of the ecosystem. Due to its attractiveness and elasticity, the ecosystem concept has been applied to a wide range of phenomena by a variety of scholarly perspectives and under varying monikers such as “innovation ecosystems,” “business ecosystems,” “technology ecosystems,” “platform ecosystems,” “entrepreneurial ecosystems,” and “knowledge ecosystems.” This conceptual and application heterogeneity has contributed to conceptual and terminological confusion, which threatens to undermine the utility of the concept in supporting cumulative insight. In this article, we seek to reintroduce some order into this conceptual heterogeneity by reviewing how the ecosystem concept has been applied to variably overlapping phenomena and by highlighting key terminological and conceptual inconsistencies and their sources. We find that conceptual inconsistency in the ecosystem terminology relates to two key dimensions: the “unit” of analysis and the type of “ecosystem service”—that is the ecosystem output collectively generated. We then argue that although there is considerable heterogeneity in application, the concept nevertheless offers promise in its potential to support insights that are distinctive relative to other concepts describing collectives of organizations, such as those of “industry,” “supply chain,” “cluster,” and “network.” We also find that despite such proliferation, the concept nevertheless describes collectives that are distinctive in that they uniquely combine participant heterogeneity, coherence of ecosystem outputs, participant interdependence, and nonhierarchical governance. Based on our identified dimensions of conceptual heterogeneity, we offer a typology of the different ecosystem concepts, thereby helping reorganize this proliferating domain. The typology is based upon three distinct ecosystem outputs—ecosystem-level value offering for a defined audience, the collective generation of business model innovation, and the collective generation of research-based knowledge—and three research emphases that resonate with alternative “units” of analysis—community dynamics, output cogeneration, and interdependence management. Together, these allow us to clearly differentiate between the concepts of innovation ecosystems, business ecosystems, platform ecosystems, technology ecosystems, entrepreneurial ecosystems, and knowledge ecosystems. Based on the three distinct types of ecosystem outputs, our typology identifies three major types of ecosystems: innovation ecosystems, entrepreneurial ecosystems, and knowledge ecosystems. Under the rubric of “innovation ecosystems,” we further distinguish between business ecosystems, modular ecosystems, and platform ecosystems. We conclude by considering innovation ecosystem dynamics, highlighting the important role of digitalization, and reviewing the implications of our model for ecosystem emergence, competition, coevolution, and resilience.