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Article

Jennifer Kuan

Open Innovation, published in 2003, was a ground-breaking work by Henry Chesbrough that placed technology and innovation at the center of attention for managers of large firms. The term open innovation refers to the ways in which firms can generate and commercialize innovation by engaging outside entities. The ideas have attracted the notice of scholars, spawning annual world conferences and a large literature in technology and innovation management (including numerous journal special issues) that documents diverse examples of innovations and the often novel business models needed to make the most of those innovations. The role of business models in open innovation is the focus of Open Business Models, Chesbrough’s 2006 follow-up to Open Innovation. Managers have likewise flocked to Chesbrough’s approach, as the hundreds of thousands of hits from an online search using the term open innovation can attest. Surveys show that the majority of large firms were engaging in open innovation practices in 2017, compared to only 20% in 2003 when Open Innovation was published.

Article

Virtual work has become critical to competing in the global information economy for many organizations. Successfully working through technology across time and space, especially on collaborative tasks, however, remains challenging. Virtual work can lead to feelings of isolation, communication and coordination difficulties, and decreased innovation. Researchers attribute many of these challenges to a lack of common ground. Virtual worlds, one type of virtualization technology, offer a potentially promising solution. Despite initial interest, organizational adoption of virtual worlds has been slower than researchers and proponents expected. The challenges of virtual work, however, remain, and research has identified virtual world technology affordances that can support virtual collaboration. Virtual world features such as multi-user voice and chat, persistence, avatars, and three-dimensional environment afford, in particular, social actions associated with successful collaboration. This suggests that the greatest value virtual worlds may offer to organizations is their potential to support virtual collaboration. Organizational scholars increasingly use a technology affordance lens to examine how features of malleable communication technologies influence organizational behavior and outcomes. Technology affordances represent possibilities of action enabled by technology features or combinations of features. Particularly relevant to virtual world technology are social affordances—affordances of social mediating technologies that support users’ social and psychological needs. To be useful to organizations, there must be a match between virtual world technology affordances, organizational practices, and a technology frame or organizing vision. Recent studies suggest a growing appreciation of the influence of physical organizational spaces on individual and organizational outcomes and increasing awareness of the need for virtual intelligence in individuals. This appreciation provides a possible basis for an emerging organizing vision that, along with recent technology developments and societal comfort with virtual environments, may support wider organizational adoption of virtual worlds and other virtualization technologies.

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

E-learning expands options for teaching and learning using technology. This nomenclature has been solidly in use for the last ten years. The expansive and ever fertile frontier of e-learning—a term used interchangeably with distance and online learning—has become standard fare as an educational delivery solution designed to enhance knowledge and performance. Many educational institutions, corporate enterprises and other entities are utilizing web-based teaching and learning methodologies to deliver education either partially or wholly online using electronic platforms. The learning value chain, including management and delivery, has created multimodal systems, content, and processes to increase accessibility, measurability, and cost effectiveness by infusing advanced learning techniques, such as adaptive learning or communities of practice, among students, employee groups, and lifelong learners. It is interesting to note that e-learning encapsulates internet based courseware and all other asynchronous and synchronous learning, as well as other capabilities for supporting learning experiences. Student success and advancements in technology are now inextricably linked as a result of higher education institutions embracing and offering e-learning options. The absence of direct instructor guidance makes distance learning particularly difficult for some students. Certain students struggle with the lack of guidance inherent in online learning and the requisite need to work independently. In particular, the lack of high touch strategies in e-learning often leads students to drop or fail courses. While some students struggle to remain engaged in technology-enabled learning, technology is often the vehicle for keeping these same students on task. There are a variety of electronic tools designed to augment online learning and keep online learners on task. Podcasts, for example, can be easily downloaded, then played back on a student’s media player or mobile device at a later date. The student is not tied to a computer, which results in a more comprehensive learning experience. In many cases, e-learning has become a very lucrative and desirable marketplace for higher education institutions. The business case for e-learning is a clarion call for tight integration among business, human resources, and knowledge and performance management. Hence, it is incumbent upon educational institutions to instill approaches that focus on the learner, learning, and improved performance, more so than the tools and technology. Of further importance is the need for higher education institutions to provide stratagems for developing and supporting caring online relationships, individualized student environments, collaboration, communication, and e-learning culture. Ultimately, institutions should measure not only improved business and performance, but also improved student online learning aptitudes (more self-motivated, self-directed, and self-assessed learning).

Article

For decades researchers have studied various aspects of the technology transfer and commercialization process in universities in hopes of discovering effective methods for enabling more research to leave the university as technologies that benefit society. However, this effort has fallen short, as only a very small percentage of applied research finds its way to the marketplace through licenses to large companies or to new ventures. Furthermore, the reasons for this failure have yet to be completely explained. In some respects, this appears to be an ontological problem. In their effort to understand the phenomenon of university commercialization, researchers tend to reduce the process into its component parts and study each part in isolation. The result is conclusions that ignore a host of variables that interact with the part being studied and frameworks that describe a linear process from invention to market rather than a complex system. To understand how individuals in the technology commercialization system make strategic choices around outcomes, studies have been successful in identifying some units of analysis (the tech transfer office, the laboratory, the investment community, the entrepreneurship community); but they have been less effective at integrating the commercialization process, contexts, behaviors, and potential outcomes to explain the forces and reciprocal interactions that might alter those outcomes. The technology commercialization process that leads to new technology products and entrepreneurial ventures needs to be viewed as a complex adaptive system that operates under conditions of risk and uncertainty with nonlinear inputs and outputs such that the system is in a constant state of change and reorganization. There is no overall project manager managing tasks and relationships; therefore, the individuals in the system act independently and codependently. No single individual is aware of what is going on in any other part of the system at any point in time, and each individual has a different agenda with different metrics on which their performance is judged. What this means is that a small number of decision makers in the university commercialization system can have a disproportionate impact on the effectiveness and success of the entire system and its research outcomes. Critics of reductionist research propose that understanding complex adaptive systems, such as university technology commercialization, requires a different mode of thinking—systems thinking—which looks at the interrelationships and dependencies among all the parts of the system. Combined with real options reasoning, which enables resilience in the system to mitigate uncertainty and improve decision-making, it may hold the key to better understanding the complexity of the university technology commercialization process and why it has not been as effective as it could be.

Article

Hettie A. Richardson and Marcia J. Simmering

Nonresponse and the missing data that it produces are ubiquitous in survey research, but they are also present in archival and other forms of research. Nonresponse and missing data can be especially problematic in organizational contexts where the risks of providing personal or organizational data might be perceived as (or actually) greater than in public opinion contexts. Moreover, nonresponse and missing data are presenting new challenges with the advent of online and mobile survey technology. When observational units (e.g., individuals, teams, organizations) do not provide some or all of the information sought by a researcher and the reasons for nonresponse are systematically related to the survey topic, nonresponse bias can result and the research community may draw faulty conclusions. Due to concerns about nonresponse bias, scholars have spent several decades seeking to understand why participants choose not to respond to certain items and entire surveys, and how best to avoid nonresponse through actions such as improved study design, the use of incentives, and follow-up initiatives. At the same time, researchers recognize that it is virtually impossible to avoid nonresponse and missing data altogether, and as such, in any given study there will likely be a need to diagnose patterns of missingness and their potential for bias. There will likewise be a need to statistically deal with missing data by employing post hoc mechanisms that maximize the sample available for hypothesis testing and minimize the extent to which missing data obscures the underlying true characteristics of the dataset. In this connection, a large body of programmatic research supports maximum likelihood (ML) and multiple imputation (MI) as useful data replacement procedures; although in some situations, it might be reasonable to use simpler procedures instead. Despite strong support for these statistical techniques, organizational scholars have yet to embrace them. Instead they tend to rely on approaches such as listwise deletion that do not preserve underlying data characteristics, reduce the sample available for statistical analysis, and in some cases, actually exacerbate the potential problems associated with missing data. Although there are certainly remaining questions that can be addressed about missing data techniques, these techniques are also well understood and validated. There remains, however, a strong need for exploration into the nature, causes, and extent of nonresponse in various organizational contexts, such when using online and mobile surveys. Such research could play a useful role in helping researchers avoid nonresponse in organizational settings, as well as extend insight about how best and when to apply validated missing data techniques.

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.

Article

Scientific advance and innovation are major sources of economic growth and are crucial for making development socially and environmentally sustainable. A critical question is: Will private enterprises invest sufficiently in research technological development and innovation and, if not, to what degree and how should governments engage in the support of science, technology, and innovation? While neoclassical economists point to market failure as the main rationale for innovation policy, evolutionary economists point to the role of government in building stronger innovation systems and creating wider opportunities for innovation. Research shows that the transmission mechanisms between scientific advance and innovation are complex and indirect. There are other equally important sources of innovation including experience-based learning. Innovation is increasingly seen as a systemic process, where the feedback from users needs to be taken into account when designing public policy. Science and innovation policy may aim at accelerating knowledge production along well-established trajectories, or it may aim at giving new direction to the production and use of knowledge. It may be focused exclusively on economic growth, or it may give attention to impact on social inclusion and the natural environment. An emerging topic is to what extent national perspectives continue to be relevant in a globalizing learning economy facing multiple global complex challenges, including the issue of climate change. Scholars point to a movement toward transformative innovation policy and global knowledge sharing as a response to current challenges.

Article

Fariborz Damanpour

Innovation is a complex construct and overlaps with a few other prevalent concepts such as technology, creativity, and change. Research on innovation spans many fields of inquiry including business, economics, engineering, and public administration. Scholars have studied innovation at different levels of analysis such as individual, group, organization, industry, and economy. The term organizational innovation refers to the studies of innovation in business and public organizations. Studies of innovations in organizations are multidimensional, multilevel, and context-dependent. They investigate what external and internal conditions induce innovation, how organizations manage innovation process, and in what ways innovation changes organizational conduct and outcome. Indiscreet application of findings from one discipline or context to another, lack of distinction between generating (creating) and adopting (using) innovations, and likening organizational innovation with technological innovation have clouded the understanding of this important concept, hampering its advancement. This article organizes studies of organizational innovation to make them more accessible to interested scholars and combines insights from various strands of innovation research to help them design and conduct new studies to advance the field. The perspectives of organizational competition and performance and organizational adaptation and progression are introduced to serve as platforms to position organizational innovation in the midst of innovation concepts, elaborate differences between innovating and innovativeness, and decipher key typologies, primary sets of antecedents, and performance consequences of generating and adopting innovations. The antecedents of organizational innovation are organized into three dimensions of environmental (external, contextual), organizational (structure, culture), and managerial (leadership, human capital). A five-step heuristic based on innovation type and process is proposed to ease understanding of the existing studies and select suitable dimensions and factors for conducting new studies. The rationale for the innovation–performance relationship in strands of organizational innovation research, and the employment of types of innovation and performance indicators, is articulated by first-mover advantage and performance gap theory, in conjunction with the perspectives of competition and performance and of adaptation and progression. Differences between effects of technological and nontechnological innovation and stand-alone and synchronous innovations are discussed to articulate how and to what extent patterns of the introduction of different types of innovation could contribute to organizational performance or effectiveness. In conclusion, ideas are proposed to demystify organizational innovation to allure new researches, facilitate their learning, and provide opportunities for the development of new studies to advance the state of knowledge on organizational innovation.