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Article

Andy El-Zayaty and Russell Coff

Many discussions of the creation and appropriation of value stop at the firm level. Imperfections in the market allow for a firm to gain competitive advantage, thereby appropriating rents from the market. What has often been overlooked is the continued process of appropriation within firms by parties ranging from shareholders to managers to employees. Porter’s “five forces” model and the resource-based view of the firm laid out the determinants of value creation at the firm level, but it was left to others to explore the onward distribution of that value. Many strategic management and strategic human capital scholars have explored the manner in which employees and managers use their bargaining power vis-à-vis the firm to appropriate value—sometimes in a manner that may not align with the interests of shareholders. In addition, cooperative game theorists provided unique insights into the way in which parties divide firm surplus among each other. Ultimately, the creation of value is merely the beginning of a complex, multiparty process of bargaining and competition for the rights to claim rents.

Article

Nydia MacGregor and Tammy L. Madsen

A substantial volume of research in economic geography, organization theory, and strategy examines the geographic concentration of interconnected firms, industries, and institutions. Theoretical and empirical work has named a host of agglomeration advantages (and disadvantages) with much agreement on the significance of clusters for firms, innovation, and regional growth. The core assertion of this vein of research is that geographically concentrated factors of production create self-reinforcing benefits, yielding increasing returns over time. The types of externalities (or agglomeration economies) generally fall into four categories: specialized labor or inputs, knowledge spillovers, diversity of actors and activity, and localized competition. Arising from multiple sources, each of these externalities attracts new and established firms and skilled workers. Along with recent advancements in evolution economics, newer research embraces the idea that the agglomeration mechanisms that benefit clusters may evolve over time. While some have considered industry and cluster life-cycle approaches, the complex adaptive systems (CAS) theory provides a well-founded framework for developing a theory of cluster evolution for several reasons. In particular, the content and stages of complex adaptive systems directly connect with those of a cluster, comprising its multiple, evolving dimensions and their interplay over time. Importantly, this view emphasizes that the externalities associated with agglomeration may not have stable effects, and thus, what fosters advantage in a cluster will change as the cluster evolves. Furthermore, by including a cluster’s degree of resilience and ability for renewal, the CAS lens addresses two significant attributes absent from cyclical approaches. Related research in various disciplines may further contribute to our understanding of cluster evolution. Studies of regional resilience (usually focused on a specific spatial unit rather than its industrial sectors) may correspond to the reorganization phase associated with clusters viewed as complex adaptive systems. In a similar vein, examining the shifting temporal dynamics and development trajectories resulting from discontinuous shocks may explain a cluster’s emergence and ultimate long-term renewal. Finally, the strain of research examining the relationship between policy initiatives and cluster development remains sparse. To offer the greatest theoretical and empirical traction, future research should examine policy outcomes aligned with specific stages of cluster evolution and include the relevant levels and scope of analysis. In sum, there is ample opportunity to further explore the complexities and interactions among firms, industries, networks, and institutions evident across the whole of a cluster’s evolution.

Article

Linus Dahlander and Henning Piezunka

Crowdsourcing—a form of collaboration across organizational boundaries—provides access to knowledge beyond an organization’s local knowledge base. There are four basic steps to crowdsourcing: (a) define a problem, (b) broadcast the problem to an audience of potential solvers, (c) take actions to attract solutions, and (d) select from the set of submitted ideas. To successfully innovate via crowdsourcing, organizations must complete all these steps. Each step requires an organization to make various decisions. For example, organizations need to decide whether its selection is made internally. Organizations must take into account interdependencies among these four steps. For example, the choice between qualitative and quantitative selection mechanisms affects how widely organizations should broadcast a problem and how many solutions they should attract. Organizations must make many decisions, and they must take into account the many interdependencies in each key step.

Article

Kai-Lung Hui and Jiali Zhou

Hacking is becoming more common and dangerous. The challenge of dealing with hacking often comes from the fact that much of our wisdom about conventional crime cannot be directly applied to understand hacking behavior. Against this backdrop, hacking studies are reviewed in view of the new features of cybercrime and how these features affect the application of the classical economic theory of crime in the cyberspace. Most findings of hacking studies can be interpreted with a parsimonious demand-and-supply framework. Hackers decide whether and how much to “supply” hacking by calculating the return on hacking over other opportunities. Defenders optimally tolerate some level of hacking risks because defense is costly. This tolerance can be interpreted as an indirect “demand” for hacking. Variations in law enforcement, hacking benefits, hacking costs, legal alternatives, private defense, and the dual-use problem can variously affect the supply or demand for hacking, and in turn the equilibrium amount of hacking in the market. Overall, it is suggested that the classical economic theory of crime remains a powerful framework to explain hacking behaviors. However, the application of this theory calls for considerations of different assumptions and driving forces, such as psychological motives and economies of scale in offenses, that are often less prevalent in conventional (offline) criminal behaviors but that tend to underscore hacking in the cyberspace.

Article

Matthew R. Marvel

Entrepreneur coachability is the degree to which an entrepreneur seeks, carefully considers, and integrates feedback to improve a venture’s performance. There is increasing evidence that entrepreneur coachability is important for attracting the social and financial resources necessary for venture growth. Although entrepreneur coachability has emerged as an especially relevant construct for practitioners, start-up ecosystem leaders, and scholars alike, research on this entrepreneurial behavior is in its infancy. What appears to be a consistent finding across studies is that some entrepreneurs are more coachable than others, which affects downstream outcomes—particularly resource acquisition. However, there are sizable theoretical and empirical gaps that limit our understanding about the value of coachability to entrepreneurship research. As a body of literature develops, it is useful to take inventory of the work that has been accomplished thus far and to build from the lessons learned to identify insightful new directions. The topic of entrepreneur coachability has interdisciplinary appeal, and there is a surge of entrepreneur coaching taking place across start-up ecosystems. Research on coaching is diverse, and scholarship has developed across the academic domains of athletics, marketing, workplace coaching, and entrepreneurship. To identify progress to date, promising research gaps, and paths for future exploration, the literature on entrepreneur coachability is critically reviewed. To consider the future development of entrepreneur coachability scholarship, a research agenda is organized by the antecedents of entrepreneurship coachability, outcomes of entrepreneur coachability, and how entrepreneur–coach fit affects learning and development. Future scholarship is needed to more fully explore the antecedents, mechanisms, and/or consequences of entrepreneur coachability. The pursuit and development of this research stream represent fertile ground for meaningful contributions to entrepreneurship theory and practice.

Article

Though concern for environmental issues dates back to the 1960s, research and practice in the field of sustainability innovation gained significant attention from academia, practitioners, and NGOs in the early 1990s, and has evolved rapidly to become mainstream. Organizations are changing their business practices so as to become more sustainable, in response to pressure from internal and external stakeholders. Sustainability innovation broadly relates to the creation of products, processes, technologies, capabilities, or even whole business models that require fewer resources to produce and consume, and also support the environment and communities, while simultaneously providing value to consumers and being financially rewarding for businesses. Sustainability innovation is a way of thinking about how to sustain a firm’s growth while sustainably managing depleting natural resources like raw materials, water, and energy, as well as preventing pollution and unethical business practices wherever the firm operates. Sustainability innovation represents a very diverse and dynamic area of scholarship contributing to a wide range of disciplines, including but not limited to general management, strategy, marketing, supply chain and operations management, accounting, and financial disciplines. As addressing sustainability is a complex undertaking, sustainability innovation strategies can be varied in nature and scope depending upon the firm’s capabilities. They may range from incremental green product introductions to radical innovations leading to changes in the way business is conducted while balancing all three pillars of sustainability—economic, environmental, and social outcomes. Sustainability innovation strategies often require deep structural transformations in organizations, supply chains, industry networks, and communities. Such transformations can be hard to implement and are sometimes resisted by those affected. Importantly, as sustainability concerns continue to increase globally, innovation provides a significant approach to managing the human, social, and economic dimensions of this profound society-wide transformation. Therefore, a thorough assessment of the current state of thinking in sustainability innovation research is a necessary starting point from which to improve society’s ability to achieve triple bottom line for current and future generations.

Article

Lukas Neumann and Oliver Gassmann

Frugal innovation as a concept was initially sparked by a groundbreaking article published in The Economist in 2010. In it, the conception and application of a handheld electrocardiogram (ECG), the Mac 400, specifically designed to serve the rural population in India, was introduced. Every aspect of this product and its ecosystem was designed to serve the customer at less than 25% of the original cost. Since this publication, a lively discussion around this concept has developed in academia as well as in the industry. As a term, “frugal innovation” refers to solutions (products or services), methods, or designs that focus on serving new customers in resource-constrained contexts at the bottom of the pyramid (BoP) and/or emerging and developing markets. This understanding has broadened somewhat as such innovations gain increasing attention and relevance throughout all customer segments across the globe. What remains consistent is that frugal innovation is based on a new type of value architecture that is specifically developed to serve customers’ needs in the respective context by utilizing as few resources as possible. This approach leads to many cases where frugal innovations are novel and disruptive to their market environment. Research shows that for firms, especially traditional “Western” ones, these innovations require significant changes in firms’ activities along the entire value chain.

Article

Likoebe Maruping and Yukun Yang

Open innovation is defined as an approach to innovation that encourages a broad range of participants to engage in the process of identifying, creating, and deploying novel products or services. It is open in the sense that there is little to no restriction on who can participate in the innovation process. Open innovation has attracted a substantial amount of research and widespread adoption by individuals and commercial, nonprofit, and government organizations. This is attributable to three main factors. First, open innovation does not restrict who can participate in the innovation process, which broadens the access to participants and expertise. Second, to realize participants’ ideas, open innovation harnesses the power of crowds who are normally users of the product or service, which enhances the quality of innovative output. Third, open innovation often leverages digital platforms as a supporting technology, which helps entities scale up their business. Recent years have witnessed a rise in the emergence of a number of digital platforms to support various open innovation activities. Some platforms achieve notable success in continuously generating innovations (e.g., InnoCentive.com, GitHub), while others fail or experience a mass exodus of participants (e.g., MyStarbucksIdea.com, Sidecar). Prior commentaries have conducted postmortems to diagnose the failures, identifying possible reasons, such as overcharging one side of the market, failing to develop trust with users, and inappropriate timing of market entry. At the root of these and other challenges that digital platforms face in open innovation is the issue of governance. In the article, governance is conceptualized as the structures determining how rigidly authority is exerted and who has authority to make decisions and craft rules for orchestrating key activities. Unfortunately, there is no comprehensive framework for understanding governance as applied to open innovation that takes place on digital platforms. A governance perspective can lend insight on the structure of how open innovation activities on digital platforms are governed in creating and capturing value from these activities, attracting and matching participants with problems or solutions, and monitoring and controlling the innovation process. To unpack the mystery of open innovation governance, we propose a framework by synthesizing and integrating accreted knowledge from the platform governance literature that has been published in prominent journals over the past 10 years. Our framework is built around four key considerations for governance in open innovation: platform model (firm-owned, market, or community), innovation output ownership (platform-owned, pass-through, or shared), innovation engagement model (transactional, collaborative, or embedded), and nature of innovation output (idea or artifact). Further, we reveal promising research avenues on the governance of digital open innovation platforms.

Article

Lorenzo Massa and Christopher L. Tucci

Starting from the mid-1990s, business models have received increased attention from both academics and practitioners. At a general level, a business model refers to the core logic that a firm or other type of organization employs to achieve its goals. Thus, in general terms, the business model construct attempts to capture the way organizations “do business” or operate to create, deliver, and capture value. Business model innovation (BMI) constitutes a unique dimension of innovation, different from and complementary to other dimensions of innovation, such as product/service, process, or organizational innovation. This distinction is important in that different dimensions of innovation have different antecedents, different processes, and, eventually, different outcomes. Business models have been the subject of extensive research, giving birth to several lines of inquiry. Among them, one line focuses on business models in relation to innovation. This is a vast, somewhat fragmented, and evolving line of inquiry. Despite this limitation, it is possible to recognize that, at the core, business models are relevant to innovation in at least two main ways. First, business models can act as vehicles for the diffusion of innovation by bridging inventions, innovative technologies, and ideas to (often distant) markets and application domains. Therefore, business models speak to the phenomenon of technology transfer from the point of view of academic entrepreneurship and of corporate innovation. Thus, an important role of the business model in relation to innovation is to support the diffusion and adoption of new technologies and scientific discoveries by bridging them with the realization of economic output in markets. This is a considerable endeavor that relies on a complex process entailing the search for, and recombination of, complementary knowledge and capabilities. Second, business models are a subject of innovation that can become a source of innovation in and of themselves. For example, offerings that reinvent value to the customer—as opposed to offerings that incrementally add value to existing offerings—often involve designing novel business models. Relatedly, BMI refers to both a process (i.e., the dynamics involved in innovating business models) as well as the output of that process. In relation to BMI as a process, the literature has suggested distinguishing between business model reconfiguration (BMR; i.e., the reconfiguration of an existing business model), and business model design (BMD; i.e., the design of a new business model from scratch). This distinction allows us to identify three possible instances, namely general BMR in incumbent firms, BMD in incumbent firms, and BMD in newly formed organizations and startups. These are arguably different phenomena involving different processes as well as different moderators. BMR could be understood as an evolutionary process occurring because of changes in activities and adjustments within an existing configuration. BMD involves facing considerable uncertainty, thus putting a premium on discovery-driven approaches that emphasize experimentation and learning and a considerable degree of knowledge search and recombination.

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

Fred Gault and Luc Soete

Innovation indicators support research on innovation and the development of innovation policy. Once a policy has been implemented, innovation indicators can be used to monitor and evaluate the result, leading to policy learning. Producing innovation indicators requires an understanding of what innovation is. There are many definitions in the literature, but innovation indicators are based on statistical measurement guided by international standard definitions of innovation and of innovation activities. Policymakers are not just interested in the occurrence of innovation but in the outcome. Does it result in more jobs and economic growth? Is it expected to reduce carbon emissions, to advance renewable energy production and energy storage? How does innovation support the Sustainable Development Goals? From the innovation indicator perspective, innovation can be identified in surveys, but that only shows that there is, or there is not, innovation. To meet specific policy needs, a restriction can be imposed on the measurement of innovation. The population of innovators can be divided into those meeting the restriction, such as environmental improvements, and those that do not. In the case of innovation indicators that show a change over time, such as “inclusive innovation,” there may have to be a baseline measurement followed by a later measurement to see if inclusiveness is present, or growing, or not. This may involve social as well as institutional surveys. Once the innovation indicators are produced, they can be made available to potential users through databases, indexes, and scoreboards. Not all of these are based on the statistical measurement of innovation. Some use proxies, such as the allocation of financial and human resources to research and development, or the use of patents and academic publications. The importance of the databases, indexes, and scoreboards is that the findings may be used for the ranking of “innovation” in participating countries, influencing their behavior. While innovation indicators have always been influential, they have the potential to become more so. For decades, innovation indicators have focused on innovation in the business sector, while there have been experiments on measuring innovation in the public (general government sector and public institutions) and the household sectors. Historically, there has been no standard definition of innovation applicable in all sectors of the economy (business, public, household, and non-profit organizations serving households sectors). This changed with the Oslo Manual in 2018, which published a general definition of innovation applicable in all economic sectors. Applying a general definition of innovation has implications for innovation indicators and for the decisions that they influence. If the general definition is applied to the business sector, it includes product innovations that are made available to potential users rather than being introduced on the market. The product innovation can be made available at zero price, which has influence on innovation indicators that are used to describe the digital transformation of the economy. The general definition of innovation, the digital transformation of the economy, and the growing importance of zero price products influence innovation indicators.

Article

Alfredo De Massis, Emanuela Rondi, and Samuel Wayne Appleton

The involvement of families in firms’ ownership, management, and governance is a key driver of organizational attitudes, behaviors, and performances, especially those related to innovation. Starting from the beginning of the 21st century, the academic interest toward family firm innovation has bloomed. This body of research has mostly emerged from family firm scholars, while mainstream innovation scholars have often overlooked family variables in their studies. Indeed, innovation is one of the main areas in family firm research, integrating family and business aspects, leading to a plethora of sometimes contradictory findings. Initially, research compared innovation between family and nonfamily firms. While this approach has been beneficial to the rise of this stream of research and underlined the idiosyncratic characteristics of family firms on this matter, it soon emerged that within family firms there is a high degree of heterogeneity, especially in their attributes and the way they relate to innovation. Therefore, scholars have delved deeper into the heterogeneous influence that different types and degrees of family involvement in the firm can exert on innovation. This vast body of literature can be reconciled according to an antecedents–activities–outcomes framework allowing to attune current understanding of family firm innovation and recommend directions for future research. While most of current research has examined the antecedents of family business innovation, further examination of the activity of innovating in family firms is needed. Fostering accessibility to this literature allows students, practitioners, and scholars to grasp and digest this insightful area of family business research. It also encourages an extension of the range of perspectives adopted to examine innovation in family firms, contributing to advance current knowledge.

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

Greg Fisher

Starting an entrepreneurial endeavor is an uncertain and ambiguous project. This uncertainty and ambiguity make it difficult for entrepreneurs to generate much needed resources and support. In order to address this difficulty, a new venture needs to establish legitimacy, which entails being perceived as desirable, proper, or appropriate within the socially constructed system of norms, values, beliefs, and definitions within which it operates. New venture legitimacy is generated from various sources and hence has three broad dimensions—a cognitive, a moral, and a pragmatic dimension. The cognitive dimension accounts for the extent to which the activities and purpose of a venture are understood by key audiences and how knowledge about that venture spreads. The moral dimension reflects the extent to which a venture is perceived to be doing the right thing. The pragmatic dimension accounts for the extent to which a new venture serves the interests of critical constituents. All three of these dimensions factor into a legitimacy assessment of a new venture. Legitimacy is important for new ventures because it helps them overcome their liabilities of newness, allowing them to mobilize resources and engage in transactions, thereby increasing their chances of survival and success. Although legitimacy matters for almost all new ventures, it is most critical if an entrepreneur engages in activities that are new and novel, such as establishing a new industry or market or creating a new product or technology. In these circumstances, it is most important for entrepreneurs to strategically establish and manage a new venture’s legitimacy. The strategic establishment and management of new venture legitimacy may entail arranging venture elements to conform with the existing environment, selecting key environments in which to operate, manipulating elements of the external environment to align with venture activities, or creating a whole new social context to accommodate a new venture. Enacting each of these new venture legitimation strategies may necessitate employing identity, associative, and organizational mechanisms. Identity mechanisms include cultural tools and identity claims such as images, symbols, and language by entrepreneurs to enhance new venture legitimacy. Associative mechanisms reflect the formation of relationships and connections with other individuals and entities to establish new venture legitimacy. Organizational mechanisms account for manipulating the organization and structure of a new venture and the achievement of success measures by that venture to attain legitimacy. Ultimately all of this is done so that various external parties, with different logics and perspectives, will evaluate a new venture as legitimate and be prepared to provide that venture with resources and support.

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

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

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.

Article

Pankaj Setia, Franck Soh, and Kailing Deng

Organizations are widely building digital platforms to transform operations. Digital platforms represent a new way of organizing, as they leverage technology to interconnect providers and consumers. Using digital technologies, organizations are platformizing operations, as they open their rigid and closed boundaries by interconnecting providers and consumers through advanced application programming interfaces (APIs). Early research examined platformized development of technology products, with software development companies—such as Mozilla Foundation—leading the way. However, contemporary organizations are platformizing nontechnology offerings (e.g., ride-sharing or food delivery). With growing interest in platforms, the basic tenets underlying platformization are still not clear. This article synthesizes previous literature examining platforms, with the aim of examining what platformization is and how and why organizations platformize.

Article

Vinícius Chagas Brasil and J.P. Eggers

In competitive strategy, firms manage two primary (non-financial) portfolios—the product portfolio and the innovation portfolio. Portfolio management involves resource allocation to balance the important tradeoff of risk reduction and upside maximization, with important decisions around the evaluation, prioritization and selection of products and innovation projects. These two portfolios are interdependent in ways that create reinforcing dynamics—the innovation portfolio is the array of potential future products, while the product portfolio both informs innovation strategy and provides inputs to future innovation efforts. Additionally, portfolio management processes operate at two levels, which is reflected in the literature's structure. The first is a micro lens which focuses on management frameworks to boost portfolio performance and success through project-level selection tools. This research has its roots in financial portfolio management, relates closely to research on new product development and marketing product management, and explores the effects of portfolio management decisions on other organizational functions (e.g., operations). The second lens is a macro lens on portfolio management research, which considers the portfolio as a whole and integrates key organizational and competitive concepts such as entry timing, portfolio management resource allocation regimes (e.g., real options reasoning), organizational experience, and the culling of products and projects. This literature aims to set portfolio management as higher level organizational decision-making capability that embodies the growth strategy of the organization. The organizational ability to manage both the product and innovation portfolios connects portfolio management to key strategic organizational capabilities, including ambidexterity and dynamic capabilities, and operationalizes strategic flexibility. We therefore view portfolio management as a source of competitive advantage that supports organizational renewal.

Article

George M. Puia and Mark D. Potts

Although risk is an essential element of the business landscape and one of the more widely researched topics in business, there is noticeably less scholarship on strategic risk. Business risk literature tends to only delineate characteristics of risk that are operational rather than strategic in nature. The current operational risk paradigm focuses primarily on only two dimensions of risk: the probability of its occurrence and the severity of its outcomes. In contrast, literature in the natural and social sciences exhibits greater dimensionality in the risk lexicon, including temporal risk dimensions absent from academic business discussions. Additionally, descriptions of operational risk included minimal linkage to strategic outcomes that could constrain or enable resources, markets, or competition. When working with a multidimensional model of risk, one can adjust the process of environmental scanning and risk assessment in ways that were potentially more measurable. Given the temporal dimensions of risk, risk management cannot always function proactively. In risk environments with short risk horizons, rapid risk acceleration, or limited risk reaction time, firms must utilize dynamic capabilities. The literature proposes multiple approaches to managing risk that are often focused on single challenges or solutions. By combining a strategic management focus with a multidimensional model of strategic risk, one can match risk management protocols to specific strategic challenges. Lastly, one of more powerful dimensions of risky events is their ability to differentially affect competitors, changing the basis of competition. Risk need not solely be viewed as defending against potential losses; many risky occurrences may represent new strategic opportunities.