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.
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Article
Rangapriya Kannan and Paola Perez-Aleman
Innovation adoption is challenging at both intra-organizational and interorganizational levels. Several decades of innovation adoption research have identified various barriers at both levels. Intra-organizational barriers are often related to the characteristics of the innovation, adopters, managers, environment, and ecosystem but can also include an incompatibility with an organization’s strategy, structural impediments, organizational resource constraints, a lack of fit of the innovation with an organizational culture and climate, decision making challenges, a lack of integration with an organization’s knowledge management, human resource management practices, dynamic capabilities, and active innovation resistance from customers. Interorganizational barriers include uncertainty with learning and implementation, the distributed nature of the innovation process, differences in production systems, disparities in regulatory systems, variation within local contexts, and the nature of embedded knowledge adopted in diverse organizational contexts.
One of the key missing aspects in understanding innovation adoption is how extant practices within an organizational or interorganizational context enhance or hinder innovation adoption. Although the practices of innovation adoption emerge and evolve dynamically, existing research does not highlight fine-grained practices that lead to its success or failure. A practice lens focuses on people’s recurrent actions and helps to understand social life as an ongoing production that results from these actions. The durability of practices results from the reciprocal interactions between agents and structures that are embedded within daily routines. A practice lens allows us to study practices from three different perspectives. The first perspective, empirically explores how people act in organizational contexts. The second, a theoretical focus investigates the structure of organizational life. This perspective also delves into the relations between the actions that people take over time and in varying contexts. Finally, the third perspective which is a philosophical one focuses on how practices reproduce organizational reality.
By focusing on the unfolding of constellations of everyday activities in relation to other practices within and across time and space, a practice lens hones in on everyday actions. Everyday actions are consequential in producing the structural contours of social life. A practice lens emphasizes what people do repeatedly and how those repetitive actions impact the social world. A practice theory lens also challenges the assumption that things are separable and independent. Instead, it focuses on relationality of mutual constitution to understand how one aspect of the issue creates another aspect. Relationality of mutual constitution is the notion that things such as identities, ideas, institutions, power, and material goods take on meaning only when they are enacted through practices instead of these being innate features of these things Focusing on duality forces us to address the assumptions that underlie the separation.
A practice perspective on innovation adoption highlights the concepts of duality, dynamics, reciprocal interactions, relationality, and distributed agency to inform both the theory and practice of innovation adoption. Understanding these concepts enables a practice lens for successful adoption of innovations that impact organizational and societal outcomes, such as economic development, productivity enhancement, entrepreneurship, sustainability, equity, health, and other economic, social, and environmental changes.
Article
Martin Obschonka and Christian Fisch
Advances in Artificial Intelligence (AI) are intensively shaping businesses and the economy as a whole, and AI-related research is exploding in many domains of business and management research. In contrast, AI has received relatively little attention within the domain of entrepreneurship research, while many entrepreneurship scholars agree that AI will likely shape entrepreneurship research in deep, disruptive ways.
When summarizing both the existing entrepreneurship literature on AI and potential avenues for future research, the growing relevance of AI for entrepreneurship research manifests itself along two dimensions. First, AI applications in the real world establish a distinct research topic (e.g., whether and how entrepreneurs and entrepreneurial ventures use and develop AI-based technologies, or how AI can function as an external enabler that generates and enhances entrepreneurial outcomes). In other words, AI is changing the research object in entrepreneurship research.
The second dimension refers to drawing on AI-based research methods, such as big data techniques or AI-based forecasting methods. Such AI-based methods open several avenues for researchers to gain new, influential insights into entrepreneurs and entrepreneurial ventures that are more difficult to assess using traditional methods. In other words, AI is changing the research methods. Given that, so far, human intelligence could not fully uncover and comprehend the secrets behind the entrepreneurial process that is so deeply embedded in uncertainty and opportunity, AI-supported research methods might achieve new breakthrough discoveries.
We conclude that the field needs to embrace AI as a topic and research method more enthusiastically while maintaining the essential research standards and scientific rigor that guarantee the field’s well-being, reputation, and impact.
Article
Neva Bojovic and Vincent Mangematin
Companies need business models to profit from innovation and technology. However, the success of a certain technology depends on whether and how it is used. Usage is important not only as an indicator of technology adoption, but also as a way for companies to design business models—as a way to create and capture value from technology. Usage is inscribed by the designers in the technology, but users in their ongoing practice can alter the designers’ intentions, which sometimes leads to innovation. Users can also combine different technologies in practice to accomplish a specific usage. In essence, usage is constitutive of technology and its value.
Technology usage-based business modeling is a way to explore business modeling for technology that looks into how different technologies are integrated, either by users or platform actors, into solutions to address specific usage needs. To understand this notion of usage for business model design, one must first understand how value is created and captured from technology. At the same time, it is also important to know different streams of literature that have investigated technology usage: user-centered design, user innovation and lead users, form, function, affordances of technology, and the practice-based view.
While usage-based business modeling has implications for all kinds of technologies, it is of particular importance for emerging, enabling, and embedding technologies, where the value of technology depends on the usage across multiple applications and connectedness between different users.
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
Jarryd Daymond and Eric Knight
Design thinking is a human-centered, innovation-focused problem-solving approach that employs various tools and methods for creative purposes. It is a dynamic process and often prioritizes the needs and experiences of people while considering both technical and economic aspects of a solution. The prominence of design thinking in practice has seen its use move beyond product development teams to take a more central role in shaping how organizations approach problems, develop strategies, build capabilities, and drive cultural change. It is common for organizations to employ executives with a specific focus on design, and traditionally “nondesign” organizations increasingly build, buy, or borrow design capabilities. The utility of design thinking stretches beyond organizational outcomes, with educators and employers recognizing that understanding and proficiency in design thinking is a valuable and transferrable skill. A rich scholarly tradition in design sciences and engineering underpins design thinking. These traditions provide the foundational understandings of problem definition and need-finding, information gathering and analysis, and creative expression and ideation, from which design thinking gained prominence. Although not often acknowledged in contemporary scholarship, design thinking research builds on these traditions and offers unique perspectives on the practice of design thinking and its theoretical underpinnings: The cognitive perspective focuses on how unique ways of thinking shape the practice of design thinking; the instrumental perspective attends to how design thinking is done, including the methods or tools used in design thinking; and the organizational-level perspective concerns the implementation of design thinking in organizations and its influence on organizational culture and capabilities. While the various research traditions preceding design thinking are receiving greater attention in contemporary research, rich insights from these established fields offer deep theoretical knowledge to develop several promising research areas. These avenues for future research include how design thinking can inform the redevelopment of services and customer experiences, tackle societal challenges, and build capabilities to benefit communities and society more generally.
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
Devashish Pujari and Anna Sadovnikova
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
Per Davidsson, Jan Recker, and Frederik von Briel
“External enabler” (EE) denotes nontrivial changes to the business environment—such as new technology, regulatory change, demographic and sociocultural trends, macroeconomic swings, and changes to the natural environment—that enable entrepreneurial pursuits. The EE framework was developed to increase knowledge accumulation in entrepreneurship and strategy research regarding the influence of environmental factors on entrepreneurial endeavors. The framework provides detailed structure and carefully defined terminology to describe, analyze, and explain the influence of changes in the business environment on entrepreneurial pursuits. EE characteristics specify the environmental changes’ range of impact in terms of spatial, sectoral, sociocultural, and temporal scope as well as the degree of suddenness and predictability of their onset. EE mechanisms specify the types of benefits individual ventures may derive from EEs. Among others, these include cost saving, resource provision, making possible new or improved products/services, and demand expansion. EE roles situate these (anticipated) mechanisms in entrepreneurial processes as triggering and/or shaping and/or outcome-enhancing. EE’s influence is conceived of as mediated by entrepreneurial agency that—in addition to agent characteristics—is contingent on the opacity (difficulty to identify) and agency-intensity (difficulty to exploit) of EE mechanisms, with the ensuing enablement being variously fortuitous or resulting from strategic deliberation.
Article
Carine Peeters
Both the absorptive capacity (AC) and international business (IB) literatures are interested in knowledge processes and learning in organizations. Although originating from different streams of research, AC and IB were thus meant to meet and reinforce each other.
Fundamentally, the role of AC in IB is to condition the performance outcome of firms’ internationalization efforts. Firms benefit from their IB activities conditional on being able to absorb new knowledge and learn. In other words, multinational corporations (MNCs) need to have the necessary AC to overcome their liabilities of foreignness and outsidership. Short of AC, the costs and challenges of entering foreign markets and operating across countries are likely to outweigh potential performance gains. Moreover, AC plays a role in the technological upgrade and economic development of nations, as it helps firms in emerging economies to benefit from spillovers of foreign direct investments by MNCs from more economically advanced economies. And national governments can play an important role to facilitate this effect by developing appropriate economic and innovation policies that support knowledge creation and learning.
Firms can also proactively develop AC. For instance, MNCs can nurture a broad knowledge base that can be leveraged in different contexts and opt for a decentralized structure with mechanisms that help subsidiaries access the knowledge base of the parent organization. They can also practice specific routines to identify and access relevant knowledge from their external environment, transfer that knowledge in their organization, and assimilate it in their own knowledge creation processes. Moreover, MNCs can adopt human resources management practices that help raise the capacity and motivation of their employees to acquire and exploit new knowledge.
Ultimately, the most important contribution of AC in IB might be to help MNCs develop the strategic flexibility that enables them to thrive in dynamic environments. High-AC MNCs may indeed be in a better position than other firms to (a) build diverse options to prepare for uncertain evolutions in the market, (b) access flexible resources to allocate to new courses of actions, and (c) redeploy resources across options over time. Unpacking the exact mechanisms as well as boundary conditions for the role of AC in building strategic flexibility offers ample opportunities for future research on a highly relevant topic for MNCs.
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
Andreas Kaplan
Artificial intelligence (AI), commonly defined as “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation,” can be classified into analytical, human-inspired, and humanized AI depending upon its application of cognitive, emotional, and social intelligence. AI’s foundations took place in the 1950s. A sequence of vicissitudes of funding, interest in, and support for AI followed subsequently. In 2015 AlphaGo, Google’s AI-driven system, won against the human grandmaster in the highly complex board game Go. This is considered one of the most significant milestones in the development of AI and marks the starting of a new period, enabling several AI innovations in a variety of sectors and industries.
Higher education, the fashion industry, and the arts serve as illustrations of areas wherein ample innovation based on AI occurs. Using these domains, various angles of innovation in AI can be presented and decrypted. AI innovation in higher education, for example, indicates that at some point, AI-powered robots might take over the role of human teachers. For the moment, however, AI in academia is solely used to support human beings, not to replace them. The apparel industry, specifically fast fashion—one of the planet’s biggest polluters—shows how innovation in AI can help the sector move toward sustainability and eco-responsibility through, among other ways, improved forecasting, increased customer satisfaction, and more efficient supply chain management. An analysis of AI-driven novelty in the arts, notably in museums, shows that developing highly innovative, AI-based solutions might be a necessity for the survival of a strongly declining cultural sector.
These examples all show the role AI already plays in these sectors and its likely importance in their respective futures. While AI applications imply many improvements for academia, the apparel industry, and the arts, it should come as no surprise that it also has several drawbacks. Enforcing laws and regulations concerning AI is critical in order to avoid its adverse effects. Ethics and the ethical behavior of managers and leaders in various sectors and industries is likewise crucial. Education will play an additional significant role in helping AI positively influence economies and societies worldwide. Finally, international entente (i.e., the cooperation of the world’s biggest economies and nations) must take place to ensure AI’s benefit to humanity and civilization. Therefore, these challenges and areas (i.e., enforcement, ethics, education, and entente) can be summarized as the four summons of AI.
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
Kazuhiro Asakawa and Jaeyong Song
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.
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