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.
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A Practice-Based View of Innovation Adoption
Rangapriya Kannan and Paola Perez-Aleman
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Artificial Intelligence and Entrepreneurship Research
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
Design Thinking in Business and Management: Research History, Themes, and Opportunities
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
Entrepreneur Coachability
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.
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External Enablers of Entrepreneurship
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
Innovation and Business Models
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
Innovation Ecosystems in Management: An Organizing Typology
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.
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Innovation in Artificial Intelligence: Illustrations in Academia, Apparel, and the Arts
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
Innovation in Family Business
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
National Systems of Innovation
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
New Venture Legitimacy
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
Open Innovation
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.
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Risk in Strategic Management
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.
Article
The Role of Human Agency in Entrepreneurship
Keith M. Hmieleski
Human agency stands as a foundational element of entrepreneurship, embodying individuals’ proactive ability to shape their destinies, innovate, and navigate the complexities of new venture creation and development. Rooted in social cognitive theory, this concept underscores the interactive interplay between personal characteristics, behaviors, and environmental influences in driving entrepreneurial endeavors. Within this framework, agentic personal characteristics, comprising both socially admired attributes (e.g., entrepreneurial self-efficacy, dispositional positive affect, grit, and locus of control) and socially deviant features (e.g., narcissism, Machiavellianism, and psychopathy) provide the motivational force and resilience needed to tackle entrepreneurial endeavors. These personal characteristics are associated with engagement in a range of agentic behaviors (e.g., improvisation, transformational leadership, learning, and personal initiative) that embody entrepreneurial action exhibited by business founders as they work to effectively shape and adapt their ventures. Situational factors (e.g., institutional forces, political barriers, and industry-specific dynamics), in turn, can positively or negatively impact the expression of agentic personal characteristics and behaviors. Thus, understanding human agency in entrepreneurship necessitates a holistic examination of the intertwined dynamics between personal characteristics, behaviors, and contextual factors. Despite significant strides in comprehending human agency in entrepreneurship, numerous avenues for exploration remain. These include investigating gender disparities in agentic versus communal orientations among entrepreneurs, the impacts of artificial intelligence on entrepreneurial agency, trajectories of entrepreneurial agency over time, strategies for fostering collective agency in new venture teams, and exploring the darker (or unproductive) aspects of entrepreneurial agency. Developing a deeper understanding of human agency in the realm of entrepreneurship not only enriches the comprehension of the new venture creation and development process but also lays the groundwork for crafting more impactful strategies, policies, interventions, and educational initiatives to cultivate and leverage the full potential of business founders and their ventures.
Article
University Technology Commercialization
Kathleen R. Allen
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.
