Bernd Wurth

Entrepreneurial ecosystems have become a prominent concept, yet in its current state, the concept itself represents a paradox. While it draws on a rich intellectual history and provides an opportunity to synthesize different strands of research, it is also under-theorized and the mechanisms that govern ecosystem evolution are not well understood. This paper takes stock of recent advancements in ecosystem scholarship and synthesizes the empirical reality of the causal mechanisms. We use these dynamics to position ecosystems in a broader context, within and beyond the domain of entrepreneurship research, and propose a transdisciplinary research program for ecosystem research and practice.

Innovation-driven entrepreneurship has become a focus for economic development and received increasing attention from policy makers and academics over the last decades. While consensus has been reached that context matters for innovation and entrepreneurship, little evidence and decision support exists for policy makers to effectively shape the environment for growth-oriented companies. We present the entrepreneurial ecosystem concept as a complex systems-based approach to the study of innovation-driven entrepreneurial economies. The concept, in combination with novel data sources, offers new opportunities for research and policy, but also comes with new challenges. The aim of this paper is to take stock of the literature and build bridges for more transdisciplinary research. First, we review emergent trends in ecosystem research and provide a typology of four overarching problems based on current limitations. These problems connect operational research scholars to the context and represent focal points for their contributions. Second, we review the operational research literature and provide an overview of how these problems have been addressed and outline opportunities for future research, both for the specific problems as well as cross-cutting themes. Operational research has been invaluable in supporting decision-makers facing complex problems in several fields. This paper provides a conceptual and methodological agenda to increase its contribution to the study and governance of entrepreneurial ecosystems.

The idea and practice of the entrepreneurial university has emerged in response to growing expectations of universities contributing to economic development and has, in turn, been subject to a growing body of research. However, much of the work is focused on individual activities or institutions, typically overemphasising commercialisation activities and certain types of universities. Furthermore, much of this research is de-contextualised and does not consider the systems in which universities operate. As a result, we have a variety of unit theories of constituent parts of the entrepreneurial university without considering the wider (feedback) effects and implications — in other words: we are, in effect, not seeing the forest for the trees. Drawing on in-depth quantitative and qualitative field work and the literature, we develop a programmatic theory of the entrepreneurial university and the institutionalised entrepreneurial activities. Using causal loop diagrams, we capture the systemness and the interdependencies between universities’ entrepreneurial activities and their dynamic capabilities. The model highlights how universities are part of a larger system and how this influences their external engagement activities. The result is a more holistic understanding of entrepreneurial universities that reconciles existing work and guides future research. We discuss practical implications and policy levers derived from this systemic perspective.

Artificial intelligence (AI) is widely portrayed as enhancing entrepreneurial decision-making through improved prediction, pattern recognition, and data-driven evaluation. Yet entrepreneurship frequently unfolds under conditions of Knightian uncertainty, where action depends on entrepreneurial judgment about uncertain and evolving futures rather than calculable prediction alone. We theorize how AI-based systems reshape the epistemic conditions under which entrepreneurial beliefs are formed and evaluated. We argue that AI systems are not epistemically neutral: by generating plausibility signals from historical data, they influence which ventures appear credible and worthy of support. We conceptualize these dynamics as AI-mediated plausibility regimes and identify a dual epistemic risk. In generative modes, AI may inflate evaluative confidence beyond what the available epistemic grounding warrants (false positives). In evaluative and screening modes, AI may disadvantage low-precedent but potentially transformative ventures (false negatives). By distinguishing these system classes and their distributional effects, we specify when AI may expand entrepreneurial imagination and when it may narrow entrepreneurial variety.