Abstract Entrepreneurial ecosystems (EE) have received increasing attention from scholars, practitioners, and policymakers. Recently, EE are being conceptualized as complex adaptative systems (CAS), with ontological and epistemological implications for the field. Despite such conceptualization, scarce empirical evidence exists on whether EE can indeed be considered CAS. Building on established methods from complexity research, this study aims at answering the following research question: Do EE exhibit complex and/or chaotic behavior? Using time-series data from U.S. Metropolitan Statistical Areas (MSAs), we apply three techniques from chaos and complexity theory, already utilized in the field of EE—the Pointwise D2 (PD2), the Brock–Dechert–Scheinkman (BDS) test, and Local Largest Lyapunov Exponents (LLLEs). While prior work has applied these methods at the national level, our study extends the analysis to a more granular regional dataset, substantially increasing the empirical evidence available on the complex nature of EE. Results reveal that EE consistently display significant structural complexity, but deterministic chaos is not the dominant dynamic. Instead, EE evolve through nonlinear dependencies and structured complexity without widespread chaotic instability. Our findings open new conversations in the field of EE by demonstrating that EE can be understood as complex, but not necessarily chaotic, systems. This enhances the possibility of studying generalizable dynamics rather than relying solely on case-specific explanations, with important consequences for theory, empirical research, and policy. In particular, this implies that regional EE policy is not fundamentally constrained to purely bespoke, case-by-case interventions, but can instead draw on comparative evidence, transferable mechanisms, and adaptive policy design grounded in recurrent systemic patterns.

Abstract Recent policy discussions have focused attention on what some refer to as Small Business Innovation Research (SBIR) “mills.” One characterization of an SBIR mill focuses on firms as multiple award winners (MAWs). A second emphasizes reliance on SBIR awards for a large portion of a firm’s revenue. This last characterization is the focus of this paper. The analysis relates the percentage of a firm’s revenue comprised by SBIR awards to four outcomes: the generation of at least one spin-off firm, the number of spin-off firms generated, the receipt of at least one patent, and the number of patents received. The results suggest that the marginal effect of an increase of one percentage point of revenue from the SBIR program, evaluated at each firm’s observed percentage of revenue, is positively related to each of the four outcomes. However, when considering predictions across the entire range of reliance on SBIR funding, the results are more mixed. Notably, the predicted number of patents peaks at 70 percent of revenue and declines thereafter. Comparisons of predictions from multivariate models to simple descriptive statistics suggest that some of the apparent negative relationship at high percentages of SBIR revenue seen in comparisons of means may be due to uncontrolled differences in other firm characteristics.

Abstract Reverse engineering (RE) allows firms to learn about critical components and design features of competitors’ technologies. From an innovation strategy point of view, relying on RE involves a trade-off between acquiring others’ knowledge rather cheap and quickly, and being trapped into a follower strategy that rests on technology developments propelled by other firms. This paper aims to explore which type of firms use RE, and which type of innovation results from an RE-based knowledge acquisition strategy. Using data from the German part of the Community Innovation Survey (CIS), we find that RE is more often used by firms that operate under fierce price competition and have stronger financial constraints. RE using firms obtain higher product innovation output compared to firms with other knowledge acquisition strategies, though their innovations have a low degree of novelty (i.e., imitations). The market success of these innovations seem to rest on a price advantage, based on higher cost savings from process innovation.