Abstract Research Summary In this manuscript, we argue that the Entrepreneurs‐as‐Scientists (E‐a‐S) research program requires a fundamental shift from predominantly conceptual development toward an empirical agenda to achieve its normative goal: developing a scalable algorithm that improves entrepreneurial learning and value creation. The E‐a‐S approach prescribes that entrepreneurs learn by articulating causal theories, testing them, updating their beliefs, and revising their theories. We identify three risks of continued emphasis on conceptual development: overlooked complementarities, insufficient attention to boundary conditions, and potential overprescription based on limited evidence. We propose an empirical research agenda, as well as specific topics within it, to ground the E‐a‐S approach in systematic evidence, advancing a research program that is as rigorous in its methods as the entrepreneurial practices it seeks to improve. Managerial Summary This manuscript examines a growing approach that encourages entrepreneurs to think and act like scientists—by clearly articulating assumptions, testing them through experiments, updating beliefs, and refining their strategies. The goal is to develop a reliable, scalable method that improves how entrepreneurs learn and create value. However, current insights and ongoing research projects predominantly focus on conceptual work. We highlight three practical risks of such a focus: missing complementarities, overlooking when the learning method works best, and applying it too broadly without sufficient validation. We suggest that entrepreneurs use this approach flexibly since it was not designed to be a fixed formula. Ongoing research will clarify how to adapt it across contexts, helping entrepreneurs make more informed, disciplined, and effective strategic decisions.
Camuffo et al. (Fri,) studied this question.