Roger Masclans

Roger Masclans (Working), Science, Startups, and the Problem of Value Capture: Thin Acquisition Markets, Weak Outside Options.

Abstract: Startups commercializing science-based innovations are crucial for tackling pressing challenges, yet, in critical sectors such as energy, industrials, and materials, entrepreneurial activity remains limited. This paper investigates whether weak value capture at exit constrains these ventures. I estimate value creation and capture in startup acquisitions by combining acquisition prices with acquirer stock returns, adjusting for market noise to isolate the economic signal attributable to the acquisition. Science-based startups capture 46 cents per dollar of acquisitioninduced surplus, compared to 61 cents for non-science startups—a 24% penalty. Conversely, they create 20% more joint surplus, consistent with continued entry despite the capture penalty. To explain these patterns, I examine a central mechanism: the structure of a startup’s exit conditions. I argue that science-based startups face thinner, more concentrated acquisition markets and limited ability to scale independently, features that weaken the startup’s bargaining power. Indeed, I find that science-based startups face up to 40% fewer potential acquirers, who are 53% larger on average, and that their value capture is more sensitive to acquirer concentration. Concentrated markets have a dual effect: large incumbents enable greater surplus creation, but also shift bargaining power away from startups, allowing acquirers to extract most of the gains from innovation. Finally, I find that the capture penalty diminishes when startups can scale commercialization independently. The results suggest that constrained exit environments limit returns to science-based innovation, highlighting the importance of competitive acquisition markets and independent commercialization pathways in incentivizing upstream innovation.

Roger Masclans, Cohen, Wesley, Hasan, Sharique (2025), Measuring the Commercial Potential of Science, Strategic Management Journal, 46 (6).

Abstract: We develop an ex-ante measure of commercial potential of science, an otherwise unobservable variable driving the performance of innovation-intensive firms. To do so, we rely on LLMs and neural networks to predict whether scientific articles will influence firms' use of science. Incorporating time-varying models and the quantification of uncertainty, the measure is validated through both traditional methods and out-of-sample exercises, leveraging a major university’s technology transfer data. To illustrate the methodological contributions of our measure, we apply it to examining the impact of university reputation and university privatization of science, finding that firms’ reliance on reputation may lead to foregone opportunities, and privatization (i.e., patenting) appears to increase firms’ use of the science of one university. We make our measure and method available to researchers.