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We hypothesize that “low-contrast categories” (those lacking sharp differentiation from adjacent categories) catalyze the creation of groundbreaking inventions by influencing two key stages in the life of an invention: (1) idea-creation and (2) idea-positioning. During “idea-creation,” low-contrast categories increase the likelihood that descendant inventions will combine the focal invention with more (a) boundary-spanning, (b) novel, (c) original, and (d) atypical knowledge inputs. During “idea-positioning,” they allow greater leeway in articulating how descendant inventions depart from the focal invention’s lineage and chart new technological directions. We find robust support for our hypothesis using data from the United States Patent and Trademark Office’s classification system spanning nearly four decades. Further analyses demonstrate that the catalyzing effect of low-contrast categories has important material consequences: inventions classified in low-contrast categories spur descendant inventions that generate substantially higher economic value and exert more enduring technological impact than those in high-contrast categories. By introducing the concept of catalyzing categories, this study offers a novel theoretical perspective on the genesis of groundbreaking inventions and the role of categorical structures in the inventive process.

With permission of the Academy of Management

We generalize the captive-and-shopper model of sales to allow asymmetries in production costs and captive audiences in an oligopoly. Both kinds of asymmetry determine the firms that compete (via randomized sales) to serve the price-comparing shoppers, while other firms exploit their captive audiences. In contrast to a model with symmetric costs (but asymmetric captive audiences) there are natural situations in which more than two firms use sales by engaging in pairwise battles across different price intervals. We then study the choice of production technologies via costly process innovations. A distinctive asymmetry emerges endogenously: one firm innovates more and becomes the dominant supplier of shoppers. The pattern of innovations connects to the size of firms’ captive bases and the shape of technological opportunity. We also provide a trio of extensions to consider costly acquisitions of captives and shoppers, and captives’ choice of captor.

Copyright ©2025 by the American Economic Association.

Large, generalist, technology firms—so-called “big-tech” firms—powerful in their primary market, routinely enter secondary markets consisting of specialist firms. Naturally, one might expect a specialist firm to be fiercely protective of its data as a way to maintain its market position in the secondary market. Counter to this intuition, we demonstrate that a specialist firm willingly shares its market data with an intruding generalist. We do so by developing a model of cross-market competition in which the data collected via consumer usage in one market can improve product quality in another. We show that a specialist firm shares its data to strategically create codependence between the two firms, thereby softening competition and transforming the generalist firm from a traditional competitor into a coopetitor. For the generalist intruder, data from the specialist firm substitute for its own investments in product quality in the secondary market. As such, the act of sharing data makes the generalist a stakeholder in the data collected by the specialist, and consequently in the specialist’s continued success. Moreover, although the firms benefit from data sharing, consumers can be worse off from weakened price competition and lower investments in innovation. Our results have managerial and policy implications, notably on account of backlash against data collection and the market power of big-tech firms.

@2025, INFORMS

How can the selection of innovation projects be designed to reduce false positives and false negatives? Prior research has provided theoretical insights into organizing to reduce errors, yet we know little about how organizations adapt selection over time and the effects of this on selection outcomes. Drawing from qualitative data from 126 interviews conducted over several years, we explore how an accelerator evolved through three selection regimes for high-stakes funding decisions, focusing on the organizational changes and their underlying reasons. We then analyze quantitative data from all 3,580 submissions they received, assessing false positives and false negatives across these regimes. Our findings reveal a persistent occurrence of both types of errors, with relatively small differences across the regimes despite deliberate efforts to enhance the process. In the final regime, which increased submission quality by emphasizing applicant track record and adding additional layers of screening, evaluators surprisingly became more prone to making selection errors. This finding stands net of accounting for (1) differences in the pool of submissions, (2) differences in treatment effects through training and resources provided, (3) learning, and (4) market evolution. By combining qualitative and quantitative data, we explain this through two mechanisms: (1) mean reversion in combination with increased emphasis on applicant track record and (2) within-type adverse selection enabled by a more stringent selection process. The study reveals that evolving an organization’s selection regime may require adjustments across multiple aspects, resulting in unintended consequences.

© 2025, INFORMS