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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.

© 2026, INFORMS

© 2026 The Author(s), under exclusive license to Springer Nature Switzerland AG

We study a Bertrand oligopoly with asymmetric costs in which each seller has some “captive” buyers. In the limit as captive buyers vanish, the lowest-cost firm sells to all buyers at a price equal to the second-lowest marginal cost. However, the closest competing price arises from non-degenerate mixed strategies, firms play exclusively undominated strategies, and with positive probability all but one firm sets the monopoly price.

With permission of Elsevier

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