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Early-stage technology startups rely critically on talented scientists and engineers to commercialize new technologies. And yet, they compete with large technology firms to hire the best workers. Theories of ability sorting predict that high ability workers will choose jobs in established firms that offer greater complementary assets and higher pay, leaving low ability workers to take lower-paying and riskier jobs in startups. We propose an alternative view in which heterogeneity in both worker ability and preferences enable startups to hire talented workers who have a taste for a startup environment, even at lower pay. Using a longitudinal survey that follows 2,394 science and engineering PhDs from graduate school into industrial employment, we overcome common empirical challenges by observing ability and stated preferences prior to first-time employment. We find that both ability and career preferences strongly predict startup employment, with high ability workers who prefer startup employment being the most likely to work in a startup. We show that this is due in part to the dual selection effects of worker preferences resulting in a large pool of startup job applicants, and startups “cherry picking” the most talented workers to make job offers to. Additional analyses confirm that startup employees earn approximately 17% lower pay. This gap is greatest for high ability workers and persists over workers’ early careers, suggesting that they accept a negative compensating differential in exchange for the non-pecuniary benefits of startup employment. This is further supported by data on job attributes and stated reasons for job choice.

© 2022, INFORMS

We consider the information design problem of a demand-maximizing firm launching a product of unknown quality to a market consisting of customers who have heterogeneous prior beliefs about quality. The firm publicly discloses information about quality to all customers. These customers can subsequently opt to acquire additional information about the product at a cost from sources beyond the firm's control. Our study is motivated by the common practice of firms conducting public pilot tests or soliciting reviews from opinion leaders before launching a new product to inform potential customers about its quality. To analyze this problem, we construct a game-theoretic model of Bayesian persuasion between the firm and its customers. We characterize the firm's optimal information policy and show that it can range from fully disclosing quality to exaggerating or downplaying quality to not disclosing quality at all depending on market characteristics. We delineate the impact of market heterogeneity and access to additional information on the optimal information disclosure policy of the firm. Our analysis provides managerial guidance for firms in designing information provision strategies and operationalizing them for different market characteristics.

© 2024 John Wiley & Sons, Ltd.

In 2013, Trumpf, a global market leader machine tools from Germany, acquired the majority shares in JFY, a smaller machine tool manufacturer from China. With this acquisition, Trumpf wanted to enter the fast-growing low-cost segment of the market. Until then, JFY had performed very well on the Chinese market, but the company's success increasingly waned after the acquistion. Other Chinese competitors performed significantly better. After JFY even had to report losses for the first time in 2019, Trumpf changed the management at JFY. Under the new management, initial successes were achieved, but even two and a half years later, JFY still did not reach the profit targets which all business units at Trumpf had to meet. In a Trumpf management meeting in October 2022, a decision was therefore to be made as to whether JFY should remain part of Trumpf or be sold off again.