The #MeToo movement, triggered by the Harvey Weinstein scandal, highlighted gender inequality not only in labor markets but also in firms’ product choices. Using Hollywood as a setting, we examine whether movie producers responded to these events by changing their project selection and how their responses differed depending on whether the team included female representation. We leverage the idea that producers associated with the scandal had greater motivation to respond, which helps isolate project changes driven by the demand of #MeToo rather than confounding trends. We find a sharp contrast by gender composition of the team; all-male Weinstein-associated teams increased female-protagonist projects relative to their non-associated counterparts, whereas Weinstein-associated teams with women shifted toward male-protagonist projects. Moreover, female protagonists created by all-male Weinstein-associated teams deviated more from traditional stereotypes, and these projects performed worse than projects of Weinstein-associated teams with women. These patterns highlight the role of #MeToo in helping female talent overcome entry barriers in male-dominated domains. They also underscore the heterogeneous effects of a scandal on the associated parties; they may create reputational risks for some but opportunities for others, depending on whether they have demonstrated progress in addressing the issues revealed by the scandal. Supplemental Material: The online appendix is available at https://doi.org/10.1287/stsc.2024.0292 .
Innovation-sourcing acquisitions provide firms with rapid access to new knowledge, but their success depends on how effectively the acquiring firm integrates this knowledge. Prior research has focused on firm-level absorptive capacity, yet little is known about how such acquisitions affect inventors within acquiring firms who embody much of this capacity. We theorize that acquiring firm inventors' postacquisition performance will be shaped by the interaction between their own knowledge and the firms' characteristics, namely (a) the technological distance between the acquiring and target firms and (b) the alignment of the inventors' knowledge with the firms' postacquisition innovation trajectory. We argue that, although acquiring firm inventors generally experience a decline in postacquisition innovation performance, generalists face a smaller decline than specialists in distant acquisitions, and specialists experience a smaller decline than generalists in close acquisitions. Our predictions are conditional on whether the inventors continue to patent after the acquisition: inventors will be more likely to continue patenting when their knowledge base aligns with the firm's postacquisition innovation trajectory. Using panel data on 334 pharmaceutical acquisitions between 1990 and 2007, we find support for our hypotheses.
We show that demand surges can lead to disintermediation through an analysis of the effects of the 2011 Japanese tsunami on the U.S. used car market. The tsunami devastated Japanese new car production, which led to an increase in demand for used cars. Wholesale and retail prices for used Japanese cars rose, but dealer margins declined by 2.3 percentage points. This decline is consistent with surges in demand leading to buyers and sellers bypassing intermediaries. The decline in dealer margins was most pronounced for popular cars trading in thick markets for which dealers were relatively more dispensable for trade and for younger used vehicles. Consistent with the adjustment costs literature, Japanese dealers, unlike their non-Japanese counterparts, were unable to exit markets for generally younger used Japanese models.
Receiving venture capital can dramatically shape the trajectory and ultimate success of startup firms. Because these investments occur under significant uncertainty, investors rely heavily on subjective assessments, often backing entrepreneurs who are similar to themselves. Yet, research on this tendency has largely relied on coarse demographic categories, potentially obscuring more granular visually assessed forms of similarity that influence investment decisions. This paper introduces "face distance," a novel measure of visual similarity derived from facial recognition models, to explore this subtle channel. Using a mixedmethods approach that combines observational data from a top startup accelerator with an online controlled experiment, I find that facial similarity between an entrepreneur and an investor is a powerful predictor of investment. This relationship is more pronounced when there is relatively higher uncertainty, suggesting that facial similarity is a heuristic investors tend to rely on when concrete "hard" information is scarce. In addition, investments between visually similar entrepreneurs and investors underperform in terms of successful exits, which is consistent with a costly distortion. These findings highlight that homophily operates at a highly granular, visual level in early-stage investment and that this is not only inequitable but also a potentially inefficient heuristic.
