Supervised machine learning (ML) methods are a powerful toolkit for discovering robust patterns in quantitative data. The patterns identified by ML could be used for exploratory inductive or abductive research, or for post-hoc analysis of regression results to detect patterns that may have gone unnoticed. However, ML models should not be treated as the result of a deductive causal test. To demonstrate the application of ML for pattern discovery, we implement ML algorithms to study employee turnover at a large technology company. We interpret the relationships between variables using partial dependence plots, which uncover surprising nonlinear and interdependent patterns between variables that may have gone unnoticed using traditional methods. To guide readers evaluating ML for pattern discovery, we provide guidance for evaluating model performance, highlight human decisions in the process, and warn of common misinterpretation pitfalls. An online appendix provides code and data to implement the algorithms demonstrated in the paper.

Although previous work has examined how audiences evaluate category-spanning organizations, little is known about the conditions under which such spanners affect audience evaluations of other organizations. We posit that the emergence of atypical categorical combinations, pioneered by startups, signals an altered future state—and seeds doubt about certain incumbents’ future prospects within a re-ordered industry. We test this hypothesis using startup funding announcements as an information shock to investors evaluating incumbents in the financial services sector from 2010 to 2017—a period marked by significant category-spanning entry from non-traditional Fintech startups. We find that announcements by startups that spanned unusual combinations of industry categories led to lower cumulative average returns for incumbents, compared to entry by typical startups. Our theory and results contribute to research on categorization in markets and to theories of disruptive innovation, industry evolution, and technology change.

Alibaba's Rural Taobao initiative had been launched in 2014 as a public service initiative to increase e-commerce adoption in China’s remote rural areas. In the first two iterations of the initiative, dubbed “1.0” and “2.0,” Alibaba had partnered with local businesses and young returnees to operate village stations. Village stations served as logistical and educational hubs to help villagers make online purchases. Despite significant growth in sales through village stations, the business model was operating at a loss, and competitors JD and Pinduoduo were growing quickly, threatening to challenge Alibaba by claiming the rural e-commerce markets. Bill Wang, the head of the initiative, was tasked with designing and implementing the next model for Alibaba’s future expansion into rural areas. The decision centers around whether Alibaba should copy the successes of competitors JD and Pinduoduo, escalate commitment to its original value proposition (hands-on service to villagers), or embrace an entirely new model. The case highlights the competing pressures of financial performance, CSR and corporate mission, government relations, and long-term competitive positioning that are common to major strategic decisions.