Peering into a crystal ball: Forecasting behavior and industry foresight

• 成果类型: Article
• 署名作者: Kapoor, Rahul; Wilde, Daniel
• 署名单位: University of Pennsylvania
• 刊物名称: STRATEGIC MANAGEMENT JOURNAL
• ISSN/ISSBN: 0143-2095
• DOI: 10.1002/smj.3450
• 发表日期: 2023
• 页码: 704-736
• 关键词: industry evolution; forecasting; Belief updating; automobile industry; Bayesian process

摘要

Research Summary What makes some managers and entrepreneurs better at forecasting the industry context than others? We argue that, regardless of experience or expertise, a learning-based forecasting behavior in which individuals attend to and incorporate new relevant information from the environment into an updated belief that aligns with the Bayesian belief updating process is likely to generate superior industry foresight. However, the effectiveness of such a cognitively demanding process diminishes under high levels of uncertainty. We find support for these arguments using an experimental design of forecasting tournaments in the managerially relevant context of the global automotive industry from 2016 to 2019. The study provides a novel account of individual-level forecasting behavior and its effectiveness in an evolving industry and suggests important implications for managers and entrepreneurs. Managerial Summary How a focal industry will evolve is a key forecasting problem faced by managers and entrepreneurs as they seek to identify opportunities and make strategic decisions. However, developing superior industry foresight in the face of significant change, and limited and often contradictory information, can be especially challenging. We study how individuals forecast the ongoing transformation of the global automotive industry with respect to electrification and autonomy, using a novel research design of forecasting tournaments. A forecasting process in which individuals update their beliefs by neither ignoring prior information nor overacting to new information helps to generate superior industry foresight. There was a significant penalty to forecasting accuracy when individuals did not update their beliefs at all, or when they updated, but overreacted to new information.