Analytic models in strategy, organizations, and management research: A guide for consumers

• 成果类型: Article
• 署名作者: Hannah, Douglas P.; Tidhar, Ron; Eisenhardt, Kathleen M.
• 署名单位: Boston University; Stanford University
• 刊物名称: STRATEGIC MANAGEMENT JOURNAL
• ISSN/ISSBN: 0143-2095
• DOI: 10.1002/smj.3223
• 发表日期: 2021
• 页码: 329-360
• 关键词: formal theory; game theory; Modeling; research methods; theory development

摘要

Research summary Analytic models are a powerful approach for developing theory, yet are often poorly understood in the strategy and organizations community. Our goal is to enhance the influence of the method by clarifying for consumers of modeling research how to understand and appreciate analytic modeling and use modeling results to enhance their own research. Our primary contribution is a guide for reading analytic models. Using comparisons with other methods and exemplar analytic models, we explore key features as well as counterintuitive aspects and common misconceptions. We also add by illuminating strengths and weaknesses of analytic modeling relative to other theory-building methods. Finally, we identify under-exploited opportunities for pairing analytic models with complementary methods. Overall, our aim is enhancing the influence of analytic modeling by better-informing consumers. Managerial summary In this paper, we explore the use of analytic (mathematical) models for developing strategy and organizations theory. Analytic modeling is common in related fields like economics but is often poorly understood among the broader of strategy and organizations community. Whereas existing resources on analytic modeling are geared towards modelers, our aim is to enhance understanding and appreciation of the method among potential consumers of modeling research. We offer three specific contributions in this regard. The first is a guide for reading analytic models, including key features, counterintuitive aspects, and common misconceptions. Second, we clarify the strengths and weaknesses of analytic modeling relative to other theory-building methods. Finally, we discuss promising opportunities for pairing methods.