Models of causal inference: Imperfect but applicable is better than perfect but inapplicable

• 成果类型: Article
• 署名作者: Ellsaesser, Florian; Tsang, Eric W. K.; Runde, Jochen
• 署名单位: University of Texas System; University of Texas Dallas; University of Cambridge
• 刊物名称: STRATEGIC MANAGEMENT JOURNAL
• ISSN/ISSBN: 0143-2095
• DOI: 10.1002/smj.2164
• 发表日期: 2014
• 页码: 1541-1551
• 关键词: causation; causal graph modeling; vector space modeling; resource-based view; Competitive advantage

摘要

We assess a recent paper by Durand and Vaara (2009) that advances causal graph modeling as a tool for inferring causes in strategy research. We focus on the Markov condition, a key assumption on which causal graph modeling is based, and show why this condition is invariably violated in strategic management in general and the resource-based view of the firm in particular. We then introduce vector space modeling as a quantitative alternative to causal graph modeling, and consider how improved methods of causal inference might enhance our ability to test some of the central propositions of the resource-based view. Copyright (c) 2013 John Wiley & Sons, Ltd.