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Using Bayesian methods in strategy research: an extension of Hansen et al .
Author(s) -
Hahn Eugene D.,
Doh Jonathan P.
Publication year - 2006
Publication title -
strategic management journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 11.035
H-Index - 286
eISSN - 1097-0266
pISSN - 0143-2095
DOI - 10.1002/smj.539
Subject(s) - bayesian probability , computer science , macro , bayesian inference , resource (disambiguation) , range (aeronautics) , management science , machine learning , artificial intelligence , operations research , data science , economics , mathematics , engineering , programming language , computer network , aerospace engineering
Hansen, Perry, and Reese (2004) recently argued for and demonstrated the utility of Bayesian methods for research associated with the resource‐based view (RBV) of the firm. In this paper, we propose that Bayesian approaches are highly relevant not only for strategy problems based on the RBV, but also to its extensions in the areas of dynamic capabilities and co‐evolution of industries and firms. Further, we argue that Bayesian methods are equally applicable for a wide range of strategy research questions at both the micro‐ and macro‐level. Bayesian techniques are especially useful in addressing specific methodological challenges related to firm‐ and individual‐level effects, firm‐level predictive results, precision with small samples, asymmetric distributions, and the treatment of missing data. Moreover, Bayesian methods readily permit the engineering and updating of more realistic, complex models. We provide a specific illustration of the utility of Bayesian approaches in strategy research on entry order and pioneering advantage to show how they can help to inform research that integrates micro‐ and macro‐phenomena within a dynamic and interactive environment. Copyright © 2006 John Wiley & Sons, Ltd.