A Bayesian operationalization of the resource‐based view

Abstract

This paper argues that the gap between the theoretical utility and the practical utility of the resource-based view (RBV) may be narrowed by operationalizing the theory more consistently with Penrose's original framework. The operationalization proposed here is a twofold approach. First, the RBV may be enhanced by the explicit recognition of Penrose's two classes of resources, namely, administrative resources and productive resources. This distinction suggests a focus on the administrative decisions of managers that lead to economic performance. Second, we argue that the RBV is a theory about extraordinary performers or outliers-not averages. Therefore, the statistical methods used in applying the theory must account for individual firm differences, and not be based on means, which statistically neutralize firm differences. We propose a novel Bayesian hierarchical methodology to examine the relationship between administrative decisions and economic performance over time. We develop and explain a measure of competitive advantage that goes beyond comparisons of economic performance. This Bayesian methodology allows us to make meaningful probability statements about specific, individual firms and the effects of the administrative decisions examined in this study.

Path Dependence, VRIN Resource Endowments, and Managers: Towards an Integration of Resource-Based Theory and Upper Echelons Theory

“…She classifies resources as falling into two categories -productive versus administrative. While productive resources constitute the strategic resources themselves, administrative resources signify the quality of administrative or managerial decisions that are made connected with these resources, which ultimately leads to resource performance (Hansen, Perry & Reese, 2004). Similarly, in his ten-year retrospective on the RBT Wernerfelt identifies the "governance structure within which a firm can leverage its resources" as being a critical missing piece of the RBV (1995: 172).…”

Causation, counterfactuals, and competitive advantage

“…Causal modeling is obviously not the only approach that has been developed to deal with such problems. For instance, taking a Bayeasian perspective has been found to be useful in various fields of social science, including strategy research (Hahn and Doh, 2006;Hansen, Perry, and Reese, 2004;Powell, 2001). However, for space limitations, we focus here on causal modeling as a particularly fruitful method of counterfactual analysis.…”

“…Bayesian methodology allows a full estimation of individual effects, a prediction of 'what-if' results, and robust results with small samples or skewed data (for further discussion, see Berry, 1996;Hahn and Doh, 2006;Hansen et al, 2004;Powell, 2001). This approach can lead to promising results around the RBV resource-performance relationship, complementing the causal modeling approach we focus on in this paper.…”

Offshoring of Information-Based Services: Structural Breaks in Industry Life Cycles

“…The utility of the Bayesian framework in investigating management phenomena has recently been discussed by authors such as Hansen, Perry and Reese (2004) and Hahn and Doh (2006). Here, an additional benefit is that the Bayesian framework permits simultaneous model comparison of an arbitrary number of non-nested models as opposed to the classical pairwise model comparison of nested models.…”