A recentering approach for interpreting interaction effects from logit, probit, and other nonlinear models

Abstract

Strategic management has seen numerous studies analyzing interaction terms in nonlinear models since Hoetker's (Strat Mgmt J., 2007,28(4), 331–343) best-practice recommendations and Zelner's (Strat Mgmt J.,2009,30(12), 1335–1348) simulation-based approach. We suggest an alternative recentering approach to assess the statistical and economic importance of interaction terms in nonlinear models. Our approach does not rely on making assumptions about the values of the control variables; it takes the existing model and data as is and requires fewer computational steps. The recentering approach not only provides a consistent answer about statistical meaningfulness of the interaction term at a given point of interest, but also helps to assess the effect size using the template that we offer in this study. We demonstrate how to implement our approach and discuss the implications for strategy researchers. In industry settings, the relationship between multiple corporate strategy-related inputs and corporate performance is often nonlinear in nature. Furthermore, such relationships tend to vary for different types of firms represented within the broader population of firms in a given industry. It is thus imperative for managers to know how to take nonlinear relationships between related business factors into account when they make strategic decisions. We suggest a simple and easily implementable way of assessing and interpreting interactions in a nonlinear setting, which we term a recentering approach. We demonstrate how to apply our approach to a strategic management setting.