In defense of diversity in theory-building approaches

The complexity of organizations and myriad ways in which they engage with their environments necessitates using our entire arsenal of approaches to theory-building. We respond to Arend's (2023) commentary on Chen, Elfenbein, Posen, and Wang (2023), in which he questions the appropriateness of computational models and critiques our model for delivering too little realism. Building on prior work in this journal, we argue that all theory-building approaches embody trade-offs across generalizability, simplicity, and accuracy. Because of these trade-offs, a diversity in theory-building approaches is required, and attempts to circumscribe theory-development methods should be rejected. We examine two additional aspects of Arend’s commentary. First, we disagree with the claim that computational models are the right tool only “when data do not exist.” This claim confuses the theory-building purpose of computational models with the theory-testing purpose of empirical analysis. Second, we point out that computational and analytical methods are merely different ways to solve formal models, made necessary by differing trade-offs in model design. While our field benefits from criticism and debates, proscribing certain theory-development approaches is unlikely to improve management scholarship. We encourage scholars to continue to use computational models to broaden our perspectives on the complex phenomena in management scholarship.