Management research mainly relies on frequentist inference to develop and test theory. Yet, these approaches are limited by their dependence on null hypothesis significance testing, arbitrary p-values, and assumptions about infinite repeated samples. Bayesian techniques address some of these issues by directly modeling probability distributions of parameter estimates, but their reliance on subjective priors can limit theory-building, especially when extant evidence is scarce. Recognizing this need for an inferential technique that combines the accessibility of frequentist inference with the interpretive richness of Bayesian methods, we introduce Generalized Fiducial Inference (GFI) to management research. Grounded in Fisher’s fiducial argument, GFI constructs parameter distributions directly from observed data without requiring priors. As such, GFI provides both the clarity and familiarity of frequentist approaches, enabling researchers to assess whether relationships exist and the probabilistic nuance of Bayesian inference. This allows scholars to understand the likely distribution of these relationships. By removing the necessity of subjective priors, GFI facilitates theory development, improves interpretability, and encourages cumulative knowledge advancement. To demonstrate GFI’s utility, we present simulation studies and an empirical application to showcase its empirical application. Our findings and discussions highlight how GFI can enrich theoretical inferences and foster more robust, meaningful, and cumulative theory-building in management research.
