Inferential accountability and theory corroboration: rethinking empirical claims

Purpose This paper aims to interrogate a widely accepted assumption in organizational research and related applied social science domains: that the routine use of structural equation modeling (SEM) constitutes rigorous theory testing. It argues that, in much SEM-dominated scholarship, statistical model confirmation is often interpreted as theory corroboration, producing an illusion of theory testing that undermines explanatory rigor and limits cumulative knowledge development. To address this, the paper advances Inferential accountability as a unifying framework that clarifies the conditions under which empirical evidence can legitimately support theoretical claims. Design/methodology/approach This paper adopts a conceptual and inferential approach. Drawing on the philosophy of science, causal inference and methodological debates surrounding SEM, it analytically distinguishes model confirmation from theory corroboration and examines how dominant SEM evaluation practices systematically enable inferential overreach. Findings The analysis shows that common SEM practices, reliance on global fit indices, path significance, mediation testing and higher-order constructs, establish statistical compatibility but rarely provide sufficient inferential leverage to corroborate theoretical explanations. As a result, SEM-based research often accumulates structurally similar models, proliferates abstract constructs, stabilizes dominant theoretical narratives and advances inflated generalization claims without corresponding explanatory refinement. Research limitations/implications The paper advances Inferential accountability as a corrective orientation for SEM-based theory testing. Inferential accountability reframes theory testing as a disciplined inferential practice requiring explicit theoretical constraints, engagement with plausible alternatives, independent articulation of mechanisms and calibration of claims to evidentiary leverage. This perspective clarifies what SEM can and cannot establish, redirecting cumulative theory development from model replication toward explanatory discrimination. Practical implications For authors, the framework encourages treating theory as a source of constraints rather than as a post hoc justification for estimable models. For reviewers and editors, it provides principled criteria for evaluating theoretical contribution beyond statistical adequacy, helping distinguish technically competent studies that merely confirm models from those that meaningfully advance theory. This perspective is particularly relevant for management and organizational research, where SEM-based evidence frequently informs claims regarding leadership, capabilities, resilience, innovation, sustainability and organizational performance. Originality/value Rather than critiquing SEM as a technique, the paper repositions it within an inferential framework that restores theoretical risk, transparency and accountability. By shifting attention from methodological sophistication to inferential responsibility, the paper offers a field-level intervention with implications for how theory testing, evaluation and cumulative progress are understood in SEM-dominated literatures.