WHAT SURVEY-BASED STRUCTURAL MODELS IN MARKETING ACTUALLY ESTABLISH: AN INFERENCE LADDER AND DESIGN-ALIGNED CLAIMS

Abstract

Purpose: Survey-based structural equation modelling (SEM) has become a widely used empirical approach in marketing research. This article clarifies the inferential limitations of such studies, identifies where conclusions may extend beyond what the underlying research design can support, and specifies the additional design features required to substantiate stronger causal, behavioural, and managerial claims. Design/methodology/approach: The article develops a conceptual and methodological framework rather than estimating a new empirical model. Inference is organised into six levels, ranging from observed survey responses to managerial recommendations. Conventional measurement and structural model statistics are mapped to the types of claims they can legitimately support. Four progressively more demanding research designs are also compared in terms of their inferential scope, principal limitations, and relative implementation costs. Findings: Cross-sectional questionnaire data can establish patterns in observed responses, provide evidence regarding measurement properties, and identify model-conditional associations among constructs. However, these findings do not, on their own, establish causal direction, demonstrate that stated intentions translate into observed behaviour, or confirm that managerial interventions targeting a particular antecedent will improve an outcome. Reliability and discriminant validity statistics address important aspects of measurement quality, but they are insufficient, in isolation, to establish the substantive meaning or causal status of a construct. The central methodological concern is therefore not the use of survey-based SEM itself, but the potential misalignment between the strength of the claims made and the inferential capabilities of the underlying research design. Originality/value: The article shifts the methodological focus from estimator selection to alignment between research design and inferential claims. It introduces an operational ladder of inference for authors, reviewers, and doctoral researchers; links commonly reported SEM statistics to the levels of inference they can support; and demonstrates how targeted improvements in research design can strengthen the defensibility of conclusions without overstating the evidence provided by the data.