PLS-SEM and reflective constructs: A response to recent criticism and a constructive path forward

2025/05/19 by Peter Guenther, Miriam Guenther, Christian M. Ringle +2 · 38 citations
Business, Management and Accounting · Decision Sciences · Psychology · Social Sciences · #Complex Systems and Decision Making #Computer network #Computer science #Constructive #Constructive criticism #Criticism #Information Systems Theories and Implementation #Innovation and Knowledge Management #Law #Path (computing) #Political science #Process (computing) #Programming language #Psychology

paper · doi:10.1016/j.indmarman.2025.05.003

published in Industrial Marketing Management 128, 1-9 (Elsevier BV)

openalex publication_date 2025/05/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This article addresses criticisms asserting that reflective construct measurement and its associated evaluation criteria are unsuitable for partial least squares structural equation modeling (PLS-SEM). More specifically, critics contend that reflective measurement models correspond exclusively to common factor models, a premise that is both inaccurate and misleading. Reflective measurement models represent theoretically grounded and conceptualized constructs. Statistical methods such as common factor model estimation, composite model estimation, and sum score regression enable researchers to estimate method-specific proxies that serve as approximations for theoretically established conceptual constructs in empirical research. These proxies vary depending on the statistical models and assumptions inherent to each method. In this context, it is important to highlight that the use of reflective evaluation criteria is not restricted to common factor models. When applied to composite model estimation, it does not compromise the validity of the results. Moreover, this article advocates for embracing the complementary strengths of diverse SEM methods within a multimethod approach, rather than positioning one method in opposition to another. We believe that this contribution provides critical insights and guidance, fostering advancements in SEM methodology, and its practical applications.

Citations

• The Effect of Estimation Methods on SEM Fit Indices
• Improving PLS-SEM use for business marketing research
• CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting
• Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods
• The Use of Partial Least Squares Structural Equation Modeling in Strategic Management Research: A Review of Past Practices and Recommendations for Future Applications
• Rethinking Partial Least Squares Path Modeling: In Praise of Simple Methods
• An assessment of the use of partial least squares structural equation modeling in marketing research
• A Critical Review of Construct Indicators and Measurement Model Misspecification in Marketing and Consumer Research
• What is a statistical model?
• Index Construction with Formative Indicators: An Alternative to Scale Development
• Alternative Ways of Assessing Model Fit
• A Simple Theory of the Survey Response: Answering Questions versus Revealing Preferences
• Comparative fit indexes in structural models.
• Specification, evaluation, and interpretation of structural equation models

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• PLS-SEM and reflective constructs: a response to recent criticism and a constructive path forward
• Going beyond the untold facts in PLS–SEM and moving forward
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