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Item type:Publication, Access status: with files , PLS-SEM and reflective constructs: a response to recent criticism and a constructive path forward(Elsevier, 2025-05-19); ; ; ; 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.Publicationtype: Review Article22 12 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Improving PLS-SEM use for business marketing research(2023-05); ; ; A review of studies published in Industrial Marketing Management over the past two decades and more shows that these studies not only used partial least squares structural equation modeling (PLS-SEM) widely to estimate and empirically substantiate theoretically established models with constructs, but did so increasingly. In line with their study goals, researchers provided reasons for using PLS-SEM (e.g., model complexity, limited sample size, and prediction). These reasons are frequently not fully convincing, requiring further clarification. Additionally, our review reveals that researchers' assessment and reporting of their measurement and structural models are insufficient. Certain tests and thresholds that they use are also inappropriate. Finally, researchers seldom apply more advanced PLS-SEM analytic techniques, although these can support the results' robustness and may create new insights. This paper addresses the issues by reviewing business marketing studies to clarify PLS-SEM's appropriate use. Furthermore, the paper provides researchers and practitioners in the business marketing field with a best practice orientation and describes new opportunities for using PLS-SEM. To this end, the paper offers guidelines and checklists to support future PLS-SEM applications.Publicationtype: Journal Article33