How to Specify, Estimate, and Validate Higher-Order Constructs in PLS-SEM

By Marko Sarstedt, Joseph F. Hair, Jun-Hwa (Jacky) Cheah, Jan-Michael Becker, Christian M. Ringle. Australasian Marketing Journal, 2019

2,911 citations recorded by the source corpus.

Higher-order constructs, which facilitate modeling a construct on a more abstract higher-level dimension and its more concrete lower-order subdimensions, have become an increasingly visible trend in applications of partial least squares structural equation modeling (PLS-SEM). Unfortunately, researchers frequently confuse the specification, estimation, and validation of higher-order constructs, for example, when it comes to assessing their reliability and validity. Addressing this concern, this paper explains how to evaluate the results of higher-order constructs in PLS-SEM using the repeated indicators and the two-stage approaches, which feature prominently in applied social sciences research. Focusing on the reflective-reflective and reflective-formative types of higher-order constructs, we use the well-known corporate reputation model example to illustrate their specification, estimation, and validation. Thereby, we provide the guidance that scholars, marketing researchers, and practitioners need when using higher-order constructs in their studies.

Subfields: Marketing, Organizational Behavior and Human Resource Management, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/dc7ede8a5c5f0c500e6f9eee9ea12748daee4655

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Citations in the corpus

Investigating the role of social media marketing on value co-creation and engagement: An empirical study in China and Hong Kong (2020, methodology): “…Second, PLS-SEM supports the simultaneous analysis of a conceptual model with both reflective-reflective and reflective-formative hierarchical constructs (Sarstedt et al., 2019); Third, PLS-SEM is suitable for studies with smaller sample sizes (e.g. ⩽ 500) (Hair et al., 2017; Leung et al., 2019).”
Assessing measurement model quality in PLS-SEM using confirmatory composite analysis (2020, methodology): “Additionally, Franke and Sarstedt (2019) recently recommended additional significance testing that includes confidence intervals to further assess HTMT ratios and discriminant validity.”
Impact of Green Training on Environmental Performance through Mediating Role of Competencies and Motivation (2021, methodology): “Next, assessment of the Structural Model was performed by examining the values of Coefficient of Determination (R2), path coefficients and Predictive Relevance (Q2) and effect size (f2) [63].”