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

TLDR

Higher‑order constructs in PLS‑SEM are increasingly used to model abstract dimensions and their sub‑dimensions, but researchers often mis‑specify, estimate, or validate them. This paper explains how to evaluate higher‑order constructs in PLS‑SEM using repeated‑indicator and two‑stage approaches. The authors illustrate the reflective‑reflective and reflective‑formative specifications with the corporate reputation model. The guidance equips scholars, marketing researchers, and practitioners to correctly specify, estimate, and validate higher‑order constructs.

Abstract

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.