Finance

Machine learning

Physics

Philosophy

Sociology

Business

Mathematics education

Psychology

Power (physics)

Order (exchange)

Programming language

Pure mathematics

Partial least squares regression

Feature (linguistics)

Construct (python library)

Dimension (graph theory)

Mathematics

Social science

Computer science

Linguistics

Formative assessment

Reliability (semiconductor)

Reputation

Knowledge management

Quantum mechanics

Structural equation modeling

Australasian Marketing Journal (AMJ)

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.

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