Estimating and assessing second-order constructs using PLS-PM: the case of composites of composites

Abstract

Purpose

The purpose of this study is threefold: (1) to propose partial least squares path modeling (PLS-PM) as a way to estimate models containing composites of composites and to compare the performance of the PLS-PM approaches in this context, (2) to provide and evaluate two testing procedures to assess the overall fit of such models and (3) to introduce user-friendly step-by-step guidelines.

Design/methodology/approach

A simulation is conducted to examine the PLS-PM approaches and the performance of the two proposed testing procedures.

Findings

The simulation results show that the two-stage approach, its combination with the repeated indicators approach and the extended repeated indicators approach perform similarly. However, only the former is Fisher consistent. Moreover, the simulation shows that guidelines neglecting model fit assessment miss an important opportunity to detect misspecified models. Finally, the results show that both testing procedures based on the two-stage approach allow for assessment of the model fit.

Practical implications

Analysts who estimate and assess models containing composites of composites should use the authors’ guidelines, since the majority of existing guidelines neglect model fit assessment and thus omit a crucial step of structural equation modeling.

Originality/value

This study contributes to the understanding of the discussed approaches. Moreover, it highlights the importance of overall model fit assessment and provides insights about testing the fit of models containing composites of composites. Based on these findings, step-by-step guidelines are introduced to estimate and assess models containing composites of composites.

Citation

Schuberth, F., Rademaker, M.E. and Henseler, J. (2020), "Estimating and assessing second-order constructs using PLS-PM: the case of composites of composites", Industrial Management & Data Systems, Vol. 120 No. 12, pp. 2211-2241. https://doi.org/10.1108/IMDS-12-2019-0642