Testing moderating effects in PLS path models with composite variables

Research output: Contribution to journalArticle

32 Citations (Scopus)
55 Downloads (Pure)

Abstract

Purpose - The purpose of this paper is to explain how to model moderating effects of composites using partial least squares (PLS) path modeling. It provides the methodological underpinning of moderating effects in general and describes the various approaches for forming the interaction term, i.e., the product indicator approach, the two-stage approach, and orthogonalization. Design/methodology/approach - The paper discusses the use of standardized vs unstandardized construct scores and introduces spotlight analysis as a useful way to report findings. Findings - Researchers should rely on unstandardized estimates when analyzing moderating effects. Centering or orthogonalization can help improve the interpretability of path coefficients. Practical implications - PLS software implementations should facilitate unstandardized estimates. Originality/value - This paper formulates step by step guidelines for analyzing moderating effects of composites using PLS path modeling. It is the first to propose spotlight analysis for PLS path modeling.

Original languageEnglish
Pages (from-to)1887-1900
Number of pages14
JournalIndustrial Management & Data Systems
Volume116
Issue number9
DOIs
Publication statusPublished - 2016

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Composite materials
Testing
Path model
Moderating effect
Partial least squares
Modeling
Software
Interaction terms
Design methodology
Coefficients

Keywords

  • Composite measurement
  • Moderation
  • Partial least squares

Cite this

@article{637d0386801d489e9650819e2eae5f47,
title = "Testing moderating effects in PLS path models with composite variables",
abstract = "Purpose - The purpose of this paper is to explain how to model moderating effects of composites using partial least squares (PLS) path modeling. It provides the methodological underpinning of moderating effects in general and describes the various approaches for forming the interaction term, i.e., the product indicator approach, the two-stage approach, and orthogonalization. Design/methodology/approach - The paper discusses the use of standardized vs unstandardized construct scores and introduces spotlight analysis as a useful way to report findings. Findings - Researchers should rely on unstandardized estimates when analyzing moderating effects. Centering or orthogonalization can help improve the interpretability of path coefficients. Practical implications - PLS software implementations should facilitate unstandardized estimates. Originality/value - This paper formulates step by step guidelines for analyzing moderating effects of composites using PLS path modeling. It is the first to propose spotlight analysis for PLS path modeling.",
keywords = "Composite measurement, Moderation, Partial least squares",
author = "Georg Fassott and J{\"o}rg Henseler and Coelho, {Pedro Sim{\~o}es}",
note = "Fassott, G., Henseler, J., & Coelho, P. S. (2016). Testing moderating effects in PLS path models with composite variables. Industrial Management & Data Systems, 116(9), 1887-1900. https://doi.org/10.1108/IMDS-06-2016-0248",
year = "2016",
doi = "10.1108/IMDS-06-2016-0248",
language = "English",
volume = "116",
pages = "1887--1900",
journal = "Industrial Management & Data Systems",
issn = "0263-5577",
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}

Testing moderating effects in PLS path models with composite variables. / Fassott, Georg; Henseler, Jörg; Coelho, Pedro Simões.

In: Industrial Management & Data Systems, Vol. 116, No. 9, 2016, p. 1887-1900.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Testing moderating effects in PLS path models with composite variables

AU - Fassott, Georg

AU - Henseler, Jörg

AU - Coelho, Pedro Simões

N1 - Fassott, G., Henseler, J., & Coelho, P. S. (2016). Testing moderating effects in PLS path models with composite variables. Industrial Management & Data Systems, 116(9), 1887-1900. https://doi.org/10.1108/IMDS-06-2016-0248

PY - 2016

Y1 - 2016

N2 - Purpose - The purpose of this paper is to explain how to model moderating effects of composites using partial least squares (PLS) path modeling. It provides the methodological underpinning of moderating effects in general and describes the various approaches for forming the interaction term, i.e., the product indicator approach, the two-stage approach, and orthogonalization. Design/methodology/approach - The paper discusses the use of standardized vs unstandardized construct scores and introduces spotlight analysis as a useful way to report findings. Findings - Researchers should rely on unstandardized estimates when analyzing moderating effects. Centering or orthogonalization can help improve the interpretability of path coefficients. Practical implications - PLS software implementations should facilitate unstandardized estimates. Originality/value - This paper formulates step by step guidelines for analyzing moderating effects of composites using PLS path modeling. It is the first to propose spotlight analysis for PLS path modeling.

AB - Purpose - The purpose of this paper is to explain how to model moderating effects of composites using partial least squares (PLS) path modeling. It provides the methodological underpinning of moderating effects in general and describes the various approaches for forming the interaction term, i.e., the product indicator approach, the two-stage approach, and orthogonalization. Design/methodology/approach - The paper discusses the use of standardized vs unstandardized construct scores and introduces spotlight analysis as a useful way to report findings. Findings - Researchers should rely on unstandardized estimates when analyzing moderating effects. Centering or orthogonalization can help improve the interpretability of path coefficients. Practical implications - PLS software implementations should facilitate unstandardized estimates. Originality/value - This paper formulates step by step guidelines for analyzing moderating effects of composites using PLS path modeling. It is the first to propose spotlight analysis for PLS path modeling.

KW - Composite measurement

KW - Moderation

KW - Partial least squares

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JF - Industrial Management & Data Systems

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