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Cited by 6,479Psychometric Methodologies and TestingTechnology Adoption and User BehaviourStatistical Methods and Applications

A Partial Least Squares Latent Variable Modeling Approach for Measuring Interaction Effects: Results from a Monte Carlo Simulation Study and an Electronic-Mail Emotion/Adoption Study

Chin, Wynne W., Marcolin, Barbara L., Newsted, Peter R.·2003·Information Systems Research
DOI 10.1287/isre.14.2.189.16018
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Abstract

The ability to detect and accurately estimate the strength of interaction effects are critical issues that are fundamental to social science research in general and IS research in particular. Within the IS discipline, a significant percentage of research has been devoted to examining the conditions and contexts under which relationships may vary, often under the general umbrella of contingency theory (cf. McKeen et al. 1994, Weill and Olson 1989). In our survey of such studies, the majority failed to either detect or provide an estimate of the effect size. In cases where effect sizes are estimated, the numbers are generally small. These results have led some researchers to question both the usefulness of contingency theory and the need to detect interaction effects (e.g., Weill and Olson 1989). This paper addresses this issue by providing a new latent variable modeling approach that can give more accurate estimates of interaction effects by accounting for the measurement error that attenuates the estimated relationships. The capacity of this approach at recovering true effects in comparison to summated regression is demonstrated in a Monte Carlo study that creates a simulated data set in which the underlying true effects are known. Analysis of a second, empirical data set is included to demonstrate the technique's use within IS theory. In this second analysis, substantial direct and interaction effects of enjoyment on electronic-mail adoption are shown to exist.

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  • PLS has few restrictions on distributional assumptions and sample size.

    “Chin, Marcolin, and Newsted (2003) noted that PLS has minimal restrictions in terms of distributional assumptions and sample size.”

    From Technology Acceptance Model 3 and a Research Agenda on Interventions (2008) · cites this paper as Chin, Marcolin, and Newsted (2003)

  • PLS is considered appropriate because it allows researchers to simultaneously assess measurement model parameters and structural path coefficients, and allows both formative and reflective constructs to be tested together.

    “PLS is considered appropriate as it allows researchers to simultaneously assess measurement model parameters and structural path coefficients. Furthermore, it allows both formative and reflective constructs to be tested together (Chin et al., 2003).”

    From Individual level culture influence on online consumer iTrust aspects towards purchase intention across cultures: A S-O-R model (2015) · cites this paper as Chin et al., 2003

  • The PLS approach to SEM minimises the demands on measurement scales, sample size and residual distributions and simultaneously models structural and measurement paths with the main objective of prediction.

    “The PLS approach to SEM minimises the demands on measurement scales, sample size and residual distributions (Chin, 1998) and simultaneously models the structural paths (the theoretical relationships among latent variables) and measurement paths (relationships between a latent variable and its indicators) with the main objective of prediction (Chin et al., 2003).”

    From Examining the antecedents of the technology acceptance model within e-procurement (2018) · cites this paper as Chin et al., 2003

  • Past studies have found that the size of the interaction effect was generally small in most empirical studies, and that an effect size of 0.02 is regarded as an optimistic interaction effect.

    “Past studies have found that the size of the interaction effect was generally small in most empirical studies (Chin et al. 2003; Weill and Olson 1989) and that an effect size of 0.02 is regarded as an optimistic interaction effect (Aguinis 2004).”

    From Trust, Satisfaction, and Online Repurchase Intention: The Moderating Role of Perceived Effectiveness of E-Commerce Institutional Mechanisms1 (2014) · cites this paper as Chin et al. 2003

  • Even small interaction effects using the product-indicator approach can suggest important moderating relationships.

    “Whilst even small interaction effects using the productindicator approach can suggest important moderating relationships (Chin et al. 2003; Lowry et al. 2009)”

    From Trust, Satisfaction, and Online Repurchase Intention: The Moderating Role of Perceived Effectiveness of E-Commerce Institutional Mechanisms1 (2014) · cites this paper as Chin et al. 2003

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Authors
Chin, W. W., Marcolin, B. L., & Newsted, P. R.
Affiliations
  • University of Houston (US)
  • University of Calgary (CA)
  • Athabasca University (CA)
ORCID
Chin, Wynne W.
Venue
Information Systems Research
Publisher
Institute for Operations Research and the Management Sciences (INFORMS)
Published
2003-06
Volume
14
Issue
2
Pages
189-217
ISSN
  • 1047-7047 (ISSN-L)
  • 1526-5536
DOI
10.1287/isre.14.2.189.16018
OpenAlex
W2098685541
Cited by
6,479
References
130
Citations by year
2026: 201 · 2025: 317 · 2024: 372 · 2023: 382 · 2022: 464 · 2021: 446 · 2020: 376 · 2019: 372 · 2018: 335 · 2017: 338 · 2016: 350 · 2015: 392 · 2014: 355 · 2013: 349 · 2012: 308
FWCI
68.17
Type
journal article
Language
en
Open access
closed
Locations
Information Systems Research (publishedVersion)
Topics
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
Keywords
interaction effects, contingency theory, effect size estimation, enjoyment, information systems theory

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