The DOI matches its Crossref record; 129 of the 130 references it cites were found in a registry or an index.
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
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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“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)
In the citing paper
“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
In the citing paper
“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
In the citing paper
“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
In the citing paper
“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
In the citing paper
“The commonly held reason for low effect size is the low reliability of the product terms (Cohen et al. 2003; Jaccard and Turrisi 2003) not the importance of theoretical model 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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Metadata
- 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
- Published
- 2003-06
- Volume
- 14
- Issue
- 2
- Pages
- 189-217
- ISSN
- 1047-7047 (ISSN-L)
- 1526-5536
- OpenAlex
- W2098685541
- Cited by
- 6,479
- References
- 130
- Citations by year
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- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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