The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Mediation analysis in partial least squares path modeling: Helping researchers discuss more sophisticated models
Open accessAbstract
Purpose: Indirect or mediated effects constitute a type of relationship between constructs that often occurs in partial least squares (PLS) path modeling. Over the past few years, the methods for testing mediation have become more sophisticated. However, many researchers continue to use outdated methods to test mediating effects in PLS, which can lead to erroneous results. One reason for the use of outdated methods or even the lack of their use altogether is that no systematic tutorials on PLS exist that draw on the newest statistical findings. The paper aims to discuss these issues. Design/methodology/approach: This study illustrates the state-of-the-art use of mediation analysis in the context of PLS-structural equation modeling (SEM). Findings: This study facilitates the adoption of modern procedures in PLS-SEM by challenging the conventional approach to mediation analysis and providing more accurate alternatives. In addition, the authors propose a decision tree and classification of mediation effects. Originality/value: The recommended approach offers a wide range of testing options (e.g. multiple mediators) that go beyond simple mediation analysis alternatives, helping researchers discuss their studies in a more accurate way.
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Cites
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SPSS and SAS procedures for estimating indirect effects in simple mediation models
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Asymptotic Confidence Intervals for Indirect Effects in Structural Equation Models
Sobel, M. E. (1982) · Sociological Methodology · doi:10.2307/270723 · Cited by 12,810
Reconsidering Baron and Kenny: myths and truths about mediation analysis
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Beyond Baron and Kenny: Statistical Mediation Analysis in the New Millennium
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An assessment of the use of partial least squares structural equation modeling in marketing research
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Cited by
When to Use and How to Report the Results of PLS-SEM
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019) · European Business Review · doi:10.1108/ebr-11-2018-0203 · Cited by 27,541
Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook
Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021) · Classroom Companion: Business · doi:10.1007/978-3-030-80519-7 · Cited by 10,343
Assessing measurement model quality in PLS-SEM using confirmatory composite analysis
Hair, J. F., Howard, M. C., & Nitzl, C. (2020) · Journal of Business Research · doi:10.1016/j.jbusres.2019.11.069 · Cited by 5,446
Self-citationPartial least squares structural equation modeling
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2017) · Handbook of Market Research · doi:10.1007/978-3-319-05542-8_15-1 · Cited by 3,285
Partial least squares structural equation modeling in HRM research
Ringle, C. M., Sarstedt, M., Mitchell, R., & Gudergan, S. P. (2020) · The International Journal of Human Resource Management · doi:10.1080/09585192.2017.1416655 · Cited by 1,943
Metadata
- Authors
- Nitzl, C., Roldan, J. L., & Cepeda, G.
- Venue
- Industrial Management & Data Systems
- Publisher
- Emerald
- Published
- 2016-10-17
- Volume
- 116
- Issue
- 9
- Pages
- 1849-1864
- ISSN
- 0263-5577 (ISSN-L)
- OpenAlex
- W2417760852
- Cited by
- 2,318
- References
- 19
- FWCI
- 91.60
- Type
- journal article
- Language
- en
- Open access
- green
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