The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Beyond a tandem analysis of SEM and PROCESS: Use of PLS-SEM for mediation analyses!
Open accessAbstract
Mediation and conditional process analyses have become popular approaches for examining the mechanisms by which effects operate and the factors that influence them. To estimate mediation models, researchers often augment their structural equation modeling (SEM) analyses with additional regression analyses using the PROCESS macro. This duality is surprising considering that research has long acknowledged the limitations of regression analyses when estimating models with latent variables. In this article, we argue that much of the confusion regarding SEM’s efficacy for mediation analyses results from a singular focus on factor-based methods, and there is no need for a tandem use of SEM and PROCESS. Specifically, we highlight that composite-based SEM methods overcome the limitations of both regression and factor-based SEM analyses when estimating even highly complex mediation models. We further conclude that composite-based SEM methods such as partial least squares (PLS-SEM) are the preferred and superior approach when estimating mediation and conditional process models, and that the PROCESS approach is not needed when mediation is examined with PLS-SEM.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003) · Information Systems Research · doi:10.1287/isre.14.2.189.16018 · Cited by 6,479
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-citationPredictive model assessment in PLS-SEM: guidelines for using PLSpredict
Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019) · European Journal of Marketing · doi:10.1108/ejm-02-2019-0189 · Cited by 4,618
Self-citationCommon Beliefs and Reality About PLS: Comments on Rönkkö and Evermann (2013)
Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., Ketchen, D. J., Hair, J. F., Hult, G. T. M., & Calantone, R. J. (2014) · Organizational Research Methods · doi:10.1177/1094428114526928 · Cited by 3,332
Self-citationHair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., & Thiele, K. O. (2017) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-017-0517-x · Cited by 2,712
Self-citationSpencer, S. J., Zanna, M. P., & Fong, G. T. (2005) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.89.6.845 · Cited by 2,452
Rethinking some of the rethinking of partial least squares
Hair, J. F., Sarstedt, M., & Ringle, C. M. (2019) · European Journal of Marketing · doi:10.1108/ejm-10-2018-0665 · Cited by 1,839
Self-citationIacobucci, D., Saldanha, N., & Deng, X. (2007) · Journal of Consumer Psychology · doi:10.1016/s1057-7408(07)70020-7 · Cited by 1,362
Aguinis, H., Beaty, J. C., Boik, R. J., & Pierce, C. A. (2005) · Journal of Applied Psychology · doi:10.1037/0021-9010.90.1.94 · Cited by 1,216 · cited 1×
“Another issue is the interaction effects are typically small compared with the direct effects, thereby reducing the statistical power to detect interactions (Aguinis et al., 2005; Murphy & Russell, 2017).”
BackgroundTesting Interaction Effects in LISREL: Examination and Illustration of Available Procedures
Cortina, J. M., Chen, G., & Dunlap, W. P. (2001) · Organizational Research Methods · doi:10.1177/109442810144002 · Cited by 512
Cited by
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
Self-citationHair, J., & Alamer, A. (2022) · Research Methods in Applied Linguistics · doi:10.1016/j.rmal.2022.100027 · Cited by 2,833
Self-citationPartial Least Squares Structural Equation Modeling
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2022) · Handbook of Market Research · doi:10.1007/978-3-319-57413-4_15 · Cited by 1,483
Self-citationPartial Least Squares Structural Equation Modeling
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2021) · Handbook of Market Research · doi:10.1007/978-3-319-05542-8_15-2 · Cited by 876
Self-citationExecuting and interpreting applications of PLS-SEM: updates for family business researchers
Hair, J. F., Astrachan, C. B., Moisescu, O. I., Radomir, L., Sarstedt, M., Vaithilingam, S., & Ringle, C. M. (2021) · Journal of Family Business Strategy · doi:10.1016/j.jfbs.2020.100392 · Cited by 860
Self-citationImproving PLS-SEM Use for Business Marketing Research
Guenther, P., Guenther, M., Ringle, C. M., Zaefarian, G., & Cartwright, S. (2023) · Industrial Marketing Management · doi:10.1016/j.indmarman.2023.03.010 · Cited by 800
Self-citationRasoolimanesh, S. M., Ringle, C. M., Sarstedt, M., & Olya, H. (2021) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-10-2020-1164 · Cited by 536
Self-citationSaif, N., Khan, S. U., Shaheen, I., ALotaibi, F. A., Alnfiai, M. M., & Arif, M. (2024) · Computers in Human Behavior · doi:10.1016/j.chb.2023.108097 · Cited by 301
Social media influencer marketing: the moderating role of materialism
Koay, K. Y., Cheung, M. L., Soh, P. C.-H., & Teoh, C. W. (2022) · European Business Review · doi:10.1108/ebr-02-2021-0032 · Cited by 139
Anasori, E., De Vita, G., & Gürkan Küçükergin, K. (2023) · The Service Industries Journal · doi:10.1080/02642069.2022.2147514 · Cited by 77
University of South Alabama, Howard, M. C., Hair, J. F., & University of South Alabama (2023) · AIS Transactions on Human-Computer Interaction · doi:10.17705/1thci.00084 · Cited by 27
Self-citationMuljana, P. S., Dabas, C. S., & Luo, T. (2023) · Journal of Research on Technology in Education · doi:10.1080/15391523.2021.1936703 · Cited by 24
Hu, X., Xu, X., & Chen, C. (. (2025) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2025.2495845 · Cited by 14
Tang, Y., & He, W. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1668567 · Cited by 12
Evaluating the Reality and Prospects of IPSAS Implementation in Algeria
Benzine, W. (2024) · Finance, Accounting and Business Analysis · doi:10.37075/faba.2024.2.03 · Cited by 2
Metadata
- Authors
- Sarstedt, M., Hair, J. F., Nitzl, C., Ringle, C. M., & Howard, M. C.
- Venue
- International Journal of Market Research
- Publisher
- SAGE Publications
- Published
- 2020-04-26
- Volume
- 62
- Issue
- 3
- Pages
- 288-299
- ISSN
- 1470-7853 (ISSN-L)
- 2515-2173
- OpenAlex
- W3018428404
- Cited by
- 898
- References
- 10
- FWCI
- 53.54
- Type
- journal article
- Language
- en
- Open access
- green
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 ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
