The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Using Partial Least Squares Structural Equation Modeling in Tourism Research: A Review of Past Research and Recommendations for Future Applications
Abstract
Although the number of empirical applications of partial least–squares structural equation modeling (PLS-SEM) in tourism has increased in the last two years, Assaker, Huang, and Hallak have conducted the only assessment on the use of PLS-SEM in four studies and with a limited number of criteria. Thus, this study aims to critically analyze how the PLS-SEM method has been applied in 44 articles published in 11 leading tourism journals from 2000 to 2014 in terms of four key criteria: (1) themes explored and main motivations for using PLS-SEM; (2) characteristics of proposed models; (3) how the models were evaluated; and (4) the use of more advanced analyses within the method. The findings revealed that although applications in tourism have improved in recent years, problematic aspects in the application of PLS-SEM in tourism research still exist. The article provides suggestions on how to improve the use of PLS-SEM in future tourism applications.
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
An assessment of the use of partial least squares structural equation modeling in marketing research
Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-011-0261-6 · Cited by 8,088
Modern Methods for Business Research
(1998) · doi:10.4324/9781410604385 · Cited by 6,534
An empirical comparison of the efficacy of covariance-based and variance-based SEM
Reinartz, W., Haenlein, M., & Henseler, J. (2009) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2009.08.001 · Cited by 2,906
Ringle, C. M., Sarstedt, M., & Straub, D. W. (2012) · MIS Quarterly · doi:10.2307/41410402 · Cited by 2,184
Sarstedt, M., Henseler, J., & Ringle, C. M. (2011) · Advances in International Marketing · doi:10.1108/s1474-7979(2011)0000022012 · Cited by 1,326
Grewal, R., Cote, J. A., & Baumgartner, H. (2004) · Marketing Science · doi:10.1287/mksc.1040.0070 · Cited by 1,228
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
An updated and expanded assessment of PLS-SEM in information systems research
Hair, J., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. L. (2017) · Industrial Management & Data Systems · doi:10.1108/imds-04-2016-0130 · Cited by 4,043
Partial 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
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
Partial 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
Metadata
- Authors
- do Valle, P. O., & Assaker, G.
- Venue
- Journal of Travel Research
- Publisher
- SAGE Publications
- Published
- 2015-02-23
- Volume
- 55
- Issue
- 6
- Pages
- 695-708
- ISSN
- 0047-2875 (ISSN-L)
- 1552-6763
- OpenAlex
- W1970571431
- Cited by
- 388
- References
- 6
- FWCI
- 29.93
- Type
- journal article
- Language
- en
- Open access
- closed
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.