genpathmox: An R Package to Tackle Numerous Categorical Variables and Heterogeneity in Partial Least Squares Structural Equation Modeling

Partial least squares structural equation modeling (PLS-SEM), combined with the analysis of the effects of categorical variables after estimating the model, is a well-established statistical approach to the study of complex relationships between variables. However, the statistical methods and software packages available are limited when we are interested in assessing the effects of several categorical variables and shaping different groups following different models. Following the framework established by Lamberti et al. (2016), we have developed the  genpathmox R package for handling a large number of categorical variables when faced with heterogeneity in PLS-SEM. The package has functions for various aspects of the analysis of heterogeneity in PLS-SEM models, including estimation, visualization, and hypothesis testing. In this paper, we describe the implementation of genpathmox in detail and demonstrate its usefulness by analyzing employee satisfaction data.

Giuseppe Lamberti, (Department of Business,)
2023-11-01

0.1 Supplementary materials

Supplementary materials are available in addition to this article. It can be downloaded at RJ-2023-051.zip

J. M. Becker, J. H. Cheah, R. Gholamzade, C. M. Ringle and M. Sarstedt. PLS-SEM’s most wanted guidance. International Journal of Contemporary Hospitality Management, 2022. URL https://doi.org/10.1108/IJCHM-04-2022-0474.
W. W. Chin and J. Dibbern. An introduction to a permutation based procedure for multi-group PLS analysis: Results of tests of differences on simulated data and a cross cultural analysis of the sourcing of information system services between germany and the USA. In Handbook of partial least squares, pages. 171–193 2010. Berlin Heidelberg: Springer. URL https://doi.org/10.1007/978-3-540-32827-8_8. ISBN 978-3-540-32825-4.
G. C. Chow. Tests of equality between sets of coefficients in two linear regressions. Econometrica: Journal of the Econometric Society, 591–605, 1960. URL https://doi.org/10.2307/1910133.
T. K. Dijkstra and J. Henseler. Consistent partial least squares path modeling. MIS quarterly, 39(2): 297–316, 2015. URL https://www.jstor.org/stable/26628355.
V. Esposito Vinzi, L. Trinchera, S. Squillacciotti and M. Tenenhaus. REBUS-PLS: A response-based procedure for detecting unit segments in PLS path modelling. Applied Stochastic Models in Business and Industry, 24(5): 439–458, 2008. URL https://doi.org/10.1002/asmb.728.
J. Evermann and M. Rönkkö. Recent developments in PLS. Communications of the Association for Information Systems, 44: 123–132, 2021. URL https://doi.org/10.17705/1CAIS.044XX.
J. F. Hair Jr, G. T. M. Hult, C. M. Ringle, M. Sarstedt, N. P. Danks and S. Ray. Partial least squares structural equation modeling (PLS-SEM) using r: A workbook. Los Angeles: Springer Nature, 2021. URL https://doi.org/10.1007/978-3-030-80519-7. ISBN 9783030805197.
J. F. Hair Jr, G. T. M. Hult, C. Ringle and M. Sarstedt. A primer on partial least squares structural equation modeling (PLS-SEM). Los Angeles: saGe publications, 2016. URL https://doi.org/10.1007/978-3-030-80519-7. ISBN 978-1-5443-9640-8.
J. F. Hair Jr, L. M. Matthews, R. L. Matthews and M. Sarstedt. PLS-SEM or CB-SEM: Updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2): 107–123, 2017a. URL https://doi.org/10.1504/IJMDA.2017.087624.
J. F. Hair Jr, J. J. Risher, M. Sarstedt and C. M. and Ringle. When to use and how to report the results of PLS-SEM. European business review, 31(1): 2–24, 2019. URL https://doi.org/10.1108/EBR-11-2018-0203.
J. F. Hair Jr, M. Sarstedt, C. M. Ringle and S. P. Gudergan. Advanced issues in partial least squares structural equation modeling. Los Angeles: saGe publications, 2017b. ISBN 9781483377391.
J. Henseler. Composite-based structural equation modeling: Analyzing latent and emergent variables. New York: Guilford Publications, 2020. ISBN 9781462545605.
J. Henseler, C. M. Ringle and M. Sarstedt. Testing measurement invariance of composites using partial least squares. International marketing review, 3(3): 405–431, 2016. URL https://doi.org/10.1108/IMR-09-2014-0304.
J. Henseler, C. M. Ringle and R. R. Sinkovics. The use of partial least squares path modeling in international marketing. In New challenges to international marketing, pages. 277–319 2009. Bingley: Emerald Group Publishing Limited. URL https://doi.org/10.1108/S1474-7979(2009)0000020014. ISBN 978-1-84855-468-9.
M. Keil, B. C. Tan, K. K. Wei, T. Saarinen, V. Tuunainen and A. Wassenaar. A cross-cultural study on escalation of commitment behavior in software projects. MIS quarterly, 299–325, 2000. URL https://doi.org/10.2307/3250940.
M. Klesel, F. Schuberth, J. Henseler and B. Niehaves. A test for multigroup comparison using partial least squares path modeling. Internet research, 29(3): 464–477, 2019. URL https://doi.org/10.1108/IntR-11-2017-0418.
M. Klesel, F. Schuberth, B. Niehaves and J. Henseler. Multigroup analysis in information systems research using PLS-PM: A systematic investigation of approaches. ACM SIGMIS Database: the DATABASE for Advances in Information Systems, 53(3): 26–48, 2022. URL https://doi.org/10.1145/3551783.3551787.
T. Kollmann, C. Stöckmann, J. M. Kensbock and A. Peschl. What satisfies younger versus older employees, and why? An aging perspective on equity theory to explain interactive effects of employee age, monetary rewards, and task contributions on job satisfaction. Human Resource Management, 59(1): 101–115, 2020. URL https://doi.org/10.1002/hrm.21981.
G. Lamberti. Genpathmox: Pathmox approach segmentation tree analysis. 2022. URL https://CRAN.R-project.org/package=genpathmox. R package version 0.9.
G. Lamberti. Hybrid multigroup partial least squares structural equation modelling: An application to bank employee satisfaction and loyalty. Quality \(\&\) Quantity, 1–23, 2021. URL https://doi.org/10.1007/s11135-021-01096-9.
G. Lamberti, T. Aluja Banet and J. Rialp Criado. Work climate drivers and employee heterogeneity. The International Journal of Human Resource Management, 33(3): 472–504, 2020. URL https://doi.org/10.1080/09585192.2020.1711798.
G. Lamberti, T. B. Aluja and G. Sanchez. The pathmox approach for PLS path modeling segmentation. Applied Stochastic Models in Business and Industry, 32(4): 453–468, 2016. URL https://doi.org/10.1002/asmb.2168.
G. Lamberti, T. Banet Aluja and G. Sanchez. The pathmox approach for PLS path modeling: Discovering which constructs differentiate segments. Applied Stochastic Models in Business and Industry, 33(6): 674–689, 2017. URL https://doi.org/10.1002/asmb.2270.
L. Lebart, A. Morineau and J. P. Fenelon. Traitement des donnees statistiques. Paris: Dunod, 1979. ISBN 10. 2040107878.
W. Y. Loh and Y. S. Shih. Split selection methods for classification trees. Statistica sinica, 815–840, 1997. URL https://www.jstor.org/stable/24306157.
M. E. Rademaker and F. Schuberth. cSEM: Composite-based structural equation modeling. 2020. URL https://CRAN.R-project.org/package=cSEM. Package version: 0.4.0.
S. Ray, N. P. Danks and A. C. Valdez. Seminr: Building and estimating structural equation models. 2020. URL https://CRAN.R-project.org/package=seminr. Package version: 0.4.0.
M. Rönkkö. Matrixpls: Matrix-based partial least squares estimation. 2017. URL https://github.com/mronkko/matrixpls. R package version 1.0.5.
Y. Rosseel. Lavaan: An r package for structural equation modeling. Journal of Statistical Software, 48(2): 1–36, 2012. URL https://doi.org/10.18637/jss.v048.i02.
G. Sanchez and T. Aluja. Pathmox: A PLS-PM segmentation algorithm. Proceedings of KNEMO, 69: 2006.
G. Sanchez, L. Trinchera and G. Russolillo. Plspm: Tools for partial least squares path modeling (PLS-PM). 2015. URL https://github.com/gastonstat/plspm. R package version 0.4.9.
M. Sarstedt, J. F. Hair, M. Pick, B. D. Liengaard, L. Radomir and C. M. Ringle. Progress in partial least squares structural equation modeling use in marketing research in the last decade. Psychology \(\&\) Marketing, 39(5): 1035–1064, 2022a. URL https://doi.org/10.1002/mar.21640.
M. Sarstedt, J. F. Hair Jr and C. M. Ringle. PLS-SEM: Indeed a silver bullet–retrospective observations and recent advances. Journal of Marketing Theory and Practice, 1–15, 2022b. URL https://doi.org/10.1080/10696679.2022.2056488.
M. Sarstedt, L. Radomir, O. I. Moisescu and C. M. Ringle. Latent class analysis in PLS-SEM: A review and recommendations for future applications. Journal of Business Research, 138: 398–407, 2022c. URL https://doi.org/10.1016/j.jbusres.2021.08.051.
G. Shmueli, S. Ray, J. M. V. Estrada and S. B. Chatla. The elephant in the room: Predictive performance of PLS models. Journal of Business Research, 69(10): 4552–4564, 2016. URL https://doi.org/10.1016/j.jbusres.2016.03.049.
G. Shmueli, M. Sarstedt, J. F. Hair, J. H. Cheah, H. Ting, S. Vaithilingam and C. M. Ringle. Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. European journal of marketing, 53(11): 2322–2347, 2019. URL https://doi.org/10.1108/EJM-02-2019-0189.
K. Soetaert. Diagram: Functions for visualising simple graphs (networks), plotting flow diagrams. 2020. URL https://CRAN.R-project.org/package=diagram. R package version 1.6.5.
H. Wold. Partial least squares. In Encyclopedia of statistical sciences, pages. 581–591 1985. John Wiley; Sons, Ltd. URL https://doi.org/10.1002/0471667196.ess1914.pub2.

References

Reuse

Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".

Citation

For attribution, please cite this work as

Lamberti,, "genpathmox: An R Package to Tackle Numerous Categorical Variables and Heterogeneity in Partial Least Squares Structural Equation Modeling", The R Journal, 2023

BibTeX citation

@article{RJ-2023-051,
  author = {Lamberti,, Giuseppe},
  title = {genpathmox: An R Package to Tackle Numerous Categorical Variables and Heterogeneity in Partial Least Squares Structural Equation Modeling},
  journal = {The R Journal},
  year = {2023},
  note = {https://doi.org/10.32614/RJ-2023-051},
  doi = {10.32614/RJ-2023-051},
  volume = {15},
  issue = {2},
  issn = {2073-4859},
  pages = {294-308}
}

© The R Foundation, web page contact.