Comparative Analysis of SmartPLS, WarpPLS, and R Studio: Accuracy, Features, Usability, and Licensing

Fajar Hari Prasetyo, Dika Kurnia Edo Kisworo, Indra Bagus Mahardhika, Shoim Ghifari

Abstract


Partial Least Squares Structural Equation Modeling (PLS-SEM) is widely applied to analyze relationships among latent variables using different software tools. This study compared SmartPLS, WarpPLS, and R Studio from quantitative and qualitative perspectives. A synthetic dataset (N=100) with a simple reflective model (X1, X2→Y) was analyzed under equivalent settings, including reflective indicators and bootstrapping with 5,000 resamplings, to ensure structural equivalence and highlight algorithmic differences. Quantitative results showed consistent external loadings above 0.70, with small numerical deviations (overall MAD=0.039) and the largest variation in item Y4 (MaxDiff=0.087). Internal model estimates were stable, with minor differences in path coefficients (MaxDiff≤0.040) and larger variation in R² for R Studio (MaxDiff=0.098), reflecting differences in latent score calculations. Bootstrapping confirmed significance (T>1.96; p<0.05), though variability in T statistics was observed across software. Qualitatively, SmartPLS excelled in usability and visualization, WarpPLS in nonlinear analysis, and R Studio in flexibility and cost-effectiveness. Computationally, SmartPLS consumed the most memory, R Studio was moderate, and WarpPLS was most efficient, with all execution times under five seconds. These findings suggest that software choice should consider not only numerical accuracy but also usability, licensing, and computational efficiency to align with research objectives and user competencies.

Keywords


PLS-SEM; Comparison; SmartPLS; WarpPLS; R-Studio

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References


. J. F. Hair, G. T. M. Hult, C. M. Ringle, and A. Sarstedt, A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 2nd ed. Thousand Oaks, CA: SAGE, 2017. [Online]. Available: https://doi.org/10.15358/9783800653614

. F. Fajar, W. Warsito, and A. Sugiharto, “Pengembangan aplikasi analisis PLS-SEM berbasis R Shiny dan penerapan UTAUT2 untuk evaluasi penerimaan sistem informasi,” JST (Jurnal Sains dan Teknologi), vol. 13, no. 1, pp. 147–158, 2024. [Online]. Available: https://doi.org/10.23887/jstundiksha.v13i1.68568

. F. Ali, S. M. Rasoolimanesh, M. Sarstedt, C. M. Ringle, and K. Ryu, “An assessment of the use of partial least squares structural equation modeling (PLS-SEM) in hospitality research,” Int. J. Contemp. Hosp. Manag., vol. 30, no. 1, pp. 514–538, 2018. [Online]. Available: https://doi.org/10.1108/IJCHM-10-2016-0568

. M. Tenenhaus, V. E. Vinzi, Y.-M. Chatelin, and C. Lauro, “PLS path modeling,” Comput. Stat. Data Anal., vol. 48, no. 1, pp. 159–205, 2004. [Online]. Available: https://doi.org/10.1016/j.csda.2004.03.005

. G. Cepeda-Carrion, J. G. Cegarra-Navarro, and D. Jiménez-Jiménez, “The effect of absorptive capacity on innovation performance: Comparing PLS-SEM and CB-SEM,” Technol. Forecast. Soc. Change, vol. 150, 2019. [Online]. Available: https://doi.org/10.1016/j.techfore.2019.119762

. G. Cepeda and J. L. Roldán, “Using PLS-SEM in management research: A practical guide,” Eur. J. Manag. Bus. Econ., 2019. [Online]. Available: https://doi.org/10.1108/EJMBE-11-2018-0116

.

. J. M. Becker, A. Rai, C. M. Ringle, and F. Völckner, “Discovering unobserved heterogeneity in PLS path modeling,” MIS Q., vol. 37, no. 3, 2013. [Online]. Available: https://doi.org/10.25300/MISQ/2013/37.3.01

. J. Henseler, “Bridging design and behavioral research with variance-based SEM,” J. Advert., vol. 46, no. 1, 2017. [Online]. Available: https://doi.org/10.1080/00913367.2017.1281780

. M. A. Memon, J. H. Cheah, T. Ramayah, H. Ting, and F. Chuah, “Mediation analysis issues and recommendations,” J. Appl. Struct. Eq. Model., 2018. [Online]. Available: https://doi.org/10.47263/jasem.2(1)01

. E. E. Rigdon, “Choosing PLS path modeling as analytical method,” Eur. Manag. J., vol. 34, no. 6, pp. 598–605, 2016. [Online]. Available: https://doi.org/10.1016/j.emj.2016.05.006

. J. Benitez, J. Henseler, A. Castillo, and F. Schuberth, “How to perform and report an impactful analysis using PLS-SEM,” Inf. Manag., vol. 57, no. 2, pp. 103–120, 2020. [Online]. Available: https://doi.org/10.1016/j.im.2019.05.003

. N. Kock, “Common method bias in PLS-SEM,” Int. J. e-Collab., vol. 11, no. 4, pp. 1–10, 2015. [Online]. Available: https://doi.org/10.4018/ijec.2015100101

. G. Shmueli, S. Ray, J. M. V. Estrada, and S. B. Chatla, “The elephant in the room: Predictive performance of PLS models,” J. Bus. Res., vol. 69, pp. 4552–4564, 2016. [Online]. Available: https://doi.org/10.1016/j.jbusres.2016.03.049

. J. Cheah, M. Sarstedt, C. M. Ringle, and H. Ting, “A comparison of PLS-SEM and CB-SEM: Evidence from international marketing research,” Int. J. Mark. Res., 2022. [Online]. Available: https://doi.org/10.1177/14707853221074679

. J. Evermann and M. Tate, “Assessing predictive performance of PLS path models,” J. Bus. Res., vol. 69, no. 10, pp. 4529–4535, 2016. [Online]. Available: https://doi.org/10.1016/j.jbusres.2016.03.050

. J. Evermann and M. Tate, “Assessing predictive performance of PLS models,” J. Bus. Res., vol. 117, pp. 458–468, 2020. [Online]. Available: https://doi.org/10.1016/j.jbusres.2020.05.037

. M. Kumar and K. Purani, “Model selection in PLS-SEM: An empirical comparison,” J. Hosp. Tour. Technol., vol. 9, no. 3, pp. 278–291, 2018. [Online]. Available: https://doi.org/10.1108/JHTT-11-2017-0122

. R. F. Falk and N. B. Miller, A Primer for Soft Modeling with Partial Least Squares. Cham, Switzerland: Springer, 2021. [Online]. Available: https://doi.org/10.1007/978-3-030-78331-0

. H. Latan and R. Noonan, Partial Least Squares Path Modeling. Cham, Switzerland: Springer, 2017. [Online]. Available: https://doi.org/10.1007/978-3-319-64069-3

. M. Sarstedt, C. M. Ringle, and J. F. Hair, “Progress in PLS-SEM use in marketing research: An update and assessment,” Psychol. Mark., 2022. [Online]. Available: https://doi.org/10.1002/mar.21658

. J. Chiarelli et al., “Calculating the precision of student-generated datasets using RStudio,” J. Chem. Educ., vol. 102, no. 2, pp. 909–916, 2025. [Online]. Available: https://doi.org/10.1021/acs.jchemed.4c00870

. Bleichrodt et al., “StatModPredict: A user-friendly R-Shiny interface for fitting and forecasting with statistical models,” PLoS One, vol. 20, no. 8, e0329791, 2025. [Online]. Available: https://doi.org/10.1371/journal.pone.0329791

. C. M. Ringle and M. Sarstedt, “The future of PLS-SEM,” Eur. Bus. Rev., 2024. [Online]. Available: https://doi.org/10.1108/EBR-11-2023-0203

. J. F. Hair, L. M. Matthews, R. L. Matthews, and M. Sarstedt, “Advances in PLS-SEM: Theory, methodology, and practice,” J. Bus. Res., vol. 152, pp. 345–360, 2023. [Online]. Available: https://doi.org/10.1016/j.jbusres.2022.06.004

. B. W. Cintika and V. Gunawan, “Development of customer loyalty measurement application using R Shiny,” JITEKI, vol. 9, no. 4, pp. 45–56, 2023. [Online]. Available: https://doi.org/10.26555/jiteki.v9i4.26649

. C. Fornell and D. F. Larcker, “Evaluating structural equation models with unobservable variables and measurement error,” J. Mark. Res., vol. 18, no. 1, pp. 39–50, 1981. [Online]. Available: https://doi.org/10.1177/002224378101800104

. T. K. Dijkstra and J. Henseler, “Consistent partial least squares path modeling,” MIS Q., vol. 39, no. 2, pp. 297–316, 2015. [Online]. Available: https://doi.org/10.25300/MISQ/2015/39.2.02

. W. W. Chin, “The partial least squares approach to structural equation modeling,” in Modern Methods for Business Research, G. A. Marcoulides, Ed. Mahwah, NJ: Lawrence Erlbaum, 1998, pp. 295–336. [Online]. Available: https://doi.org/10.4324/9781410604385-9

. Monecke and F. Leisch, “semPLS: Structural equation modeling using partial least squares,” J. Stat. Soft., vol. 48, no. 3, pp. 1–32, 2012. [Online]. Available: https://doi.org/10.18637/jss.v048.i03

. Chanda, A., Vafaei-Zadeh, H., Hanifah, H., and Thurasamy, R., “Modeling eco-friendly house purchasing intention: A combined study of PLS-SEM and FSQCA approaches,” Int. J. Housing Markets Anal., vol. 18, no. 1, pp. 123–157, 2025. [Online]. Available: https://doi.org/10.1108/IJHMA-04-2023-0059

. J. F. Hair and C. M. Ringle, “Covariance-based structural equation modeling (CB-SEM): A SmartPLS 4 software tutorial,” J. Market Anal., vol. 13, pp. 709–724, 2025. [Online]. Available: https://doi.org/10.1057/s41270-025-00414-6

. Tavana, M., Soltanifar, M., and Santos, J., “Analytical hierarchy process: Revolution and evolution,” Ann. Oper. Res., vol. 326, pp. 879–907, 2023. [Online]. Available: https://doi.org/10.1007/s10479-021-04432-2




DOI: http://dx.doi.org/10.30829/zero.v9i3.27040

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