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Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook
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This is an open-access step-by-step guide to analyzing PLS models using R, especially using the SEMinR package and with in-depth case studies.
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R: A Language and Environment for Statistical Computing
R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,893
Evaluating Structural Equation Models with Unobservable Variables and Measurement Error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.2307/3151312 · Cited by 109,314
Evaluating structural equation models with unobservable variables and measurement error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.1177/002224378101800104 · Cited by 109,190
Statistical Power Analysis for the Behavioral Sciences
Muller, K. E., & Cohen, J. (1989) · Technometrics · doi:10.2307/1270020 · Cited by 99,603
Estimating the Dimension of a Model
Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033
(2013) · Cited by 44,563
A new criterion for assessing discriminant validity in variance-based structural equation modeling
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-014-0403-8 · Cited by 38,459
Self-citationStatistical power analyses using G∗Power 3.1: tests for correlation and regression analyses
Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009) · Behavior Research Methods · doi:10.3758/brm.41.4.1149 · Cited by 37,615
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
Self-citationA Theory of Cognitive Dissonance
Festinger, L. (1957) · doi:10.1515/9781503620766 · Cited by 24,241
Research works printed but not linked: 119
- Henseler, J. (2021). Composite-based structural equation modeling: Analyzing latent and emergent vari- ables. New York, NY: Guilford Press.
- Mehmetoglu, M., & Venturini, S. (2021). Structural equation modelling with partial least squares using Stata and R. London: Routledge.
- Memon, M. A., Ramayah, T., Cheah, J.-H., Ting, H., Chuah, F., & Cham, T. H. (2021). PLS-SEM statistical programs: A review. Journal of Applied Structural Equation Modeling, 5(1), i-xiv.
- Monecke, A., & Leisch, F. (2012). semPLS: Structural equation modeling using partial least squares. Journal of Statistical Software, 48(3), 1-32.
- Ramayah, T., Cheah, J.-H., Chuah, F., Ting, H., & Memon, M. A. (2016). Partial least squares struc- tural equation modeling (PLS-SEM) using SmartPLS 3.0: An updated and practical guide to sta- tistical analysis. Kuala Lumpur: Pearson Malaysia.
- Sarstedt, M., & Cheah, J. H. (2019). Partial least squares structural equation modeling using Smart- PLS: A software review. Journal of Marketing Analytics, 7(3), 196-202.
- Barclay, D. W., Higgins, C. A., & Thompson, R. (1995). The partial least squares approach to causal modeling: Personal computer adoption and use as illustration. Technology Studies, 2(2), 285-309.
- Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295-358). Mahwah, NJ: Erlbaum.
- Chin, W. W., & Newsted, P. R. (1999). Structural equation modeling analysis with small samples using partial least squares. In R. H. Hoyle (Ed.), Statistical strategies for small sample research (pp. 307-341). Thousand Oaks, CA: Sage.
- Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155-159.
- do Valle, P. O., & Assaker, G. (2016). Using partial least squares structural equation modeling in tour- ism research: A review of past research and recommendations for future applications. Journal of Travel Research, 55(6), 695-708. Chapter 1 • An Introduction to Structural Equation Modeling 1
- Ghasemy, M., Teeroovengadum, V., Becker, J.-M., & Ringle, C. M. (2020). This fast car can move faster: A review of PLS-SEM application in higher education research. Higher Education, 80, 1121-1152.
- Guttman, L. (1955). The determinacy of factor score matrices with implications for five other basic problems of common-factor theory. British Journal of Statistical Psychology, 8(2), 65-81.
- Hair, J. F., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. L. (2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data Systems, 117(3), 442-458.
- Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Thousand Oaks, CA: Sage.
- Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139-151.
- Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24.
- Hair, J. F., & Sarstedt, M. (2019). Composites vs. factors: Implications for choosing the right SEM method. Project Management Journal, 50(6), 1-6.
- 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). Common beliefs and reality about partial least squares: Comments on Rönkkö & Evermann (2013). Organizational Research Methods, 17(1), 182-209.
- In K. G. Jöreskog & H. Wold (Eds.), Systems under indirect observation, part II (pp. 119-130). Amsterdam: North-Holland.
- Khan, G., Sarstedt, M., Shiau, W.-L., Hair, J. F., Ringle, C. M., & Fritze, M. (2019). Methodological research on partial least squares structural equation modeling (PLS-SEM): A social network analysis. Internet Research, 29(3), 407-429.
- Marcoulides, G. A., & Saunders, C. (2006). PLS: A silver bullet? MIS Quarterly, 30(2), iii-ix.
- Rasoolimanesh, S. M., Ringle, C. M., Sarstedt, M., & Olya, H. (2021). The combined use of predic- tion-oriented approaches: Partial least squares-structural equation modeling and fuzzyset quali- Chapter 1 • An Introduction to Structural Equation Modeling 1 tative comparative analysis. International Journal of Contemporary Hospitality Management, 33(5), 1571-1592.
- Richter, N. F., Cepeda Carrión, G., Roldán, J. L., & Ringle, C. M. (2016). European management research using partial least squares structural equation modeling (PLS-SEM): Editorial. European Management Journal, 34(6), 589-597.
- Ringle, C. M., Sarstedt, M., & Straub, D. W. (2012). A critical look at the use of PLS-SEM in MIS Quarterly. MIS Quarterly, 36(1), iii-xiv.
- Roldán, J. L., & Sánchez-Franco, M. J. (2012). Variance-based structural equation modeling: Guidelines for using partial least squares in information systems research. In M. Mora, O. Gelman, A. L. Steenkamp, & M. Raisinghani (Eds.), Research methodologies, innovations and philosophies in software systems engineering and information systems (pp. 193-221).
- Sarstedt, M., Hair, J. F., Cheah, J.-H., Becker, J.-M., & Ringle, C. M. (2019). How to specify, estimate, and validate higher-order models. Australasian Marketing Journal, 27(3), 197-211.
- Sarstedt, M., Hair, J. F., Nitzl, C., Ringle, C. M., & Howard, M. C. (2020). Beyond a tandem analysis of SEM and PROCESS: Use PLS-SEM for mediation analyses! International Journal of Market Research, 62(3), 288-299.
- Sarstedt, M., Hair, J. F., Ringle, C. M., Thiele, K. O., & Gudergan, S. P. (2016). Estimation issues with PLS and CB-SEM: Where the bias lies! Journal of Business Research, 69(10), 3998-4010.
- Usakli, A., & Kucukergin, K. G. (2018). Using partial least squares structural equation modeling in hospitality and tourism: Do researchers follow practical guidelines? International Journal of Contemporary Hospitality Management, 30(11), 3462-3512.
- Wold, H. (1982). Soft modeling: The basic design and some extensions. In K. G. Jöreskog & H. Wold (Eds.), Systems under indirect observations, part II (pp. 1-54). Amsterdam: North-Holland.
- Wold, H. (1985). Partial least squares. In S. Kotz & N. L. Johnson (Eds.), Encyclopedia of statistical sciences (pp. 581-591). New York, NY: John Wiley.
- Zeng, N., Liu, Y., Gong, P., Hertogh, M., & König, M. (2021). Do right PLS and do PLS right: A critical review of the application on PLS in construction management research. Frontiers of Engineering Management, 8(3), 356-369.
- Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295-358). Mahwah, NJ: Erlbaum.
- Hair, J. F., Binz Astrachan, C., Moisescu, O. I., Radomir, L., Sarstedt, M., Vaithilingam, S., & Ringle, C. M. (2020). Executing and interpreting applications of PLS-SEM: Updates for family business researchers. Journal of Family Business Strategy, 12(3), 100392. Chapter 1 • An Introduction to Structural Equation Modeling 1
- Jöreskog, K. G., & Wold, H. (1982). The ML and PLS techniques for modeling with latent variables: Historical and comparative aspects. In H. Wold & K. G. Jöreskog (Eds.), Systems under indirect observation, part I (pp. 263-270). Amsterdam: North-Holland.
- Rigdon, E. E. (2012). Rethinking partial least squares path modeling: In praise of simple methods. Long Range Planning, 45(5-6), 341-358.
- Tenenhaus, M., Esposito Vinzi, V., Chatelin, Y.-M., & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48(1), 159-205.
- Wold, H. (1982). Soft modeling: The basic design and some extensions. In K. G. Jöreskog & H. Wold (Eds.), Systems under indirect observations, part II (pp. 1-54). Amsterdam: North-Holland. References
- Eklöf, J. A., & Westlund, A. H. (2002). The pan-European customer satisfaction index program: Current work and the way ahead. Total Quality Management, 13(8), 1099-1106.
- Hornik, K., & Leisch, F. (2002). Vienna and R: Love, marriage and the future. In R. Dutter (Ed.), Festschrift 50 Jahre Österreichische Statistische Gesellschaft (pp. 61-70). Vienna: Österreichische Statistische Gesellschaft.
- Rigdon, E. E., Sarstedt, M., & Becker, J.-M. (2020). Quantify uncertainty in behavioral research. Nature Human Behaviour, 4, 329-331. Chapter 2 • Overview of R and RStudio RStudio Team. (2021). RStudio: Integrated development for R [computer software]. Boston, MA: RStudio, PBC. Retrieved from: http://www. rstudio. com/
- Tenenhaus, M., Esposito Vinzi, V., Chatelin, Y.-M., & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48(1), 159-205.
- Wickham, H., & Grolemund, G. (2016). R for data science. Sebastopol, CA: O'Reilly Media. Retrieved from: https://r4ds. had. co. nz/ Suggested Readings
- Grolemund, G. (2014). Hands-on programming with R: Write your own functions and simulations. Sebastopol, CA: O'Reilly Media.
- Wickham, H. (2019). Advanced R. Boca Raton, FL: CRC press. Retrieved from: https://adv-r. hadley. nz/
- Wickham, H., & Grolemund, G. (2016). R for data science. Sebastopol, CA: O'Reilly Media. Retrieved from: https://r4ds. had. co. nz/ References
- Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295-358). Mahwah, NJ: Erlbaum.
- Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Thousand Oaks, CA: Sage.
- Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24.
- Helm, S., Eggert, A., & Garnefeld, I. (2010). Modelling the impact of corporate reputation on cus- tomer satisfaction and loyalty using PLS. In V. Esposito Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares: Concepts, methods and applications in market- ing and related fields (Springer Handbooks of Computational Statistics Series) (Vol. II, pp. 515- 534). Berlin: Springer.
- Rigdon, E. E. (2012). Rethinking partial least squares path modeling: In praise of simple methods. Long Range Planning, 45(5-6), 341-358.
- Rosseel, Y. (2012). Lavaan: An R package for structural equation modeling and more. Version 0.5-12 (beta). Journal of Statistical Software, 48(2), 1-36.
- Streukens, S., & Leroi-Werelds, S. (2016). Bootstrapping and PLS-SEM: A step-by-step guide to get more out of your bootstrapping results. European Management Journal, 34(6), 618-632.
- Tenenhaus, M., Esposito Vinzi, V., Chatelin, Y.-M., & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48(1), 159-205.
- Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295-358). Mahwah, NJ: Erlbaum.
- Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Thousand Oaks, CA: Sage.
- Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24.
- Tenenhaus, M., Esposito Vinzi, V., Chatelin, Y.-M., & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48(1), 159-205. References
- Aguirre-Urreta, M. I., & Rönkkö, M. (2018). Statistical inference with PLSc using bootstrap confi- dence intervals. MIS Quarterly, 42(3), 1001-1020.
- Dijkstra, T. K. (2014). PLS' Janus face-response to professor Rigdon's 'rethinking partial least squares modeling: In praise of simple methods. Long Range Planning, 47(3), 146-153.
- Hair, J. F., Hult, T., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Thousand Oaks: Sage.
- Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24.
- Streukens, S., & Leroi-Werelds, S. (2016). Bootstrapping and PLS-SEM: A step-by-step guide to get more out of your bootstrapping results. European Management Journal, 34(6), 618-632.
- Hair, J. F., Hult, T., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Thousand Oaks: Sage.
- Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24.
- Hair, J. F., Sarstedt, M., & Ringle, C. M. (2019). Rethinking some of the rethinking of partial least squares. European Journal of Marketing, 53(4), 566-584.
- Aguirre-Urreta, M. I., & Rönkkö, M. (2018). Statistical inference with PLSc using bootstrap confi- dence intervals. MIS Quarterly, 42(3), 1001-1020.
- Bollen, K. A., & Diamantopoulos, A. (2017). In defense of causal-formative indicators: A minority report. Psychological Methods, 22(3), 581-596.
- Cheah, J. H., Sarstedt, M., Ringle, C. M., Ramayah, T., & Ting, H. (2018). Convergent validity assessment of formatively measured constructs in PLS-SEM. International Journal of Contemporary Hospitality Management, 30(11), 3192-3210.
- Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295-336.
- Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155-159.
- Diamantopoulos, A., Sarstedt, M., Fuchs, C., Wilczynski, P., & Kaiser, S. (2012). Guidelines for choosing between multi-item and single-item scales for construct measurement: A predictive validity perspective. Journal of the Academy of Marketing Science, 40(3), 434-449.
- Eberl, M. (2010). An application of PLS in multi-group analysis: The need for differentiated corpo- rate-level marketing in the mobile communications industry. In V. Esposito Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of Partial Least Squares: Concepts, Methods and Applications. (Springer handbooks of computational statistics series, vol. II) (pp. 487-514). Berlin: Springer.
- Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149-1160.
- Grimm, M. S., & Wagner, R. (2020). The impact of missing values on PLS, ML and FIML model fit. Archives of Data Science, Series A, 6(1), 04.
- Hair, J. F., Hult, T., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Thousand Oaks, CA: Sage.
- Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24.
- Kock, N., & Hadaya, P. (2018). Minimum sample size estimation in PLS-SEM: The inverse square root and gamma-exponential methods. Information Systems Journal, 28(1), 227-261.
- Sarstedt, M., Wilczynski, P., & Melewar, T. (2013). Measuring reputation in global markets: A com- parison of reputation measures' convergent and criterion validities. Journal of World Business, 48(3), 329-339.
- Schwaiger, M., Sarstedt, M., & Taylor, C. R. (2010). Art for the sake of the corporation: Audi, BMW Group, DaimlerChrysler, Montblanc, Siemens, and Volkswagen help explore the effect of spon- sorship on corporate reputations. Journal of Advertising Research, 50(1), 77-90.
- Hair, J. F., Hult, T., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Thousand Oaks, CA: Sage.
- Sarstedt, M., Hair, J. F., Ringle, C. M., Thiele, K. O., & Gudergan, S. P. (2016). Estimation issues with PLS and CBSEM: Where the bias lies! Journal of Business Research, 69(10), 3998-4010. Chapter 5 • Evaluation of Formative Measurement Models References
- Aguirre-Urreta, M. I., & Rönkkö, M. (2018). Statistical inference with PLSc using bootstrap confi- dence intervals. MIS Quarterly, 42(3), 1001-1020.
- Becker, J.-M., Ringle, C. M., Sarstedt, M., & Völckner, F. (2015). How collinearity affects mixture regression results. Marketing Letters, 26(4), 643-659.
- Burnham, K. P., & Anderson, D. R. (2004). Multimodel inference: Understanding AIC and BIC in model selection. Sociological Research Methods, 33(2), 261-304.
- Chin, W. W., Cheah, J.-H., Liu, Y., Ting, H., Lim, X.-J., & Cham, T. H. (2020). Demystifying the role of causal-predictive modeling using partial least squares structural equation modeling in infor- mation systems research. Industrial Management & Data Systems, 120(12), 2161-2209.
- Hair, J. F. (2021). Next generation prediction metrics for composite-based PLS-SEM. Industrial Management & Data Systems, 121(1), 5-11.
- Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139-151.
- Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24.
- Hair, J. F., & Sarstedt, M. (2021). Explanation plus prediction -The logical focus of project manage- ment research. Project Management Journal, 52(4), 319-322.
- Hair, J. F., Sarstedt, M., & Ringle, C. M. (2019). Rethinking some of the rethinking of partial least squares. European Journal of Marketing, 53(4), 566-584.
- Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., et al. (2014). Common beliefs and reality about partial least squares: Comments on Rönkkö & Evermann (2013). Organizational Research Methods, 17(1), 182-209.
- Memon, M. A., Cheah, J. H., Ramayah, T., Ting, H., Chuah, F., & Cham, T. H. (2019). Moderation analysis: Issues and guidelines. Journal of Applied Structural Equation Modeling, 3(1), i-xi.
- Rigdon, E. E. (2012). Rethinking partial least squares path modeling: In praise of simple methods. Long Range Planning, 45(5-6), 341-358.
- Sarstedt, M. & Danks, N. P. (2021). Prediction in HRM research-A gap between rhetoric and real- ity. Human Resource Management Journal, forthcoming.
- Sarstedt, M., Hair, J. F., Cheah, J.-H., Becker, J.-M., & Ringle, C. M. (2019). How to specify, estimate, and validate higher-order models. Australasian Marketing Journal, 27(3), 197-211.
- Shmueli, G., Ray, S., Estrada, J. M. V., & Chatla, S. B. (2016). The elephant in the room: Predictive performance of PLS models. Journal of Business Research, 69(10), 4552-4564.
- Streukens, S., & Leroi-Werelds, S. (2016). Bootstrapping and PLS-SEM: A step-by-step guide to get more out of your bootstrap results. European Management Journal, 34(6), 618-632.
- Suggested Reading Chin, W. W., Cheah, J.-H., Liu, Y., Ting, H., Lim, X.-J., & Cham, T. H. (2020). Demystifying the role of causal-predictive modeling using partial least squares structural equation modeling in infor- mation systems research. Industrial Management & Data Systems, 120(12), 2161-2209.
- Hair, J. F., Hult, T., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Thousand Oaks, CA: Sage.
- Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J. H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322-2347.
- Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Thousand Oaks, CA: Sage.
- Nitzl, C., Roldán, J. L., & Cepeda Carrión, G. (2016). Mediation analysis in partial least squares path modeling. Industrial Management & Data Systems, 119(9), 1849-1864.
- Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Thousand Oaks, CA: Sage.
- Hayes, A. F. (2018). Introduction to mediation, moderation, and conditional process analysis: A regres- sion-based approach (2nd ed.). New York, NY: Guilford Press.
- Nitzl, C., Roldán, J. L., & Cepeda Carrión, G. (2016). Mediation analysis in partial least squares path modeling. Industrial Management & Data Systems, 119(9), 1849-1864.
- Preacher, K. J., & Hayes, A. F. (2008). Contemporary approaches to assessing mediation in communi- cation research. In A. F. Hayes, D. Slater, & L. B. Snyder (Eds.), The SAGE sourcebook of advanced data analysis methods for communication research (pp. 13-54). Thousand Oaks, CA: Sage.
- Sarstedt, M., Hair, J. F., Nitzl, C., Ringle, C. M., & Howard, M. C. (2020). Beyond a tandem analysis of SEM and PROCESS: Use of PLS-SEM for mediation analyses! International Journal of Market Research, 62(3), 288-299.
- Zhao, X., Lynch, J. G., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37(2), 197-206. Chapter 7 • Mediation Analysis References
- Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003). A partial least squares latent variable model- ing approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information Systems Research, 14(2), 189-217.
- Diamantopoulos, A., Sarstedt, M., Fuchs, C., Kaiser, S., & Wilczynski, P. (2012). Guidelines for choosing between multi-item and single-item scales for construct measurement: A predictive validity perspective. Journal of the Academy of Marketing Science, 40(3), 434-449.
- Gardner, R. G., Harris, T. B., Li, N., Kirkman, B. L., & Mathieu, J. E. (2017). Understanding "it depends" in organizational research: A theory-based taxonomy, review, and future research agenda concerning interactive and quadratic relationships. Organizational Research Methods, 20(4), 610-638.
- Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Thousand Oaks, CA: Sage.
- Memon, M. A., Cheah, J.-H., Ramayah, T., Ting, H., Chuah, F., & Cham, T. H. (2018). Moderation analysis: Issues and guidelines. Journal of Applied Structural Equation Modeling, 3(1), i-ix.
- Sarstedt, M., Diamantopoulos, A., Salzberger, T., & Baumgartner, P. (2016). Selecting single items to measure doubly-concrete constructs: A cautionary tale. Journal of Business Research, 69(8), 3159-3167.
- Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Thousand Oaks, CA: Sage.
- Memon, M. A., Cheah, J.-H., Ramayah, T., Ting, H., Chuah, F., & Cham, T. H. (2018). Moderation analysis: Issues and guidelines. Journal of Applied Structural Equation Modeling, 3(1), i-ix.
- J. F. Hair Jr. et al., Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R, Classroom Companion: Business, https://doi.org/10.1007/978-3-030-80519-7 · DOI
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Recent decade expansion of aquatic vegetation covering in china's lakes
Cao, Z. X., Zhang, Y., Liu, Z., Guan, B., Lai, L., Yang, Q., & Chen, X. (2024) · Ecological Indicators · doi:10.1016/j.ecolind.2024.111603 · Cited by 19
Role of public relations practices in content management: the mediating role of new media platforms
Hadeed, A. Y. B. A., Maysari, I., Aldroubi, M. M., Attar, R. W., Olaimat, F. A., & Habes, M. (2024) · Frontiers in Sociology · doi:10.3389/fsoc.2023.1273371 · Cited by 40
Consumer’s acceptance of retail service robots: mediating role of pleasure and arousal
Alam, S., Abdullah, S., Kokash, H. A., Ahmed, S., & Omar, N. A. (2024) · Journal of Decision System · doi:10.1080/12460125.2024.2318527 · Cited by 20
Manshadi, Z. D., Doost, H. T. N., & Jobson, L. (2024) · European Journal of Psychotraumatology · doi:10.1080/20008066.2024.2320041 · Cited by 19
Ratmono, D., Mail, R., Cahyonowati, N., & Janie, D. N. A. (2024) · Environmental Economics · doi:10.21511/ee.15(1).2024.04 · Cited by 19
Doing Good and Doing Well? CSR Climate as a Driver of Team Empowerment and Team Performance
Kluijtmans, T., Meyfroodt, K., & Crucke, S. (2024) · Journal of Business Ethics · doi:10.1007/s10551-024-05678-z · Cited by 19
Rahimi, A. R. (2024) · Smart Learning Environments · doi:10.1186/s40561-024-00297-7 · Cited by 22
Alkatheeri, H., & Ahmad, S. Z. (2024) · Journal of Modelling in Management · doi:10.1108/jm2-08-2023-0186 · Cited by 19
Digital Natives' Mobile Learning Adoption in terms of UTAUT-2 Model: a Structural Equation Model
Talan, T., Doğan, Y., & Kalınkara, Y. (2024) · Innoeduca International Journal of Technology and Educational Innovation · doi:10.24310/ijtei.101.2024.17440 · Cited by 30
Wang, Z., & Jiang, Q. (2024) · Systems · doi:10.3390/systems12060217 · Cited by 23
Orden-Mejía, M. A., Carvache‐Franco, M., Huertas, A., Carvache‐Franco, O., & Carvache‐Franco, W. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2425882 · Cited by 24
“According to Hair et al. (2021), a VIF greater than 5.0 is problematic, indicating multicollinearity.”
InfluentialIrshad, O., Ahmad, S., & Mahmood, S. (2024) · Sustainability · doi:10.3390/su162310584 · Cited by 21
Yu, T., Teoh, A. P., Liao, J., & Wang, C. (2025) · Journal of Travel & Tourism Marketing · doi:10.1080/10548408.2025.2455421 · Cited by 19
Chawla, S., Prakash, G., Singh, G., & Kaur, J. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12716-w · Cited by 18
“The path coefficient of the model has been obtained from the standard error through t statistics in bootstrapping with 5000 iterations (Hair et al., 2021).”
Ahmed, Z., Khosa, M., Nguyen, N. T., Faqera, A. F. O., & Rehman, S. U. (2024) · Creativity and Innovation Management · doi:10.1111/caim.12614 · Cited by 24
Innovatoion capability: Digital transformation of human resources and digital talent in SMEs
Budiarti, I., & Firmansyah, D. (2024) · Journal of Eastern European and Central Asian Research (JEECAR) · doi:10.15549/jeecar.v11i3.1709 · Cited by 47
Al‐Romeedy, B. S., & Zaki, H. S. (2024) · Journal Of Vacation Marketing · doi:10.1177/13567667241268667 · Cited by 20
“In terms of convergent validity, the study examined internal consistency reliability (should be > 0.70), composite reliability (should be > 0.70), and average variance extraction (AVE) (should be > 0.5) according to Hair et al. (2021).”
InfluentialMukuka, A., & Alex, J. K. (2024) · Journal of Mathematics Teacher Education · doi:10.1007/s10857-024-09657-z · Cited by 28
“…the 10-times minimum sample size rule for structural equation modeling analysis (Barclay et al., 1995), we were cognizant of the fact that this rule has been criticized for its failure to account for the population size and other power analysis procedures (Hair et al., 2021; Kock & Hadaya, 2018).”
Alnaim, M., & Metwally, A. B. M. (2024) · Administrative Sciences · doi:10.3390/admsci14120311 · Cited by 18
Wu, S., Xiao, Y., Pǎcalǎ, A., Bădulescu, A., & Khan, S. (2024) · Sustainability · doi:10.3390/su162411059 · Cited by 23
Chen, J., Li, H., Luo, S., Su, D., Zang, T., Kinoshita, T., & Yang, L. (2024) · Urban forestry & urban greening · doi:10.1016/j.ufug.2024.128649 · Cited by 20
Alrawhani, E. M., Romli, A., & Al-Sharafi, M. A. (2024) · Journal of Open Innovation Technology Market and Complexity · doi:10.1016/j.joitmc.2024.100463 · Cited by 18
Hossain, M. I., Islam, M. T., Kumar, J., & Jamadar, Y. (2025) · Journal of Hospitality and Tourism Insights · doi:10.1108/jhti-09-2024-1041 · Cited by 18
Benhacene, H. L. M., & Musa, A. M. H. (2025) · Sustainability · doi:10.3390/su17020768 · Cited by 18
Hu, C., Din, Q. M. U., & Tahir, A. (2025) · Systems · doi:10.3390/systems13040216 · Cited by 25
Gong, Y., Xu, C., Luo, S., & Lin, J. (2025) · Scientific Reports · doi:10.1038/s41598-025-03298-9 · Cited by 32
“According to Hair et al. 115 , the minimum required sample size is tenfold the measurement items of the most complex factor.”
A Systematic Review of UTAUT and UTAUT2 for AI Adoption in Education
Xue, L., Ghazali, N., & Mahat, J. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2552867 · Cited by 34
Alhelal, A. A., Alshiha, A. A., & Al‐Romeedy, B. S. (2025) · Sustainability · doi:10.3390/su17198903 · Cited by 18
Agag, G., Yousaf, A., & Adıgüzel, F. (2025) · European Journal of Marketing · doi:10.1108/ejm-01-2024-0029 · Cited by 19
Abbasi, A. Z., Rehman, U., Afaq, Z., Rafeh, M. A., Hlavacs, H., Mamun, M. A., & Shah, M. U. (2021) · JMIR Serious Games · doi:10.2196/30310 · Cited by 22
Gârdan, I. P., Mauri, A. G., Dumitru, I., Gârdan, D. A., Maican, S., & Moise, D. (2022) · Electronics · doi:10.3390/electronics11162570 · Cited by 24
Lestari, E. D., Hamid, N. A., Rizkalla, N., Purnamaningsih, P., & Urus, S. T. (2022) · GATR Global Journal of Business Social Sciences Review · doi:10.35609/gjbssr.2022.10.3(3 · Cited by 18
Shahzad, K., Ahmed, H. N., & Ali, S. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.1046293 · Cited by 25
Selim, I., & Kee, D. M. H. (2022) · Information · doi:10.3390/info14010012 · Cited by 24
Areiza-Padilla, J. A., & Cervera, A. (2023) · Cross Cultural & Strategic Management · doi:10.1108/ccsm-03-2022-0050 · Cited by 18
The Impacts of Social Media on Job Satisfaction: Task-Oriented Use and Relationship-Oriented Use
Wang, X., Lin, X., & Gonzales, A. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2199628 · Cited by 18
Liang, Y., Ren, L., Wei, C., & Shi, Y. (2023) · Sustainability · doi:10.3390/su15118938 · Cited by 18
Alqahtani, T. M., Yusop, F. D., & Halili, S. H. (2023) · Humanities and Social Sciences Communications · doi:10.1057/s41599-023-01754-3 · Cited by 18
Digital marketing adoption of microenterprises in a technology acceptance approach
Buvár, Á., & Gáti, M. (2023) · Management & Marketing · doi:10.2478/mmcks-2023-0008 · Cited by 18
Yuan, Z., Deng, X., Ding, T., Liu, J., & Tan, Q. (2023) · Electronic Research Archive · doi:10.3934/era.2023287 · Cited by 18
Li, X., Niu, Y., & Xu, J. (2023) · Behavioral Sciences · doi:10.3390/bs13080689 · Cited by 28
Understanding the drivers of organizational business performance from the human capital perspective
Serenko, A., Abubakar, A. M., & Bontis, N. (2023) · Knowledge and Process Management · doi:10.1002/kpm.1763 · Cited by 25
Başer, M. Y., Büyükbeşe, T., & Durmaz, Y. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2254617 · Cited by 18
Vu, T. D., Nguyễn, H. V., Vu, P. T., Tran, T. H. H., & Vu, H. V. (2023) · Sustainability · doi:10.3390/su152014776 · Cited by 18
Teixeira, S., de Oliveira, Z. M., Teixeira, S., & Teixeira, S. (2023) · Sustainability · doi:10.3390/su152014784 · Cited by 26
Wu, S., Yan, Y., Pan, J., & Wu, K. (2023) · Buildings · doi:10.3390/buildings13102634 · Cited by 18
Saihi, A., Ben‐Daya, M., & As’ad, R. A. (2023) · Computers & Industrial Engineering · doi:10.1016/j.cie.2023.109699 · Cited by 18
Modeling the intention and adoption of food waste prevention practices among Chinese households
Ma, Y., Mamun, A. A., Ali, M. H., Hoque, M. E., & Lili, Z. (2023) · Humanities and Social Sciences Communications · doi:10.1057/s41599-023-02348-9 · Cited by 26
Aldreabi, H., Halalsheh, N. Z., Alrawashdeh, M. N., Alnajdawi, A. M., Alsawalqa, R. O., & Al-Shboul, M. A. (2023) · Frontiers in Education · doi:10.3389/feduc.2023.1226718 · Cited by 18
The Effects of ESG Management on Business Performance: The Case of Incheon International Airport
Lee, S., Park, J.-W., & Choi, D. (2023) · Sustainability · doi:10.3390/su152416831 · Cited by 26
Ngo, T. T. A., Bui, C. T., Chau, H. K. L., & Tran, N. P. N. (2023) · Innovative Marketing · doi:10.21511/im.19(4).2023.22 · Cited by 20
Gnecco, V. M., Pigliautile, I., & Pisello, A. L. (2023) · Building and Environment · doi:10.1016/j.buildenv.2023.111147 · Cited by 18
Jo, H., & Baek, E.-M. (2024) · BMC Public Health · doi:10.1186/s12889-023-17618-z · Cited by 18
Pons‐Escoda, A., García-Ruiz, A., Naval‐Baudin, P., Martínez‐Zalacaín, I., Castell, J., Camins, À., Vidal, N., Bruna, J., Cos, M., Pérez-López, R., et al. (2024) · European Radiology · doi:10.1007/s00330-024-10611-z · Cited by 19
Musa, H., Ahmad, N. H. B., & Nor, A. M. (2024) · Cogent Economics & Finance · doi:10.1080/23322039.2024.2306536 · Cited by 18
Anti-Brand Activism in the Collectivist Orientation: The Case of Online-Consumers-Run Communities
Do, H., Bich, N., Nguyen, T. M., & Nguyen, H. M. T. (2024) · Journal of Promotion Management · doi:10.1080/10496491.2024.2318665 · Cited by 25
Quality 4.0 social and strategic readiness factors: sufficient and Necessary Condition Analysis
Rico, J. P., Calvo‐Mora, A., Molina, C. M., & Alves, H. (2024) · Total Quality Management & Business Excellence · doi:10.1080/14783363.2024.2323185 · Cited by 18
Yoon, T. K., Myeong, J. Y., Lee, Y., Choi, Y. E., Lee, S., Lee, S., & Byun, C. (2024) · Forest Policy and Economics · doi:10.1016/j.forpol.2024.103184 · Cited by 18
Zheng, C., Ling, S., Cho, D., & Kim, Y. G. (2024) · Journal of theoretical and applied electronic commerce research · doi:10.3390/jtaer19020038 · Cited by 20
Principals’ well-being: understanding its multidimensional nature
Wang, F. (2024) · School Leadership and Management · doi:10.1080/13632434.2024.2334942 · Cited by 26
Huang, Q., Kumarasinghe, P. J., & Jayarathna, R. M. G. S. (2024) · Interdisciplinary Journal of Information Knowledge and Management · doi:10.28945/5277 · Cited by 27
Zeng, Y., Yu, J., Wu, H., & Liu, W. (2024) · Behavioral Sciences · doi:10.3390/bs14050370 · Cited by 18
The moderating role of tourism intensity on residents’ intentions towards pro-tourism behaviours
Lança, M., Silva, J. A., Andraz, J. M., Nunes, R., & Pereira, L. N. (2024) · Journal of Sustainable Tourism · doi:10.1080/09669582.2024.2349925 · Cited by 18
Zhang, Z. J. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-03174-3 · Cited by 18
AlAli, A. M., Hassan, T. H., & Abdelmoaty, M. A. (2024) · Sustainability · doi:10.3390/su16114746 · Cited by 22
Dalavong, P., Im, H. N., & Choi, C. G. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1394930 · Cited by 18
“For formative constructs, we examined the variance inflation factor (VIF), outer weight, and outer loading (Sarstedt et al., 2020; Hair et al., 2021).”
BackgroundMethodInfluentialBîlbîie, A., Puiu, A.-I., Mihăilă, V., & Burcea, M. (2024) · Healthcare · doi:10.3390/healthcare12151531 · Cited by 22
Hendrawan, D. C. P., & Mußhoff, O. (2024) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2024.143692 · Cited by 27
Kholifah, N., Nurtanto, M., Mutohhari, F., Subakti, H., Ramadhan, M. A., & Majid, N. W. A. (2024) · Cogent Education · doi:10.1080/2331186x.2024.2421094 · Cited by 64
Schultz, C. D., & Zacheus, P. (2024) · Journal of Consumer Behaviour · doi:10.1002/cb.2426 · Cited by 22
Customer trust and willingness to use shopping assistant humanoid chatbot
Wang, Y., Mohsin, M., & Jamil, K. (2024) · Service Industries Journal · doi:10.1080/02642069.2024.2432935 · Cited by 20
Sun, Y., Zhu, L., Zhang, N., Wu, H., Chen, Q., & Wang, H. (2024) · Land · doi:10.3390/land13122144 · Cited by 23
Lee, E. (2024) · Journal of Public Relations Research · doi:10.1080/1062726x.2024.2437775 · Cited by 22
“…version 4, I evaluated relevant criteria, including (a) convergent validity, (b) multicollinearity, (c) significance and size of path weights, and (d) nomological validity of the formative second-order measurement model of perceived authenticity in CSA (Cheah et al., 2018; Hair et al., 2021).”
InfluentialZhang, P., & Ramayah, T. (2024) · Systems · doi:10.3390/systems13010003 · Cited by 21
Socioeconomic status and internet addiction: double-mediated moderation
Chen, W., Gao, Y., Ren, R., Bi, Y., & Liao, Y. (2025) · BMC Public Health · doi:10.1186/s12889-024-21153-w · Cited by 18
Pan, J., Wu, K., Huang, D.-M., & Sun, S. (2025) · Nutrients · doi:10.3390/nu17050746 · Cited by 36
Nonnis, M., Agus, M., Corona, F., Aru, N., Urban, A., & Cortese, C. G. (2024) · Sustainability · doi:10.3390/su16020893 · Cited by 17
Pourazad, N., Stocchi, L., Michaelidou, N., & Pare, V. (2023) · Journal of Strategic Marketing · doi:10.1080/0965254x.2023.2236615 · Cited by 17
How does adaptive gamification impact different types of student motivation over time?
Reyssier, S., Serna, A., Hallifax, S., Marty, J.-C., Simonian, S., & Lavoué, É. (2023) · Interactive Learning Environments · doi:10.1080/10494820.2023.2248220 · Cited by 17
Inhibitors of Battery Electric Vehicle Adoption in Morocco
Nasreddin, D., Hafdaoui, H. E., Jelti, F., Boumelha, A., & Khallaayoun, A. (2023) · World Electric Vehicle Journal · doi:10.3390/wevj15010006 · Cited by 23
Pattanaik, P. K., Gupta, S., Pani, A. K., Himanshu, U., & Pappas, I. O. (2024) · Information Systems Frontiers · doi:10.1007/s10796-024-10522-w · Cited by 17
Hadi, N. U. (2024) · Sustainability · doi:10.3390/su16177647 · Cited by 17
Niguse, T., Kant, S., & Adula, M. (2024) · Advances in marketing, customer relationship management, and e-services book series · doi:10.4018/979-8-3693-5340-0.ch014 · Cited by 17
Building Community Resilience and Adaptive Capacity Through Investments in Tourism and Conservation
Clark, C., Nyaupane, G. P., & Xiao, X. (2024) · Journal of Travel Research · doi:10.1177/00472875241304482 · Cited by 17
“The items with factor loadings that were lower than 0.60 were excluded from each construct (Hair et al., 2021).”
InfluentialSuyitno, S., Nurtanto, M., Jatmoko, D., Widiyono, Y., Purwoko, R. Y., Abdillah, A., Setuju, S., & Hermawan, Y. (2025) · European Journal of Educational Research · doi:10.12973/eu-jer.14.1.309 · Cited by 17
Hurani, J., & Abdel-Haq, M. K. (2025) · International Journal of Financial Studies · doi:10.3390/ijfs13010011 · Cited by 30
Pagnanelli, M. A., Colamatteo, A., Cassia, F., & Sansone, M. (2025) · International Journal of Quality & Reliability Management · doi:10.1108/ijqrm-02-2024-0062 · Cited by 17
The Effect of Human Resource Analytics on Organizational Performance: Insights from Ethiopia
Adugna, T. S., Yang, S., & Chen, C. (2025) · Systems · doi:10.3390/systems13020134 · Cited by 44
Takagi, N. H., Menezes, R., Varajão, J. E., & Faria, S. (2025) · International Journal of Managing Projects in Business · doi:10.1108/ijmpb-10-2024-0251 · Cited by 22
Zhao, Y., Gómez, M. C. S., Llorente, A. M. P., & Prieto, R. S. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1541475 · Cited by 17
Liu, Y., & Liu, Y. (2025) · Sustainability · doi:10.3390/su17093869 · Cited by 18
Alsyouf, A., Al-Momani, A. M., Alsubahi, N., Lutfi, A., Al-Mugheed, K., Almaiah, M. A., Anshasi, R. J., Alolayyan, M., Alsaad, A., & Alrawad, M. (2025) · Digital Health · doi:10.1177/20552076251336271 · Cited by 5
Malik, A., Abrar, D., Khan, N. A., & Alhussami, A. M. (2025) · Discover Sustainability · doi:10.1007/s43621-025-01288-5 · Cited by 25
Rashid, S. (2025) · Education Sciences · doi:10.3390/educsci15060756 · Cited by 26
Navigating vulnerable community-based urban heat adaptation under SDG 11
Lao, X., Istrate, A.-L., & Pilla, F. (2025) · npj Urban Sustainability · doi:10.1038/s42949-025-00235-7 · Cited by 17
Zervas, I. M., & Stiakakis, E. (2025) · Administrative Sciences · doi:10.3390/admsci15070267 · Cited by 30
Yu-peng, L., & Yu, Z. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-03861-1 · Cited by 23
“Q 2 values greater than zero allowed the researchers to compare the mean absolute error (MAE) or the root mean square error (RMSE) with the benchmarks calculated by the linear regression model (LM) (Hair et al. 2021).”
Salifu, I., Arthur, F., Acquah, B. Y. S., Opoku, E., Nortey, S. A., & Boateng, E. (2025) · Discover Education · doi:10.1007/s44217-025-00603-6 · Cited by 22
“The coefficient of determination (R 2 ) indicates the extent to which exogenous constructs explain the variance in an endogenous construct [34].”
Jaén, J. M. S., León-Gómez, A., Martínez, M. D. C. V., & Muñoz, V. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13712-4 · Cited by 17
“Finally, the average variance extracted (AVE) was used to assess convergent validity, with all values exceeding 0.5, thereby confirming the internal consistency of the constructs (Hair et al., 2021).”
InfluentialBernuy, J. B. V., Nolasco-Mamani, M. A., Rodríguez, N. C. V., Quelopana, R. L. G., Valdivia, A. N. M., & Vidaurre, S. M. E. (2025) · Sustainability · doi:10.3390/su17188329 · Cited by 17
Metadata
- Authors
- Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S.
- Affiliations
- University of South Alabama (US)
- Hamburg University of Technology (DE)
- Otto-von-Guericke-Universität Magdeburg (DE)
- Trinity College Dublin (IE)
- National Tsing Hua University (TW)
- ORCID
- Venue
- Classroom Companion: Business
- Publisher
- Springer International Publishing
- Published
- 2021
- ISSN
- 2662-2866 (ISSN-L)
- 2662-2874
- OpenAlex
- W3210383951
- Cited by
- 10,343
- References
- 171
- Citations by year
- 2026: 3947 · 2025: 2779 · 2024: 1862 · 2023: 1196 · 2022: 498 · 2021: 60
- FWCI
- 418.20
- Type
- book
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Classroom companion: business (publishedVersion, cc-by)
- Directory of Open access Books (OAPEN Foundation) (submittedVersion, other-oa)
- Trinity's Access to Research Output (TARA) (Trinity College Dublin) (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Keywords
- partial least squares structural equation modeling, PLS-SEM, R programming language, structural equation modeling, case studies
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