CoChat
Sign Up
Cited by 10,343Psychometric Methodologies and TestingAdvanced Statistical Methods and ModelsStatistical Methods and Applications

The DOI matches its Crossref record; 164 of the 171 references it cites were found in a registry or an index.

Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook

Open access
Hair, Joseph F., Hult, G. Tomas M., Ringle, Christian M., Sarstedt, Marko, Danks, Nicholas P., Ray, Soumya·2021·Classroom Companion: Business
DOI 10.1007/978-3-030-80519-7
Open this paper in CoChat — free accountOpen PDF

Abstract

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.

Overview

Integrity signals

Registry

Crossref · 10.1007/978-3-030-80519-7

Cites

164 of 164 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

The text we found for this paper looks like another document, so its claims and self-check weren’t run. The registry and citation checks still apply.

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The text we found for this paper looks like another document, so the self-check wasn’t run.

Citations

Cites

Show all 171 references

Research works printed but not linked: 119

  1. Henseler, J. (2021). Composite-based structural equation modeling: Analyzing latent and emergent vari- ables. New York, NY: Guilford Press.
  2. Mehmetoglu, M., & Venturini, S. (2021). Structural equation modelling with partial least squares using Stata and R. London: Routledge.
  3. 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.
  4. Monecke, A., & Leisch, F. (2012). semPLS: Structural equation modeling using partial least squares. Journal of Statistical Software, 48(3), 1-32.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155-159.
  11. 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
  12. 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.
  13. 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.
  14. 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.
  15. 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.
  16. 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.
  17. 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.
  18. Hair, J. F., & Sarstedt, M. (2019). Composites vs. factors: Implications for choosing the right SEM method. Project Management Journal, 50(6), 1-6.
  19. 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.
  20. In K. G. Jöreskog & H. Wold (Eds.), Systems under indirect observation, part II (pp. 119-130). Amsterdam: North-Holland.
  21. 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.
  22. Marcoulides, G. A., & Saunders, C. (2006). PLS: A silver bullet? MIS Quarterly, 30(2), iii-ix.
  23. 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.
  24. 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.
  25. 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.
  26. 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).
  27. 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.
  28. 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.
  29. 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.
  30. 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.
  31. 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.
  32. 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.
  33. 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.
  34. 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.
  35. 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
  36. 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.
  37. Rigdon, E. E. (2012). Rethinking partial least squares path modeling: In praise of simple methods. Long Range Planning, 45(5-6), 341-358.
  38. Tenenhaus, M., Esposito Vinzi, V., Chatelin, Y.-M., & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48(1), 159-205.
  39. 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
  40. 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.
  41. 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.
  42. 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/
  43. Tenenhaus, M., Esposito Vinzi, V., Chatelin, Y.-M., & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48(1), 159-205.
  44. Wickham, H., & Grolemund, G. (2016). R for data science. Sebastopol, CA: O'Reilly Media. Retrieved from: https://r4ds. had. co. nz/ Suggested Readings
  45. Grolemund, G. (2014). Hands-on programming with R: Write your own functions and simulations. Sebastopol, CA: O'Reilly Media.
  46. Wickham, H. (2019). Advanced R. Boca Raton, FL: CRC press. Retrieved from: https://adv-r. hadley. nz/
  47. Wickham, H., & Grolemund, G. (2016). R for data science. Sebastopol, CA: O'Reilly Media. Retrieved from: https://r4ds. had. co. nz/ References
  48. 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.
  49. 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.
  50. 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.
  51. 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.
  52. Rigdon, E. E. (2012). Rethinking partial least squares path modeling: In praise of simple methods. Long Range Planning, 45(5-6), 341-358.
  53. 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.
  54. 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.
  55. Tenenhaus, M., Esposito Vinzi, V., Chatelin, Y.-M., & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48(1), 159-205.
  56. 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.
  57. 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.
  58. 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.
  59. Tenenhaus, M., Esposito Vinzi, V., Chatelin, Y.-M., & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48(1), 159-205. References
  60. Aguirre-Urreta, M. I., & Rönkkö, M. (2018). Statistical inference with PLSc using bootstrap confi- dence intervals. MIS Quarterly, 42(3), 1001-1020.
  61. 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.
  62. 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.
  63. 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.
  64. 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.
  65. 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.
  66. 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.
  67. 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.
  68. Aguirre-Urreta, M. I., & Rönkkö, M. (2018). Statistical inference with PLSc using bootstrap confi- dence intervals. MIS Quarterly, 42(3), 1001-1020.
  69. Bollen, K. A., & Diamantopoulos, A. (2017). In defense of causal-formative indicators: A minority report. Psychological Methods, 22(3), 581-596.
  70. 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.
  71. Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295-336.
  72. Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155-159.
  73. 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.
  74. 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.
  75. 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.
  76. 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.
  77. 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.
  78. 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.
  79. 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.
  80. 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.
  81. 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.
  82. 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.
  83. 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
  84. Aguirre-Urreta, M. I., & Rönkkö, M. (2018). Statistical inference with PLSc using bootstrap confi- dence intervals. MIS Quarterly, 42(3), 1001-1020.
  85. Becker, J.-M., Ringle, C. M., Sarstedt, M., & Völckner, F. (2015). How collinearity affects mixture regression results. Marketing Letters, 26(4), 643-659.
  86. Burnham, K. P., & Anderson, D. R. (2004). Multimodel inference: Understanding AIC and BIC in model selection. Sociological Research Methods, 33(2), 261-304.
  87. 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.
  88. Hair, J. F. (2021). Next generation prediction metrics for composite-based PLS-SEM. Industrial Management & Data Systems, 121(1), 5-11.
  89. 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.
  90. 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.
  91. Hair, J. F., & Sarstedt, M. (2021). Explanation plus prediction -The logical focus of project manage- ment research. Project Management Journal, 52(4), 319-322.
  92. 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.
  93. 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.
  94. 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.
  95. Rigdon, E. E. (2012). Rethinking partial least squares path modeling: In praise of simple methods. Long Range Planning, 45(5-6), 341-358.
  96. Sarstedt, M. & Danks, N. P. (2021). Prediction in HRM research-A gap between rhetoric and real- ity. Human Resource Management Journal, forthcoming.
  97. 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.
  98. 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.
  99. 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.
  100. 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.
  101. 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.
  102. 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.
  103. 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.
  104. 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.
  105. 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.
  106. Hayes, A. F. (2018). Introduction to mediation, moderation, and conditional process analysis: A regres- sion-based approach (2nd ed.). New York, NY: Guilford Press.
  107. 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.
  108. 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.
  109. 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.
  110. 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
  111. 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.
  112. 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.
  113. 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.
  114. 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.
  115. 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.
  116. 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.
  117. 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.
  118. 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.
  119. 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

Cited by

Sorted by citations

Related

See all

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
DOI
10.1007/978-3-030-80519-7
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
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

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