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Partial Least Squares Structural Equation Modeling

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Abstract

Partial least squares structural equation modeling (PLS-SEM) has become a popular method for estimating path models with latent variables and their relationships. A common goal of PLS-SEM analyses is to identify key success factors and sources of competitive advantage for important target constructs such as customer satisfaction, customer loyalty, behavioral intentions, and user behavior. Building on an introduction of the fundamentals of measurement and structural theory, this chapter explains how to specify and estimate path models using PLS-SEM. Complementing the introduction of the PLS-SEM method and the description of how to evaluate analysis results, the chapter also offers an overview of complementary analytical techniques. A PLS-SEM application of the widely recognized corporate reputation model illustrates the method.

Keywords

Partial least squares structural equation modeling PLS-SEM Path model analysis Composite modeling Results evaluation 
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Notes

Acknowledgments

This chapter uses the statistical software SmartPLS 3 (https://www.smartpls.com). Ringle acknowledges a financial interest in SmartPLS.

References

  1. Aaker, D. A. (1991). Managing brand equity: Capitalizing on the value of a brand name. New York: Free Press.Google Scholar
  2. Aguirre-Urreta, M. I., & Rönkkö, M. (2018). Statistical inference with PLSc using bootstrap confidence intervals. MIS Quarterly, 42(3), 1001–1020.CrossRefGoogle Scholar
  3. Akter, S., Fosso Wamba, S., & Dewan, S. (2017). Why PLS-SEM is suitable for complex modeling? An empirical illustration in big data analytics quality. Production Planning & Control, 28(11–12), 1011–1021.CrossRefGoogle Scholar
  4. Albers, S. (2010). PLS and success factor studies in marketing. 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. 409–425). Berlin/Heidelberg: Springer.CrossRefGoogle Scholar
  5. Ali, F., Rasoolimanesh, S. M., Sarstedt, M., Ringle, C. M., & Ryu, K. (2018). An assessment of the use of partial least squares structural equation modeling (PLS-SEM) in hospitality research. The International Journal of Contemporary Hospitality Management, 30(1), 514–538.CrossRefGoogle Scholar
  6. Avkiran, N. K., & Ringle, C. M. (Eds.). (2018). Partial least squares structural equation modeling: Recent advances in banking and finance. Cham: Springer.Google Scholar
  7. Baumgartner, H., & Homburg, C. (1996). Applications of structural equation modeling in marketing and consumer research: A review. International Journal of Research in Marketing, 13(2), 139–161.CrossRefGoogle Scholar
  8. Bayonne, E., Marin-Garcia, J. A., & Alfalla-Luque, R. (2020). Partial least squares (PLS) in operations management research: Insights from a systematic literature review. Journal of Industrial Engineering and Management, 13(3), 565–597.CrossRefGoogle Scholar
  9. Becker, J.-M., & Ismail, I. R. (2016). Accounting for sampling weights in PLS path modeling: Simulations and empirical examples. European Management Journal, 34(6), 606–617.CrossRefGoogle Scholar
  10. Becker, J.-M., Rai, A., & Rigdon, E. E. (2013a). Predictive validity and formative measurement in structural equation modeling: Embracing practical relevance. In 2013 Proceedings of the International Conference on Information Systems, Milan.Google Scholar
  11. Becker, J.-M., Rai, A., Ringle, C. M., & Völckner, F. (2013b). Discovering unobserved heterogeneity in structural equation models to avert validity threats. MIS Quarterly, 37(3), 665–694.CrossRefGoogle Scholar
  12. Bentler, P. M., & Huang, W. (2014). On components, latent variables, PLS and simple methods: Reactions to Rigdon’s rethinking of PLS. Long Range Planning, 47(3), 138–145.CrossRefGoogle Scholar
  13. Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley.CrossRefGoogle Scholar
  14. Bollen, K. A. (2002). Latent variables in psychology and the social sciences. Annual Review of Psychology, 53(1), 605–634.CrossRefGoogle Scholar
  15. Bollen, K. A. (2011). Evaluating effect, composite, and causal indicators in structural equation models. MIS Quarterly, 35(2), 359–372.CrossRefGoogle Scholar
  16. Bollen, K. A., & Bauldry, S. (2011). Three Cs in measurement models: Causal indicators, composite indicators, and covariates. Psychological Methods, 16(3), 265–284.CrossRefGoogle Scholar
  17. Bollen, K. A., & Diamantopoulos, A. (2017). In defense of causal–formative indicators: A minority report. Psychological Methods, 22(3), 581–596.CrossRefGoogle Scholar
  18. Bollen, K. A., & Lennox, R. (1991). Conventional wisdom on measurement: A structural equation perspective. Psychological Bulletin, 110(2), 305–314.CrossRefGoogle Scholar
  19. Borsboom, D., Mellenbergh, G. J., & van Heerden, J. (2003). The theoretical status of latent variables. Psychological Review, 110(2), 203–219.CrossRefGoogle Scholar
  20. Burnham, K. P., & Anderson, D. R. (2002). Model selection and multimodel inference: A practical information-theoretic approach (2nd ed.). Heidelberg: Springer.Google Scholar
  21. Carlson, K. D., & Herdman, A. O. (2012). Understanding the impact of convergent validity on research results. Organizational Research Methods, 15(1), 17–32.CrossRefGoogle Scholar
  22. Cenfetelli, R. T., & Bassellier, G. (2009). Interpretation of formative measurement in information systems research. MIS Quarterly, 33(4), 689–708.CrossRefGoogle Scholar
  23. Cepeda Carrión, G., Cegarra-Navarro, J.-G., & Cillo, V. (2019). Tips to use partial least squares structural equation modelling (PLS-SEM) in knowledge management. Journal of Knowledge Management, 23(1), 67–89.CrossRefGoogle Scholar
  24. 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.CrossRefGoogle Scholar
  25. Cheah, J.-H., Roldán, J. L., Ciavolino, E., Ting, H., & Ramayah, T. (2020). Sampling weight adjustments in partial least squares structural equation modeling: Guidelines and illustrations. Total Quality Management & Business Excellence, forthcoming.Google Scholar
  26. Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295–336). Mahwah: Lawrence Erlbaum.Google Scholar
  27. Chin, W. W. (2010). How to write up and report PLS analyses. 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. 655–690). Heidelberg: Springer.CrossRefGoogle Scholar
  28. Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003). A partial least squares latent variable modeling 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.CrossRefGoogle Scholar
  29. 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 information systems research. Industrial Management & Data Systems, 120(12), 2161–2209.CrossRefGoogle Scholar
  30. Cho, G., & Choi, J. Y. (2020). An empirical comparison of generalized structured component analysis and partial least squares path modeling under variance-based structural equation models. Behaviormetrika, 47, 243–272.CrossRefGoogle Scholar
  31. Cho, G., Hwang, H., Kim, S., Lee, J., Sarstedt, M., & Ringle, C. M. (2021). A comparative study of the predictive power of component-based approaches to structural equation modeling. Working Paper.Google Scholar
  32. Chou, C.-P., Bentler, P. M., & Satorra, A. (1991). Scaled test statistics and robust standard errors for non-Normal data in covariance structure analysis: A Monte Carlo study. British Journal of Mathematical and Statistical Psychology, 44(2), 347–357.CrossRefGoogle Scholar
  33. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Mahwah: Lawrence Erlbaum.Google Scholar
  34. Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159.CrossRefGoogle Scholar
  35. Danks, N., & Ray, S. (2018). Predictions from partial least squares models. In F. Ali, S. M. Rasoolimanesh, & C. Cobanoglu (Eds.), Applying partial least squares in tourism and hospitality research (pp. 35–52). Bingley: Emerald.CrossRefGoogle Scholar
  36. Danks, N. P., Sharma, P. N., & Sarstedt, M. (2020). Model selection uncertainty and multimodel inference in partial least squares structural equation modeling (PLS-SEM). Journal of Business Research, 113, 13–24.CrossRefGoogle Scholar
  37. Diamantopoulos, A. (2006). The error term in formative measurement models: Interpretation and modeling implications. Journal of Modelling in Management, 1(1), 7–17.CrossRefGoogle Scholar
  38. Diamantopoulos, A. (2011). Incorporating formative measures into covariance-based structural equation models. MIS Quarterly, 35(2), 335–358.CrossRefGoogle Scholar
  39. Diamantopoulos, A., & Winklhofer, H. M. (2001). Index construction with formative indicators: An alternative to scale development. Journal of Marketing Research, 38(2), 269–277.CrossRefGoogle Scholar
  40. 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.CrossRefGoogle Scholar
  41. Dijkstra, T. K. (2010). Latent variables and indices: Herman Wold’s basic design and partial least squares. 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. 23–46). Berlin/Heidelberg: Springer.CrossRefGoogle Scholar
  42. 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.CrossRefGoogle Scholar
  43. Dijkstra, T. K., & Henseler, J. (2015a). Consistent and asymptotically normal PLS estimators for linear structural equations. Computational Statistics & Data Analysis, 81, 10–23.Google Scholar
  44. Dijkstra, T. K., & Henseler, J. (2015b). Consistent partial least squares path modeling. MIS Quarterly, 39(2), 297–316.CrossRefGoogle Scholar
  45. do Valle, P. O., & Assaker, G. (2016). Using partial least squares structural equation modeling in tourism research: A review of past research and recommendations for future applications. Journal of Travel Research, 55(6), 695–708.CrossRefGoogle Scholar
  46. Douglas, H. E. (2009). Reintroducing prediction to explanation. Philosophy of Science, 76(4), 444–463.CrossRefGoogle Scholar
  47. Eberl, M. (2010). An application of PLS in multi-group analysis: The need for differentiated corporate-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/Heidelberg: Springer.CrossRefGoogle Scholar
  48. Eberl, M., & Schwaiger, M. (2005). Corporate reputation: Disentangling the effects on financial performance. European Journal of Marketing, 39(7/8), 838–854.CrossRefGoogle Scholar
  49. Edwards, J. R., & Bagozzi, R. P. (2000). On the nature and direction of relationships between constructs and measures. Psychological Methods, 5(2), 155–174.CrossRefGoogle Scholar
  50. Esposito Vinzi, V., Chin, W. W., Henseler, J., & Wang, H. (Eds.). (2010). Handbook of partial least squares: Concepts, methods and applications (Springer handbooks of computational statistics series) (Vol. II). Heidelberg: Springer.Google Scholar
  51. Evermann, J., & Tate, M. (2016). Assessing the predictive performance of structural equation model estimators. Journal of Business Research, 69(10), 4565–4582.CrossRefGoogle Scholar
  52. Falk, R. F., & Miller, N. B. (1992). A primer for soft modeling. Akron: University of Akron Press.Google Scholar
  53. Fordellone, M., & Vichi, M. (2020). Finding groups in structural equation modeling through the partial least squares algorithm. Computational Statistics & Data Analysis, 147, 106957.CrossRefGoogle Scholar
  54. Fornell, C. G., & Bookstein, F. L. (1982). Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research, 19(4), 440–452.CrossRefGoogle Scholar
  55. Fornell, C. G., Johnson, M. D., Anderson, E. W., Cha, J., & Bryant, B. E. (1996). The American customer satisfaction index: Nature, purpose, and findings. Journal of Marketing, 60(4), 7–18.CrossRefGoogle Scholar
  56. Franke, G., & Sarstedt, M. (2019). Heuristics versus statistics in discriminant validity testing: A comparison of four procedures. Internet Research, 29(3), 430–447.CrossRefGoogle Scholar
  57. Garson, G. D. (2016). Partial least squares regression and structural equation models. Asheboro: Statistical Associates.Google Scholar
  58. George, D., & Mallery, P. (2019). IBM SPSS statistics 25 step by step: A simple guide and reference (15th ed.). New York: Routledge.CrossRefGoogle Scholar
  59. Geweke, J., & Meese, R. (1981). Estimating regression models of finite but unknown order. International Economic Review, 22(1), 55–70.CrossRefGoogle Scholar
  60. 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.Google Scholar
  61. Goodhue, D. L., Lewis, W., & Thompson, R. (2012). Does PLS have advantages for small sample size or non-Normal data? MIS Quarterly, 36(3), 981–1001.CrossRefGoogle Scholar
  62. Grace, J. B., & Bollen, K. A. (2008). Representing general theoretical concepts in structural equation models: The role of composite variables. Environmental and Ecological Statistics, 15(2), 191–213.CrossRefGoogle Scholar
  63. Gregor, S. (2006). The nature of theory in information systems. MIS Quarterly, 30(3), 611–642.CrossRefGoogle Scholar
  64. Gudergan, S. P., Ringle, C. M., Wende, S., & Will, A. (2008). Confirmatory tetrad analysis in PLS path modeling. Journal of Business Research, 61(12), 1238–1249.CrossRefGoogle Scholar
  65. Haenlein, M., & Kaplan, A. M. (2004). A Beginner's guide to partial least squares analysis. Understanding Statistics, 3(4), 283–297.CrossRefGoogle Scholar
  66. Hahn, C., Johnson, M. D., Herrmann, A., & Huber, F. (2002). Capturing customer heterogeneity using a finite mixture PLS approach. Schmalenbach Business Review, 54(3), 243–269.CrossRefGoogle Scholar
  67. Hair, J. F. (2021). Next-generation prediction metrics for composite-based PLS-SEM. Industrial Management & Data Systems, 121(1), 5–11.Google Scholar
  68. Hair, J. F., & Sarstedt, M. (2019). Composites vs. factors: Implications for choosing the right SEM method. Project Management Journal, 50(6), 1–6.CrossRefGoogle Scholar
  69. Hair, J. F., & Sarstedt, M. (2021a). Data, measurement, and causal inferences in machine learning: Opportunities and challenges for marketing. Journal of Marketing Theory & Practice, 29(1), 65–77.Google Scholar
  70. Hair, J. F., & Sarstedt, M. (2021b). Explanation plus prediction – The logical focus of project management research. Project Management Journal, forthcoming.Google Scholar
  71. 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.CrossRefGoogle Scholar
  72. Hair, J. F., Sarstedt, M., Pieper, T. M., & Ringle, C. M. (2012a). The use of partial least squares structural equation modeling in strategic management research: A review of past practices and recommendations for future applications. Long Range Planning, 45(5-6), 320–340.CrossRefGoogle Scholar
  73. Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012b). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40(3), 414–433.CrossRefGoogle Scholar
  74. Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Planning, 46(1-2), 1–12.CrossRefGoogle Scholar
  75. Hair, J. F., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. L. (2017a). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data Systems, 117(3), 442–458.CrossRefGoogle Scholar
  76. Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., & Thiele, K. O. (2017b). Mirror, mirror on the wall: A comparative evaluation of composite-based structural equation modeling methods. Journal of the Academy of Marketing Science, 45(5), 616–632.Google Scholar
  77. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2018a). Multivariate data analysis (8th ed.). Mason: Cengage.Google Scholar
  78. Hair, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2018b). Advanced issues in partial least squares structural equation modeling (PLS-SEM). Thousand Oaks: Sage.Google Scholar
  79. Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019a). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24.CrossRefGoogle Scholar
  80. Hair, J. F., Sarstedt, M., & Ringle, C. M. (2019b). Rethinking some of the rethinking of partial least squares. European Journal of Marketing, 53(4), 566–584.CrossRefGoogle Scholar
  81. Hair, J. F., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101–110.Google Scholar
  82. 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: Sage.Google Scholar
  83. Helm, S., Eggert, A., & Garnefeld, I. (2010). Modelling the impact of corporate reputation on customer 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 (Springer handbooks of computational statistics series) (Vol. II, pp. 515–534). Heidelberg: Springer.CrossRefGoogle Scholar
  84. Henseler, J. (2017). Using variance-based structural equation modeling for empirical advertising research at the Interface of design and behavioral research. Journal of Advertising, 46(1), 178–192.CrossRefGoogle Scholar
  85. Henseler, J. (2021). Composite-based structural equation modeling: Analyzing latent and emergent variables. New York: Guilford Press.Google Scholar
  86. Henseler, J., & Sarstedt, M. (2013). Goodness-of-fit indices for partial least squares path modeling. Computational Statistics, 28(2), 565–580.CrossRefGoogle Scholar
  87. Henseler, J., & Schuberth, F. (2020). Using confirmatory composite analysis to assess emergent variables in business research. Journal of Business Research, 120, 147–156.CrossRefGoogle Scholar
  88. Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In R. R. Sinkovics & P. N. Ghauri (Eds.), Advances in international marketing (Vol. 20, pp. 277–320). Bingley: Emerald.Google Scholar
  89. Henseler, J., Ringle, C. M., & Sarstedt, M. (2012). Using partial least squares path modeling in international advertising research: Basic concepts and recent issues. In S. Okazaki (Ed.), Handbook of research in international advertising (pp. 252–276). Cheltenham: Edward Elgar Publishing.Google Scholar
  90. 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(2), 182–209.CrossRefGoogle Scholar
  91. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135.CrossRefGoogle Scholar
  92. Henseler, J., Hubona, G. S., & Ray, P. A. (2016a). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management & Data Systems, 116(1), 2–20.CrossRefGoogle Scholar
  93. Henseler, J., Ringle, C. M., & Sarstedt, M. (2016b). Testing measurement invariance of composites using partial least squares. International Marketing Review, 33(3), 405–431.CrossRefGoogle Scholar
  94. Houston, M. B. (2004). Assessing the validity of secondary data proxies for marketing constructs. Journal of Business Research, 57(2), 154–161.CrossRefGoogle Scholar
  95. Hui, B. S., & Wold, H. (1982). Consistency and consistency at large of partial least squares estimates. In K. G. Jöreskog & H. Wold (Eds.), Systems under indirect observation, part II (pp. 119–130). Amsterdam: North-Holland.Google Scholar
  96. Hult, G. T. M., Hair, J. F., Dorian, P., Ringle, C. M., Sarstedt, M., & Pinkwart, A. (2018). Addressing endogeneity in marketing applications of partial least squares structural equation modeling. Journal of International Marketing, 26(3), 1–21.Google Scholar
  97. Hwang, H., Sarstedt, M., Cheah, J.-H., & Ringle, C. M. (2020). A concept analysis of methodological research on composite-based structural equation modeling: Bridging PLSPM and GSCA. Behaviormetrika, 47(1), 219–241.CrossRefGoogle Scholar
  98. Jöreskog, K. G. (1971). Simultaneous factor analysis in several populations. Psychometrika, 36(4), 409–426.CrossRefGoogle Scholar
  99. Jöreskog, K. G. (1973). A general method for estimating a linear structural equation system. In A. S. Goldberger & O. D. Duncan (Eds.), Structural equation models in the social sciences (pp. 255–284). New York: Seminar Press.Google Scholar
  100. 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.Google Scholar
  101. Kaufmann, L., & Gaeckler, J. (2015). A structured review of partial least squares in supply chain management research. Journal of Purchasing and Supply Management, 21(4), 259–272.CrossRefGoogle Scholar
  102. 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.CrossRefGoogle Scholar
  103. 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.Google Scholar
  104. Latan, H., & Noonan, R. (Eds.). (2017). Partial least squares structural equation modeling: Basic concepts, methodological issues and applications. Berlin/Heidelberg: Springer.Google Scholar
  105. Lee, L., Petter, S., Fayard, D., & Robinson, S. (2011). On the use of partial least squares path modeling in accounting research. International Journal of Accounting Information Systems, 12(4), 305–328.CrossRefGoogle Scholar
  106. Lei, P.-W., & Wu, Q. (2012). Estimation in structural equation modeling. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (pp. 164–179). New York: Guilford Press.Google Scholar
  107. Liengaard, B. D., Sharma, P. N., Hult, G. T. M., Jensen, M. B., Sarstedt, M., Hair, J. F., & Ringle, C. M. (2021). Prediction: Coveted, yet forsaken? Introducing a cross-validated predictive ability test in partial least squares path modeling. Decision Sciences, 52(2), 362–292.Google Scholar
  108. Leischnig, A., Henneberg, S. C., & Thornton, S. C. (2016). Net versus combinatory effects of firm and industry antecedents of sales growth. Journal of Business Research, 69(9), 3576–3583.Google Scholar
  109. Lohmöller, J.-B. (1989). Latent variable path modeling with partial least squares. Heidelberg: Physica.CrossRefGoogle Scholar
  110. Manley, S. C., Hair, J. F., Williams, R. I., & McDowell, W. C. (2020). Essential new PLS-SEM analysis methods for your entrepreneurship analytical toolbox. International Entrepreneurship and Management Journal, forthcoming.Google Scholar
  111. Marcoulides, G. A., & Chin, W. W. (2013). You write, but others read: Common methodological misunderstandings in PLS and related methods. In H. Abdi, W. W. Chin, V. Esposito Vinzi, G. Russolillo, & L. Trinchera (Eds.), New perspectives in partial least squares and related methods (Springer proceedings in Mathematics & Statistics) (Vol. 56, pp. 31–64). New York: Springer.CrossRefGoogle Scholar
  112. Marcoulides, G. A., & Saunders, C. (2006). Editor’s comments: PLS: A silver bullet? MIS Quarterly, 30(2), iii–ix.CrossRefGoogle Scholar
  113. Marcoulides, G. A., Chin, W. W., & Saunders, C. (2012). When imprecise statistical statements become problematic: A response to Goodhue, Lewis, and Thompson. MIS Quarterly, 36(3), 717–728.CrossRefGoogle Scholar
  114. Mason, C. H., & Perreault, W. D. (1991). Collinearity, power, and interpretation of multiple regression analysis. Journal of Marketing Research, 28(3), 268–280.CrossRefGoogle Scholar
  115. Mateos-Aparicio, G. (2011). Partial least squares (PLS) methods: Origins, evolution, and application to social sciences. Communications in Statistics - Theory and Methods, 40(13), 2305–2317.CrossRefGoogle Scholar
  116. Matthews, L. (2017). Applying multigroup analysis in PLS-SEM: A step-by-step process. In H. Latan & R. Noonan (Eds.), Partial least squares path modeling: Basic concepts, methodological issues and applications (pp. 219–243). Cham: Springer.CrossRefGoogle Scholar
  117. McDonald, R. P. (1996). Path analysis with composite variables. Multivariate Behavioral Research, 31(2), 239–270.CrossRefGoogle Scholar
  118. Mehmetoglu, M., & Venturini, S. (2021). Structural equation modelling with partial least squares using Stata and R. Boca Raton: CRC Press.CrossRefGoogle Scholar
  119. Memon, M. A., Cheah, J. H., Ramayah, H. T., Chuah, F., & Cham, T. H. (2019). Moderation analysis: Issues and guidelines. Journal of Applied Structural Equation Modeling, 3(1), i–xi.CrossRefGoogle Scholar
  120. Nitzl, C. (2016). The use of partial least squares structural equation modelling (PLS-SEM) in management accounting research: Directions for future theory development. Journal of Accounting Literature, 37, 19–35.CrossRefGoogle Scholar
  121. Nitzl, C., & Chin, W. W. (2017). The case of partial least squares (PLS) path modeling in managerial accounting. Journal of Management Control, 28(2), 137–156.CrossRefGoogle Scholar
  122. Nitzl, C., Roldán, J. L., & Cepeda Carrión, G. (2016). Mediation analysis in partial least squares path modeling: Helping researchers discuss more sophisticated models. Industrial Management & Data Systems, 119(9), 1849–1864.CrossRefGoogle Scholar
  123. Noonan, R., & Wold, H. (1982). PLS path modeling with indirectly observed variables: A comparison of alternative estimates for the latent variable. In K. G. Jöreskog & H. Wold (Eds.), Systems under indirect observations: Part II (pp. 75–94). Amsterdam: North-Holland.Google Scholar
  124. Nunnally, J. C., & Bernstein, I. (1994). Psychometric theory (3rd ed.). New York: McGraw Hill.Google Scholar
  125. Olsson, U. H., Foss, T., Troye, S. V., & Howell, R. D. (2000). The performance of ML, GLS, and WLS estimation in structural equation modeling under conditions of misspecification and nonnormality. Structural Equation Modeling: A Multidisciplinary Journal, 7(4), 557–595.CrossRefGoogle Scholar
  126. Peng, D. X., & Lai, F. (2012). Using partial least squares in operations management research: A practical guideline and summary of past research. Journal of Operations Management, 30(6), 467–480.CrossRefGoogle Scholar
  127. Raithel, S., & Schwaiger, M. (2015). The effects of corporate reputation perceptions of the general public on shareholder value. Strategic Management Journal, 36(6), 945–956.CrossRefGoogle Scholar
  128. Raithel, S., Sarstedt, M., Scharf, S., & Schwaiger, M. (2012). On the value relevance of customer satisfaction: Multiple drivers and multiple markets. Journal of the Academy of Marketing Science, 40(4), 509–525.CrossRefGoogle Scholar
  129. Ramayah, T., Cheah, J., Chuah, F., Ting, H., & Memon, M. A. (2016). Partial least squares structural equation modeling (PLS-SEM) using SmartPLS 3.0: An updated and practical guide to statistical analysis. Kuala Lumpur: Pearson.Google Scholar
  130. Rasoolimanesh, S. M., Ringle, C. M., Sarstedt, M., & Olya, H. (2021). The combined use of symmetric and asymmetric approaches: Partial least squares-structural equation modeling and fuzzy-set qualitative comparative analysis. International Journal of Contemporary Hospitality Management, forthcoming.Google Scholar
  131. Reinartz, W. J., Haenlein, M., & Henseler, J. (2009). An empirical comparison of the efficacy of covariance-based and variance-based SEM. International Journal of Research in Marketing, 26(4), 332–344.CrossRefGoogle Scholar
  132. Rhemtulla, M., van Bork, R., & Borsboom, D. (2020). Worse than measurement error: Consequences of inappropriate latent variable measurement models. Psychological Methods, 25(1), 30–45.CrossRefGoogle Scholar
  133. Richter, N. F., Sinkovics, R. R., Ringle, C. M., & Schlägel, C. (2016). A critical look at the use of SEM in international business research. International Marketing Review, 33(3), 376–404.CrossRefGoogle Scholar
  134. Richter, N. F., Schubring, S., Hauff, S., Ringle, C. M.. & Sarstedt, M. (2020). When predictors of outcomes are necessary: Guidelines for the combined use of PLS-SEM and NCA. Industrial Management & Data Systems, 120(12), 2243–2267.Google Scholar
  135. Rigdon, E. E. (2012). Rethinking partial least squares path modeling: In praise of simple methods. Long Range Planning, 45(5–6), 341–358.CrossRefGoogle Scholar
  136. Rigdon, E. E. (2013). Partial least squares path modeling. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling. A second course (2nd ed., pp. 81–116). Charlotte: Information Age Publishing.Google Scholar
  137. Rigdon, E. E. (2016). Choosing PLS path modeling as analytical method in European management research: A realist perspective. European Management Journal, 34(6), 598–605.CrossRefGoogle Scholar
  138. Rigdon, E. E., Becker, J.-M., Rai, A., Ringle, C. M., Diamantopoulos, A., Karahanna, E., Straub, D., & Dijkstra, T. K. (2014). Conflating antecedents and formative indicators: A comment on Aguirre-Urreta and Marakas. Information Systems Research, 25(4), 780–784.CrossRefGoogle Scholar
  139. Rigdon, E. E., Sarstedt, M., & Ringle, C. M. (2017). On comparing Results from CB-SEM and PLS-SEM. Five perspectives and five recommendations. Marketing ZFP–Journal of Research and Management, 39(3), 4–16.CrossRefGoogle Scholar
  140. Rigdon, E. E., Becker, J. M., & Sarstedt, M. (2019). Factor indeterminacy as metrological uncertainty: Implications for advancing psychological measurement. Multivariate Behavioral Research, 54(3), 429–443.CrossRefGoogle Scholar
  141. Ringle, C. M. (2019). What makes a great textbook? Lessons learned from joe Hair. In B. J. Babin & M. Sarstedt (Eds.), The great facilitator: Reflections on the contributions of Joseph F. Hair, Jr. to marketing and business research (pp. 131–150). Cham: Springer.CrossRefGoogle Scholar
  142. Ringle, C. M., & Sarstedt, M. (2016). Gain more insight from your PLS-SEM results: The importance-performance map analysis. Industrial Management & Data Systems, 116(9), 1865–1886.CrossRefGoogle Scholar
  143. Ringle, C. M., Sarstedt, M., & Straub, D. W. (2012). Editor’s comments: A critical look at the use of PLS-SEM in MIS quarterly. MIS Quarterly, 36(1), iii–xiv.CrossRefGoogle Scholar
  144. Ringle, C. M., Sarstedt, M., Schlittgen, R., & Taylor, C. R. (2013). PLS path modeling and evolutionary segmentation. Journal of Business Research, 66(9), 1318–1324.CrossRefGoogle Scholar
  145. Ringle, C. M., Sarstedt, M., & Schlittgen, R. (2014). Genetic algorithm segmentation in partial least squares structural equation modeling. OR Spectrum, 36(1), 251–276.CrossRefGoogle Scholar
  146. Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS 3 [computer software]. Bönningstedt: SmartPLS. Retrieved from https://www.smartpls.com.
  147. Ringle, C. M., Sarstedt, M., Mitchell, R., & Gudergan, S. P. (2020). Partial least squares structural equation modeling in HRM research. International Journal of Human Resource Management, 31(12), 1617–1643.CrossRefGoogle Scholar
  148. 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). Hershey: IGI Global.CrossRefGoogle Scholar
  149. Russo, D., & Stol, K. J. (2021). PLS-SEM for software engineering research: An introduction and survey. ACM Computing Surveys, 54(4), 1–38.Google Scholar
  150. Sarstedt, M. (2019). Der Knacks and a Silver Bullet. In B. J. Babin & M. Sarstedt (Eds.), The great facilitator: Reflections on the contributions of Joseph F. Hair, Jr. to marketing and business research (pp. 155–164). Cham: Springer.CrossRefGoogle Scholar
  151. Sarstedt, M., & Cheah, J.-H. (2019). Partial least squares structural equation modeling using SmartPLS: A software review. Journal of Marketing Analytics, 7(3), 196–202.CrossRefGoogle Scholar
  152. Sarstedt, M., & Mooi, E. (2019). A concise guide to market research: The process, data, and methods using IBM SPSS statistics (3rd ed.). Berlin/Heidelberg: Springer.Google Scholar
  153. Sarstedt, M., Becker, J.-M., Ringle, C. M., & Schwaiger, M. (2011). Uncovering and treating unobserved heterogeneity with FIMIX-PLS: Which model selection criterion provides an appropriate number of segments? Schmalenbach Business Review, 63(1), 34–62.CrossRefGoogle Scholar
  154. Sarstedt, M., Wilczynski, P., & Melewar, T. C. (2013). Measuring reputation in global markets – A comparison of reputation measures’ convergent and criterion validities. Journal of World Business, 48(3), 329–339.Google Scholar
  155. Sarstedt, M., Ringle, C. M., Smith, D., Reams, R., & Hair, J. F. (2014). Partial least squares structural equation modeling (PLS-SEM): A useful tool for family business researchers. Journal of Family Business Strategy, 5(1), 105–115.CrossRefGoogle Scholar
  156. 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.Google Scholar
  157. 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.CrossRefGoogle Scholar
  158. Sarstedt, M., Hair, J. F., Nitzl, C., Ringle, C. M., & Howard, M. C. (2020a). Beyond a tandem analysis of SEM and PROCESS: Use PLS-SEM for mediation analyses! International Journal of Market Research, 62(3), 288–299.CrossRefGoogle Scholar
  159. Sarstedt, M., Ringle, C. M., Cheah, J. H., Ting, H., Moisescu, O. I., & Radomir, L. (2020b). Structural model robustness checks in PLS-SEM. Tourism Economics, 26(4), 531–554.CrossRefGoogle Scholar
  160. Schlittgen, R., Ringle, C. M., Sarstedt, M., & Becker, J.-M. (2016). Segmentation of PLS path models by iterative reweighted regressions. Journal of Business Research, 69(10), 4583–4592.CrossRefGoogle Scholar
  161. Schloderer, M. P., Sarstedt, M., & Ringle, C. M. (2014). The relevance of reputation in the nonprofit sector: The moderating effect of socio-demographic characteristics. International Journal of Nonprofit and Voluntary Sector Marketing, 19(2), 110–126.CrossRefGoogle Scholar
  162. Schuberth, F., Henseler, J., & Dijkstra, T. K. (2018). Confirmatory composite analysis. Frontiers in Psychology, 9, 2541.CrossRefGoogle Scholar
  163. Schwaiger, M. (2004). Components and parameters of corporate reputation: An empirical study. Schmalenbach Business Review, 56(1), 46–71.CrossRefGoogle Scholar
  164. Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461–464.CrossRefGoogle Scholar
  165. Shah, R., & Goldstein, S. M. (2006). Use of structural equation modeling in operations management research: Looking back and forward. Journal of Operations Management, 24(2), 148–169.Google Scholar
  166. Sharma, P. N., Shmueli, G., Sarstedt, M., Danks, N., & Ray S. (2018). Prediction-oriented model selection in partial least squares path modeling. Decision Sciences, forthcoming.Google Scholar
  167. Sharma, P. N., Liengaard, B. D., Hair, J. F., Sarstedt, M., & Ringle C. M. (2021). Predictive model assessment and selection in composite-based modeling using PLS-SEM: Extensions and guidelines for using CVPAT. Working Paper.Google Scholar
  168. Shmueli, G. (2010). To explain or to predict? Statistical Science, 25(3), 289–310.CrossRefGoogle Scholar
  169. Shmueli, G., & Koppius, O. R. (2011). Predictive analytics in information systems research. MIS Quarterly, 35(3), 553–572.CrossRefGoogle Scholar
  170. Shmueli, G., Ray, S., Velasquez Estrada, J. M., & Chatla, S. B. (2016). The elephant in the room: Evaluating the predictive performance of PLS models. Journal of Business Research, 69(10), 4552–4564.CrossRefGoogle Scholar
  171. Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322–2347.CrossRefGoogle Scholar
  172. Shugan, S. (2009). Relevancy is robust prediction, not alleged realism. Marketing Science, 28(5), 991–998.CrossRefGoogle Scholar
  173. Stieglitz, S., Linh, D.-X., Bruns, A., & Neuberger, C. (2014). Social media analytics. An interdisciplinary approach and its implications for information systems. Business and Information Systems Engineering, 6, 89–96Google Scholar
  174. 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.CrossRefGoogle Scholar
  175. Tenenhaus, M., Esposito Vinzi, V., Chatelin, Y.-M., & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48(1), 159–205.CrossRefGoogle Scholar
  176. 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.CrossRefGoogle Scholar
  177. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.CrossRefGoogle Scholar
  178. Wagenmakers, E. J., & Farrell, S. (2004). AIC model selection using Akaike weights. Psychonomic Bulletin & Review, 11(1), 192–196.CrossRefGoogle Scholar
  179. Westland, J. C. (2019). Partial least squares path analysis. In Structural equation models: From paths to networks (2nd ed., pp. 17–38). Cham: Springer.CrossRefGoogle Scholar
  180. Willaby, H. W., Costa, D. S. J., Burns, B. D., MacCann, C., & Roberts, R. D. (2015). Testing complex models with small sample sizes: A historical overview and empirical demonstration of what partial least squares (PLS) can offer differential psychology. Personality and Individual Differences, 84, 73–78.CrossRefGoogle Scholar
  181. Wold, H. (1975). Path models with latent variables: The NIPALS approach. In H. M. Blalock, A. Aganbegian, F. M. Borodkin, R. Boudon, & V. Capecchi (Eds.), Quantitative sociology: International perspectives on mathematical and statistical modeling (pp. 307–357). New York: Academic.CrossRefGoogle Scholar
  182. Wold, H. (1980). Model construction and evaluation when theoretical knowledge is scarce: Theory and application of PLS. In J. Kmenta & J. B. Ramsey (Eds.), Evaluation of econometric models (pp. 47–74). New York: Academic.CrossRefGoogle Scholar
  183. 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.Google Scholar
  184. Wold, H. (1985). Partial least squares. In S. Kotz & N. L. Johnson (Eds.), Encyclopedia of statistical sciences (Vol. 6, pp. 581–591). New York: Wiley.Google Scholar
  185. Wong, K. K. K. (2019). Mastering partial least squares structural equation modeling (PLS-SEM) with SmartPLS in 38 hours. Bloomington: iUniverse.Google Scholar
  186. 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 reserarch. Frontiers of Engineering Management, forthcoming.Google Scholar

Authors and Affiliations

  1. 1.Faculty of Economics and ManagementOtto-von-Guericke University MagdeburgMagdeburgGermany
  2. 2.Faculty of Economics and Business AdministrationBabeș-Bolyai-UniversityClujRomania
  3. 3.Institute of Human Resource Management and Organizations (HRMO)Hamburg University of Technology (TUHH)HamburgGermany
  4. 4.Waikato Management SchoolUniversity of WaikatoHamiltonNew Zealand
  5. 5.Cleverdon Chair of BusinessMitchell College of Business, University of South AlabamaMobileUSA

Section editors and affiliations

  • Christian Homburg
    • 1
  • Martin Klarmann
    • 2
  • Arnd Vomberg
    • 3
  1. 1.University of MannheimMannheimGermany
  2. 2.Karlsruhe Institute of TechnologyKarlsruheGermany
  3. 3.University of GroningenGroningenNiederlande

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