Partial Least Squares Path Modeling: Updated Guidelines

  • Jörg Henseler
  • Geoffrey Hubona
  • Pauline Ash Ray
Chapter

Abstract

Partial least squares (PLS) path modeling is a variance-based structural equation modeling technique that is widely applied in business and social sciences. It is the method of choice if a structural equation model contains both factors and composites. This chapter aggregates new insights and offers a fresh look at PLS path modeling. It presents the newest developments, such as consistent PLS, confirmatory composite analysis, and the heterotrait-monotrait ratio of correlations (HTMT). PLS path modeling can be regarded as an instantiation of generalized canonical correlation analysis. It aims at modeling relationships between composites, i.e., linear combinations of observed variables. A recent extension, consistent PLS, makes it possible to also include factors in a PLS path model. The chapter illustrates how to specify a PLS path model consisting of construct measurement and structural relationships. It also shows how to integrate categorical variables. A particularly important consideration is model identification: Every construct measured by multiple indicators must be embedded into a nomological net, which means that there must be at least one other construct with which it is related. PLS path modeling results are useful for exploratory and confirmatory research. The chapter provides guidelines for assessing the fit of the overall model, the reliability and validity of the measurement model, and the relationships between constructs. Moreover, it provides a glimpse on various extensions of PLS, many of which will be described in more detail in later chapters of the book.

References

  1. Aguirre-Urreta, M., & Rönkkö, M. (2015). Sample size determination and statistical power analysis in PLS using R: An annotated tutorial. Communications of the Association for Information Systems, 36(3), 33–51.Google Scholar
  2. 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 (pp. 409–425). Berlin: Springer.CrossRefGoogle Scholar
  3. Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2010). On making causal claims: A review and recommendations. The Leadership Quarterly, 21(6), 1086–1120. doi:10.1016/j.leaqua.2010.10.010.CrossRefGoogle Scholar
  4. Becker, J.-M., Rai, A., & Rigdon, E. E. (2013a). Predictive validity and formative measurement in structural equation modeling: Embracing practical relevance. Proceedings of the International Conference on Information Systems (ICIS), Milan, Italy.Google Scholar
  5. 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
  6. Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588–606. doi:10.1037/0033-2909.88.3.588.CrossRefGoogle Scholar
  7. 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. doi:10.1016/j.lrp.2014.02.005.CrossRefGoogle Scholar
  8. Bollen, K. A., & Stine, R. A. (1992). Bootstrapping goodness-of-fit measures in structural equation models. Sociological Methods & Research, 21(2), 205–229. doi:10.1177/0049124192021002004.CrossRefGoogle Scholar
  9. Braojos-Gomez, J., Benitez-Amado, J., & Llorens-Montes, F. J. (2015). How do small firms learn to develop a social media competence? International Journal of Information Management, 35(4), 443–458. doi:10.1016/j.ijinfomgt.2015.04.003.CrossRefGoogle Scholar
  10. Buckler, F., & Hennig-Thurau, T. (2008). Identifying hidden structures in marketing’s structural models through universal structure modeling. An explorative Bayesian neural network complement to LISREL and PLS Market. Marketing: Journal of Research and Management, 4, 47–66.Google Scholar
  11. Byrne, B. M. (2013). Structural equation modeling with LISREL, PRELIS, and SIMPLIS: Basic concepts, applications, and programming. Stanford, CA: Psychology Press.MATHGoogle Scholar
  12. Cepeda Carrión, G., Henseler, J., Ringle, C. M., & Roldán, J. L. (2016). Prediction-oriented modeling in business research by means of PLS path modeling. Journal of Business Research, 69(10), 4545–4551. doi:10.1016/j.jbusres.2016.03.048.CrossRefGoogle Scholar
  13. Chen, Y., Wang, Y., Nevo, S., Benitez-Amado, J., & Kou, G. (2015). IT capabilities and product innovation performance: The roles of corporate entrepreneurship and competitive intensity. Information Management, 52(6), 643–657.CrossRefGoogle Scholar
  14. Chin, W. W. (2010). Bootstrap cross-validation indices for PLS path model assessment. In V. Esposito Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares: Concepts, methods and applications (pp. 83–97). New York: Springer.CrossRefGoogle Scholar
  15. Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Mahwah, NJ: Lawrence Erlbaum.MATHGoogle Scholar
  16. Cohen, J. (1994). The earth is round (p<.05). The American Psychologist, 49(12), 997–1003. doi:10.1037/0003-066X.49.12.997.CrossRefGoogle Scholar
  17. 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 Academy of Market Science, 40(3), 434–449. doi:10.1007/s11747-011-0300-3.CrossRefGoogle Scholar
  18. 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 (pp. 23–46). New York: Springer.CrossRefGoogle Scholar
  19. Dijkstra, T. K., & Henseler, J. (2011). Linear indices in nonlinear structural equation models: Best fitting proper indices and other composites. Quality and Quantity, 45(6), 1505–1518. doi:10.1007/s11135-010-9359-z.CrossRefGoogle Scholar
  20. Dijkstra, T. K., & Henseler, J. (2014). Assessing and testing the goodness-of-fit of PLS path models. Third VOC Conference, Leiden.Google Scholar
  21. Dijkstra, T. K., & Henseler, J. (2015a). Consistent and asymptotically normal PLS estimators for linear structural equations. Computational Statistics and Data Analysis, 81(1), 10–23. doi:10.1016/j.csda.2014.07.008.MathSciNetCrossRefGoogle Scholar
  22. Dijkstra, T. K., & Henseler, J. (2015b). Consistent partial least squares path modeling. MIS Quarterly, 39(2), 297–316.CrossRefGoogle Scholar
  23. Dijkstra, T. K., & Schermelleh-Engel, K. (2014). Consistent partial least squares for nonlinear structural equation models. Psychometrika, 79(4), 585–604. doi:10.1007/S11336-013-9370-0.MATHMathSciNetCrossRefGoogle Scholar
  24. Esposito Vinzi, V., Trinchera, L., & Amato, S. (2010). PLS path modeling: From foundations to recent developments and open issues for model assessment and improvement. Chap. 2. In V. E. Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares: Concepts, methods and applications (pp. 47–82). Berlin: Springer.CrossRefGoogle Scholar
  25. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. doi:10.2307/3151312.CrossRefGoogle Scholar
  26. French, B. F., & Finch, W. H. (2006). Confirmatory factor analytic procedures for the determination of measurement invariance. Structural Equation Modeling, 13(3), 378–402.MathSciNetCrossRefGoogle Scholar
  27. Goodhue, D. L., Lewis, W., & Thompson, R. L. (2011). A dangerous blind spot in IS research: False positives due to multicollinearity combined with measurement error. Paper presented at the AMCIS 2011, Detroit, MI.Google Scholar
  28. Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152.CrossRefGoogle Scholar
  29. 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. doi:10.1016/j.lrp.2012.09.008.CrossRefGoogle Scholar
  30. 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 Academy of Market Science, 40(3), 414–433. doi:10.1007/s11747-011-0261-6.CrossRefGoogle Scholar
  31. Henseler, J. (2010). On the convergence of the partial least squares path modeling algorithm. Computational Statistics, 25(1), 107–120. doi:10.1007/s00180-009-0164-x.MATHMathSciNetCrossRefGoogle Scholar
  32. Henseler, J. (2012). Why generalized structured component analysis is not universally preferable to structural equation modeling. Journal of Academy of Market Science, 40(3), 402–413. doi:10.1007/s11747-011-0298-6.CrossRefGoogle Scholar
  33. Henseler, J. (2015). Is the whole more than the sum of its parts? On the interplay of marketing and design research. Inaugural Lecture, University of Twente.Google Scholar
  34. Henseler, J., & Chin, W. W. (2010). A comparison of approaches for the analysis of interaction effects between latent variables using partial least squares path modeling. Structural Equation Modeling, 17(1), 82–109. doi:10.1080/10705510903439003.MathSciNetCrossRefGoogle Scholar
  35. Henseler, J., & Dijkstra, T. K. (2015). ADANCO 2.0. Kleve: Composite Modeling GmbH. Retrieved from http://www.compositemodeling.com.Google Scholar
  36. Henseler, J., & Fassott, G. (2010). Testing moderating effects in PLS path models: An illustration of available procedures. In V. Esposito Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares: Concepts, methods and applications (pp. 713–735). Berlin: Springer.CrossRefGoogle Scholar
  37. Henseler, J., & Sarstedt, M. (2013). Goodness-of-fit indices for partial least squares path modeling. Computational Statistics, 28(2), 565–580. doi:10.1007/s00180-012-0317-1.MATHMathSciNetCrossRefGoogle Scholar
  38. Henseler, J., Fassott, G., Dijkstra, T. K., & Wilson, B. (2012). Analysing quadratic effects of formative constructs by means of variance-based structural equation modelling. European Journal of Information Systems, 21(1), 99–112. doi:10.1057/ejis.2011.36.CrossRefGoogle Scholar
  39. Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., et al. (2014). Common beliefs and reality about PLS: Comments on Rönkkö & Evermann (2013). Organizational Research Methods, 17(2), 182–209. doi:10.1177/1094428114526928.CrossRefGoogle Scholar
  40. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of Academy of Market Science, 43(1), 115–135. doi:10.1007/s11747-014-0403-8.CrossRefGoogle Scholar
  41. Henseler, J., Ringle, C. M., & Sarstedt, M. (2016). Testing measurement invariance of composites using partial least squares. International Marketing Review, 33(3), 405–431. doi:10.1108/IMR-09-2014-0304.CrossRefGoogle Scholar
  42. Höök, K., & Löwgren, J. (2012). Strong concepts: Intermediate-level knowledge in interaction design research. ACM Transactions on Computer-Human Interaction (TOCHI), 19(3). doi:10.1145/2362364.2362371.
  43. Hu, L.-T., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3(4), 424–453. doi:10.1037/1082-989X.3.4.424.CrossRefGoogle Scholar
  44. Hu, L.-T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. doi:10.1080/10705519909540118.CrossRefGoogle Scholar
  45. Hwang, H., & Takane, Y. (2004). Generalized structured component analysis. Psychometrika, 69(1), 81–99. doi:10.1007/BF02295841.MATHMathSciNetCrossRefGoogle Scholar
  46. Kettenring, J. R. (1971). Canonical analysis of several sets of variables. Biometrika, 58(3), 433–451. doi:10.1093/biomet/58.3.433.MATHMathSciNetCrossRefGoogle Scholar
  47. Ketterlinus, R. D., Bookstein, F. L., Sampson, P. D., & Lamb, M. E. (1989). Partial least squares analysis in developmental psychopathology. Development and Psychopathology, 1(4), 351–371.CrossRefGoogle Scholar
  48. Krijnen, W. P., Dijkstra, T. K., & Gill, R. D. (1998). Conditions for factor (in)determinacy in factor analysis. Psychometrika, 63(4), 359–367. doi:10.1007/BF02294860.MATHMathSciNetCrossRefGoogle Scholar
  49. Lancelot-Miltgen, C., Henseler, J., Gelhard, C., & Popovič, A. (2016). Introducing new products that affect consumer privacy: A mediation model. Journal of Business Research, 69(10), 4659–4666.CrossRefGoogle Scholar
  50. Landis, R. S., Beal, D. J., & Tesluk, P. E. (2000). A comparison of approaches to forming composite measures in structural equation models. Organizational Research Methods, 3(2), 186–207.CrossRefGoogle Scholar
  51. Lohmöller, J.-B. (1989). Latent variable path modeling with partial least squares. Berlin: Springer.MATHCrossRefGoogle Scholar
  52. Maraun, M. D., & Halpin, P. F. (2008). Manifest and latent variates. Measurement: Interdisciplinary Research and Perspectives, 6(1–2), 113–117.Google Scholar
  53. Marcoulides, G. A., & Saunders, C. (2006). PLS: A silver bullet? MIS Quarterly, 30(2), iii–iix.CrossRefGoogle Scholar
  54. McDonald, R. P. (1996). Path analysis with composite variables. Multivariate Behavioral Research, 31(2), 239–270. doi:10.1207/s15327906mbr3102_5.CrossRefGoogle Scholar
  55. McDonald, R. P. (1999). Test theory: A unified treatment. Mahwah, NJ: Erlbaum.Google Scholar
  56. Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York: McGraw-Hill.Google Scholar
  57. 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. doi:10.1016/j.ijresmar.2009.08.001.CrossRefGoogle Scholar
  58. Rhemtulla, M., Brosseau-Liard, P. E., & Savalei, V. (2012). When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions. Psychological Methods, 17(3), 354–373. doi:10.1037/a0029315.CrossRefGoogle Scholar
  59. Rigdon, E. E. (2012). Rethinking partial least squares path modeling: In praise of simple methods. Long Range Planning, 45(5–6), 341–358. doi:10.1016/j.lrp.2012.09.010.CrossRefGoogle Scholar
  60. Rigdon, E. E. (2014). Rethinking partial least squares path modeling: Breaking chains and forging ahead. Long Range Planning, 47(3), 161–167. doi:10.1016/j.lrp.2014.02.003.MathSciNetCrossRefGoogle Scholar
  61. Rigdon, E. E., Becker, J.-M., Rai, A., Ringle, C. M., et al. (2014). Conflating antecedents and formative indicators: A comment on Aguirre-Urreta and Marakas. Information Systems Research, 25(4), 780–784. doi:10.1287/isre.2014.0543.CrossRefGoogle Scholar
  62. Rindskopf, D. (1984). Using phantom and imaginary latent variables to parameterize constraints in linear structural models. Psychometrika, 49(1), 37–47. doi:10.1007/BF02294204.CrossRefGoogle Scholar
  63. Ringle, C. M., Sarstedt, M., & Mooi, E. A. (2010a). Response-based segmentation using finite mixture partial least squares: Theoretical foundations and an application to American customer satisfaction index data. Annals of Information Systems, 8, 19–49.CrossRefGoogle Scholar
  64. Ringle, C. M., Sarstedt, M., & Schlittgen, R. (2010b). Finite mixture and genetic algorithm segmentation in partial least squares path modeling: Identification of multiple segments in a complex path model. In A. Fink, B. Lausen, W. Seidel, & A. Ultsch (Eds.), Advances in data analysis, data handling and business intelligence (pp. 167–176). Berlin: Springer.Google Scholar
  65. Ringle, C. M., Wende, S., & Will, A. (2010c). Finite mixture partial least squares analysis: Methodology and numerical examples. 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. 195–218). New York: Springer.CrossRefGoogle Scholar
  66. 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.Google Scholar
  67. Ringle, C. M., Sarstedt, M., & Schlittgen, R. (2014). Genetic algorithm segmentation in partial least squares structural equation modeling. OR Spectrum, 36(1), 251–276.MATHCrossRefGoogle Scholar
  68. Sahmer, K., Hanafi, M., & Qannari, M. (2006). Assessing unidimensionality within the PLS path modeling framework. In M. Spiliopoulou, R. Kruse, C. Borgelt, A. Nürnberger, & W. Gaul (Eds.), From data and information analysis to knowledge engineering (pp. 222–229). Berlin: Springer.CrossRefGoogle Scholar
  69. Sarstedt, M., Henseler, J., & Ringle, C. (2011). Multi-group analysis in partial least squares (PLS) path modeling: Alternative methods and empirical results. Advances in International Marketing, 22, 195–218. doi:10.1108/S1474-7979(2011)0000022012.CrossRefGoogle Scholar
  70. Sarstedt, M., Ringle, C. M., Henseler, J., & Hair, J. F. (2014). On the emancipation of PLS-SEM: A commentary on Rigdon (2012). Long Range Planning, 47(3), 154–160. doi:10.1016/j.lrp.2014.02.007.CrossRefGoogle Scholar
  71. Schuberth, F., Henseler, J., & Dijkstra, T. K. (2016). Partial least squares path modeling using ordinal categorical indicators. Quality and Quantity, 1–27. doi:10.1007/s11135-016-0401-7.
  72. Shmueli, G., & Koppius, O. R. (2011). Predictive analytics in information systems research. MIS Quarterly, 35(3), 553–572.CrossRefGoogle Scholar
  73. Sijtsma, K. (2009). On the use, the misuse, and the very limited usefulness of Cronbach’s alpha. Psychometrika, 74(1), 107–120. doi:10.1007/s11336-008-9101-0.MATHMathSciNetCrossRefGoogle Scholar
  74. Streukens, S., Wetzels, M., Daryanto, A., & de Ruyter, K. (2010). Analyzing factorial data using PLS: Application in an online complaining context. In V. Esposito Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares: Concepts, methods and applications (pp. 567–587). New York: Springer.CrossRefGoogle Scholar
  75. Tenenhaus, M. (2008). Component-based structural equation modelling. Total Quality Management and Business Excellence, 19(7), 871–886. doi:10.1080/14783360802159543.CrossRefGoogle Scholar
  76. Tenenhaus, A., & Tenenhaus, M. (2011). Regularized generalized canonical correlation analysis. Psychometrika, 76(2), 257–284. doi:10.1007/S11336-011-9206-8.MATHMathSciNetCrossRefGoogle Scholar
  77. Tenenhaus, M., Amato, S., & Esposito Vinzi, V. (2004). A global goodness-of-fit index for PLS structural equation modelling (pp. 739–742). Proceedings of the XLII SIS Scientific Meeting, CLEUP, Padova.Google Scholar
  78. Tenenhaus, M., Esposito Vinzi, V., Chatelin, Y.-M., & Lauro, C. (2005). PLS path modeling. Computational Statistics and Data Analysis, 48(1), 159–205. doi:10.1016/j.csda.2004.03.005.MATHMathSciNetCrossRefGoogle Scholar
  79. Turkyilmaz, A., Oztekin, A., Zaim, S., & Fahrettin Demirel, O. (2013). Universal structure modeling approach to customer satisfaction index. Industrial Management & Data Systems, 113(7), 932–949. doi:10.1108/IMDS-12-2012-0444.CrossRefGoogle Scholar
  80. Voorhees, C. M., Brady, M. K., Calantone, R., & Ramirez, E. (2016). Discriminant validity testing in marketing: An analysis, causes for concern, and proposed remedies. Journal of Academy of Market Science, 44(1), 119–134. doi:10.1007/s11747-015-0455-4.CrossRefGoogle Scholar
  81. Wold, H. (1974). Causal flows with latent variables: Partings of the ways in the light of NIPALS modelling. European Economic Review, 5(1), 67–86. doi:10.1016/0014-2921(74)90008-7.CrossRefGoogle Scholar
  82. Wold, H. (1982). Soft modeling: The basic design and some extensions. In K. G. Jöreskog & H. O. A. Wold (Eds.), Systems under indirect observations: Part II (pp. 1–54). Amsterdam: North-Holland.Google Scholar
  83. 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. doi:10.1086/651257.CrossRefGoogle Scholar
  84. Ziggers, G.-W., & Henseler, J. (2016). The reinforcing effect of a firm’s customer orientation and supply-base orientation on performance. Industrial Marketing Management, 52(1), 18–26. doi:10.1016/j.indmarman.2015.07.011.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Jörg Henseler
    • 1
  • Geoffrey Hubona
    • 2
  • Pauline Ash Ray
    • 3
  1. 1.University of TwenteEnschedeThe Netherlands
  2. 2.Georgia R SchoolChattanoogaUSA
  3. 3.Business DivisionThomas UniversityThomasvilleUSA

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