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Partial Least Squares Path Modeling: Updated Guidelines

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Partial Least Squares Path Modeling

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.

This chapter is an updated reprint of Henseler, J., Hubona, G., Ray, P. A. (2016). Using PLS path modeling in new technology research: updated guidelines. Industrial Management & Data Systems, 116(1), 2–20 doi:10.1108/IMDS-09-2015-0382. The first author acknowledges a financial interest in ADANCO and its distributor, Composite Modeling.

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Notes

  1. 1.

    Note that also factors are nothing else than proxies (Rigdon 2012).

  2. 2.

    This assumption should be relaxed in case of non-recursive models (Dijkstra and Henseler 2015a).

  3. 3.

    An allegedly higher statistical power of PLS (Reinartz et al. 2009) can be traced back to model misspecification, namely, making use of a composite model although the factor model would have been true (Goodhue et al. 2011).

  4. 4.

    For an application of the NFI, see Ziggers and Henseler (2016).

  5. 5.

    Interestingly, the methodological literature on factor models is quite silent about what to do if the test speaks against a factor model. Some researchers suggest considering the alternative of a composite model, because it is less restrictive (Henseler et al. 2014) and not subject to factor indeterminacy (Rigdon 2012).

  6. 6.

    The AVE must be calculated based on consistent loadings; otherwise, the assessment of convergent and discriminant validity based on the AVE is meaningless.

References

  • 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 

  • 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.

    Chapter  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • Byrne, B. M. (2013). Structural equation modeling with LISREL, PRELIS, and SIMPLIS: Basic concepts, applications, and programming. Stanford, CA: Psychology Press.

    MATH  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Mahwah, NJ: Lawrence Erlbaum.

    MATH  Google Scholar 

  • Cohen, J. (1994). The earth is round (p<.05). The American Psychologist, 49(12), 997–1003. doi:10.1037/0003-066X.49.12.997.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Dijkstra, T. K., & Henseler, J. (2014). Assessing and testing the goodness-of-fit of PLS path models. Third VOC Conference, Leiden.

    Google Scholar 

  • 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.

    Article  MathSciNet  Google Scholar 

  • Dijkstra, T. K., & Henseler, J. (2015b). Consistent partial least squares path modeling. MIS Quarterly, 39(2), 297–316.

    Article  Google Scholar 

  • 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.

    Article  MATH  MathSciNet  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • 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.

    Article  Google Scholar 

  • French, B. F., & Finch, W. H. (2006). Confirmatory factor analytic procedures for the determination of measurement invariance. Structural Equation Modeling, 13(3), 378–402.

    Article  MathSciNet  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  MATH  MathSciNet  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  MathSciNet  Google Scholar 

  • Henseler, J., & Dijkstra, T. K. (2015). ADANCO 2.0. Kleve: Composite Modeling GmbH. Retrieved from http://www.compositemodeling.com.

    Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • 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.

    Article  MATH  MathSciNet  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Hwang, H., & Takane, Y. (2004). Generalized structured component analysis. Psychometrika, 69(1), 81–99. doi:10.1007/BF02295841.

    Article  MATH  MathSciNet  Google Scholar 

  • Kettenring, J. R. (1971). Canonical analysis of several sets of variables. Biometrika, 58(3), 433–451. doi:10.1093/biomet/58.3.433.

    Article  MATH  MathSciNet  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  MATH  MathSciNet  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Lohmöller, J.-B. (1989). Latent variable path modeling with partial least squares. Berlin: Springer.

    Book  MATH  Google Scholar 

  • Maraun, M. D., & Halpin, P. F. (2008). Manifest and latent variates. Measurement: Interdisciplinary Research and Perspectives, 6(1–2), 113–117.

    Google Scholar 

  • Marcoulides, G. A., & Saunders, C. (2006). PLS: A silver bullet? MIS Quarterly, 30(2), iii–iix.

    Article  Google Scholar 

  • McDonald, R. P. (1996). Path analysis with composite variables. Multivariate Behavioral Research, 31(2), 239–270. doi:10.1207/s15327906mbr3102_5.

    Article  Google Scholar 

  • McDonald, R. P. (1999). Test theory: A unified treatment. Mahwah, NJ: Erlbaum.

    Google Scholar 

  • Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York: McGraw-Hill.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  MathSciNet  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Chapter  Google Scholar 

  • 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 

  • Ringle, C. M., Sarstedt, M., & Schlittgen, R. (2014). Genetic algorithm segmentation in partial least squares structural equation modeling. OR Spectrum, 36(1), 251–276.

    Article  MATH  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

  • Shmueli, G., & Koppius, O. R. (2011). Predictive analytics in information systems research. MIS Quarterly, 35(3), 553–572.

    Article  Google Scholar 

  • 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.

    Article  MATH  MathSciNet  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • Tenenhaus, M. (2008). Component-based structural equation modelling. Total Quality Management and Business Excellence, 19(7), 871–886. doi:10.1080/14783360802159543.

    Article  Google Scholar 

  • Tenenhaus, A., & Tenenhaus, M. (2011). Regularized generalized canonical correlation analysis. Psychometrika, 76(2), 257–284. doi:10.1007/S11336-011-9206-8.

    Article  MATH  MathSciNet  Google Scholar 

  • 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 

  • 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.

    Article  MATH  MathSciNet  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

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Henseler, J., Hubona, G., Ray, P.A. (2017). Partial Least Squares Path Modeling: Updated Guidelines. In: Latan, H., Noonan, R. (eds) Partial Least Squares Path Modeling. Springer, Cham. https://doi.org/10.1007/978-3-319-64069-3_2

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