Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods

Abstract

Composite-based structural equation modeling (SEM), and especially partial least squares path modeling (PLS), has gained increasing dissemination in marketing. To fully exploit the potential of these methods, researchers must know about their relative performance and the settings that favor each method’s use. While numerous simulation studies have aimed to evaluate the performance of composite-based SEM methods, practically all of them defined populations using common factor models, thereby assessing the methods on erroneous grounds. This study is the first to offer a comprehensive assessment of composite-based SEM techniques on the basis of composite model data, considering a broad range of model constellations. Results of a large-scale simulation study substantiate that PLS and generalized structured component analysis are consistent estimators when the underlying population is composite model-based. While both methods outperform sum scores regression in terms of parameter recovery, PLS achieves slightly greater statistical power.

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Notes

  1. 1.

    Note that researchers frequently distinguish between latent variables/constructs and composites. We use the term latent variable/construct to refer to the entities that represent conceptual variables in a structural equation model.

  2. 2.

    Our comparison does not consider consistent PLS (PLSc; Dijkstra 2014; Dijkstra and Henseler 2015) that corrects the PLS estimates for attenuation to mimic common factor models. As our objective is to compare composite-based SEM techniques on the basis of composite model data, PLSc is not relevant to our study.

  3. 3.

    Note that constructs in factor-based SEM are also proxies for the conceptual variables under investigation (Rigdon 2012).

  4. 4.

    Table A1 in the Online Appendix shows the indicator weights for different numbers of indicators.

  5. 5.

    For further details about the non-normal data, see the additional information on the data generation in the Online Appendix.

  6. 6.

    As the analyses show only marginal differences between normal and non-normal data, the following results presentations use the joint outcomes of the different data distribution types considered in this simulation study.

  7. 7.

    For example, for the condition with 500 observations, two indicators with equal weights of 0.625, PLS yields a MAE value of 0.05814 in the measurement models, which translates into an MARE value of 0.093. On the contrary, a very similar MAE value of 0.06029 for the condition with 500 observations, eight indicators with equal weights of 0.25 translates into a MARE value of 0.241.

  8. 8.

    Note that the MARE is not defined for the two null paths γ 4 and γ 5 (Fig. 2). Hence, we did not include these two paths in the MARE computations.

References

  1. Anderson, E. W., & Fornell, C. G. (2000). Foundations of the American customer satisfaction index. Total Quality Management, 11(7), 869–882.

    Article  Google Scholar 

  2. Anderson, E. W., Fornell, C. G., & Mazvancheryl, S. K. (2004). Customer satisfaction and shareholder value. Journal of Marketing, 68(4), 172–185.

    Article  Google Scholar 

  3. Angulo-Ruiz, F., Donthu, N., Prior, D., & Rialp, J. (2014). The financial contribution of customer-oriented marketing capability. Journal of the Academy of Marketing Science, 42(4), 380–399.

    Article  Google Scholar 

  4. Atinc, G., Simmering, M. J., & Kroll, M. J. (2012). Control variable use and reporting in macro and micro management research. Organizational Research Methods, 15(1), 57–74.

    Article  Google Scholar 

  5. Babin, B. J., Hair, J. F., & Boles, J. S. (2008). Publishing research in marketing journals using structural equation modeling. Journal of Marketing Theory and Practice, 16(4), 279–285.

    Article  Google Scholar 

  6. Barroso, C., Carrión, G. C., & Roldán, J. L. (2010). Applying maximum likelihood and PLS on different sample sizes: studies on SERVQUAL model and employee behavior model. In V. Esposito Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares: concepts, methods and applications (pp. 427–447). Berlin: Springer.

    Google Scholar 

  7. Becker, J. M., Klein, K., & Wetzels, M. (2012). Hierarchical latent variable models in PLS-SEM: guidelines for using reflective-formative type models. Long Range Planning, 45(5–6), 359–394.

    Article  Google Scholar 

  8. Becker, J. M., Rai, A., & Rigdon, E. E. (2013a). Predictive validity and formative measurement in structural equation modeling: Embracing practical relevance. In 2013a Proceedings of the international conference on information systems. Milan.

  9. 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.

    Google Scholar 

  10. Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley.

    Google Scholar 

  11. Brady, M. K., Voorhees, C. M., & Brusco, M. J. (2012). Service sweethearting: its antecedents and customer consequences. Journal of Marketing, 76(2), 81–98.

    Article  Google Scholar 

  12. Burke, S. J. (2011). Competitive positioning strength: market measurement. Journal of Strategic Marketing, 19(5), 421–428.

    Article  Google Scholar 

  13. Cassel, C., Hackl, P., & Westlund, A. H. (1999). Robustness of partial least-squares method for estimating latent variable quality structures. Journal of Applied Statistics, 26(4), 435–446.

    Article  Google Scholar 

  14. 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: Erlbaum.

    Google Scholar 

  15. 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 (pp. 83–97). Berlin: Springer.

    Google Scholar 

  16. 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.

    Google Scholar 

  17. 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.

    Article  Google Scholar 

  18. Cliff, N. (1983). Some cautions concerning the application of causal modeling methods. Multivariate Behavioral Research, 18(1), 115–126.

    Article  Google Scholar 

  19. Cohen, J. (1988). Statistical power analysis for the behavioural sciences (2nd ed.). Hillsdale: Lawrence Erlbaum Associates.

    Google Scholar 

  20. Core Team, R. (2016). R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.

    Google Scholar 

  21. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.

    Article  Google Scholar 

  22. Dellande, S., Gilly, M. C., & Graham, J. L. (2004). Gaining compliance and losing weight: the role of the service provider in health care services. Journal of Marketing, 68(3), 78–91.

    Article  Google Scholar 

  23. Diamantopoulos, A., & Riefler, P. (2011). Using formative measures in international marketing models: a cautionary tale using consumer animosity as an example. In M. Sarstedt, M. Schwaiger, & C. R. Taylor (Eds.), Advances in international marketing (Vol. 22, pp. 11–30). Bingley: Emerald.

    Google Scholar 

  24. 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). Berlin: Springer.

    Google Scholar 

  25. 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.

    Article  Google Scholar 

  26. Dijkstra, T. K., & Henseler, J. (2011). Linear indices in nonlinear structural equation models: best fitting proper indices and other composites. Quality & Quantity, 45(6), 1505–1518.

    Article  Google Scholar 

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

    Article  Google Scholar 

  28. 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.

    Article  Google Scholar 

  29. Evermann, J., & Tate, M. (2016). Assessing the predictive performance of structural equation model estimators. Journal of Business Research, 69(10), 4565–4582.

    Article  Google Scholar 

  30. 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.

    Article  Google Scholar 

  31. Fornell, C., Morgeson, F. V., & Hult, G. T. M. (2016). Stock returns on customer satisfaction do beat the market: gauging the effect of a marketing intangible. Journal of Marketing, 80(5), 92–107.

    Article  Google Scholar 

  32. Gelbrich, K. (2010). Anger, frustration, and helplessness after service failure: coping strategies and effective informational support. Journal of the Academy of Marketing Science, 38(5), 567–585.

    Article  Google Scholar 

  33. Goodhue, D. L., Lewis, W., & Thompson, R. (2012a). Does PLS have advantages for small sample size or non-normal data? MIS Quarterly, 36(3), 981–1001.

    Google Scholar 

  34. Goodhue, D. L., Lewis, W., & Thompson, R. (2012b). Comparing PLS to regression and LISREL: a response to Marcoulides, Chin, and Saunders. MIS Quarterly, 36(3), 703–716.

    Google Scholar 

  35. Green, D. H., Donald, W. B., & Ryans, A. B. (1995). Entry strategy and long-term performance: conceptualization and empirical examination. Journal of Marketing, 59(4), 1–16.

    Article  Google Scholar 

  36. Habel, J., & Klarmann, M. (2015). Customer reactions to downsizing: when and how is satisfaction affected? Journal of the Academy of Marketing Science, 43(6), 768–789.

    Article  Google Scholar 

  37. Haenlein, M., & Kaplan, A. M. (2004). A beginner's guide to partial least squares analysis. Understanding Statistics, 3(4), 283–297.

    Article  Google Scholar 

  38. Haenlein, M., & Kaplan, A. M. (2011). The influence of observed heterogeneity on path coefficient significance: technology acceptance within the marketing discipline. Journal of Marketing Theory and Practice, 19(2), 153–168.

    Article  Google Scholar 

  39. 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.

    Article  Google Scholar 

  40. Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012a). 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.

    Article  Google Scholar 

  41. Hair, J. F., Sarstedt, M., Pieper, T. M., & Ringle, C. M. (2012b). 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.

    Article  Google Scholar 

  42. 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.

    Article  Google Scholar 

  43. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Thousand Oaks, CA: Sage.

    Google Scholar 

  44. Heidenreich, S., Wittkowski, K., Handrich, M., & Falk, T. (2015). The dark side of customer co-creation: exploring the consequences of failed co-created services. Journal of the Academy of Marketing Science, 43(3), 279–296.

    Article  Google Scholar 

  45. Hennig-Thurau, T., Groth, M., Paul, M., & Gremler, D. D. (2006). Are all smiles created equal? How emotional contagion and emotional labor affect service relationships. Journal of Marketing, 70(3), 58–73.

    Article  Google Scholar 

  46. Henseler, J. (2010). On the convergence of the partial least squares path modeling algorithm. Computational Statistics, 25(1), 107–120.

    Article  Google Scholar 

  47. Henseler, J. (2012). Why generalized structured component analysis is not universally preferable to structural equation modeling. Journal of the Academy of Marketing Science, 40(3), 402–413.

    Article  Google Scholar 

  48. 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.

    Google Scholar 

  49. Henseler, J., & Sarstedt, M. (2013). Goodness-of-fit indices for partial least squares path modeling. Computational Statistics, 28(2), 565–580.

    Article  Google Scholar 

  50. 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 

  51. 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.

    Article  Google Scholar 

  52. 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.

    Article  Google Scholar 

  53. Henseler, J., Hubona, G. S., & Ray, P. A. (2016). Using PLS path modeling in new technology research: updated guidelines. Industrial Management & Data Systems, 116(1), 1–19.

    Article  Google Scholar 

  54. Hui, B. S., & Wold, H. O. A. (1982). Consistency and consistency at large of partial least squares estimates. In K. G. Jöreskog & H. O. A. Wold (Eds.), Systems under indirect observation, part II (pp. 119–130). Amsterdam: North Holland.

    Google Scholar 

  55. Hulland, J., Ryan, M. J., & Rayner, R. K. (2010). Modeling customer satisfaction: a comparative performance evaluation of covariance structure analysis versus 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. 307–325). Berlin: Springer.

    Google Scholar 

  56. Hult, G. T. M., Morgeson III, F. V., Morgan, N. A., Mithas, S., & Fornell, C. (2017). Do managers know what their customers think and why? Journal of the Academy of Marketing Science, 45(1), 37–54.

    Article  Google Scholar 

  57. Hwang, H. (2009). Regularized generalized structured component analysis. Psychometrika, 74(3), 517–530.

    Article  Google Scholar 

  58. Hwang, H., Malhotra, N. K., Kim, Y., Tomiuk, M. A., & Hong, S. (2010). A comparative study on parameter recovery of three approaches to structural equation modeling. Journal of Marketing Research, 47(4), 699–712.

    Article  Google Scholar 

  59. 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, NJ: Seminar Press.

    Google Scholar 

  60. Jöreskog, K. G., & Wold, H. O. A. (1982). The ML and PLS techniques for modeling with latent variables: historical and comparative aspects. In H. O. A. Wold & K. G. Jöreskog (Eds.), Systems under indirect observation, part I (pp. 263–270). Amsterdam: North-Holland.

    Google Scholar 

  61. Kaplan, A. M., Schoder, D., & Haenlein, M. (2007). Factors influencing the adoption of mass customization: the impact of base category consumption frequency and need satisfaction. Journal of Product Innovation Management, 24(2), 101–116.

    Article  Google Scholar 

  62. 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.

    Article  Google Scholar 

  63. Knaus, J. (2013). R package snowfall: Easier cluster computing (version: 1.84–6). cran.r-project.org/web/packages/snowfall/.

  64. Lee, N., & Cadogan, J. W. (2013). Problems with formative and higher-order reflective variables. Journal of Business Research, 66(2), 242–247.

    Article  Google Scholar 

  65. Lohmöller, J.-B. (1989). Latent variable path modeling with partial least squares. Heidelberg: Physica.

    Google Scholar 

  66. 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 (pp. 31–64). New York, NJ: Springer.

    Google Scholar 

  67. 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.

    Google Scholar 

  68. McDonald, R. P. (1996). Path analysis with composite variables. Multivariate Behavioral Research, 31(2), 239–270.

    Article  Google Scholar 

  69. McIntosh, C. N., Edwards, J. R., & Antonakis, J. (2014). Reflections on partial least squares path modeling. Organizational Research Methods, 17(2), 210–251.

    Article  Google Scholar 

  70. Monecke, A., & Leisch, F. (2012). semPLS: structural equation modeling using partial least squares. Journal of Statistical Software, 48(3), 1–32.

    Article  Google Scholar 

  71. Paxton, P., Curran, P. J., Bollen, K. A., Kirby, J., & Chen, F. (2001). Monte Carlo experiments: design and implementation. Structural Equation Modeling, 8(2), 287–312.

    Article  Google Scholar 

  72. Ranjan, K. R., & Read, S. (2016). Value co-creation: concept and measurement. Journal of the Academy of Marketing Science, 44(3), 290–315.

    Article  Google Scholar 

  73. Rego, L. L., Morgan, N. A., & Fornell, C. (2013). Reexamining the market share–customer satisfaction relationship. Journal of Marketing, 77(5), 1–20.

    Article  Google Scholar 

  74. 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.

    Article  Google Scholar 

  75. Rigdon, E. E. (1998). Structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 251–294). Mahwah: Erlbaum.

    Google Scholar 

  76. Rigdon, E. E. (2012). Rethinking partial least squares path modeling: in praise of simple methods. Long Range Planning, 45(5–6), 341–358.

    Article  Google Scholar 

  77. Rigdon, E. E. (2014). Rethinking partial least squares path modeling: breaking chains and forging ahead. Long Range Planning, 47(3), 161–167.

    Article  Google Scholar 

  78. 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.

    Article  Google Scholar 

  79. Rigdon, E. E., Ringle, C. M., & Sarstedt, M. (2010). Structural modeling of heterogeneous data with partial least squares. In N. K. Malhotra (Ed.), Review of marketing research (pp. 255–296). Armonk: Sharpe.

    Google Scholar 

  80. Rigdon, E. E., Becker, J.-M., Rai, A., Ringle, C. M., Diamantopoulos, A., Karahanna, E., Straub, D. W., & Dijkstra, T. K. (2014). Conflating antecedents and formative indicators: a comment on Aguirre-Urreta and Marakas. Information Systems Research, 25(4), 780–784.

    Article  Google Scholar 

  81. 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.

    Article  Google Scholar 

  82. Ringle, C. M., Sarstedt, S., & Straub, D. W. (2012). A critical look at the use of PLS-SEM in MIS Quarterly. MIS Quarterly, 36(1), iii–xiv.

    Google Scholar 

  83. 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  Google Scholar 

  84. Romdhani, H., Grinek, S., Hwang, H., & Labbe, A. (2014). R package ASGSCA: Association studies for multiple SNPs and multiple traits using generalized structured equation models (Version 1.4.0), http://bioconductor.org/packages/ASGSCA/.

  85. Rönkkö, M., & Evermann, J. (2013). A critical examination of common beliefs about partial least squares path modeling. Organizational Research Methods, 16(3), 425–448.

    Article  Google Scholar 

  86. Rönkkö, M., McIntosh, C. N., & Antonakis, J. (2015). On the adoption of partial least squares in psychological research: caveat emptor. Personality and Individual Differences, 87, 76–84.

    Article  Google Scholar 

  87. Rönkkö, M., McIntosh, C. N., Antonakis, J., & Edwards, J. R. (2016). Partial least squares path modeling: time for some serious second thoughts. Journal of Operations Management, 47-48(November), 9–27.

    Article  Google Scholar 

  88. Rubera, G., Chandrasekaran, D., & Ordanini, A. (2016). Open innovation, product portfolio innovativeness and firm performance: the dual role of new product development capabilities. Journal of the Academy of Marketing Science, 44(2), 166–184.

    Article  Google Scholar 

  89. 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.

    Article  Google Scholar 

  90. 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.

    Article  Google Scholar 

  91. Schneeweiß, H. (1991). Models with latent variables: LISREL versus PLS. Statistica Neerlandica, 45(2), 145–157.

    Article  Google Scholar 

  92. Schönemann, P. H., & Steiger, J. H. (1978). On the validity of indeterminate factor scores. Bulletin of the Psychonomic Society, 12(4), 287–290.

    Article  Google Scholar 

  93. Schönemann, P. H., & Wang, M.-M. (1972). Some new results on factor indeterminacy. Psychometrika, 37(1), 61–91.

    Article  Google Scholar 

  94. Schuberth, F., Henseler, J., & Dijkstra, T. K. (2016). Partial least squares path modeling using ordinal categorical indicators. Quality & Quantity, forthcoming.

  95. Schubring, S., Lorscheid, I., Meyer, M., & Ringle, C. M. (2016). The PLS agent: predictive modeling with PLS-SEM and agent-based simulation. Journal of Business Research, 69(10), 4604–4612.

    Article  Google Scholar 

  96. Shmueli, G. (2010). To explain or to predict? Statistical Science, 25(3), 289–310.

    Article  Google Scholar 

  97. Shmueli, G., Ray, D., Manuel, J., Estrada, V., & Chatla, S. B. (2016). The elephant in the room: evaluating the predictive performance of PLS models. Journal of Business Research, 69(10), 4552–4564.

    Article  Google Scholar 

  98. Spearman, C. (1927). The abilities of man. London: MacMillan.

    Google Scholar 

  99. Steenkamp, J.-B. E. M., & Baumgartner, H. (2000). On the use of structural equation models for marketing modeling. International Journal of Research in Marketing, 17(2/3), 195–202.

    Article  Google Scholar 

  100. Sundaram, S., Schwarz, A., Jones, E., & Chin, W. W. (2007). Technology use on the front line: how information technology enhances individual performance. Journal of the Academy of Marketing Science, 35(1), 101–112.

    Article  Google Scholar 

  101. Tenenhaus, M. (2008). Component-based structural equation modelling. Total Quality Management & Business Excellence, 19(7–8), 871–886.

    Article  Google Scholar 

  102. Tenenhaus, A., & Tenenhaus, M. (2011). Regularized generalized canonical correlation analysis. Psychometrika, 76(2), 257–284.

    Article  Google Scholar 

  103. Tenenhaus, M., Esposito Vinzi, V., Chatelin, Y.-M., & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48(1), 159–205.

    Article  Google Scholar 

  104. Thurstone, L, L. (1947). Multiple factor analysis. Chicago: The University of Chicago Press.

  105. Treiblmaier, H., Bentler, P. M., & Mair, P. (2011). Formative constructs implemented via common factors. Structural Equation Modeling: A Multidisciplinary Journal, 18(1), 1–17.

    Article  Google Scholar 

  106. van der Heijden, G. A. H., Schepers, J. J. L., Nijssen, E. J., & Ordanini, A. (2013). Don’t just fix it, make it better! Using frontline service employees to improve recovery performance. Journal of the Academy of Marketing Science, 41(5), 515–530.

    Article  Google Scholar 

  107. 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.

    Google Scholar 

  108. Vilares, M. J., & Coelho, P. S. (2013). Likelihood and PLS estimators for structural equation modeling: an assessment of sample size, skewness and model misspecification effects. In J. Lita da Silva, F. Caeiro, I. Natário, & C. A. Braumann (Eds.), Advances in regression, survival analysis, extreme values, Markov processes and other statistical applications (pp. 11–33). Berlin: Springer.

    Google Scholar 

  109. Wold, H. O. A. (1974). Causal flows with latent variables: partings of ways in the light of NIPALS modelling. European Economic Review, 5(1), 67–86.

    Article  Google Scholar 

  110. Wold, H. O. A. (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, NJ: Academic Press.

    Google Scholar 

  111. Wold, H. O. A. (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 

  112. Wold, S., Sjöström, M., & Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58(2), 109–130.

    Article  Google Scholar 

  113. Wolter, J. S., & Cronin, J. J. (2016). Re-conceptualizing cognitive and affective customer-company identification: the role of self-motives and different customer-based outcomes. Journal of the Academy of Marketing Science, 44(3), 397–413.

    Article  Google Scholar 

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Acknowledgements

Earlier versions of the manuscript have been presented at the 2015 Academy of Marketing Science Annual Conference held in Denver, Colorado, and the 2nd International Symposium on Partial Least Squares Path Modeling: The Conference for PLS Users held in Seville, 2015. The authors would like to thank Jan-Michael Becker, University of Cologne, Jörg Henseler, University of Twente, and Rainer Schlittgen, University of Hamburg, for their support and helpful comments when developing the simulation study and its data generation in order to improve earlier versions of the manuscript. Even though this research does not explicitly refer to the use of the statistical software SmartPLS (http://www.smartpls.com), Ringle acknowledges a financial interest in SmartPLS.

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Correspondence to G. Tomas M. Hult.

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John Hulland served as Area Editor for this article.

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Hair, J.F., Hult, G.T.M., Ringle, C.M. et al. Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods. J. of the Acad. Mark. Sci. 45, 616–632 (2017). https://doi.org/10.1007/s11747-017-0517-x

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Keywords

  • Composite
  • Generalized structured component analysis
  • GSCA
  • Partial least squares
  • PLS
  • SEM
  • Simulation
  • Structural equation modeling
  • Sum scores regression