An assessment of the use of partial least squares structural equation modeling in marketing research

  • Joe F. Hair
  • Marko Sarstedt
  • Christian M. Ringle
  • Jeannette A. Mena
Methodological Paper

Abstract

Most methodological fields undertake regular critical reflections to ensure rigorous research and publication practices, and, consequently, acceptance in their domain. Interestingly, relatively little attention has been paid to assessing the use of partial least squares structural equation modeling (PLS-SEM) in marketing research—despite its increasing popularity in recent years. To fill this gap, we conducted an extensive search in the 30 top ranked marketing journals that allowed us to identify 204 PLS-SEM applications published in a 30-year period (1981 to 2010). A critical analysis of these articles addresses, amongst others, the following key methodological issues: reasons for using PLS-SEM, data and model characteristics, outer and inner model evaluations, and reporting. We also give an overview of the interdependencies between researchers’ choices, identify potential problem areas, and discuss their implications. On the basis of our findings, we provide comprehensive guidelines to aid researchers in avoiding common pitfalls in PLS-SEM use. This study is important for researchers and practitioners, as PLS-SEM requires several critical choices that, if not made correctly, can lead to improper findings, interpretations, and conclusions.

Keywords

Empirical research methods Partial least squares Path modeling Structural equation modeling 

Notes

Acknowledgments

The authors would like to thank three anonymous reviewers, Jörg Henseler (University of Nijmegen), and Edward E. Rigdon (Georgia State University) for their helpful remarks on earlier versions of this article.

References

  1. Babakus, E., Ferguson, C. E., & Jöreskog, K. G. (1987). The sensitivity of confirmatory maximum likelihood factor analysis to violations of measurement scale and distributional assumptions. Journal of Marketing Research, 24(2), 222–228.CrossRefGoogle Scholar
  2. Babin, B. J., Hair, J. F., & Boles, J. S. (2008). Publishing research in marketing journals using structural equation modeling. Journal of Marketing Theory & Practice, 16(4), 279–285.CrossRefGoogle Scholar
  3. Bagozzi, R. P. (1994). Structural equation models in marketing research: Basic principles. In R. P. Bagozzi (Ed.), Principles of marketing research (pp. 317–385). Oxford: Blackwell.Google Scholar
  4. Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94.CrossRefGoogle Scholar
  5. Barclay, D. W., Higgins, C. A., & Thompson, R. (1995). The partial least squares approach to causal modeling: personal computer adoption and use as illustration. Technology Studies, 2(2), 285–309.Google Scholar
  6. 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
  7. Baumgartner, H., & Pieters, R. (2003). The structural influence of marketing journals: a citation analysis of the discipline and its subareas over time. Journal of Marketing, 67(2), 123–139.CrossRefGoogle Scholar
  8. Beebe, K. R., Pell, R. J., & Seasholtz, M. B. (1998). Chemometrics: A practical guide. New York: Wile.Google Scholar
  9. Bergkvist, L., & Rossiter, J. R. (2007). The predictive validity of multiple-item versus single-item measures of the same constructs. Journal of Marketing Research, 44(2), 175–184.CrossRefGoogle Scholar
  10. Bergkvist, L., & Rossiter, J. R. (2009). Tailor-made single-item measures of doubly concrete constructs. International Journal of Advertising, 28(4), 607–621.CrossRefGoogle Scholar
  11. Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley.Google Scholar
  12. Bollen, K. A. (2011). Evaluating effect, composite, and causal indicators in structural equation models. MIS Quarterly, 35(2), 359–372.Google Scholar
  13. Bollen, K. A., & Davies, W. R. (2009). Causal indicator models: identification, estimation, and testing. Structural Equation Modeling. An Interdisciplinary Journal, 16(3), 498–522.CrossRefGoogle Scholar
  14. Bollen, K. A., & Ting, K.-F. (2000). A tetrad test for causal indicators. Psychological Methods, 5(1), 3–22.CrossRefGoogle Scholar
  15. Boomsma, A., & Hoogland, J. J. (2001). The robustness of LISREL modeling revisited. In R. Cudeck, S. du Toit, & D. Sörbom (Eds.), Structural equation modeling: Present and future (pp. 139–168). Chicago: Scientific Software International.Google Scholar
  16. Brannick, M. T. (1995). Critical comments on applying covariance structure modeling. Journal of Organizational Behavior, 16(3), 201–213.CrossRefGoogle Scholar
  17. 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.CrossRefGoogle Scholar
  18. Cenfetelli, R. T., & Bassellier, G. (2009). Interpretation of formative measurement in information system research. MIS Quarterly, 33(4), 689–707.Google Scholar
  19. Chernick, M. R. (2008). Bootstrap methods. A guide for practitioners and researchers (2nd ed.). Hoboken, New Jersey: Wiley.Google Scholar
  20. 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, New Jersey: Lawrence Erlbaum Associates.Google Scholar
  21. Chin, W. W. (2003). PLS Graph 3.0. Houston: Soft Modeling Inc.Google Scholar
  22. Chin, W. W. (2010). How to write up and report PLS analyses. In V. E. Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares: Concepts, methods and applications in marketing and related fields (pp. 655–690). Berlin: Springer.Google Scholar
  23. 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: Sage.Google Scholar
  24. Churchill, G. A., & Iacobucci, D. (2010). Marketing research: Methodological foundations. Mason: South-Western Cengage Learning.Google Scholar
  25. Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Hillsdale, New Jersey: Lawrence Erlbaum Associates.Google Scholar
  26. Coltman, T., Devinney, T. M., Midgley, D. F., & Venaik, S. (2008). Formative versus reflective measurement models: two applications of formative measurement. Journal of Business Research, 61(12), 1250–1262.CrossRefGoogle Scholar
  27. 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
  28. Diamantopoulos, A., & Riefler, P. (2011). Using formative measures in international marketing models: A cautionary tale using consumer animosity as an example. Advances in International Marketing, forthcoming.Google Scholar
  29. Diamantopoulos, A., & Siguaw, J. A. (2006). Formative vs. reflective indicators in organizational measure development: A comparison and empirical illustration. British Journal of Management, 17(4), 263–282.CrossRefGoogle Scholar
  30. 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
  31. Diamantopoulos, A., Riefler, P., & Roth, K. P. (2008). Advancing formative measurement models. Journal of Business Research, 61(12), 1203–1218.CrossRefGoogle Scholar
  32. Dijkstra, T. K. (2010). Latent variables and indices: Herman Wold’s basic design and partial least squares. In V. E. Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares: Concepts, methods and applications in marketing and related fields (pp. 23–46). Berlin: Springer.Google Scholar
  33. Efron, B. (1981). Nonparametric estimates of standard error: the jackknife, the bootstrap and other methods. Biometrika, 68(3), 589–599.CrossRefGoogle Scholar
  34. 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
  35. Fornell, C. G., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.CrossRefGoogle Scholar
  36. Fuchs, C., & Diamantopoulos, A. (2009). Using single-item measures for construct measurement in management research. Conceptual issues and application guidelines. Die Betriebswirtschaft, 69(2), 195–210.Google Scholar
  37. Gardner, D. G., Dunham, R. B., Cummings, L. L., & Pierce, J. L. (1989). Focus of attention at work: construct definition and empirical validation. Journal of Occupational Psychology, 62(1), 61–77.CrossRefGoogle Scholar
  38. Garver, M. S., & Mentzer, J. T. (1999). Logistics research methods: employing structural equation modeling to test for construct validity. Journal of Business Logistics, 20(1), 33–57.Google Scholar
  39. Gefen, D., Rigdon, E. E., & Straub, D. (2011). Editor's comments: an update and extension to SEM guidelines for administrative and social science research. MIS Quarterly, 35(2), III–XIV.Google Scholar
  40. Geisser, S. (1974). A predictive approach to the random effects model. Biometrika, 61(1), 101–107.CrossRefGoogle Scholar
  41. Goodhue, D., Lewis, W., & Thompson, R. (2006). PLS, small sample size, and statistical power in MIS research. In HICSS’06: Proceedings of the 39th annual Hawaii international conference on system sciences. Washington: IEEE Computer Society.Google Scholar
  42. Grégoire, Y., & Fisher, R. J. (2006). The effects of relationship quality on customer retaliation. Marketing Letters, 17(1), 31–46.CrossRefGoogle Scholar
  43. 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
  44. Haenlein, M., & Kaplan, A. M. (2004). A beginner’s guide to partial least squares analysis. Understanding Statistics, 3(4), 283–297.CrossRefGoogle Scholar
  45. 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.Google Scholar
  46. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Englewood Cliffs: Prentice Hall.Google Scholar
  47. 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
  48. Henseler, J. (2010). On the convergence of the partial least squares path modeling algorithm. Computational Statistics, 25(1), 107–120.CrossRefGoogle Scholar
  49. 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: A Multidisciplinary Journal, 17(1), 82–109.CrossRefGoogle Scholar
  50. Henseler, J., & Fassott, G. (2010). Testing moderating effects in PLS path models: An illustration of available procedures. In V. E. Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares: Concepts, methods and applications in marketing and related fields (pp. 713–735). Berlin: Springer.Google Scholar
  51. Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. Advances in international marketing, 20, 277–319.CrossRefGoogle Scholar
  52. Hu, L.-T., & Bentler, P. M. (1995). Evaluating model fit. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 76–99). Thousand Oaks, California: Sage.Google Scholar
  53. 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
  54. Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: a review of four recent studies. Strategic Management Journal, 20(2), 195–204.CrossRefGoogle Scholar
  55. Hult, G. T. M., Neese, W. T., & Bashaw, R. E. (1997). Faculty perceptions of marketing journals. Journal of Marketing Education, 19(1), 37–52.CrossRefGoogle Scholar
  56. Hult, G. T. M., Reimann, M., & Schilke, O. (2009). Worldwide faculty perceptions of marketing journals: Rankings, trends, comparisons, and segmentations. GlobalEDGE Business Review, 3(3), 1–23.Google Scholar
  57. 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.CrossRefGoogle Scholar
  58. Jakobowicz, E., & Derquenne, C. (2007). A modified PLS path modeling algorithm handling reflective categorical variables and a new model building strategy. Computational Statistics & Data Analysis, 51(8), 3666–3678.CrossRefGoogle Scholar
  59. Jarvis, C. B., MacKenzie, S. B., & Podsakoff, P. M. (2003). A critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of Consumer Research, 30(2), 199–218.CrossRefGoogle Scholar
  60. Jedidi, K., Jagpal, H. S., & DeSarbo, W. S. (1997). Finite-mixture structural equation models for response-based segmentation and unobserved heterogeneity. Marketing Science, 16(1), 39–59.CrossRefGoogle Scholar
  61. Johnson, M. D., Herrmann, A., & Huber, F. (2006). The evolution of loyalty intentions. Journal of Marketing, 70(2), 122–132.CrossRefGoogle Scholar
  62. Jöreskog, K. G. (1978). Structural analysis of covariance and correlation matrices. Psychometrika, 43(4), 443–477.CrossRefGoogle Scholar
  63. Jöreskog, K. G. (1993). Testing structural equation models. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 294–316). Newbury Park: Sage.Google Scholar
  64. Jöreskog, K. G., & Wold, H. (1982). The ML and PLS techniques for modeling with latent variables: Historical and comparative aspects. In K. G. Jöreskog & H. Wold (Eds.), Systems under indirect observation: Part I (pp. 263–270). Amsterdam: North-Holland.Google Scholar
  65. Kwon, H., & Trail, G. (2005). The feasibility of single-item measures in sport loyalty research. Sport Management Review, 8(1), 69–89.CrossRefGoogle Scholar
  66. Lee, D. Y. (1994). The impact of firms’ risk-taking attitudes on advertising budgets. Journal of Business Research, 31(2–3), 247–256.CrossRefGoogle Scholar
  67. Lohmöller, J.-B. (1987). LVPLS 1.8. Cologne.Google Scholar
  68. Lohmöller, J.-B. (1989). Latent variable path modeling with partial least squares. Heidelberg: Physica.Google Scholar
  69. MacKenzie, S. B., Podsakoff, P. M., & Podsakoff, N. P. (2011). Construct measurement and validation procedures in MIS and behavioral research: integrating new and existing techniques. MIS Quarterly, 35(2), 293–334.Google Scholar
  70. Malhotra, N. K. (2010). Marketing research: An applied orientation. Upper Saddle River: Prentice Hall.Google Scholar
  71. Marcoulides, G. A., & Saunders, C. (2006). PLS: a silver bullet? MIS Quarterly, 30(2), III–IX.Google Scholar
  72. Medsker, G. J., Williams, L. J., & Holahan, P. J. (1994). A review of current practices for evaluating causal models in organizational behavior and human resources management research. Journal of Management, 20(2), 439–464.Google Scholar
  73. Nunnally, J. C. (1967). Psychometric theory. New York: McGraw Hill.Google Scholar
  74. Raykov, T. (2007). Reliability if deleted, not “alpha if deleted”: evaluation of scale reliability following component deletion. British Journal of Mathematical and Statistical Psychology, 60(2), 201–216.CrossRefGoogle Scholar
  75. Reinartz, W. J., Haenlein, M., & Henseler, J. (2009). An empirical comparison of the efficacy of covariance-based and variance-based SEM. International Journal of Market Research, 26(4), 332–344.CrossRefGoogle Scholar
  76. Rigdon, E. E. (1998). Structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 251–294). Mahwah: Erlbaum.Google Scholar
  77. 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. Armonk: Sharpe, 7, 255–296.Google Scholar
  78. Ringle, C., Wende, S., & Will, A. (2005). SmartPLS 2.0 (Beta). Hamburg, (www.smartpls.de).
  79. Ringle, C. M., Götz, O., Wetzels, M., & Wilson, B. (2009). On the use of formative measurement specifications in structural equation modeling: A Monte Carlo simulation study to compare covariance-based and partial least squares model estimation methodologies. In METEOR Research Memoranda (RM/09/014): Maastricht University.Google Scholar
  80. 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
  81. Ringle, C. M., Wende, S., & Will, A. (2010b). Finite mixture partial least squares analysis: Methodology and numerical examples. In V. E. Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares: Concepts, methods and applications in marketing and related fields (pp. 195–218). Berlin: Springer.Google Scholar
  82. Sackett, P. R., & Larson, J. R. (1990). Research strategies and tactics in I/O psychology. In M. D. Dunnette, P. L. Ackerman, & L. M. Hough (Eds.), Handbook of industrial and organizational psychology (2nd edition, Vol. 1, pp. 419–488). Palo Alto: Consulting Psychology Press.Google Scholar
  83. Sarstedt, M., Becker, J.-M., Ringle, C. M., & Schwaiger, M. (2011a). 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.Google Scholar
  84. Sarstedt, M., Henseler, J., & Ringle, C. M. (2011b). Multi-group analysis in partial least squares (PLS) path modeling: Alternative methods and empirical results. Advances in International Marketing, forthcoming.Google Scholar
  85. Sarstedt, M., & Ringle, C. M. (2010). Treating unobserved heterogeneity in PLS path modeling: a comparison of FIMIX-PLS with different data analysis strategies. Journal of Applied Statistics, 37(7–8), 1299–1318.CrossRefGoogle Scholar
  86. Sarstedt, M., & Wilczynski, P. (2009). More for less? A comparison of single-item and multi-item measures. Die Betriebswirtschaft, 69(2), 211–227.Google Scholar
  87. 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.CrossRefGoogle Scholar
  88. Shook, C. L., Ketchen, D. J., Hult, G. T. M., & Kacmar, K. M. (2004). An assessment of the use of structural equation modeling in strategic management research. Strategic Management Journal, 25(4), 397–404.CrossRefGoogle Scholar
  89. Sirohi, N., McLaughlin, E. W., & Wittink, D. R. (1998). A model of consumer perceptions and store loyalty intentions for a supermarket retailer. Journal of Retailing, 74(2), 223–245.CrossRefGoogle Scholar
  90. Slack, N. (1994). The importance-performance matrix as a determinant of improvement priority. International Journal of Operations and Production Management, 14(5), 59–75.CrossRefGoogle Scholar
  91. Sosik, J. J., Kahai, S. S., & Piovoso, M. J. (2009). Silver bullet or voodoo statistics? A primer for using partial least squares data analytic technique in group and organization research. Group & Organization Management, 34(1), 5–36.CrossRefGoogle Scholar
  92. 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.CrossRefGoogle Scholar
  93. Steenkamp, J.-B. E. M., & van Trijp, H. C. M. (1991). The use of LISREL in validating marketing constructs. International Journal of Research in Marketing, 8(4), 283–299.CrossRefGoogle Scholar
  94. Stewart, D. W. (2009). The role of method: some parting thoughts from a departing editor. Journal of the Academy of Marketing Science, 37(4), 381–383.CrossRefGoogle Scholar
  95. Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society, 36(2), 111–147.Google Scholar
  96. Tenenhaus, M., Amato, S., & Esposito Vinzi, V (2004). A global goodness-of-fit index for PLS structural equation modeling. In Proceedings of the XLII SIS Scientific Meeting (pp. 739–742). Padova: CLEUP.Google Scholar
  97. Theoharakis, V., & Hirst, A. (2002). Perceptual differences of marketing journals: a worldwide perspective. Marketing Letters, 13(4), 389–402.CrossRefGoogle Scholar
  98. Völckner, F., Sattler, H., Hennig-Thurau, T., & Ringle, C. M. (2010). The role of parent brand quality for service brand extension success. Journal of Service Research, 13(4), 379–396.CrossRefGoogle Scholar
  99. Wetzels, M., Oderkerken-Schröder, G., & van Oppen, C. (2009). Using PLS path modeling for assessing hierarchical construct models: guidelines and empirical illustration. MIS Quarterly, 33(1), 177–195.Google Scholar
  100. 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.Google Scholar
  101. 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
  102. Wold, H. (1985). Partial least squares. In S. Kotz & N. L. Johnson (Eds.), Encyclopedia of statistical sciences (pp. 581–591). New York: Wiley.Google Scholar
  103. Xu, X., Venkatesh, V., Tam, K. Y., & Hong, S.-J. (2010). Model of migration and use of platforms: role of hierarchy, current generation, and complementarities in consumer settings. Management Science, 56(8), 1304–1323.CrossRefGoogle Scholar

Copyright information

© Academy of Marketing Science 2011

Authors and Affiliations

  • Joe F. Hair
    • 1
  • Marko Sarstedt
    • 2
    • 3
  • Christian M. Ringle
    • 3
    • 4
  • Jeannette A. Mena
    • 5
  1. 1.Kennesaw State University, KSU Center, DBA ProgramKennesawUSA
  2. 2.Ludwig-Maximilians-University Munich (LMU), Institute for Market-based Management (IMM)MunichGermany
  3. 3.University of Newcastle, Faculty of Business and LawNewcastleAustralia
  4. 4.Hamburg University of Technology (TUHH), Institute for Human Resource Management and Organizations (HRMO)HamburgGermany
  5. 5.University of South Florida, College of BusinessTampaUSA

Personalised recommendations