Skip to main content
SpringerLink
Log in

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

  • Methodological Paper
  • Published:
Journal of the Academy of Marketing Science Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Notes

  1. It is important to note that the SEM-related literature does not always employ the same terminology when referring to elements of the model. Publications addressing CB-SEM (e.g., Hair et al. 2010) often refer to “structural model” and “measurement models,” whereas those focusing on PLS-SEM (e.g., Lohmöller 1989) use the terms “inner model” and “outer models.” As this paper deals with PLS-SEM, related terminology is used.

  2. The search process was completed on January 31, 2011.

  3. In the following, we consistently use the term “studies” when referring to the 204 journal articles and the term “models” when referring to the 311 PLS-SEM applications in these articles.

  4. The same holds for comparative scales such as rank order, paired comparison, or constant sum scales.

  5. It has to be noted that, contrary to Bergkvist and Rossiter’s (2007, 2009) findings, several studies have shown that single-item measures do not necessarily match multi-item measures in terms of psychometric properties (e.g., Gardner et al. 1989; Kwon and Trail 2005; Sarstedt and Wilczynski 2009).

  6. We also computed the means, medians, and ranges of statistics related to outer (and inner) model assessment (e.g., composite reliability, indicator weights). These are available from the authors upon request.

  7. This analysis includes only those models specified as including only reflective and a mixture of reflective and formative latent variables. Models in which the measurement mode was not specified are not included.

  8. In addition, we contrasted PLS-SEM use between studies that appeared in the five highest ranked and the five lowest ranked journals (20 studies with 23 model estimations) considered in this specific analysis. These analyses produced highly similar results compared to those reported here and are available from the authors upon request.

References

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Beebe, K. R., Pell, R. J., & Seasholtz, M. B. (1998). Chemometrics: A practical guide. New York: Wile.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Bergkvist, L., & Rossiter, J. R. (2009). Tailor-made single-item measures of doubly concrete constructs. International Journal of Advertising, 28(4), 607–621.

    Article  Google Scholar 

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

    Google Scholar 

  • Bollen, K. A. (2011). Evaluating effect, composite, and causal indicators in structural equation models. MIS Quarterly, 35(2), 359–372.

    Google Scholar 

  • Bollen, K. A., & Davies, W. R. (2009). Causal indicator models: identification, estimation, and testing. Structural Equation Modeling. An Interdisciplinary Journal, 16(3), 498–522.

    Article  Google Scholar 

  • Bollen, K. A., & Ting, K.-F. (2000). A tetrad test for causal indicators. Psychological Methods, 5(1), 3–22.

    Article  Google Scholar 

  • 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 

  • Brannick, M. T. (1995). Critical comments on applying covariance structure modeling. Journal of Organizational Behavior, 16(3), 201–213.

    Article  Google Scholar 

  • 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 

  • Cenfetelli, R. T., & Bassellier, G. (2009). Interpretation of formative measurement in information system research. MIS Quarterly, 33(4), 689–707.

    Google Scholar 

  • Chernick, M. R. (2008). Bootstrap methods. A guide for practitioners and researchers (2nd ed.). Hoboken, New Jersey: Wiley.

    Google Scholar 

  • 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 

  • Chin, W. W. (2003). PLS Graph 3.0. Houston: Soft Modeling Inc.

  • 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 

  • 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 

  • Churchill, G. A., & Iacobucci, D. (2010). Marketing research: Methodological foundations. Mason: South-Western Cengage Learning.

    Google Scholar 

  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Hillsdale, New Jersey: Lawrence Erlbaum Associates.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Diamantopoulos, A. (2006). The error term in formative measurement models: interpretation and modeling implications. Journal of Modelling in Management, 1(1), 7–17.

    Article  Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • Diamantopoulos, A., & Winklhofer, H. M. (2001). Index construction with formative indicators: an alternative to scale development. Journal of Marketing Research, 38(2), 269–277.

    Article  Google Scholar 

  • Diamantopoulos, A., Riefler, P., & Roth, K. P. (2008). Advancing formative measurement models. Journal of Business Research, 61(12), 1203–1218.

    Article  Google Scholar 

  • 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 

  • Efron, B. (1981). Nonparametric estimates of standard error: the jackknife, the bootstrap and other methods. Biometrika, 68(3), 589–599.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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 

  • Geisser, S. (1974). A predictive approach to the random effects model. Biometrika, 61(1), 101–107.

    Article  Google Scholar 

  • 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.

  • Grégoire, Y., & Fisher, R. J. (2006). The effects of relationship quality on customer retaliation. Marketing Letters, 17(1), 31–46.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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 

  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Englewood Cliffs: Prentice Hall.

    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), 139–151.

    Article  Google Scholar 

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

    Article  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: A Multidisciplinary Journal, 17(1), 82–109.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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 

  • 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.

    Article  Google Scholar 

  • Hult, G. T. M., Neese, W. T., & Bashaw, R. E. (1997). Faculty perceptions of marketing journals. Journal of Marketing Education, 19(1), 37–52.

    Article  Google Scholar 

  • 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 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Johnson, M. D., Herrmann, A., & Huber, F. (2006). The evolution of loyalty intentions. Journal of Marketing, 70(2), 122–132.

    Article  Google Scholar 

  • Jöreskog, K. G. (1978). Structural analysis of covariance and correlation matrices. Psychometrika, 43(4), 443–477.

    Article  Google Scholar 

  • 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 

  • 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 

  • Kwon, H., & Trail, G. (2005). The feasibility of single-item measures in sport loyalty research. Sport Management Review, 8(1), 69–89.

    Article  Google Scholar 

  • Lee, D. Y. (1994). The impact of firms’ risk-taking attitudes on advertising budgets. Journal of Business Research, 31(2–3), 247–256.

    Article  Google Scholar 

  • Lohmöller, J.-B. (1987). LVPLS 1.8. Cologne.

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

    Google Scholar 

  • 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 

  • Malhotra, N. K. (2010). Marketing research: An applied orientation. Upper Saddle River: Prentice Hall.

    Google Scholar 

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

    Google Scholar 

  • 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 

  • Nunnally, J. C. (1967). Psychometric theory. New York: McGraw Hill.

    Google Scholar 

  • 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.

    Article  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 Market Research, 26(4), 332–344.

    Article  Google Scholar 

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

    Google Scholar 

  • 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.

  • Ringle, C., Wende, S., & Will, A. (2005). SmartPLS 2.0 (Beta). Hamburg, (www.smartpls.de).

  • 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.

  • 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., 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 

  • 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 

  • 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 

  • 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.

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Slack, N. (1994). The importance-performance matrix as a determinant of improvement priority. International Journal of Operations and Production Management, 14(5), 59–75.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society, 36(2), 111–147.

    Google Scholar 

  • 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.

  • Theoharakis, V., & Hirst, A. (2002). Perceptual differences of marketing journals: a worldwide perspective. Marketing Letters, 13(4), 389–402.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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.

    Article  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

  1. Kennesaw State University, KSU Center, DBA Program, Kennesaw, GA, 30144, USA

    Joe F. Hair

  2. Ludwig-Maximilians-University Munich (LMU), Institute for Market-based Management (IMM), Kaulbachstr. 45, 80539, Munich, Germany

    Marko Sarstedt

  3. University of Newcastle, Faculty of Business and Law, Newcastle, Australia

    Marko Sarstedt & Christian M. Ringle

  4. Hamburg University of Technology (TUHH), Institute for Human Resource Management and Organizations (HRMO), Schwarzenbergstraße 95 (D), 21073, Hamburg, Germany

    Christian M. Ringle

  5. University of South Florida, College of Business, 4202 E. Fowler Avenue, Tampa, FL, 33620-5500, USA

    Jeannette A. Mena

Authors
  1. Joe F. Hair
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marko Sarstedt
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christian M. Ringle
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jeannette A. Mena
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Joe F. Hair.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hair, J.F., Sarstedt, M., Ringle, C.M. et al. An assessment of the use of partial least squares structural equation modeling in marketing research. J. of the Acad. Mark. Sci. 40, 414–433 (2012). https://doi.org/10.1007/s11747-011-0261-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11747-011-0261-6

Keywords

Search

Navigation