Skip to main content
SpringerLink
Log in

Partial Least Squares Structural Equation Modeling

Book cover Handbook of Market Research

Abstract

Partial least squares structural equation modeling (PLS-SEM) has become a popular method for estimating (complex) path models with latent variables and their relationships. Building on an introduction of the fundamentals of measurement and structural theory, this chapter explains how to specify and estimate path models using PLS-SEM. Complementing the introduction of the PLS-SEM method and the description of how to evaluate analysis results, the chapter also offers an overview of complementary analytical techniques. An application of the PLS-SEM method to a well-known corporate reputation model using the SmartPLS 3 software illustrates the concepts.

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

Access this chapter

Log in via an institution

Institutional subscriptions

References

  • Aaker, D. A. (1991). Managing brand equity: Capitalizing on the value of a brand name. New York: Free Press.

    Google Scholar 

  • Aguirre-Urreta, M. I., & Rönkkö, M. (2017). Statistical inference with PLSc using bootstrap confidence intervals. MIS Quarterly, forthcoming.

    Google Scholar 

  • Akter, S., Wamba, S. F., & Dewan, S. (2017). Why PLS-SEM is suitable for complex modeling? An empirical illustration in big data analytics quality. Production Planning & Control, 28(11-12), 1011–1021.

    Google Scholar 

  • Ali, F., Rasoolemanesh, S. M., Sarstedt, M., Ringle, C. M., & Ryu, K. (2017). An assessment of the use of partial least squares structural equation modeling (PLS-SEM) in hospitality research. International Journal of Contemporary Hospitality Management, forthcoming.

    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 

  • Becker, J.-M., & Ismail, I. R. (2016). Accounting for sampling weights in PLS path modeling: Simulations and empirical examples. European Management Journal, 34(6), 606–617.

    Article  Google Scholar 

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

    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.

    Google Scholar 

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

    Book  Google Scholar 

  • Bollen, K. A. (2002). Latent variables in psychology and the social sciences. Annual Review of Psychology, 53(1), 605–634.

    Article  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., & Bauldry, S. (2011). Three Cs in measurement models: Causal indicators, composite indicators, and covariates. Psychological Methods, 16(3), 265–284.

    Article  Google Scholar 

  • Bollen, K. A., & Diamantopoulos, A. (2017). In defense of causal–formative indicators: A minority report. Psychological Methods, forthcoming.

    Google Scholar 

  • Bollen, K. A., & Lennox, R. (1991). Conventional wisdom on measurement: A structural equation perspective. Psychological Bulletin, 110(2), 305–314.

    Article  Google Scholar 

  • Borsboom, D., Mellenbergh, G. J., & van Heerden, J. (2003). The theoretical status of latent variables. Psychological Review, 110(2), 203–219.

    Article  Google Scholar 

  • Cenfetelli, R. T., & Bassellier, G. (2009). Interpretation of formative measurement in information systems research. MIS Quarterly, 33(4), 689–708.

    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. 295–358). Mahwah: Erlbaum.

    Google Scholar 

  • Chin, W. W. (2010). How to write up and report PLS analyses. 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. 655–690). Heidelberg/Dordrecht/London/New York: Springer.

    Google Scholar 

  • 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 

  • Chou, C.-P., Bentler, P. M., & Satorra, A. (1991). Scaled test statistics and robust standard errors for non-normal data in covariance structure analysis: A Monte Carlo study. British Journal of Mathematical and Statistical Psychology, 44(2), 347–357.

    Article  Google Scholar 

  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Mahwah: 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 Modeling in Management, 1(1), 7–17.

    Article  Google Scholar 

  • Diamantopoulos, A. (2011). Incorporating formative measures into covariance-based structural equation models. MIS Quarterly, 35(2), 335–358.

    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., 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 the Academy of Marketing Science, 40(3), 434–449.

    Article  Google Scholar 

  • Dijkstra, T. K. (2010). Latent variables and indices: Herman world’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 (Springer Handbooks of Computational Statistics Series, Vol. II, pp. 23–46). Heidelberg/Dordrecht/London/New York: Springer.

    Google Scholar 

  • 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 

  • Dijkstra, T. K., & Henseler, J. (2015a). Consistent and asymptotically normal PLS estimators for linear structural equations. Computational Statistics & Data Analysis, 81(1), 10–23.

    Article  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.

    Article  Google Scholar 

  • do Valle, P. O., & Assaker, G. (2016). Using partial least squares structural equation modeling in tourism research: A review of past research and recommendations for future applications. Journal of Travel Research, 55(6), 695–708.

    Article  Google Scholar 

  • Eberl, M. (2010). An application of PLS in multi-group analysis: The need for differentiated corporate-level marketing in the mobile communications industry. 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. 487–514). Heidelberg/Dordrecht/London/New York: Springer.

    Google Scholar 

  • Eberl, M., & Schwaiger, M. (2005). Corporate reputation: Disentangling the effects on financial performance. European Journal of Marketing, 39(7/8), 838–854.

    Article  Google Scholar 

  • Edwards, J. R., & Bagozzi, R. P. (2000). On the nature and direction of relationships between constructs and measures. Psychological Methods, 5(2), 155–174.

    Article  Google Scholar 

  • Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. New York: Chapman & Hall.

    Book  Google Scholar 

  • Esposito Vinzi, V., Chin, W. W., Henseler, J., & Wang, H. (Eds.) (2010). Handbook of partial least squares: Concepts, methods and applications (Springer Handbooks of Computational Statistics Series, Vol. II). Heidelberg/Dordrecht/London/New York: Springer.

    Google Scholar 

  • 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 

  • Falk, R. F., & Miller, N. B. (1992). A primer for soft modeling. Akron: University of Akron Press.

    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 

  • Garson, G. D. (2016). Partial least squares regression and structural equation models. Asheboro: Statistical Associates.

    Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  • Götz, O., Liehr-Gobbers, K., & Krafft, M. (2010). Evaluation of structural equation models using the partial least squares (PLS) approach. 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. 691–711). Heidelberg/Dordrecht/London/New York: Springer.

    Google Scholar 

  • Grace, J. B., & Bollen, K. A. (2008). Representing general theoretical concepts in structural equation models: The role of composite variables. Environmental and Ecological Statistics, 15(2), 191–213.

    Article  Google Scholar 

  • Gregor, S. (2006). The nature of theory in information systems. MIS Quarterly, 30(3), 611–642.

    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., 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 

  • 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.

    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 the Academy of Marketing Science, 40(3), 414–433.

    Article  Google Scholar 

  • 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 

  • Hair, J. F., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. L. (2017a). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data Systems, 117(3), 442–458.

    Article  Google Scholar 

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

    Google Scholar 

  • Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., & Thiele, K. O. (2017c). Mirror, mirror on the wall: A comparative evaluation of composite-based structural equation modeling methods. Journal of the Academy of Marketing Science, forthcoming.

    Google Scholar 

  • Hair, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2018). Advanced issues in partial least squares structural equation modeling (PLS-SEM). Thousand Oaks: Sage.

    Google Scholar 

  • Helm, S., Eggert, A., & Garnefeld, I. (2010). Modelling the impact of corporate reputation on customer satisfaction and loyalty using PLS. 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. 515–534). Heidelberg/Dordrecht/London/New York: Springer.

    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. (2017). Using variance-based structural equation modeling for empirical advertising research at the interface of design and behavioral research. Journal of Advertising, 46(1), 178–192.

    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, 17(1), 82–109.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • Henseler, J., Fassott, G., Dijkstra, T. K., & Wilson, B. (2012a). Analyzing quadratic effects of formative constructs by means of variance-based structural equation modelling. European Journal of Information Systems, 21(1), 99–112.

    Article  Google Scholar 

  • Henseler, J., Ringle, C. M., & Sarstedt, M. (2012b). Using partial least squares path modeling in international advertising research: Basic concepts and recent issues. In S. Okazaki (Ed.), Handbook of research in international advertising (pp. 252–276). Cheltenham: Edward Elgar Publishing.

    Google Scholar 

  • 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 

  • 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 

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

    Article  Google Scholar 

  • Henseler, J., Ringle, C. M., & Sarstedt, M. (2016b). Testing measurement invariance of composites using partial least squares. International Marketing Review, 33(3), 405–431.

    Article  Google Scholar 

  • Houston, M. B. (2004). Assessing the validity of secondary data proxies for marketing constructs. Journal of Business Research, 57(2), 154–161.

    Article  Google Scholar 

  • Hu, L.-t., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3(4), 424–453.

    Article  Google Scholar 

  • 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 

  • Jöreskog, K. G. (1971). Simultaneous factor analysis in several populations. Psychometrika, 36(4), 409–426.

    Article  Google Scholar 

  • 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: Seminar Press.

    Google Scholar 

  • 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 

  • 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 

  • Kock, N., & Hadaya, P. (2017). Minimum sample size estimation in PLS-SEM: The inverse square root and gamma-exponential methods. Information Systems Journal, forthcoming.

    Google Scholar 

  • Latan, H., & Noonan, R. (Eds.). (2017). Partial least squares structural equation modeling: Basic concepts, methodological issues and applications. Heidelberg: Springer.

    Google Scholar 

  • Lee, L., Petter, S., Fayard, D., & Robinson, S. (2011). On the use of partial least squares path modeling in accounting research. International Journal of Accounting Information Systems, 12(4), 305–328.

    Article  Google Scholar 

  • Lei, P.-W., & Wu, Q. (2012). Estimation in structural equation modeling. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (pp. 164–179). New York: Guilford Press.

    Google Scholar 

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

    Book  Google Scholar 

  • 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 (Springer Proceedings in Mathematics & Statistics, Vol. 56, pp. 31–64). New York: Springer.

    Google Scholar 

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

    Google Scholar 

  • Marcoulides, G. A., Chin, W. W., & Saunders, C. (2009). Foreword: A critical look at partial least squares modeling. MIS Quarterly, 33(1), 171–175.

    Google Scholar 

  • 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 

  • Mateos-Aparicio, G. (2011). Partial least squares (PLS) methods: Origins, evolution, and application to social sciences. Communications in Statistics – Theory and Methods, 40(13), 2305–2317.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Nitzl, C. (2016). The use of partial least squares structural equation modelling (PLS-SEM) in management accounting research: Directions for future theory development. Journal of Accounting Literature, 37, 19–35.

    Article  Google Scholar 

  • Nitzl, C., & Chin, W. W. (2017). The case of partial least squares (PLS) path modeling in managerial accounting. Journal of Management Control, 28(2), 137–156.

    Google Scholar 

  • Nitzl, C., Roldán, J. L., & Cepeda Carrión, G. (2016). Mediation analysis in partial least squares path modeling: Helping researchers discuss more sophisticated models. Industrial Management & Data Systems, 119(9), 1849–1864.

    Article  Google Scholar 

  • Noonan, R., & Wold, H. O. A. (1982). PLS path modeling with indirectly observed variables: A comparison of alternative estimates for the latent variable. In K. G. Jöreskog & H. O. A. Wold (Eds.), Systems under indirect observations: Part II (pp. 75–94). Amsterdam: North-Holland.

    Google Scholar 

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

    Google Scholar 

  • Olsson, U. H., Foss, T., Troye, S. V., & Howell, R. D. (2000). The performance of ML, GLS, and WLS estimation in structural equation modeling under conditions of misspecification and nonnormality. Structural Equation Modeling: A Multidisciplinary Journal, 7(4), 557–595.

    Article  Google Scholar 

  • Peng, D. X., & Lai, F. (2012). Using partial least squares in operations management research: A practical guideline and summary of past research. Journal of Operations Management, 30(6), 467–480.

    Article  Google Scholar 

  • Raithel, S., & Schwaiger, M. (2015). The effects of corporate reputation perceptions of the general public on shareholder value. Strategic Management Journal, 36(6), 945–956.

    Article  Google Scholar 

  • Raithel, S., Sarstedt, M., Scharf, S., & Schwaiger, M. (2012). On the value relevance of customer satisfaction. Multiple drivers and multiple markets. Journal of the Academy of Marketing Science, 40(4), 509–525.

    Article  Google Scholar 

  • Ramayah, T., Cheah, J., Chuah, F., Ting, H., & Memon, M. A. (2016). Partial least squares structural equation modeling (PLS-SEM) using SmartPLS 3.0: An updated and practical guide to statistical analysis. Singapore: Pearson.

    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.

    Article  Google Scholar 

  • Richter, N. F., Sinkovics, R. R., Ringle, C. M., & Schlägel, C. (2016a). A critical look at the use of SEM in international business research. International Marketing Review, 33(3), 376–404.

    Article  Google Scholar 

  • Richter, N. F., Cepeda, G., Roldán, J. L, Ringle, C. M. (2016b) European management research using partial least squares structural equation modeling (PLS-SEM). European Management Journal 34 (6):589-597

    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.

    Article  Google Scholar 

  • Rigdon, E. E. (2013a). Partial least squares path modeling. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling: A second course, seconded (Vol. 1). Charlotte: Information Age Publishing.

    Google Scholar 

  • Rigdon, E. E. (2013b). Partial least squares path modeling. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling. A second course (2nd ed., pp. 81–116). Charlotte: Information Age Publishing.

    Google Scholar 

  • Rigdon, E. E. (2014a). Comment on “Improper use of endogenous formative variables”. Journal of Business Research, 67(1), 2800–2802.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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 

  • Rigdon, E. E., Becker, J.-M., Rai, A., Ringle, C. M., Diamantopoulos, A., Karahanna, E., Straub, D., & 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 

  • Rigdon, E. E., Sarstedt, M., & Ringle, C. M. (2017). On comparing results from CB-SEM And PLS-SEM. Five perspectives and five recommendations. Marketing ZFP – Journal of Research and Management, forthcoming.

    Google Scholar 

  • 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 

  • Ringle, C. M., Sarstedt, M., & Straub, D. W. (2012). 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., & Taylor, C. R. (2013). PLS path modeling and evolutionary segmentation. Journal of Business Research, 66(9), 1318–1324.

    Article  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  Google Scholar 

  • Roldán, J. L., & Sánchez-Franco, M. J. (2012). Variance-based structural equation modeling: Guidelines for using partial least squares in information systems research. In M. Mora, O. Gelman, A. L. Steenkamp, & M. Raisinghani (Eds.), Research methodologies, innovations and philosophies in software systems engineering and information systems (pp. 193–221). Hershey: IGI Global.

    Chapter  Google Scholar 

  • Sarstedt, M., & Mooi, E. A. (2014). A concise guide to market research: The process, data, and methods using IBM SPSS statistics. Heidelberg: Springer.

    Book  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. In M. Sarstedt, M. Schwaiger, & C. R. Taylor (Eds.), Advances in international marketing (Vol. 22, pp. 195–218). Bingley: Emerald.

    Google Scholar 

  • Sarstedt, M., Wilczynski, P., & Melewar, T. C. (2013). Measuring reputation in global markets – A comparison of reputation measures’ convergent and criterion validities. Journal of World Business, 48(3), 329–339.

    Article  Google Scholar 

  • Sarstedt, M., Ringle, C. M., Smith, D., Reams, R., & Hair, J. F. (2014). Partial least squares structural equation modeling (PLS-SEM): A useful tool for family business researchers. Journal of Family Business Strategy, 5(1), 105–115.

    Article  Google Scholar 

  • Sarstedt, M., Diamantopoulos, A., Salzberger, T., & Baumgartner, P. (2016a). Selecting single items to measure doubly-concrete constructs: A cautionary tale. Journal of Business Research, 69(8), 3159–3167.

    Article  Google Scholar 

  • Sarstedt, M., Hair, J. F., Ringle, C. M., Thiele, K. O., & Gudergan, S. P. (2016b). Estimation issues with PLS and CBSEM: Where the bias lies! Journal of Business Research, 69(10), 3998–4010.

    Article  Google Scholar 

  • Sarstedt, M., Bengart, P., Shaltoni, A. M., & Lehmann, S. (2017). The use of sampling methods in advertising research: A gap between theory and practice. International Journal of Advertising, forthcoming.

    Google Scholar 

  • Schlittgen, R., Ringle, C. M., Sarstedt, M., & Becker, J.-M. (2016). Segmentation of PLS path models by iterative reweighted regressions. Journal of Business Research, 69(10), 4583–4592.

    Article  Google Scholar 

  • Schloderer, M. P., Sarstedt, M., & Ringle, C. M. (2014). The relevance of reputation in the nonprofit sector: The moderating effect of socio-demographic characteristics. International Journal of Nonprofit and Voluntary Sector Marketing, 19(2), 110–126.

    Article  Google Scholar 

  • Schwaiger, M. (2004). Components and parameters of corporate. Reputation: An empirical study. Schmalenbach Business Review, 56(1), 46–71.

    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 

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

    Article  Google Scholar 

  • Shmueli, G., Ray, S., Velasquez Estrada, J. M., & 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 

  • Sosik, J. J., Kahai, S. S., & Piovoso, M. J. (2009). Silver bullet or voodoo statistics? A primer for using the partial least squares data analytic technique in group and organization research. Group & Organization Management, 34(1), 5–36.

    Article  Google Scholar 

  • Stieglitz, S., Dang-Xuan, L., Bruns, A., & Neuberger, C. (2014). Social media analytics. Business & Information Systems Engineering, 6(2), 89–96.

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

    Article  Google Scholar 

  • Westland, J. C. (2015). Partial least squares path analysis. In Structural equation models: From paths to networks (pp. 23–46). Cham: Springer International Publishing.

    Google Scholar 

  • Willaby, H. W., Costa, D. S. J., Burns, B. D., MacCann, C., & Roberts, R. D. (2015). Testing complex models with small sample sizes: A historical overview and empirical demonstration of what partial least squares (PLS) can offer differential psychology. Personality and Individual Differences, 84, 73–78.

    Article  Google Scholar 

  • Wold H. O. A. (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. 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: Academic.

    Google Scholar 

  • 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 

  • Wold H. O. A. (1985). Partial least squares. In S. Kotz & N. L. Johnson (Eds.), Encyclopedia of statistical sciences (Vol. 6, pp. 581–591). New York: Wiley.

    Google Scholar 

Download references

Acknowledgment

This chapter uses the statistical software SmartPLS 3 (http://www.smartpls.com). Ringle acknowledges a financial interest in SmartPLS.

Author information

Authors and Affiliations

  1. Otto-von-Guericke University, Magdeburg, Germany

    Marko Sarstedt

  2. Hamburg University of Technology (TUHH), Hamburg, Germany

    Christian M. Ringle

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

    Marko Sarstedt & Christian M. Ringle

  4. University of South Alabama, Mobile, AL, USA

    Joseph F. Hair

Authors
  1. Marko Sarstedt
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christian M. Ringle
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Joseph F. Hair
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marko Sarstedt .

Editor information

Editors and Affiliations

  1. Universität Mannheim, Mannheim, Germany

    Christian Homburg

  2. Inst. Informations Systems and Marketing Marketing & Sales Research Group, Karlsruher Institut für Technologie, Karlsruhe, Germany

    Martin Klarmann

  3. LS für ABWL und Marketing I, Universität Mannheim, Mannheim, Baden-Württemberg, Germany

    Arnd Vomberg

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this entry

Cite this entry

Sarstedt, M., Ringle, C.M., Hair, J.F. (2017). Partial Least Squares Structural Equation Modeling. In: Homburg, C., Klarmann, M., Vomberg, A. (eds) Handbook of Market Research. Springer, Cham. https://doi.org/10.1007/978-3-319-05542-8_15-1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-05542-8_15-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-05542-8

  • Online ISBN: 978-3-319-05542-8

  • eBook Packages: Springer Reference Business and ManagementReference Module Humanities and Social SciencesReference Module Business, Economics and Social Sciences

Publish with us

Policies and ethics

Chapter history

  1. Latest

    Partial Least Squares Structural Equation Modeling
    Published:
    22 July 2021

    DOI: https://doi.org/10.1007/978-3-319-05542-8_15-2

  2. Original

    Partial Least Squares Structural Equation Modeling
    Published:
    22 August 2017

    DOI: https://doi.org/10.1007/978-3-319-05542-8_15-1

Search

Navigation