Skip to main content
SpringerLink
Log in

Introduction to the Partial Least Squares Path Modeling: Basic Concepts and Recent Methodological Enhancements

  • Chapter
  • First Online:
Partial Least Squares Path Modeling

  • 520 Accesses

Abstract

This chapter aims to provide a brief overview of the three primary structural equation modeling approaches, which include partial least squares-path modeling (PLS-PM), covariance-based structural equation modeling (CB-SEM), and generalized structure component analysis (GSCA). We also provide guidelines regarding the appropriate situation to apply each of the three SEM methods. In addition, we describe recent methodological developments in SEM, particularly the method of PLS-PM, as well as applications of selected essential features of PLS-PM. We identify these topics as essential emerging tools for PLS-PM scholars since increasingly their understanding and application will be required in research utilizing PLS-PM. In the end, we summarize our observations and conclusions regarding the evolving state of PLS-PM.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Adelman, I., & Lohmöller, J.-B. (1994). Institutions and development in the nineteenth century: A latent variable regression model. Structural Change and Economic Dynamics, 5(2), 329–359.

    Article  Google Scholar 

  • Apel, H., & Wold, H. (1982). Soft modeling with latent variables in two or more dimensions: PLS estimation and testing for predictive relevance. In K. G. Joreskog & H. Wold (Eds.), Systems under indirect observation: Causality, structure, prediction (Vol. 2, pp. 209–247). North Holland.

    Google Scholar 

  • Basco, R., Hair, J. F., Ringle, C. M., & Sarstedt, M. (2022). Advancing family business research through modeling nonlinear relationships: Comparing PLS-SEM and multiple regression. Journal of Family Business Strategy, 13(3), 100457.

    Google Scholar 

  • Becker, J.-M., Cheah, J. H., Gholamzade, R., Ringle, C. M., & Sarstedt, M. (2023). PLS-SEM’s most wanted guidance. International Journal of Contemporary Hospitality Management, 53(1), 321–346.

    Article  Google Scholar 

  • Becker, J.-M., Rai, A., Ringle, C. M., & Völckner, F. (2013). Discovering unobserved heterogeneity in structural equation models to avert validity threats. MIS Quarterly, 37(3), 665–694.

    Article  Google Scholar 

  • Cheah, J.-H., Nitzl, C., Roldán, J. L., Cepeda-Carrion, G., & Gudergan, S. P. (2021). A primer on the conditional mediation analysis in PLS-SEM. The DATA BASE for Advances in Information Systems, 52, 43–100.

    Article  Google Scholar 

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

    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 System Research, 14(2), 189–217.

    Article  Google Scholar 

  • Cho, G., Kim, S., Lee, J., Hwang, H., Sarstedt, M., & Ringle, C. M. (2023). A comparative study of the predictive power of component-based approaches to structural equation modeling. European Journal of Marketing, 57(6), 1641–1661.

    Google Scholar 

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

    Google Scholar 

  • Fassott, G., Henseler, J., & Coelho, P. S. (2016). Testing moderating effects in PLS path models with composite variables. Industrial Management & Data Systems, 116(9), 1887–1900.

    Article  Google Scholar 

  • Geladi, P., & Kowalski, B. R. (1986). Partial least squares regression: A tutorial. Analytica Chimica Acta, 185, 1–17.

    Article  Google Scholar 

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

    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 

  • Hair, J. F. (2022). A celebration of a decade of PLS-SEM applications and improvement. Paper presented at the International Conference on Partial Least Squares Structural Equation Modeling (PLS-SEM), Babes-Bolyai University, Cluj-Napoca, Romania, September 6–9, 2022.

    Google Scholar 

  • Hair, J. F., & Alamer, A. (2022). Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics, 1, 100027.

    Article  Google Scholar 

  • Hair, J. F., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101–110.

    Article  Google Scholar 

  • Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2014). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications.

    MATH  Google Scholar 

  • Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage Publications.

    MATH  Google Scholar 

  • Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook. Springer.

    Book  Google Scholar 

  • Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., & Thiele, K. O. (2017). Mirror, mirror on the wall: A comparative evaluation of composite-based structural equation modeling methods. Journal of the Academy Marketing Science, 45(5), 616–632.

    Article  Google Scholar 

  • Hair, J. F., Ringle, C. M., Gudergan, S. P., Fischer, A., Nitzl, C., & Menictas, C. (2019a). Partial least squares structural equation modeling based discrete choice modeling: An illustration in modeling retailer choice. Business Research, 12, 115–142.

    Article  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., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019b). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24.

    Article  Google Scholar 

  • Hair, J. F., & Sarstedt, M. (2021). Explanation plus prediction—The logical focus of project management research. Project Management Journal, 52(4), 319–322.

    Article  Google Scholar 

  • Hair, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2023). Advanced issues in partial least squares structural equation modeling (2nd ed.). Sage Publications.

    Google Scholar 

  • Hayes, A. F. (2018). Partial, conditional, and moderated moderated mediation: Quantification, inference, and interpretation. Communication Monographs, 85(1), 4–40.

    Article  Google Scholar 

  • Hayes, A. F. (2022). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (3rd ed.). Guilford Press.

    Google Scholar 

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

    Article  Google Scholar 

  • Hoyle, R. H. (Ed.). (2023). Handbook of structural equation modeling (2nd ed.). Guilford Press.

    Google Scholar 

  • Hwang, H., & Takane, Y. (2004). Generalized structure component analysis. Psychometrika, 69(1), 81–99.

    Article  MathSciNet  MATH  Google Scholar 

  • Hwang, H., & Takane, Y. (2015). Generalized structured component analysis: A component-based approach to structural equation modeling. CRC Press.

    MATH  Google Scholar 

  • Iacobucci, D. (2008). Mediation analysis. Sage Publications.

    Book  Google Scholar 

  • Jaccard, J., & Jacoby, J. (2020). Theory construction and model-building skills: A practical guide for social scientists (2nd ed.). Guilford Press.

    Google Scholar 

  • Jöreskog, K. G. (1973). Structural analysis of covariance and correlation matrices. Psychometrika, 43, 443–477.

    Article  MathSciNet  MATH  Google Scholar 

  • Jöreskog, K. G., Olsson, U. H., & Wallentin, F. Y. (2016). Multivariate analysis with LISREL. Springer.

    Book  MATH  Google Scholar 

  • Kline, R. B. (2023). Principles and practice of structural equation modeling (5th ed.). Gulford Press.

    MATH  Google Scholar 

  • Latan, H. (2013). Structural equation modeling using AMOS. Alfabeta.

    Google Scholar 

  • Latan, H. (2014). Generalized structure component analysis: Theory, concepts and applications. Alfabeta.

    Google Scholar 

  • Latan, H. (2018). PLS path modeling in hospitality and tourism research: The golden age and days of future past. In F. Ali, S. M. Rasoolimanesh, & C. Cobanoglu (Eds.), Applying partial least squares in tourism and hospitality research (pp. 53–83). Bingley.

    Chapter  Google Scholar 

  • Latan, H., Chiappetta Jabbour, C. J., & Lopes de Sousa Jabbour, A. B. (2019). Ethical awareness, ethical judgment and whistleblowing: A moderated mediation analysis. Journal of Business Ethics, 155, 289–304.

    Article  Google Scholar 

  • Latan, H., & Ghozali, I. (2022). Partial least squares using SmartPLS (4th ed.). Diponegoro University Press.

    Google Scholar 

  • Legate, A. E., Hair, J. F., Chretien, J. L., & Risher, J. J. (2023). PLS-SEM: Prediction-oriented solutions for HRD researchers. Human Resource Development Quarterly, 34(1), 91–109.

    Article  Google Scholar 

  • Liengaard, B. D., Sharma, P. N., Hult, G. T. M., Jensen, M. B., Sarstedt, M., Hair, J. F., et al. (2020). Prediction: Coveted, yet forsaken? Introducing a cross-validated predictive ability test in partial least squares path modeling. Decision Sciences, 52(2), 362–392.

    Article  Google Scholar 

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

    Book  MATH  Google Scholar 

  • Manley, S. C., Hair, J. F., Williams, R. I., & McDowell, W. C. (2021). Essential new PLS-SEM analysis methods for your entrepreneurship analytical toolbox. International Entrepreneurship and Management Journal, 17, 1805–1825.

    Article  Google Scholar 

  • Mehmetoglu, M., & Venturini, S. (2021). Structural equation modelling with partial least squares using Stata and R. CRC Press.

    Book  MATH  Google Scholar 

  • Negrão, L. L. L., Lopes de Sousa, A. B., Latan, H., Godinho Filho, M., Chiappetta Jabbour, C. J., & Ganga, G. M. D. (2020). Lean manufacturing and business performance: Testing the S-curve theory. Production Planning & Control, 31(10), 771–785.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Noonan, R., & Wold, H. (1977). NIPALS path modeling with latent variables: Analyzing school survey data using nonlinear iterative partial least squares. Scandinavian Journal of Educational Research, 21(1), 33–61.

    Article  Google Scholar 

  • Noonan, R., & Wold, H. (1980). PLS path modeling with latent variables: Analyzing school survey data using partial least squares—Part II. Scandinavian Journal of Educational Research, 24(1), 1–24.

    Article  Google Scholar 

  • Noonan, R., & Wold, H. (1982). PLS path modelling with indirectly observed variables: A comparison of alternative estimates for the latent variable. In K. G. Joreskog & H. Wold (Eds.), System under indirect observation: Causality, structure, prediction (Vol. 2, pp. 75–94). North-Holland.

    Google Scholar 

  • Noonan, R., & Wold, H. (1983). Evaluating school systems using partial least squares. In H. J. Walberg & T. N. Postlethwaite (Eds.), Evaluation in education: An international review series (Vol. 7, pp. 219–364). Pergamon Press.

    Google Scholar 

  • Richter, N. F., Hauff, S., Ringle, C. M., & Gudergan, S. P. (2022). The use of partial least squares structural equation modeling and complementary methods in international management research. Management International Review, 62(4), 449–470.

    Article  Google Scholar 

  • Rigdon, E. E., Sarstedt, M., & Becker, J. M. (2020). Quantify uncertainty in behavioral research. Nature Human Behaviour, 4(4), 329–331.

    Article  Google Scholar 

  • Ringle, C. M., Wende, S., & Becker, J.-M. (2022). SmartPLS 4. Oststeinbek: SmartPLS GmbH. http://www.smartpls.com.

  • Ringle, C. M., Wende, S., & Will, A. (2005). SmartPLS 2.0 M3 (beta). Hamburg.

    Google Scholar 

  • Sabol, M. A., Hair Jr, J. F., Cepeda, G., & Roldan, J. L. (2023). PLS-SEM in information systems: Seizing the opportunity and marching ahead full speed. Industrial Management and Information Systems, forthcoming.

    Google Scholar 

  • Sanchez, G. (2013). PLS path modeling with R. Creative Commons Attribution: NonCommercial-ShareAlike 3.0 Unported License.

    Google Scholar 

  • Sarstedt, M., Hair, J. F., Nitzl, C., Ringle, C. M., & Howard, M. C. (2020a). Beyond a tandem analysis of SEM and PROCESS: Use of PLS-SEM for mediation analyses! International Journal of Market Research, 62(3), 288–299.

    Article  Google Scholar 

  • Sarstedt, M., Hair, J. F., Pick, M., Liengaard, B. D., Radomir, L., & Ringle, C. M. (2022). Progress in partial least squares structural equation modeling use in marketing research in the last decade. Psychology & Marketing, 39(5), 1035–1064.

    Article  Google Scholar 

  • Sarstedt, M., Henseler, J., & Ringle, C. M. (2011). Multigroup analysis in partial least squares (PLS) path modeling: Alternative methods and empirical results. In M. Sarstedt, M. Schwaiger, & C. R. Taylor (Eds.), Measurement and research methods in international marketing: Advances in international marketing (Vol. 22, pp. 195–218). Emerald Group Publishing Limited.

    Google Scholar 

  • Sarstedt, M., Ringle, C. M., Cheah, J.-H., Ting, H., Moisescu, O. I., & Radomir, L. (2020b). Structural model robustness checks in PLS-SEM. Tourism Economics, 26(4), 531–554.

    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 

  • Schuberth, F., Henseler, J., & Dijkstra, T. K. (2018). Confirmatory composite analysis. Frontiers in Psychology, 1–14. https://doi.org/10.3389/fpsyg.2018.02541

  • Sharma, P. N., Liengaard, B. D. D., Hair, J. F., Sarstedt, M., & Ringle, C. M. (2023). Predictive model assessment and selection in composite-based modeling using PLS-SEM: Extensions and guidelines for using CVPAT. European Journal of Marketing, 57(6), 1662–1677.

    Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  • Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., Vaithilingam, S., et al. (2019). Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. European Journal of Marketing, 53(11).

    Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  • Tobias, R. D. (1997). An introduction to partial least squares regression (pp. 1–8). SAS Institute Inc.

    Google Scholar 

  • Whittaker, T. A., & Schumacker, R. E. (2022). A beginner’s guide to structural equation modeling (5th ed.). Routledge.

    Book  Google Scholar 

  • Wold, H. (1965). A fix-point theorem with econometric background Part II. Illustrations. Further Developments. Arkiv for Matematik, 6(13), 221–240.

    MATH  Google Scholar 

  • Wold, H. (1966). Nonlinear estimation by iterative least squares procedures. In F. N. David (Ed.), Festschrift for J. Neyman: Research papers in statistics (pp. 411–444). Wiley.

    Google Scholar 

  • Wold, H. (1975). Soft modelling by latent variables: The non-linear iterative partial least squares (NIPALS) approach. In J. Gani (Ed.), Perspectives in probability and statistics: Papers in honour of M.S. Bartlett on the occasion of his sixty-fifth birthday (pp. 117–142). Applied Probability Trust, Academic.

    Google Scholar 

  • Wold, H. (1980a). Model construction and evaluation when theoretical knowledge is scarce: Theory and application of partial least squares. In J. Kmenta & J. B. Ramsey (Eds.), Evaluation of econometrics models (pp. 47–74). Academic Press.

    Chapter  Google Scholar 

  • Wold, H. (1980b). Soft modelling: Intermediate between traditional model building and data analysis. In Mathematical statistics (Vol. 6, pp. 333–346). Polish Scientific Publishers.

    Google Scholar 

  • Wold, H. (Ed.). (1981). The fix-point approach to independent systems. North-Holland.

    MATH  Google Scholar 

  • Wold, H. (1982). Soft modeling: The basic design and some extensions. In K. G. Joreskog & H. Wold (Eds.), Systems under indirect observation: Causality, structure, prediction (Vol. 2, pp. 1–54). North Holland.

    Google Scholar 

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

    Google Scholar 

  • Wold, H. (1989). Introduction to the second generation of multivariate analysis. In H. Wold (Ed.), Theoretical empiricism: A general rationale for scientific model-building (pp. VII–XL). Paragon House.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. FTD Institute, Ambon, 97234, Indonesia

    Hengky Latan

  2. HLC Consulting, Jl. Kertanegara Selatan V No 5B, Semarang, 50241, Indonesia

    Hengky Latan

  3. Mitchell College of Business, University of South Alabama, 307 N University Blvd, Mobile, AL, 36688, USA

    Joseph F. Hair Jr. & Misty Sabol

  4. Institute of International Education, Stockholm University, Stockholm, Sweden

    Richard Noonan

Authors
  1. Hengky Latan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Joseph F. Hair Jr.
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Richard Noonan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Misty Sabol
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Misty Sabol .

Editor information

Editors and Affiliations

  1. HLC Consulting, Semarang, Indonesia

    Hengky Latan

  2. Mitchell College of Business, University of South Alabama, Mobile, AL, USA

    Joseph F. Hair, Jr.

  3. Institute of International Education, Stockholm University, Stockholm, Sweden

    Richard Noonan

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Latan, H., Hair, J.F., Noonan, R., Sabol, M. (2023). Introduction to the Partial Least Squares Path Modeling: Basic Concepts and Recent Methodological Enhancements. In: Latan, H., Hair, Jr., J.F., Noonan, R. (eds) Partial Least Squares Path Modeling. Springer, Cham. https://doi.org/10.1007/978-3-031-37772-3_1

Download citation

Publish with us

Policies and ethics

Search

Navigation