Skip to main content
SpringerLink
Log in
Book cover

Partial Least Squares Structural Equation Modeling pp 1–29Cite as

Rise of the Partial Least Squares Structural Equation Modeling: An Application in Banking

  • Chapter
  • First Online:

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 267))

Abstract

Researchers across a wide range of disciplines exploited the capabilities of partial least squares structural equation modeling (PLS-SEM). The rise in popularity of PLS-SEM is particularly noticeable 2013 onwards. The banking and finance discipline, however, hardly exploits the advantages of the PLS-SEM approach. PLS-SEM can be used for prediction and exploration in complex models with relaxed expectations on data. PLS-SEM is useful in identifying relationships between constructs. If the primary objective is theory development, PLS-SEM is appropriate.

This is a preview of subscription content, 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
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
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

Learn about institutional subscriptions

Notes

  1. 1.

    A video recording of PLS-SEM analysis using SmartPLS and R code can be viewed at https://youtu.be/SzQ_LJWnqgQ; this recording is based on the article that can be downloaded from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2634184.

References

  • Avkiran, N. K. (1995). A multivariate model of integrated branch performance and potential focusing on personal banking. Ph.D. Thesis, Victoria University, Australia. Retrieved from http://www.users.on.net/~necmi/financesite/profile.htm.

  • Avkiran, N. K. (2017). An in-depth discussion and illustration of partial least squares structural equation modeling in health care. Health Care Management Science. https://doi.org/10.1007/s10729-017-9393-7.

  • 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 

  • 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., 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 

  • Bentler, P. M., & Huang, W. (2014). On components, latent variables, PLS and simple methods: Reactions to Rigdon’s rethinking of PLS. Long Range Planning, 47(3), 138–145.

    Article  Google Scholar 

  • Bollen, K. A., & Diamantopoulos, A. (2017). In defense of causal-formative indicators: A minority report. Psychological Methods, 22(3), 581–596. https://doi.org/10.1037/met0000056.

    Article  Google Scholar 

  • Boyatzis, R. E. (1982). The competent manager. New York: Wiley.

    Google Scholar 

  • Cepeda Carrión, G., Henseler, J., Ringle, C. M., & Roldán, J. L. (2016). Prediction-oriented modeling in business research by means of PLS path modeling. Journal of Business Research, 69(10), 4545–4551.

    Article  Google Scholar 

  • Chataway, J. G. (1982). Bank work: A study of the everyday work activity of four suburban branch managers. Ph.D. Thesis, Monash University.

    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. E. Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares: Concepts, methods and applications: Vol. 2. Springer handbooks of computational statistics series (pp. 655–690). Heidelberg: Springer.

    Google Scholar 

  • Chin, W. W., & Dibbern, J. (2010). A permutation based procedure for multi-group PLS analysis: Results of tests of differences on simulated data and a cross cultural analysis of the sourcing of information system services between Germany and the USA. In V. Esposito Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares: Concepts, methods and applications: Vol. 2. Springer handbooks of computational statistics series (pp. 171–193). Heidelberg: Springer.

    Google Scholar 

  • Chu, P. Y., Lee, G. Y., & Chao, Y. (2012). Service quality, customer satisfaction, customer trust, and loyalty in an e-banking context. Social Behavior and Personality: An International Journal, 40(8), 1271–1283.

    Article  Google Scholar 

  • Dijkstra, T. K. (2014). PLS’ Janus face—Response to Professor Rigdon’s ‘Rethinking Partial Least Squares Modeling: In Praise of Simple Methods’. Long Range Planning, 47(3), 146–153.

    Article  Google Scholar 

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

    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 

  • 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 

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

    Google Scholar 

  • Gefen, D., Rigdon, E. E., & Straub, D. W. (2011). Editor’s comment: An update and extension to SEM guidelines for administrative and social science research. MIS Quarterly, 35(2), iii–xiv.

    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., 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., Ringle, C. M., & Sarstedt, M. (2012a). Partial least squares: The better approach to structural equation modeling? Long Range Planning, 45(5–6), 312–319.

    Article  Google Scholar 

  • Hair, J. F., Sarstedt, M., Pieper, T. M., & Ringle, C. M. (2012b). The use of partial least squares structural equation modeling in strategic management research: A review of past practices and recommendations for future applications. Long Range Planning, 45(5–6), 320–340.

    Article  Google Scholar 

  • Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012c). 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, 1–12.

    Article  Google Scholar 

  • Hair, J. F., Sarstedt, M., Matthews, L., & Ringle, C. M. (2016). Identifying and treating unobserved heterogeneity with FIMIX-PLS: Part I—method. European Business Review, 28(1), 63–76.

    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, 45(5), 616–632.

    Article  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 

  • Henseler, J., & Sarstedt, M. (2013). Goodness-of-fit indices for partial least squares path modeling. Computational Statistics, 28, 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.), New challenges to international marketing: Advances in international marketing (Vol. 20, pp. 277–319). Bingley: Emerald Group.

    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, 115–135.

    Article  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 

  • Höck, C., Ringle, C. M., & Sarstedt, M. (2010). Management of multi-purpose stadiums: Importance and performance measurement of service interfaces. International Journal of Services Technology and Management, 14(2/3), 188–207.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Hwang, H., & Takane, Y. (2014). Generalized structured component analysis: A component-based approach to structural equation modeling. New York: Chapman & Hall.

    Google Scholar 

  • Hwang, H., Ho, M.-H., & Lee, J. (2010). Generalized structured component analysis with latent interactions. Psychometrika, 75(2), 228–242.

    Article  Google Scholar 

  • Jöreskog, K. G. (1979). Basic ideas of factor and component analysis. In K. G. Jöreskog & D. Sörbom (Eds.), Advances in factor analysis and structural equation models (pp. 5–20). New York: University Press of America.

    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: Causality, structure, prediction, 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, 259–272.

    Article  Google Scholar 

  • Kumar, B. R., & Waheed, K. A. (2015). Determinants of dividend policy: Evidence from GCC market. Accounting and Finance Research, 4(1), 17–29.

    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, 305–328.

    Article  Google Scholar 

  • Lei, P.-W., & Wu, Q. (2007). Introduction to structural equation modeling: Issues and practical considerations. Educational Measurement: Issues and Practice, 26(3), 33–43.

    Article  Google Scholar 

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

    Book  Google Scholar 

  • Martensen, A., & Grønholdt, L. (2003). Improving library users’ perceived quality, satisfaction and loyalty: An integrated measurement and management system. The Journal of Academic Librarianship, 29(3), 140–147.

    Article  Google Scholar 

  • Matthews, L., Sarstedt, M., Hair, J. F., & Ringle, C. M. (2016). Identifying and treating unobserved heterogeneity with FIMIX-PLS: Part II—A case study. European Business Review, 28(2), 208–224.

    Article  Google Scholar 

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

    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(December), 19–35.

    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, 467–480.

    Article  Google Scholar 

  • Petter, S., Straub, D. W., & Rai, A. (2007). Specifying formative constructs in information systems research. MIS Quarterly, 31(4), 623–656.

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  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. (2014). 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., 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, iii–xiv.

    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 

  • Ringle, C. M., Wende, S., & Becker, J. M. (2015). SmartPLS 3. Bönningstedt: SmartPLS GmbH.

    Google Scholar 

  • Rose, P. S. (1986). The bank branch: Which way to the future? Canadian Banker, 93(6), 42–50.

    Google Scholar 

  • Sarstedt, M., Henseler, J., & Ringle, C. M. (2011). 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., 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., Hair, J. F., Ringle, C. M., Thiele, K. O., & Gudergan, S. P. (2016). Estimation issues with PLS and CBSEM: Where the bias lies! Journal of Business Research, 69(10), 3998–4010.

    Article  Google Scholar 

  • Sarstedt, M., Ringle, C. M., & Hair, J. F. (2018). Partial least squares structural equation modeling. In C. Homburg, M. Klarmann, & A. Vomberg (Eds.), Handbook of market research. Heidelberg: Springer.

    Google Scholar 

  • Schlittgen, R. (2017). Estimation of generalized structured component analysis models with alternating least squares. Computational Statistics. 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, 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 

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

    Google Scholar 

  • Sohn, S. Y., Han, H. K., & Jeon, H. J. (2007). Development of an air force warehouse logistics index to continuously improve logistics capabilities. European Journal of Operational Research, 183, 148–161.

    Article  Google Scholar 

  • Tenenhaus, M., Amato, S., & Vinzi, V. E. (2004). A global goodness-of-fit index for PLS structural equation modeling. Proceedings of the XLII SIS Scientific Meeting, 739–742.

    Google Scholar 

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

    Article  Google Scholar 

  • Teo, A. C., Tan, G. W. H., Ooi, K. B., & Lin, B. (2015). Why consumers adopt mobile payment? A partial least squares structural equation modelling (PLS-SEM) approach. International Journal of Mobile Communications, 13(5), 478–497.

    Article  Google Scholar 

  • Thiele, K.-O., Sarstedt, M., & Ringle, M. C. (2015). Mirror, mirror on the wall. A comparative evaluation of new and established structural equation modeling methods. 2nd International Symposium on Partial Least Squares Path Modeling, Seville, Spain.

    Google Scholar 

  • Vinzi, E. V., Trinchera, L., & Amato, S. (2010). PLS path modeling: From foundations to recent developments and open issues for model assessment and improvement. In V. E. Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares: Concepts, methods and applications: Vol. 2. Springer handbooks of computational statistics series (pp. 47–82). Heidelberg: Springer.

    Google Scholar 

  • Voorhees, C. M., Brady, M. K., Calantone, R., & Ramirez, E. (2016). Discriminant validity testing in marketing: An analysis, causes for concern, and proposed remedies. Journal of the Academy of Marketing Science, 44(1), 119–134.

    Article  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. (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. (2006). Partial least squares encyclopedia of statistical sciences. New York: Wiley.

    Google Scholar 

  • Wu, W. W., Lan, L. W., & Lee, Y. T. (2012). Exploring the critical pillars and causal relations within the NRI: An innovative approach. European Journal of Operational Research, 218, 230–238.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Business, University of Queensland, St Lucia, QLD, Australia

    Necmi K. Avkiran

Authors
  1. Necmi K. Avkiran
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Necmi K. Avkiran .

Editor information

Editors and Affiliations

  1. School of Business, University of Queensland, St Lucia, Queensland, Australia

    Necmi K. Avkiran

  2. Institute of HRM, Hamburg University of Technology, Hamburg, Germany

    Christian M. Ringle

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Avkiran, N.K. (2018). Rise of the Partial Least Squares Structural Equation Modeling: An Application in Banking. In: Avkiran, N., Ringle, C. (eds) Partial Least Squares Structural Equation Modeling. International Series in Operations Research & Management Science, vol 267. Springer, Cham. https://doi.org/10.1007/978-3-319-71691-6_1

Download citation

Publish with us

Policies and ethics

Search

Navigation