Skip to main content
SpringerLink
Log in

Use of Partial Least Squares Path Modeling Within and Across Business Disciplines

  • Chapter
  • First Online:
Partial Least Squares Path Modeling

Abstract

The acceptance and application of PLS-PM vary dramatically across business disciplines. Some business disciplines, such as marketing and information systems, have used PLS-PM for decades. Other disciplines, such as accounting, have been slower at incorporating path models and PLS-PM in their research studies. The differences in adoption of PLS-PM across business disciplines can be confusing for authors interested in applying, using, and reporting the PLS-PM results in published research within their own discipline or across business disciplines. To address this concern, this chapter reviews the use and application of PLS-PM in Financial Times (FT50) journals. Our results identify the prevalence of PLS-PM use within and across business disciplines. This chapter reviews the rationales provided by authors for their use of PLS-PM within and across business disciplines, discusses questionable and appropriate rationales for PLS-PM, and offers guidance for authors intending to publish articles using 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

Notes

  1. 1.

    Providing a full description of the PLS-PM method and its proper application and use is beyond the scope of this book chapter. We refer readers to the many books, journal articles, and websites explaining the PLS-PM methodology and the interpretation of its results.

  2. 2.

    In addition to non-parametric tests, some CB-SEM tools provide test statistics for non-normal data.

  3. 3.

    Refer to Henseler et al. (2014) for a detailed description of the differences in common factor and composite models.

  4. 4.

    Originally, confirmatory tetrad analysis was developed to discover additional causal relationships within the data in the context of CB-SEM (Glymour et al., 1987). Bollen and Ting (2000) later discussed how to use confirmatory tetrad analysis to test if variables in the model are causal (i.e., formative) or effect (i.e., reflective) indicators. Gudergan et al. (2008) further extended this work to PLS-PM by explaining how authors could explore and confirm the specification of the measurement model (i.e., formative vs. reflective items).

References

  • Abedifar, P., Molyneux, P., & Tarazi, A. (2013). Risk in Islamic banking*. Review of Finance, 17(6), 2035–2096. https://doi.org/10.1093/rof/rfs041

    Article  Google Scholar 

  • Aguirre-Urreta, M., & Rönkkö, M. (2015). Sample size determination and statistical power analysis in PLS using R: An annotated tutorial. Communications of the Association for Information Systems, 36(1). https://doi.org/10.17705/1CAIS.03603

  • Ali, F., Rasoolimanesh, S. M., Sarstedt, M., Ringle, C. M., & Ryu, K. (2018). An assessment of the use of partial least squares structural equation modeling (PLS-SEM) in hospitality research. International Journal of Contemporary Hospitality Management, 30(1), 514–538. https://doi.org/10.1108/IJCHM-10-2016-0568

    Article  Google Scholar 

  • Amoroso, D. L., & Cheney, P. H. (1991). Testing a causal model of end-user application effectiveness. Journal of Management Information Systems, 8(1), 63–89. https://doi.org/10.1080/07421222.1991.11517911

    Article  Google Scholar 

  • Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. https://doi.org/10.1037/0022-3514.51.6.1173

    Article  Google Scholar 

  • Bayonne, E., Marin-Garcia, J. A., & Alfalla-Luque, R. (2020). Partial least squares (PLS) in operations management research: Insights from a systematic literature review. Journal of Industrial Engineering and Management, 13(3), 565–597. https://doi.org/10.3926/jiem.3416

    Article  Google Scholar 

  • Becker, J. -M., Klein, K., & Wetzels, M. (2012). Hierarchical latent variable models in PLS-SEM: Guidelines for using reflective-formative type models. Long Range Planning, 45(5–6), 359–394. https://doi.org/10.1016/j.lrp.2012.10.001

  • Bedford, D. S., Bisbe, J., & Sweeney, B. (2019). Performance measurement systems as generators of cognitive conflict in ambidextrous firms. Accounting, Organizations and Society, 72, 21–37. https://doi.org/10.1016/j.aos.2018.05.010

    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 

  • Browning, T. R., & de Treville, S. (2018). Editorial: New developments at the journal of operations management. Journal of Operations Management, 64(1), 1–6. https://doi.org/10.1016/j.jom.2018.12.005

    Article  Google Scholar 

  • Burton-Jones, A., Boh, W. F., Oborn, & Padmanabhan, B. (2021). Advancing research transparency at MIS quarterly: A pluralistic approach. MIS Quarterly, 45(2), iii–xviii.

    Google Scholar 

  • Cepeda-Carrion, G., Cegarra-Navarro, J.-G., & Cillo, V. (2018). Tips to use partial least squares structural equation modelling (PLS-SEM) in knowledge management. Journal of Knowledge Management, 23(1), 67–89. https://doi.org/10.1108/JKM-05-2018-0322

    Article  Google Scholar 

  • Chatterjee, D., & Ravichandran, T. (2013). Governance of interorganizational information systems: A resource dependence perspective. Information Systems Research, 24(2), 261–278. https://doi.org/10.1287/isre.1120.0432

    Article  Google Scholar 

  • Chenhall, R. H. (2005). Integrative strategic performance measurement systems, strategic alignment of manufacturing, learning and strategic outcomes: An exploratory study. Accounting, Organizations and Society, 30(5), 395–422. https://doi.org/10.1016/j.aos.2004.08.001

    Article  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. https://doi.org/10.1287/isre.14.2.189.16018

    Article  Google Scholar 

  • Chin, W., Cheah, J.-H., Liu, Y., Ting, H., Lim, X.-J., & Cham, T. H. (2020). Demystifying the role of causal-predictive modeling using partial least squares structural equation modeling in information systems research. Industrial Management & Data Systems, 120(12), 2161–2209. https://doi.org/10.1108/IMDS-10-2019-0529

    Article  Google Scholar 

  • Cool, K., Dierickx, I., & Jemison, D. (1989). Business strategy, market structure and risk-return relationships: A structural approach. Strategic Management Journal, 10(6), 507–522. https://doi.org/10.1002/smj.4250100602

    Article  Google Scholar 

  • de Vries, J., de Koster, R., & Stam, D. (2016). Safety does not happen by accident: Antecedents to a safer warehouse. Production & Operations Management, 25(8), 1377–1390. https://doi.org/10.1111/poms.12546

    Article  Google Scholar 

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

    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. https://doi.org/10.1016/j.lrp.2014.02.004

  • 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. https://doi.org/10.1177/0047287515569779

  • Evermann, J., & Rönkkö, M. (2023). Recent developments in PLS. Communications of the Association for Information Systems, 52, 663–667. https://doi.org/10.17705/1CAIS.05229

  • Fornell, C., & 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. https://doi.org/10.1177/002224378201900406

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Glymour, C., Schemes, R., Spirits, P., & Kelly, K. (1987). Discovering causal structure: Artificial intelligence, philosophy of science, and statistical modeling. Academic Press.

    Google Scholar 

  • Goodhue, D., Lewis, W., & Thompson, R. (2007). Research note—Statistical power in analyzing interaction effects: Questioning the advantage of PLS with product indicators. Information Systems Research, 18(2), 211–227. https://doi.org/10.1287/isre.1070.0123

    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-A16. https://doi.org/10.2307/41703490

    Article  Google Scholar 

  • Goodhue, D., Lewis, W., & Thompson, R. (2023). Comments on Evermann and Rönkkö: Recent developments in PLS. Communications of the Association for Information Systems, 52, 751–755. https://doi.org/10.17705/1CAIS.05235

  • 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 

  • Guide Jr., V. D. R., & Ketokivi, M. (2015). Notes from the editors: Redefining some methodological criteria for the journal. Journal of Operations Management, 37(1), v–viii. https://doi.org/10.1016/S0272-6963(15)00056-X

  • Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152.

    Article  Google Scholar 

  • Hair, J. F., Sarstedt, M., Pieper, T. M., & Ringle, C. M. (2012a). Applications of partial least squares path modeling in management journals: A review of past practices and recommendations for future applications. Long Range Planning, 45(5–6), 320–340. https://doi.org/10.1016/j.lrp.2012.09.008

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

    Article  Google Scholar 

  • Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Editorial—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., Hult, G. T. M., Ringle, C. M., Sarstedt, M., & Thiele, K. O. (2017b). 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. https://doi.org/10.1007/s11747-017-0517-x

    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.

    Google Scholar 

  • Hajli, N. (2018). Ethical environment in the online communities by information credibility: A social media perspective. Journal of Business Ethics, 149(4), 799–810. https://doi.org/10.1007/s10551-016-3036-7

    Article  Google Scholar 

  • Harmeling, C. M., Palmatier, R. W., Houston, M. B., Arnold, M. J., & Samaha, S. A. (2015). Transformational relationship events. Journal of Marketing, 79(5), 39–62. https://doi.org/10.1509/jm.15.0105

    Article  Google Scholar 

  • Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., et al. (2014). Common beliefs and reality about PLS: Comments on Rönkkö and Evermann (2013). Organizational Research Methods, 17(2), 182–209. https://doi.org/10.1177/1094428114526928

    Article  Google Scholar 

  • Henseler, J., Hubona, G., & Ray, P. A. (2016a). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management & Data Systems, 116(1), 2–20. https://doi.org/10.1108/IMDS-09-2015-0382

    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. https://doi.org/10.1108/IMR-09-2014-0304

    Article  Google Scholar 

  • Hughes, M. A., Price, R. L., & Marrs, D. W. (1986). Linking theory construction and theory testing: models with multiple indicators of latent variables. Academy of Management Review, 11(1), 128–144. https://doi.org/10.5465/amr.1986.4282643

    Article  Google Scholar 

  • James, T. L., Lowry, P. B., Wallace, L., & Warkentin, M. (2017). The effect of belongingness on obsessive-compulsive disorder in the use of online social networks. Journal of Management Information Systems, 34(2), 560–596. https://doi.org/10.1080/07421222.2017.1334496

    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. https://doi.org/10.1086/376806

    Article  Google Scholar 

  • Jarvis, C. B., MacKenzie, S. B., & Podsakoff, P. M. (2012). The negative consequences of measurement model misspecification: A response to Aguirre-Urreta and Marakas. MIS Quarterly, 36(1), 139–146.

    Article  Google Scholar 

  • Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Pyschometrika, 34(2), 183–202.

    Article  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. https://doi.org/10.1016/j.pursup.2015.04.005

    Article  Google Scholar 

  • Kock, N. (2023). Contributing to the success of PLS in SEM: An action research perspective. Communications of the Association for Information Systems, 52, 730–734. https://doi.org/10.17705/1CAIS.05233

  • Kock, N., & Hadaya, P. (2018). Minimum sample size estimation in PLS-SEM: The inverse square root and gamma-exponential methods. Information Systems Journal, 28(1), 227–261. https://doi.org/10.1111/isj.12131

    Article  Google Scholar 

  • Liengaard, B. D., Sharma, P. N., Hult, G. T. M., Jensen, M. B., Sarstedt, M., Hair, J. F., & Ringle, C. M. (2021). Prediction: Coveted, yet forsaken? Introducing a cross-validated predictive ability test in partial least squares path modeling. Decision Sciences, 52(2), 362–392. https://doi.org/10.1111/deci.12445

    Article  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(4), 1805–1825. https://doi.org/10.1007/s11365-020-00687-6

    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 

  • Patel, P. C., Terjesen, S., & Li, D. (2012). Enhancing effects of manufacturing flexibility through operational absorptive capacity and operational ambidexterity. Journal of Operations Management, 30(3), 201–220. https://doi.org/10.1016/j.jom.2011.10.004

    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. https://doi.org/10.1016/j.jom.2012.06.002

    Article  Google Scholar 

  • Petter, S., Straub, D., & Rai, A. (2007). Specifying formative constructs in information systems research. MIS Quarterly, 31(4), 623–656. https://doi.org/10.2307/25148814

    Article  Google Scholar 

  • Petter, S., Rai, A., & Straub, D. (2012). The critical importance of construct measurement specification: A response to Aguirre-Urreta and Marakas. MIS Quarterly, 36(1), 147–156.

    Article  Google Scholar 

  • Petter, S., & Hadavi, Y. (2021). With great power comes great responsibility: The use of partial least squares in information systems research. The DATA BASE for Advances in Information Systems, 52(SI), 10–23.

    Google Scholar 

  • Preacher, K. J., & Hayes, A. F. (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, Instruments, & Computers, 36(4), 717–731. https://doi.org/10.3758/BF03206553

    Article  Google Scholar 

  • Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891. https://doi.org/10.3758/BRM.40.3.879

    Article  Google Scholar 

  • Qureshi, I., & Compeau, D. (2009). Assessing between-group differences in information systems research: A comparison of covariance- and component-based SEM. MIS Quarterly, 33(1), 197–214. https://doi.org/10.2307/20650285

    Article  Google Scholar 

  • Richter, N. F., Sinkovics, R. R., Ringle, C. M., & Schlägel, C. (2016). A critical look at the use of SEM in international business research. International Marketing Review, 33(3), 376–404. https://doi.org/10.1108/IMR-04-2014-0148

    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. https://doi.org/10.1016/j.emj.2016.05.006

    Article  MathSciNet  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, 39(3), 4–16.

    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), iiv–8.

    Article  Google Scholar 

  • Ringle, C. M., Sarstedt, M., Mitchell, R., & Gudergan, S. P. (2020). Partial least squares structural equation modeling in HRM research. The International Journal of Human Resource Management, 31(12), 1617–1643. https://doi.org/10.1080/09585192.2017.1416655

    Article  Google Scholar 

  • Rönkkö, M., & Evermann, J. (2013). A critical examination of common beliefs about partial least squares path modeling. Organizational Research Methods, 16(3), 425–448.

    Article  Google Scholar 

  • Rönkkö, M., McIntosh, C. N., Antonakis, J., & Edwards, J. R. (2016). Partial least squares path modeling: Time for some serious second thoughts. Journal of Operations Management, 47–48(1), 9–27. https://doi.org/10.1016/j.jom.2016.05.002

    Article  Google Scholar 

  • Russo, D., & Stol, K. (2023). Don’t throw the baby out with the bathwater: Comments on “Recent Developments in PLS”. Communications of the Association for Information Systems, 52, 700–704. https://doi.org/10.17705/1CAIS.05231

  • 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. https://doi.org/10.1016/j.jbusres.2016.06.007

    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 (Vol. 22, pp. 195–218). Emerald Group Publishing Limited. https://doi.org/10.1108/S1474-7979(2011)0000022012

  • Sarstedt, M., Ringle, C. M., & Hair, J. F. (2021). Partial least squares structural equation modeling. In C. Homburg, M. Klarmann, & Vomberg A. E. (Eds.), Handbook of Market Research. Switzerland: Springer Cham. https://doi.org/10.1007/978-3-319-05542-8_15-2

  • Sharma, P. N., Liengaard, B. D., Sarstedt, M., Hair, J. F., & Ringle, C. M. (2023). Extraordinary claims require extraordinary evidence: A comment on “recent developments in PLS. Communications of the Association for Information Systems, 52, 739–742. https://doi.org/10.17705/1CAIS.05234

  • Shmueli, G., & Koppius, O. R. (2011). Predictive analytics in information systems research. MIS Quarterly, 35(3), 553–572.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Tiwana, A., & Keil, M. (2009). Control in internal and outsourced software projects. Journal of Management Information Systems, 26(3), 9–44. https://doi.org/10.2753/MIS0742-1222260301

    Article  Google Scholar 

  • Udo, G., Bagchi, K., & Maity, M. (2016). Exploring factors affecting digital piracy using the norm activation and UTAUT models: The role of national culture. Journal of Business Ethics, 135(3), 517–541.

    Article  Google Scholar 

  • Usakli, A., & Kucukergin, K. G. (2018). Using partial least squares structural equation modeling in hospitality and tourism: Do researchers follow practical guidelines? International Journal of Contemporary Hospitality Management, 30(11), 3462–3512. https://doi.org/10.1108/IJCHM-11-2017-0753

    Article  Google Scholar 

  • Wetzels, M., Odekerken-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. https://doi.org/10.2307/20650284

    Article  Google Scholar 

  • Wold, H. (1966). Estimation of principal components and related models by iterative least squares. In Multivariate Analysis: Proceedings of an international sympoium (pp. 391–420). New York: Academic Press.

    Google Scholar 

  • Wold, H. (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 

  • Yu, S., Mishra, A. N., Gopal, A., Slaughter, S., & Mukhopadhyay, T. (2015). E-procurement infusion and operational process impacts in MRO procurement: Complementary or substitutive effects? Production & Operations Management, 24(7), 1054–1070. https://doi.org/10.1111/poms.12362

    Article  Google Scholar 

  • Zhao, X., Lynch, J. G., Jr., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37(2), 197–206. https://doi.org/10.1086/651257

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Business, Wake Forest University, NC, 27109, Winston-Salem, USA

    Stacie Petter

  2. Department of Information Systems and Business Analytics, Baylor University, One Bear Place #98005, TX, 76798-8005, Waco, USA

    Yasamin Hadavi

Authors
  1. Stacie Petter
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yasamin Hadavi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Stacie Petter .

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

Petter, S., Hadavi, Y. (2023). Use of Partial Least Squares Path Modeling Within and Across Business Disciplines. 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_3

Download citation

Publish with us

Policies and ethics

Search

Navigation