nach oben

Erschienen in:

2022 | OriginalPaper | Buchkapitel

Partial Least Squares Structural Equation Modeling

verfasst von : Marko Sarstedt, Christian M. Ringle, Joseph F. Hair

Erschienen in: Handbook of Market Research

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Partial least squares structural equation modeling (PLS-SEM) has become a popular method for estimating path models with latent variables and their relationships. A common goal of PLS-SEM analyses is to identify key success factors and sources of competitive advantage for important target constructs such as customer satisfaction, customer loyalty, behavioral intentions, and user behavior. 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. A PLS-SEM application of the widely recognized corporate reputation model illustrates the method.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Structural Equation Modeling

Nächstes Kapitel Automated Text Analysis

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

Aguirre-Urreta, M. I., & Rönkkö, M. (2018). Statistical inference with PLSc using bootstrap confidence intervals. MIS Quarterly, 42(3), 1001–1020.CrossRef

Akter, S., Fosso Wamba, S., & 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.CrossRef

Albers, S. (2010). PLS and success factor studies in marketing. 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. 409–425). Berlin/Heidelberg: Springer.CrossRef

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. The International Journal of Contemporary Hospitality Management, 30(1), 514–538.CrossRef

Avkiran, N. K., & Ringle, C. M. (Eds.). (2018). Partial least squares structural equation modeling: Recent advances in banking and finance. Cham: Springer.

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.CrossRef

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.CrossRef

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.CrossRef

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.

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.CrossRef

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.CrossRef

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

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

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

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

Bollen, K. A., & Diamantopoulos, A. (2017). In defense of causal–formative indicators: A minority report. Psychological Methods, 22(3), 581–596.CrossRef

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

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

Burnham, K. P., & Anderson, D. R. (2002). Model selection and multimodel inference: A practical information-theoretic approach (2nd ed.). Heidelberg: Springer.

Carlson, K. D., & Herdman, A. O. (2012). Understanding the impact of convergent validity on research results. Organizational Research Methods, 15(1), 17–32.CrossRef

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

Cepeda Carrión, G., Cegarra-Navarro, J.-G., & Cillo, V. (2019). Tips to use partial least squares structural equation modelling (PLS-SEM) in knowledge management. Journal of Knowledge Management, 23(1), 67–89.CrossRef

Cheah, J.-H., Sarstedt, M., Ringle, C. M., Ramayah, T., & Ting, H. (2018). Convergent validity assessment of formatively measured constructs in PLS-SEM. International Journal of Contemporary Hospitality Management, 30(11), 3192–3210.CrossRef

Cheah, J.-H., Roldán, J. L., Ciavolino, E., Ting, H., & Ramayah, T. (2020). Sampling weight adjustments in partial least squares structural equation modeling: Guidelines and illustrations. Total Quality Management & Business Excellence, forthcoming.

Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295–336). Mahwah: Lawrence Erlbaum.

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: Springer.CrossRef

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.CrossRef

Chin, W. 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.CrossRef

Cho, G., & Choi, J. Y. (2020). An empirical comparison of generalized structured component analysis and partial least squares path modeling under variance-based structural equation models. Behaviormetrika, 47, 243–272.CrossRef

Cho, G., Hwang, H., Kim, S., Lee, J., Sarstedt, M., & Ringle, C. M. (2021). A comparative study of the predictive power of component-based approaches to structural equation modeling. Working Paper.

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.CrossRef

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Mahwah: Lawrence Erlbaum.

Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159.CrossRef

Danks, N., & Ray, S. (2018). Predictions from partial least squares models. In F. Ali, S. M. Rasoolimanesh, & C. Cobanoglu (Eds.), Applying partial least squares in tourism and hospitality research (pp. 35–52). Bingley: Emerald.CrossRef

Danks, N. P., Sharma, P. N., & Sarstedt, M. (2020). Model selection uncertainty and multimodel inference in partial least squares structural equation modeling (PLS-SEM). Journal of Business Research, 113, 13–24.CrossRef

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

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

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

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.CrossRef

Dijkstra, T. K. (2010). Latent variables and indices: Herman Wold’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). Berlin/Heidelberg: Springer.CrossRef

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.CrossRef

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

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

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.CrossRef

Douglas, H. E. (2009). Reintroducing prediction to explanation. Philosophy of Science, 76(4), 444–463.CrossRef

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). Berlin/Heidelberg: Springer.CrossRef

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

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

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: Springer.

Evermann, J., & Tate, M. (2016). Assessing the predictive performance of structural equation model estimators. Journal of Business Research, 69(10), 4565–4582.CrossRef

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

Fordellone, M., & Vichi, M. (2020). Finding groups in structural equation modeling through the partial least squares algorithm. Computational Statistics & Data Analysis, 147, 106957.CrossRef

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.CrossRef

Fornell, C. G., Johnson, M. D., Anderson, E. W., Cha, J., & Bryant, B. E. (1996). The American customer satisfaction index: Nature, purpose, and findings. Journal of Marketing, 60(4), 7–18.CrossRef

Franke, G., & Sarstedt, M. (2019). Heuristics versus statistics in discriminant validity testing: A comparison of four procedures. Internet Research, 29(3), 430–447.CrossRef

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

George, D., & Mallery, P. (2019). IBM SPSS statistics 25 step by step: A simple guide and reference (15th ed.). New York: Routledge.CrossRef

Geweke, J., & Meese, R. (1981). Estimating regression models of finite but unknown order. International Economic Review, 22(1), 55–70.CrossRef

Ghasemy, M., Teeroovengadum, V., Becker, J.-M., & Ringle, C. M. (2020). This fast car can move faster: A review of PLS-SEM application in higher education research. Higher Education, 80, 1121–1152.

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.CrossRef

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.CrossRef

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

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.CrossRef

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

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.CrossRef

Hair, J. F. (2021). Next-generation prediction metrics for composite-based PLS-SEM. Industrial Management & Data Systems, 121(1), 5–11.

Hair, J. F., & Sarstedt, M. (2019). Composites vs. factors: Implications for choosing the right SEM method. Project Management Journal, 50(6), 1–6.CrossRef

Hair, J. F., & Sarstedt, M. (2021a). Data, measurement, and causal inferences in machine learning: Opportunities and challenges for marketing. Journal of Marketing Theory & Practice, 29(1), 65–77.

Hair, J. F., & Sarstedt, M. (2021b). Explanation plus prediction – The logical focus of project management research. Project Management Journal, forthcoming.

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2018a). Multivariate data analysis (8th ed.). Mason: Cengage.

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

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

Hair, J. F., Sarstedt, M., & Ringle, C. M. (2019b). Rethinking some of the rethinking of partial least squares. European Journal of Marketing, 53(4), 566–584.CrossRef

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.

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.). Thousand Oaks: Sage.

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: Springer.CrossRef

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.CrossRef

Henseler, J. (2021). Composite-based structural equation modeling: Analyzing latent and emergent variables. New York: Guilford Press.

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

Henseler, J., & Schuberth, F. (2020). Using confirmatory composite analysis to assess emergent variables in business research. Journal of Business Research, 120, 147–156.CrossRef

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.

Henseler, J., Ringle, C. M., & Sarstedt, M. (2012). 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.

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.CrossRef

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.CrossRef

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), 2–20.CrossRef

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

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

Hui, B. S., & Wold, H. (1982). Consistency and consistency at large of partial least squares estimates. In K. G. Jöreskog & H. Wold (Eds.), Systems under indirect observation, part II (pp. 119–130). Amsterdam: North-Holland.

Hult, G. T. M., Hair, J. F., Dorian, P., Ringle, C. M., Sarstedt, M., & Pinkwart, A. (2018). Addressing endogeneity in marketing applications of partial least squares structural equation modeling. Journal of International Marketing, 26(3), 1–21.

Hwang, H., Sarstedt, M., Cheah, J.-H., & Ringle, C. M. (2020). A concept analysis of methodological research on composite-based structural equation modeling: Bridging PLSPM and GSCA. Behaviormetrika, 47(1), 219–241.CrossRef

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

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.

Jöreskog, K. G., & Wold, H. (1982). The ML and PLS techniques for modeling with latent variables: Historical and comparative aspects. In H. Wold & K. G. Jöreskog (Eds.), Systems under indirect observation, part I (pp. 263–270). Amsterdam: North-Holland.

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.CrossRef

Khan, G., Sarstedt, M., Shiau, W.-L., Hair, J. F., Ringle, C. M., & Fritze, M. (2019). Methodological research on partial least squares structural equation modeling (PLS-SEM): A social network analysis. Internet Research, 29(3), 407–429.CrossRef

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.

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

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.CrossRef

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.

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), 362–292.

Leischnig, A., Henneberg, S. C., & Thornton, S. C. (2016). Net versus combinatory effects of firm and industry antecedents of sales growth. Journal of Business Research, 69(9), 3576–3583.

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

Manley, S. C., Hair, J. F., Williams, R. I., & McDowell, W. C. (2020). Essential new PLS-SEM analysis methods for your entrepreneurship analytical toolbox. International Entrepreneurship and Management Journal, forthcoming.

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.CrossRef

Marcoulides, G. A., & Saunders, C. (2006). Editor’s comments: PLS: A silver bullet? MIS Quarterly, 30(2), iii–ix.CrossRef

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.CrossRef

Mason, C. H., & Perreault, W. D. (1991). Collinearity, power, and interpretation of multiple regression analysis. Journal of Marketing Research, 28(3), 268–280.CrossRef

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.CrossRef

Matthews, L. (2017). Applying multigroup analysis in PLS-SEM: A step-by-step process. In H. Latan & R. Noonan (Eds.), Partial least squares path modeling: Basic concepts, methodological issues and applications (pp. 219–243). Cham: Springer.CrossRef

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

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

Memon, M. A., Cheah, J. H., Ramayah, H. T., Chuah, F., & Cham, T. H. (2019). Moderation analysis: Issues and guidelines. Journal of Applied Structural Equation Modeling, 3(1), i–xi.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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

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

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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. Kuala Lumpur: Pearson.

Rasoolimanesh, S. M., Ringle, C. M., Sarstedt, M., & Olya, H. (2021). The combined use of symmetric and asymmetric approaches: Partial least squares-structural equation modeling and fuzzy-set qualitative comparative analysis. International Journal of Contemporary Hospitality Management, forthcoming.

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.CrossRef

Rhemtulla, M., van Bork, R., & Borsboom, D. (2020). Worse than measurement error: Consequences of inappropriate latent variable measurement models. Psychological Methods, 25(1), 30–45.CrossRef

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.CrossRef

Richter, N. F., Schubring, S., Hauff, S., Ringle, C. M.. & Sarstedt, M. (2020). When predictors of outcomes are necessary: Guidelines for the combined use of PLS-SEM and NCA. Industrial Management & Data Systems, 120(12), 2243–2267.

Rigdon, E. E. (2012). Rethinking partial least squares path modeling: In praise of simple methods. Long Range Planning, 45(5–6), 341–358.CrossRef

Rigdon, E. E. (2013). 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.

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.CrossRef

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.CrossRef

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.CrossRef

Rigdon, E. E., Becker, J. M., & Sarstedt, M. (2019). Factor indeterminacy as metrological uncertainty: Implications for advancing psychological measurement. Multivariate Behavioral Research, 54(3), 429–443.CrossRef

Ringle, C. M. (2019). What makes a great textbook? Lessons learned from joe Hair. In B. J. Babin & M. Sarstedt (Eds.), The great facilitator: Reflections on the contributions of Joseph F. Hair, Jr. to marketing and business research (pp. 131–150). Cham: Springer.CrossRef

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.CrossRef

Ringle, C. M., Sarstedt, M., & Straub, D. W. (2012). Editor’s comments: A critical look at the use of PLS-SEM in MIS quarterly. MIS Quarterly, 36(1), iii–xiv.CrossRef

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.CrossRef

Ringle, C. M., Sarstedt, M., & Schlittgen, R. (2014). Genetic algorithm segmentation in partial least squares structural equation modeling. OR Spectrum, 36(1), 251–276.CrossRef

Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS 3 [computer software]. Bönningstedt: SmartPLS. Retrieved from https://www.smartpls.com.

Ringle, C. M., Sarstedt, M., Mitchell, R., & Gudergan, S. P. (2020). Partial least squares structural equation modeling in HRM research. International Journal of Human Resource Management, 31(12), 1617–1643.

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.CrossRef

Russo, D., & Stol, K. J. (2021). PLS-SEM for software engineering research: An introduction and survey. ACM Computing Surveys, 54(4), 1–38.

Sarstedt, M. (2019). Der Knacks and a Silver Bullet. In B. J. Babin & M. Sarstedt (Eds.), The great facilitator: Reflections on the contributions of Joseph F. Hair, Jr. to marketing and business research (pp. 155–164). Cham: Springer.CrossRef

Sarstedt, M., & Cheah, J.-H. (2019). Partial least squares structural equation modeling using SmartPLS: A software review. Journal of Marketing Analytics, 7(3), 196–202.CrossRef

Sarstedt, M., & Mooi, E. (2019). A concise guide to market research: The process, data, and methods using IBM SPSS statistics (3rd ed.). Berlin/Heidelberg: Springer.

Sarstedt, M., Becker, J.-M., Ringle, C. M., & Schwaiger, M. (2011). 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.CrossRef

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.

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.CrossRef

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.

Sarstedt, M., Hair, J. F., Cheah, J.-H., Becker, J.-M., & Ringle, C. M. (2019). How to specify, estimate, and validate higher-order models. Australasian Marketing Journal, 27(3), 197–211.CrossRef

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

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.CrossRef

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.CrossRef

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.CrossRef

Schuberth, F., Henseler, J., & Dijkstra, T. K. (2018). Confirmatory composite analysis. Frontiers in Psychology, 9, 2541.CrossRef

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

Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461–464.CrossRef

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.

Sharma, P. N., Shmueli, G., Sarstedt, M., Danks, N., & Ray S. (2018). Prediction-oriented model selection in partial least squares path modeling. Decision Sciences, forthcoming.

Sharma, P. N., Liengaard, B. D., Hair, J. F., Sarstedt, M., & Ringle C. M. (2021). Predictive model assessment and selection in composite-based modeling using PLS-SEM: Extensions and guidelines for using CVPAT. Working Paper.

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

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

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.CrossRef

Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322–2347.CrossRef

Shugan, S. (2009). Relevancy is robust prediction, not alleged realism. Marketing Science, 28(5), 991–998.CrossRef

Stieglitz, S., Linh, D.-X., Bruns, A., & Neuberger, C. (2014). Social media analytics. An interdisciplinary approach and its implications for information systems. Business and Information Systems Engineering, 6, 89–96

Streukens, S., & Leroi-Werelds, S. (2016). Bootstrapping and PLS-SEM: A step-by-step guide to get more out of your bootstrap results. European Management Journal, 34(6), 618–632.CrossRef

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

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.CrossRef

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.CrossRef

Wagenmakers, E. J., & Farrell, S. (2004). AIC model selection using Akaike weights. Psychonomic Bulletin & Review, 11(1), 192–196.CrossRef

Westland, J. C. (2019). Partial least squares path analysis. In Structural equation models: From paths to networks (2nd ed., pp. 17–38). Cham: Springer.CrossRef

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.CrossRef

Wold, H. (1975). Path models with latent variables: The NIPALS approach. In H. M. Blalock, A. Aganbegian, F. M. Borodkin, R. Boudon, & V. Capecchi (Eds.), Quantitative sociology: International perspectives on mathematical and statistical modeling (pp. 307–357). New York: Academic.CrossRef

Wold, H. (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.CrossRef

Wold, H. (1982). Soft modeling: The basic design and some extensions. In K. G. Jöreskog & H. Wold (Eds.), Systems under indirect observations: Part II (pp. 1–54). Amsterdam: North-Holland.

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.

Wong, K. K. K. (2019). Mastering partial least squares structural equation modeling (PLS-SEM) with SmartPLS in 38 hours. Bloomington: iUniverse.

Zeng, N., Liu, Y., Gong, P, Hertogh, M., & König, M. (2021). Do right PLS and do PLS right: A critical review of the application on PLS in construction management reserarch. Frontiers of Engineering Management, forthcoming.

Titel: Partial Least Squares Structural Equation Modeling
verfasst von: Marko Sarstedt
Christian M. Ringle
Joseph F. Hair
Verlag: Springer International Publishing
Buch: Handbook of Market Research
Print ISBN: 978-3-319-57411-0

Electronic ISBN: 978-3-319-57413-4

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-3-319-57413-4_15