Skip to main content
SpringerLink
Log in

Treating Unobserved Heterogeneity in PLS-SEM: A Multi-method Approach

  • Chapter
  • First Online:
Partial Least Squares Path Modeling

Abstract

Accounting for unobserved heterogeneity has become a key concern to ensure the validity of results when applying partial least squares structural equation modeling (PLS-SEM). Recent methodological research in the field has brought forward a variety of latent class techniques that allow for identifying and treating unobserved heterogeneity. This chapter raises and discusses key aspects that are fundamental to a full and adequate understanding of how to apply these techniques in PLS-SEM. More precisely, in this chapter, we introduce a systematic procedure for identifying and treating unobserved heterogeneity in PLS path models using a combination of latent class techniques. The procedure builds on the FIMIX-PLS method to decide if unobserved heterogeneity has a critical impact on the results. Based on these outcomes, researchers should use more recently developed latent class methods, which have been shown to perform superior in recovering the segment-specific model estimates. After introducing these techniques, the chapter continues by discussing the means to identify explanatory variables that characterize the latent segments. Our discussion also broaches the issue of measurement invariance testing, which is a fundamental requirement for a subsequent comparison of parameters across groups by means of a multigroup analysis.

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 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight 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.

    While ten repetitions achieve a sound balance between accuracy and computational time, future research should systematically explore the effect of model complexity and sample size on the number of repetitions to use.

  2. 2.

    The following link gives access to a Microsoft Excel file that supports computing the omnibus test of group differences for more than two segments: http://derwinchan.iwopop.com/MG-PLS.

References

  • Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In B. N. Petrov & F. Csáki (Eds.), Second international symposium on information theory (pp. 267–281). Budapest: Académiai Kiadó.

    Google Scholar 

  • Alonso-Dos-Santos, M., Vveinhardt, J., Calabuig-Moreno, F., & Montoro-Rios, F. J. (2016). Involvement and image transfer in sports sponsorship. The Engineering Economist, 27, 78–89.

    Google Scholar 

  • Andrews, R. L., & Currim, I. S. (2003). Retention of latent segments in regression-based marketing models. International Journal of Research in Marketing, 20, 315–321.

    Article  Google Scholar 

  • Barnes, S. J., & Mattson, J. (2011). Segmenting brand value perceptions of consumers in virtual worlds: An empirical analysis using the FIMIX method. International Journal of Online Marketing, 1, 1–11.

    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, 665–694.

    Article  Google Scholar 

  • Becker, J.-M., Ringle, C. M., Sarstedt, M., & Völckner, F. (2015). How collinearity affects mixture regression results. Marketing Letters, 26, 643–659.

    Article  Google Scholar 

  • Bozdogan, H. (1987). Model selection and Akaike’s information criterion (AIC): The general theory and its analytical extensions. Psychometrika, 52, 345–370.

    Article  MATH  MathSciNet  Google Scholar 

  • Bozdogan, H. (1994). Mixture-model cluster analysis using model selection criteria in a new information measure of complexity. In H. Bozdogan (Ed.), Proceedings of the first US/Japan conference on frontiers of statistical modelling: An information approach (pp. 69–113). Dordrecht: Kluwer Academic.

    Chapter  Google Scholar 

  • Byrne, B. M. (2016). Structural equation modeling with AMOS: Basic concepts, applications, and programming (multivariate applications). New York: Routledge.

    Google Scholar 

  • Caniëls, M. C. J., Lenaerts, H. K. L., & Gelderman, C. J. (2015). Explaining the internet usage of SMEs: The impact of market orientation, behavioural norms, motivation and technology acceptance. Internet Research, 25, 358–377.

    Article  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, Springer handbooks of computational statistics series (Vol. II, pp. 171–193). Heidelberg: Springer.

    Chapter  Google Scholar 

  • Dempster, A. P., Laird, N., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society B, 39, 1–38.

    MATH  MathSciNet  Google Scholar 

  • Diamantopoulos, A., & Siguaw, J. A. (2000). Introducing LISREL. Thousand Oaks, CA: Sage.

    Book  Google Scholar 

  • Esposito Vinzi, V., Ringle, C. M., Squillacciotti, S., & Trinchera, L. (2007). Capturing and treating unobserved heterogeneity by response based segmentation in PLS path modeling: A comparison of alternative methods by computational experiments. ESSEC Research Center, Working Paper No. 07019, Cergy Pontoise Cedex.

    Google Scholar 

  • Esposito Vinzi, V., Trinchera, L., Squillacciotti, S., & Tenenhaus, M. (2008). REBUS-PLS: A response-based procedure for detecting unit segments in PLS path modelling. Applied Stochastic Models in Business and Industry, 24(5), 439–458.

    Article  MATH  MathSciNet  Google Scholar 

  • Ferrari, G., Mondéjar-Jiménez, J., & Vargas-Vargas, M. (2010). Environmental sustainable management of small rural tourist enterprises. International Journal of Environmental Research, 4, 407–414.

    Google Scholar 

  • Hahn, C., Johnson, M. D., Herrmann, A., & Huber, F. (2002). Capturing customer heterogeneity using a finite mixture PLS approach. Schmalenbach Business Review, 54, 243–269.

    Article  Google Scholar 

  • Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). 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. doi:10.1007/s11747-011-0261-6.

    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., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2017a). Advanced issues in partial least squares structural equation modeling (PLS-SEM). Thousand Oaks, CA: Sage.

    Google Scholar 

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

    Article  Google Scholar 

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

    MATH  Google Scholar 

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

    Google Scholar 

  • Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. doi:10.1007/s11747-014-0403-8.

    Article  Google Scholar 

  • Human, G., & Naudé, P. (2014). Heterogeneity in the quality–satisfaction–loyalty framework. Industrial Marketing Management, 43, 920–928.

    Article  Google Scholar 

  • Jedidi, K., Jagpal, H. S., & Desarbo, W. S. (1997). Finite-mixture structural equation models for response-based segmentation and unobserved heterogeneity. Marketing Science, 16(1), 39–59.

    Article  MATH  Google Scholar 

  • Jiménez-Castillo, D., Sánchez-Fernández, R., & Iniesta-Bonillo, M. Á. (2013). Segmenting university graduates on the basis of perceived value, image and identification. International Review on Public and Nonprofit Marketing, 10, 235–252.

    Article  Google Scholar 

  • Kessel, F., Ringle, C. M., & Sarstedt, M. (2010). On the impact of missing values on model selection in FIMIX-PLS. Proceedings of the 2010 INFORMS Marketing Science Conference, Cologne.

    Google Scholar 

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

    Google Scholar 

  • Kotler, P. (1989). From mass marketing to mass customization. Planning Review, 17, 10–47.

    Article  Google Scholar 

  • Kotler, P., & Keller, K. L. (2015). Marketing management. Upper Saddle River, NJ: Pearson/Prentice-Hall.

    Google Scholar 

  • Lenaerts, H. K. L., & Gelderman, C. J. (2015). Explaining the internet usage of SMEs. Internet Research: Electronic Networking Applications and Policy, 25, 358–377.

    Article  Google Scholar 

  • Liang, Z., Jaszak, R. J., & Coleman, R. E. (1992). Parameter estimation of finite mixtures using the EM algorithm and information criteria with applications to medical image processing. IEEE Transactions on Nuclear Science, 39, 1126–1133.

    Article  Google Scholar 

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

    Book  MATH  Google Scholar 

  • Loureiro, S. (2012). Love and loyalty in car brands: Segmentation using finite mixture partial least squares. In A. W. Gaul, A. Geyer-Schulz, L. Schmidt-Thieme, & J. Kunze (Eds.), Challenges at the interface of data analysis, computer science, and optimization: Proceedings of the 34th Annual Conference of the Gesellschaft für Klassifikation, Karlsruhe, July 21–23, 2010 (pp. 503–510). Berlin: Springer.

    Chapter  Google Scholar 

  • Loureiro, S. M. C., & Miranda, F. J. (2011). Brand equity and brand loyalty in the internet banking context: FIMIX-PLS market segmentation. Journal of Service Science and Management, 4, 476–485.

    Article  Google Scholar 

  • Mancha, R., Leung, M. T., Clark, J., & Sun, M. (2014). Finite mixture partial least squares for segmentation and behavioral characterization of auction bidders. Decision Support Systems, 57, 200–211.

    Article  Google Scholar 

  • Marques, C., & Reis, E. (2015). How to deal with heterogeneity among tourism constructs? Annals of Tourism Research, 52, 172–174.

    Article  Google Scholar 

  • Matthews, L. (2018). Applying multi-group analysis in PLS-SEM: A step-by-step process. In H. Latan & R. Noonan (Eds.), Partial least squares structural equation modeling: Basic concepts, methodological issues and applications. New York: Springer.

    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. doi:10.1108/EBR-09-2015-0095.

    Article  Google Scholar 

  • Matzler, K., Strobl, A., Thurner, N., & Füller, J. (2015). Switching experience, customer satisfaction, and switching costs in the ICT industry. Journal of Service Management, 26, 117–136.

    Article  Google Scholar 

  • Mazanec, J. A., & Ring, A. (2011). Tourism destination competitiveness: Second thoughts on the world economic forum reports. Tourism Economics, 17, 725–751.

    Article  Google Scholar 

  • Mclachlan, G. J. (1988). On the choice of starting values for the EM algorithm in fitting mixture models. Journal of the Royal Statistical Society Series D (The Statistician), 37, 417–425.

    Google Scholar 

  • Mclachlan, G. J., & Peel, D. (2000). Finite mixture models. New York: Wiley.

    Book  MATH  Google Scholar 

  • Mondéjar-Jiménez, J., Segarra-Oña, M., Peiró-Signes, Á., Payá-Martínez, A. M., & Sáez-Martínez, F. J. (2015). Segmentation of the Spanish automotive industry with respect to the environmental orientation of firms: Towards an ad-hoc vertical policy to promote eco-innovation. Journal of Cleaner Production, 86, 238–244.

    Article  Google Scholar 

  • Money, K. G., Hillenbrand, C., Henseler, J., & Da Camara, N. (2012). Exploring unanticipated consequences of strategy amongst stakeholder segments: The case of a European revenue service. Long Range Planning, 45, 395–423.

    Article  Google Scholar 

  • Navarro, A., Acedo, F. J., Losada, F., & Ruzo, E. (2011). Integrated model of export activity: Analysis of heterogeneity in managers’ orientations and perceptions on strategic marketing management in foreign markets. Journal of Marketing Theory and Practice, 19, 187–204.

    Article  Google Scholar 

  • Oyewobi, L. O., Windapo, A. O., & Rotimi, J. O. B. (2016). Environment, competitive strategy, and organizational characteristics: A path analytic model of construction organizations’ performance in South Africa. Canadian Journal of Administrative Sciences/Revue Canadienne des Sciences de l’Administration, 33, 213–226.

    Article  Google Scholar 

  • Ramaswamy, V., Desarbo, W. S., Reibstein, D. J., & Robinson, W. T. (1993). An empirical pooling approach for estimating marketing mix elasticities with PIMS data. Marketing Science, 12, 103–124.

    Article  Google Scholar 

  • Rigdon, E. E. (1998). Structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 251–294). Mahwah, NJ: Erlbaum.

    Google Scholar 

  • Rigdon, E. E. (2005). Structural equation modeling: Nontraditional alternatives. In B. Everitt & D. Howell (Eds.), Encyclopedia of statistics in behavioral science (pp. 1934–1941). New York: Wiley.

    Google Scholar 

  • Rigdon, E. E., Ringle, C. M., & Sarstedt, M. (2010). Structural modeling of heterogeneous data with partial least squares. In N. K. Malhotra (Ed.), Review of marketing research (pp. 255–296). Armonk, NY: Sharpe.

    Chapter  Google Scholar 

  • Rigdon, E. E., Ringle, C. M., Sarstedt, M., & Gudergan, S. P. (2011). Assessing heterogeneity in customer satisfaction studies: Across industry similarities and within industry differences. Advances in International Marketing, 22(1), 169–194.

    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 (in press).

    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., & Mooi, E. A. (2010a). Response-based segmentation using finite mixture partial least squares: Theoretical foundations and an application to American Customer Satisfaction Index data. Annals of Information Systems, 8, 19–49.

    Article  Google Scholar 

  • Ringle, C. M., Wende, S., & Will, A. (2010b). Finite mixture partial least squares analysis: Methodology and numerical examples. 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. 195–218). New York: Springer.

    Chapter  Google Scholar 

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

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  • Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS 3. Bönningstedt: SmartPLS GmbH. Retrieved from http://www.smartpls.com.

    Google Scholar 

  • Sarstedt, M. (2008). A review of recent approaches for capturing heterogeneity in partial least squares path modelling. Journal of Modelling in Management, 3, 140–161.

    Article  Google Scholar 

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

    Book  Google Scholar 

  • Sarstedt, M., & Ringle, C. M. (2010). Treating unobserved heterogeneity in PLS path modelling: A comparison of FIMIX-PLS with different data analysis strategies. Journal of Applied Statistics, 37, 1299–1318.

    Article  MathSciNet  Google Scholar 

  • Sarstedt, M., Schwaiger, M., & Ringle, C. M. (2009). Do we fully understand the critical success factors of customer satisfaction with industrial goods? – Extending Festge and Schwaiger’s model to account for unobserved heterogeneity. Journal of Business Market Management, 3(3), 185–206.

    Article  Google Scholar 

  • Sarstedt, M., Becker, J.-M., Ringle, C. M., & Schwaiger, M. (2011a). Uncovering and treating unobserved heterogeneity with FIMIX-PLS: Which model selection criterion provides an appropriate number of segments? Schmalenbach Business Review, 63, 34–62.

    Article  Google Scholar 

  • Sarstedt, M., Henseler, J., & Ringle, C. M. (2011b). Multi-group analysis in partial least squares (PLS) path modeling: Alternative methods and empirical results. In M. Sarstedt, M. Schwaiger, & C. R. Taylor (Eds.), Advances in international marketing (Vol. 22, pp. 195–218). Bingley: Emerald. doi:10.1108/S1474-7979(2011)0000022012.

    Google Scholar 

  • Sarstedt, M., Hair, J. F., Ringle, C. M., Thiele, K. O., & Gudergan, S. P. (2016a). Estimation issues with PLS and CBSEM: Where the bias lies! Journal of Business Research, 69(10), 3998–4010. doi:10.1016/j.jbusres.2016.06.007.

    Article  Google Scholar 

  • Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2016b). Guidelines for treating unobserved heterogeneity in tourism research: A comment on Marques and Reis (2015). Annals of Tourism Research, 57, 279–284. doi:10.1016/j.annals.2015.10.006.

    Article  Google Scholar 

  • Schafer, J. L. (1997). Analysis of incomplete multivariate data. London: Chapman and Hall.

    Book  MATH  Google Scholar 

  • Schlägel, C., & Sarstedt, M. (2016). Assessing the measurement invariance of the four-dimensional cultural intelligence scale across countries: A composite model approach. European Management Journal, 34(6), 633–649.

    Article  Google Scholar 

  • Schlittgen, R. (2011). A weighted least-squares approach to clusterwise regression. Advances in Statistical Analysis, 95, 205–217.

    Article  MathSciNet  Google Scholar 

  • Schlittgen, R., Ringle, C. M., Sarstedt, M., & Becker, J.-M. (2015). Segmentation of PLS path models by iterative reweighted regressions. In J. Henseler, C. M. Ringle, J. L. Roldán, & G. Cepeda (Eds.), Second international symposium on partial least squares path modeling: The conference for PLS users, Seville.

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Schwarz, G. (1978). Estimating the dimensions of a model. The Annals of Statistics, 6, 461–464.

    Article  MATH  MathSciNet  Google Scholar 

  • Semina, H., & Muris, C. (2013). Segmentation of information systems users: The finite mixture partial least squares method. Journal of Organizational and End User Computing, 25, 1–26.

    Google Scholar 

  • Squillacciotti, S. (2005). Prediction oriented classification in PLS path modeling. In T. Aluja, J. Casanovas, V. Esposito Vinzi, & M. Tenenhaus (Eds.), PLS & marketing: Proceedings of the 4th international symposium on PLS and related methods (pp. 499–506). Paris: DECISIA.

    Google Scholar 

  • Squillacciotti, S. (2010). Prediction-oriented classification in PLS path modeling. In V. Esposito Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares: Concepts, methods and applications (pp. 219–233). New York: Springer.

    Chapter  Google Scholar 

  • Steenkamp, J.-B. E. M., & Baumgartner, H. (1998). Assessing measurement invariance in cross-national consumer research. Journal of Consumer Research, 25, 78–107.

    Article  Google Scholar 

  • Steinley, D. (2003). Local optima in K-means clustering: What you don’t know may hurt you. Psychological Methods, 8, 294–304.

    Article  Google Scholar 

  • Stewart, W. H., Jr., May, R. C., & Ledgerwood, D. E. (2015). Do you know what I know? Intent to share knowledge in the US and Ukraine. Management International Review, 55, 737–773.

    Article  Google Scholar 

  • Teller, C., & Gittenberger, E. (2011). Patronage behaviour of elderly supermarket shoppers: Antecedents and unobserved heterogeneity. The International Review of Retail, Distribution and Consumer Research, 21, 483–499.

    Article  Google Scholar 

  • Valette-Florence, P., Guizanib, H., & Merunka, D. (2011). The impact of brand personality and sales promotions on brand equity. Journal of Business Research, 64, 24–28.

    Article  Google Scholar 

  • Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3, 4–70.

    Article  Google Scholar 

  • Völckner, F., Sattler, H., Hennig-Thurau, T., & Ringle, C. M. (2010). The role of parent brand quality for service brand extension success. Journal of Service Research, 13(4), 379–396.

    Article  Google Scholar 

  • Wedel, M., & Kamakura, W. A. (2000). Market segmentation: Conceptual and methodological foundations. Boston, MA: Kluwer.

    Book  Google Scholar 

  • Wilden, R., & Gudergan, S. P. (2015). The impact of dynamic capabilities on operational marketing and technological capabilities: Investigating the role of environmental turbulence. Journal of the Academy of Marketing Science, 43, 181–199.

    Article  Google Scholar 

  • Wold, H. (1975). Path models with latent variables: The NIPALS approach. In H. M. Blalock et al. (Eds.), Quantitative sociology: International perspectives on mathematical and statistical modeling (pp. 307–357). New York: Academic Press.

    Chapter  Google Scholar 

  • 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.

    Google Scholar 

Download references

Acknowledgments

This chapter builds on the articles published by Hair et al. (2016) and Matthews et al. (2016) in the European Business Review journal, the article by Sarstedt et al. (2016b) in Annals of Tourism Research, and the chapter on uncovering unobserved heterogeneity in the book on PLS-SEM advances by Hair et al. (2017a). This chapter refers to the use of the statistical software SmartPLS (http://www.smartpls.com). Ringle acknowledges a financial interest in SmartPLS.

Author information

Authors and Affiliations

  1. Institute of Marketing, Otto-von-Guericke University Magdeburg, Magdeburg, Germany

    Marko Sarstedt

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

    Marko Sarstedt & Christian M. Ringle

  3. Institute of Human Resource Management and Organizations (HRMO), Hamburg University of Technology (TUHH), Hamburg, Germany

    Christian M. Ringle

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

    Joseph F. Hair

Authors
  1. Marko Sarstedt
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christian M. Ringle
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Joseph F. Hair
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marko Sarstedt .

Editor information

Editors and Affiliations

  1. Department of Accounting, STIE Bank BPD Jateng and Petra Christian University, Semarang-Surabaya, Indonesia

    Hengky Latan

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

    Richard Noonan

Appendix

Appendix

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Sarstedt, M., Ringle, C.M., Hair, J.F. (2017). Treating Unobserved Heterogeneity in PLS-SEM: A Multi-method Approach. In: Latan, H., Noonan, R. (eds) Partial Least Squares Path Modeling. Springer, Cham. https://doi.org/10.1007/978-3-319-64069-3_9

Download citation

Publish with us

Policies and ethics

Search

Navigation