Partial Least Squares Path Modeling pp 197-217 | Cite as
Treating Unobserved Heterogeneity in PLS-SEM: A Multi-method Approach
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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.
Notes
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.
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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
- Becker, J.-M., Ringle, C. M., Sarstedt, M., & Völckner, F. (2015). How collinearity affects mixture regression results. Marketing Letters, 26, 643–659.CrossRefGoogle Scholar
- Bozdogan, H. (1987). Model selection and Akaike’s information criterion (AIC): The general theory and its analytical extensions. Psychometrika, 52, 345–370.CrossRefzbMATHMathSciNetGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.zbMATHMathSciNetGoogle Scholar
- Diamantopoulos, A., & Siguaw, J. A. (2000). Introducing LISREL. Thousand Oaks, CA: Sage.CrossRefGoogle 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.CrossRefzbMATHMathSciNetGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.zbMATHGoogle 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.CrossRefGoogle Scholar
- Human, G., & Naudé, P. (2014). Heterogeneity in the quality–satisfaction–loyalty framework. Industrial Marketing Management, 43, 920–928.CrossRefGoogle 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.CrossRefzbMATHGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
- Lohmöller, J.-B. (1989). Latent variable path modeling with partial least squares. Berlin: Springer.CrossRefzbMATHGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
- Marques, C., & Reis, E. (2015). How to deal with heterogeneity among tourism constructs? Annals of Tourism Research, 52, 172–174.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
- Mazanec, J. A., & Ring, A. (2011). Tourism destination competitiveness: Second thoughts on the world economic forum reports. Tourism Economics, 17, 725–751.CrossRefGoogle 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.CrossRefzbMATHGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefzbMATHGoogle 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.CrossRefGoogle Scholar
- Sarstedt, M., & Mooi, E. A. (2014). A concise guide to market research: The process, data, and methods using IBM SPSS statistics. Berlin: Springer.CrossRefGoogle 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.CrossRefMathSciNetGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
- Schafer, J. L. (1997). Analysis of incomplete multivariate data. London: Chapman and Hall.CrossRefzbMATHGoogle 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.CrossRefGoogle Scholar
- Schlittgen, R. (2011). A weighted least-squares approach to clusterwise regression. Advances in Statistical Analysis, 95, 205–217.CrossRefMathSciNetGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
- Schwarz, G. (1978). Estimating the dimensions of a model. The Annals of Statistics, 6, 461–464.CrossRefzbMATHMathSciNetGoogle 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.CrossRefGoogle Scholar
- Steenkamp, J.-B. E. M., & Baumgartner, H. (1998). Assessing measurement invariance in cross-national consumer research. Journal of Consumer Research, 25, 78–107.CrossRefGoogle Scholar
- Steinley, D. (2003). Local optima in K-means clustering: What you don’t know may hurt you. Psychological Methods, 8, 294–304.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
- Wedel, M., & Kamakura, W. A. (2000). Market segmentation: Conceptual and methodological foundations. Boston, MA: Kluwer.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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