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

Sarstedt, Marko, Ringle, Christian M., Hair, Joseph F.·2017·Partial Least Squares Path Modeling
DOI 10.1007/978-3-319-64069-3_9
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  1. 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.
  2. 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.
  3. 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. · DOI
  4. 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. · DOI
  5. 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. · DOI · Paper page
  6. Becker, J.-M., Ringle, C. M., Sarstedt, M., & Völckner, F. (2015). How collinearity affects mixture regression results. Marketing Letters, 26, 643–659. · DOI · Paper page
  7. 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. · DOI
  8. 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. · DOI
  9. 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. · DOI · Paper page
  10. Diamantopoulos, A., & Siguaw, J. A. (2000). Introducing LISREL. Thousand Oaks, CA: Sage. · DOI · Paper page
  11. 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. · DOI
  12. 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.
  13. 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. · DOI · Paper page
  14. 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. · DOI · Paper page
  15. 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.
  16. 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. · DOI · Paper page
  17. 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.
  18. Human, G., & Naudé, P. (2014). Heterogeneity in the quality–satisfaction–loyalty framework. Industrial Marketing Management, 43, 920–928. · DOI
  19. 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. · DOI · Paper page
  20. 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. · DOI
  21. 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.
  22. Kotler, P. (1989). From mass marketing to mass customization. Planning Review, 17, 10–47. · DOI · Paper page
  23. Kotler, P., & Keller, K. L. (2015). Marketing management. Upper Saddle River, NJ: Pearson/Prentice-Hall.
  24. Lenaerts, H. K. L., & Gelderman, C. J. (2015). Explaining the internet usage of SMEs. Internet Research: Electronic Networking Applications and Policy, 25, 358–377. · DOI
  25. 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. · DOI
  26. Lohmöller, J.-B. (1989). Latent variable path modeling with partial least squares. Berlin: Springer. · DOI
  27. 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. · DOI
  28. 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. · DOI
  29. 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. · DOI
  30. Marques, C., & Reis, E. (2015). How to deal with heterogeneity among tourism constructs? Annals of Tourism Research, 52, 172–174. · DOI
  31. 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.
  32. 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 . · DOI
  33. 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. · DOI
  34. Mazanec, J. A., & Ring, A. (2011). Tourism destination competitiveness: Second thoughts on the world economic forum reports. Tourism Economics, 17, 725–751. · DOI
  35. 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.
  36. 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. · DOI
  37. 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. · DOI
  38. 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. · DOI
  39. 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. · DOI
  40. 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. · DOI · Paper page
  41. 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.
  42. 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. · doi:10.1108/s1548-6435(2010)0000007011 · Paper page
  43. 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. · doi:10.1108/s1474-7979(2011)0000022011
  44. 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). · DOI · Paper page
  45. 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. · DOI · Paper page
  46. 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. · DOI
  47. 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. · DOI
  48. Ringle, C. M., Sarstedt, M., Schlittgen, R., & Taylor, C. R. (2013). PLS path modeling and evolutionary segmentation. Journal of Business Research, 66, 1318–1324. · DOI
  49. Ringle, C. M., Sarstedt, M., & Schlittgen, R. (2014). Genetic algorithm segmentation in partial least squares structural equation modeling. OR Spectrum, 36(1), 251–276. · DOI
  50. Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS 3. Bönningstedt: SmartPLS GmbH. Retrieved from http://www.smartpls.com .
  51. 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. · DOI
  52. Sarstedt, M., & Mooi, E. A. (2014). A concise guide to market research: The process, data, and methods using IBM SPSS statistics. Berlin: Springer. · DOI · Paper page
  53. 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. · DOI
  54. 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. · DOI
  55. 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. · DOI · Paper page
  56. 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 . · DOI
  57. 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 . · DOI
  58. 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. · DOI
  59. Schlittgen, R. (2011). A weighted least-squares approach to clusterwise regression. Advances in Statistical Analysis, 95, 205–217. · DOI
  60. 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. · DOI
  61. 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. · DOI
  62. 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. · DOI
  63. Schwarz, G. (1978). Estimating the dimensions of a model. The Annals of Statistics, 6, 461–464. · DOI · Paper page
  64. 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.
  65. 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.
  66. 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. · DOI
  67. Steenkamp, J.-B. E. M., & Baumgartner, H. (1998). Assessing measurement invariance in cross-national consumer research. Journal of Consumer Research, 25, 78–107. · DOI · Paper page
  68. Steinley, D. (2003). Local optima in K-means clustering: What you don’t know may hurt you. Psychological Methods, 8, 294–304. · DOI · Paper page
  69. 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. · DOI
  70. 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. · DOI
  71. 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. · DOI
  72. 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. · DOI · Paper page
  73. 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. · DOI
  74. Wedel, M., & Kamakura, W. A. (2000). Market segmentation: Conceptual and methodological foundations. Boston, MA: Kluwer. · DOI · Paper page
  75. 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. · DOI · Paper page
  76. 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. · DOI · Paper page
  77. 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.

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Metadata

Authors
Sarstedt, M., Ringle, C. M., & Hair, J. F.
Venue
Partial Least Squares Path Modeling
Publisher
Springer International Publishing
Published
2017
Pages
197-217
DOI
10.1007/978-3-319-64069-3_9
OpenAlex
W2766590219
Cited by
450
References
11
FWCI
41.05
Type
book chapter
Language
en
Open access
closed
Licence
www.springer.com/tdm (unspecified)

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