Data Mining pp 19-49 | Cite as

Response-Based Segmentation Using Finite Mixture Partial Least Squares

Theoretical Foundations and an Application to American Customer Satisfaction Index Data
Part of the Annals of Information Systems book series (AOIS, volume 8)

Abstract

When applying multivariate analysis techniques in information systems and social science disciplines, such as management information systems (MIS) and marketing, the assumption that the empirical data originate from a single homogeneous population is often unrealistic. When applying a causal modeling approach, such as partial least squares (PLS) path modeling, segmentation is a key issue in coping with the problem of heterogeneity in estimated cause-and-effect relationships. This chapter presents a new PLS path modeling approach which classifies units on the basis of the heterogeneity of the estimates in the inner model. If unobserved heterogeneity significantly affects the estimated path model relationships on the aggregate data level, the methodology will allow homogenous groups of observations to be created that exhibit distinctive path model estimates. The approach will, thus, provide differentiated analytical outcomes that permit more precise interpretations of each segment formed. An application on a large data set in an example of the American customer satisfaction index (ACSI) substantiates the methodology’s effectiveness in evaluating PLS path modeling results.

Keywords

Entropy Covariance Income Marketing Lution 

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References

  1. 1.
    Ritu Agarwal and Elena Karahanna. Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24(4):665–694, 2000.CrossRefGoogle Scholar
  2. 2.
    Eugene W. Anderson, Claes Fornell, and Donald. R. Lehmann. Customer satisfaction, market share and profitability: Findings from Sweden. Journal of Marketing, 58(3):53–66, 1994.CrossRefGoogle Scholar
  3. 3.
    Richard P. Bagozzi. On the meaning of formative measurement and how it differs from reflective measurement: Comment on Howell, Breivik, and Wilcox (2007). Psychological Methods, 12(2):229–237, 2007.CrossRefGoogle Scholar
  4. 4.
    Richard P. Bagozzi and Youjae Yi. Advanced topics in structural equation models. In Richard P. Bagozzi, editor, Principles of Marketing Research, pages 1–52. Blackwell, Oxford, 1994.Google Scholar
  5. 5.
    Thomas Bayes. Studies in the history of probability and statistics: IX. Thomas Bayes’s essay towards solving a problem in the doctrine of chances; Bayes’s essay in modernized notation. Biometrika, 45:296–315, 1763/1958.Google Scholar
  6. 6.
    David Biggs, Barry de Ville, and Ed Suen. A method of choosing multiway partitions for classification and decision trees. Journal of Applied Statistics,, 18(1):49–62, 1991.CrossRefGoogle Scholar
  7. 7.
    Kenneth A. Bollen. Interpretational confounding is due to misspecification, not to type of indicator: Comment on Howell, Breivik, and Wilcox (2007). Psychological Methods, 12(2):219–228, 2007.CrossRefGoogle Scholar
  8. 8.
    Wynne W. Chin. The partial least squares approach to structural equation modeling. In George A. Marcoulides, editor, Modern Methods for Business Research, pages 295–358. Lawrence Erlbaum, Mahwah, NJ, 1998.Google Scholar
  9. 9.
    Sven F. Crone, Stefan Lessmann, and Robert Stahlbock. The impact of preprocessing on data mining: An evaluation of classifier sensitivity in direct marketing. European Journal of Operational Research, 173(3):781–800, 2006.CrossRefGoogle Scholar
  10. 10.
    Fred D. Davis. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3):319–340, 1989.CrossRefGoogle Scholar
  11. 11.
    Adamantios Diamantopoulos. The C-OAR-SE procedure for scale development in marketing: A comment. International Journal of Research in Marketing, 22(1):1–10, 2005.CrossRefGoogle Scholar
  12. 12.
    Adamantios Diamantopoulos, Petra Riefler, and Katharina P. Roth. Advancing formative measurement models. Journal of Business Research, 61(12):1203–1218, 2008.CrossRefGoogle Scholar
  13. 13.
    Jens Dibbern and Wynne W. Chin. Multi-group comparison: Testing a PLS model on the sourcing of application software services across germany and the usa using a permutation based algorithm. In Friedhelm W. Bliemel, Andreas Eggert, Georg Fassott, and Jörg Henseler, editors, Handbuch PLS-Pfadmodellierung. Methode, Anwendung, Praxisbeispiele, pages 135–160. Schäffer-Poeschel, Stuttgart, 2005.Google Scholar
  14. 14.
    William Dumouchel, Chris Volinsky, Theodore Johnson, Corinna Cortes, and Daryl Pregibon. Squashing flat files flatter. In Proceedings of the 5th ACM SIGKDD International Conferennce on Knowledge Discovery in Data Mining, pages 6–15, San Diego, CA, 1999. ACM Press.Google Scholar
  15. 15.
    Jacob Eskildsen, Kai Kristensen, Hans J. Juhl, and Peder Østergaard. The drivers of customer satisfaction and loyalty: The case of Denmark 2000–2002. Total Quality Management, 15(5-6):859–868, 2004.Google Scholar
  16. 16.
    Vincenzo Esposito Vinzi, Christian M. Ringle, Silvia Squillacciotti, and Laura Trinchera. Capturing and treating unobserved heterogeneity by response based segmentation in PLS path modeling: A comparison of alternative methods by computational experiments. Technical report, ESSEC Business School Paris-Singapore, 2007.Google Scholar
  17. 17.
    Vincenzo Esposito Vinzi, Laura Trinchera, Silvia Squillacciotti, and Michel Tenenhaus. REBUS-PLS: A response-based procedure for detecting unit segments in PLS path modeling. Applied Stochastic Models in Business and Industry, 24(5):439–458, 2008.CrossRefGoogle Scholar
  18. 18.
    Fabian Festge and Manfred Schwaiger. The drivers of customer satisfaction with industrial goods: An international study. In Charles R. Taylor and Doo-Hee Lee, editors, Advances in International Marketing – Cross-Cultural Buyer Behavior, volume 18, pages 179–207. Elsevier, Amsterdam, 2007.Google Scholar
  19. 19.
    Adam Finn and Ujwal Kayande. How fine is C-OAR-SE? a generalizability theory perspective on rossiter’s procedure. International Journal of Research in Marketing, 22(1):11–22, 2005.CrossRefGoogle Scholar
  20. 20.
    Claes Fornell and Fred L. Bookstein. Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research, 19(4):440–452, 1982.CrossRefGoogle Scholar
  21. 21.
    Claes Fornell, Michael D. Johnson, Eugene W. Anderson, Jaesung Cha, and Barbara Everitt Johnson. The American customer satisfaction index: Nature, purpose, and findings. Journal of Marketing, 60(4):7–18, 1996.CrossRefGoogle Scholar
  22. 22.
    Claes Fornell and David F. Larcker. Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1):39–50, 1981.CrossRefGoogle Scholar
  23. 23.
    Claes Fornell, Peter Lorange, and Johan Roos. The cooperative venture formation process: A latent variable structural modeling approach. Management Science, 36(10):1246–1255, 1990.Google Scholar
  24. 24.
    Claes Fornell, William T. Robinson, and Birger Wernerfelt. Consumption experience and sales promotion expenditure. Management Science, 31(9):1084–1105, 1985.Google Scholar
  25. 25.
    David Gefen and Detmar W. Straub. Gender differences in the perception and use of e-mail: An extension to the technology acceptance model. MIS Quarterly, 21(4):389–400, 1997.CrossRefGoogle Scholar
  26. 26.
    Oliver Götz, Kerstin Liehr-Göbbers, and Manfred Krafft. Evaluation of structural equation models using the partial least squares (PLS-) approach. In Vincenzo Esposito Vinzi, Wynne W. Chin, Jörg Henseler, and Huiwen Wang, editors, Handbook of Partial Least Squares: Concepts, Methods and Applications in Marketing and Related Fields, forthcoming. Springer, Berlin-Heidelberg, 2009.Google Scholar
  27. 27.
    Peter H. Gray and Darren B. Meister. Knowledge sourcing effectiveness. Management Science, 50(6):821–834, 2004.CrossRefGoogle Scholar
  28. 28.
    Yany Grégoire and Robert J. Fisher. The effects of relationship quality on customer retaliation. Marketing Letters, 17(1):31–46, 2006.CrossRefGoogle Scholar
  29. 29.
    Siegfried P. Gudergan, Christian M. Ringle, Sven Wende, and Alexander Will. Confirmatory tetrad analysis in PLS path modeling. Journal of Business Research, 61(12):1238–1249, 2008.CrossRefGoogle Scholar
  30. 30.
    Peter Hackl and Anders H. Westlund. On structural equation modeling for customer satisfaction measurement. Total Quality Management, 11:820–825, 2000.Google Scholar
  31. 31.
    Carsten Hahn, Michael D. Johnson, Andreas Herrmann, and Frank Huber. Capturing customer heterogeneity using a finite mixture PLS approach. Schmalenbach Business Review, 54(3):243–269, 2002.Google Scholar
  32. 32.
    Jörg Henseler, Christian M. Ringle, and Rudolf R. Sinkovics. The use of partial least squares path modeling in international marketing. In Rudolf R. Sinkovics and Pervez N. Ghauri, editors, Advances in International Marketing, volume 20, pages 277–320 Emerald, Bingley, 2009Google Scholar
  33. 33.
    Roy D. Howell, Einar Breivik, and James B. Wilcox. Is formative measurement really measurement? reply to Bollen (2007) and Bagozzi (2007). Psychological Methods, 12(2): 238–245, 2007.CrossRefGoogle Scholar
  34. 34.
    Roy D. Howell, Einar Breivik, and James B. Wilcox. Reconsidering formative measurement. Psychological Methods, 12(2):205–218, 2007.CrossRefGoogle Scholar
  35. 35.
    John Hulland. Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20(2):195–204, 1999.CrossRefGoogle Scholar
  36. 36.
    Magid Igbaria, Nancy Zinatelli, Paul Cragg, and Angele L. M. Cavaye. Personal computing acceptance factors in small firms: A structural equation model. MIS Quarterly, 21(3): 279–305, 1997.CrossRefGoogle Scholar
  37. 37.
    Kamel Jedidi, Harsharanjeet S. Jagpal, and Wayne S. DeSarbo. Finite-fixture structural equation models for response-based segmentation and unobserved heterogeneity. Marketing Science, 16(1):39–59, 1997.CrossRefGoogle Scholar
  38. 38.
    Karl G. Jöreskog. Structural analysis of covariance and correlation matrices. Psychometrika, 43(4):443–477, 1978.CrossRefGoogle Scholar
  39. 39.
    Richard E. Kihlstrom and Michael H. Riordan. Advertising as a signal. Journal of Political Economy, 92(3):427–450, 1984.CrossRefGoogle Scholar
  40. 40.
    Philip Kotler and Kevin Lane Keller. Marketing Management. Prentice Hall, Upper Saddle River, NJ, 12 edition, 2006.Google Scholar
  41. 41.
    Kai Kristensen, Anne Martensen, and Lars Grønholdt. Customer satisfaction measurement at post denmark: Results of application of the European customer satisfaction index methodology. Total Quality Management, 11(7):1007–1015, 2000.Google Scholar
  42. 42.
    Wei-Yin Loh and Yu-Shan Shih. Split selection methods for classification trees. Statistica Sinica, 7(4):815–840, 1997.Google Scholar
  43. 43.
    Jan-Bernd Lohmöller. Latent Variable Path Modeling with Partial Least Squares. Physica, Heidelberg, 1989.Google Scholar
  44. 44.
    Scott B. MacKenzie, Philip M. Podsakoff, and Cheryl B. Jarvis. The problem of measurement model misspecification in behavioral and organizational research and some recommended solutions. Journal of Applied Psychology, 90(4):710–730, 2005.CrossRefGoogle Scholar
  45. 45.
    Ranjan Maitra. Clustering massive data sets with applications in software metrics and tomography. Technometrics, 43(3):336–346, 2001.CrossRefGoogle Scholar
  46. 46.
    Geoffrey J. McLachlan and Kaye E. Basford. Mixture Models: Inference and Applications to Clustering. Dekker, New York, NY, 1988.Google Scholar
  47. 47.
    Geoffrey J. McLachlan and Thriyambakam Krishnan. The EM Algorithm and Extensions. Wiley, Chichester, 2004.Google Scholar
  48. 48.
    Geoffrey J. McLachlan and David Peel. Finite Mixture Models. Wiley, New York, NY, 2000.CrossRefGoogle Scholar
  49. 49.
    Christopher Meek, Bo Thiesson, and David Heckerman. Staged mixture modeling and boosting. In In Proceedings of the 18th Annual Conference on Uncertainty in Artificial Intelligence, pages 335–343, San Francisco, CA, 2002. Morgan Kaufmann.Google Scholar
  50. 50.
    Marina Meilă and David Heckerman. An experimental comparison of model-based clustering methods. Machine Learning, 40(1/2):9–29, 2001.Google Scholar
  51. 51.
    Paul Milgrom and John Roberts. Price and advertising signals of product quality. Journal of Political Economy, 94(4):796–821, 1986.CrossRefGoogle Scholar
  52. 52.
    Vikas Mittal, Eugene W. Anderson, Akin Sayrak, and Pandu Tadikamalla. Dual emphasis and the long-term financial impact of customer satisfaction. Marketing Science, 24(4): 531–543, 2005.CrossRefGoogle Scholar
  53. 53.
    Neil Morgan, Eugene W. Anderson, and Vikas Mittal. Understanding firms’ customer satisfaction information usage. Journal of Marketing, 69(3):121–135, 2005.CrossRefGoogle Scholar
  54. 54.
    Bengt O. Muthèn. Latent variable modeling in heterogeneous populations. Psychometrika, 54(4):557–585, 1989.CrossRefGoogle Scholar
  55. 55.
    Linda K. Muthén and Bengt O. Muthén. Mplus User’s Guide. Muthén 8 Muthén, Los Angeles, CA, 4th edition, 1998.Google Scholar
  56. 56.
    Jum C. Nunnally and Ira Bernstein. Psychometric Theory. McGraw Hill, New York, NY, 3rd edition, 1994.Google Scholar
  57. 57.
    Francesco Palumbo, Rosaria Romano, and Vincenzo Esposito Vinzi. Fuzzy PLS path modeling: A new tool for handling sensory data. In Christine Preisach, Hans Burkhardt, Lars Schmidt-Thieme, and Reinhold Decker, editors, Data Analysis, Machine Learning and Applications – Proceedings of the 31st Annual Conference of the Gesellschaft für Klassifikation e.V., Albert-Ludwigs-Universität Freiburg, March 7–9, 2007, pages 689–696, Berlin-Heidelberg, 2008. Springer.Google Scholar
  58. 58.
    Venkatram Ramaswamy, Wayne S. DeSarbo, David J. Reibstein, and William T. Robinson. An empirical pooling approach for estimating marketing mix elasticities with PIMS data. Marketing Science, 12(1):103–124, 1993.CrossRefGoogle Scholar
  59. 59.
    Edward E. Rigdon. Structural equation modeling. In George A. Marcoulides, editor, Modern methods for business research, Quantitative Methodology Series, pages 251–294. Lawrence Erlbaum, Mahwah, 1998.Google Scholar
  60. 60.
    Edward E. Rigdon, Christian M. Ringle, and Marko Sarstedt. Structural modeling of heterogeneous data with partial least squares. Review of Marketing Research, forthcoming, 2010.Google Scholar
  61. 61.
    Christian M. Ringle. Segmentation for path models and unobserved heterogeneity: The finite mixture partial least squares approach.,Research Papers on Marketing and Retailing No. 035, University of Hamburg, 2006.Google Scholar
  62. 62.
    Christian M. Ringle and Karl-Werner Hansmann. Enterprise-networks and strategic success: An empirical analysis. In Theresia Theurl and Eric C. Meyer, editors, Strategies for Cooperation, pages 133–152. Shaker, Aachen, 2005.Google Scholar
  63. 63.
    Christian M. Ringle and Rainer Schlittgen. A genetic segmentation approach for uncovering and separating groups of data in PLS path modeling. In Harald Martens, Tormod Næs, and Magni Martens, editors, PLS’07 International Symposium on PLS and Related Methods – Causalities Explored by Indirect Observation, pages 75–78, \rAs, 2007. Matforsk.Google Scholar
  64. 64.
    Christian M. Ringle, Sven Wende, and Alexander Will. Customer segmentation with FIMIX-PLS. In Tom\‘as Aluja, Josep Casanovas, Vincenzo Esposito Vinzi, and Michel Tenenhaus, editors, PLS’05 International Symposium on PLS and Related Methods – PLS and Marketing, pages 507–514, Paris, 2005. Decisia.Google Scholar
  65. 65.
    Christian M. Ringle, Sven Wende, and Alexander Will. SmartPLS 2.0 (beta), 2005.Google Scholar
  66. 66.
    Christian M. Ringle, Sven Wende, and Alexander Will. The finite mixture partial least squares approach: Methodology and application. In Vincenzo Esposito Vinzi, Wynne W. Chin, Jörg Henseler, and Huiwen Wang, editors, Handbook of Partial Least Squares: Concepts, Methods and Applications in Marketing and Related Fields forthcoming. Springer, Berlin-Heidelberg, 2009.Google Scholar
  67. 67.
    Christian M. Ringle, Marko Sarstedt, and Rainer Schlittgen. Finite mixture and genetic algorithm segmentation in partial least squares path modeling.In Andreas Fink, Berthold Lausen, Wilfried Seidel, and Alfred Ultsch, editors, Advances in Data Analysis, Data Handling and Business Intelligence.Proceedings of the 32nd Annual Conference of the German Classification Society (GfKl), Springer, Heidelberg and Berlin, forthcoming.Google Scholar
  68. 68.
    John R. Rossiter. The C-OAR-SE procedure for scale development in marketing. International Journal of Research in Marketing, 19(4):305–335, 2002.CrossRefGoogle Scholar
  69. 69.
    John R. Rossiter. Reminder: A horse is a horse. International Journal of Research in Marketing, 22(1):23–25, 2005.CrossRefGoogle Scholar
  70. 70.
    Gastón Sánchez and Tomàs Aluja. A simulation study of PATHMOX (PLS path modeling segmentation tree) sensitivity. In Harald Martens, Tormod Næs, and Magni Martens, editors, PLS’07 International Symposium on PLS and Related Methods – Causalities Explored by Indirect Observation, pages 33–36, Ås, 2007. Matforsk.Google Scholar
  71. 71.
    Marko Sarstedt and Christian M. Ringle. Treating unobserved heterogeneity in PLS path modelling: A comparison of FIMIX-PLS with different data analysis strategies. Journal of Applied Statistics, forthcoming, 2010.Google Scholar
  72. 72.
    Marko Sarstedt, Christian M. Ringle, and Manfred Schwaiger. 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, 2009.Google Scholar
  73. 73.
    Marko Sarstedt. Market segmentation with mixture regression models. Journal of Targeting, Measurement and Analysis for Marketing, 16(3):228–246, 2008.CrossRefGoogle Scholar
  74. 74.
    Marko Sarstedt. A review of recent approaches for capturing heterogeneity in partial least squares path modelling. Journal of Modelling in Management, 3(2):140–161, 2008.CrossRefGoogle Scholar
  75. 75.
    Michel Tenenhaus, Vincenzo Esposito Vinzi, Yves-Marie Chatelin, and Carlo Lauro. PLS path modeling. Computational Statistics 8 Data Analysis, 48(1):159–205, 2005.CrossRefGoogle Scholar
  76. 76.
    Viswanath Venkatesh and Ritu Agarwal. Turning visitors into customers: A usability-centric perspective on purchase behavior in electronic channels. Management Science, 52(3): 367–382, 2006.CrossRefGoogle Scholar
  77. 77.
    Michel Wedel and Wagner Kamakura. Market Segmentation: Conceptual and Methodological Foundations. Kluwer, London, 2nd edition, 2000.Google Scholar
  78. 78.
    Ron Wehrens, Lutgarde M. C. Buydens, Chris Fraley, and Adrian E. Raftery. Model-based clustering for image segmentation and large datasets via sampling. Journal of Classification, 21(2):231–253, 2004.CrossRefGoogle Scholar
  79. 79.
    John Williams, Dirk Temme, and Lutz Hildebrandt. A Monte Carlo study of structural equation models for finite mixtures.SFB 373 Discussion Paper No. 48, Humboldt University Berlin, 2002.Google Scholar
  80. 80.
    Herman Wold. Soft modeling: The basic design and some extensions. In Karl G. Jöreskog and Herman Wold, editors, Systems Under Indirect Observations, pages 1–54. North-Holland, Amsterdam, 1982.Google Scholar
  81. 81.
    Jianan Wu and Wayne S. DeSarbo. Market segmentation for customer satisfaction studies via a new latent structure multidimensional scaling model. Applied Stochastic Models in Business and Industry, 21(4/5):303–309, 2005.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Institute for Industrial Management and OrganizationsUniversity of HamburgHamburgGermany
  2. 2.Centre for Management and Organisation Studies (CMOS)University of Technology Sydney (UTS)HaymarketAustralia
  3. 3.Institute for Market-based ManagementUniversity of MunichMunichGermany
  4. 4.Aston Business SchoolAston UniversityBirminghamUK

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