Advertisement

Marketing Letters

, Volume 2, Issue 2, pp 129–146 | Cite as

Simultaneous multidimensional unfolding and cluster analysis: An investigation of strategic groups

  • Wayne Desarbo
  • Kamel Jedidi
  • Karel Cool
  • Dan Schendel
Article

Abstract

This paper develops a maximum likelihood based methodology for simultaneously performing multidimensional unfolding and cluster analysis on two-way dominance or profile data. This new procedure utilizes mixtures of multivariate conditional normal distributions to estimate a joint space of stimulus coordinates and K ideal points, one for each cluster or group, in a T-dimensional space. The conditional mixture, maximum likelihood methodology is introduced together with an E-M algorithm utilized for parameter estimation. A marketing strategy application is provided with an analysis of PIMS data for a set of firms drawn from the same competitive industry to determine strategic groups, while simultaneously depicting strategy-performance relationships.

Key words

Marketing Strategy Multidimensional Scaling Cluster Analysis Strategic Groups 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Akaike, H. (1974). “A New Look at Statistical Model Identification,” IEEE Transactions on Automatic Control 6, 716–723.Google Scholar
  2. Anderberg, M. 1973. Cluster Analysis for Applications. Academic Press, New York.Google Scholar
  3. Arabie, P., J. D. Carroll, and W. S. DeSarbo. (1987). Three-Way Scaling and Clustering. Beverly Hills: Sage Publications.Google Scholar
  4. Baker, F. B., and L. J. Hubert. (1976). “A Graph-Theoretic Approach to Goodness-of-Fit in Complete Link Hierarchical Clustering,” Journal of the American Statistical Association 71, 870–878.Google Scholar
  5. Bozdogan, H. (1987). “Model Selection and Akaike's Information Criterion (AIC): The General Theory and Its Analytical Extensions,” Psychometrika, 52, 345–370.Google Scholar
  6. Bozdogan, H., and S. L. Sclove. (1984). “Multi-sample Cluster Analysis Using Akaike's Information Criterion,” Annals of the Institute of Statistical Mathematics 36, 163–180.Google Scholar
  7. Brock, H. H. (1985). “On Some Significance Tests in Cluster Analysis,” Journal of Classification 2, 77–108.Google Scholar
  8. Carroll, J. D. (1976). “Spatial, Non-Spatial and Hybird Models for Scaling,” Psychometrika 41, 439–463.Google Scholar
  9. Carroll, J. D. (1980). “Models and Methods for Multidimensional Analysis of Preferential Choice Data,” in E. D. Lantermann and H. Feger (eds.), Similarity and Choice. Bern: Hans Huber.Google Scholar
  10. Caves, R. E., and M. E. Porter. (1977). “From Entry Barriers to Mobility Barriers: Conjectural Decisions and Contrived Deterrence to New Competition,” Quarterly Journal of Economics 91, 421–441.Google Scholar
  11. Chang, W. C. (1983). “On Using Principal Components Before Separating a Mixture of Two Multivariate Normal Distributions,” Applied Statistics 32, 267–275.Google Scholar
  12. Cool, K., and D. Schendel. (1987). “Strategic Group Formation and Performance: The Case of the U.S. Pharmaceutical Industry, 1963–82,” Management Science 33(9), 1102–1124.Google Scholar
  13. Cool, K., and D. Schendel. (1988). “Performance Differences Among Strategic Group Members,” Strategic Management Journal 9(2), 207–223.Google Scholar
  14. Day, D. L., W. S. DeSarbo, and J. A. Oliva. (1987). “Strategy Maps: A Spatial Representation of Intra-Industry Competitive Strategy,” Management Science 33, 1534–1551.Google Scholar
  15. Dempster, A. P., N. M. Laird, and D. B. Rubin. (1977). “Maximum Likelihood From Incomplete Data Via the E-M Algorithm,” Journal of The Royal Statistical Society-B 39, 1–38.Google Scholar
  16. DeSarbo, W. S., and V. R. Rao. (1984). “GENFOLD2: A Set of Models and Algorithms for the General Unfolding Analysis of Preference/Dominance Data,” Journal of Classification 1, 146–185.Google Scholar
  17. DeSarbo, W. S., and V. R. Rao. (1986). “A New Constrained Unfolding Model for Product Positioning,” Marketing Science 5, 1–19.Google Scholar
  18. DeSarbo, W. S., D. Howard, and K. Jedidi. (1991). “MULTICLUS: A New Methodology for Simultaneously Performing Multidimensional Scaling and Cluster Analysis,” Psychometrika, forthcoming.Google Scholar
  19. Dess, G., and P. Davis. (1984). “Porter's Generic Strategies as Determinants of Strategic Group Membership and Organizational Performance,” Academy of Management Journal 27, 467–488.Google Scholar
  20. Dillon, W. R., Mulani, N., and Frederick, D. G. (1989). “On the Use of Component Scores in the Presence of Group Structure,” Journal of Consumer Research 16, 106–112.Google Scholar
  21. Gnanadesikan, R., and J. R. Kettenring. (1972). “Robust Residuals and Outlier Defection with Multiresponse Data,” Biometrics 28, 81–124.Google Scholar
  22. Harrigan, K. R. (1985). “An Application of Clustering for Strategic Group Analysis,” Strategic Management Journal 6, 55–73.Google Scholar
  23. Hartigan, J. A. (1975). Clustering Algorithm. New York: John Wiley & Son.Google Scholar
  24. Hartigan, J. A. (1977). “Distribution Problems in Clustering,” in J. paVan Ryzin (ed.), Classification and Clustering. New York: Academic Press.Google Scholar
  25. Hartigan, J. A., and P. M. Hartigan. (1985). “The Dip Test of Unimodality,” Annals of Statistics 13, 70–84.Google Scholar
  26. Ling, R. F. (1971). Cluster Analysis. Ann Arbor, MI: University Microfilms, No. 71-22356.Google Scholar
  27. McGee J., and H. Thomas. “Strategic Groups: Theory, Research and Taxonomy,” Strategic Management Journal 7(2), 141–160.Google Scholar
  28. McLachlan, G. J., and K. E. Basford. (1988). Mixture Models: Inference and Applications to Clustering, New York: Marcel Dekkar.Google Scholar
  29. Porter, M. E. (1979). “The Structure Within Industries and Companies' Performance,” Review of Economics and Statistics 61, 214–227.Google Scholar
  30. Porter, M. E. (1980). Competitive Strategy. New York: Free Press.Google Scholar
  31. Pruzansky, S., A. Tversky, and J. D. Carroll. (1982). “Spatial Versus Tree Representations of Proximity Data,” Psychometrika, 47, 3–24.Google Scholar
  32. Rumelt, R., (1984). “Toward a Strategic Theory of the Firm,” in R. Lamb (ed.), Competitive Strategic Management. Englewood Cliffs: Prentice Hall.Google Scholar
  33. Schendel, D. E., and G. P. Patton. (1978). “A Simultaneous Equation Model of Corporate Strategy,” Management Science 24, 1611–1621.Google Scholar
  34. Sclove, S. L. (1987). “Application of Model-selection Criteria to Some Problems in Multivariate Analysis,” Psychometrika 52, 333–343.Google Scholar
  35. Titterington, D. M., A. F. M. Smith, and V. E. Makov. (1985). Statistical Analysis of Finite Mixture Distributions. New York: John Wiley & Sons, 1985.Google Scholar
  36. Ward, J. H. (1963). “Hierarchical Grouping to Optimize an Objective Function,” Journal of the American Statistical Association 58, 236–244.Google Scholar
  37. White, R. (1986). “Generic Business Strategies, Organizational Context, and Performance: An Empirical Investigation,” Strategic Management Journal 7(3), 217–231.Google Scholar
  38. Wilkinson, L. (1988). SYSTAT: The System for Statistics. Evanston, Ill.: SYSTAT, Inc.Google Scholar
  39. Wolfe, J. H. (1970). “Pattern Clustering by Multivariate Mixture Analysis,” Multivariate Behavioral Research 5, 329–350.Google Scholar

Copyright information

© Kluwer Academic Publishers 1991

Authors and Affiliations

  • Wayne Desarbo
    • 1
    • 2
  • Kamel Jedidi
    • 3
  • Karel Cool
  • Dan Schendel
    • 4
  1. 1.Marketing DepartmentUniversity of Michigan, School of Business AdministrationAnn ArborUSA
  2. 2.Statistics DepartmentUniversity of Michigan, School of Business AdministrationAnn ArborUSA
  3. 3.Columbia UniversityColumbiaUSA
  4. 4.Purdue UniversityUSA

Personalised recommendations