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Quality & Quantity

, Volume 49, Issue 1, pp 385–405 | Cite as

Visual conjoint analysis (VCA): a topology of preferences in multi-attribute decision making

  • Peter Sarlin
  • Shahrokh Nikou
  • József Mezei
  • Harry Bouwman
Article

Abstract

This paper proposes an approach denoted visual conjoint analysis (VCA). Conjoint analysis is commonly used in marketing to understand consumers’ decision criteria, particularly why consumers prefer and select certain products and their variations. Yet, little efforts have been made to provide visual means for exploring and visualizing preferences and utilities of consumers. In this paper, we propose an approach that enables identifying a low-dimensional topology of consumer profiles and their demographic characteristics. Through a two-step approach, VCA makes use of techniques for (i) data reduction and (ii) dimension reduction in combination with conjoint analysis. It provides a two-dimensional representation (dimension reduction) of a small number of respondent segments (data reduction). This provides means for two key tasks: (i) identifying the topology of multivariate respondent profiles in a lower dimension, focusing on neighborhood relations, and (ii) visual representations of information describing the respondent profiles, as well as the combination of the two tasks. The approach is applied to a real-world case of consumers’ preferences of mobile platform ecosystems.

Keywords

Conjoint analysis Cluster analysis Data reduction Dimension reduction Visual conjoint analysis 

Notes

Acknowledgments

Shahrokh Nikou gratefully acknowledges financial support from the Nokia Foundation and Foundation for Economic Education.

References

  1. Bezdek, J.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)CrossRefGoogle Scholar
  2. Cattin, P., Wittink, D.: Commercial use of conjoint analysis: a survey. J. Mark. 46, 44–53 (1982)CrossRefGoogle Scholar
  3. Compeau, D., Marcolin, B., Kelly, H., Higgins, C.: Generalizability of information systems research using student subjects: a reflection on our practices and recommendation for future research. Inf. Syst. Res. 23(4), 1093–1109 (2012)CrossRefGoogle Scholar
  4. Darian, J., Tucci, L.: Developing marketing strategies to increase vegetable consumption. J. Consum. Mark. 30(5), 427–435 (2013)CrossRefGoogle Scholar
  5. Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. B 39(1), 1–38 (1977)Google Scholar
  6. Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Simoudis, E., Han, J., Fayyad, U. (eds.) Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD 96), pp. 226–231. AAAI Press, Menlo Park (1996)Google Scholar
  7. Forte, J., Letrémy, P., Cottrell, M.: Advantages and drawbacks of the batch Kohonen algorithm. In: Proceedings of the European Symposium on Artificial Neural Networks (ESANN 02), Bruges, Belgium, pp. 223–230 (2002)Google Scholar
  8. Green, P., Rao, V.: Conjoint measurement for quantifying judgmental data. J. Mark. Res. 8, 355–363 (1971)CrossRefGoogle Scholar
  9. Green, P.E., Srinivasan, V.: Conjoint analysis in consumer research: Issues and outlook. J Consumer Res 5(2), 103–123 (1978)Google Scholar
  10. Green, P., Krieger, A., Wind, Y.: Thirty years of conjoint analysis: reflections and prospects. Interfaces 31(3), 56–73 (2001)CrossRefGoogle Scholar
  11. Haaker, T., de Vos, H., Bouwman, H.: Mobile service bundles: the example of navigation services. Electron. Mark. 17(1), 28–38 (2007)Google Scholar
  12. Hagerty, M.: Improving the predictive power of conjoint analysis: The use of factor analysis and cluster analysis. J. Mark. Res. 22(2), 168–184 (1985)CrossRefGoogle Scholar
  13. Harrower, M., Brewer, C.: ColorBrewer.org: an online tool for selecting color schemes for maps. Cartogr. J. 40(1), 27–37 (2003)CrossRefGoogle Scholar
  14. Head, M., Ziolkowski, N.: Understanding student attitudes of mobile phone features: rethinking adoption through conjoint, cluster and sem analyses. Comput. Hum. Behav. 28(6), 2331–2339 (2012)CrossRefGoogle Scholar
  15. Johnson, R., Orme, B.: How many questions should you ask in choice-based conjoint studies. Sawtooth software technical paper (1996)Google Scholar
  16. Kangas, J.: Sample weighting when training self-organizing maps for image compression. In: Proceedings of the 1995 IEEE Workshop on Neural Networks for Signal Processing, pp. 343–350 (1995)Google Scholar
  17. Karren, R., Barringer, M.: A review and analysis of the policy-capturing methodology in organizational research: guidelines for research and practice. Organ. Res. Methods 5(4), 337–361 (2002)CrossRefGoogle Scholar
  18. Kaski, S., Lagus, K.: Comparing self-organizing maps. In: Proceedings of the International Conference on Artificial Neural Networks (ICANN ’96), pp. 809–814. Springer, Bochum (1996)Google Scholar
  19. Kaufman, L., Rousseeuw, P.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York (1990)CrossRefGoogle Scholar
  20. Kim, K., Ra, J.: Edge preserving vector quantization using self-organizing map based on adaptive learning. In: Proceedings of the International Joint Conference on Neural Networks, pp. 1219–1222. IEEE Press, New York (1993)Google Scholar
  21. Kiviluoto, K.: Topology preservation in self-organizing maps. In: Proceedings of the IEEE International Conference on Artificial Neural Networks, Piscataway, New Jersey, USA, pp. 294–299 (1996)Google Scholar
  22. Kohonen, T.: Things you haven’t heard about the self-organizing map. In: Proceedings of the International Conference on Neural Networks, pp. 1147–1156 (1993)Google Scholar
  23. Kohonen, T.: Self-organized formation of topologically correct feature maps. Biol. Cybern. 43, 59–69 (1982)CrossRefGoogle Scholar
  24. Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer, Berlin (2001)CrossRefGoogle Scholar
  25. Lampinen, J., Oja, E.: Clustering properties of hierarchical self-organizing maps. J. Math. Imaging Vis. 2(2–3), 261–272 (1992)CrossRefGoogle Scholar
  26. Lee, J., Verleysen, M.: Nonlinear Dimensionality Reduction. Information Science and Statistics Series. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  27. Li, J., Gao, X., Jiao, L.: A novel typical-sample-weighted clustering algorithm for large data sets. In: International Conference on Computational Intelligence and Security, Springer, China, pp. 696–703 (2005)Google Scholar
  28. Louviere, J.: Analyzing Decision Making: Metric Conjoint Analysis. Sage, Newbury Park (1988)Google Scholar
  29. MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297. University of California Press, Berkeley, CA (1967)Google Scholar
  30. Marcelloni, F.: Feature selection based on a modified fuzzy c-means algorithm with supervision. Inf. Sci. 151, 201–226 (2003)CrossRefGoogle Scholar
  31. Marghescu, D.: Multidimensional data visualization techniques for exploring financial performance data. In: Proceedings of 13th Americas Conference on Information Systems, AIS Electronic Library, Keystone, Colorado, USA (2007)Google Scholar
  32. McBurney, P., Parsons, S., Green, J.: Forecasting market demand for new telecommunications services: an introduction. Telemat. Inform. 19(3), 225–249 (2002)CrossRefGoogle Scholar
  33. Mesías, F., Martínez-Carrasco, F., Martínez, J.M., Gaspar, P.: Functional and organic eggs as an alternative to conventional production: a conjoint analysis of consumers’ preferences. J. Sci. Food Agric. 91(3), 532–538 (2011)CrossRefGoogle Scholar
  34. Modha, D., Spangler, W.: Feature weighting in k-means clustering. Mach. Learn. 52, 217–237 (2003)CrossRefGoogle Scholar
  35. Nock, R., Nielsen, F.: On weighting clustering. IEEE Trans. Pattern Anal. Mach. Intell. 28, 1223–1235 (2006)CrossRefGoogle Scholar
  36. Pedrycz, W.: Conditional fuzzy c-means. Pattern Recognit. Lett. 17, 625–632 (1996)CrossRefGoogle Scholar
  37. Pignone, P., Brenner, A., Hawley, S., Sheridan, S., Lewis, C., Jonas, D., Howard, K.: Conjoint analysis versus rating and ranking for values elicitation and clarification in colorectal cancer screening. J. Gen. Intern. Med. 27(1), 45–50 (2012)CrossRefGoogle Scholar
  38. Rose, K.: Deterministic annealing for clustering, compression, classification, regression, and related optimization problems. In: Proceedings of the IEEE, vol. 86, pp. 2210–2239 (1998)Google Scholar
  39. Rossi, P., Anderson, A.: The factorial survey approach: an introduction. In: P.H., Rossi, S.L., Noch (eds.) Measuring Social Judgments: The Factorial Survey Approach, pp. 15–67. Sage, Beverly Hills (1982)Google Scholar
  40. Rossi, P., Nock, S.: Measuring Social Judgments: The Factorial Survey Approach. Sage, Beverly Hills (1982)Google Scholar
  41. Sammon, J.: A non-linear mapping for data structure analysis. IEEE Trans. Comput. 18(5), 401–409 (1969)CrossRefGoogle Scholar
  42. Samuelson, P.: A note on the pure theory of consumers’ behaviour. Economica 5, 61–71 (1938)CrossRefGoogle Scholar
  43. Sarlin, P.: Self-organizing time map: an abstraction of temporal multivariate patterns. Neurocomputing 99(1), 496–508 (2013)CrossRefGoogle Scholar
  44. Sarlin, P.: A weighted SOM for classifying data with instance-varying importance. Int. J. Mach. Learn. Cybern. 5(1), 101–110 (2014a)Google Scholar
  45. Sarlin, P.: Data and dimension reduction for visual financial performance analysis. Inf. Vis. (2014b). doi: 10.1177/1473871613504102
  46. Smith, W.: Product differentiation and market segmentation as alternative marketing strategies. J. Mark. 21, 3–8 (1956)CrossRefGoogle Scholar
  47. Sorenson, D., Bogue, J.: A conjoint-based approach to concept optimisation: probiotic beverages. Br. Food J. 107(11), 870–883 (2005)CrossRefGoogle Scholar
  48. Teichert, T., Shehu, E.: Investigating research streams of conjoint analysis: a bibliometric study. BuR Bus. Res. J. 3(1), 49–68 (2010)CrossRefGoogle Scholar
  49. Vesanto, J., Himberg, J., Alhoniemi, E., Parhankangas, J.: Self-organizing map in matlab: the SOM toolbox. In: Proceedings of the Matlab DSP Conference, pp. 35–40 (1999)Google Scholar
  50. Vesanto, J.: SOM-based data visualization methods. Intell. Data Anal. 3(2), 111–126 (1999)CrossRefGoogle Scholar
  51. Vesanto, J., Alhoniemi, E.: Clustering of the self-organizing map. IEEE Trans. Neural Netw. 11(3), 586–600 (2000)CrossRefGoogle Scholar
  52. Ward, J.: Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58, 236–244 (1963)CrossRefGoogle Scholar
  53. Yu, J.: General c-means clustering model. IEEE Trans. Pattern Anal. Mach. Intell. 27, 1197–1211 (2005)CrossRefGoogle Scholar
  54. Yu, J., Yang, M.-S., Lee, S.: Sample-weighted clustering methods. Comput. Math. Appl. 62(5), 2200–2208 (2011)CrossRefGoogle Scholar
  55. Zedeck, S., Kafry, D.: Capturing rater policies for processing evaluation data. Organ. Behav. Hum. Perform. 18(2), 269–294 (1977)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Peter Sarlin
    • 1
    • 2
  • Shahrokh Nikou
    • 3
  • József Mezei
    • 3
  • Harry Bouwman
    • 4
    • 5
  1. 1.Centre of Excellence SAFEGoethe University FrankfurtFrankfurtGermany
  2. 2.RiskLab at IAMSRÅbo Akademi University and Arcada University of Applied SciencesTurkuFinland
  3. 3.Department of Information Technologies, Institute for Advanced Management Systems ResearchÅbo Akademi UniversityTurkuFinland
  4. 4.Faculty of Technology, Policy, and ManagementDelft University of TechnologyDelftThe Netherlands
  5. 5.Institute for Advanced Management Systems ResearchÅbo Akademi UniversityTurkuFinland

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