Abstract
This paper proposes a new methodology for conjoint analysis by fuzzifying the rank or the rating data. The fuzzy conjoint model is solved as a fuzzy regression problem assuming the error term to be a random fuzzy variable. The paper investigates whether or not the proposed fuzzy conjoint model is superior to its non-fuzzy counterpart. A test of superiority is carried out on a reallife example. It appears that the choice of the membership function is extremely critical for the superiority of the fuzzy model. The author conjectures that if the membership functions could be estimated directly from the respondents, the performance of the fuzzy model would definitely improve.
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References
Paul E. Green and Vithala R Rao, Conjoint Measurement for Quantifying Judgmental Data, Journal of Marketing Research,8 (August 1971) 355–363.
Paul E. Green, Hybrid Models for Conjoint Analysis: An Expository Review, Journal of Marketing Research,Vol. XXI (May 1984) 155–169.
Paul E. Green and V. Srinivasan, Conjoint Analysis in Marketing: New developments with Implications for Research and Practice, Journal of Marketing,54, (October 1990) 3–19.
Paul E. Green, Donald S. Tull and Gerald Albaum, Research for Marketing Decisions ( Fifth Edition ), ( Prentice Hall of India Private Limited, 1994 ).
I. Burhan Turksen and Ian A. Wilson, A fuzzy set preference model for consumer choice, Fuzzy Sets and Systems,68 (1994) 253–266.
A. G. Lapiga and V. V. Polyakov, On statistical methods in fuzzy decision-making, Fuzzy Sets and Systems,47 (1992) 303–311.
D. Dubois and H. Prade, Fuzzy Sets and Systems: Theory and Applications ( Academic Press. New York, 1980).
A. Kandel, Fuzzy Techniques in Pattern Recognition ( Wiley, New York, 1990).
G. J. Klir and B. Yuan, Fuzzy Sets and Fuzzy Logic: Theory and Applications ( Prentice-Hall, New Jersey, 1995).
T. Terano, K. Asai and M. Sugeno, Fuzzy Systems Theory and its Applications (Academic Press, London, 1992) ( English edition).
L.A. Zadeh, Probability measures of fuzzy events, J. Math. Anal. Appl. 23 (1968) 421–427.
Ping-Teng Chang and E. Stanley Lee, Fuzzy least absolute deviations regression based on the ranking of fuzzy numbers, IEEE (1994).
Ralf Körner, Linear models with random fuzzy variables, Ph.D. Thesis, Faculty of Mathematics and Computer Sciences, Freiberg University of Mining and Technology, Freiberg, Germany (1997).
Ch. Chui-Yu and Ch. S. Park, Fuzzy cash flow analysis using present worth criterion, The Engineering Economist, 39, 113–137 (1994).
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Bhattacharyya, M. (2002). Fuzzy Conjoint Analysis. In: Grzegorzewski, P., Hryniewicz, O., Gil, M.Á. (eds) Soft Methods in Probability, Statistics and Data Analysis. Advances in Intelligent and Soft Computing, vol 16. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1773-7_25
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DOI: https://doi.org/10.1007/978-3-7908-1773-7_25
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-1526-9
Online ISBN: 978-3-7908-1773-7
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