The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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New Design of Choice Sets for Choice-based Conjoint Analysis

  • Kim, Bu-Yong (Department of Statistics, Sookmyung Women's University)
  • Received : 2012.05.03
  • Accepted : 2012.09.15
  • Published : 2012.10.31
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Abstract

This article is concerned with choice-based conjoint analysis versus rating-based and ranking-based conjoint analysis. Choice-based conjoint analysis has a definite advantage in that the respondent's task of choosing the most preferred profile from several competing profiles adequately mimics consumer marketplace behavior. It is crucial to design the choice sets appropriate for the choice-based conjoint. Thus, this article suggests a new method to design the choice sets that are well-balanced. It augments the balanced incomplete block design and then obtains the dual design of the result to accommodate various numbers of profiles. In consequence, the choice sets designed by the new method have the desirable characteristics that each profile is presented to the same number of respondents, and pairs of any two distinct profiles occur together in the same number of choice sets. The balancing of the design increases the efficiency of the conjoint analysis. In addition, the pair-comparison scheme can improve the quality of data through the identification of contradictory responses.

Keywords

References

  1. Barone, S. and Lombardo, A. (2004). Service quality design through a smart use of conjoint analysis, The Asian Journal of Quality, 5, 34-42. https://doi.org/10.1108/15982688200400004
  2. Berkson, J. (1955). Maximum likelihood and minimum chi-square estimates of the logistic function, Journal of the American Statistical Association, 50, 130-162.
  3. Berkson, J. (1980). Minimum chi-square, not maximum likelihood, The Annals of Statistics, 8, 457-487. https://doi.org/10.1214/aos/1176345003
  4. Chakraborty, G., Ball, D., Gaeth, G. J. and Jun, S. (2002). The ability of rating and choice conjoint to predict market shares: A Monte Carlo simulation, Journal of Business Research, 55, 237-249. https://doi.org/10.1016/S0148-2963(00)00127-2
  5. Desarbo, W. S., Ramaswamy, V. and Cohen, S. H. (1995). Market segmentation with choice-based conjoint analysis, Marketing Letters, 6, 137-147. https://doi.org/10.1007/BF00994929
  6. Elrod, T., Louviere, J. J. and Davey, K. S. (1992). An empirical comparison of rating-based and choice-based conjoint models, Journal of Marketing Research, 29, 368-377. https://doi.org/10.2307/3172746
  7. Giancristofaro, R. A. (2003). A new conjoint analysis procedure with application to marketing research, Communications in Statistics - Theory and Methods, 32, 2271-2283. https://doi.org/10.1081/STA-120024480
  8. Green, P. E., Carroll, D. and Goldberg, S. M. (1981). A general approach to product design optimization via conjoint analysis, Journal of Marketing, 45, 17-37. https://doi.org/10.2307/1251539
  9. Green, P. E. and Srinivasan, V. (1990). Conjoint analysis in marketing: New developments with implications for research and practice, Journal of Marketing, 54, 3-19. https://doi.org/10.2307/1251756
  10. Haaijer, R., Kamakura, W. and Wedel, M. (2001). The no-choice alternative in conjoint choice experiments, International Journal of Market Research, 43, 93-106.
  11. ohnson, R. M. and Orme, B. K. (1996). How many questions should you ask in choice-based conjoint studies?, Research Paper Series, Sawtooth Software, Inc.
  12. Kamakura, W. A. (1988). A least squares procedure for benefit segmentation with conjoint experiments, Journal of Marketing Research, 25, 157-167. https://doi.org/10.2307/3172647
  13. Kim, B. Y. (2005). Conjoint analysis for the development of new cellular phone, Journal of the Korean Society for Quality Management, 33, 103-110.
  14. Kuhfeld, W. F. and Tobias, R. D. (2005). Large factorial designs for product engineering and marketing research applications, Technometrics, 47, 132-141. https://doi.org/10.1198/004017004000000653
  15. Kuhfeld, W. F., Tobias, R. D. and Garratt, M. (1994). Efficient experimental design with marketing research applications, Journal of Marketing Research, 31, 545-557. https://doi.org/10.2307/3151882
  16. Kutner, M. H., Nachtsheim, C. J., Neter, J. and Li, W. (2005). Applied Linear Statistical Models, McGraw Hill.
  17. Lazari, A. G. and Anderson, D. A. (1994). Designs of discrete choice set experiments for estimating both attribute and availability cross effects, Journal of Marketing Research, 31, 375-383. https://doi.org/10.2307/3152224
  18. Lim, B., Ahn, K. and Park, U. (2006). A study on the comparison of the predictability among traditional and choice-based conjoint analysis in the choice of service products, Journal of Global Scholars Marketing Science, 16, 37-52.
  19. Marshall, P. and Bradlow, E. T. (2002). A unified approach to conjoint analysis models, Journal of the American Statistical Association, 97, 674-682. https://doi.org/10.1198/016214502388618410
  20. Moore, W. L. (2004). A cross-validity comparison of rating-based and choice-based conjoint analysis models, International Journal of Research in Marketing, 21, 299-312. https://doi.org/10.1016/j.ijresmar.2004.01.002
  21. Orme, B. (2010). Getting Started with Conjoint Analysis: Strategies for Product Design and Pricing Research, Madison, Research Publishers LLC, Wisconsin.
  22. Shin, Y. J., Kim, B. Y. and Hyun, Y. J. (2007). Conjoint analysis for the effects of cigarette warning label and packaging on intention to quit, Journal of Health and Social Affairs, 27, 27-51.

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