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An Empirical Comparison of Predictability of Ranking-based and Choice-based Conjoint Analysis

순위기반 컨조인트분석과 선택기반 컨조인트분석의 예측력에 대한 실증적 비교

  • Kim, Bu-Yong (Department of Statistics, Sookmyung Women's University)
  • 김부용 (숙명여자대학교 통계학과)
  • Received : 2014.06.18
  • Accepted : 2014.08.13
  • Published : 2014.10.31

Abstract

Ranking-based conjoint analysis(RBCA) and choice-based conjoint analysis(CBCA) have attracted significant interest in various fields such as marketing research. When conducting research, the researcher has to select one suitable approach in consideration of strengths and weaknesses. This article performs an empirical comparison of the predictability of RBCA and CBCA in order to provide criterion for the selection. A new concept of measurement set is developed by combining the ranking set and choice set. The measurement set enables us to apply two approaches separately on the same consumer group that allows a fair comparison of predictability. RBCA and CBCA are conducted on consumer preferences for RTD-coffee; subsequently, the predicted values of market shares and hit rates are compared. The study result reveals that their predictabilities are not significantly different. Further, the result indicates that RBCA is recommended if the researcher wants to improve data quality by filtering out poor responses or to implement the market segmentation. In contrast, CBCA is recommended if the researcher wants to lessen the burden on the respondents or to measure preferences under similar conditions with the actual marketplace.

마케팅조사 등 다양한 분야에서 순위기반 컨조인트분석과 선택기반 컨조인트분석이 많이 활용되고 있다. 컨조인트 분석가들은 각 분석 기법의 상대적인 강점과 약점들을 고려하여 상황에 적합하다고 판단되는 기법을 선택하여 사용한다. 본 연구는 컨조인트분석 기법을 선택할 때 참고할 수 있는 준거를 제공하기 위하여 두가지 기법의 예측력을 실증적으로 비교하고자 한다. 순위집합과 선택집합을 통합한 측정집합 개념을 새롭게 도입함으로써 동일한 소비자 집단에 두 가지 분석기법을 동시에 적용할 수 있는 조사를 설계하였다. 실제로 측정집합을 설계하여 RTD커피에 대한 선호도를 측정하고 순위기반과 선택기반 컨조인트분석을 적용하여 소비자 선호도를 분석하고 두 기법에 의한 시장점유율 예측치와 적중률을 비교하였다. 비교결과 두 기법의 예측력에 유의적인 차이가 없는 것으로 나타났다. 따라서 응답자의 응답결과를 사전에 점검하여 부실한 자료를 제외시킴으로써 자료의 품질을 향상시키려 하거나 컨조인트분석 결과를 바탕으로 시장세분화 작업을 하기 원하는 경우에는 순위기반 컨조인트분석을 채택하고, 선호도 측정과정에서 응답자의 부담을 덜어주고 실제 시장과 가장 유사한 상황에서 선호도를 측정하고자 하는 경우에는 선택기반 조인트분석을 채택할 것을 제안한다.

Keywords

References

  1. Adanacioglu, H. and Albayram, Z. (2012). A conjoint analysis of consumer preference for traditional cheeses in Turkey: A case study on Tulum cheese, Korean Journal for Food Science of Animal Resources, 32, 458-466. https://doi.org/10.5851/kosfa.2012.32.4.458
  2. Annunziata, A. and Vecchio, R. (2013). Consumer perception of functional food: A conjoint analysis with probiotics, Food Quality and Preference, 28, 348-355. https://doi.org/10.1016/j.foodqual.2012.10.009
  3. Ariji, M. (2010). Conjoint analysis of consumer preference for blue-fin tuna, Fisheries Science, 76, 1023-1028. https://doi.org/10.1007/s12562-010-0297-4
  4. Berkson, J. (1955). Maximum likelihood and minimum Chi-square estimates of the logistic function, Journal of the American Statistical Association, 50, 130-162.
  5. Boyle, K. J., Holmes, T. P., Teisl, M. F. and Roe, B. (2001). A comparison of conjoint analysis response formats, American Journal of Agricultural Economics, 83, 441-454. https://doi.org/10.1111/0002-9092.00168
  6. Bridges, J. F. P., Lataille, A. T., Buttorff, C. B., White, S. and Niparko, J. K. (2012). Consumer preferences for hearing aid attributes: A comparison of rating and conjoint analysis methods, Trends in Amplification, 16, 40-48. https://doi.org/10.1177/1084713811434617
  7. 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
  8. Chen, Y. H., Hsu, I. C. and Lin, C. C. (2010). Website attributes that increase consumer purchase intention: A conjoint analysis, Journal of Business Research, 63, 1007-1014. https://doi.org/10.1016/j.jbusres.2009.01.023
  9. Choi, W. S., Seo, K. H. and Lee, S. B. (2012). A study on the development of HMR products of Korean foods using conjoint analysis, The Korean Journal of Culinary Research, 18, 156-167.
  10. Chung, H. S., Hong, H. D., Kim, K., Cho, C. W., Moskowitz, H. R. and Lee, S. Y. (2011). Consumer attitudes and expectations of ginseng food products assessed by focus groups and conjoint analysis, Journal of Sensory Studies, 26, 346-357. https://doi.org/10.1111/j.1745-459X.2011.00350.x
  11. Deliza, R., Rosenthal, A., Hedderley, D. and Jaeger, S. (2010). Consumer perception of irradiated fruit: A case study using choice-based conjoint analysis, Journal of Sensory Studies, 25, 184-200. https://doi.org/10.1111/j.1745-459X.2009.00250.x
  12. Eggers, F. and Eggers, F. (2011). Where have all the flowers gone? Forecasting green trends in the automobile industry with a choice-based conjoint adoption model, Technological Forecasting and Social Change, 78, 51-62. https://doi.org/10.1016/j.techfore.2010.06.014
  13. 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
  14. Endrizzi, I., Menichelli, E., Johansen, S. B., Olsen, N. V. and Naes, T. (2011). Handling of individual differences in rating-based conjoint analysis, Food Quality and Preference, 22, 241-254. https://doi.org/10.1016/j.foodqual.2010.10.005
  15. Gensler, S., Hinz, O., Skiera, B. and Theysohn, S. (2012). Willingness-to-pay estimation with choice-based conjoint analysis: Addressing extreme response behavior with individually adapted designs, European Journal of Operation Research, 219, 368-378. https://doi.org/10.1016/j.ejor.2012.01.002
  16. Hong, J. S., Jeon, J. Y. and Kim, Y. S. (2012). Study on consumers' restaurant selection criteria by using conjoint analysis, Journal of East Asian Society of Dietary Life, 22, 315-321.
  17. Jervis, S. M., Lopetcharat, K. and Drake, M. A. (2012). Application of ethnography and conjoint analysis to determine key consumer attributes for latte-style coffee beverages, Journal of Sensory Studies, 27, 48-58. https://doi.org/10.1111/j.1745-459X.2011.00366.x
  18. Jin, B., Park, J. Y. and Ryu, J. S. (2010). Comparison of Chinese and Indian consumers' evaluative criteria when selecting denim jeans: A conjoint analysis, Journal of Fashion Marketing and Management, 14, 180-194. https://doi.org/10.1108/13612021011025492
  19. Jo, M. N. (2010). Conjoint analysis of restaurant attributes on customer intentions to choose restaurant, The Korean Journal of Culinary Research, 16, 254-268.
  20. Jung, U. (2012). Conjoint analysis on the academia-industrial cooperative research project attributes for culture technology research, Management Science and Financial Engineering, 29, 13-21. https://doi.org/10.7737/KMSR.2012.29.3.013
  21. Karniouchina, E. V., Moore, W. L., Rhee, B. and Verma, R. (2009). Issues in the use of ratings-based versus choice-based concomitant analysis in operations management research, European Journal of Operations Research, 197, 340-348. https://doi.org/10.1016/j.ejor.2008.05.029
  22. Kaufman, S., Kunzel, K. and Loock, M. (2013). Customer value of smart metering: Explosive evidence from a choice-based conjoint study in Switzerland, Energy Policy, 53, 229-239. https://doi.org/10.1016/j.enpol.2012.10.072
  23. Kim, B. Y. (2012). New design of choice sets for choice-based conjoint analysis, The Korean Journal of Applied Statistics, 25, 847-857. https://doi.org/10.5351/KJAS.2012.25.5.847
  24. Kim, B. Y. (2014). New method for preference measurement in ranking-based conjoint analysis, The Korean Journal of Applied Statistics, 27, 185-195. https://doi.org/10.5351/KJAS.2014.27.2.185
  25. Kim, Y. J. and Kim, B. S. (2012). An analysis on consumers' preference of agricultural products cultivated from plants factory system, Journal of the Korean Academia-Industrial Cooperation Society, 13, 5052-5059. https://doi.org/10.5762/KAIS.2012.13.11.5052
  26. Kim, B. Y., Kim, J. and Kahn, Y. (2012). Analysis of consumer preferences for cosmetic essence-for-men via choice-based conjoint with new design of choice sets, Korean Journal of Applied Statistics, 25, 987-997. https://doi.org/10.5351/KJAS.2013.25.6.987
  27. Kim, J. H., Kim, J. H. and Kim, M. K. (2010). Development of dental services markets segmentation and strategy by use of conjoint analysis, Korean Journal of Health Policy and Administration, 20, 1-20. https://doi.org/10.4332/KJHPA.2010.20.3.001
  28. 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
  29. Kutner, M. H., Nachtsheim, C. J., Neter, J. and Li, W. (2005). Applied Linear Statistical Models, McGraw Hill.
  30. Lebeau, K., Mierlo, J. V., Lebeau, P., Mairesse, O. and Macharis, C. (2012). The market potential for plugin hybrid and battery electric vehicles in Flanders: A choice-based conjoint analysis, Transportation Research Part D, 17, 592-597. https://doi.org/10.1016/j.trd.2012.07.004
  31. Lee, E. Y., Park, Y. W. and Lee, S. B. (2010). An exploratory study on selection attributes of food in the cultural tourism festival through conjoint analysis, The Korean Journal of Culinary Research, 16, 94-113.
  32. 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 Academy of Marketing Science, 16, 39-54.
  33. Mesias, F. J., Martinez-Carrasco, F., Martinez, J. M. and Gaspar, P. (2010). Functional and organic eggs as an alternative to conventional production: A conjoint analysis of consumers' preferences, Journal of the Science of Food and Agriculture, 91, 532-538.
  34. 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
  35. Nikou, S., Bouwman, H. and Reuver, M. (2012). The potential of converged mobile telecommunication services: A conjoint analysis, The Journal of Policy, Regulation and Strategy for Telecommunications, Information and Media, 14, 21-35.
  36. Ong, F. S., Kitchen, P. F. and Chew, S. S. (2010). Marketing a consumer durable brand in Malaysia: A conjoint analysis and market simulation, Journal of Consumer Marketing, 27, 507-515. https://doi.org/10.1108/07363761011078244
  37. Park, R. J. and Lee, D. H. (2011). Information security risk: Application of the conjoint analysis, Journal of Korean Data and Information Science Society, 22, 207-215.
  38. Ryu, H. S. and Roh, E. K. (2010). Preference and subjective evaluation of washed fabric hand using conjoint analysis, Textile Research Journal, 80, 2167-2175. https://doi.org/10.1177/0040517510376270
  39. Sayadi, S., Roa, M. C. G. and Requena, J. C. (2005). Ranking versus scale rating in conjoint analysis: Evaluating landscapes in mountainous regions in southeastern Spain, Ecological Economics, 55, 539-550. https://doi.org/10.1016/j.ecolecon.2004.12.010
  40. Voelkel, J. G. (2005). The efficiencies of fractional factorial designs, Technometrics, 47, 488-494. https://doi.org/10.1198/004017005000000274