The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
권호 표지
DOI QR코드

DOI QR Code

A comparative study on validity of AHP and conjoint analysis: a case of cosmetics preference

계층적 의사결정과 컨조인트 분석의 타당성 비교: 화장품 선호 사례 조사

  • Lee, Ji Hye (Department of Applied Statistics, University of Suwon) ;
  • Jeong, Hyeong Chul (Department of Applied Statistics, University of Suwon)
  • 이지혜 (수원대학교 응용통계학과) ;
  • 정형철 (수원대학교 응용통계학과)
  • Received : 2016.05.25
  • Accepted : 2016.07.27
  • Published : 2016.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we consider the comparisons of the personal preferences of analytic hierarchy process (AHP) and conjoint analysis (CA) which contain very relatively small number of alternatives. However, a direct performance comparison is not easy because these two methods have a much different process to achieve the final decision. Therefore, we adopt a validity and reference method with empirical case study for cosmetics preference of female college students. In case study, conjoint analysis has the merit of measuring internal validity; however, AHP has the merit of measuring predictive validity.

본 연구는 대안이 많지 않은 의사결정에서 계층적 의사결정론(analytic hierarchy process)과 컨조인트 분석 간의 비교를 다루었다. 계층적 의사결정론은 속성들의 쌍대비교 과정을 거쳐 속성의 중요도를 추정한 후 대안들의 순위를 추정하는 방법이며, 컨조인트 분석은 대안의 순서로부터 속성의 효용을 추정하는 방법으로, 의사결정의 과정이 다르기에 두 방법을 직접적으로 비교하는 것은 다소 한계가 있다. 본 연구에서는 Scholl (2004)의 타당도 척도를 사용하여 두 방법을 S대학 여학생들의 화장품 선택 사례 연구를 통하여 두 방법을 서로 비교하였다. 사례연구 결과 컨조인트 분석은 내적타당도가 높게 나타났으며, 계층적 의사결정분석 방법은 예측타당도가 높게 나타남을 볼 수 있었다.

Keywords

References

  1. Chang, T. (2009). A methodological review of using AHP and conjoint analysis for measuring importance, Journal of Practical Research in Advertising and Public Relations, 2, 7-20.
  2. Green, P. E. and Srinivasan, V. (1978). Conjoint analysis in consumer research: issues and outlook, Journal of Consumer Research, 5, 103-123. https://doi.org/10.1086/208721
  3. 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
  4. Helm, R., Manthey, L., Scholl, A., and Steiner, M. (2003). Empirical evaluation of preference elicitation tech- niques from marketing and decision analysis (Technical Paper), Friedrich-Schiller University, Available from: http://www.wiwi.uni-jena.de
  5. Huh, M. (1994). Optimal Scaling, Freeacademy, Seoul.
  6. Jeong, H. C. (2010). Study on AHP and non-parametric verification on the importance of the diagnosis indicators of personal information security level, Journal of the Korean Data Analysis Society, 12, 1499-1510.
  7. Jeong, H. C., Lee, J. C., and Jhun, M. (2012). A study for obtaining weights in pairwise comparison matrix in AHP, The Korean Journal of Applied Statistics, 25, 531-541. https://doi.org/10.5351/KJAS.2012.25.3.531
  8. Lee, J. C., Jhun, M., and Jeong, H. C. (2014). A statistical testing of the consistency index in analytic hierarchy process, The Korean Journal of Applied Statistics, 27, 103-114. https://doi.org/10.5351/KJAS.2014.27.1.103
  9. Saaty, T. L. (1980). The Analytic Hierarchy Process, McGraw-Hill, New York.
  10. Satty, T. L. (1990). How to make a decision: the analytic hierarchy process, European Journal of Operational Research, 48, 9-26. https://doi.org/10.1016/0377-2217(90)90057-I
  11. Scholl, A., Manthey, L., Helm, R., and Steiner, M. (2004). Solving multiattribute design problems with analytic hierarchy process and conjoint analysis: an empirical comparison, European Journal of Operational Research, 164, 760-777.