Advanced SearchSearch Tips
Analysis of Consumer Preferences for Cosmetic Essence-for-Men via Choice-Based Conjoint with New Design of Choice Sets
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Analysis of Consumer Preferences for Cosmetic Essence-for-Men via Choice-Based Conjoint with New Design of Choice Sets
Kim, Bu-Yong; Kim, Jiyoung; Kan, Yu-Yeong;
  PDF(new window)
The sales volume of men's cosmetics has drastically increased in Korea. In recent years, men's needs for cosmetics have been diversified and the consumer demand for functional cosmetics has greatly risen. In particular, male consumers have become more interested in essence product that is a light and concentrated treatment to correct skin problems. This research analyzes consumer preferences for essence-for-men through the use of choice-based conjoint analysis. This approach is adopted since the task of respondents to choose the most preferred option from several alternatives closely mimics actual marketplace purchasing behavior by consumers. New technique for the construction of choice sets is suggested based on the balanced incomplete block design, to accommodate a larger number of product profiles. The proposed design for choice sets is balanced and provides a tool to filter the contradictory choices. Conjoint analyses are performed to assess the relative importance of attributes and identify the most preferred profile of essence-for-men with respect to attributes such as emphasized function, price, type of content, and design of container. Some differences are indicated in the analysis results between age brackets as well as between groups classified by the amount of fashion item expenditures.
Consumer preference;essence-for-men;choice-based conjoint analysis;choice set;
 Cited by
순위기반 컨조인트분석과 선택기반 컨조인트분석의 예측력에 대한 실증적 비교,김부용;

응용통계연구, 2014. vol.27. 5, pp.681-691 crossref(new window)
An Empirical Comparison of Predictability of Ranking-based and Choice-based Conjoint Analysis, Korean Journal of Applied Statistics, 2014, 27, 5, 681  crossref(new windwow)
Berkson, J. (1955). Maximum likelihood and minimum Chi-square estimates of the logistic function, Journal of the American Statistical Association, 50, 130-162.

Cassab, H. (2009). Investigating the dynamics of service attributes in multi-channel environments, Journal of Retailing and Consumer Services, 16, 25-30. crossref(new window)

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. crossref(new window)

Cohen, S. H. (1997). Perfect union: CBCA marries the best of conjoint and discrete choice models, Marketing Research, 9, 12-17.

DeSarbo, W. S., Ramaswamy, V. and Cohen, S. H. (1995). Market segmentation with choice-based conjoint analysis, Marketing Letters, 6, 137-147. crossref(new window)

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. crossref(new window)

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. crossref(new window)

Giancristofaro, R. A. (2003). A new conjoint analysis procedure with application to marketing research, Communications in Statistics - Theory and Methods, 32, 2271-2283. crossref(new window)

Green, P. E., Krieger, A. M. and Wind, Y. (2001). Thirty years of conjoint analysis: Reflections and prospects, Interfaces, 31, 56-73.

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. crossref(new window)

Haaijer, R., Kamakura, W. and Wedel, M. (2001). The 'no-choice' alternative in conjoint choice experiments, International Journal of Market Research, 43, 93-106.

Hong, J. K. (2008). A study on skin recognition and cosmetics use necessity in men by age, Journal of the Korean Society of Cosmetology, 14, 1230-1243.

Jeon, H. R. and Jae, M. K. (2009). 20s-30s men's cosmetics purchase decision factors, Journal of Korean Association of Human Ecology, 18, 1237-1246. crossref(new window)

Johnson, R. M. and Orme, B. K. (1996). How many questions should you ask in choice-based conjoint studies? Research Paper Series, Sequim, WA: Sawtooth Software Inc.

Kim, B. Y. (2005). Conjoint analysis for the development of new cellular phone, Journal of the Korean Society for Quality Management, 33, 103-110.

Kuhfeld, W. F. and Tobias, R. D. (2005). Large factorial designs for product engineering and marketing research applications, Technometrics, 47, 132-141. crossref(new window)

Kuhfeld, W. F., Tobias, R. D. and Garratt, M. (1994). Efficient experimental design with marketing research applications, Journal of Marketing Research, 31, 545-557. crossref(new window)

Kutner, M. H., Nachtsheim, C. J., Neter, J. and Li, W. (2005). Applied Linear Statistical Models, McGraw Hill.

Marshall, P. and Bradlow, E. T. (2002). A unified approach to conjoint analysis models, Journal of the American Statistical Association, 97, 674-682. crossref(new window)

Meibner, M. and Decker, R. (2009). Eye-tracking information processing in choice-based conjoint analysis, International Journal of Market Research, 52, 591-610.

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. crossref(new window)

Park, E. J. (2008). Men's actual usage and purchase pattern of cosmetics according to their appearancerelated attitude, Journal of the Korean Society of Fashion and Beauty, 6, 267-277.

Ramaswamy, V. and Cohen, S. H. (2001). Latent class models for conjoint analysis, Conjoint Measurement: Methods and Application, ed. Gustafsson, A., Herrmann, A., and Huber, F., Springer-Verlag, Berlin, 415-446.

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.

Toubia, O., Hauser, J. R. and Simester, D. I. (2004). Polyhedral methods for adaptive choice-based conjoint analysis, Journal of Marketing Research, 41, 116-131. crossref(new window)

Wittink, D. R., Vriens, M. and Burhenne, W. (1994). Commercial use of conjoint analysis in Europe: Results and critical reflections, International Journal of Research in Marketing, 11, 41-52. crossref(new window)