ENHANCING CONJOINT ANALYSIS WITH HIERARCHICAL FACTOR ANALYSIS AS CLUSTERING TECHNIQUE
Keywords:
Product Design, Factor Analysis, Conjoint AnalysisAbstract
Competitive advantage is achieved by those firms which able to develop their product or service to fulfill a consumer’s need. The market preferences following adequate evaluation of how people measure different features of an individual product. The product or service design using Conjoint analysis, a quantitative research tool widely employed in product management and marketing struggled the over information load for decision-making in the ranking preference value. Our proposed method, the integration of Hierarchical
Factor analysis and conjoint analysis, can improve the product design more efficiently. The experiment on electric pump design has found that the seven attributes were segmented into five clusters, and each cluster consisted of a full factorial design with suitable product concepts for man-kind decision-making.
References
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Anderson, N.H. (1982). Methods of Information Integration Theory. New York: Academic Press.
Balakrishnan P. V. (Sundar), & Jacob, V. S. (1996). Genetic algorithms for product design. Management Science, 42(8), 1105-1117.
Easton L., D.J. Murphy, J.N. Pearson (2002). Purchasing performance evaluation: with data envelopment analysis, European Journal of Purchasing & Supply Management, 8, 123-134.
Friedman L. & Sinuany-Stern Z. (1997). Scaling units via the canonical correlation analysis in the DEA context, European Journal of Operations Research, 100(3), 629-637.
Green, P.E. and Krieger, A.M. (1993). Conjoint Analysis with Product-Positioning Applications, in Handbooks in OR&MS, Jehoshua Eliashberg and Gary L. Lilien, eds. Elsevier Science Publishers, 5, 467-513.
Green, P. E., & Krieger, A. M. (1996). Individualized hybrid models for conjoint analysis. Management Science, 42(6), 850-867.
Green, P.E.; Krieger, A.M. and Wind, Y. (2001). Thirty Years of Conjoint Analysis: Reflections and Prospects, Interfaces, 31, S56-S73.
Hair, J.F.; Anderson, R.E. Tatham, R.L. and Black, W.C. (1999). Multivariate Analysis. 5th ed. Madrid, Prentice Hall.
Hise, R. & Young-Tae, C. (2011). Are US companies employing standardization or adaptation strategies in their international markets?, Journal of International Business and cultural studies, 4, 1-29.
Johnson, M. (1988). Comparability and hierarchical processing in multi attribute Choice. Journal of Consumer Research, 15, December, 303-314.
Kotler, P. & Keller, K. (2006). Marketing Management. 12th Edi, Upper Saddle River, New Jersey: Pearson Prentice Hall.
Louviere, J.J. (1988). Analyzing Decision Making. Metric Conjoint Analysis, Newbury Park, Sage Publications Inc.
Louviere, J.J. and Timmermans, H.J.P. (1990a). Hierarchical Information Integration applied to residential choice behavior. Geographical Analysis, 22, 127-145.
Louviere, J.J. and Timmermans, H.J.P. (1990b). Using hierarchical information integration to model consumer responses to possible planning actions: recreation destination choice illustration. Environment and Planning A, 22, 291-309.
Iyengar, S., & Lepper, M. (2000). When choice is demotivating: Can one desire too much of a good thing?. Journal of Personality and Social Psychology, 79, 995-1006.
Louviere, J. J., Hensher, D. A., & Swait, J. D. (2004). Stated preference methods: Analysis and application. Cambridge. UK: Cambridge University Press.
Mick, D. G. (2005). Choice writ larger. Newsletter of the Association for Consumer Research. Retrieved March 6, 2017, from http://www.acrwebsite.org/
Molin, E.J.E. and Timmermans, J.P. (2009). Hierarchical Information Integration Experiments and Integrated Choice Experiments. Transport Reviews, 1-21.
Myers, J.H. and Mullet, G.M. (2003). Managerial Applications of Multivariate Analysis In Marketing. American Marketing Association, Chicago, Illinois.
Oppewal, H. Virens, M. (2000). Measuring perceived service quality using integrated conjoint experiments. International Journal of Bank Marketing, 18(4), 154-169.
Strack, F., Werth, L., & Deutsch, R. (2006). Reflective and impulsive determinants of Consumer behavior. Journal of Consumer Psychology, 16,205-216.
Waldman, S. (1992). The tyranny of choice: Why the consumer revolution is ruining your life. New Republic, 22–25.
Anderson, N.H. (1981). Foundations of Information Integration Theory. New York: Academic Press.
Anderson, N.H. (1982). Methods of Information Integration Theory. New York: Academic Press.
Balakrishnan P. V. (Sundar), & Jacob, V. S. (1996). Genetic algorithms for product design. Management Science, 42(8), 1105-1117.
Easton L., D.J. Murphy, J.N. Pearson (2002). Purchasing performance evaluation: with data envelopment analysis, European Journal of Purchasing & Supply Management, 8, 123-134.
Friedman L. & Sinuany-Stern Z. (1997). Scaling units via the canonical correlation analysis in the DEA context, European Journal of Operations Research, 100(3), 629-637.
Green, P.E. and Krieger, A.M. (1993). Conjoint Analysis with Product-Positioning Applications, in Handbooks in OR&MS, Jehoshua Eliashberg and Gary L. Lilien, eds. Elsevier Science Publishers, 5, 467-513.
Green, P. E., & Krieger, A. M. (1996). Individualized hybrid models for conjoint analysis. Management Science, 42(6), 850-867.
Green, P.E.; Krieger, A.M. and Wind, Y. (2001). Thirty Years of Conjoint Analysis: Reflections and Prospects, Interfaces, 31, S56-S73.
Hair, J.F.; Anderson, R.E. Tatham, R.L. and Black, W.C. (1999). Multivariate Analysis. 5th ed. Madrid, Prentice Hall.
Hise, R. & Young-Tae, C. (2011). Are US companies employing standardization or adaptation strategies in their international markets?, Journal of International Business and cultural studies, 4, 1-29.
Johnson, M. (1988). Comparability and hierarchical processing in multi attribute Choice. Journal of Consumer Research, 15, December, 303-314.
Kotler, P. & Keller, K. (2006). Marketing Management. 12th Edi, Upper Saddle River, New Jersey: Pearson Prentice Hall.
Louviere, J.J. (1988). Analyzing Decision Making. Metric Conjoint Analysis, Newbury Park, Sage Publications Inc.
Louviere, J.J. and Timmermans, H.J.P. (1990a). Hierarchical Information Integration applied to residential choice behavior. Geographical Analysis, 22, 127-145.
Louviere, J.J. and Timmermans, H.J.P. (1990b). Using hierarchical information integration to model consumer responses to possible planning actions: recreation destination choice illustration. Environment and Planning A, 22, 291-309.
Iyengar, S., & Lepper, M. (2000). When choice is demotivating: Can one desire too much of a good thing?. Journal of Personality and Social Psychology, 79, 995-1006.
Louviere, J. J., Hensher, D. A., & Swait, J. D. (2004). Stated preference methods: Analysis and application. Cambridge. UK: Cambridge University Press.
Mick, D. G. (2005). Choice writ larger. Newsletter of the Association for Consumer Research. Retrieved March 6, 2017, from http://www.acrwebsite.org/
Molin, E.J.E. and Timmermans, J.P. (2009). Hierarchical Information Integration Experiments and Integrated Choice Experiments. Transport Reviews, 1-21.
Myers, J.H. and Mullet, G.M. (2003). Managerial Applications of Multivariate Analysis In Marketing. American Marketing Association, Chicago, Illinois.
Oppewal, H. Virens, M. (2000). Measuring perceived service quality using integrated conjoint experiments. International Journal of Bank Marketing, 18(4), 154-169.
Strack, F., Werth, L., & Deutsch, R. (2006). Reflective and impulsive determinants of Consumer behavior. Journal of Consumer Psychology, 16,205-216.
Waldman, S. (1992). The tyranny of choice: Why the consumer revolution is ruining your life. New Republic, 22–25.
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Published
2019-08-15
How to Cite
พิริยะกุล อ., & ศรีสังวาลย์ จ. (2019). ENHANCING CONJOINT ANALYSIS WITH HIERARCHICAL FACTOR ANALYSIS AS CLUSTERING TECHNIQUE. Academic Journal Phranakhon Rajabhat University, 10(2), 268–283. Retrieved from https://so01.tci-thaijo.org/index.php/AJPU/article/view/193800
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