Applied conjoint analysis

Applied Conjoint Analysis

Volumn 9783540877530, Issue , 2014, Pages 1-389

  Rao, Vithala R ^a  

^a The Samuel Curtis Johnson Graduate School of Management   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84929934853     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/978-3-540-87753-0     Document Type: Book

References (437)

1. Allenby, G. M., Arora, N., & Gonter, J. L. (1995). Incorporating prior knowledge into the analysis of conjoint studies. Journal of Marketing Research, 34, 152-162.
2. Anderson, N. H. (1970). Functional measurement and psychological judgment. Psychological Review, 77, 153-170.
3. Ben-Akiva, M., & Lerman, S. R. (1991). Discrete choice analysis. Cambridge, MA: MIT Press.
4. Carroll, J. D., & Green, P. E. (1995). Psychometric methods in marketing research: Part I, conjoint analysis. Journal of Marketing Research, 32, 385-391.
5. Cattin, P., & Wittink, D. R. (1982). Commercial use of conjoint analysis: A survey. Journal of Marketing, 46, 44-53.
6. Cattin, P., Gelfand, A., & Danes, J. (1983). A simple Bayesian procedure for estimation in a conjoint model. Journal of Marketing Research, 20, 29-35.
7. Corstjens, M. L., & Gautschi, D. A. (1983). A comparative analysis of specification tests for the utility function. Management Science, 29(12), 1393-1413.
8. Dawes, R. M., & Corrigan, B. (1974). Linear models in decision making. Psychological Bulletin, 81, 95-106.
9. Fishbein, M. (Ed.). (1967). Readings in attitude theory and measurement. New York: Wiley.
10. Green, P. E., & Rao, V. R. (1971). Conjoint measurement for quantifying judgmental data. Journal of Marketing Research, 8, 355-363.
11. Green, P. E., & Srinivasan, V. (1978). Conjoint analysis in consumer research: Issues and outlook. Journal of Consumer Research, 5, 103-123.
12. Green, P. E., & Srinivasan, V. (1990). Conjoint analysis in marketing: New developments with implications for research and practice. Journal of Marketing, 54, 3-19.
13. Green, P. E., & Wind, Y. (1975). New way to measure consumers' judgments. Harvard Business Review, 53, 107-117.
14. Green, P. E., Krieger, A. M., & Vavra, T. G. (1997). Evaluating new products. Marketing Research, 9, 12-19.
15. Huber, J. Conjoint analysis: How we got here and where we are. Sawtooth Conference, 2004.
16. Johnson, R. M. (1974). Trade-off analysis of consumer values. Journal of Marketing Research, 11, 121-127.
17. Keeney, R. L., & Raiffa, H. (1976). Decisions with multiple objectives: preferences and value tradeoffs. New York: Wiley.
18. Krantz, D. H., David, H., Duncan Luce, R., Tversky, A., & Suppes, P. (1971). Foundations of measurement volume I: Additive and polynomial representations. New York/London: Academic.
19. Louviere, J. J., Hensher, D. A., & Swait, J. (2000). Stated choice methods: Analysis and applications. Cambridge, UK: Cambridge University Press.
20. Luce, D., & Tukey, J. (1964). Simultaneous conjoint measurement: A new type of fundamental measurement. Journal of Mathematical Psychology, 1, 1-27.
21. McFadden, D. (1974). Conditional logit analysis of qualitative choice behavior. In P. Zarembka (Ed.), Frontiers in econometrics (pp. 105-142). New York: Academic Press.
22. Rao, V. R. (1977). Conjoint measurement in marketing analysis. In J. N. Sheth (Ed.), Multivariate methods in market and survey research. Chicago: American Marketing Association.
23. Rao, V. R. (2009). Developments in conjoint analysis. In B. Weiringa (Ed.), Handbook of marketing decision models. New York: Springer.
24. Rao, V. R., & Soutar, G. N. (1975). Subjective evaluations for product design decisions. Decision Sciences, 6(1), 120-134.
25. Rosenberg, M. J. (1956). Cognitive structure and attitudinal affect. Journal of Abnormal and Social Psychology, 53, 367-372.
26. Srinivasan, V., & Park, C. S. (1997). Surprising robustness of self-explicated approach to customer preference structure measurement. Journal of Marketing Research, 34(2), 286-291.
27. Srinivasan, V., & Wyner, G. A. (1989). CASEMAP: Computer-assisted self-explication of multi-attributed preferences. In W. Henry, M. Menasco, & H. Takada (Eds.), New product development and testing (pp. 91-111). Lexington: Lexington Books.
28. Thurstone, L. (1927). A law of comparative judgment. Psychological Review, 34, 273-286.
29. Wilkie, W. L., & Pessemier, E. A. (1973). Issues in marketing's use of multi-attribute-models. Journal of Marketing Research, 10, 428-441.
30. Wind, Y., Green, P. E., Shifflet, D., & Scarbrough, M. (1989). Courtyard by Marriott: designing a hotel with consumer-based marketing. Interfaces, 19(January-February), 25-47.
31. Wittink, D. R., & Cattin, P. (1989). Commercial use of conjoint analysis: An update. Journal of Marketing, 53, 91-96.
32. Wittink, D. R., Vriens, M., & Burhenne, W. (1994). Commercial use of conjoint analysis in Europe: Results and critical reflections. International Journal of Research in Marketing, 11, 41-52.
33. Carroll, J. D. (1969). Categorical conjoint measurement. Paper presented at Meeting of Mathematical Psychology, Ann Arbor, MI.
34. Cattin, P, & Dick, R.W. (1976). A Monte Carlo study of metric and nonmetric estimation methods for multiattribute models. Research Paper No. 341, Graduate School of Business, Stanford University.
35. De Sarbo, W. S., Gelfand, A. E., & Danes, J. (1983). A simple Bayesian procedure for estimation in a conjoint model. Journal of Marketing Research, 20, 29-35.
36. Green, P. E. (1984). Hybrid models for conjoint analysis: An expository review. Journal of Marketing Research, 21, 155-169.
37. Green, P. E., & De Sarbo, W. S. (1979). Componential segmentation in the analysis of consumer tradeoffs. Journal of Marketing, 43, 83-91.
38. Green, P. E., Goldberg, S. M., & Montemayor, M. (1981). A hybrid utility estimation model for conjoint analysis. Journal of Marketing, 45, 33-41.
39. Hagerty, M. R. (1985). Improving the predictive power of conjoint analysis: The use of factor analysis and cluster analysis. Journal of Marketing Research, 22, 168-184.
40. Herman, S. (1988). Software for full-profile conjoint analysis. In M. Metegrano (Ed.), Proceeding of the Sawtooth conference on perceptual mapping, conjoint analysis, and computer interviewing (pp. 117-130). Ketchum, ID: Sawtooth Software.
41. Johnson, R. M. (1987). Adaptive conjoint analysis. In M. Metegrano (Ed.), Proceeding of the Sawtooth conference on perceptual mapping, conjoint analysis, and computer interviewing (pp. 253-265). Ketchum, ID: Sawtooth Software.
42. Kamakura, W. (1988). A least squares procedure for benefit segmentation with conjoint experiments. Journal of Marketing Research, 25, 157-167.
43. Krishnamurthi, L, & Wittink, D. R. (1989). The partworth model and its applicability in conjoint analysis. Working Paper, College of Business Administration, University of Illinois.
44. Kruskal, J. B. (1965). Analysis of factorial experiments by estimating monotone transformations of the data. Journal of the Royal Statistical Society, 27, 251-263. Series B.
45. Pekelman, D., & Sen, S. L. (1979). Improving prediction in conjoint analysis. Journal of the American Statistical Association, 75, 801-816.
46. Shocker, A. D., & Srinivasan, V. (1977). LINMAP (Version II): A FORTRAN IV computer program for analyzing ordinal preference (Dominance) judgments via linear programming techniques for conjoint measurement. Journal of Marketing Research, 14, 101-103.
47. Srinivasan, V. (1988). A conjunctive-compensatory approach to the self-explication of multiattributed preferences. Decision Sciences, 19, 295-305.
48. Srinivasan, V., Jain, A. K., & Malhotra, N. K. (1983). Improving the predictive power of conjoint analysis by constrained parameter estimation. Journal of Marketing Research, 20, 433-438.
49. Srinivasan, V., & Wyner, G. A. (1989b). CASEMAP: Computer-assisted self-explication of multi-attributed preferences. In W. Henry, M. Menasco, & H. Takada (Eds.), New product development and testing (pp. 91-111). Lexington, MA: Lexington Books.
50. van der Lans, I. A., & Heiser, W. H. (1992). Constrained part-worth estimation in conjoint analysis using the self-explicated utility model. International Journal of Research in Marketing, 9, 325-344.
51. Adamowicz, W., Swait, J., Boxal, P., Louviere, J., & Williams, M. (1997). Perceptions versus objective measures of environmental quality in combined revealed and stated preference models of environmental valuation. Journal of Environmental Economics and Management, 32, 65-84.
52. Baarsma, B. (2003). The valuation of the IJmeer nature reserve using conjoint analysis. Environmental and Resource Economics, 25(3), 343-356.
53. Beenstock, M., Goldin, E., & Haitovsky, Y. (1998). Response bias in a conjoint analysis of power outages. Energy Economics, 20, 135-156.
54. Finn, A., & Louviere, J. J. (1992). Determining the appropriate response to evidence of public concern: The case of food safety. Journal of Public Policy & Marketing, 11, 2-25.
55. Gerardz, K., Shanahanz, M., & Louviere, J. J. (2003). Using stated preference discrete choice modelling to inform healthcare decision-making: A pilot study of breast screening participation. Applied Economics, 35, 1073-1085.
56. Haefele, M., & Loomis, J. B. (2001). Improving statistical efficiency and testing robustness of conjoint marginal valuations. American Journal of Agricultural Economics, 83(5), 1321-1327.
57. Hensher, D. A., Louviere, J. J., & Swait, J. (1999). Combining sources of preference data. Journal of Econometrics, 89, 197-221.
58. Kienast, P., MacLachlan, D., McAlister, L., & Sampson, D. (1983). Employing conjoint analysis in making compensation decisions. Personnel Psychology, 36(2), 301-313.
59. Maddala, T., Phillips, K. A., & Johnson, F. R. (2003). An experiment on simplifying conjoint analysis designs for measuring preferences. Health Economics, 12, 1035-1047.
60. Miguel, S. F., Ryan, M., & McIntosh, E. (2000). Applying conjoint analysis in economic evaluations: An application to menorrhagia. Applied Economics, 32, 823-833.
61. Morrison, M., Bennett, J., Blamey, R., & Louviere, J. J. (2002). Choice modeling and tests of benefit transfer. American Journal of Agricultural Economics, 84, 161-170.
62. Poortinga, W., Steg, L., Vlek, C., & Wiersma, G. (2003). Household preferences for energy saving measures: A conjoint analysis. Journal of Economic Psychology, 24(1), 49-64.
63. Roe, B., Boyle, K. J., & Teisel, M. F. (1996). Using conjoint analysis to derive estimates of compensating variation. Journal of Environmental Economics and Management, 31, 145-159.
64. Schroeder, H. W., & Louviere, J. (1999). Stated choice models for predicting the impact of user fees at public recreation sites. Journal of Leisure Research, 31(3), 300-324.
65. Skjoldborg, U. S., & Gyrd-Hansen, D. (2003). Conjoint analysis. The cost variable: An Achilles' heel? Health Economics, 12, 479-491.
66. Telser, H., & Zweifel, P. (2002). Measuring willingness to pay for risk reduction: An application of conjoint analysis. Health Economics, 11, 129-139.
67. Viney, R., Savage, E., & Louviere, J. (2005). Empirical investigation of experimental design properties of discrete choice experiments in health care. Health Economics, 14, 349-362.
68. Zinkham, F. C., & Zinkham, G. M. (1994). An application of conjoint analysis to capital budgeting: The case of innovative land management systems. Managerial Finance, 20(7), 37-50.
69. Acito, F., & Jain, A. K. (1980). Evaluation of conjoint analysis results a comparison of methods. Journal of Marketing Research, 17, 106-112.
70. Addelman, S. (1962a). Orthogonal main-effect plans for asymmetrical factorial experiments. Technometrics, 4, 21-46.
71. Addelman, S. (1962b). Symmetrical and asymmetrical factorial plans. Technometrics, 4, 47-58.
72. Akaah, I., & Korgaonkar, P. K. (1983). An empirical comparison of the predictive validity of selfexplicated, Huber-hybrid, traditional conjoint, and hybrid conjoint models. Journal of Marketing Research, 20, 187-197.
73. Cochran, W. G., & Cox, G. M. (1957). Experimental designs (2nd ed.). New York: Wiley.
74. Green, P. E. (1974). On the design of choice experiments involving multifactor alternatives. Journal of Consumer Research, 1, 61-68.
75. Green, P. E. (1984). Hybrid models for conjoint analysis: An expository view. Journal of Marketing Research, 21, 155-169.
76. Green, P. E., Carroll, J. D., & Carmone, F. J. (1978). Some new types of fractional factorial designs for marketing experiments. Research in Marketing, 1, 99-122.
77. Green, P. E., Krieger, A. M., & Agarwal, M. K. (1991). Adaptive conjoint analysis: Some caveats and suggestions. Journal of Marketing Research, 28, 215-222.
78. Gustaffson, A., Herrmann, A., & Huber, F. (Eds.). (2003). Conjoint measurement (3rd ed.). Berlin/New York: Springer.
79. Huber, J., & Klein, N. M. (1991). Adapting cutoffs to the choice environment: The effects of attribute correlation and reliability. Journal of Consumer Research, 18, 346-357.
80. Huber, J., Wittink, D. R., Fiedler, J. A., & Miller, R. (1993). The effectiveness of alternative elicitation procedures in predicting choice. Journal of Marketing Research, 30, 105-114.
81. Johnson, R. M. (1987). Adaptive conjoint analysis, Sawtooth Software conference on perceptual mapping, conjoint analysis, and computer interviewing. Ketchum, ID: Sawtooth Software.
82. Johnson, R. M. (1991). Comment on adaptive conjoint analysis: Some caveats and suggestions. Journal of Marketing Research, 28, 223-225.
83. Kuhfeld, W. F., Tobias, R. D., & Garratt, M. (1994). Efficient experimental designs with marketing research applications. Journal of Marketing Research, 31, 545-557.
84. Kuhfeld, W. F. (2003). Marketing Research Methods in SAS. May 1, 2003. SAS9.1 Edition, TS-689.
85. Lenk, P. J., De Sarbo, W. S., Green, P. E., & Young, M. R. (1996). Hierarchical Bayes conjoint analysis: Recovery of partworth heterogeneity from reduced experimental design. Marketing Science, 15(2), 173-191.
86. MacLachlan, D. L., Mulhern, M. G., & Shocker, A. D. (1988). Attribute selection and representation in conjoint analysis: Reliability and validity issues. Sawtooth Software Conference, Sun Valley, Idaho.
87. McCoullough, J., & Best, R. (1979). Conjoint measurement: Temporal stability and structural reliability. Journal of Marketing Research, 16, 26-31.
88. Mehta, R., Moore, W. L., & Pavia, T. M. (1992). An examination of the use of unacceptable levels in conjoint analysis. Journal of Consumer Research, 19, 470-476.
89. Rao, V. R., & Steckel, J. H. (1995). A cross-cultural analysis of price responses to environmental changes. Marketing Letters, 6(1), 5-14.
90. Reibstein, D., Bateson, J. E. G., & Boulding, W. (1988). Conjoint analysis reliability: Empirical findings. Marketing Science, 7, 271-286.
91. SAS Institute (1995). Statistical Analysis Software. Cary, N.C.
92. Segal, M. N. (1982). Reliability of conjoint analysis: Contrasting data collection procedures. Journal of Marketing Research, 19, 187-197.
93. Steckel, J. H., DeSarbo, W. S., & Mahajan, V. (1991). On the creation of acceptable conjoint analysis experimental designs. Decision Sciences, 22(Spring), 435-442.
94. Toubia, O., Hauser, J. R., & Dahan, E. (2003). Fast polyhederal adaptive conjoint estimation. Marketing Science, 22(3), 273-303.
95. Viswanathan, M., & Narayan, S. (1992). Processing numerical versus verbal attribute information: A study using information acquisition patterns. Marketing Letters, 3(2), 201-208.
96. Wittink, D. R., Krishnamurti, L., & Nutter, J. B. (1982). Comparing derived importance weights across attributes. Journal of Consumer Research, 8, 471-474.
97. Wittink, D. R., Krishnamurti, L., & Reibstein, D. J. (1989). The effects of differences in the number of attribute levels on conjoint results. Marketing Letters, 2, 113-123.
98. Allenby, G. M., Arora, N., & Ginter, J. L. (1995). Incorporating prior knowledge into the analysis of conjoint studies. Journal of Marketing Research, 32, 152-162.
99. Andrews, R. L., Ainslie, A., & Currim, I. S. (2002). An empirical comparison of logit choice models with discrete representations of heterogeneity. Journal of Marketing Research, 39, 479-487.
100. De Sarbo, W. S., Wedel, M., Vriens, M., & Ramaswamy, V. (1992). Latent class metric conjoint analysis. Marketing Letters, 3(3), 273-288.
101. Green, P. E. (1981). A hybrid utility estimation model for conjoint analysis. Journal of Marketing, 45, 33-41.
102. Green, P. E. (1984). Hybrid models for conjoint analysis: An expository review. Journal of Marketing Research, 21, 155-169.
103. Green, P. E., & Krieger, A. M. (1988). Choice rules and sensitivity in conjoint analysis. Journal of the Academy of Marketing Science, 6, 114-127.
104. Green, P. E., & Krieger, A. M. (1995). Attribute importance weights modification in assessing a brand's competitive potential. Marketing Science, 14, 253-270.
105. Green, P. E., & Krieger, A. M. (1996). Individualized hybrid models for conjoint analysis. Management Science, 42, 850-867.
106. Green, P. E., Krieger, A. M., & Zelnio, R. N. (1989). A componential segmentation model with optimal product design features. Decision Sciences, 20, 221-238.
107. Green, P. E., Krieger, A. M., & Agarwal, M. K. (1991). A cross-validation test for four models for quantifying multiattribute preferences. Marketing Letters, 4(4), 369-380.
108. Hagerty, M. R. (1985). Improving the predictive power of conjoint analysis: The use of factor analysis and cluster analysis. Journal of Marketing Research, 22, 168-184.
109. Hagerty, M., & Srinivasan, V. (1991). Comparing the predictive powers of alternative multiple regresation models. Psychometerlica, 56(1), 77-85.
110. Johnson, R. M. (1991). Comment on 'Adaptive conjoint analysis: Some caveats and suggestions'. Journal of Marketing Research, 28, 223-225.
111. Kamakura, W. A. (1988). A least squares procedure for benefit segmentation with conjoint experiments. Journal of Marketing Research, 25, 157-167.
112. Lenk, P. J., et al. (1996). Hierarchical Bayes conjoint analysis: Recovery of part-worth heterogeneity from reduced experimental design. Marketing Science, 15(2), 173-191.
113. Mallows, C. L. (1973). Some comments on Cp. Technometrics, 15, 661-676.
114. Moore, W. L. (1980). Levels of aggregation in conjoint analysis: An empirical comparison. Journal of Marketing Research, 27, 516-523.
115. Rao, V. R., & Sattler, H. (2000). Measurement of allocative and informational effects of price. In A. Gustafsson, A. Herrmann, & F. Huber (Eds.), Conjoint measurement. New York: Springer.
116. Rossi, P. E., Allenby, G. M., & McCullough, R. (2008). Bayesian statistics and marketing. New York: Wiley.
117. Srinivasan, V., & Shocker, A. D. (1973). Linear programming techniques for multidimensional analysis of preferences. Psychometrika, 38, 337-369.
118. Toubia, O., Semester, D. I., Hauser, J. R., & Dahan, E. (2003). Fast polyhedral adaptive conjoint estimation. Marketing Science, 22(3), 273-303.
119. Toubia, O., John Hauser, J. R., & Garcia, R. (2007). Probabilistic polyhedral methods for adaptive choice-based conjoint analysis: Theory and application. Marketing Science, 26(5) (September-October), 596-610.
120. Vriens, M., Wedel, M., & Wilms, T. (1996). Metric conjoint segmentation methods. Journal of Marketing Research, 33, 73-85.
121. Allenby, G. M., Arora, N., & Ginter, J. L. (1998). On the heterogeneity of demand. Journal of Marketing Research, 35(August), 384-389.
122. Allenby, G. M., & Lenk, P. J. (1994). Modeling household purchase behavior with logistic regression. Journal of the American Statistical Association, 89(December), 1218-1231.
123. Arora, N., & Huber, J. (2001). Improving parameter estimates and model prediction by aggregate customization in choice experiments. Journal of Consumer Research, 28, 273-283.
124. Batsell, R. R., & Louviere, J. (1991). Experimental analysis of choice. Marketing Letters, 2(3), 199-214.
125. Becker, G. M., De Groot, M. H., & Marschak, J. (1964). Measuring utility by a single-response sequential method. Behavioral Science, 9(July), 226-232.
126. Ben-Akiva, M., & Lerman, S. R. (1991). Discrete choice analysis. Cambridge, MA: The MIT Press.
127. Bradlow, E. T., & Rao, V. R. (2000). A hierarchical model for assortment choice. Journal of Marketing Research, 37(May), 259-264.
128. Bradlow, E. T., Hu, Y., & Ho, T.-H. (2004). A learning-based model for imputing missing levels in partial conjoint profiles. Journal of Marketing Research, 41(November), 369-381.
129. k choice experiments. Communications in Statistics: Theory and Methods, 32(11), 2185-2206.
130. Burgess, L., & Street, D. J. (2005). Optimal designs for choice experiments with asymmetric attributes. Journal of Statistical Planning and Inference, 134, 288-301.
131. Chakraborty, G., Woodworth, G., & Gaeth, G. J. (1991). Screening for interactions between design factors and demographics in choice-based conjoint. Journal of Business Research, 23, 219-237.
132. Chung, J., & Rao, V. R. (2003). A general choice model for bundles with multiple-category products: Application to market segmentation and optimal pricing for bundles. Journal of Marketing Research, 40(May), 115-130.
133. Ding, M. (2007). An incentive-aligned mechanism for conjoint analysis. Journal of Marketing Research, 44(May), 214-223.
134. Ding, M., Agarwal, R., & Liechty, J. (2005). Incentive-aligned conjoint analysis. Journal of Marketing Research, 42(February), 67-82.
135. Elrod, T., Louviere, J. J., & Davey, K. S. (1992). An empirical comparison of ratings-based and choice-based conjoint models. Journal of Marketing Research, 29(August), 368-377.
136. Erdem, T., & Swait, J. (1998). Brand equity as a signaling phenomenon. Journal of Consumer Psychology, 7(2), 131-157.
137. Fiebig, D., Keane, M. P., Louviere, J., & Wasi, N. (2010). The generalized multinomial logit model: Accounting for scale and coefficient heterogeneity. Marketing Science, 29(3 (May-June)), 393-421.
138. Greene, W. H. (2012). Econometric analysis (7th ed.). Boston: Prentice-Hall.
139. Hahn, G.J., & Shapiro, S.S. (1966). A catalog and computer program for the design and analysis of orthogonal and asymmetric fractional factorial experiments. No. 66- C- 165. Schenectady, NY: Research and Development Center.
140. Huber, J., & Train, K. (2001). On the similarity of classical and Bayesian estimates of individual mean partworths. Marketing Letters, 12(3), 259-269.
141. Huber, J., & Zwerina, K. (1996). The importance of utility balance in efficient choice designs. Journal of Marketing Research, 33(August), 307-17.
142. Khuri, A. I., Mukherjee, B., Sinha, B. K., & Ghose, M. (2006). Design issues for generalized linear models: A review. Statistical Science, 21(3), 3760399.
143. Krieger, A., & Green, P. E. (1991). Designing pareto optimal stimuli for multiattribute choice experiments. Marketing Letters, 2(4), 337-348.
144. Kuhfeld, W. F. (2000). Experimental design, efficiency, coding, and choice designs. Chapter (TS- 722C). http://support.sas.com/techsup/tnote-stat.html#market.
145. Kuhfeld, W. F., Tobias, R. D., & Garratt, M. (1994). Efficient experimental designs with marketing research applications. Journal of Marketing Research, 31, 545-557.
146. Lazari, A. G., & Anderson, D. A. (1994). Designs of discrete choice set experiments for estimating both attribute and availability cross effects. Journal of Marketing Research, 31(August), 375-383.
147. Louviere, J. J. (1984). Using discrete choice experiments and multinomial logit choice models to forecast trial in a competitive retail environment in a differentiated market with price competition. Journal of Retailing, 60(4), 81-107.
148. Louviere, J. J. (1988). Modeling individual decisions: Metric conjoint analysis, theory, methods and applications (Sage University Press series). Newbury Park: Sage.
149. Louviere, J. J. (1991). Experimental choice analysis: Introduction and overview. Journal of Business Research, 23, 291-297.
150. Louviere, J. J., & Woodworth, G. (1983). Design and analysis of simulated choice or allocated experiments: An approach based on aggregated data. Journal of Marketing Research, 20(November), 350-467.
151. Louviere, J. J., Hensher, D. A., & Swait, J. D. (2001). Stated choice models: Analysis and application. Cambridge, UK: Cambridge University Press.
152. Maddala, G. S. (1983). Limited-dependent and qualitative variables in econometrics. Cambridge, MA: Cambridge University Press.
153. McFadden, D. (1986). The choice theory approach to market research. Marketing Science, 5(4), 275-297.
154. Meyer, R., & Johnson, E. J. (1995). Empirical generalizations in the modeling of consumer choice. Marketing Science, 14(3), G180-G189. Part 2.
155. Orme, B. (1999). CBC user manual, Version 2.0. Sequim, WA: Sawtooth Software.
156. Sandor, Z., & Wedel, M. (2001). Designing conjoint choice experiments using managers' prior beliefs. Journal of Marketing Research, 38(November), 430-444.
157. SPSS (1997 and 2012). http://www-01.ibm.com/software/analysis/spss/.
158. Street, D. J., & Burgess, L. (2007). The construction of optimal stated choice experiments: Theory and methods. Hoboken, NJ: Wiley.
159. Toubia, O., & Hauser, J. R. (2007). On managerial efficiency for experimental designs. Marketing Science, 26(6), 851-858.
160. Toubia, O., Hauser, J. R., & Simester, D. (2004). Polyhedral methods for adaptive choice-based conjoint analysis. Journal of Marketing Research, 41(February), 116-131.
161. Toubia, O., Hauser, J. R., & Garcia, R. (2007). Probabilistic polyhedral methods for adaptive choice-based conjoint analysis: Theory and application. Marketing Science, 26(5 (September- October)), 596-610.
162. Wertenbroch, K., & Skiera, B. (2002). Measuring consuemrs' willingness to pay at the point of purchase. Journal of Marketing Research, 39(May), 228-241.
163. Agarwal, M. K., & Green, P. E. (1991). Adaptive conjoint analysis versus self-explicated models: Some empirical results. International Journal of Research in Marketing, 8, 141-146.
164. Akaah, I., & Korgaonkar, P. K. (1983). An empirical comparison of the predictive validity of self-explicated, huber-hybrid, traditional conjoint, and hybrid conjoint models. Journal of Marketing Research, 20(May), 187-197.
165. Alba, J. W., & Cooke, A. D. J. (2004). When absence begets inference in conjoint analysis. Journal of Marketing Research, 41(November), 382-387.
166. Allenby, G. M., Arora, N., & Ginter, J. L. (1998). On the heterogeneity of demand. Journal of Marketing Research, 35(August), 384-389.
167. Baalbaki, I. B., & Malhotra, N. K. (1995). Standardization versus customization in international marketing: An investigation using bridging conjoint analysis. Journal of the Academy of Marketing Science, 23(3), 182-194.
168. Becker, G. M., De Groot, M. H., & Marschak, J. (1964). Measuring utility by a single-response sequential method. Behavioral Science, 9(July), 226-232.
169. Bradlow, E. T., Hu, Y., & Ho, T.-H. (2004). A learning-based model for imputing missing levels in partial conjoint profiles. Journal of Marketing Research, 41(November), 369-381. 392-396.
170. Clatworthy, W. H. (1955). Partially balanced incomplete designs with two associate classes and two treatments per block. Journal of Research of the National Bureau of Standards, 54(April), 177-190.
171. Cochran, W. W., & Cox, G. M. (1955). Experimental designs. New York: Wiley.
172. Cui, D., & Curry, D. (2005). Prediction in marketing using the support vector machine. Marketing Science, 24(Fall), 595-615.
173. Ding, M. (2007). An incentive-aligned mechanism for conjoint analysis. Journal of Marketing Research, 44(May), 214-223.
174. Ding, M., Agarwal, R., & Liechty, J. (2005). Incentive-aligned conjoint analysis. Journal of Marketing Research, 42(February), 67-82.
175. Evgeniou, T., Boussios, C., & Zacharia, G. (2005). Generalized robust conjoint estimation. Marketing Science, 24(3), 415-429.
176. Finkbeiner, C., & Platz, P. J. (1986 October). Computerized versus paper and pencil methods: A comparison study. Paper presented at the association for consumer research conference, Toronto.
177. Fisher, R. A. (1942). The theory of confounding in factorial experiments in relation to theory of groups. Annals of Eugenics, 11, 341-353.
178. Ghose, S., & Rao, V. R. (2007). A choice model of bundles features and meta-attributes: An application to product design. Working paper. Cornell University.
179. Green, P. E. (1981). A hybrid utility estimation model for conjoint analysis. Journal of Marketing, 45(Winter), 33-41.
180. Green, P. E. (1984). Hybrid models for conjoint analysis: An expository view. Journal of Marketing Research, 21(May), 155-169.
181. Green, P. E., & Srinivasan, V. S. (1978). Conjoint analysis in consumer research: Issues and outlook. Journal of Consumer Research, 5(September), 103-123.
182. Green, P. E., & Srinivasan, V. S. (1990). Conjoint analysis in marketing: New developments with implications for research and practice. Journal of Marketing, 54(4), 3-19.
183. Green, P. E., Goldberg, S. M., & Montemayor, M. (1981). A hybrid utility estimation nodel for conjoint analysis. Journal of Marketing, 45(Winter), 33-41.
184. Green, P. E., Krieger, A. M., & Agarwal, M. K. (1991). Adaptive conjoint analysis: Some caveats and suggestions. Journal of Marketing Research, 28(May), 215-222.
185. Hauser, J. R., & Rao, V. R. (2004). Conjoint analysis, related modeling, and applications. In J. Wind (Ed.), Advances in marketing research: Progress and prospects. Boston, MA: Kluwer Academic.
186. Huber, J. C., Wittink, D. R., Fiedler, J. A., & Miller, R. L. (1993). The effectiveness of alternative preference elicitation procedures in predicting choice. Journal of Marketing Research, 30(February), 105-114.
187. Johnson, R. M. (1987). Adaptive conjoint analysis. In Sawtooth software conference on perceptual mapping, conjoint analysis, and computer interviewing (pp. 253-265). Ketchum: Sawtooth Software.
188. Johnson, R. M. (1991). Comment on 'adaptive conjoint analysis: Some caveats and suggestions'. Journal of Marketing Research, 28(February), 223-225.
189. Leigh, T. W., MacKay, D. B., & Summers, J. O. (1984). Reliability and validity of conjoint analysis and self-explicated weights: A comparison. Journal of Marketing Research, 21(November), 456-462.
190. Louviere, J. J. (1984). Hierarchical information integration: A new method for the design and analysis of complex multiattribute judgment problems. Advances in Consumer Research, 11, 148-155.
191. Louviere, J. J., & Gaeth, G. J. (1987). Decomposing the determinants of retail facility choice using the method of hierarchical information integration: A supermarket illustration. Journal of Retailing, 63, 25-48.
192. McFadden, D. (1973). Conditional logit analysis of qualitative choice behavior. In P. Zarembka (Ed.), Frontiers in econometrics. New York: Academic.
193. Moore, W. L., & Semenik, R. J. (1988). Measuring preference with hybrid conjoint analysis: The impact of a different number of attributes in the master design. Journal of Business Research, 16(3), 261-274.
194. Netzer, O., & Srinivasan, V. (2011). Adaptive self-explication of multi-attribute preferences. Journal of Marketing Research, 48 (February), 140-156.
195. Oppewal, H., Louviere, J., & Timmermans, H. J. P. (1994). Modeling hierarchical conjoint process with integrated choice experiments. Journal of Marketing Research, 31(February), 92-105.
196. Park, Y.-H., Ding, M., & Rao, V. R. (2008). Eliciting preference for complex products: A webbased upgrading method. Journal of Marketing Research, 45(October), 562-574.
197. Rao, V. R. (2004). Comments on conjoint analysis with partial profiles. Journal of Marketing Research, 41(November), 388-389.
198. Rao, V. R. (2008). Developments in conjoint analysis. In B. Wierenga (Ed.), Handbook of marketing decision models. New York: Springer.
199. Rao, V. R., Kartono, B., & Su, M. (2008). Methods for handling massive number of attributes in conjoint analysis. Review of Marketing Research, 5, 104-129.
200. Srinivasan, V. (1988). A Conjunctive-compensatory approach to the self-explication of multiattributed preferences. Decision Sciences, 19(2), 295-305.
201. Srinivasan, V., & Park, C. S. (1997). Surprising robustness of self-explicated approach to customer preference structure measurement. Journal of Marketing Research, 34(2), 286-291.
202. Toubia, O., Hauser, J. R., & Simester, D. I. (2004). Polyhedral methods for adaptive choice-based conjoint analysis. Journal of Marketing Research, 41, 116-131.
203. Wilkie, W. L., & Pessemier, E. A. (1973). Issues in marketing's use of multiattribute attitude models. Journal of Marketing Research, 10(November), 428-471.
204. Wind, J., Green, P. E., Shifflet, D., & Scarbrough, M. (1989). Courtyard by Marriott: Designing a hotel facility with consumer-based marketing. Interfaces, 19(January-February), 25-47.
205. Winer, B. J. (1973). Statistical principles in experimental design (2nd ed.). New York:McGraw-Hill.
206. Balakrishnan, P.V. (Sundar). (2009). PRODLINE: Pedagogical version software. Download from: https://catalysttools.washington.edu/webq/servey/sundar/45656.
207. Balakrishnan, P. V. (Sundar), & Varghese, S. J. (1996). Genetic algorithms for product design. Management Science, 42(8), 1105-1117.
208. Balakrishnan, P. V., & Roos, J. M. T. (2008). Case: Television 4'US optimal product line designs. http://faculty.washington.edu/sundar/PRODLINE-RELEASE.
209. Balakrishnan, P. V., Desai, A., & Storbeck, J. E. (1994). Efficiency evaluation of retail outlets networks. Environmental Planning B: Planning Design, 21, 477-488.
210. Balakrishnan, P. V. (Sundar), Rakesh, G., & Varghese, S. J. (2004). Development of hybrid genetic algorithms for product line designs. IEEE Transactions on Systems, Management, and Cybernetics - Part B: Cybernetics, 34(1), 468-483.
211. Balakrishnan, P. V. (Sundar), Varghese, S. J., & Hao, X. (2009). PRODLINE: Architecture of an artificial intelligence based marketing decision support system for PRODuct LINE designs. Working paper.
212. Belloni, A., Freund, R., Selove, M., & Simester, D. (2008). Optimizing product line designs: Efficient methods and comparisons. Management Science, 54(9), 1544-1552.
213. Cunningham, M. A., Gaeth, G. J., Chifei, J., & Goutam, C. (1999). Using choice-based conjoin to determine the relative importance of dental benefit plan aatributes. Journal of Dental Education, 63(5), 391-399.
214. Dobson, G., & Khalish, S. (1993). Heuristics for pricing and positioning a product line using conjoint and cost data. Management Science, 39(February), 160-175.
215. Dufhues, T., Heidhues, F., & Buchenrieder, G. (2004). Participatory product design by using conjoint analysis in the rural financial market of Northern Vietnam. Asian Economic Journal, 18(1), 81-114.
216. Green, P. E., & Krieger, A. M. (1985). Models and heuristics for product line selection. Marketing Science, 4(Winter), 1-19.
217. Green, P. E., & Krieger, A. M. (1987a). On the generation of 'Good' product candidates for product line optimization. In R. L. Schultz (Ed.), Applications of management science. Greenwich: JAI Press.
218. Green, P. E., & Krieger, A. M. (1987b). A consumer-based approach to designing product line extensions. Journal of Product Innovation Management, 4, 21-32.
219. Green, P. E., & Krieger, A. M. (1992). An application of a product positioning model to pharmaceutical products. Marketing Science, 11(2), 117-132.
220. Green, P. E., & Krieger, A. M. (1993). Conjoint analysis with product positioning applications. In J. Eliashberg & G. L. Lilien (Eds.), Handbook of OR & MS (Vol. 5, pp. 467-516). Amsterdam: Marketing.
221. Green, P. E., Carroll, J. D., & Goldberg, S. M. (1981). A general approach to product design optimization via conjoint analysis. Journal of Marketing, 45(Summer), 17-37.
222. Green, P. E., Krieger, A., & Vavra, T. (1997). Evaluating new products conjoint analysis offers a level of detail that few concept testing methods can match. Marketing Research, 9(Winter), 12-21.
223. Gupta, S., & Kohli, R. (1990). Designing products and services for consumer welfare using conjoint analysis: Theoretical and empirical issues. Marketing Science, 9(3), 230-246.
224. John, H., & Holland, J. H. (1975). Adaptation in natural and artificial systems. Ann Arbor: University of Michigan Press.
225. Katzenstein, H., Kavil, S., Mummalaneni, V., & Dubas, K. (1994). Design of an ideal direct marketing course from the students' perspective. Journal of Direct Marketing, 4(Spring), 66-72.
226. Kaul, A., & Rao, V. R. (1995). Research for product positioning and design decisions: An academic review. International Journal of Reserach in Marketing, 12, 293-320.
227. Luo, L., Kannan, P. K., & Ratchford, B. T. (2007). New product development under channel acceptance. Marketing Science, 26(2, March-April), 149-163.
228. Michalek, J., Feinberg, F., & Papalambros, P. Y. (2005). Linking marketing and engineering product design decisions via analytical target cascading. Journal of Product Innovation Management, 22(1), 42-62.
229. Michalek, J., Feinberg, F., Adiguzel, F., Ebbes, P., & Papalambros, P. Y. (2011). Enhancing marketing with engineering: Optimal product line design for heterogeneous markets. International Journal of Research in Marketing, 28, 1-12.
230. Nair, S. K., Thakur, L. S., & Wen, K. (1995). Near optimal solutions for product line design and selection: Beam search heuristics. Management Science, 41, 767-785.
231. Page, A. L., & Rosenbaum, H. F. (1987). Redesigning product lines with conjoint analysis: How Sunbeam does it. Journal of Product Innovation Management, 4, 120-137.
232. Page, A. L., & Rosenbaum, H. F. (1989). Redesigning product lines with conjoint analysis: How Sunbeam does it. Journal of Product Innovation Management, 4, 120-137.
233. Rao, V. R., & Steckel, J. H. (1998). Analysis for strategic marketing. Reading: Addison-Wesley.
234. Rao, V. R., & Winter, F. (1978). An application of the multivariate probit model to market segmentation and product design. Journal of Marketing Research, 15(August), 361-368.
235. Urban, G. L., & Hauser, J. (1993). Design and marketing of new products (2nd ed.). Englewood Cliffs: Prentice-Hall.
236. Wind, J., Green, P. E., Shifflet, D., & Scarbrough, M. (1989). Courtyard by Marriott: Designing a hotel facility with consumer-based marketing. Interfaces, 19(January-February), 25-47.
237. Wittink, D. R. (1989). Redesigning product lines with conjoint analysis: A comment. Journal of Product Innovation Management, 6, 289-292.
238. Wittink, D. R., Lakshman, K., & Nutter, J. B. (1982). Comparing derived importance weights across attributes. Journal of Consumer Research, 8, 471-474.
239. Yoo, D., & Ohta, H. (1994). Optimal product-planning for new multiattribute products based on conjoint analysis. Computers and Industrial Engineering, 27(1-4), 11-14.
240. De Sarbo, W. S., Ramaswamy, V., & Cohen, S. H. (1995). Market segmentation with choice-based conjoint study. Marketing Letters, 6(2), 137-147.
241. Green, P. E., & Krieger, A. M. (1991a). Product design strategies for target-market positioning. Journal of Product Innovation Management, 8, 189-202.
242. Green, P. E., & Krieger, A. M. (1991b). Segmenting markets with conjoint analysis. Journal of Marketing, 55(October), 20-31.
243. Hagerty, M. R. (1985). Improving the predictive power of conjoint analysis: The use of factor analysis and cluster analysis. Journal of Marketing Research, 22(May), 168-184.
244. Kaul, A., & Rao, V. R. (1995). Research for product positioning and design decisions: An academic review. International Journal of Research in Marketing, 12, 293-320.
245. Kotler, P., & Keller, K. L. (2012). Marketing management (14th ed.). Upper saddle River: Prentice-Hall.
246. Page, A. L., & Rosenbaum, H. F. (1987). Redesigning product lines with conjoint analysis: How sunbeam does it. Journal of Product Innovation Management, 4, 120-137.
247. Page, A. L., & Rosenbaum, H. F. (1989). Redesigning product lines with conjoint analysis: How sunbeam does it. Journal of Product Innovation Management, 4, 120-137.
248. Rao, V. R., & Winter, F. W. (1978). An Application of multivariate probit model to market segmentation and product design. Journal of Marketing Research, 15, 361-368.
249. Rao, V. R., & Steckel, J. H. (1998). Analysis for strategic marketing. Reading: Addison-Wesley.
250. Vriens, M., Wedel, M., & Wilms, T. (1996). Metric conjoint segmentation methods: A monte carlo comparison. Journal of Marketing Research, 33(February), 73-85.
251. Wedel, M., & Kistemaker, C. (1989). Consumer benefit segmentation using clusterwise linear regression. International Journal of Research in Marketing, 6(1), 45-59.
252. Wedel, M., & Steenkamp, J. E. B. M. (1989). A fuzzy clusterwise regression approach to benefit segmentation. International Journal of Research in Marketing, 6(4), 241-258.
253. Wedel, M., & Kamakura, W. A. (2000). Market segmentation: Conceptual and methodological foundations (2nd ed.). Norwell: Kluwer Academic Publishers.
254. Datoo, B. (1994). Measuring price elasticity. Marketing Research, 6(2), 30-34.
255. Erickson, G. M., & Johansson, J. K. (1985). The role of price in multi-attribute product evaluations. Journal of Consumer Research, 17, 195-199.
256. Gautschi, D. A., & Rao, V. R. (1990). A methodology for specification and aggregation in product concept testing. In A. DE Fontenay, M. H. Shugard, & D. S. Sibley (Eds.), Telecommunications demand modeling (pp. 37-63). Amsterdam/New York: North-Holland.
257. Goldberg, S. M., Green, P. E., & Wind, Y. (1984). Conjoint analysis of price premiums for hotel amenities. Journal of Business, 57(1, pt. 2), S111-S132.
258. Green, P. E., & Savitz, J. (1994). Applying conjoint analysis to product assortment and pricing in retailing research. Pricing Strategy and Practice, 2(3), 4-19.
259. Iyengar, R., & Jedidi, K. (2012). A conjoint model for quantity discounts. Marketing Science, 31 (2), 334-350.
260. Iyengar, R., Jedidi, K., & Kohli, R. (2008). A conjoint approach to multipart pricing. Marketing Science, 28(4), 620-636.
261. Kalish, S., & Nelson, P. (1991). A comparison of ranking, rating and reservation price measurement in conjoint analysis. Marketing Letters, 2(4), 327-325.
262. Kannan, P. K., Pope, B. K., & Jain, S. (2009). Pricing digital content product lines: A model and application to the national academies press. Marketing Science, 28(4), 620-636.
263. Kohli, R., & Mahajan, V. (1991). A reservation-price model for optimal pricing of multiattribute products in conjoint analysis. Journal of Marketing Research, 28, 347-354.
264. Little, J. D. C. (1970). Models and managers: The concept of decision calulus. Management Science, 16(April), B446-B485.
265. Mahajan, V., Green, P. E., & Goldberg, S. M. (1982). A conjoint model for measuring self- And cross- price/demand relationships. Journal of Marketing Research, 19(August), 334-342.
266. Mohn, N. C. (1995). Pricing research for decision making. Marketing Research, 7(1), 11-19.
267. Rao, V. R., & Sattler, H. (2000). Measurement of allocative and informational effects of price. In A. Gustafsson, A. Herrmann, & F. Huber (Eds.), Conjoint measurement. Berlin: Springer.
268. Rao, V. R., & Steckel, J. H. (1995). A cross-cultural analysis of price responses to environmental changes. Marketing Letters, 6(1), 5-14.
269. Simon, H. (1969). Price management. Amsterdam: North-Holland Elsevier.
270. Simon, H. (1992). Pricing opportunities-And how to exploit them. Sloan Management Review, 33(Winter), 55-65.
271. Tellis, G. J. (1988). The price-elasticity of selective demand: A meta-analysis of econometric models of sales. Journal of Marketing Research, 25, 331-341.
272. Völckner, F., & Sattler, H. (2005). Separating negative and positive effects of price with choicebased conjoint analysis. Marketing- JRM, 1, 5-13.
273. Wang, T., Venkatesh, R., & Chatteyre, R. (2007). Reservation price as a range: An incentive compatible measurement approach. Journal of Marketing Research, 44, 200-213.
274. Agarwal, A. (2007). The impact of front-end usability guidelines on consumer evaluation of e-commerce web pages. M.S. Thesis, Cornell University, Ithaca.
275. Agarwal, M. K., & Rao, V. R. (1996). An empirical comparison of consumer-based measures of brand equity. Marketing Letters, 7(3), 237-247.
276. Choi, S. C., & De Sarbo, W. S. (1993). Game theoretic derivations of competitive strategies in conjoint analysis. Marketing Letters, 4(4), 337-348.
277. Darmon, R. (1979). Setting sales quotas with conjoint analysis. Journal of Marketing Research, 16, 133-140.
278. Durvasula, S., Sharma, S., & Andrews, J. C. (1992). STORELOC: A retail store location model based on managerial judgments. Journal of Retailing, 68(Winter), 420-444.
279. Foutz, Y. N. Z., Rao, V. R., & Yang, S. (2002). Incorporating reference effects into conjoint choice models. Working paper, Cornell University.
280. Ghosh, A., & Craig, S. (1991). FRANSYS: A franchise distribution system location model. Journal of Retailing, 67(4), 466-495.
281. Green, P. E., & Krieger, A. M. (1995). User's guide to VOICE for WindowsTM: An analytical and predictive model for customer satisfaction data. The Wharton School, University of Pennsylvania.
282. Green, P. E., & Krieger, A. (1997). Using conjoint analysis to view competitive interaction through the customer's eyes. In G. S. Day, D. J. Reibstein, & R. Gunther (Eds.), Wharton on dynamic competitive strategy (pp. 343-367). New York: Wiley.
283. Levy, M., Webster, J., & Kerin, R. (1983). Formulating push marketing strategies: A method and application. Journal of Marketing, 47(Winter), 25-34.
284. Ofek, E., & Srinivasan, V. (2002). How much does the market value an improvement in a product attribute? Marketing Science, 21(4), 398-411.
285. Priem, R. L. (1992). An application of metric conjoint analysis for the evaluation of top managers' individual strategic decision making processes: A research note. Strategic Management Journal, 13, 143-151.
286. Agarwal, J., Malhotra, N. K., DeSarbo, W. S., & Rao, V. R. (2013). A contemporary review of the research in conjoint analysis: Recent developments and potential directions for future research. Working paper.
287. Ben-Akiva, M., Morikawa, T., Benjamin, J., Novak, T., Oppewal, H., & Rao, V. (1994). Combining revealed and stated preference data. Marketing Letters, 5(4), 335-350.
288. Bettman, J. R., Luce, M. F., & Payne, J. W. (1998). Constructive consumer choice processes. Journal of Consumer Research, 25(3), 187-217.
289. Bettman, J. R., Luce, M. F., & Payne, J. W. (2008). Preference construction and preference stability: Putting the pillow to rest. Journal of Consumer Psychology, 18(3), 170-174.
290. Bradlow, E. T. (2005). Current issues and a Wish List for conjoint analysis. Journal Applied Stochastic Models in Business and Industry, 21, 319-323.
291. Chung, J., & Rao, V. R. (2003). A general choice model for bundles with multiple category products: Application to market segmentation and optimal pricing for bundles. Journal of Marketing Research, 40(2), 115-130.
292. Cooke, A. D. J., Janiszewski, C., Cunha, M. Jr., Nasco, S. A., & De Wilde, E. (2004). Stimulus context and the formation of consumer ideals. Journal of Consumer Research, 31, 112-124.
293. Deng, X., & Hutchinson, J. W. (2010). Aesthetic self-design: Just do it yourself. Working paper, Wharton School, University of Pennsylvania, Philadelphia.
294. Ding, M., Park, Y.-H., & Bradlow, E. T. (2009). Barter markets for conjoint analysis. Management Science, 55(6), 1003-1017.
295. Farquhar, P. H., & Rao, V. R. (1976). A balance model for evaluating subsets of multiattributed items. Management Science, 22, 528-539.
296. Finn, A., & Louviere, J. J. (1993). Determining the appropriate response to evidence of public concerns: The case of food safety. Journal of Public Policy and Marketing, 11(1), 12-25.
297. Franke, N., & Schreier, M. (2010). Why customers value self-designed products: The importance of process effort and enjoyment. Journal of Product Innovation Management, 27, 1020-1031.
298. Gilbride, T. J., & Allenby, G. M. (2004). A choice model with conjunctive, disjunctive, and compensatory screening rules. Marketing Science, 23(2), 391-406.
299. Hauser, J. R., & Rao, V. R. (2004). Conjoint analysis, related modeling, and applications. In Y. Wind & P. E. Green (Eds.), Marketing research and modeling: Progress and prospects: A tribute to Paul E. Green. Norwell: Kluwer Academic.
300. Hauser, J. R., & Toubia, O. (2005). The impact of utility balance and endogeneity in conjoint analysis. Marketing Science, 24(3), 498-507.
301. Jedidi, K., & Kohli, R. (2005). Probabilistic subset - conjunctive models for heterogeneous consumers. Journal of Marketing Research, 42(4), 483-494.
302. Kivetz, R., Netzer, O., & Srinivasan, V. (2004). Alternative models for capturing the compromise effect. Journal of Marketing Research, 41, 237-257.
303. Kohli, R., & Jedidi, K. (2007). Representation and inference of lexicographic preference models and their variants. Marketing Science, 26(3), 380-399.
304. Liechty, J. C., Ramaswamy, V., & Cohen, S. H. (2001). Choice menus for mass customization: An experimental approach for analyzing customer demand with an application to a web-based information service. Journal of Marketing Research, 38(2), 183-196.
305. Liechty, J. C., Fong, D. K. H., & De Sarbo, W. (2005). Dynamic models incorporating individual heterogeneity: Utility evolution in conjoint analysis. Marketing Science, 24(2), 285-293.
306. Liu, Q., Otter, T., & Allenby, G. M. (2007). Investigating endogeneity bias in conjoint models. Marketing Science, 26(5), 642-650.
307. Louviere, J. J., & Islam, T. (2008). A comparison of importance weights and willingness-to-pay measures derived from choice-based conjoint, constant sum scales and best-worst scaling. Journal of Business Research, 61, 903-911.
308. Marley, A. A. J., & Louviere, J. J. (2005). Some probabilistic models of best, worst, and best-worst choices. Journal of Mathematical Psychology, 49, 464-480.
309. Miller, K. M., Hofstetter, R., Krohmer, H., & John Zhang, Z. (2011). How should consumers' willingness to pay be measured? An empirical comparison of the state-of-the art approaches. Journal of Marketing Research, 48, 172-184.
310. Morikawa, T., Akiva, M.-B., & Yamada, K. (1991). Forecasting intercity rail ridership using revealed preference and stated preference data. Transportation Research Record, 1328, 30-35.
311. Narayan, V., Rao, V. R., & Saunders, C. (2011). How peer influence affects attribute preferences: A bayesian updating mechanism. Marketing Science, 30(2), 368-384.
312. Payne, J. W., Bettman, J. R., & Johnson, E. J. (1993). The adaptive decision maker. Cambridge: Cambridge University Press.
313. Raghavarao, D., & Wiley, J. B. (2009). Conjoint measurement with constraints on attribute levels: A mixture-amount model approach. International Statistical Review, 77(2), 167-178.
314. Raghavarao, D., Wiley, J. B., & Chitturi, P. (2011). Choice-based conjoint models and designs. Boca Raton: Chapman & Hall/CRC.
315. Rao, V. R. (2008). Developments in conjoint analysis. In B. Wierenga (Ed.), Handbook of marketing decisions models. New York/London: Springer.
316. Simonson, I. (2005). Determinants of customers' responses to customized offers: Conceptual framework and research propositions. Journal of Marketing, 69, 32-45.
317. Simonson, I. (2008a). Will I like a medium pillow? Another look at constructed and inherent preferences. Journal of Consumer Psychology, 18, 155-169.
318. Simonson, I. (2008b). Regarding inherent preferences. Journal of Consumer Psychology, 18, 191-196.
319. Srinivasan, V., & Park, C. S. (1997). Surprising robustness of the self-explicated approach to customer preference structure measurement. Journal of Marketing Research, 34, 286-291.
320. Torgersen, W. S. (1958). Theory and methods of scaling. New York: Wiley.
321. Toubia, O., de Jong, M. G., Stieger, D., & Füller, J. (2012). Measuring consumer preferences using conjoint poker. Marketing Science, 31, 138-156.
322. Yee, M., Dahan, E., Hauser, J. R., & Orlin, J. (2007). Greedoid-based noncompensatory inference. Marketing Science, 26(4), 532-549.
323. Adamowicz, W., Bunch, D., Cameron, T.-A., Dellaert, B. G. C., Hanneman, M., Keane, M., et al. (2008). Behavioral frontiers in choice modeling. Marketing Letters. doi:10.1007/s11002-008-9038-11.
324. Addelman, S. (1962). Symmetrical and asymmetrical fractional factorial plans. Technometrics, 4, 47-58.
325. Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 15(6), 734-749.
326. Amaldoss, W., Ho, T.-H., Krishna, A., Chen, K.-Y., Desai, P., Iyer, G., et al. (2008). Experiments on strategic choices and markets. Marketing Letters. doi:10.1007/s11002-008-9040-77.
327. Ansari, A., Essegaier, S., & Kohli, R. (2000). Internet recommendation systems. Journal of Marketing Research, 37, 363-375.
328. Arora, N., & Allenby, G. (1999). Measuring the influence of individual preference structures in group decision making. Journal of Marketing Research, 36(4), 476-487.
329. Arora, N., Ghose, A., Hess, J. D., Iyengar, R., Jing, B., Joshi, Y., et al. (2008). Putting one-to-one marketing to work: Personalization, customization and choice. Marketing Letters, 19, 305-321.
330. Ben-Akiva, M., Bradley, M., Morikawa, T., Benjamin, J., Novak, T. P., Thomas, P., et al. (1994). Combining revealed and stated preference data. Marketing Letters, 5(4), 335-350.
331. Ben-Akiva, M., McFadden, D., Train, K., Walker, J., Bhat, C., Bierlaire, M., et al. (2002). Hybrid choice models, progress and challenges. Marketing Letters, 13(3), 163-175.
332. Blattberg, R. C., & George, E. I. (1992). Estimation under profit-driven loss functions. Journal of Business and Economic Statistics, 10, 437-444.
333. Bleichrodt, H., & Pinto, J. L. (2000). A parameter-free elicitation of the probability weighting function in medical decision analysis. Management Science, 46(11), 1485-1496.
334. Bradlow, E. T. (2005). Current issues and a wish-list for conjoint analysis. Applied Stochastic Models in Business and Industry, 4-5, 319-323.
335. Bradlow, E. T., Hu, Y., & Ho, T.-H. (2004). A learning-based model for imputing missing levels in partial conjoint profiles. Journal of Marketing Research, 41(4), 369-381.
336. Cattin, P., & Wittink, D. R. (1982). Commercial use of conjoint analysis: A survey. Journal of Marketing, 46(3), 44-53.
337. Chaloner, K., & Verdinelli, I. (1995). Bayesian experimental design: A review. Statistical Science, 10(3), 273-304.
338. Cui, D., & Curry, D. (2005). Prediction in marketing using the support vector machine. Marketing Science, 24(4), 595-615.
339. Dahan, E. (2007). Conjoint adaptive ranking database system. Working paper, University of California, Los Angeles.
340. Dahan, E., & Hauser, J. R. (2002). The virtual customer. Journal of Product Innovation Management, 19, 332-353.
341. Dahan, E., & Srinivasan, V. (2000). The predictive power of internet-based product concept testing using visual depiction and animation. Journal of Product Innovation Management, 17, 99-109.
342. Dahan, E., Lo, A., Poggio, T., Chan, N., & Kim, A. (2007a). Securities trading of concepts (STOC). Working paper, University of California, Los Angeles.
343. Dahan, E., Soukhoroukova, A., & Spann, M. (2007b). Preference markets: Organizing securities markets for opinion surveys with infinite scalability. Working paper, University of California, Los Angeles.
344. De Bruyn, A., Liechty, J. C., Huizingh, E. K. R. E., & Lilien, G. L. (2008). Offering online recommendations with minimum customer input through conjoint-based decision aids. Marketing Science, 27, 443-460.
345. De Sarbo, W., Fong, D. K. H., Liechty, J. C., & Coupland, J. C. (2005). Evolutionary preferences/utility functions: A dynamic perspective. Psychometrika, 70(1), 179.
346. Ding, M. (2007). An incentive-aligned mechanism for conjoint analysis. Journal of Marketing Research, 44, 214-223.
347. Ding, M., & Eliashberg, J. (2007). A dynamic competitive forecasting model incorporating dyadic decision-making. Management Science, 54, 820-834.
348. Ding, M., Grewal, R., & Liechty, J. (2005). Incentive-aligned conjoint analysis. Journal of Marketing Research, 42, 67-82.
349. Dobson, G., & Kalish, S. (1993). Heuristics for pricing and positioning a product-line using conjoint and cost data. Management Science, 7(2), 107-125.
350. Erdem, T., Keane, M., Öncü, S., & Strebel, J. (2005). Learning about computers: An analysis of information search and technology choice. Quantitative Marketing and Economics, 3(3), 207-247.
351. Evgeniou, T., Boussios, C., & Zacharia, G. (2005). Generalized robust conjoint estimation. Marketing Science, 24(3), 415-429.
352. Evgeniou, T., Pontil, M., & Toubia, O. (2007). A convex optimization approach to modeling consumer heterogeneity in conjoint estimation. Marketing Science, 26(6), 805-818.
353. Fehr, E., & Goette, L. (2007). Do workers work more if the wages are higher: Evidence from a randomized field experiment. The American Economic Review, 97(1), 298-317.
354. Feit, E. M., Beltramo, M. A., & Feinberg, F. M. (2007). Combining survey and market data to enhance consumer preference models. Working paper, University of Michigan.
355. Gensler, S., Theysohn, S., Hinz, O., & Skiera, B. (2007). Individually adjusted choice-based conjoint analysis. Working paper, Frankfurt University, Germany.
356. Ghose, S., & Rao, V. R. (2007). A choice model of bundles features and meta-attributes: An application to product design. Working paper, Cornell University.
357. Gilbride, T. J., & Allenby, G. M. (2004). A choice model with conjunctive, disjunctive, and compensatory screening rules. Marketing Science, 23(3), 391-406.
358. Gilbride, T. J., Lenk, P. J., & Brazell, J. D. (2006). Market share constraints and the loss function in choice based conjoint analysis. Working paper, University of Notre Dame.
359. Gilula, Z., McCulloch, R. E., & Rossi, P. E. (2006). A direct approach to data fusion. Journal of Marketing Research, 43(1), 73-83.
360. Godes, D., & Mayzlin, D. (2004). Using online conversations to study word-of-mouth communication. Marketing Science, 23(4), 545-560.
361. Goldenberg, J., Libai, B., & Muller, E. (2002). Riding the saddle: How cross-market communications can create a major slump in sales. Journal of Marketing, 66(2), 1-16.
362. Gonzalez, R., & Wu, G. (1999). On the shape of the probability weighting function. Cognitive Psychology, 38, 129-166.
363. Green, P. E., & Krieger, A. M. (1985). Models and heuristics for product line selection. Marketing Science, 4(1), 1-19.
364. Green, P. E., & Rao, V. R. (1971). Conjoint measurement for quantifying judgmental data. Journal of Marketing Research, 8, 355-363.
365. Green, P. E., & Srinivasan, V. (1990). Conjoint analysis in marketing: New developments with implications for research and practice. Journal of Marketing Research, 54, 3-19.
366. Green, P. E., Goldberg, S. M., & Montemayor, M. (1981). A hybrid utility estimation model for conjoint analysis. Journal of Marketing, 45, 33-41.
367. Green, P. E., Krieger, A. M., & Bansal, P. (1988). Completely unacceptable levels in conjoint analysis: A cautionary note. Journal of Marketing Research, 25, 293-300.
368. Gustafsson, A., Herrmann, A., & Huber, F. (2007). Conjoint measurement: Methods and applications (4th ed.). Berlin: Springer.
369. Haaijer, R., Wedel, M., Vriens, M., & Wansbeek, T. (1998). Utility covariances and context effects in conjoint MNP models. Marketing Science, 17(3), 236-252.
370. Haaijer, M. E., Kamakura, W. A., & Wedel, M. (2000). Response latencies in the analysis of conjoint choice experiments. Journal of Marketing Research, 37(3), 376-382.
371. Häubl, G., & Murray, K. B. (2003). Preference construction and preference in digital marketplaces: The role of electronic recommendation agents. Journal of Consumer Psychology, 13, 75-91.
372. Häubl, G., & Trifts, V. (2000). Interactive decision aids. Marketing Science, 19(1), 4-21.
373. Horsky, D., Misra, S., & Nelson, P. (2006). Observed and unobserved preference heterogeneity in brand-choice models. Marketing Science, 25(4), 322-335.
374. Hui, S. K., Bradlow, E. T., & Fader, P. S. (2008a). An integrated model of grocery store shopping path and purchase behavior. Working paper, The Wharton School, University of Pennsylvania.
375. Hui, S. K., Fader, P. S., & Bradlow, E. T. (2009). Path data in marketing: An integrative framework and prospectus for model-building. Marketing Science, 28(2), 320-335.
376. Iyengar, R., Jedidi, K., & Kohli, R. (2008). A conjoint approach to multi-part pricing. Journal of Marketing Research, 45(2), 195-210.
377. Jarnebrant, P., Toubia, O., & Johnson, E. J. (2009). The silver lining effect: Formal analysis and experiments. Management Science, 55(11), 1832-1841.
378. Jedidi, K., & Kohli, R. (2005). Probabilistic subset-conjunctive models for heterogeneous consumers. Journal of Marketing Research, 42(4), 483-494.
379. Johnson, R. M. (1987). Adaptive conjoint analysis. In Sawtooth Software Conference Proceedings, Ketchum: Sawtooth Software, pp. 253-265.
380. Johnson, J., Tellis, G. J., & MacInnis, D. J. (2005). Losers, winners and biased trades. Journal of Consumer Research, 32(2), 324-329.
381. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-292.
382. Kim, J. G. (2004). Dynamic heterogeneous choice heuristics: A Bayesian hidden Markov mixture model approach. Working paper, MIT Sloan School of Management, Cambridge, MA.
383. Kim, J. G., Menzefricke, U., & Feinberg, F. M. (2005). Modeling parametric evolution in a random utility framework. Journal of Business and Economic Statistics, 23(5), 282-294.
384. Kim, J. G., Menzefricke, U., & Feinberg, F. M. (2007). Capturing flexible heterogeneous utility curves: Bayesian spline approach. Management Science, 53(2), 340-354.
385. Kivetz, R., Netzer, O., & Srinivasan, V. (2004a). Alternative models for capturing the compromise effect. Journal of Marketing Research, 41(3), 237-257.
386. Kivetz, R., Netzer, O., & Srinivasan, V. (2004b). Extending compromise effect models to complex buying situations and other context effects. Journal of Marketing Research, 41(3), 262-268.
387. Kohli, R., & Jedidi, K. (2007). Representation and inference of lexicographic preference models and their variants. Marketing Science, 26(3), 380-399.
388. Kohli, R., & Sukumar, R. (1990). Heuristics for product-line design using conjoint analysis. Management Science, 36(12), 1464-1478.
389. Kuhfeld, W. F., Tobias, R. D., & Garratt, M. (1994). Efficient experimental design with marketing applications. Journal of Marketing Research, 31(4), 545-557.
390. Lachaab, M., Ansari, A., Jedidi, K., & Trabelsi, A. (2006). Modeling preference evolution in discrete choice models: A Bayesian state-space approach. Quantitative Marketing and Economics, 4, 57-81.
391. Lee, T. Y., & Bradlow, E. T. (2008). Automatic construction of conjoint attributes and levels from online customer reviews. Working paper, The Wharton School, University of Pennsylvania.
392. Lenk, P. J., De Sarbo, W. S., Green, P. E., & Young, M. R. (1996). Hierarchical Bayes conjoint analysis: Recovery of partworth heterogeneity from reduced experimental designs. Marketing Science, 15(2), 173-191.
393. Liechty, J., Ramaswamy, V., & Cohen, S. H. (2001). Choice menus for mass customization: An experimental approach for analyzing customer demand with an application to a web-based information service. Journal of Marketing Research, 38(2), 183-196.
394. Liechty, J., Pieters, R., & Wedel, M. (2003). Global and local covert visual attention: evidence from a Bayesian hidden Markov model. Psychometrika, 68, 519-542.
395. Liechty, J. C., Fong, D. K. H., & DeSarbo, W. S. (2005). Dynamic models incorporating individual heterogeneity: utility evolution in conjoint analysis. Marketing Science, 24(2), 285-293.
396. Louviere, J. J., Meyer, R. J., Bunch, D. S., Carson, R., Dellaert, B., Hanemann, M., et al. (1999). Combining sources of preference data for modeling complex decision processes. Marketing Letters, 10(3), 187-204.
397. Luo, L., Kannan, P. K., Besharati, B., & Azarm, S. (2005). Design of robust new products under variability: Marketing meets design. Journal of Product Innovation Management, 22, 177-192.
398. Luo, L., Kannan, P. K., & Ratchford, B. (2008). Incorporating subjective characteristics in product design and evaluations. Journal of Marketing Research, 45(2), 182-194.
399. Marshall, P., & Bradlow, E. T. (2002). A unified approach to conjoint analysis models. Journal of the American Statistical Association, 97(459), 674-682.
400. McBride, R., & Zufryden, F. S. (1988). An integer programming approach to the optimal product line selection problem. Marketing Science, 7(2), 126-140.
401. Michalek, J. J., Feinberg, F. M., & Papalambros, P. Y. (2005). Linking marketing and engineering product design decisions via analytical target cascading. Journal of Product Innovation Management, 22, 42-62.
402. Michalek, J. J., Feinberg, F. M., Ebbes, P., Adigüzel, F., & Papalambros, P. Y. (2007). Optimal feasible product line design for heterogeneous markets. Working paper, Department of Mechanical Engineering, Carnegie-Mellon University.
403. Netzer, O., & Srinivasan, V. (2008). Adaptive self-explication of multi-attribute preferences. Working paper, Columbia Business School, New York.
404. Netzer, O., Schrift, R., & Toubia, O. (2008). Modeling and exploiting response time in conjoint analysis. Working paper, Columbia Business School, New York.
405. Otter, T., Allenby, G. M., & van Zandt, T. (2008). An integrated model of discrete choice and response time. Journal of Marketing Research, 45(5), 593-607.
406. Park, Y. H., Ding, M., & Rao, V. R. (2008). Eliciting preference for complex products: A web-based upgrading method. Journal of Marketing Research, 45, 562-574.
407. Parker, B. R., & Srinivasan, V. (1976). A consumer preference approach to the planning of rural primary health-care facilities. Operations Research, 24(5), 991-1025.
408. Prelec, D. (1998). The probability weighting function. Econometrica, 66(3), 497-527.
409. Prelec, D. (2001). A two-person scoring rule for subjective reports. Working paper, Center for Innovation in Product Development, Massachusetts Institute of Technology, Cambridge, MA.
410. Rao, V. R., & Steckel, J. H. (1991). A polarization model for describing group preferences. Journal of Consumer Research, 18(1), 108-118.
411. Rossi, P. E., & Allenby, G. M. (2003). Bayesian statistics and marketing. Marketing Science, 22(3), 304-328.
412. Saigal, C., Dahan, E., & Cumberland, W. (2007). Measuring prostate cancer treatnment preferences with standard gambles, time tradeoffs and conjoint analysis. NIH Research Proposal.
413. Sandor, Z., & Wedel, M. (2001). Designing conjoint choice experiments using managers prior beliefs. Journal of Marketing Research, 38, 430-444.
414. Sandor, Z., & Wedel, M. (2005). Heterogeneous conjoint choice designs. Journal of Marketing Research, 42, 210-218.
415. Sonnier, G., Ainslie, A., & Otter, T. (2007). Heterogeneity distributions of willingness-to-pay in choice models. Quantitative Marketing and Economics, 5(3), 313-331.
416. Srebro, N., Rennie, J. D. M., & Jaakkola, T. (2005). Maximum margin matrix factorization. Advances In Neural Information Processing Systems, 17, 1329-1336.
417. Srinivasan, V. (1988). A conjunctive-compensatory approach to the self-explication of multiattributed preferences. Decision Sciences, 19(Spring), 295-305.
418. Srinivasan, V., & Shocker, A. D. (1973a). Linear programming techniques for multidimensional analysis of preferences. Psychometrika, 38(3), 337-369.
419. Srinivasan, V., & Shocker, A. D. (1973b). Estimating the weights for multiple attributes in a composite criterion using pairwise judgments. Psychometrika, 38(4), 473-493.
420. Srinivasan, V., Lovejoy, W. S., & Beach, D. (1997). Integrated product design for marketability and manufacturing. Journal of Marketing Research, 34(1), 154-163.
421. Su, M., & Rao, V. R. (2007). Evolution in willingness to pay at attribute level and its impact on new product adoption: A continuous conjoint analysis. Working paper, Cornell university.
422. Tanaka, T., Camerer, C. F., & Nguyen, Q. (2007). Risk and time preferences: Experimental and household survey data from Vietnam. Working paper, California Institute of Technology.
423. Ter Hofstede, F., Kim, Y., & Wedel, M. (2002). Bayesian prediction in hybrid conjoint analysis. Journal of Marketing Research, 34(2), 253-261.
424. Toubia, O., & Hauser, J. R. (2007). On managerially efficient experimental designs. Marketing Science, 26(6), 851-858.
425. Toubia, O., Simester, D., Hauser, J. R., & Dahan, E. (2003). Fast polyhedral conjoint estimation. Marketing Science, 22(3), 274-303.
426. Toubia, O., Hauser, J. R., & Simester, D. (2004). Polyhedral methods for adaptive choice based conjoint analysis. Journal of Marketing Research, 41(1), 116-131.
427. Toubia, O., Evgeniou, T., & Hauser, J. R. (2007). Optimization-based and machine-learning methods for conjoint analysis: Estimation and question design. In A. Gustafsson, A. Herrmann, & F. Huber (Eds.), Conjoint measurement: Methods and applications (4th ed.). Berlin: Springer.
428. Toubia, O., Hauser, J. R., & Garcia, R. (2007). Probabilistic polyhedral methods for adaptive choice-based conjoint analysis: Theory and application. Marketing Science, 26(5), 596-610.
429. Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4), 297-323.
430. Vadali, S., Liechty, J., & Rangaswamy, A. (2007). Generalized hierarchical Bayes estimation for polyhedral conjoint analysis. Working paper, Pennsylvania State University.
431. von Hippel, E., & Katz, R. (2002). Shifting innovation to users via toolkits. Management Science, 48(7), 821-833.
432. Wassenaar, H. J., Chen, W., Cheng, J., & Sudjianto, A. (2005). Enhancing discrete choice demand modeling for decision-based design. ASME Journal of Mechanical Design, 127(4), 514-523.
433. Wind, J., Green, P. E., Shifflet, D., & Scarbrough, M. (1989). Courtyard by Marriott: Designing a hotel facility with consumer-based marketing models. Interfaces, 19, 25-47.
434. Wittink, D. R., & Cattin, P. (1989). Commercial use of conjoint analysis: An update. Journal of Marketing, 53(3), 91-96.
435. Wittink, D. R., Krishnamurthi, L., & Reibstein, D. J. (1989). The effect of differences in the number of attribute levels on conjoint results. Marketing Letters, 1, 113-123.
436. Yee, M., Dahan, E., Hauser, J. R., & Orlin, J. (2008). Greedoid-based noncompensatory inference. Marketing Science, 26(4), 532-549.
437. Ying, Y., Feinberg, F. M., & Wedel, M. (2006). Leveraging missing ratings to improve online recommendation systems. Journal of Marketing Research, 43(3), 355-365.