The Impact of Utility Balance and Endogeneity in Conjoint Analysis

Published Online:https://doi.org/10.1287/mksc.1040.0108

References

  • Allenby Greg M., Arora Neeraj. Incorporating prior knowledge into the analysis of conjoint studies. J. Marketing Res. (1995) 32(2):152–163CrossrefGoogle Scholar
  • Arora Neeraj, Huber Joel. Improving parameter estimates and model prediction by aggregate customization in choice experiments. J. Consumer Res. (2001) 28(September):273–283CrossrefGoogle Scholar
  • Capen E. C., Clapp R. V., Campbell W. M. Competitive bidding in high-risk situations. J. Petroleum Tech. (1971) 23(June):641–653CrossrefGoogle Scholar
  • Carroll J. Douglas, Green Paul E. Psychometric methods in marketing research: Part I, Conjoint analysis. J. Marketing Res. (1995) 32(November):385–391CrossrefGoogle Scholar
  • Choi S. Chan, DeSarbo Wayne S. A conjoint-based product designing procedure incorporating price competition. J. Product Innovation Management (1994) 11:451–459CrossrefGoogle Scholar
  • Evgeniou Theodoros, Boussios Constantinos, Zacharia Giorgos. Generalized robust conjoint estimation. Marketing Sci. (2004) 24(3):415–429LinkGoogle Scholar
  • Green Paul E., Krieger Abba. Attribute importance weights modification in assessing a brand’s competitive potential. Marketing Sci. (1995) 14(3, Part 1 of 2):253–270LinkGoogle Scholar
  • Green Paul E., Krieger Abba, Agarwal Manoj K. Adaptive conjoint analysis: Some caveats and suggestions. J. Marketing Res. (1991) 28(2):215–222CrossrefGoogle Scholar
  • Greene William H.Econometric Analysis (1993) 2nd ed.(Prentice-Hall, Inc., Englewood Cliffs, NJ) Google Scholar
  • Haaijer Rinus, Kamakura Wagner, Wedel Michel. Response latencies in the analysis of conjoint choice experiments. J. Marketing Res. (2000) 37(August):376–382CrossrefGoogle Scholar
  • Hauser John R., Shugan Steven M. Intensity measures of consumer preference. Oper. Res. (1980) 28(2, March–April):278–320LinkGoogle Scholar
  • Huber Joel, Hansen David, Wallendorf Melanie, Anderson Paul. Testing the impact of dimensional complexity and affective differences of paired concepts in adaptive conjoint analysis. Advances in Consumer Research (1986) 14(Associations of Consumer Research, Provo, UT) 159–163Google Scholar
  • Huber Joel, Zwerina Klaus. The importance of utility balance in efficient choice designs. J. Marketing Res. (1996) 33(August):307–317CrossrefGoogle Scholar
  • Huber Joel, Wittink Dick R., Fiedler John A., Miller Richard. The effectiveness of alternative preference elicitation procedures in predicting choice. J. Marketing Res. (1993) 30(1):105–114CrossrefGoogle Scholar
  • Jedidi Kamel, Jagpal Sharan, Manchanda Puneet. Measuring heterogeneous reservation prices for product bundles. Marketing Sci. (2003) 22(1, Winter):107–130LinkGoogle Scholar
  • Johnson Richard. Comment on adaptive conjoint analysis: Some caveats and suggestions. J. Marketing Res. (1991) 28(May):223–225CrossrefGoogle Scholar
  • Johnson Richard, Huber Joel, Bacon Lynd. Adaptive choice-based conjoint. (2003) (Sawtooth Software, Sequim, WA) Google Scholar
  • Judge G. G., Griffiths W. E., Hill R. C., Lutkepohl H., Lee T. C.The Theory and Practice of Econometrics (1985) (John Wiley and Sons, New York) Google Scholar
  • Kagel John H., Levin Dan. The winner’s curse and public information in common value auctions. Amer. Econom. Rev. (1986) 76(5, December):894–920Google Scholar
  • Kanninen Barbara J. Optimal design for multinomial choice experiments. J. Marketing Res. (2002) 39(May):214–227CrossrefGoogle Scholar
  • Kuhfeld Warren F., Tobias Randall D., Garratt Mark. Efficient experimental design with marketing research applications. J. Marketing Res. (1994) 31(4, November):545–557CrossrefGoogle Scholar
  • Lenk Peter J., DeSarbo Wayne S., Green Paul E., Young Martin R. Hierarchical Bayes conjoint analysis: Recovery of partworth heterogeneity from reduced experimental designs. Marketing Sci. (1996) 15(2):173–191LinkGoogle Scholar
  • Liechty John, Fong Duncan, DeSarbo Wayne S. The evolution of consumer utility functions in conjoint analysis. Marketing Sci. (2005) 24(2):285–293LinkGoogle Scholar
  • Marshall Pablo, Bradlow Eric T. A unified approach to conjoint analysis models. J. Amer. Statist. Assoc. (2002) 97(459, September):674–682CrossrefGoogle Scholar
  • Orme Bryan. ACA, CBC, or both: Effective strategies for conjoint research. (1999) . Working paper, Sawtooth Software, Sequim, WAGoogle Scholar
  • Orme Bryan, Huber Joel. Improving the value of conjoint simulations. Marketing Res. (2000) 12(4, Winter):12–20Google Scholar
  • Orme Bryan, King W. Christopher. Improving ACA algorithms: Challenging a twenty-year-old approach. Advance Res. Tech. Conf. (2002) (American Marketing Association, Vail, Co) Google Scholar
  • Sandor Zsolt, Wedel Michel. Designing conjoint choice experiments using managers’ prior beliefs. J. Marketing Res. (2001) 38(4, November):430–444CrossrefGoogle Scholar
  • Sandor Zsolt, Wedel Michel. Profile construction in experimental choice designs for mixed logit models. Marketing Sci. (2002) 21(4, Fall):455–475LinkGoogle Scholar
  • Sándor Zsolt, Wedel Michel. Differentiated Bayesian conjoint choice designs, April 29, 2003. (2003) . ERIM Report Series Reference No. ERS-2003-016-MKT. http://ssrn.com/abstract=41161Google Scholar
  • Sawtooth Software The ACA/hierarchical Bayes technical paper. (2001) (Sawtooth Software, Inc., Sequim, WA) Google Scholar
  • Sawtooth Software ACA 5.0 technical paper. (2002) (Sawtooth Software, Inc., Sequim, WA) Sawtooth Software Technical Paper SeriesGoogle Scholar
  • Sawtooth Software Update on relative conjoint analysis usage. Sawtooth Solutions (2004) Summer). http://www.sawtoothsoftware.com/productforms/ssolutions/ss20.shtml#ss20usageGoogle Scholar
  • Shugan Steven M. The cost of thinking. J. Consumer Res. (1980) 7(2, September):99–111CrossrefGoogle Scholar
  • Swait Joffre, Andrews Rick L. Enriching scanner panel models with choice experiments. Marketing Sci. (2003) 22(4):442–460LinkGoogle Scholar
  • Thaler Richard H.The Winner’s Curse: Paradoxes and Anomalies of Economic Life (1992) (Princeton University Press, Princeton, NJ) Google Scholar
  • Toubia Olivier, Hauser John R., Simester Duncan I. Polyhedral methods for adaptive choice-based conjoint analysis. J. Marketing Res. (2004) 41(1):116–131CrossrefGoogle Scholar
  • Toubia Olivier, Simester Duncan, Hauser John R., Dahan Ely. Fast polyhedral adaptive conjoint estimation. Marketing Sci. (2003) 22(3):273–303LinkGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.