Construction of Heterogeneous Conjoint Choice Designs: A New Approach

Published Online:https://doi.org/10.1287/mksc.2014.0897

References

  • Arora N, Huber J (2001) Improving parameter estimates and model prediction by homogeneous customization in choice experiments. J. Consumer Res. 28(2):273–283.CrossrefGoogle Scholar
  • Atkinson A, Donev A, Tobias R (2007) Optimum Experimental Designs, with SAS (Oxford University Press, New York).CrossrefGoogle Scholar
  • Ben-Akiva M, Lerman S (1985) Discrete Choice Analysis: Theory and Application to Travel Demand (MIT Press, Cambridge, MA).Google Scholar
  • Birnbaum MH (2000) Psychological Experiments on the Internet (Academic Press, San Diego).CrossrefGoogle Scholar
  • Chaloner K, Verdinelli I (1995) Bayesian experimental design: A review. Statist. Sci. 10(3):273–304.CrossrefGoogle Scholar
  • Chrzan K, Orme B (2000) An overview and comparison of design strategies for choice-based conjoint analysis. Technical report, Sawtooth software. http://www.sawtoothsoftware.com/download/techpap/desgncbc.pdf.Google Scholar
  • Ding M (2007) An incentive-aligned mechanism for conjoint analysis. J. Marketing Res. 44(2):214–223.CrossrefGoogle Scholar
  • Ding M, Grewal R, Liechty J (2005) Incentive-aligned conjoint analysis. J. Marketing Res. 42(1):67–82.CrossrefGoogle Scholar
  • Ding M, Park YH, Bradlow ET (2009) Barter markets for conjoint analysis. Management Sci. 55(6):1003–1017.LinkGoogle Scholar
  • Fang KT, Lin DKJ (2003) Uniform experimental design and its applications in industry. Rao CR, Khattree R, eds. Handbook of Statistics in Industry, Vol. 22 (North Holland, Amsterdam), 131–170.CrossrefGoogle Scholar
  • Fedorov VV, Hackl P (1997) Model-Oriented Design of Experiments (Springer Verlag, New York).CrossrefGoogle Scholar
  • Gilbride TJ, Allenby GM (2004) A choice model with conjunctive, disjunctive, and compensatory screening rules. Marketing Sci. 23(3):391–406.LinkGoogle Scholar
  • Gilbride TJ, Allenby GM (2006) Estimating heterogeneous EBA and economic screening rule choice models. Marketing Sci. 25(5):494–509.LinkGoogle Scholar
  • Green PE, Srinivasan V (1990) Conjoint analysis in marketing: New developments with implications for research and practice. J. Marketing 54(4):3–19.CrossrefGoogle Scholar
  • Haaijer R, Kamakura WA, Wedel M (2001) The no-choice alternative in conjoint choice experiments. Internat. J. Market Res. 43(1):93–106.Google Scholar
  • Huber J, Zwerina K (1996) The importance of utility balance in efficient choice designs. J. Marketing Res. 33(3):307–317.CrossrefGoogle Scholar
  • Johnson R, Huber J, Bacon L (2003) Adaptive choice-based conjoint designs. Sawtooth Software Conf. Proc. 333–343. http://www.sawtoothsoftware.com/support/technical-papers/conference-proceedings.Google Scholar
  • Johnson R, Orme B, Huber J, Pinnell J (2005) Testing adaptive choice-based conjoint designs. Technical paper, Sawtooth software. http://www.sawtoothsoftware.com/download/techpap/acbc3.pdf.Google Scholar
  • Kessels R, Goos P, Vandebroek M (2006) A comparison of criteria to design efficient choice experiments. J. Marketing Res. 43(3):409–419.CrossrefGoogle Scholar
  • Kessels R, Jones B, Goos P, Vanderbroek M (2009) An efficient algorithm for constructing Bayesian optimal choice designs. J. Bus. Econom. Statist. 27(2):279–291.CrossrefGoogle Scholar
  • Kiefer J (1974) General equivalence theory for optimum designs (approximate theory). Ann. Statist. 2(5):849–879.CrossrefGoogle Scholar
  • Kuhfeld WF, Tobias R (2005) Large factorial designs for product engineering and marketing research applications. Technometrics 47(2):132–141.CrossrefGoogle Scholar
  • Kuhfeld WF, Tobias RD, Garratt M (1994) Efficient experimental design with marketing research applications. J. Marketing Res. 31(4):545–557.CrossrefGoogle Scholar
  • Liu Q, Arora N (2011) Efficient choice designs for a consider-then-choose model. Marketing Sci. 30(2):321–338.LinkGoogle Scholar
  • Liu Q, Dean AM, Allenby GM (2012) Bayesian designs for hierarchical linear models. Statistica Sinica 22(1):393–417.CrossrefGoogle Scholar
  • McFadden D (1974) Conditional logit analysis of qualitative choice behavior. Zarembka P, ed. Frontiers in Econometrics (Academic Press, New York), 105–142.Google Scholar
  • Meyer R, Nachtsheim C (1995) The coordinate-exchange algorithm for constructing exact optimal experimental designs. Technometrics 37(1):60–69.CrossrefGoogle Scholar
  • Netzer O, Toubia O, Bradlow ET, Dahan E, Evgeniou T, Feinberg FM, Feit EMet al. (2008) Beyond conjoint analysis: Advances in preference measurement. Marketing Lett. 19(3–4):337–354.CrossrefGoogle Scholar
  • Newton M, Raftery A (1994) Approximate Bayesian inference with weighted likelihood bootstrap. J. Royal Statist. Soc. Series B 56(1):3–48.Google Scholar
  • Pukelsheim F (1993) Optimal Design of Experiments (John Wiley, New York).Google Scholar
  • Sándor Z, Wedel M (2001) Designing conjoint choice experiments using managers’ prior beliefs. J. Marketing Res. 38(4):430–444.CrossrefGoogle Scholar
  • Sándor Z, Wedel M (2002) Alternative construction in experimental choice designs for mixed logit models. Marketing Sci. 21(4):455–475.LinkGoogle Scholar
  • Sándor Z, Wedel M (2005) Heterogeneous conjoint choice designs. J. Marketing Res. 42(2):210–218.CrossrefGoogle Scholar
  • Silvey SD (1980) Optimal Design (Chapman and Hall, London).CrossrefGoogle Scholar
  • Swait J, Adamowicz W (2001) The influence of task complexity on consumer choice: A latent class model of decision strategy switching. J. Consumer Res. 28(1):135–148.CrossrefGoogle Scholar
  • Toubia O, Hauser JR (2007) On managerial efficiency for experimental designs. Marketing Sci. 26(6):851–858.LinkGoogle Scholar
  • Toubia O, Hauser JR, Garcia R (2007) Probabilistic polyhedral methods for adaptive choice-based conjoint analysis: Theory and application. Marketing Sci. 26(5):596–610.LinkGoogle Scholar
  • Toubia O, Hauser JR, Simester DI (2004) Polyhedral methods for adaptive choice-based conjoint analysis. J. Marketing Res. 41(1):116–131.CrossrefGoogle Scholar
  • Toubia O, de Jong MG, Stieger D, Fueller J (2012) Measuring consumer preferences using conjoint poker. Marketing Sci. 31(1):138–156.LinkGoogle Scholar
  • Wittink DR, Bergestuen T (1999) Forecasting with conjoint analysis. Armstrong JS, ed. Principles of Forecasting: A Handbook for Researchers and Practitioners (Kluwer Academic, Norwell, MA), 147–167.Google Scholar
  • Yang M, Stufken J (2009) Support points of locally optimal designs for nonlinear models with two parameters. Ann. Statist. 37(1):518–541.CrossrefGoogle Scholar
  • Yang M, Biedermann S, Tang E (2013) On optimal designs for nonlinear models: A general and efficient algorithm. J. Amer. Statist. Assoc. 108(504):1411–1420.CrossrefGoogle Scholar
  • Yu J, Goos P, Vandebroek M (2008) Model-robust design of conjoint choice experiments. Comm. Statist.: Simulation Comput. 37(8):1603–1621.CrossrefGoogle Scholar
  • Yu J, Goos P, Vanderbroek M (2009) Efficient conjoint choice designs in the presence of respondent heterogeneity. Marketing Sci. 28(1):122–135.LinkGoogle Scholar
  • Zhou YD, Fang KT (2012) An efficient method for constructing uniform designs with large size. Comput. Statist. 28(3):1319–1331.CrossrefGoogle Scholar
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