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Individually adapted sequential Bayesian conjoint-choice designs in the presence of consumer heterogeneity

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  • Yu, Jie
  • Goos, Peter
  • Vandebroek, Martina

Abstract

We propose an efficient individually adapted sequential Bayesian approach for constructing conjoint-choice experiments, which uses Bayesian updating, a Bayesian analysis, and a Bayesian design criterion to generate a conjoint-choice design for each individual respondent based on the previous answers of that particular respondent. The proposed design approach is compared with three non-adaptive design approaches, two aggregate-customization approaches (based on the conditional logit model and on a mixed logit model), and the (nearly) orthogonal design approach, under various degrees of response accuracy and consumer heterogeneity. A simulation study shows that the individually adapted sequential Bayesian conjoint-choice designs perform better than the benchmark approaches in all scenarios we investigated in terms of the efficient estimation of individual-level part-worths and the prediction of individual choices. In the presence of high consumer heterogeneity, the improvements are impressive. The new method also performs well when the response accuracy is low, in contrast with the recently proposed adaptive polyhedral approach. Furthermore, the new methodology yields precise population-level parameter estimates, even though the design criterion focuses on the individual-level parameters.

Suggested Citation

  • Yu, Jie & Goos, Peter & Vandebroek, Martina, 2011. "Individually adapted sequential Bayesian conjoint-choice designs in the presence of consumer heterogeneity," International Journal of Research in Marketing, Elsevier, vol. 28(4), pages 378-388.
  • Handle: RePEc:eee:ijrema:v:28:y:2011:i:4:p:378-388
    DOI: 10.1016/j.ijresmar.2011.06.002
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    5. Jason Soria & Shelly Etzioni & Yoram Shiftan & Amanda Stathopoulos & Eran Ben-Elia, 2022. "Microtransit adoption in the wake of the COVID-19 pandemic: evidence from a choice experiment with transit and car commuters," Papers 2204.01974, arXiv.org.
    6. Crabbe, Marjolein & Akinc, Deniz & Vandebroek, Martina, 2014. "Fast algorithms to generate individualized designs for the mixed logit choice model," Transportation Research Part B: Methodological, Elsevier, vol. 60(C), pages 1-15.
    7. Danaf, Mazen & Atasoy, Bilge & de Azevedo, Carlos Lima & Ding-Mastera, Jing & Abou-Zeid, Maya & Cox, Nathaniel & Zhao, Fang & Ben-Akiva, Moshe, 2019. "Context-aware stated preferences with smartphone-based travel surveys," Journal of choice modelling, Elsevier, vol. 31(C), pages 35-50.
    8. Vishva Danthurebandara & Jie Yu & Martina Vandebroek, 2011. "Sequential choice designs to estimate the heterogeneity distribution of willingness-to-pay," Quantitative Marketing and Economics (QME), Springer, vol. 9(4), pages 429-448, December.
    9. Crabbe, M. & Vandebroek, M., 2012. "Improving the efficiency of individualized designs for the mixed logit choice model by including covariates," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 2059-2072.
    10. Onesun Steve Yoo & Tingliang Huang & Kenan Arifoğlu, 2021. "A Theoretical Analysis of the Lean Start-up Method," Marketing Science, INFORMS, vol. 40(3), pages 395-412, May.
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    13. Denis Sauré & Juan Pablo Vielma, 2019. "Ellipsoidal Methods for Adaptive Choice-Based Conjoint Analysis," Operations Research, INFORMS, vol. 67(2), pages 315-338, March.

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