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Probabilistic Polyhedral Methods for Adaptive Choice-Based Conjoint Analysis: Theory and Application

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Cited by:

  1. Gensler, Sonja & Hinz, Oliver & Skiera, Bernd & Theysohn, Sven, 2012. "Willingness-to-pay estimation with choice-based conjoint analysis: Addressing extreme response behavior with individually adapted designs," European Journal of Operational Research, Elsevier, vol. 219(2), pages 368-378.
  2. Mohammad Ghaderi & Kamel Jedidi & Miłosz Kadziński & Bas Donkers, 2025. "Random preference model," Economics Working Papers 1913, Department of Economics and Business, Universitat Pompeu Fabra.
  3. 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.
  4. Daria Dzyabura & John R. Hauser, 2011. "Active Machine Learning for Consideration Heuristics," Marketing Science, INFORMS, vol. 30(5), pages 801-819, September.
  5. Denis Sauré & Juan Pablo Vielma, 2019. "Ellipsoidal Methods for Adaptive Choice-Based Conjoint Analysis," Operations Research, INFORMS, vol. 67(2), pages 315-338, March.
  6. Hauser, John R., 2014. "Consideration-set heuristics," Journal of Business Research, Elsevier, vol. 67(8), pages 1688-1699.
  7. Julio López & Sebastián Maldonado & Ricardo Montoya, 2017. "Simultaneous preference estimation and heterogeneity control for choice-based conjoint via support vector machines," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(11), pages 1323-1334, November.
  8. Mingyu Joo & Michael L. Thompson & Greg M. Allenby6, 2019. "Optimal Product Design by Sequential Experiments in High Dimensions," Management Science, INFORMS, vol. 65(7), pages 3235-3254, July.
  9. Oded Netzer & Olivier Toubia & Eric Bradlow & Ely Dahan & Theodoros Evgeniou & Fred Feinberg & Eleanor Feit & Sam Hui & Joseph Johnson & John Liechty & James Orlin & Vithala Rao, 2008. "Beyond conjoint analysis: Advances in preference measurement," Marketing Letters, Springer, vol. 19(3), pages 337-354, December.
  10. Olivier Toubia & Eric Johnson & Theodoros Evgeniou & Philippe Delquié, 2013. "Dynamic Experiments for Estimating Preferences: An Adaptive Method of Eliciting Time and Risk Parameters," Management Science, INFORMS, vol. 59(3), pages 613-640, June.
  11. Dongling Huang & Lan Luo, 2016. "Consumer Preference Elicitation of Complex Products Using Fuzzy Support Vector Machine Active Learning," Marketing Science, INFORMS, vol. 35(3), pages 445-464, May.
  12. Tammo Bijmolt & Michel Velden, 2012. "Multiattribute perceptual mapping with idiosyncratic brand and attribute sets," Marketing Letters, Springer, vol. 23(3), pages 585-601, September.
  13. James Agarwal & Wayne DeSarbo & Naresh K. Malhotra & Vithala Rao, 2015. "An Interdisciplinary Review of Research in Conjoint Analysis: Recent Developments and Directions for Future Research," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 2(1), pages 19-40, March.
  14. Steven M. Shugan & Jihwan Moon & JQiaoni Shi & Nanda S. Kumar, 2017. "Product Line Bundling: Why Airlines Bundle High-End While Hotels Bundle Low-End," Marketing Science, INFORMS, vol. 36(1), pages 124-139, January.
  15. Peter Gibbard & Kelson Sadlier, 2025. "Optimal adaptive Bayesian design in choice experiments: performance for the population versus individuals," Marketing Letters, Springer, vol. 36(3), pages 547-559, September.
  16. Maxime C. Cohen & Ilan Lobel & Renato Paes Leme, 2020. "Feature-Based Dynamic Pricing," Management Science, INFORMS, vol. 66(11), pages 4921-4943, November.
  17. Rossella Berni & Nedka Dechkova Nikiforova & Patrizia Pinelli, 2024. "An Optimal Design through a Compound Criterion for Integrating Extra Preference Information in a Choice Experiment: A Case Study on Moka Ground Coffee," Stats, MDPI, vol. 7(2), pages 1-16, June.
  18. Eggers, Felix & Sattler, Henrik, 2009. "Hybrid individualized two-level choice-based conjoint (HIT-CBC): A new method for measuring preference structures with many attribute levels," International Journal of Research in Marketing, Elsevier, vol. 26(2), pages 108-118.
  19. Junpei Komiyama & Shunya Noda, 2021. "Deviation-Based Learning: Training Recommender Systems Using Informed User Choice," Papers 2109.09816, arXiv.org, revised Aug 2022.
  20. Qing Liu & Yihui (Elina) Tang, 2015. "Construction of Heterogeneous Conjoint Choice Designs: A New Approach," Marketing Science, INFORMS, vol. 34(3), pages 346-366, May.
  21. Srinivasan, V. Seenu & Netzer, Oded, 2007. "Adaptive Self-Explication of Multi-attribute Preferences," Research Papers 1979, Stanford University, Graduate School of Business.
  22. Vishva Danthurebandara & Jie Yu & Martina Vandebroek, 2015. "Designing choice experiments by optimizing the complexity level to individual abilities," Quantitative Marketing and Economics (QME), Springer, vol. 13(1), pages 1-26, March.
  23. Steven M. Shugan, 2009. "—Relevancy Is Robust Prediction, Not Alleged Realism," Marketing Science, INFORMS, vol. 28(5), pages 991-998, 09-10.
  24. repec:bge:wpaper:1502 is not listed on IDEAS
  25. Christian Schlereth & Bernd Skiera, 2017. "Two New Features in Discrete Choice Experiments to Improve Willingness-to-Pay Estimation That Result in SDR and SADR: Separated (Adaptive) Dual Response," Management Science, INFORMS, vol. 63(3), pages 829-842, March.
  26. Dimitris Bertsimas & Allison O'Hair, 2013. "Learning Preferences Under Noise and Loss Aversion: An Optimization Approach," Operations Research, INFORMS, vol. 61(5), pages 1190-1199, October.
  27. Maldonado, Sebastián & Montoya, Ricardo & Weber, Richard, 2015. "Advanced conjoint analysis using feature selection via support vector machines," European Journal of Operational Research, Elsevier, vol. 241(2), pages 564-574.
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