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Advanced conjoint analysis using feature selection via support vector machines

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  • Maldonado, Sebastián
  • Montoya, Ricardo
  • Weber, Richard

Abstract

One of the main tasks of conjoint analysis is to identify consumer preferences about potential products or services. Accordingly, different estimation methods have been proposed to determine the corresponding relevant attributes. Most of these approaches rely on the post-processing of the estimated preferences to establish the importance of such variables. This paper presents new techniques that simultaneously identify consumer preferences and the most relevant attributes. The proposed approaches have two appealing characteristics. Firstly, they are grounded on a support vector machine formulation that has proved important predictive ability in operations management and marketing contexts and secondly they obtain a more parsimonious representation of consumer preferences than traditional models. We report the results of an extensive simulation study that shows that unlike existing methods, our approach can accurately recover the model parameters as well as the relevant attributes. Additionally, we use two conjoint choice experiments whose results show that the proposed techniques have better fit and predictive accuracy than traditional methods and that they additionally provide an improved understanding of customer preferences.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:ejores:v:241:y:2015:i:2:p:564-574
    DOI: 10.1016/j.ejor.2014.09.051
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    2. Vairetti, Carla & González-Ramírez, Rosa G. & Maldonado, Sebastián & Álvarez, Claudio & Voβ, Stefan, 2019. "Facilitating conditions for successful adoption of inter-organizational information systems in seaports," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 333-350.
    3. Zhang, Yishi & Zhu, Ruilin & Chen, Zhijun & Gao, Jie & Xia, De, 2021. "Evaluating and selecting features via information theoretic lower bounds of feature inner correlations for high-dimensional data," European Journal of Operational Research, Elsevier, vol. 290(1), pages 235-247.
    4. Narine Yegoryan & Daniel Guhl & Friederike Paetz, 2023. "When Zeros Count: Confounding in Preference Heterogeneity and Attribute Non-attendance," Rationality and Competition Discussion Paper Series 482, CRC TRR 190 Rationality and Competition.
    5. Oztekin, Asil & Al-Ebbini, Lina & Sevkli, Zulal & Delen, Dursun, 2018. "A decision analytic approach to predicting quality of life for lung transplant recipients: A hybrid genetic algorithms-based methodology," European Journal of Operational Research, Elsevier, vol. 266(2), pages 639-651.
    6. Yegoryan, Narine & Guhl, Daniel & Klapper, Daniel, 2018. "Inferring Attribute Non-Attendance Using Eye Tracking in Choice-Based Conjoint Analysis," Rationality and Competition Discussion Paper Series 111, CRC TRR 190 Rationality and Competition.
    7. Yegoryan, Narine & Guhl, Daniel & Klapper, Daniel, 2020. "Inferring attribute non-attendance using eye tracking in choice-based conjoint analysis," Journal of Business Research, Elsevier, vol. 111(C), pages 290-304.
    8. Díaz, Verónica & Montoya, Ricardo & Maldonado, Sebastián, 2023. "Preference estimation under bounded rationality: Identification of attribute non-attendance in stated-choice data using a support vector machines approach," European Journal of Operational Research, Elsevier, vol. 304(2), pages 797-812.
    9. Karlson Pfannschmidt & Pritha Gupta & Bjorn Haddenhorst & Eyke Hullermeier, 2019. "Learning Context-Dependent Choice Functions," Papers 1901.10860, arXiv.org, revised Oct 2021.
    10. Franke, Melanie & Nadler, Claudia, 2019. "Energy efficiency in the German residential housing market: Its influence on tenants and owners," Energy Policy, Elsevier, vol. 128(C), pages 879-890.
    11. Hein, Maren & Goeken, Nils & Kurz, Peter & Steiner, Winfried J., 2022. "Using Hierarchical Bayes draws for improving shares of choice predictions in conjoint simulations: A study based on conjoint choice data," European Journal of Operational Research, Elsevier, vol. 297(2), pages 630-651.
    12. Bathke, Henrik & Hartmann, Evi, 2021. "Accepting a crowdsourced delivery - A choice-based conjoint analysis," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Jahn, Carlos & Kersten, Wolfgang & Ringle, Christian M. (ed.), Adapting to the Future: Maritime and City Logistics in the Context of Digitalization and Sustainability. Proceedings of the Hamburg International Conf, volume 32, pages 65-95, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.

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