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A Bayesian approach for incorporating expert opinions into decision support systems: A case study of online consumer-satisfaction detection

Author

Listed:
  • Kristof Coussement

    (LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique)

  • D.F. Benoit
  • M. Antioco

Abstract

Interest in the use of (big) company data and data-mining models to guide decisions exploded in recent years. In many domains there are human experts whose knowledge is essential in building, interpreting and applying these models. However, the impact of integrating expert opinions into the decision-making process has not been sufficiently investigated. This research gap deserves attention because the triangulation of information sources is critical for the success of analytical projects. This paper contributes to the decision-making literature by (a) detailing the natural advantages of the Bayesian framework for fusing multiple information sources into one decision support system (DSS), (b) confirming the necessity for adjusted methods in this data-explosion era, and (c) opening the path to future applications of Bayesian DSSs in other organizational research contexts. In concrete, we propose a Bayesian decision support framework that formally fuses subjective human expert opinions with more objective organizational information. We empirically test the proposed Bayesian fusion approach in the context of a customer-satisfaction prediction study and show how it improves the prediction performance of the human experts and a data-mining model ignoring expert information.

Suggested Citation

  • Kristof Coussement & D.F. Benoit & M. Antioco, 2015. "A Bayesian approach for incorporating expert opinions into decision support systems: A case study of online consumer-satisfaction detection," Post-Print hal-02990768, HAL.
  • Handle: RePEc:hal:journl:hal-02990768
    DOI: 10.1016/j.dss.2015.07.006
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    Citations

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    Cited by:

    1. Antioco, Michael & Coussement, Kristof, 2018. "Misreading of consumer dissatisfaction in online product reviews: Writing style as a cause for bias," International Journal of Information Management, Elsevier, vol. 38(1), pages 301-310.
    2. Kim, Phillip H. & Kotha, Reddi & Fourné, Sebastian P.L. & Coussement, Kristof, 2019. "Taking leaps of faith: Evaluation criteria and resource commitments for early-stage inventions," Research Policy, Elsevier, vol. 48(6), pages 1429-1444.
    3. Tan, Kim Hua & Ji, Guojun & Chung, Leanne & Wang, Ching-Hsin & Chiu, Anthony & Tseng, M.L., 2019. "Riding the wave of belt and road initiative in servitization: Lessons from China," International Journal of Production Economics, Elsevier, vol. 211(C), pages 15-21.
    4. Zhan, Yuanzhu & Tan, Kim Hua, 2020. "An analytic infrastructure for harvesting big data to enhance supply chain performance," European Journal of Operational Research, Elsevier, vol. 281(3), pages 559-574.
    5. Arno de Caigny & Kristof Coussement & Koen W. de Bock & Stefan Lessmann, 2019. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," Post-Print hal-02275958, HAL.
    6. Steven Debaere & Floris Devriendt & Johanna Brunneder & Wouter Verbeke & Tom de Ruyck & Kristof Coussement, 2019. "Reducing inferior member community participation using uplift modeling: Evidence from a field experiment," Post-Print hal-02990787, HAL.
    7. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W. & Lessmann, Stefan, 2020. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1563-1578.
    8. Swaminathan, Kritika & Venkitasubramony, Rakesh, 2024. "Demand forecasting for fashion products: A systematic review," International Journal of Forecasting, Elsevier, vol. 40(1), pages 247-267.

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