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How Tourist Group Books Hotels Meeting the Majority Affective Expectations: A Group Selection Frame with Kansei Text Mining and Consensus Coordinating

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  • Jia-Li Chang

    (Nankai University)

  • Hui Li

    (Nankai University)

  • Jian Wu

    (Shanghai Maritime University)

Abstract

The decision on which hotel to book is a high-cost process for individuals and even more for groups due to the information explosion on online travel agent platforms. To assist group selecting the optimal hotel that meets the majority affective expectations, this study proposes a group hotel selection frame for booking with Kansei text mining and consensus coordinating. Firstly, to quantify the tourists’ affective needs for hotels, a Kansei-related dictionary towards hotel domain is constructed by Kansei text mining. Secondly, the bilattice-based Kansei score of hotels is defined and measured to represent hotel affective information scientifically. Then, a group consensus model with minimum adjustment cost is introduced to coordinate consistency of individual preferences in a tourist group. Moreover, a group hotel ranking based on group affective preference is interactively generated to meet the group personalized demands. Finally, a case study on Trip.com is conducted to demonstrate the proposed method.

Suggested Citation

  • Jia-Li Chang & Hui Li & Jian Wu, 2023. "How Tourist Group Books Hotels Meeting the Majority Affective Expectations: A Group Selection Frame with Kansei Text Mining and Consensus Coordinating," Group Decision and Negotiation, Springer, vol. 32(2), pages 327-358, April.
    • Handle: RePEc:spr:grdene:v:32:y:2023:i:2:d:10.1007_s10726-022-09810-0
    DOI: 10.1007/s10726-022-09810-0
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    References listed on IDEAS

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    1. Jian Jin & Danping Jia & Kejia Chen, 2022. "Mining online reviews with a Kansei-integrated Kano model for innovative product design," International Journal of Production Research, Taylor & Francis Journals, vol. 60(22), pages 6708-6727, November.
    2. Si Liu & David Ríos Insua, 2020. "Group Decision Making with Affective Features," Group Decision and Negotiation, Springer, vol. 29(5), pages 843-869, October.
    3. Jian-Wu Bi & Yang Liu & Zhi-Ping Fan & Erik Cambria, 2019. "Modelling customer satisfaction from online reviews using ensemble neural network and effect-based Kano model," International Journal of Production Research, Taylor & Francis Journals, vol. 57(22), pages 7068-7088, November.
    4. Dong, Yucheng & Xu, Yinfeng & Li, Hongyi & Feng, Bo, 2010. "The OWA-based consensus operator under linguistic representation models using position indexes," European Journal of Operational Research, Elsevier, vol. 203(2), pages 455-463, June.
    5. Hong-Bin Yan & Ming Li, 2021. "An uncertain Kansei Engineering methodology for behavioral service design," IISE Transactions, Taylor & Francis Journals, vol. 53(5), pages 497-522, May.
    6. Xiao-kang Wang & Sheng-hui Wang & Hong-yu Zhang & Jian-qiang Wang & Lin Li, 2021. "The Recommendation Method for Hotel Selection Under Traveller Preference Characteristics: A Cloud-Based Multi-Criteria Group Decision Support Model," Group Decision and Negotiation, Springer, vol. 30(6), pages 1433-1469, December.
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