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Identifying emerging hotel preferences using Emerging Pattern Mining technique

Author

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  • Li, Gang
  • Law, Rob
  • Vu, Huy Quan
  • Rong, Jia
  • Zhao, Xinyuan (Roy)

Abstract

Hotel managers continue to find ways to understand traveler preferences, with the aim of improving their strategic planning, marketing, and product development. Traveler preference is unpredictable; for example, hotel guests used to prefer having a telephone in the room, but now favor fast Internet connection. Changes in preference influence the performance of hotel businesses, thus creating the need to identify and address the demands of their guests. Most existing studies focus on current demand attributes and not on emerging ones. Thus, hotel managers may find it difficult to make appropriate decisions in response to changes in travelers' concerns. To address these challenges, this paper adopts Emerging Pattern Mining technique to identify emergent hotel features of interest to international travelers. Data are derived from 118,000 records of online reviews. The methods and findings can help hotel managers gain insights into travelers' interests, enabling the former to gain a better understanding of the rapid changes in tourist preferences.

Suggested Citation

  • Li, Gang & Law, Rob & Vu, Huy Quan & Rong, Jia & Zhao, Xinyuan (Roy), 2015. "Identifying emerging hotel preferences using Emerging Pattern Mining technique," Tourism Management, Elsevier, vol. 46(C), pages 311-321.
    • Handle: RePEc:eee:touman:v:46:y:2015:i:c:p:311-321
    DOI: 10.1016/j.tourman.2014.06.015
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    Cited by:

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    5. A Fronzetti Colladon & B Guardabascio & R Innarella, 2021. "Using social network and semantic analysis to analyze online travel forums and forecast tourism demand," Papers 2105.07727, arXiv.org.
    6. Blazquez, Desamparados & Domenech, Josep, 2018. "Big Data sources and methods for social and economic analyses," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 99-113.
    7. Ahani, Ali & Nilashi, Mehrbakhsh & Yadegaridehkordi, Elaheh & Sanzogni, Louis & Tarik, A. Rashid & Knox, Kathy & Samad, Sarminah & Ibrahim, Othman, 2019. "Revealing customers’ satisfaction and preferences through online review analysis: The case of Canary Islands hotels," Journal of Retailing and Consumer Services, Elsevier, vol. 51(C), pages 331-343.
    8. Xiuming Yu & Meijing Li & Kyung Ah Kim & Jimoon Chung & Keun Ho Ryu, 2016. "Emerging Pattern-Based Clustering of Web Users Utilizing a Simple Page-Linked Graph," Sustainability, MDPI, vol. 8(3), pages 1-18, March.
    9. Park, Eunhye & Park, Jinah & Hu, Mingming, 2021. "Tourism demand forecasting with online news data mining," Annals of Tourism Research, Elsevier, vol. 90(C).
    10. Pantano, Eleonora & Priporas, Constantinos-Vasilios & Stylos, Nikolaos, 2017. "‘You will like it!’ using open data to predict tourists' response to a tourist attraction," Tourism Management, Elsevier, vol. 60(C), pages 430-438.
    11. Mellinas, Juan Pedro & Nicolau, Juan Luis, 2019. "Asymmetric effects of WiFi on overall satisfaction," Annals of Tourism Research, Elsevier, vol. 78(C), pages 1-1.
    12. Kedar Pandurang Joshi & Amol Dhaigude, 2021. "Revenue management for homestay with TODIM-integrated EMSR-b," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(2), pages 134-148, April.
    13. Gang Chen & Shuaiyong Xiao & Chenghong Zhang & Huimin Zhao, 2023. "A Theory-Driven Deep Learning Method for Voice Chat–Based Customer Response Prediction," Information Systems Research, INFORMS, vol. 34(4), pages 1513-1532, December.
    14. Silvia Emili & Paolo Figini & Andrea Guizzardi, 2020. "Modelling international monthly tourism demand at the micro destination level with climate indicators and web-traffic data," Tourism Economics, , vol. 26(7), pages 1129-1151, November.
    15. Guizzardi, Andrea & Pons, Flavio Maria Emanuele & Angelini, Giovanni & Ranieri, Ercolino, 2021. "Big data from dynamic pricing: A smart approach to tourism demand forecasting," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1049-1060.
    16. Marie Al-Ghossein & Talel Abdessalem & Anthony Barré, 2018. "Open data in the hotel industry: leveraging forthcoming events for hotel recommendation," Information Technology & Tourism, Springer, vol. 20(1), pages 191-216, December.
    17. Chloé Duvivier & Claire Bussière, 2022. "The contingent nature of broadband as an engine for business startups in rural areas," Journal of Regional Science, Wiley Blackwell, vol. 62(5), pages 1329-1357, November.
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