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Customer-Oriented Strategic Planning for Hotel Competitiveness Improvement Based on Online Reviews

Author

Listed:
  • Yuan Yuan

    (School of Business Administration, Northeastern University, Shenyang 110169, China)

  • Tianhui You

    (School of Business Administration, Northeastern University, Shenyang 110169, China)

  • Tian’ai Xu

    (School of Business Administration, Northeastern University, Shenyang 110169, China)

  • Xun Yu

    (School of Tourism and Hospitality Management, Shenyang Normal University, Shenyang 110034, China)

Abstract

The hotel industry has been facing fierce competition in recent years. It is important for hotels to conduct effective strategic planning for competitiveness improvement to achieve sustainable development. Prior studies on hotel strategic planning mainly use questionnaire data or hotel internal data, which have the problems of insufficient data or neglecting customer perspectives. The purpose of this study was to develop an integrated method for customer-oriented strategic planning for hotel competitiveness improvement based on text mining of online reviews. First, text mining of online reviews was conducted to extract customer-concerned service attributes and evaluate customer concern level and the performance of the service attributes through Latent Dirichlet Allocation (LDA) and sentiment analysis. Second, the competitive structures of the hotels were analyzed and the main competitors were identified from the competitive hotels through correspondence analysis. Third, SWOT analysis of the target hotel toward the main competitors was conducted, and the priorities of factors in each SWOT category were determined. An empirical study on a five-star hotel is given to illustrate the feasibility and effectiveness of the proposed method. The results indicate that the proposed method can help managers in strategic planning to obtain more specific strategies for hotel competitiveness improvement.

Suggested Citation

  • Yuan Yuan & Tianhui You & Tian’ai Xu & Xun Yu, 2022. "Customer-Oriented Strategic Planning for Hotel Competitiveness Improvement Based on Online Reviews," Sustainability, MDPI, vol. 14(22), pages 1-30, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:15299-:d:976152
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    References listed on IDEAS

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    1. King, Robert Allen & Racherla, Pradeep & Bush, Victoria D., 2014. "What We Know and Don't Know About Online Word-of-Mouth: A Review and Synthesis of the Literature," Journal of Interactive Marketing, Elsevier, vol. 28(3), pages 167-183.
    2. Phadermrod, Boonyarat & Crowder, Richard M. & Wills, Gary B., 2019. "Importance-Performance Analysis based SWOT analysis," International Journal of Information Management, Elsevier, vol. 44(C), pages 194-203.
    3. Chang Liu & Yueli Xu, 2021. "Consumer Sentiment Involvement in Big Data Analytics and Its Impact on Product Design Innovation," Sustainability, MDPI, vol. 13(21), pages 1-12, October.
    4. 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.
    5. Elbanna, Said, 2016. "Managers' autonomy, strategic control, organizational politics and strategic planning effectiveness: An empirical investigation into missing links in the hotel sector," Tourism Management, Elsevier, vol. 52(C), pages 210-220.
    6. Moro, Sérgio & Lopes, Rui J. & Esmerado, Joaquim & Botelho, Miguel, 2020. "Service quality in airport hotel chains through the lens of online reviewers," Journal of Retailing and Consumer Services, Elsevier, vol. 56(C).
    7. Xiang, Zheng & Du, Qianzhou & Ma, Yufeng & Fan, Weiguo, 2017. "A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism," Tourism Management, Elsevier, vol. 58(C), pages 51-65.
    8. Heinonen, Kristina & Campbell, Colin & Lord Ferguson, Sarah, 2019. "Strategies for creating value through individual and collective customer experiences," Business Horizons, Elsevier, vol. 62(1), pages 95-104.
    9. Yae-Ji Kim & Hak-Seon Kim, 2022. "The Impact of Hotel Customer Experience on Customer Satisfaction through Online Reviews," Sustainability, MDPI, vol. 14(2), pages 1-13, January.
    10. Guo, Yue & Barnes, Stuart J. & Jia, Qiong, 2017. "Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation," Tourism Management, Elsevier, vol. 59(C), pages 467-483.
    11. Mitra, Satanik & Jenamani, Mamata, 2020. "OBIM: A computational model to estimate brand image from online consumer review," Journal of Business Research, Elsevier, vol. 114(C), pages 213-226.
    12. Dyson, Robert G., 2004. "Strategic development and SWOT analysis at the University of Warwick," European Journal of Operational Research, Elsevier, vol. 152(3), pages 631-640, February.
    13. Phillips, Paul & Moutinho, Luiz, 2014. "Critical review of strategic planning research in hospitality and tourism," Annals of Tourism Research, Elsevier, vol. 48(C), pages 96-120.
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