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An opinion-driven decision-support framework for benchmarking hotel service

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  • Park, Jaehun
  • Lee, Byung Kwon

Abstract

Service-quality is a major determinant of tourists’ choice of hotel. Tourists are likely to refer to user-created online reviews on online forums and other social networks comprising critical text data resources that represent the service quality experienced. This study develops a decision-support framework for hotel managers to comprehensively estimate the degree of guest satisfaction (i.e., service-quality measure) together with benchmarking guidelines on service quality improvement. The decision-support framework facilitates the discovery of the most important service attributes from the online reviews of 52 five-star hotels in South Korea (data preprocessing component). It clusters the reviews according to the discovered service attributes and conducts sentiment analysis to estimate the magnitude of positive opinions (sentiment analysis component). Further, it applies an output-oriented data envelopment analysis to calculate the degree of guest satisfaction and service positioning for each hotel (benchmarking analysis component). The framework also investigates how the service quality of each hotel is associated with that of others in terms of each service attribute (quality association analysis component). The framework enables managers to comprehensively understand the achievement goals of service quality for the service attributes by means of online review data analytics and modeling.

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  • Park, Jaehun & Lee, Byung Kwon, 2021. "An opinion-driven decision-support framework for benchmarking hotel service," Omega, Elsevier, vol. 103(C).
    • Handle: RePEc:eee:jomega:v:103:y:2021:i:c:s0305048321000244
    DOI: 10.1016/j.omega.2021.102415
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    1. David A. Hensher & Paola Prioni, 2002. "A Service Quality Index for Area-wide Contract Performance Assessment," Journal of Transport Economics and Policy, University of Bath, vol. 36(1), pages 93-113, January.
    2. Yuanzhu Zhan & Kim Hua Tan & Yina Li & Ying Kei Tse, 2018. "Unlocking the power of big data in new product development," Annals of Operations Research, Springer, vol. 270(1), pages 577-595, November.
    3. B. Hollingsworth & P. Smith, 2003. "Use of ratios in data envelopment analysis," Applied Economics Letters, Taylor & Francis Journals, vol. 10(11), pages 733-735.
    4. Ng, Wan Lung, 2007. "A simple classifier for multiple criteria ABC analysis," European Journal of Operational Research, Elsevier, vol. 177(1), pages 344-353, February.
    5. Oliveira, Ricardo & Pedro, Maria Isabel & Marques, Rui Cunha, 2013. "Efficiency and its determinants in Portuguese hotels in the Algarve," Tourism Management, Elsevier, vol. 36(C), pages 641-649.
    6. Zhou, Peng & Fan, Liwei, 2007. "A note on multi-criteria ABC inventory classification using weighted linear optimization," European Journal of Operational Research, Elsevier, vol. 182(3), pages 1488-1491, November.
    7. Joe Zhu, 2014. "Data Envelopment Analysis," International Series in Operations Research & Management Science, in: Quantitative Models for Performance Evaluation and Benchmarking, edition 3, chapter 1, pages 1-9, Springer.
    8. Yin, Pengzhen & Chu, Junfei & Wu, Jie & Ding, Jingjing & Yang, Min & Wang, Yuhong, 2020. "A DEA-based two-stage network approach for hotel performance analysis: An internal cooperation perspective," Omega, Elsevier, vol. 93(C).
    9. Hong-Sheng Chang, 2008. "Increasing hotel customer value through service quality cues in Taiwan," The Service Industries Journal, Taylor & Francis Journals, vol. 28(1), pages 73-84, January.
    10. Chiang Kao, 2017. "Network Data Envelopment Analysis," International Series in Operations Research and Management Science, Springer, number 978-3-319-31718-2, December.
    11. Cook, Wade D. & Tone, Kaoru & Zhu, Joe, 2014. "Data envelopment analysis: Prior to choosing a model," Omega, Elsevier, vol. 44(C), pages 1-4.
    12. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    13. Moutaz Haddara & Jenny Hsieh & Asle Fagerstrøm & Niklas Eriksson & Valdimar Sigurðsson, 2020. "Exploring customer online reviews for new product development: The case of identifying reinforcers in the cosmetic industry," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 41(2), pages 250-273, March.
    14. Lado-Sestayo, Rubén & Fernández-Castro, Ángel Santiago, 2019. "The impact of tourist destination on hotel efficiency: A data envelopment analysis approach," European Journal of Operational Research, Elsevier, vol. 272(2), pages 674-686.
    15. William W. Cooper & Lawrence M. Seiford & Kaoru Tone, 2007. "Data Envelopment Analysis," Springer Books, Springer, edition 0, number 978-0-387-45283-8, December.
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    2. Adjei Peter Darko & Decui Liang & Yinrunjie Zhang & Agbodah Kobina, 2023. "Service quality in football tourism: an evaluation model based on online reviews and data envelopment analysis with linguistic distribution assessments," Annals of Operations Research, Springer, vol. 325(1), pages 185-218, June.
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    5. Yinfeng Du & Zhen-Song Chen & Jie Yang & Juan Antonio Morente-Molinera & Lu Zhang & Enrique Herrera-Viedma, 2023. "A Textual Data-Oriented Method for Doctor Selection in Online Health Communities," Sustainability, MDPI, vol. 15(2), pages 1-19, January.

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