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Hotel Service Analysis by Penalty-Reward Contrast Technique for Online Review Data

Author

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  • Wen Tu Zhang

    (Department of Big Data Analytics, Graduate School, Kyung Hee University, 24 Kyungheedae-ro, Dongdaemun-gu, Seoul 02453, Korea)

  • Il Young Choi

    (Department of Big Data Analytics, Graduate School, Kyung Hee University, 24 Kyungheedae-ro, Dongdaemun-gu, Seoul 02453, Korea)

  • Yun Joo Hyun

    (Department of Business Administration, Graduate School, Kyung Hee University, 24 Kyungheedae-ro, Dongdaemun-gu, Seoul 02453, Korea)

  • Jae Kyeong Kim

    (Department of Big Data Analytics, Graduate School, Kyung Hee University, 24 Kyungheedae-ro, Dongdaemun-gu, Seoul 02453, Korea
    Department of Business Administration, Kyung Hee University, 24 Kyungheedae-ro, Dongdaemun-gu, Seoul 02453, Korea)

Abstract

Hotel reviews play an important role in the selection of hotels by travelers. Online travel platforms (e.g., Tripadvisor, Expedia) provide multi-criteria (e.g., room, service, location, sleep quality, etc.) ratings to make it easier for travelers to choose a hotel from reviews. Through penalty-reward contrast analysis (PRCA), this study aims to explore the asymmetric effects of attribute performance ( Value , Cleanliness , Location , Rooms , Service , and Sleep Quality ) on customer satisfaction with different geographic and cultural backgrounds using review data from hotels in Shanghai, Seoul, and New York. This study compares the asymmetric effects of attribute performance on customer satisfaction of hotels in different cities. At the same time, this study compares the asymmetric effects of attribute performance on customer satisfaction of reviews that are written in English and reviews that are written in the domestic language of hotels in the same city. The findings of this study help hotel managers serve customers from different cultural backgrounds and improve hotel services by identifying the criteria that affect customer satisfaction. As a result, it will be possible to improve the service and profitability of the hotel.

Suggested Citation

  • Wen Tu Zhang & Il Young Choi & Yun Joo Hyun & Jae Kyeong Kim, 2022. "Hotel Service Analysis by Penalty-Reward Contrast Technique for Online Review Data," Sustainability, MDPI, vol. 14(12), pages 1-14, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:12:p:7340-:d:839570
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    References listed on IDEAS

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    1. Wu, Xinyi & Cao, Jason & Huting, Joel, 2018. "Using three-factor theory to identify improvement priorities for express and local bus services: An application of regression with dummy variables in the Twin Cities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 113(C), pages 184-196.
    2. Youjae Yi & Suna La, 2003. "The Moderating Role of Confidence in Expectations and the Asymmetric Influence of Disconfirmation on Customer Satisfaction," The Service Industries Journal, Taylor & Francis Journals, vol. 23(5), pages 20-47, November.
    3. Michele Preziosi & Alessia Acampora & Maria Claudia Lucchetti & Roberto Merli, 2022. "Delighting Hotel Guests with Sustainability: Revamping Importance-Performance Analysis in the Light of the Three-Factor Theory of Customer Satisfaction," Sustainability, MDPI, vol. 14(6), pages 1-20, March.
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    Cited by:

    1. Harman Preet Singh & Ibrahim Abdullah Alhamad, 2022. "A Novel Categorization of Key Predictive Factors Impacting Hotels’ Online Ratings: A Case of Makkah," Sustainability, MDPI, vol. 14(24), pages 1-25, December.

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