IDEAS home Printed from https://ideas.repec.org/a/spr/infott/v24y2022i1d10.1007_s40558-022-00222-z.html
   My bibliography  Save this article

Deep learning model based on expectation-confirmation theory to predict customer satisfaction in hospitality service

Author

Listed:
  • Soyoung Oh

    (Sungkyunkwan University)

  • Honggeun Ji

    (Raon Data)

  • Jina Kim

    (Raon Data)

  • Eunil Park

    (Sungkyunkwan University)

  • Angel P. del Pobil

    (Jaume I University)

Abstract

Customer satisfaction is one of the most important measures in the hospitality industry. Therefore, several psychological and cognitive theories have been utilized to provide appropriate explanations of customer perception. Owing to recent rapid developments in artificial intelligence and big data, novel methodologies have presented to examine several psychological theories applied in the hospitality industry. Within this framework, this study combines deep learning techniques with the expectation-confirmation theory to elucidate customer satisfaction in hospitality services. Customer hotel review comments, hotel information, and images were employed to predict customer satisfaction with hotel service. The results show that the proposed fused model achieved an accuracy of 83.54%. In addition, the recall value that predicts dissatisfaction improved from 16.46–33.41%. Based on the findings of this study, both academic and managerial implications for the hospitality industry are presented.

Suggested Citation

  • Soyoung Oh & Honggeun Ji & Jina Kim & Eunil Park & Angel P. del Pobil, 2022. "Deep learning model based on expectation-confirmation theory to predict customer satisfaction in hospitality service," Information Technology & Tourism, Springer, vol. 24(1), pages 109-126, March.
    • Handle: RePEc:spr:infott:v:24:y:2022:i:1:d:10.1007_s40558-022-00222-z
    DOI: 10.1007/s40558-022-00222-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40558-022-00222-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s40558-022-00222-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Takaya Saito & Marc Rehmsmeier, 2015. "The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-21, March.
    2. Xiangbin Yan & Jing Wang & Michael Chau, 2015. "Customer revisit intention to restaurants: Evidence from online reviews," Information Systems Frontiers, Springer, vol. 17(3), pages 645-657, June.
    3. Eugene W. Anderson & Mary W. Sullivan, 1993. "The Antecedents and Consequences of Customer Satisfaction for Firms," Marketing Science, INFORMS, vol. 12(2), pages 125-143.
    4. Law, Rob & Li, Gang & Fong, Davis Ka Chio & Han, Xin, 2019. "Tourism demand forecasting: A deep learning approach," Annals of Tourism Research, Elsevier, vol. 75(C), pages 410-423.
    5. Park, Eunil, 2019. "Motivations for customer revisit behavior in online review comments: Analyzing the role of user experience using big data approaches," Journal of Retailing and Consumer Services, Elsevier, vol. 51(C), pages 14-18.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ashraf Mohamed Anas & Ahmed Hassan Abdou & Thowayeb H. Hassan & Wael Mohamed Mahmoud Alrefae & Fathi Mohammed Daradkeh & Maha Abdul-Moniem Mohammed El-Amin & Adam Basheer Adam Kegour & Hanem Mostafa M, 2023. "Satisfaction on the Driving Seat: Exploring the Influence of Social Media Marketing Activities on Followers’ Purchase Intention in the Restaurant Industry Context," Sustainability, MDPI, vol. 15(9), pages 1-21, April.
    2. Tiwari, Veenus & Mishra, Abhishek, 2023. "The effect of a hotel's star-rating-based expectations of safety from the pandemic on during-stay experiences," Journal of Retailing and Consumer Services, Elsevier, vol. 71(C).
    3. Arpaci, Ibrahim, 2023. "Predictors of financial sustainability for cryptocurrencies: An empirical study using a hybrid SEM-ANN approach," Technological Forecasting and Social Change, Elsevier, vol. 196(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hwang, Syjung & Kim, Jina & Park, Eunil & Kwon, Sang Jib, 2020. "Who will be your next customer: A machine learning approach to customer return visits in airline services," Journal of Business Research, Elsevier, vol. 121(C), pages 121-126.
    2. Wasib B Latif & Md. Aminul Islam & Idris Bin Mohd Noor, 2014. "A Conceptual Framework to Build Brand Loyalty in the Modern Marketing Environment," Journal of Asian Scientific Research, Asian Economic and Social Society, vol. 4(10), pages 547-557, October.
    3. Philipp Afèche & Mojtaba Araghi & Opher Baron, 2017. "Customer Acquisition, Retention, and Service Access Quality: Optimal Advertising, Capacity Level, and Capacity Allocation," Manufacturing & Service Operations Management, INFORMS, vol. 19(4), pages 674-691, October.
    4. Angulo-Ruiz, Fernando & Pergelova, Albena & Cheben, Juraj & Angulo-Altamirano, Eladio, 2016. "A cross-country study of marketing effectiveness in high-credence services," Journal of Business Research, Elsevier, vol. 69(9), pages 3636-3644.
    5. Andreas Herrmann & Michael D. Johnson, 1999. "Die Kundenzufriedenheit als Bestimmungsfaktor der Kundenbindung," Schmalenbach Journal of Business Research, Springer, vol. 51(6), pages 579-598, June.
    6. Yeung, Matthew C.H. & Ramasamy, Bala & Chen, Junsong & Paliwoda, Stan, 2013. "Customer satisfaction and consumer expenditure in selected European countries," International Journal of Research in Marketing, Elsevier, vol. 30(4), pages 406-416.
    7. Christopher J Greenwood & George J Youssef & Primrose Letcher & Jacqui A Macdonald & Lauryn J Hagg & Ann Sanson & Jenn Mcintosh & Delyse M Hutchinson & John W Toumbourou & Matthew Fuller-Tyszkiewicz &, 2020. "A comparison of penalised regression methods for informing the selection of predictive markers," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.
    8. Vishal Gaur & Young-Hoon Park, 2007. "Asymmetric Consumer Learning and Inventory Competition," Management Science, INFORMS, vol. 53(2), pages 227-240, February.
    9. Gaurav Khatwani & Gopal Das, 2016. "Evaluating combination of individual pre-purchase internet information channels using hybrid fuzzy MCDM technique: demographics as moderators," International Journal of Indian Culture and Business Management, Inderscience Enterprises Ltd, vol. 12(1), pages 28-49.
    10. Yanlong Guo & Jiaying Yu & Han Zhang & Zuoqing Jiang, 2022. "A Study on Cultural Context Perception in Huizhou Cultural and Ecological Reserve Based on Multi-Criteria Decision Analysis," Sustainability, MDPI, vol. 14(24), pages 1-20, December.
    11. Brady, Michael K. & Robertson, Christopher J. & Cronin, J. Joseph, 2001. "Managing behavioral intentions in diverse cultural environments: an investigation of service quality, service value, and satisfaction for American and Ecuadorian fast-food customers," Journal of International Management, Elsevier, vol. 7(2), pages 129-149.
    12. Tae-Seung Park & Jun-Su Kim & Jiyoun Kim, 2021. "The Impact of Perceived Hapkido Service Quality on Exercise Continuation and Recommendation Intentions, with a Focus on Korean Middle and High School Students," Sustainability, MDPI, vol. 13(6), pages 1-10, March.
    13. Heribert Gierl & Gunter Gehrke, 2004. "Kundenbindung in industriellen Zuliefer-Abnehmer-Beziehungen," Schmalenbach Journal of Business Research, Springer, vol. 56(3), pages 203-236, May.
    14. İhsan Erdem Kayral & Tuğba Sarı & Nisa Şansel Tandoğan Aktepe, 2023. "Forecasting the Tourist Arrival Volumes and Tourism Income with Combined ANN Architecture in the Post COVID-19 Period: The Case of Turkey," Sustainability, MDPI, vol. 15(22), pages 1-20, November.
    15. Taryn Renatta De Mendonca & Yan Zhou, 2019. "Environmental Performance, Customer Satisfaction, and Profitability: A Study among Large U.S. Companies," Sustainability, MDPI, vol. 11(19), pages 1-15, September.
    16. Zhao, Xinxing & Li, Kainan & Ang, Candice Ke En & Cheong, Kang Hao, 2023. "A deep learning based hybrid architecture for weekly dengue incidences forecasting," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    17. Le, Hong Hanh & Viviani, Jean-Laurent, 2018. "Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios," Research in International Business and Finance, Elsevier, vol. 44(C), pages 16-25.
    18. Tingting Song & Jinghua Huang & Yong Tan & Yifan Yu, 2019. "Using User- and Marketer-Generated Content for Box Office Revenue Prediction: Differences Between Microblogging and Third-Party Platforms," Service Science, INFORMS, vol. 30(1), pages 191-203, March.
    19. Polyzos, Stathis & Samitas, Aristeidis & Kampouris, Ilias, 2021. "Economic stimulus through bank regulation: Government responses to the COVID-19 crisis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 75(C).
    20. Eelco Kappe & Ashley Stadler Blank & Wayne S. DeSarbo, 2014. "A General Multiple Distributed Lag Framework for Estimating the Dynamic Effects of Promotions," Management Science, INFORMS, vol. 60(6), pages 1489-1510, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:infott:v:24:y:2022:i:1:d:10.1007_s40558-022-00222-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.