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Analyzing customer satisfaction in self-service technology adopted in airports

Author

Listed:
  • Hon Keung Yau

    (City University of Hong Kong)

  • Ho Yi Horace Tang

    (City University of Hong Kong)

Abstract

Customer satisfaction level is one of key performance indicators in the service industry. The various factors affecting this are studied to maintain an excellent relationship with customers. Self-service technology (SST) is widely implemented by companies in service sector. This paper proposes to apply the customer satisfaction survey to investigate factors influencing the customer satisfaction. The relationships among the factors are discovered using PC-algorithm. The critical factors are identified to be inputs in regression tree and ANN to estimate the customer satisfaction level. By means of comparison of models, importance of selected inputs is quantified and discussed. The results show that customer satisfaction has strong connectivity relationship with personal service attributes as well as affective and temporal commitment by running PC-algorithm. ANN validated by 10-fold cross validation is the best among the models. The most important factor influencing the satisfaction level to the companies is the customer’s desire of continuing a relationship. The key benefit of the proposed approach is to avoid making subjective decisions, for instance, building a plausible initial path models in the analysis. The analytical results facilitate the decision-making process and better resource allocation in the airline and state its future development of self-service technology.

Suggested Citation

  • Hon Keung Yau & Ho Yi Horace Tang, 2018. "Analyzing customer satisfaction in self-service technology adopted in airports," Journal of Marketing Analytics, Palgrave Macmillan, vol. 6(1), pages 6-18, March.
    • Handle: RePEc:pal:jmarka:v:6:y:2018:i:1:d:10.1057_s41270-017-0026-2
    DOI: 10.1057/s41270-017-0026-2
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    References listed on IDEAS

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    1. Amanda Beatson & Nick Lee & Leonard V. Coote, 2007. "Self-Service Technology and the Service Encounter," The Service Industries Journal, Taylor & Francis Journals, vol. 27(1), pages 75-89, January.
    2. Oliver, Richard L, 1993. "Cognitive, Affective, and Attribute BAses of the Satisfaction Response," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 20(3), pages 418-430, December.
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    Cited by:

    1. Jiang, Yirui & Tran, Trung Hieu & Williams, Leon, 2023. "Machine learning and mixed reality for smart aviation: Applications and challenges," Journal of Air Transport Management, Elsevier, vol. 111(C).
    2. Antwi, Collins Opoku & Fan, Chong-jun & Nataliia, Ihnatushchenko & Aboagye, Michael Osei & Xu, Hangyu & Azamela, John Coffie, 2020. "Do airport staff helpfulness and communication generate behavioral loyalty in transfer passengers? A conditional mediation analysis," Journal of Retailing and Consumer Services, Elsevier, vol. 54(C).
    3. Zihayat, Morteza & Ayanso, Anteneh & Davoudi, Heidar & Kargar, Mehdi & Mengesha, Nigussie, 2021. "Leveraging non-respondent data in customer satisfaction modeling," Journal of Business Research, Elsevier, vol. 135(C), pages 112-126.
    4. Pritha Ghosh & Subrata Saha & Shamindra Nath Sanyal & Swati Mukherjee, 2021. "Positioning of private label brands of men’s apparel against national brands," Journal of Marketing Analytics, Palgrave Macmillan, vol. 9(3), pages 210-227, September.

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