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Determinants of customer satisfaction with airline services: An analysis of customer feedback big data

Author

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  • Park, Eunil
  • Jang, Yeonju
  • Kim, Jina
  • Jeong, Nam Jeong
  • Bae, Kunwoo
  • del Pobil, Angel P.

Abstract

Given that the global airline industry is considered one of the most competitive markets, numerous attempts have been made to explore strategies for success in this industry. Among these, improving customers’ perceived satisfaction is an important topic for airline service providers. Thus, the current study investigates the potential determinants of customers’ perceived satisfaction with airline services through a close examination of their feedback on these services. A sentiment analysis is conducted on a dataset comprising feedback from more than 133,000 customers. Applying the structural equation modeling method in the proposed research model reveals that customers’ affective values have notable effects on their satisfaction with airline services. Moreover, notable statistical differences in customer groups exist between low-cost carrier and full-service carrier services. The current study presents both managerial and practical implications.

Suggested Citation

  • Park, Eunil & Jang, Yeonju & Kim, Jina & Jeong, Nam Jeong & Bae, Kunwoo & del Pobil, Angel P., 2019. "Determinants of customer satisfaction with airline services: An analysis of customer feedback big data," Journal of Retailing and Consumer Services, Elsevier, vol. 51(C), pages 186-190.
    • Handle: RePEc:eee:joreco:v:51:y:2019:i:c:p:186-190
    DOI: 10.1016/j.jretconser.2019.06.009
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    References listed on IDEAS

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    Cited by:

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    2. Mukherjee, Ashesh & Burnham, Thomas & King, Dan, 2021. "Anticipated firm interaction can bias expressed customer satisfaction," Journal of Retailing and Consumer Services, Elsevier, vol. 59(C).
    3. Devika Rani Sharma & Smitha Girija & Pratima Merugu, 2022. "Mediating Role of Perceived Health Risk on Customer Experience and Customer Satisfaction: Evidence from the Airline Industry in India During COVID-19," International Journal of Global Business and Competitiveness, Springer, vol. 17(1), pages 31-45, December.
    4. Farzadnia, Siavash & Raeesi Vanani, Iman, 2022. "Identification of opinion trends using sentiment analysis of airlines passengers' reviews," Journal of Air Transport Management, Elsevier, vol. 103(C).
    5. 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.
    6. Wu, Jia-Jhou & Chang, Sue-Ting, 2020. "Exploring customer sentiment regarding online retail services: A topic-based approach," Journal of Retailing and Consumer Services, Elsevier, vol. 55(C).

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