Journal of Distribution Science (유통과학연구)

Korea Distribution Science Association (한국유통과학회)
권호 표지
DOI QR코드

DOI QR Code

Distribution of Air Tickets through Online Platform Recommendation Algorithms

  • Soyeon PARK (Hanseo University)
  • Received : 2024.07.10
  • Accepted : 2024.09.05
  • Published : 2024.09.26
Download PDF
⟨ Previous Next ⟩

Abstract

Purpose: The purpose of this study is to collect and analyze a large amount of data from online ticket distribution platforms that offer multiple airlines and different routes so that they can improve their ticket distribution marketing strategies and provide services that are more suitable for consumer's needs. The results of this study will help airlines improve the quality of their online platform services to provide more benefits and convenience by providing access to multiple airlines and routes around the world on one platform. Research design, data and methodology: For the study, 200 people completed the survey between May 1 and June 15, 2024, of which 191 copies were used in the study. Results: The hypothesis testing results of this study showed that among the components of the recommendation algorithm, decision comport, novelty, and evoked interest recurrence had a positive effect on perceived recommendation quality, but curiosity did not have a positive effect on recommendation quality. The perceived recommendation quality of the online platform positively influenced recommendation satisfaction, and the higher the perceived recommendation quality, the higher the intention to continue the relationship. Finally, higher recommendation satisfaction was associated with higher relationship continuation intention. Conclusion: it's important to continue researching online ticketing platforms. Online platforms will also need to be systems that use technology and data analytics to provide a better user experience and more benefits.

Keywords

Acknowledgement

The study was written with the support of a research fund from Hanseo University.

References

  1. Adomavicius, G., & Tuzhilin, A. (2005). Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734-749.
  2. Benlian, A. (2015). Web Personalization Cues and Their Differential Effects on User Assessments of Website Value. Journal of Management Information Systems, 32(1), 225-260.
  3. Burke, R. (2002). Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction, 12(4), 331-370.
  4. Caruana, A. (2002). Service Loyalty: The Effects of Service Quality and the Mediating Role of Customer Satisfaction. European Journal of Marketing, 36(7), 811-828.
  5. Chen, Y. C., Hsieh, H. C., & Lin, H. C. (2013). Improved Precision Recommendation Scheme by BPNN Algorithm in O2O Commerce. International Conference on e-Business Engineering, 10, 324-328.
  6. Choi, K., Wang, Y., Sparks, B. A., & Choi, S. M. (2021). Privacy or Security: Does It Matter for Continued Use Intention of Travel Applications? Cornell Hospitality Quarterly, 64(2), 267-282.
  7. Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-340.
  8. De Gemmis, M., Lops, P., Semeraro, G., & Musto, C. (2015). An Investigation on the Serendipity Problem in Recommender Systems. Information Processing & Management, 51(5), 695-717.
  9. Gong, Z., Wang, H., Nie, Q., Zhang, Z., & Xiao, Q. (2022). The Personalized Recommendation for OTA Flight Cancellation and Change Services During the Pandemic. Journal of Revenue and Pricing Management, 22, 157-165.
  10. Gupta, S., Hanssens, D., Hardie, B., Kahn, W., Kumar, V., Lin, N., & Sriram, S. (2006). Modeling Customer Lifetime Value. Journal of Service Research, 9(2), 139-155.
  11. Hsieh, Y. C., & Hiang, S. T. (2004). A study of the Impacts of Service Quality on Relationship Quality in Search-Experience-Credence Services. Total Quality Management, 15(1), 43-58.
  12. Kaminskas, M. & Bridge, D. (2016). Diversity, Serendipity, Novelty, and Coverage: A Survey and Empirical Analysis of Beyond-accuracy Objectives in Recommender Systems. ACM Transactions on Interactive Intelligent Systems, 7(1), 1-42.
  13. Kathleen, S., Glenn, B. V., Andrea, L., & Godfrey, D. G. (2007). SERVCON: Development and Validation of a Multidimensional Service Convenience Scale. Journal of the Academy of Marketing Science, 35(1), 144 -156.
  14. Kedah, Z., Ismail, Y., Haque, A., & Ahmed, S. (2015). Key Success Factors of Online Food Ordering Services: An Empirical Study. Malaysian Management Review, 50(2), 19-36.
  15. Knijnenburg, B.P., Willemsen, M. C., Ganter, Z., Soncu, H., & Newell, C. (2012). Explaining the User Experience of Recommender Systems. User Modeling and User-Adapted Interaction, 22(4), 441-504.
  16. Lee, G. H., Kim, S., & Park, C. K. (2022). Development of Fashion Recommendation System Using Collaborative Deep Learning. International Journal of Clothing Science and Technology, 24(5), 732- 744.
  17. Lee, S.W., Sung, H.J., & Jeon, H.M. (2019). Determinants of Continuous Intention on Food Delivery Apps: Extending UTAUT2 with Information Quality. Sustainability, 11(1), 31-41.
  18. Lin, C. S., Wu, S. & Tsai, R. J. (2005). Integrating Perceived Playfulness into Expectation-Confirmation Model for Web Portal Context. Information and Management, 42(5), 683-693.
  19. Lu, H. P., & Cheng, Y. H. (2020). Sustainability in Online Video Hosting Services: The Effects of Serendipity and Flow Experience on Prolonged Usage Time. Sustainability, 12(3), 1271-1291.
  20. Matt, C., Benlian, A., Hess, T. & Weiss, C. (2015). Digital Transformation Strategies. Business & Information Systems Engineering, 57, 339-343
  21. McCay-Peet, L. & Toms, E. G. (2011). The Serendipity Qquotient. Proceedings of the American Society for Information Science and Technology, .48(1), 1-4.
  22. Meuter, M. L., Bitner, M. J., Ostrom, A. L., & Brown, S. W. (2005). Choosing among Alternative Service Delivery Modes: An Investigation of Customer Trial of Self-Service Technologies. Journal of Marketing, 69(2), 61-83.
  23. Morgan, R. M., & Hunt, S. D. (1994). The Commitment Trust Theory of Relationship Marketing. Journal of Marketing, 58(3), 20-38.
  24. Nilashi, M., Dietmar, J., Othman, B. I., Mohammad, D. E., & Hossein, A. (2016). Recommendation Quality, Transparency, 17 and Website Quality for Trust-building in Recommendation Agents. Electronic Commerce Research and Applications, 19, 70-84.
  25. Oliver, R. L. (1989). Measurement and Evaluation of Satisfaction Process in Retail Setting. Journal of Retailing, 57, 25-48.
  26. Parker, J. R., Lehmann, D. R. & Xie, Y. (2016). Decision Comport. Journal of Consumer Research, 43(1), 113-133.
  27. Peterson, C., & Seligman, M. P. (2004). Character strengths and virtues: A hand Book and classification. Oxford University Press.
  28. Pu, P., Chen, L., & Hu, Rong. (2011). A User-centric Evaluation Framework for Recommender Systems. Proceedings of the Fifth ACM Conference on Recommender Systems, 157-164.
  29. Suhartanto, D., Helmi Ali, M., Tan, K. H., Sjahroeddin, F., & Kusdibyo, L. (2019). Loyalty Toward Online Food Delivery Service: The Role of e-Service Quality and Food Quality. Journal of Food service Business Research, 22(1), 81-97.
  30. Tam, C., Santos, D., & Oliveira, T. (2021). Exploring the Influential Factors of Continuance Intention to Use Mobile Apps: Extending the Expectation Confirmation Model, Information Systems Frontiers, 22, 243-257.
  31. Teo, H. H., Chan, H. C., Wei, K. K., & Zhang, Z. (2003). Evaluating Information Accessibility and Community Adaptivity Features for Sustaining Virtual Learning Communities. International Journal of Human-Computer Studies, 59, 671-697.
  32. Wang, W., & Izak, B. (2009). Interactive Decision Aids for Consumer Decision Making in e-Commerce: The Influence of Perceived Restrictiveness. MIS Quarterly, 33(2), 293-320.
  33. Xiao, B., & Izak, B. (2007). E-commerce Product Recommendation Agents: Use, Characteristics, and Impact. MIS Quarterly, 31(1), 137-209.
  34. Xu, J., Izak, B., & Ronald, T. C. (2014). The Nature and Consequences of Trade-off Transparency in the Context of Recommendation Agents. MIS Quarterly, 38(2), 379-406.
  35. Xuemei, S., Jean-Eric, P., Shiying, D., & Yi, M. (2023). The Effects of Trust, Perceived Risk, Innovativeness, and Deal Proneness on Consumers' Purchasing Behavior in the Livestreaming Social Commerce Context. Sustainability, 15(23), 940-959.
  36. Yan Li., & Huping, S. (2019). Service Quality, Perceived Value, and Citizens' Continuous-Use Intention Regarding e-Government: Empirical Evidence from China, 57(3), 1-15.
  37. Zeithaml, V., Berry, L., & Parasuraman, A. (1996). The Behavioral Consequences of Service Quality. Journal of Marketing, 60 (2), 31-46.
  38. Zhao, G., Muehlmeier, D. & Kareklas, I. (2014). Remembering the Good Old Days: The Moderating Role of Consumer Affective State on the Effectiveness of Nostalgic Advertising. Journal of Advertising,43(3), 244-255.