Modeling Air-travel Booking Behavior and Market Dynamics

About the Project

This project aims at the study of consumer behavior in online airline markets.

The rise of the Internet and the shift of consumer activities in digital space has strong impact on individual and firm behavior. Consumers can reach a larger pool of offers. This results in potential increase in consumer satisfaction, but also in information overload. One solution recently developed by the industry is to offer aggregators. These websites collect a high number of offers for any consumer search. They are confronted with the question of how to present these offers to the consumer. In this respect, anticipating consumer behavior from browsing (search), to conversion (decision to buy), and further to choice (decision what to buy) becomes an important challenge.
This project aims at the in-depth study of consumer behavior in online environments and at the analysis of its implications on the example of the air-travel market. It will heavily rely on a unique, and very large pool of data describing market characteristics and consumer behavior in incredible detail provided by the industrial partner – Amadeus.

Principal Investigator
Project's partner(s)
  • Laboratories:
    • GREDEG
    • LJAD
    • North Carolina State University
    • Griffith University
  • Industrial Partner:
    • Amadeus
Duration
  • September 2018 - August 2022
Total Amount
  • 195 000 euros
Publications
  • Mirzayev E., Babutsidze Z., Rand W. and Delahaye T. (2021) Use of clustering for consideration set modelling in recommender systems. Proceedings of the 54th Hawaii International Conference on System Sciences (HICSS-54).
    https://scholarspace.manoa.hawaii.edu/bitstream/10125/71135/0420.pdf
  • Rafaï I., Babutsidze Z., Delahaye T., Hanaki N., Acuna-Agost R. (2022) No Evidence of Attraction Effect Among Recommended Options: A large-scale field experiment on an online flight aggregator. Decision Support Systems.
    https://www.sciencedirect.com/science/article/abs/pii/S0167923621001822
  • Mirzayev E., Babutsidze Z. (2022) User control and acceptance of recommender systems. Proceedings of the 24th Annual Conference of the Southern Association for Information Systems (SAIS2022).
     
Related References
  • Acuna-Agost, R., T. Delahaye, N. Bondoux, A. Nguyen & M. Boudia (2016) Data-driven models for itinerary preferences of air travelers and application for dynamic pricing optimization. 56th AGIFORS Symposium 2016, Santiago, Chile.
  • Ariely, D. (2008) Predictably Irrational: The Hidden Forces That Shape Our Decisions. Harper Collins, New York, NY.
  • Babutsidze, Z. (2012) How do consumers make choices? A survey of evidence. Journal of Economic Surveys 26(4):752–762.
  • Babutsidze, Z. (2014) Changing social network architectures. Annual Complexity in Business Conference. Washington DC.
  • Ben-Akiva, M. & S. Lerman (1985) Discrete choice analysis: Theory and application to travel demand. MIT Press.
  • Chioveanu, I. & J. Zhou (2013) Price competition with consumer confusion. Management Science 59:2450-2469.
  • Collins, A. & L. Thomas (2014) Learning competitive dynamic airline pricing under different customer models. Journal of Revenue and Pricing Management 12:416-430.
  • Hanaki, N., N. Jacquemet, S. Luchini & A. Zylbersztejin (2016) Cognitive ability and the effect of strategic uncertainty. Theory and Decision 81:101-121.
  • Huber, J., J. Payne & C. Puto (1982) “Adding asymmetrically dominated alternatives: Violations of regularity and the similarity hypothesis,” Journal of Consumer Research 9:90-98.
  • Lounici, K. (2014) High-dimensional covariance matrix estimation with missing observations. Bernoulli 20:1029–1058.
  • Rand, W. & R Rust (2011) Agent-based modeling in marketing: Guidelines for rigor. International Journal of Research in Marketing 28:181-193.
  • Shen, Z. & X. Su (2007) Customer behavior modeling in revenue management and auctions: A review and new research opportunities. Production and Operations Management 16:713- 728.
  • Train, K. (2003) Discrete choice methods with simulation. Cambridge University Press.
Related Documentation