Assessing the effects of flight delays, distance, number of passengers and seasonality on revenue

Güven M., ÇALIK E., Cetinguc B., Güloğlu B., Çalışır F.

KYBERNETES, vol.48, no.9, pp.2138-2149, 2019 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 48 Issue: 9
  • Publication Date: 2019
  • Doi Number: 10.1108/k-01-2018-0022
  • Journal Name: KYBERNETES
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.2138-2149
  • Istanbul Technical University Affiliated: Yes


Purpose This study aims to investigate the effects of flight delays, distance, number of passengers and seasonality on revenue in the Turkish air transport industry. Design/methodology/approach The domestic return routes of a Turkish airline company were examined to address this issue. Among five cities and six airports, 14 major domestic return routes were selected. The augmented mean group (AMG) estimator and common correlated effects mean group (CCEMG) estimator were conducted with a two-way fixed effects (FE) robustness test in this study. Findings The results show that arrival flight delay and departure flight delay had negative effects on revenue, whereas the distance between airports, the number of air passengers and seasonality had positive effects on revenue. Research limitations/implications - The data used in this study were retrieved from a Turkish airline company; for future research, other airline companies operating in Turkey may be included. Practical implications - These findings could be evaluated by air transportation leaders to provide a guide to make strategic decisions to achieve greater performance in this competitive environment. Originality/value The originality of the paper comes from the facts that besides distance and number of passengers, the authors control for the seasonality when assessing the effects of flight delay on revenue; they use panel data techniques, which permit them to control for individual heterogeneity, and create more variability, more efficiency and less collinearity among the variables; they use two recent panel data techniques, CCEMG and AMG, allowing for cross-section dependence.