Understanding Determinents of Making Airline Entry and Exit Decisions: Application of Logit Models

Abstract

Understanding patterns of entry and exit decisions and determinants shaping the patterns are necessary for airline planners in drawing a robust route map and gaining their own competitive advantages. The study used logit models to exam the relationship between two separate binary dependent variables: entry versus no-entry, exit versus no-exit, and multiple independent variables. Dataset was extracted from the Bureau of Transportation Statistics DB1B for Quarter 1 of 2018, then was reconstructed based on original and destination (O&D) airport pairs to gain insights. The entry decision pattern model yielded seven significant factors: total passengers, average market fare, number of carriers, distance, low-cost carriers (LCC) existence, origin hub, and destination hub. In the meantime, the exit decision pattern model yielded all the seven aforementioned factors and two other significant factors: route type and the business model of the largest share airline. The findings made a practical implication to airline network planners in considering determinants affecting entry and exit decisions to build a more efficient and profitable network.

A Data Mining Approach to Building a Predictive Model of Low-Cost Carriers' Presence in the U.S. Domestic Routes

“…Additional difficulties in the rising price of jet fuel thereafter led to a wave of consolidation among players that consequently arrived at six major LCCs in April 2018. These six major LCCs were found to have a negative relationship to route entry and exit decisions of competing airlines (Nguyen & Nguyen, 2018). Bachwich and Wittman (2017) considered factors enabling the emergence of a new variant of the LCC model, ultra-low-cost carriers (ULCCs).…”