Race to the bottom or swimming upstream: Performance analysis of US airlines

Abstract

a b s t r a c tData envelopment analysis is used to examine inter-temporal and peer group airline efficiency. Results for the US for 1985-2006 indicate that airline performance is converging over time. In particular, airlines inter-temporal inefficiency peaked earlier and then converged. Furthermore, using Tobit specifications it is seen that while demand intensity matters less in determining airlines inter-temporal inefficiency, their influence is stronger in determining peer group inefficiency. Block time, a representative of operational factors, tends to negatively impact airlines efficiency by imposing burdens on airline operations. Among the structural cost and revenue factors, fuel cost tends to affect inter-temporal inefficiency more robustly than it does to peer group efficiency. Labor pay tends to reduce inefficiency in case of inter-temporal while increasing peer group inefficiency. The events of September 11th had little or no impact on intertemporal inefficiency but tended to reduce peer group inefficiency in a significant way. Finally, airlines efficiency tends to be robustly affected by block hours; reducing them increases efficiency.

De-hubbing of airports and their recovery patterns

“…For example, Alitalia abandoned the Malpensa airport on March 31, 2008, cutting 180 flights a day and moving 14 intercontinental routes to the Rome Fiumicino airport. The partial or complete abandonment of a hub by the dominant carrier is known as "dehubbing" (Bhadra, 2009), and refers to the process of dismantling the structure of connections offered by the hub airline. De-hubbing can result in an airline's complete withdrawal from an airport.…”

An analysis of Asian airlines efficiency with two-stage TOPSIS and MCMC generalized linear mixed models

“…Furthermore, this Bauer (1990) adopted by Oum and Yu (1995) and Barbot et al (2008). And, finally, the contemporary stochastic econometric frontier models (e.g., Cornwell et al, 1990;Good et al, 1993;Sickles, 1985;Sickles et al, 1986;Captain and Sickles, 1997;Coelli et al, 1999;Inglada et al, 2006) and the DEA models (e.g., Distexhe and Perelman, 1994;Good et al, 1995;Adler and Golany, 2001;Fethi et al, 2001;Scheraga, 2004;Greer, 2008;Bhadra, 2009;Gitto and Mancuso, 2012). Caves et al (1981) assessed the productivity of eleven US airlines for the period 1972-1977.…”

Impact of Economic Performance in Organizational Capacity, Capability: A Case Study in Airline Industry

“…Some airline companies, especially governmental or public sector, receive financial support from their governments under the name of subsidy (Bhadra, 2009), which can be one of the most influential factors affecting a company's performance. According to the limitations in data and data collection, as mentioned earlier, this factor, as a moderator or internal operation variable, was not taken into consideration in the assessed and evaluated models in this research.…”