The competitiveness of global cargo airports employing the Best-Worst Method

Abstract

PurposeThe global economy and air transport business have been negatively affected owing to the COVID-19 pandemic outbreak. As countries tighten restrictions on international movements, the growing emphasis on air cargo places pressure on airports to maintain and upgrade their cargo policies, facilities and operations. Hence, ensuring the competitiveness of cargo airports is pivotal for their survival under volatile global demand. This study aims to evaluate the importance of competitiveness factors for cargo airports and identify areas for further improvement.Design/methodology/approachThis study applies the Best-Worst Method (BWM) to assess the cargo airports' competitiveness factors.FindingsThe results identified “Transport Capacity” as the most significant competitiveness factor, implying that airport connectivity is crucial in promoting cargo transportation at hub airports. This result was followed by “Airport Operations' and Facilities' Capacity” and “Economic Growth.”. Additionally, the results identified Hong Kong International Airport as the best-performing cargo airport, followed by Aéroport de Paris-Charles de Gaulle and Incheon International Airport, respectively. Furthermore, both selected European airports are the most competitive airports in terms of “Financial Performance” and appear to be aware of the significance of their brand value.Originality/valueThis study forms a reference framework for evaluating cargo airports’ competitive positions, which may help identify airports’ relative strengths and weaknesses. Moreover, this framework can also serve as a tool to facilitate the strategic design of airports that can accommodate air cargo demand flexibly under demand uncertainty.

The performance of major airports in the Europe, North America and Asia

“…Many scholarly contributions have been made to support this phenomenon by introducing diverse methodologies. They include multi-criteria decision analysis (MCDA), including DEA, TOPSIS and MACBETH, and regression analysis (Shishani et al ., 2023; Lee et al. , 2021; Kim et al ., 2021, 2022).…”

“…Many scholarly contributions have been made to support this phenomenon by introducing diverse methodologies. They include multi-criteria decision analysis (MCDA), including DEA, TOPSIS and MACBETH, and regression analysis (Shishani et al, 2023;Lee et al, 2021;Kim et al, 2021Kim et al, , 2022. However, no specific methods facilitate measuring and comparing airport performance based on methodical methods within the airport industry (Oum et al, 2003).…”