Journal of Air Transport Management最新文献

This article investigates the impact of COVID-19 on the airline industry from a corporate governance perspective. Although the impact of the pandemic was literally unprecedented and the three years of hardship it caused (2020–2022) were particularly acute for the airline industry, the aviation market quickly recovered from a much gloomier forecast. This turnaround occurred at various times in different markets, but the airline industry in most regions has returned to pre-pandemic levels. However, COVID-19 has left a profound impact on the airline industry from a corporate governance perspective. Since almost no airlines could have survived the pandemic on their own, governments bailed out the airline industry, and particularly flag carriers. The rationale for the bailout was not only airlines' economic contributions but also reinforced protectionism in the airline industry. As a consequence, governments have been more concerned about airline corporate governance while emphasizing social issues. Board members and corporate leaders of airlines that received bailouts from governments are pressured to consider the interests of all stakeholders, and not just shareholders. This article argues that stakeholderism gained more influence in the airline industry after the pandemic.

In order to take into consideration the presence of technological heterogeneity in the aviation industry, numerous studies over the past ten years have embraced the data envelopment analysis (DEA)-based meta-frontier framework. Since group targets and meta targets are calculated independently to evaluate the technology gap in airlines, there is a possibility of obtaining unrealistic targets. Estimating the projected inputs and outputs of the evaluated units separately at both the group and meta-frontier does not automatically ensure superiority on the meta-frontier compared to the group frontier. Our proposed combined Nash bargaining model that integrates group technology and metatechnology presents a potential answer to the current unreasonable targets issue and gives more reliable outcomes. The results of the empirical application on a dataset of Alliance and Non-alliance airlines show that the proposed approach generates targets for inputs and outputs which are realistic and optimal, as well as a valid and reasonable technology gap. The finding thus validates the utilisation of our suggested model and it can be used by airline managers to improve the airline operational process.

With the burgeoning expansion of air transportation, the imbalance between airspace capacity and demand is significantly prominent around the world. Air traffic flow management (ATFM) represents a crucial management concept aimed at managing the flow of air traffic in a manner that minimizes delay and maximizes the use of the entire airspace. Despite the concerted efforts of organizations such as IATA, CANSO, FAA, CAAC, and Eurocontrol, which have convened numerous workshops and organized diverse ATFM seminars to constantly update the technology and operational standards, the potential for improving the capacity provision remains significant. Therefore, in recent years, stakeholders and scholars have been relentlessly dedicated to simplifying the problem and optimizing the solution. This paper undertakes a critical appraisal of the recent advancements in this domain, with a particular emphasis on the latest strides and innovative research methodologies. Specifically, the review expounds upon the novel methodologies that have been employed in ATFM research, offering a comprehensive analysis of their comparative advantages in comparison to the present methodologies. Moreover, we have encapsulated recent groundbreaking works and outlined prospective avenues for future research. To the best of our knowledge, this represents the first comprehensive literature review systematically evaluating the latest progress in ATFM research over the past eight years.

In this study, we examine whether tourism can appreciate real exchange rates in developing countries. We use Fiji as a case study with annual data from 1980 to 2019. We apply the recently developed Fourier ARDL model which automatically corrects for the effects of structural breaks. The results indicate that tourism entails real exchange rate appreciation pressure. This effect is present with positive tourism shocks in expansionary periods. Tourism exerts a marginally stronger downward pressure on real exchange rates with negative tourism shocks during recessionary periods. Negative tourism shocks in expansions and positive tourism shocks in recessions do not influence real exchange rates. The implication is that whether tourism leads to an appreciation of real exchange rates depends on the relevant phase of the business cycle.

The objective of the current study is to examine monthly air passenger departures at the airport level considering spatial interactions between airports. In this study, we develop a novel spatial grouped generalized ordered probit (SGGOP) model system of monthly air passenger departures at the airport level. Specifically, we estimate two variants of spatial models including spatial lag model and spatial error model. In the presence of repeated demand measures for the airports, we also consider temporal variations of spatial correlation effects among proximally located airports by employing space and time-based weight matrix. The proposed model is estimated using monthly air passenger departures for five years for 369 airports across the US. The proposed spatial model is implemented using composite marginal likelihood (CML) approach that offers a computationally feasible framework. From the estimation results, it is evident that air passenger departures at the airport level are influenced by different factors including MSA specific demographic characteristics, built environment characteristics, airport specific factors, spatial factors, and temporal factors. Moreover, spatial autocorrelation parameter is found to be significant validating our hypothesis of the presence of common unobserved factors associated with the spatial unit of analysis. In this study, we also perform a validation analysis to examine the predictive performance of the proposed spatial models. The results highlight the superiority of spatial error model compared to spatial lag model and the independent model that ignores the spatial interactions. Finally, we undertake an elasticity analysis to quantify the impact of the independent variables.

Non-aeronautical business, including airport retail, is an essential source of airport revenue. The locations of retail stores in an airport terminal building significantly impact their revenue. Based on the retail store-level data from Dalian Zhoushuizi International Airport (DLC), the traditional panel data regression and machine learning methods were employed to explore the relationships between the terminal built environment and airport retail revenue. The results indicate the following: (1) non-linear relationships exist between airport retail store revenue and the terminal built environment. (2) Airport passenger traffic is still the principal factor in retail store revenue. (3) Variables including area, function halls, and per capita spending have different impacts on the revenue of food and beverage (F&B) and specialty retail (SR) stores. The optimal location sites for retail stores are as far away from the nearest washroom as possible while being as close to gates as possible.

As the first step of airline schedule planning, flight scheduling plays a pivotal role in shaping an airline’s competitiveness, defining its profitability and establishing service levels by determining the timetable for potential city pairs. Although full-service carriers consider the bank structure as an effective method to improve flight connectivity and optimise aircraft utilisation, existing literature lacks models specially focused on optimising flight schedules within the bank structure. This paper effectively addresses the existing gap by proposing an integrated multi-objective flight scheduling model to optimise airline connectivity in bank structure. The generalised formulation allows airlines to maximise their connectivity while controlling the traffic flow during flight scheduling, offering more flexibility to adjust parameters according to their specific needs. By formulating the problem as an integrated tail-dependent one, this study measures the impact of aircraft routing decisions on the set of feasible flight pairings continuously. Further, a novel heuristic-based Selective Simulated Annealing (SSA) algorithm is designed to implement and solve the proposed model promptly. Computational results demonstrate the applicability and effectiveness of the proposed approach, revealing that the systematic consideration of flight interactions leads to significant improvements in airline connectivity and aircraft utilisation. Notably, in test instances for over 200 daily flights, the proposed approach yields a solution that significantly increases airline connectivity by 18.58% while respecting the operational constraints. Validated with historical flight schedule data, the resolution approach serves as an efficient data-driven decision-making tool, which enables airlines to respond to the fast-changing air transportation market dynamics in real-time. In addition, this paper discusses and concludes with managerial insights regarding bank length verification and flight schedule optimisation.

Numerous studies have explored airline competition on routes, with a limited focus on competition in route portfolios at hub airports. Route control regulations influence this competition. This study proposes a theoretical framework for examining airline competition in route portfolios at regulated hub airports, using the Chinese market as a case. The Chinese market, characterized by strict government regulations, has seen substantial passenger traffic growth. Considering the historical airline network structure as a key evaluation criterion in China's regulations, we examine the impact of airline network structures on this competition. Findings reveal distinct airline network types: Mature, Hub-led, Direct-flight-led, Direct-flight-expansion, Hub-cultivation, and Initial. Mature and Hub-cultivation networks are associated with establishing numerous routes at airports with fewer competitor hubs. However, Mature airlines also dominate airports with significant competitor hubs. Hub-led, Direct-flight-led, and Direct-flight-expansion networks tend to establish many routes at airports with substantial competitor hubs, while Initial networks do not exhibit such competition. Aviation policy and company decisions significantly influence airline competitive positions at hub airports.