Airport Productivity Research Articles

Research on the Efficiency of Major Airports in China’s Six Major Airport Cluster: Based on Common Frontier Super-Efficiency DEA and Malmquist Method

Airport clusters are of great significance to the sustainable development of the civil aviation transportation industry. The study utilises common frontier and super-efficiency DEA methods to assess the efficiency of China’s six major airport groups. It then employs the Malmquist index method to analyse changes in airport productivity. The results highlight regional disparities in airport efficiency. The East China Airport Group and the Southwest Airport Group consistently demonstrate excellent efficiency values, while the North China Airport Group and the Northeast Airport Group have significant room for improvement. Most airports within the groups operate at low and ineffective levels, with efficiency initially increasing and then decreasing. Moreover, the technology gap ratio (TGR) for each airport group somewhat shows a downward trend. The Malmquist index indicates that the overall factor productivity of each airport has generally remained stable, with efficiency growth primarily dependent on scale efficiency. On average, technical efficiency has increased by 1.5%. However, in terms of technological changes, most airports have experienced technological regression, indicating insufficient focus on technological improvement. Therefore, it is crucial to prioritise technological innovation and enhance management efficiency to achieve efficiency improvements in airport clusters. It is necessary to formulate strategies accurately based on the specific conditions of different regions, promote coordinated development, foster regional exchanges and cooperation, address regional disparities, ensure sustainable development of China’s airport clusters, and establish a world-class airport cluster.

Airport productivity and network centrality in the pandemic outbreak: Lessons from the Turkish airports

The adverse effects of the Covid-19 outbreak on airport performance have been noted in many studies, but the determinants of airport productivity in this period still need to be adequately discussed. Identifying performance drivers during the pandemic may offer insights into the efficient management of airports. In this study, the factors affecting airport productivity during the pandemic were revealed, and the performance sources were explained with attention to network centrality (indegree, outdegree, clustering coefficient, betweenness, and eigenvector), both in the domestic and international context. To do that, the Fixed Proportion Ratio was applied to measure the performances of Turkish airports from 2017 to 2020. Next, Malmquist Productivity Index was used to calculate the technical and technological changes in productivity. Finally, Gradient Boosting Modeling was employed to test the impacts of network centrality measures and other contextual variables on total factor productivity. Results revealed that international networks lost their importance, while domestic networks were the major drivers of airport productivity during the pandemic outbreak. Accessible airports with robust domestic connections (represented by high clustering coefficient and indegree centrality) were less vulnerable in this period.

Airport Performance - a multifarious review of literature

This paper reviews literature covering three broad categories of airport performance: productivity and efficiency, financial performance, and service quality and passenger satisfaction. Data Envelopment Analysis (DEA) has become the overwhelmingly dominant method for airport productivity and efficiency studies, in large part due to the lack of publicly accessible data. The effects of ownership and regulations on airport efficiency appear to be of the most interest to researchers. However, sustainability has increasingly become one of the top priorities for many airports, thus we start to see more research incorporate environmental factors in evaluating airport productivity and efficiency. In contrast to the vast volume of literature on airport productivity and efficiency, published works on airport financial performance are scarce with most simply comparing various ratios reflecting unit revenues, unit cost and some forms of profitability, which indicates an indisputable need for more research in the area. There has been a rapid growth in literature on airport service quality over the last decade, mostly focusing on examining drivers for passenger satisfaction. Advancements in data mining and text mining techniques have led to increasing use of user generated contents from web-based platforms instead of traditional questionnaire surveys as data sources by studies of airport service quality and passenger satisfaction.

Structural efficiency assessment of regional airports: Lessons from India

This study focuses on the structural efficiency of 42 Indian regional airports over five years from 2014 to 2019. A two-stage data envelopment analysis (DEA) was applied to assess the structural efficiencies of airports. We find that distance to the closest airport, distance from the city centre, per capita income, accessibility and profitability of the airport significantly impact the structural efficiency scores. We also calculate the productivity growth of the sample airports over five years using the Malmquist Productivity Index. Airport productivity rankings and sources of inefficiency provide airport authorities and policymakers with valuable insights into the potential for improvement.

Changes in land use typology of global airports: An empirical investigation with implications for the aerotropolis concept

Aerotropolis is an urban development concept characterised by the concentration of land uses fostering economic activities around airports. However, limited empirical evidence exists about the formation of aerotropolises and their effects on the productivity of airports. This study addresses the gaps by examining changes in land use patterns around 75 major airports globally, using data extracted from Google Earth for two time periods: 2004 and 2014. A Two-Step cluster analysis technique was applied to identify natural groupings of the airports in terms of their land use patterns for both periods. Changes in cluster membership were observed between the periods to examine the process of aerotropolis formation. A multivariate multiple linear regression (MMLR) model is estimated to identify factors contributing to aerotropolis formation process. Finally, the effects of the aerotropolis formation process on airport productivity is estimated in a two-stage least square (2SLS) regression model. Cluster analysis results show that airport land side development can be classified into three groups: Island airports (surrounded by largely vacant land), Waterfront airports (adjacent to a large body of water), and Built form airports (well-developed residential, commercial and industrial land uses). Overall, built-up areas increased by 2.1% between 2004 and 2014, and only three airports transitioned from Island to Built form – suggesting a weak evidence of the aerotropolis formation process. Developable land surrounding the airports and city population growth rate contributed significantly to this process, which consequently contributed to the productivity of airports. In conclusion, aerotropolises are a promising concept, yet unreal. It needs to be integrated with city plans to direct development, and thereby, to make it a reality.

Does high-speed rail development affect airport productivity? Evidence from China and Japan

This paper examines the effects of HSR development on airport technical efficiency and the labor productivity at airports. Existing literature mainly focuses on the impacts of HSR on passenger traffic. In addition to passengers, HSR development may influence airports' other outputs such as cargo and flight movements together with various inputs. These inputs and outputs collectively determine airports' technical efficiency. With access to a dataset consisting of 46 Chinese airports and 16 Japanese airports from 2007 to 2015, the paper firstly adopts both the standard two-stage Data Envelopment Analysis (DEA) and double bootstrap methods to evaluate the impacts of HSR development on airports’ technical efficiency. We then evaluate the effects of HSR on airport labor productivity which is measured by both work-load units per employee and aircraft movements per employee. Our main findings indicate that HSR development relates to a decline in airport efficiency. Airports located in cities that have better connectivity or accessibility in the HSR network suffer more efficiency loss than the others. It is also observed that the locational advantage of HSR stations relative to airports is negatively associated with airport efficiency. By contrast, good intermodal linkage between the airport and its nearest HSR station is positively correlated with airport efficiency. Furthermore, the study reports different results between China and Japan with respect to the effects of HSR on labor productivity.

Measuring business performance in the airport context: a critical review of literature

Purpose Over 15 years have passed since the first paper was published applying data envelopment analysis (DEA) to measure airport productivity. Since then, a wave of studies has appeared refining and validating this technique as one of the most reliable in the airport context. The purpose of this paper is to conduct a critical review of this accumulated literature. Design/methodology/approach This paper presents an exhaustive review of approximately 60 peer-reviewed published papers on business performance measurement through DEA applications in the airport industry. Findings The paper analyses the research on DEA technique chronologically and by geography. Managerial and academic practitioners’ interest in conducting performance measurement studies has grown and benchmarking techniques have become more sophisticated. Research limitations/implications Despite the popularity of the methodological framework of DEA, it can be improved by further research to continue refining and exploiting deeply the basic DEA scores. Some innovations have appeared from studies using alternative approaches to DEA, e.g. a Bayesian approach. Practical implications The conclusions explore the research contribution, its final value delivered to airport management and some practical aspects and recommendations for the selected field of work. Originality/value The paper explores the contribution of research to final value delivered to airport management by describing the main complementary procedures refining DEA technique scores for improving the operational efficiencies of airports through benchmarking. As well as by presenting the results of the assessment of major determinants of efficiency at airports around the world.

Can flexibility make the difference to an airport's productivity? An assessment using cluster analysis

Airport managers are facing a tough challenge: how to maintain a costly infrastructure with a long life-cycle in an increasingly volatile market? Traditional airport development approaches, based on master plans are no longer adequate. Flexibility has been advanced as a possible solution. We can define flexibility as the capacity of an object to accommodate external pressures while still keeping its functions. The literature on flexibility is limited and the field remains relatively unexplored. One relevant topic has to do with the actual impact of flexibility on an airport's overall productivity. Although the object of substantial discussion, thus far no evidence has been gathered. The objective of this study was to contribute to remedying this gap. We applied a cluster analysis to a set of 140 North American, European and Asian airports, of which 20 are identified in the literature as being flexible. We considered a total of 14 financial and productivity indicators: if the hypothesis holds true, then the flexible airports would be less affected than the others by economic recessions or other external changes. The results do not reveal any visible differences between flexible and non-flexible airports. Despite these results, our study raises important new questions about the nature and the actual effects of flexibility.

Total factor productivity analysis of the UK airport industry: A Hicks-Moorsteen index method

This study examines the total factor productivity (TFP) change of the UK airport industry from 2001 to 2009 using a Hicks-Moorsteen index method. The results show that the industry experienced an average annual growth in TFP of 0.32 per cent with efficiency change being the main contributor to the TFP growth. We find that private UK airports enjoy slightly higher TFP growth than those in public or mixed ownership. Regulation and airport size are found to have statistically significant effects on the airport productivity level.

Airports classification and priority of their reconstruction

Purpose. It is important for Ukraine to have a network of airports, which would promote the current and long-term implementation of air transportation needs of the population and the economics. This study aims to establish criteria of airports classification to determine their role in the development of the air transport system of Ukraine. Methodology. The methods of statistical analysis were used for the processing of data according to categories of airport productivity and geographic information system for data visualization. Findings. It is established that the existing division of Ukrainian airports into international and domestic, as well as into coordinated and non-coordinated ones is not relevant for determining the role of airport in the development of air transport system of the country and accordingly for the priority in financing of their modernization. The approach to the determination of airports classifications using analysis of performance categories was developed. Originality. Classification criterions of the airports in Ukraine are proposed: by type of activity and by the maintenance of scheduled route network. It is proposed to classify the airports by the type of activity to the primary commercial, commercial, cargo primary commercial, cargo commercial and general aviation. According to the scheduled route network maintenane it is proposed to classify the airports as the primary, non-primary and auxiliary hubs. An example of classification by the given criteria is submitted. Practical value. The value of the obtained results is in the possibility of using the proposed classification in the task of determining the priorities for financing the country's airports. As opposed to the practice of directed funding procedure in the framework of the state program of airports development, it is proposed to take into account the fact that the resumption of the functioning of the airport and/or its modernization should be as a response to effective demand of consumers (in this case, airlines), which will be the guarantors of investment return.

User Requirement Analysis and IT Framework Design for Smart Airports

It takes a great deal of time for an airline passenger to go through the immigration process, security checks, and other airport-associated procedures. Furthermore, nowadays, large airports are competing to be hub airports, are striving to increase customer satisfaction, and wish to provide passengers with excellent travel experiences. When we reviewed airport processes in order to increase airport productivity and improve passenger convenience, we categorized passenger-related processes into three parts: passenger processing, guide services, and mobile services. In order to support a series of improvements in the process, a new paradigm, "Smart Airport Service Framework" is proposed to ensure faster service and greater convenience for passengers and improve work processes and resource utilization. As a part of this paradigm, and to meet the needs of passengers as regards mobile service, we reviewed a customer requirement analysis for smart phone applications using quality function deployment methodology. From this information, we drew the most necessary functions, such as the guide to airport amenities, and developed them, consequently enhancing passenger access to the airport IT systems, saving time, and increasing airport authority profits.

Airline market structure and airport efficiency: Evidence from major Northeast Asian airports

This paper investigates the impact of airline market structure on airport productivity, in which airlines are viewed as downstream users of an airport in a vertical airport–airlines structure. Our estimation is based on a sample of eleven major airports in Northeast Asia. A standard two-stage approach is employed: In the first stage, efficiency of the airports is measured by both the data envelopment analysis and stochastic frontier analysis. The resulting efficiency scores are carried over to a second-stage analysis in which Tobit regression is conducted to quantify the impact of airline concentration on efficiency, controlling for such factors as airport governance structure, airport competition and other characteristics. We find an inverse U-shaped relationship between airport efficiency and downstream airlines' market concentration: i.e., either too much or too little downstream concentration is associated with airport inefficiency. Other interesting and useful results are also obtained and discussed.

APPLYING THE METHOD OF MEASURING AIRPORT PRODUCTIVITY IN THE BALTIC REGION

The author of the article describes the theoretical assumptions of the DEA method used for measuring the productivity of airports described in the article ‘Research into the methods of analysing the productivity indicators of transport terminals’ (Jaržemskienė 2009). The essential insights presented in the above mentioned paper reveal that Data Envelopment Analysis (DEA) is a relatively new ‘data oriented’ approach to evaluating the performance of the so-called Decision Making Units (DMU) that convert multiple inputs into multiple outputs. The article focuses on the findings of the study carried out by the author in accordance with those assumptions. Research represents the Data Envelopment Analysis (DEA) method testing 15 selected airports situated in the Baltic Region, including Vilnius, Kaunas, Palanga, Riga, Tallinn, St. Petersburg, Helsinki, Turku, Stockholm, Malme, Copenhagen, Hamburg, Gdansk, Warsaw and Minsk. Airport productivity indicators are ranked considering importance and using the method of Delphi expert survey made of two rounds. The author presented the following indicators (expressed as ‘ratio’) as the major ones estimated by PAX/LAND, AIR/LAND, PAX/AIR, PAX/RW, PAX/RWA, GA and INTER experts. The succeeding indicators were introduced by PAX/TERMAREA, PAX/GATES, AIR/RW, AIR/RWA, AIR/TERMAREA, AIR/GATES, FR/LAND and FR/RW. 10 indicators were accepted as the most important and selected from the current set in the following sequence: AIR/LAND, AIR/RW, PAX/RW, PAX/LAND, AIR/RWA, PAX/AIR, PAX/RWA, AIR/TERMAREA, PAX/GATES and PAX/TERMAREA. AIR/LAND and AIR/RW were submitted as two main indicators. The acronyms are explained as follows: LAND – airport area, RWA – runway length, PAX – the number of passengers, AIR – the number of aircraft take-offs and landings, RW – the number of runways, GATES – the number of gates, FR – the amount of freight served, TERMAREA – the area of passenger terminal, GA – a general aviation market share of airport served aircraft by percentage, INTER – the percentage of international passengers considering all passengers served by airports. After two key productivity indicators were chosen conducting the expert survey, airport productivity was compared applying the DEA method.

Estimating indices of airport productivity in Greater China

In this paper, we measure the productivity changes of 30 airports in Greater China during 2000–2006 by computing their Malmquist productivity indices using parametric output distance functions. On average, the productivity of airports grew at 10.3% annually over our period. In addition, decomposing the Malmquist index did not provide evidence supporting any “catching-up” in technical efficiency between regional airports and major hubs.

Bootstrapping the Malmquist indexes for Italian airports

Abstract This paper uses data envelopment analysis to assess the operational performance of 28 Italian airports during the period of 2000 through 2006. Recent developments in bootstrapping techniques are used to correct total factor productivity estimates for bias and to assess the uncertainty surrounding such estimates. This study found that the Italian airport industry experienced a significant technological regress, with few airports achieving an increase in productivity led by improvements in efficiency. Moreover, the paper shows that the form of ownership (public majority vs. private majority) of an airport management company does not significantly affect performance. In contrast, this type of the concession agreement has positive and significant effects on airport productivity. Finally, the paper highlights the existence of a productivity gap between airports located in the North-Central part of the country and those located in the south.

Comparative Analysis of Efficiency for Major Northeast Asia Airports

AbstractGrowing airport capacity constraint, airports' increasing attention to profits, and regional hub formation in Northeast Asia suggest a need to study efficiency performance of the region's major airports. This article empirically evaluates the level and change of efficiency (productivity) of Tokyo (Narita), Osaka (Kansai), Seoul (Incheon and Gimpo), Beijing Capital, Shanghai Hongqiao, Guangzhou, and Hong Kong airports. We apply data envelopment analysis (DEA) to new panel data covering the 1994-2007 time period. We find that all the sample airports have recorded an increase in productivity over the period. Given the initial low productivity level of Chinese airports, this finding suggests that a series of deregulatory measures adopted by the Chinese government may have worked in improving airport productivity. On the other hand, no sample airports have experienced consistent growth in productivity. To improve productivity, the airports in Northeast Asia need not only technological revolutions at the industry level, but also technical improvements, such as better airport management, operations, and investment, at an airport level. Alternatively, the region may need to adopt more aggressive liberalization measures to stimulate further traffic growth. A number of future research areas are also discussed.

Comparative Analysis of Efficiency for Major Northeast Asia Airports

Abstract Growing airport capacity constraint, airports' increasing attention to profits, and regional hub formation in Northeast Asia suggest a need to study efficiency performance of the region's major airports. This article empirically evaluates the level and change of efficiency (productivity) of Tokyo (Narita), Osaka (Kansai), Seoul (Incheon and Gimpo), Beijing Capital, Shanghai Hongqiao, Guangzhou, and Hong Kong airports. We apply data envelopment analysis (DEA) to new panel data covering the 1994-2007 time period. We find that all the sample airports have recorded an increase in productivity over the period. Given the initial low productivity level of Chinese airports, this finding suggests that a series of deregulatory measures adopted by the Chinese government may have worked in improving airport productivity. On the other hand, no sample airports have experienced consistent growth in productivity. To improve productivity, the airports in Northeast Asia need not only technological revolutions at the industry level, but also technical improvements, such as better airport management, operations, and investment, at an airport level. Alternatively, the region may need to adopt more aggressive liberalization measures to stimulate further traffic growth. A number of future research areas are also discussed.

Effects of competition and policy changes on Chinese airport productivity: An empirical investigation

This paper investigates the influence of competition and aviation policy reform (for example, the airport localization program and listing airports on stock markets) in China on the efficiency of Chinese airports. By using Data Envelopment Analysis, we estimate both the productivity level and its growth for 25 sample Chinese airports. After controlling for hub status and other airports' characteristics, we find that: (i) publicly listed airports are significantly more efficient than non-listed airports; (ii) airports with more competition are more efficient than their counterparts; (iii) the airports' efficiency and the technical progress are positively correlated with the airport localization program; and (iv) the impacts of open-skies agreements and airline mergers on the airports' efficiency are statistically insignificant.

Productivity changes in Chinese airports 1995–2004

This paper reports a study of the patterns of productivity changes in 25 regional airports in China during the period 1995–2004. We found the average annual growth in airport productivity to be above 3%. By the decomposition of the Malmquist index, we identified the major source of productivity growth to be technical progress, rather than an improvement in efficiency. The kernel smoothing method revealed a trend of disparate productivity among the airports that was due to the polarization of efficiency. Our results imply that reform policies should focus on non-hub airports, non-listed airports, and airports in the northeast region if more balanced development is to be achieved.

Assessment of Airport Productivity

The move toward commercialization and privatization has pressured airports to become more productive and competitive. Airport managers around the globe are using several performance measures to monitor their businesses and improve operational efficiency. In recent years, several studies have devised an overall productivity measure. A new study contributes to those publications in two major ways. First, unlike past studies, it explicitly considers the joint production of both desirable and undesirable outputs, that is, delays. Second, it investigates the robustness of the assessment. The results indicate that the selection of inputs and desirable and undesirable output measures can dramatically affect the productivity assessment of airports, the calculated level of inefficiency, the calculation of potential output, and the determination of productivity growth.