Introduction
Every flight starts and ends at an airport. As critical
infrastructure, airports are essential for the success of the aviation
industry. Airports were traditionally owned and operated by government
entities, but there has been a worldwide trend of airports being
privatized and/or commercialized since the mid-1980s. As airports
continue to evolve from public utilities to commercially oriented
business entities, studies on airport performance have flourished and
resulted in a broad range of literature reflecting diverse interests by
academic researchers, industry analysts and practitioners, investors as
well as policy makers. These studies can be classified into three
general categories: productivity and efficiency, financial performance,
and service quality and customer satisfaction. The academic research on
airport performance tend to focus on productivity and efficiency,
applying various methodologies to different groups of airports, as
evidenced by multiple survey articles on the topic over the last two
decades including Graham (2005), Merkert et al. (2012), Liebert and
Niemeier (2013), Iyer and Jain (2019), See et al. (2023), and Adler and
Kumar (2023). While investors and airport managers are understandably
more interested in financial performance, published academic works on
airport financial performance have been relatively scarce. In the
meantime, there has been a growing volume of works on airport service
quality and customer satisfaction.
This paper aims to provide a multifarious review of literature on
airport performance including productivity and efficiency, financial
performance and service quality. Specifically, the review consists of
three parts. The first part discusses the evolution of literature on
airport productivity and efficiency. Since there have been several well
received survey articles on this topic, the discussions will be drawn
mostly through a perusal of these survey articles with respects to
research methodologies, empirical findings on various issues, and
challenges researchers have encountered. The second part of the review
is about airport financial performance. As pointed out by Raghavan and
Yu (2021), rapid growth of airline industry and the intense competition
among airlines have created tremendous challenges for airports to meet
the growing demand for significant infrastructure expansion and
improvement with limited ability to increase aeronautical charges.
Therefore, financial viability and strength are imperative for airports
to be successful, especially in light of the devasting impacts of the
COVID-19 pandemic on airlines and airports. This paper reviews different
financial performance metrics that have been used to measure airport
financial performance, and discusses their applicability to airports
under various ownership and government forms. Complications in financial
performance analysis arising from the increasing presence of global
airport operators are also discussed. Finally, the third part examines
the airport service quality and customer satisfaction. Studies in this
area transcend multiple disciplines. The paper reviews various
methodologies used and attempts to provide an interpretative overview of
the empirical results.
This paper contributes to the literature by providing a synthesis of
existent surveys of the literature on airport productivity and
efficiency, and offers interpretative reviews of the literature on
airport financial performance and airport service quality and passenger
satisfaction.
Airport Productivity and
Efficiency
Productivity is defined as the ratio of output to input. It would be
easy and straightforward to measure and compare productivity if a firm
produces one output from one input. When multiple inputs are employed to
produce multiple outputs, it becomes necessary to aggregate the outputs
and inputs. Consequently, different methodologies have been developed to
measure and compare productivity in the presence of multiple inputs and
multiple outputs, especially when these outputs and inputs are measured
in different units. Productivity itself does not tell how well
(efficiently) a firm performs without having best practice as reference
point. Efficiency measures how well a firm performs relative to
the best practice or the most output obtainable from a given input level
with the given production technology.
One of the critical elements of productivity and efficiency analysis
is to identify and define inputs and outputs. Given the complex nature
of airport business, definition and quantification of consistent and
comparable inputs and outputs are not a simple task, especially for
comparisons between airports. The fundamental difficulties associated
with inter-airport comparison arise largely from the diversity of inputs
and outputs (Graham, 2005), and from the fact that some of the inputs
are often supplied by different stakeholders at various degrees.
Nevertheless, as noted by Liebert and Niemeier (2013), there has been a
broad census among the studies on airport productivity and efficiency
that “capital, labour, materials and other external services” are
generally considered as inputs, whereas “traffic volume and selling
non-aeronautical products” are considered as outputs. Both physical and
monetary measures of inputs and outputs have been used, mostly depending
on data availability. Liebert and Niemeier (2013) provide a
comprehensive list of inputs and outputs. For example, number of
employees and staff cost have been used as labor inputs; airport area,
number of gates, runway area, and capital stock or capital value have
been used as capital inputs. Liebert and Niemeier (2013) divide outputs
into desirable outputs versus undesirable outputs. Number of passengers,
aircraft movements, work load units (WLU), aeronautical revenues, and
non-aeronautical revenues are considered as desirable outputs, whereas
delays and noise are considered as un-desirable outputs.
There are a number of issues with airport input and output measures,
albeit these issues are often due to lack of consistent data. On the
output side, number of passengers and aircraft movements are
generally accepted as airport output measures with little disagreements
other than aircraft movements not differentiating between different
aircraft size (Graham, 2005). On the other hand, not all the studies
include freight or cargo tonnage as an airport output as some argue that
freight/cargo is generally handled by airlines or 3rd party
handling companies, not airport operators (Graham, 2005). Workload unit
(WLU) originated from airlines. Although WLU has been commonly used as
an overall airport output measure, its pertinence has been questioned
(Humphreys and Francis, 2000). One of the main criticisms is that it is
computed using an arbitrary method to combine passengers and cargo
(Hooper and Hensher, 1997). It is interesting to note that Graham (2005)
states that WLU is “the most popular measure” of outputs, as many of the
earlier academic studies and industry reports reviewed by Graham (2005)
use partial productivity measures that often include WLU as an aggregate
output. Less than 8 years later, many of the studies reviewed by Liebert
and Niemeier (2013) employ methodologies that can incorporate multiple
inputs and multiple outputs to estimate overall productivity or
efficiency, thus do not use WLU as output.
The direct use of monetary value (various revenue values) as outputs
are problematic, as revenue values are affected by “output prices” that
are affected by the local overall price level, regulatory policy, nature
of air transport traffic, competition with other airports, and market
power of airlines serving the airport. An acceptable way to use revenues
as outputs is to deflate revenues to form an output quantity index as
argued by Hooper and Hensher (1997) and Oum et al. (2003). This issue is
not discussed in any of the survey articles. Non-aeronautical revenue is
discussed in the context of revenue generation indicators in Graham
(2005), but is considered as an output measure in Liebert and Niemeier
(2013), which reflects the changes in how researchers view the
production process of airports with the increasing importance of
non-aeronautical revenues for airports.
Another noticeable difference between Liebert and Niemeier (2013) and
Graham (2005) with respect to outputs is the emergence of “Undesirable
or negative output” that formally recognizes the fact that airport
production process yields side products in the form of delays, noise and
pollution that are not desirable. Liebert and Niemeier (2013) include
three studies that incorporate delays and noise as undesirable or
negative outputs in their review. Since then, there have been a few more
studies that consider undesirable or negative outputs including Fan etl
al. (2013), Lozano, et al. (2013), Martini et al (2013), Scotti, et al.
(2014), Zou et al (2015), Shamohammadi et al (2022), etc., reflecting
the increasing awareness of airports’ impacts on the environment, as
noted by See et al. (2023) in their bibliometric analysis of airport
productivity and efficiency studies over the last two decades.
The complexity of airport output measures is nothing compared to the
intricacy of inputs. Airports are composed of a tangled collection of
immense infrastructure, costly facilities and myriad equipment built and
operated by various stakeholders including airport operators, airlines,
and others. Number of employees as a commonly used labor input measure
may seem to be a straightforward measure, it is not always comparable
between airports as pointed by Liebert and Niemeier (2013). First, the
number of employees often does not distinguish between full time and
part-time employees or between annual average and end of year. Second,
the operational responsibilities of airport operators vary greatly from
airport to airport, thus the number of employees may bias the
performance results (Tretheway, 1995). For example, some European and
Asian airports provide employee extensive ground handling services
whereas airport operators in North America generally are not involved in
ground handling. Adjustment is needed to make the numbers comparable.
The use of staff costs as labor input is subject to similar problems as
those when revenues are used as outputs as they do not account for the
differences in labor prices as well as differences in accounting
systems.
Airports are capital intensive business, thus capital inputs are very
important in evaluating airport productivity and efficiency. Ideally,
capital input measures should reflect the opportunity costs of the
capital inputs consumed in the relevant period. However, it is
difficult, if not impossible, to construct consistently comparable
measures of capital inputs, as airports have very different ownership
and governance structures with respect to the funding (and accounting)
and operations of infrastructure and facilities. Most studies use some
physical measures as capital inputs, such as number of gates, terminal
area, number of runways. Fansone and Zapata-Aguirre (2016) review 60
peer-reviewed published articles on airport performance using DEA, 46 of
these studies use physical measures as capital inputs. The main problem
with using physical capital input measures is that these physical
capital inputs (such as airport area, runway length, number of gates)
are generally “fixed” in the short and medium term, but few studies
distinguish between fixed and variable inputs when performance analysis
is conducted based on annual or quarterly data. Moreover, the fact that
airlines or other stakeholders may have invested and operate some of the
facilities are not considered, such as Lufthansa’s role as both investor
and operator of Terminal 2 at Munich Airport. Because of the
difficulties of constructing meaningful capital input measures, and the
fact that airports make managerial and operational decisions within the
given state of their infrastructure and facilities in the short and
medium term, Oum and Yu (2004) argue for the use of variable factor
productivity instead of total factor productivity. There are some
studies, such as Hooper and Hensher (1997), Parker (1999), Martin and
Roman (2001), Curi et al. (2010), Coto-Millan et al. (2014), that use
capital stock or invested capital as capital inputs. It is noted that
these studies include only airports located in the same country.
A notable omission in most of the studies that use physical capital
input measures is non-labor non-capital expenses (such as materials,
utilities, outsourcing, etc) airports employ (Fansone and
Zapata-Aguirre, 2016). Such omission could potentially bias the
performance results significantly as these expenses account for 23.7% ~
94.7% of airports’ non-capital expenses in 2019 (ATRS, 2021). Oum et al.
(2003) construct a soft cost input index to account for the non-labor
non-capital expenses as the second variable input.
Methodologies for measuring
airport productivity and efficiency
Airports, trade associations and industry analyst reports make
extensive use of various partial performance measures or key performance
indicators (KPIs) as discussed by Graham (2005) and Merket et al.
(2012). In fact, Airport Council International (ACI) has published a
guide for airport managers to assess airport performance in six key
performance areas (ACI, 2012). The U.S. Federal Aviation Administration
(FAA) sponsored Airport Cooperative Research Program (ACRP) have
published three guidebooks associated with airport performance
measurements (ACRP, 2010; ACRP, 2011; ACRP. 2018). These guidebooks
provide comprehensive lists of performance metrics for different
performance areas including productivity and efficiency. Merket et al (
2012) provide a list of partial airport performance indicators compiled
from both academic articles and industry reports. These partial
performance measures are easy to compute and intuitively easy to
understand and interpret. Partial productivity measures relate an
airport’s output to a single input, and can be misleading when being
used alone. For example, airports that outsource certain activities may
appear to have lower labor productivity when compared to airports that
do everything in house, as labor productivity in insolation does not
reflect the expenses airports pay to the contractors. This limitation is
discussed by Graham (2005), Liebert and Niemeier (2013), and Merket et
al. (2012).
Index Number Method – Total
Factor Productivity
Total factor productivity (TFP) uses pre-determined sets of weights,
generally revenue shares and expenses shares, to aggregate outputs and
inputs, and the ratio of aggregated outputs over aggregated inputs is then
considered as an overall productivity measure of airports. TFP is
essentially the weighted average of partial factor productivities of all
inputs airports use. A commonly accepted way to aggregate outputs and
inputs is the translog multilateral index procedure
developed by Caves et al. (1982). This translog multilateral index
procedure allows researchers to compute a consistent output (input)
index that provides a consistent comparison across firms and over time
within a firm. The typical inputs (or outputs) aggregation formula can
be specified as follows:
Ln Zk - Ln Zj = åi
(Rik + Ri)/(2)\ln(Zik)/(\widetildeZi) - åi
|
(Rij + Ri)/(2)\ln(Zij)/(\widetildeZi) (1)
where Zk is the aggregate index of input (or output) for
kth observation, Zik is ith input (output) for kth
observation, the Rik are weights, a bar over a variable
indicates the arithmetic mean and a tilde over a variable indicates the
geometric mean. Revenue shares are often used as the weights in output
aggregation (with the assumption of constant returns to scale), while
cost shares are used as weights in input aggregation. Each observation
represents a specific firm at a particular time. This procedure allows
transitive comparisons across all observations (across firms and over
time within a firm) via a series of binary comparisons between each
observation and the means of the data.
Merket et al. (2012) and Liebert and Niemeier (2013) review a number
of studies that measure airports’ total factor productivity using the
multilateral index procedure, including Hooper and Hensher (1997),
Nyshadham and Rao (2000), Martin-Cejas (2002), Vasigh and Gorjidooz
(2006), Oum and Yu (2003). Researchers typically augment TFP with a
second stage regression analysis to examine the factors that influence
the observed or gross TFP and to estimate airport efficiency. TFP
requires detailed revenues and cost data in addition to the quantifiable
inputs and outputs. Access to detailed and comparable airport financial
and operational data is often limited, as a result, application of the
TFP method to measure airport productivity may not always be possible.
With the advancement of other methods that have less data requirements,
particularly Data Envelopment Analysis (DEA), TFP has not been widely
applied to measure airport performance.
Non-Parametric Method – Data
Envelopment Analysis (DEA)
In their bibliometric analysis of literature on airport productivity
and efficiency during the 1999-2019 period, See et al. (2023) note that
DEA has been the most commonly used method to measure airport
productivity and efficiency. Data Envelopment Analysis (DEA) is a
non-parametric method developed by Charnes, Cooper and Rhoades (1978) to
measure productive efficiency of “decision making units” (DMUs) with
multiple inputs and multiple outputs. DEA utilizes a sequence of linear
programs, one for each DMU, to construct a piecewise linear production
frontier, and to compute an efficiency index relative to the frontier.
Observations (DMUs) that lie on the production frontier are deemed
efficient, and those not on the frontier are considered as inefficient.
The relative efficiency level of each observation is measured by its
distance to the production frontier, and is essentially the ratio of the
total weighted output to the total weighted input. The weights are
determined by linear programming optimization. DEA allows each DMU to
select the weights that maximize its own efficiency score. Generally,
DMUs will place higher weights on the inputs that they use the least and
on the outputs they produce the most. In this sense DEA shows each DMU
in its best possible light. A score of 1 indicates that the DMU is
efficient relative to other DMUs in the sample, and a value less than l
indicates a DMU being inefficient. The original DEA model, often
referred to as CCR model, assumes constant returns to scale and is
either input-oriented or output-oriented. The input-oriented DEA model
attempts to minimize inputs for given level of output, while
output-oriented DEA model attempts to maximize output for given level of
input. Since various DEA models are now well documented and widely
applied to different industries, this section focuses on discussions of
DEA’s applications to the airport industry and not the methodology
itself.
While Graham (2005), Merket et al. (2012) and Liebert and Niemeier
(2013) review airport performance studies using different methodologies,
Lyer and Jain (2019) and Fasone and Zapata-Aquirre (2015) focus on
studies apply DEA to airport efficiency because of its popularity. In
particular, Fasone and Zapata-Aquirre (2015) review 60 articles
published between 1997 and July 2014, and Lyer and Jain (2019) review 61
articles published during the 2009 – 2017 period. Both reviews note that
most of the studies analyzes airports in a single country, i.e., 48 out
of the 60 articles reviewed by Fasone and Zaparta-Aquirre (2015) and 38
out of the 61 articles reviewed by Lyer and Jain (2019). Furthermore,
Lyer and Jain (2019) point out that the published studies on airport
efficiency are mostly based on large airports with little attention to small,
regional airports, and based on airports in high-income and upper-middle
income countries, which highlights the lack of airport data in lower
middle-income and low-income countries. Lyer and Jain (2019) find that
nearly half of the studies use output-oriented DEA due to the fact that
inputs such as runways and terminal area cannot be minimized. Most of
the studies use both constant returns to scale (CRS) and variable
returns to scale (VRS) DEA models. Although the studies generally find
that airports exhibit returns to scale, there is no consensus with
respects to whether airports have increasing or decreasing returns to
scale. They note four general themes among the studies. The most
prevalent theme is the explanation of variation in airport efficiency
through exogenous factors. The second theme is the examination of the
change in airport efficiency over time. The other two themes are the
application of network DEA to decompose efficiency into intermediate
level and final, and the comparison with other methods. Examination of
the factors that influence airport efficiency or drivers of efficiency
is generally done through a second stage Tobit regression or Simar and
Wilson bootstrapping regression (Fasone and Zparta-Aquirre, 2015). The
empirical findings from the studies are discussed later in a different
section.
Liebert and Niemeier (2013) and See et al. (2023) observe that a
growing number of airport efficiency studies have applied various DEA
extensions and derivatives to improve the discriminatory power of
efficiency estimates and to decompose the efficiency at different stages
of production process. For example, Barros and Dieke (2007) apply
cross-efficiency DEA model and Super-Efficiency DEA model to measure
financial and operational performance of 31 Italian airports during the
2001-2003 period. Lozano et al. (2013) develop a directional distance
function (DDF) network DEA model and apply it to data for 39 Spanish
airports in 2008. The model distinguishes airport operation into two
process, movement of aircraft, and loading (and unloading) of passengers
and cargo, and considers aircraft movement as an intermediate product
that is an input in the second stage. Their model also includes two
undesirable outputs associated with flight delays in the first stage.
Wanke, et al. (2016) assess the productive efficiency of 30 Nigerian
airports over the 2003-2013 period using Fuzzy-DEA that allow them to
capture vagueness in input and output variables. Wang and Song (2020)
apply network DEA to actual and forecasted data of 12 airports (8
Chinese and 4 other Asian airports) to evaluate operational efficiency
(with passengers, cargo and aircraft movements at intermediate outputs)
and financial efficiency (with total revenue and net income as outputs).
Yu and Rakshit (2023) develop a so-called union dynamic network DEA
model to analyze the dynamic efficiency and changes of technology gap of
50 European airports during the 2011-2017 period.
Index Number Method –
Malmquist Productivity Index
The Malmquist productivity index was first introduced by Caves,
Christensen and Diewert (1982), and is based on the concept of a
distance function introduced by Malmquist (1953). It measures the
productivity change of a decision-making unit (DMU) over time, and
attributes the productivity change to change in efficiency and technical
change. An improvement in efficiency can occur when inputs are reduced
to produce a given level of outputs under given production technology
(input oriented), or outputs are increased with a given level of inputs
under the same technology (output oriented). Technical change can
enhance productivity if new technology leads to less inputs needed to
produce a given level of outputs. The Malmquist productivity index is
often applied in association with Data Envelopment Analysis, with DEA
identifying the efficient frontier in each period. Murillo-Melchor
(1999) estimates both efficiency and productivity changes of Spanish
Airports over the 1992-1994 period using DEA and Malmquist Index. Other
studies apply DEA-Malmquist include Gillen and Lall (2001), Fung et al.
(2008), Barros et al, (2010), De Nicola and Gitto (2013), Ahn and Min
(2014), Orkcu et al. (2016), Fernandes, et al. (2019), Fragoudaki and
Giokas (2020), Thomas and Jha (2022), and Chutiphongdech and Vongsaroj
(2022).
Parametric Method – Stochastic
Frontier Analysis (SFA)
The parametric methods generally involve the
estimation of a production or cost function. The estimated production or
cost function is then used to identify changes in productivity or
efficiency. A time trend variable is commonly included in the production
function, and the derivative of the estimated production function with
respect to the time trend is used to measure the rate of technical
progress that in turn indicates the rate of productivity growth. In the
presence of multiple outputs, many studies estimate cost functions
instead of production functions to avoid endogeneity issues that often
characterize production function estimations.
Conventional econometric estimation of production and cost functions
implicitly assumes that all firms are on the efficient frontier.
Frontier production or cost functions, on the
other hand, recognize that some firms may not be on the efficient
frontier, thus a firm’s efficiency is measured based on its location
relative to the frontier. Aigner and Chu (1968) made the first attempt
to impose a parametric functional form on the production frontier within
the framework provided by Farrell. In estimating the frontier production
function, they force all observations to be on or beneath the frontier
through the introduction of a one-sided error term. Afriat (1972) was
the first to impose a statistical assumption on the one-sided errors,
and to introduce the concept of "distribution of technical
inefficiency", which makes statistical estimation of frontier functions
possible. Within the frontier function frame, a distinction is made
between two different methods: the deterministic frontier
method and the stochastic frontier method. The main
difference between the two methods is that the deterministic method
attributes all deviations from the frontier to inefficiency while the
stochastic method distinguishes the deviations into a random component
capturing statistical noise and an inefficiency component. The
stochastic frontier method was introduced by Aigner, Lovell, and Schmidt
(1977) and Meeusen and van den Broeck (1977). See et al. (2023) state
that 23 out of the 88 articles on airport efficiency included in their
analysis use stochastic frontier analysis method. Liebert and Niemeier
(2013) provide a good review of the various approaches used to estimate
stochastic frontiers in airport productivity and efficiency studies.
They note that studies in the early 2000s tend to estimate production
frontiers with physical inputs reflecting airport infrastructure, such
as number of gates and terminal size. In late 2000s, however, studies
often estimate cost frontiers to assess cost-efficiency. They further
note that translog functions are generally preferred over Cobb-Douglas
function to allow more flexibility. Exogenous factors are considered
either as impacting the production technology or the inefficiency. Lin,
et al. (2013) compare airport operating efficiency estimates derived
from variable factor productivity (VFP), DEA and SFA based on the data
for 62 major Canadian and US airports. They find that the airport
efficiency rankings for both the top 15 and the bottom 15 airports are
largely consistent across the three alternative methods, while
significant differences exist in the mid-ranked airports. They further
note that the differences are more significant between the DEA
efficiency rankings and those from the other two methods.
The main issue with estimating cost functions is that it requires
data on input prices that are not always readily available.
Nevertheless, SFA has remained as the 2nd most commonly used
method to measure and compare airport efficiency. Examples of studies
applying SFA include Pels, et al. (2001), Oum, et al. (2008), Chow and
Fung (2009), Martin et al. (2009), Assaf (2010), Diana (2010), Scotti et
al. (2012), Kutlu and McCarthy (2016), Chen and Lai (2019),
Hidalgo-Gallego and Mateo-Mantecón (2019), Gianmaria et al. (2020),
Karanki and Lim (2021), and Adachi (2023).
Selected Applications of
Airport Productivity and Efficiency Studies
Airport productivity and efficiency are affected by a wide range of
factors, including airport size, location, ownership, regulatory
environment, governance, competition, and policies/practices. It is
imperative to better understand the relative importance of these factors
and to identify strategies for improving airport productivity and
efficiency, and to provide useful insights for airport managers,
policymakers, and researchers seeking to improve airport operations and
performance.
The Effects of Ownership on
Airport Efficiency
It is not surprising to note that ownership had been the most
commonly studied influential factor for airport efficiency (Lyer and
Jain, 2019), as privatization and commercialization has been the ongoing
trend in the airport industry over the last three decades. However,
there appear to be some conflicting findings among the studies, likely
due to the fact that the studies are based on data for airports in
different countries and over different time periods. For example, some
studies find that privatized airports tend to have higher operational
efficiency (Oum et al, 2006; Vogel, 2006; Oum et al. 2008; Perelman and
Serebrisky, 2012; Alder and Liebert, 2014; lo Storto, 2018; and
Chutiphongdech and Vongsaroj, 2022), while other studies suggest
publicly owned airports perform better than privately owned airports
(D’Afonso et al, 2015; Curi et al., 2010; Martini et al. 2013; Tsui et
al., 2014, Chen and Lai, 2019). Randrianarisoa et al. (2023) find that
Asia Pacific airports owned/operated by public corporations appear to be
the most efficient in a low corruption environment, but most likely to
be subject to significant negative effect of corruption. Parker (1999),
Ahn and Min (2014), Ablando-Rosas and Gemoets (2010), Barros and Weber
(2009) do not find any significant effect of ownership on airport
efficiency.
The Effects of Economic
Regulation on Airport Efficiency
Privatized and commercialized airports are generally subject to
economic regulations to safeguard the interests of airport users (Phang,
2016). Consequently, many studies have investigated the effects of
various forms of economic regulations on airport efficiency. It is found
that unregulated and/or deregulated airports tend to perform better than
their regulated counterparts (Adler and Liebert, 2014; Barros et al.
2013). A move towards incentive regulations is associated with increased
productive efficiency (Adler, et al, 2015). Oum et al. (2004) provide
evidence to support the argument that dual-till regulation would be
better than the single-till regulation in terms of economic efficiency,
especially for large and busy airports. Assaf and Gillen (2012) argue
that the impact of regulation and airport ownership structure should be
investigated jointly. They find that price regulation is dominant in
affecting efficiency, but the starting ownership form matters. Their
results show that shifting from a regulated government enterprise to a
private sector operator with no price regulation would achieve the
biggest efficiency gains. Adler and Kumar (2023) find that a majority of the studies they reviewed show evidence that airport regulation encourages technical, financial and cost efficiency.
The Effects of Airlines on
Airport Efficiency
Airlines and airports are co-dependents. Thus, airline business
models and partnerships as well as competition among airlines are likely
to have significant impacts on airport performance. However, the
empirical results are not always in consensus. Choo and Oum (2013) find
non-monotonic relationships between the level of LCC presence and
airport efficiency based on 63 major US airports during the 2007-2010
period. In particular, they show that airport efficiency falls as LCC
presence increases from very low level, and then rises as LCC become
dominant. Based on the 24 largest UK airports during the 2002-2005,
Bottasso, et al. (2013) indicate a positive relationship between the
intensity of LCC’s presence and airports’ productivity. They also
discuss the causality issue associated with the positive relationship,
pointing out the possibility that the most efficient airports are likely
to be more attractive to LCCs. Merket and Assaf (2015) and Coto-Millan
et al. (2014) find similar results based on 33 international airports
and 35 Spanish airports, respectively.
Tsui et al. (2014) find that airports with the dominant carrier being
a member of global alliance tend to have higher efficiency based on data
for 21 Asia-Pacific airports. However, airline mergers do not seem to
have a significant correlation with productivity among Chinese airports
(Yuan and Zhang, 2009). Airline hub status appears to have a significant
positive effect on operational efficiency among the medium and small
airports in Europe (Gutierrez and Lozano, 2016). An inverse U-Shaped
relationship between airport efficiency and airlines’ market
concentration is observed by Ha et al. (2013) among major airports in
Northeast Asia, indicating that airports with either too large or too little
airline market concentration are likely to be inefficient.
In contrast to the vast volume of literature on airport productivity
and efficiency, studies solely focusing on airport financial performance
are few and far between (Raghavan and Yu, 2021), albeit there are
studies that use financial variables (expenses and revenues) as input or
output in their analysis of airport productivity or productive
efficiency as discussed in Section 2.1, and there are studies that
examine some financial metrics in addition to other performance
indicators.
Financial performance generally refers to how well a company uses its
assets to generate revenue and profits. It is the evaluation of a
companys financial health and stability, and it involves analyzing
various financial metrics such as revenue growth, profitability, return
on investment, cash flow, and asset utilization. Financial performance
is typically assessed over a period of time, such as a fiscal year or a
quarter, and is compared to past performance as well as industry
benchmarks to determine the companys overall financial strength and
potential for growth.
Among airport performance studies, the term “financial performance”
may not always be used in its conventional definition. Graham (2005)
lists revenue generation indicators, such as revenue per employee and
revenue per work load unit (WLU), and profitability, such as
profit/total asset, under Economic performance. Cahill et al. (2017)
also include a number of financial metrics in their analysis of Dublin
Airport Authority’s economic performance. Furthermore, as shown in
Fasone and Zapata-Aguirre (2016), some studies use revenues as outputs
and operating expenses as inputs in measuring airport productivity and
efficiency, which is often due to the lack of data for physical inputs
and outputs. For example, Bazargan and Vasigh (2003) include operating
and non-operating expenses among their inputs and aeronautical and
non-aeronautical revenues among their outputs in measuring the
efficiency performance of a sample of U.S. commercial service
airports.
There are also a few studies that attempt to construct an overall
airport performance indicator to combine profitability, service quality
and efficiency. For example, Merkert and Assaf (2015) include operating
margin (EBITDA) as an output in their DEA model to estimate a single
profitability / perceived service quality /traffic efficiency indicator.
Chang et al. (2018) consider Net Operating Income as a final output in
their network DEA model to generate a comprehensive performance
indicator that reflects airports’ commercial viability, customer
satisfaction, traffic volume and bonding ratings.
Revenue Generation and Cost
Effectiveness
Revenue generation is one of the most common financial performance
measures used by airports and industry analysts (ACRP, 2020; ACI, 2012),
and is usually measured by ratios of various revenues (such as total
operating revenue, aeronautical revenue, or non-aeronautical revenue)
per passenger, or per employee, or per aircraft movement, or per work
load unit (WLU). Among the published academic articles, non-aeronautical
revenue per passenger is used by Pagliari and Graham (2019), turnover per
employee by Cahill et al. (2017), revenue per gate and revenue per
runway by Vasigh and Haririan (2003), operating income per WLU by Fasone
et al. (2014), revenue per WLU by Vogel and Graham (2013) and Graham and
Dennis (2007). Cost Effectiveness is one of the core performance
measures suggested by ACI (2012) and ACRP (2010). Common metrics used to
assess cost effectiveness include operating expenses per passenger
(Pagliari and Graham, 2019), and costs per WLU (Graham and Dennis, 2007;
Vogel and Graham, 2013).
Profitability, Return on
Investment, and Return on Asset
Profitability is usually measured by EBITDA (earnings before
interest, taxes, depreciation and amortization), gross profit margin,
operating profit margin, net profit margin, which measure a companys
ability to generate profits relative to its revenue. Return on
Investment (ROI) measures the return earned on an investment relative to
its cost, often used to evaluate the effectiveness of investment
decisions, whereas return on asset (ROA) measures how effectively a
business uses its assets. EBITDA is used by Vogel (2006), Vogel and
Graham (2013), Merkert and Assaf (2015) and Pagliari and Graham (2019)
to assess airports’ profitability, whereas profit margin is used by
Zuidberg (2017) and Tavalaei and Santalo (2019). Graham and Dennis
(2007) include both EBITDA and profit margin in their analysis.
Return on capital employed (ROCE) and Return on sales (ROS) are used
by Cahill et al. (2017) to analyze Dublin Airport Authority’s economic
performance over the 1994-2014 period along with turnover per employee
and total factor productivity (TFP) analysis. Return on Investment is
one of the five financial performance indicators examined by Abbruzzo et
al. (2016) in their analysis of 10 Italian airports during the
2008-2014. Vogel (2006) uses total asset turnover, EBIDTA, return on
asset and return on equity among others to assess and compare the
financial performance of public versus private airports in Europe during
the 1990-2000 period. Return on Equity and Return on Sales are also used
by Fasone et al. (2014) in their comparison of the financial performance
of 14 Italian airports. Both Vogel (2006) and Fasone et al. (2014)
implicitly assume profit maximization for all airports no matter whether
they are private or government owned. Oum et al. (2008) measure and
compare productive efficiency and profitability among airports owned and
operated by government departments, 100% government-owned corporations,
independent airport authorities, mixed enterprises with government
majority ownership, and mixed enterprises with private majority
ownership, and their results indicate that government-owned airports may
not be pursuing profit-maximizing goals. Their results are consistent
with credit rating agencies’ practice of using different rating
methodologies for private vs. public airports as private airports are
led by profit maximization motive while public airports operate for the
municipalities or regions social and economic benefit (Moody’s, 2017;
Moody’s, 2019).
Raghavan and Yu (2021) argue that traditional profitability measures
and financial efficiency measures are not appropriate for the US
commercial services airports that are owned and operated by various
government entities. The US commercial service airports are non-profit
by nature, and expected to be financially self-sufficient and raise
capital funds mainly through the bond markets. They propose six
pertinent financial performance metrics to measure and compare the US
airports in terms of operational financial performance, leverage, and
liquidity after reviewing the literature on non-profit organization and
practices of credit rating agencies and government oversight bodies.
Instead of the traditional profitability indicators, they use two
operating financial metrics to compare the performance the US commercial
services airports: namely, operating financial ratio and net take-down
ratio. Operating financial ratio is defined as operating
expenses/operating revenue, and net take-down ratio is defined as total
revenue-operating expenses/total revenue. A high operating financial
ratio suggests very little surplus revenue available for capital
spending, and a low ratio indicates financial strength. The net
take-down ratio is a broader measure as it includes non-operating
revenues generated by an airport.
Leverage and Liquidity
Financial leverage results from using borrowed capital as a funding
source when investing to expand a firms asset base and generate returns
on risk capital. There are a range of leverage metrics to measure how
risky a firm’s position is. Debt-to-assets and debt-to-equity are the
most common leverage ratios. Other leverage measures include interest
coverage ratio, debt service coverage ratio, and leverage ratio.
Liquidity refers to how easy it is to turn an asset into cash without
losing much value. Current ratio and quick ratio are the most common
liquidity measures. Cash ratio, operating cash flow ratio and working
capital ratio are also used to measure a firm’s liquidity.
Airports are capital intensive industry with massive investments.
Therefore, leverage and liquidity are important aspects of their
financial viability and strength. However, few studies consider any
leverage and liquidity measures in their analysis of airport financial
performance. Fasone et al. (2014) and Abbruzzo et al. (2016) are the
very few studies that take into account of leverage by including
Equity/Debt ratio in their analysis. Raghavan and Yu (2021) suggest two
leverage ratios (debt-to-asset ratio and debt service safety margin) and
two liquidity ratios (current ratio and days cash on hand). A low
debt-to-asset ratio is desirable because creditors have the assurance
that asset coverage of debt is high. A high debt service safety margin,
on the other hand, shows the proportion of revenues available to service
new debt and the availability of a safety net for an airport when an
airport faces abnormally low revenues. Days Cash on Hand (DCOH) reveals
days of unrestricted cash and investments an airport has to meet its
operating expenses, whereas current ratio captures whether the current
assets of an airport are sufficient to meet the current liabilities of
the airport. These measures are used to assess and compare the financial
performance of the US commercial service airports by Raghavan and Yu
(2021).
As the trend toward privatization and commercialization has continued
across different regions of the world, investors and researchers alike
become interested in the effects of ownership and governance on airport
financial performance. Vasigh and Haririan (2003) find that public
airports generate higher unit revenues (revenue per runway, revenue per
gate) with lower unit cost (cost per runway and cost per gate) than
private airports based on a sample of 7 airports in the UK and 8
airports in the U.S.. In contrast, based on a sample of European
airports during the 1990-2000 period, Vogel (2006) find that privatized
airports achieve higher returns on total assets and revenues and are
more cost efficient, whereas public airports appear to have higher
financial leverage. Based on a sample of major Asia-Pacific, European,
and North American airports during the 2001–2003 period, Oum et al.
(2008) find that airports with majority private ownership generally
achieve higher operating profit margins, whereas airports with majority
government ownership tend to have significantly lower operating profit
margin. Fasone, et al. (2014) provide evidence that Italian airports
with a majority private ownership appear to performance better in terms
of operating income than their public airports. As pointed out by Fasone
et al (2014), there is a need to explore the causality issue associated
with the relationship between ownership and financial performance, as
private stakeholders are more likely to invest in profitable and/or
financially promising airports. Unfortunately, this remains a gap in the
literature.
Focusing on a sample of 10 commercialized national and regional
Italian airports, airports managed by private operators under “total
concession” agreements with public authorities, Abbruzzo et al. (2016)
investigate the effects of airports operational indicators on their
financial performance using a Gaussian graphical model. Their results
suggest that increasing jointly the number of flights and number of
passengers would improve revenues and profitability, which is consistent
with the finding of increased passenger traffic likely leading to higher
profits by Zuidberg (2017). Abbruzzo et al. (2016) further state that
the effects of LCC on airport financial performance have been
heterogeneous, as their results do not show any significant relationship
between the degree of LCC presence and financial performance. Graham and
Dennis (2007) find that airports with high proportion of low cost
traffic tend to have lower unit revenues, despite the strong passenger
growth and increased passenger load generated by LCCs. In the context of
the U.S. airports, Tavalaei and Santalo (2019) suggest airports that are
either dominated by legacy carriers or LCCs tend to have better
financial performance than airports with more diversified airlines
services.
Based on a sample of 23 US large hub airports, Richardson et al.
(2014) find that airports with compensatory agreements perform better in
terms of revenue generation, operating margin, operating liquidity (net
working capital) and debt ratio than airports with residual or hybrid
agreements. However, airports with residual agreements generate higher
commercial revenues than airports with compensatory agreements. Raghavan
and Yu (2021) show that the US large hub airports tend to perform better
in terms of liquidity, whereas the medium hub airports tend to have
lower leverage ratios. They further show that airports with high
productive efficiency and those with a dominant carrier appear to have
more surplus revenues for meeting their operational needs and capital
spending. Their results also indicate that airports with larger
proportion of international passengers are likely to have less surplus
revenue available for capital spending, whereas airports with
significant cargo operations are likely to have more surplus revenue.
Furthermore, the share of non-aeronautical revenues in total operating
revenues is found to have a positive association with better leverage
and higher liquidity.
Growth of Global Airport
Operators
The global airport operators continue to expand their portfolio, and
have become a significant phenomenon in the airport industry. According
to ATRS (2022), as of August 2022, VINCI Airports operates/manages 53
airports in 12 countries, not including the 13 airports under OMA (Grupo
Aeroportuario del Centro Norte) that signed an agreement giving VINCI
29.99% of ownership at the beginning of August 2022. Table 1 provides some examples of major global
airport operators.
Table 1: Global Airport Operators.
| VINCI Airports |
53 airports in 12 countries |
VINCE Airports signed an agreement to
purchase 29.99% of OMA (Grupo Aeroportuario del Centro Norte), which
will add 13 airports to its portfolio (Northern and Central Mexico) |
| GROUPE ADP |
28 airports in all continents |
GROUPE ADP has strategic holdings in TAV
(Turkey), AIG (Jordan) and GMR (India) |
| Fraport AG |
31 airports worldwide |
In cooperation with its partners, Fraport
AG is investing in the expansion of Frankfurt Airport City |
| Corporacion America Airports |
53 airports in 6 countries |
Argentina, Armenia, Brazil, Ecuador,
Italy, and Uruguay |
Source: ATRS (2022).
Asker and Kiraci (2016) conduct a trend analysis of five airport
groups, AENA, Fraport, Ferrovial, TAV Airports Groupe, and Groupe ADP,
using the 9 financial variables, terms of trade receivables, net sales,
current assets, short term liability, long-term liabilities, tangible
fixed assets, fixed assets and liabilities, and shareholders’ equity,
and discuss whether these ratios increased or decreased over the
2007-2014 period. However, there is no direct performance comparison
between the airport groups apart from their respective trends. There is
a lack of published study examining the impact of these global airport
operators on the financial performance of individual airports within each
group, as only consolidated financial data for the groups instead of
individual airports are publicly available.
Airport Service quality and
Customer Satisfaction
Airports provide services to airlines, passengers, shippers and other
customers, therefore, service quality is essential to airport operation
and management. Studies on airport service quality to airlines are very
limited (Adler and Berechman, 2001; Pabedinskaitė and Akstinaitė, 2014),
this paper focuses on airport service quality with respects to
passengers. Airport service quality refers to the level of satisfaction
that passengers have with the services and facilities provided by an
airport. There are often discrepancies between passengers’ expectation
of service quality and their perceptions of service quality (Jiang and
Zhang, 2016). Service quality is one of the most debated topics in the
service marketing literature. However, the one thing researchers appear
to agree on is that perceptions of service quality are based on multiple
dimensions. Inherently, airport service quality is a complex and
multifaceted issue that represents a broad range of passenger
experiences. According to Bakir et al. (2022a), the number of
publications on airport service quality start to show an increasing
trend since 2014, albeit with considerable drops in 2017 and 2018. Usman
et al. (2022) provide a chronicle list of the 27 top articles published
between 2000 and 2020. Panouvakis and Renzi (2016) point out that there
are two general tracks of airport service quality literature: (1) to
identify the different dimensions or attributes of airport service
quality through conceptual or empirical modeling; (2) to identify
quality drivers that lead to customers’ satisfaction.
Attributes of airport service
quality and Service Quality Evaluation.
Airport service quality studies generally start with survey conducted
in various format and/or different media, and survey results are then
analyzed using different methodologies. One of the most publicized
passenger satisfaction surveys is Airports Council International’s (ACI)
Airport Service Quality program (hereinafter ASQ), which was initiated
in 2006. The ASQ program measures service quality across eight different
categories: access, check-in, security, airport facilities, food and
beverage, retail, airport environment, and arrival/departure. Each
category is broken down into several different factors that are
evaluated through a combination of surveys and on-site assessments. In
total, 34 service attributes are included in the survey. Unfortunately,
ASQ survey results are only accessible to participating airports.
Academic researchers generally rely on surveys that are limited to a
particular airport or a small number of airports.
Rhoades et al. (2000) conduct a mail questionnaire survey of 150
airport directors and consultants, and use factor analysis to analyze
the survey results. They identify four dimensions that contain twelve
attributes, including passenger service (food and beverage, restrooms,
retail and duty free, special services), airport access (parking, rental
car, ground transportation), airline-airport interface (gate boarding
areas, baggage claim, information display), and the inter-terminal
transportation as a single attribute dimension. Also using factor
analysis, Bezerra and Gomes (2015) extract seven dimensions of airport
service quality as perceived by the passengers from an extensive survey
conducted in a main Brazilian international airport, and then examine
how each of these dimensions affects passengers’ overall satisfaction
using a probabilistic model. They further emphasize the need to consider
the effects of passenger characteristics on the perceived levels of
service quality. Based on the same survey results, Bezerra and Gomes
(2016) estimate a six-factor model to measure airport service quality
(Check-in, Security, Mobility, Ambience, Basic facilities, and
Convenience).
Based on 700 mail survey responses, Fodness and Murray (2007) propose
that passengers’ expectation of airport service quality has three key
dimensions with five subdimensions including function (effectiveness,
efficiency), interaction, and diversion (maintenance, productivity,
decor). Pantouvakis and Renzi (2016) conducted in-terminal personal
interviews at Rome Fiumicino Airport over a two month period in 2014,
and collected 922 usable responses. Three “distinct, independent and
invariant” service quality dimensions are identified based on the
responses, namely, “Servicescape and Image,” “Signage” and “Service.”
Their results also provide some evidence that passengers’ satisfaction
or dissatisfaction perception of airport service quality vary according
to their nationalities. Fakfare et al. (2021) initially identify 10
dimensions and 44 airport service quality attributes. The quality
attributes are then reviewed by a panel of experts, and one attribute is
deleted and four attributes are added at the recommendation of the
panel, resulting in 47 quality attributes to be included in an online
survey of air passengers in Thailand. The study employ Impact Range
Performance Analysis (IRPA) and Impact Asymmetry Analysis (IAA) based on
879 responses from the survey to investigate the asymmetric effect of
attributes on passenger satisfaction. Results based on surveys at a
single airport may not be generalizable to all airports as the
perception of airport service quality is context dependent as pointed
out by Bezerra and Gomes (2015) and Pantouvakis and Renzi (2016).
Instead of relying on surveys, Yeh and Kuo (2003) employ a panel of
experts to identify six “manageable” service attributes (Comfort,
Processing time, Convenience, Courtesy of staff, Information visibility,
and Security). A fuzzy multi-attribute decision making (MADM) model is
then applied to generate a service quality index to evaluate the
comparative level of passenger service performance among 14 Asia Pacific
airports. A number of studies attempt to construct airport service
quality measures using different approaches. Lupo (2014) proposes a
Fuzzy ServPerf model together with the multi-criteria decision making
ELECTRE III method to evaluate perceived service quality and apply it to
three airports in Sicily. Pandey (2016) utilizes the Fuzzy
multi-criteria decision making method and importance performance
analysis to measure the service quality of the two main airports in
Bangkok (Thailand), Suvarnabhumi (BKK) and Don Mueang (DMK).
Chonsalasin et al. (2020) use confirmatory factor analysis to identify
passengers’ expectations of airport services and measure airport service
quality based on a sample of 1037 randomly selected passengers flying on
domestic Thai airlines. Their resulting model consists of seven
dimensions of service quality: security, check-in, wayfinding, airport
environment, access, arrival services, and airport facilities.
Antwi et al. (2020) propose the so-called Airport Indicators of
Passenger Experience (AIPEX) model to measure airport service quality,
and apply it to Shanghai Pudong International Airport as an illustration.
The study also conducts a second stage analysis to examine the
relationship between airport service quality, passenger affective image
and passenger satisfaction. Nwaogbe, et al. (2021) adopt the SERVQUAL
model to assess the overall service quality and passenger satisfaction
for airlines and airports in Nigeria. Their study is based on 150
questionnaire responses collected at four airports in Nigeria over a
one-week survey period. They conclude that perceived airport service
quality is less than passengers’ expectations for the selected airport
service operations. Adeniran and Fadare (2018) also use SERVQUAL model
to assess passenger satisfaction and airport service quality at the
domestic terminal of Murtala Muhammed Airport in Nigeria.
Drivers for Passenger
Satisfaction of Airport Service Quality
Based on a survey of 201 airports worldwide, Advani and Borins (2001)
find that private airports appear to provide better services.
Suarez-Aleman and Jimenez (2016) investigate the effects of airport
management and characteristics on passenger satisfaction based on 111
international airports worldwide. Their results indicate that airports
in countries with higher levels of competition and with private
participation tend to have higher perceived service quality. Allen et
al. (2020) use Principal Component Analysis to investigate the factors
that influence airport service quality at Lamezia Terme International
Airport in Italy. They find that accessibility (e.g., flight
information, signposting, etc), control operations (e.g., waiting time
at check-in, security, etc) and environment (cleanliness, air
conditioning) in the terminal have significant effect on air service
quality.
Brida et al. (2016) examine the effects of information and
communication technologies (ICT) on passengers’ perception of airport
service quality using a mixed generalized ordered logit analysis based
on data from a survey conducted by Chilean Aviation Authority at
Santiago International Airport (SCL) in 2013, they find that factors
related to flights and airport information have an important impact on
the passengers’ perception of airport services. In particular, their
results indicate that passengers appreciate “good quality” in
information screen, and value terminal signage that makes it easy to
move in the airport. Furthermore, speaker sound quality appear to have a
significant positive impact on overall perception of airport service
quality. Rubio-Andrada et al. (2023) attempt to examine the degree of
passenger satisfaction derived from information and communication
technologies from a gender perspective and conclude that women give
higher value to the use of technologies and technology-based services at
airports. Antwi et al. (2021) show a significant positive association
between airport self-service technology (SST) performance and passenger
satisfaction with SST, and the spillover effects of satisfaction with
technology on passengers’ overall satisfaction with airport service
quality.
Gkritza et al. (2006) explore factors that affect passenger
satisfaction with airport security check points by using multinomial
logit models. It is not surprising that their results show that wait
times is the most significant determinant of passenger satisfaction.
They further show that passenger satisfaction varies among different age
groups, education levels, trip purposes, etc. Moreover, the study find
that the determinants of customer satisfaction are not stable over time.
Sakano, et al. (2016) show that passengers who show a greater level of
satisfaction with the airport security screening process perceive higher
level of airport safety. Ceccato and Masci (2017) use Chi-square
analysis and binary logistic regression to examine passengers’
satisfaction with their perceived safety at an international airport in
Europe. Their results show that passengers’ perceived safety is affected
by their perception of airport environment (e.g., elevators, overall
maintenance, etc) for both arrival and departing passengers.
de Barros et al. (2007) study the airport service quality from the
perspective of transfer passengers. They conduct a regression analysis
based on suervey responses collected at Bandaranaike International Airport in Sri Lanka,
and show that courtesy of the security check staff and the quality of
the Flight Information Display are among the most valued service
attributes by transfer passengers at that airport. Park and Jung (2011)
examine transfer passengers’ perception of airport service quality and
its effects on passenger satisfaction and airport image, etc based
on survey of 331 transfer passengers at Incheon International Airport
(S. Korea). Airport service quality is measured in terms of 22 service
attributes. Chang and Chen (2011) apply importance-performance analysis
(IPA) to examine the perceived satisfaction of passengers based on a
sample of 130 respondents with mobility disability. Their analysis
suggests barrier-free ramp and slip resistant floors in the airports are
important to the satisfaction of disabled passengers.
Based on 377 survey responses collected at Kuala Lumpur International
Airport, Batouei et al. (2020) assess the effects of airport experience
on travelers overall satisfaction as well as their intention to revisit
an airport. Airport experience is assessed through three perspectives
each consisting of multiple dimensions, namely sociological,
psychological, and service marketing. Their results reveal that service
fairness, servicescape, service encounter, and self-service technologies
have significant effects on satisfaction, which is the driver of
intention to revisit an airport. The two sociological dimensions (sense
of place and social interaction), airport anxiety, and retail experience
do not seem to have any significant influence on travelers satisfaction.
Isa et al. (2020) use the PLS-SEM method to analyze the 2016 ACI-ASQ
survey data for Kuala Lumpur International Airport Terminal 2 (KLIA2)
that is predominantly used by LCCs. Their analysis indicates that there
is a significant positive relationship between most of the ACI-ASQ
dimensions and passenger overall satisfaction at KLIA2, with the airport
environment dimension as the best predictor for the overall passenger
satisfaction.
Martín et al. (2019) apply a hybrid-fuzzy TOPSIS (Technique for Order
of Preference by Similarity to Ideal Solution) method to analyze airport
duty-free shopper’ satisfaction through a six-attribute construct. The
analysis is based on questionnaire responses collected at the
international terminal of a regional airport in Australia. Their results
indicate that place of residence and destination of the passengers have
important impact on customers’ satisfaction with duty free shopping at
the airport. Australian residents travelling short haul appear to be
more likely to be satisfied with the duty free shops than non-residents
and those traveling long haul. Freitas, el al, (2021) estimate a logit
discrete choice model based on the Permanent Passenger Survey data
collected by Brazilian National Civil Aviation Secretariat to evaluate
Customer Experience in the airport commercial areas of 20 main Brazilian
airports during the 2016-2018 period. Their analysis indicates that
passengers traveling alone are more likely to be satisfied with airport
cafeterias and restaurants, while frequent flyers tend to have lower
level of satisfaction with airport commercial services.
Li et al. (2022) show that passengers at US airports appear to have
higher satisfaction after the COVID-19 outbreak than pre-COVID period
based on their analysis of Google Map reviews of 98 US airports. Their
results also indicate that environment (cleanliness and hygiene
conditions) and personnel have stronger influence on passenger
satisfaction during the COVID-19 Pandemic period than the pre-COVID
period. Similarly, Lopez-Valpuesta and Casas-Albala (2023) find that
cleanliness and comfort is the most important factor for passenger
satisfaction during the COVID pandemic period. They further show that the
actions taken by the airports during the early months of the Pandemic
(in 2020) appear to be positively associated with passenger
satisfaction, however, passengers became less satisfied in 2021.
Bezerra and Gomes (2020) examine the relationship between the
antecedents and consequences of passenger satisfaction, taking into
account the effects of switching costs for changing airports within a
multi-airport region. It is a little surprising to note that their
analysis indicates that passenger expectations do not influence their
perception of service value and their satisfaction level. On the other
hand, airport service quality is shown to have a strong effect on
perceived value and passenger satisfaction.
Hong et al. (2020) distinguish airport users’ service quality
satisfaction between passengers and service providers. They apply
exploratory factor analysis and reliability test as well as confirmatory
factor analysis to survey responses collected from 138 passengers and
110 employees of service providers that provide direct services to
passengers at Incheon International Airport. Their results suggest that
interactional service quality (courtesy and attitude of employees) are
drivers for overall satisfaction of both passengers and service
providers. Furthermore, physical environment quality (restrooms,
lighting, humidity and temperature) are drivers for service providers’
satisfaction, whereas passengers’ satisfaction is driven by convenience
and easy to find signs and flight information, baggage, and access to
the gate.
Content and Sentiment Analysis
based on User Generated Content
Web-based opinion platforms have become popular venues for passengers
to offer voluntary reviews of airport services. These platforms let
passengers rate services with stars along with comments and reviews,
which are great sources of information for both passengers and service
providers. The rapid advancement in data mining (i.e., techniques of
discovering patterns and trends from a large data set) (Larose, 2005)
and text mining (i.e., a subset of data mining that aims at extracting
information from texts) (Zhong et al., 2012) provides various means to
analyze these rating and reviews, offering new sources of data to
capture passengers’ perception and expectations of airport service
quality on a broader scale than the traditional questionnaire
surveys.
There has been an increasing number of studies utilizing the ever
growing online resources to investigate travel and transport related
issues. For example, Bogicevic et al. (2013) conduct content analysis of
1095 traveler comments posted between 2010 and 2013 on www.AirlineQuality.com. Their
analysis indicate that cleanliness and shopping options, WIFI and
adequate seating are among the main drivers of passengers satisfaction,
and on the other hand, security check location and procedure, poor
signage and long waiting lines associated with passenger
dissatisfaction. Yavuz el al. (2020) also that find queuing time is the
main influencing factor for passenger satisfaction at European airports
based on analysis of Skytrax reviews. Gitto and Mancuso (2017) use
sentiment analysis based on comments posted on SKYTRAX website to
investigate passenger satisfaction levels at five major European
airports. Their analysis suggests that food & beverage and the
shopping area are passengers’ main concerns about airport non
aeronautical services, whereas check-in, baggage claim and security
control procedures dominate passengers’ concern about aviation services.
Nghiem-Phu and Suter (2018) analyze 427 passenger reviews of Harry Reid
International Airport (formerly Las Vegas McCarran International
Airport) posted on TripAdvisor to identify the attributes that are
associated with the image of the airport. The study suggests that Harry
Reid International Airport shares most of the common airport service
attributes identified by earlier studies, but also has several
attributes unique to its host city, Las Vegas.
Lee and Yu (2018) apply sentiment analysis and topic modeling
technique to 42,137 reviews collected from Google Maps. The sentiment
scores computed from the textual Google reviews are very good predictors
of the associated Google stars. Both the sentiment scores and Google
star ratings are found to have a fairly strong association with the ACI
ASQ ratings, indicating that the online reviews provide a good proxy for
airport service quality ratings and an effective means to cross-validate
the conventional industry standard survey results. Furthermore, the
topic modelling analysis extracts 25 latent topics from the Google
reviews that show good correspondence with the ASQ service attributes,
suggesting that the ASQ program effectively covers all the service
quality attributes of airport users. Li et al. (2022) use Google Map
reviews to investigate the factors that influence passenger satisfaction
at 86 US airports before and during the COVID-19 pandemic.
Martin-Domingo et al. (2019) apply the sentiment analysis technique to
measure airport service quality based on 4392 tweets collected from
London Heathrow Airport’s twitter account. The study identifies 23
attributes and finds that these attributes are similar to the ACI-ASQ
service attributes. The authors point out some potential issues with
applying sentiment analysis, for example, the misclassification of false
positives associated with sarcastic comments.
Bae and Chi (2021) conduct content analysis based on 1341 comments
posted on Skytrax website to explore passengers’ perception of airport
service quality at Honolulu International Airport and five other
international airports. Their analysis reveals that security, check,
flight, line, and staff are the most frequently occurring words used by
dissatisfied passengers, whereas staff, terminal, clean, time,
immigration, and free are the most common words mentioned by satisfied
passengers. Bakır et al. (2022b) use multiple regression analysis to
examine the effects of eight airport service attributes on passenger
satisfaction, and use necessary condition analysis to dissect the
necessary conditions and level of necessity to achieve passenger
satisfaction based on 1463 valid online reviews of the top 50 busiest
airports in Europe posted on Skytrax.
Summary and Concluding
Remarks
Airport productivity and efficiency has attracted substantial
interests among scholars, practitioners, industry analysts and
government regulators, resulting in a vast volume of literature. There
have been several surveys and reviews of literature on the topic. This
paper attempts to synthesize these survey articles with respects to
research methodologies, empirical findings on various issues, and
challenges researchers have encountered. Quantifiable inputs and outputs
are essential to any productivity and efficiency studies, one of the
main challengers for airport productivity and efficiency studies is lack
of data to generate meaningful input and output measures that are
comparable across airports and over time, especially with respects to
inputs. Consequently, researchers have often replied on some physical
measures, such as number of gates, number of runways, etc, that are
“fixed” in the short and medium term, or monetary values, such as
expenses or revenues, without considering the implication of different
input or output prices. The lack of data is also at least partially the
driver for DEA becoming the dominant methodology being used for airport
productivity and efficiency studies, as it has less data requirement
than stochastic frontier analysis method and various multilateral index
methods.
As airport privatization and commercialization actuated researchers’
interests in airport performance in the 1980s, it is not surprising to see
many of the published articles focusing on effects of airport ownership
on airport productivity and efficiency. Various regulatory policies have
been imposed on privatized or commercialized airports, naturally policy
makers and academic researchers have strong interests on the effects of
regulations on airport performance, leading to numerous publications on
the topic. Airports and airlines are partners and co-dependents, thus
airline strategies and market structure inherently affect performance of
airports, which has become another “popular” research topic.
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. We expect to
see more efforts in this regard, and go beyond the simple inclusion of
negative outputs.
Escalating needs for significant infrastructure improvement and
expansion is one of the main challenges for many airports, thus
financial viability and strength of airports are imperative. However,
literature on airport financial performance are limited. Most of the
studies on airport financial performance often involve comparison of
various ratios representing unit revenues or unit costs, and some
profitability metrics. Profitability analysis is generally based on the
traditional framework for for-profit firms, despite the presence of
different ownership forms and government structures. The increasing
presence of global airport operators further complicates the study of
airport financial performance. In view of the devasting financial
impacts of the COVID-19 pandemic, there is a need for more works that
would help understand and improve airport financial health and
stability.
Similar to other service industries, airports have made tremendous
efforts to improve service quality and customer satisfaction, which are
reflected in the fast growing volume of literature on the topic.
Although early work on airport service quality tend to focus on
identifying different service attributes, more recent works tend to
focus on investigating factors that influence passengers’ satisfaction.
Traditional airport service quality are generally based in in-person or
mail questionnaire surveys, user generated contents on web based
platforms (Skytrax, Google maps, TripAdvisor, Twitters) have become a
valuable source of data for studies on airport passenger satisfactions.
With the continuing advancement of sentiment and content analysis
techniques, we expect to see more studies to explore the vast data
generated by users.
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