The effects of alliances and size on airlines’ dynamic operational performance

Transportation Research Part A 106 (2017) 197–214

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Transportation Research Part A journal homepage: www.elsevier.com/locate/tra

The effects of alliances and size on airlines’ dynamic operational performance

MARK

⁎

Ming-Miin Yu, Li-Hsueh Chen , Hui Chiang Department of Transportation Science, National Taiwan Ocean University, No. 2 Pei-Ning Road, Keelung 20224, Taiwan

AR TI CLE I NF O

AB S T R A CT

Keywords: Slack based measure Dynamic network data envelopment analysis Carry-over activities Airlines Bootstrapped truncated regression

The purpose of this paper is to examine dynamic efficiency and its determinants in a set of global airlines from several countries by means of a two-stage dynamic network data envelopment analysis (DNDEA). In the first stage, the DNDEA based on a slack-based measure is used to evaluate the dynamic production efficiency, service efficiency and overall operational efficiency of 30 global airlines for the period from 2009 to 2012, which explores the linkages in operations among airlines' internal divisions as well as the dynamic operational performance, with consideration of carry-over activities. In the second stage, the impacts of alliances and size on the operational performance of airlines are explored by adopting a bootstrapped truncated regression model. The empirical results reveal the following: (1) the weight setting on importance of overall operational efficiency on a yearly basis has significant impact on the operational efficiency of airlines; (2) the overall operational efficiency shows a yearly declining trend; (3) joining an airline alliance, the airlines' total assets and the GDP have significant impacts on operational performance.

1. Introduction The fast pace of economic growth, privatization of airline industries, and the open skies policies adopted by the Asia Pacific region has vastly boosted air traffic volume, and brought about the growth of peripheral airline industries. This change has resulted in a colossal positive impact on consumers, related business activities and tourism, while airlines have been plunged into intense competition. In the face of this ever increasingly competitive environment, airlines have found themselves desperately needing to boost operational efficiency in order to deal with the rapidly changing environment. Hence, how to utilize limited resources to distinguish their own sales capacity from others’ has become something critical that no airlines can ignore, and the operational performance has become the key to evaluating how airlines are doing (Tavassoli et al., 2014). The evaluation of operational performance can provide airlines with references for them to make improvement on operational efficiency. To understand the importance of transportation services, evaluation of operational performance has become a critical indicator to the management of transportation industries. Literature specifically about airline operations has proposed some methods for the evaluation of operational performance of airlines, including total factor productivity (Oum and Yu, 1995; Barbot et al., 2008), financial ratio analysis (Park et al., 2009), multiple criteria decision making (MCDM) (Lee et al., 2005; Wang and Lee, 2007), and data envelopment analysis (DEA). Among these methods, the DEA is the most commonly used in academia, due to its simple framework. It can be implemented with the mathematical programming approach to set up an overall measurement indication, where input and output variables are used to calculate the relative efficiency of individual decision making units (DMUs) (Gillen and Lall,

⁎

Corresponding author. E-mail addresses: [email protected] (M.-M. Yu), [email protected] (L.-H. Chen), [email protected] (H. Chiang).

http://dx.doi.org/10.1016/j.tra.2017.09.015 Received 18 October 2016; Received in revised form 23 July 2017; Accepted 17 September 2017 0965-8564/ © 2017 Elsevier Ltd. All rights reserved.

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1997). Moreover, this method requires no assumptions on production function, and allows multiple input and output variables to be put simultaneously in single-term or multi-term models for evaluation of efficiencies. In recent years, many studies have used DEA to evaluate the operational performance of airlines, such as Adler and Golany (2001), Coelli et al. (2002), Ray (2004), Färe et al. (2007), and Assaf and Josiassen (2009, 2011). Traditionally, those DEA applications regard internal operations as a black box, by ignoring the impact of internal structural changes on overall operational performance. For instance, most enterprises consist of multiple divisions, each of which has its own inputs and outputs, and is connected with other divisions. For an airline company, its internal operations are composed of a production division and service division that provide passenger and cargo services and are connected to each other in an operational network. For application to real world business, the network data envelopment analysis (NDEA) model uses inter-divisional connectivity to explore the internal structural changes of DMUs. Although some articles have discussed the impact of an airline company's internal structure on its operational performance (e.g., Zhu, 2011; Yu, 2012; Chang and Yu, 2014a; Tavassoli et al., 2014), the objective of such models are usually to measure the efficiency of a DMU in a specified period of time. When the period of time is composed of clearly defined units, such as years, then there is the opportunity to estimate efficiency that incorporates carry-over activities between sub-units as well as linking activities (Avkiran, 2015). Chang et al. (2016) and Olfat et al. (2016) applied dynamic NDEA models to measure airport performance with considering the sustainable development. In order to pursue sustainable development, airline companies also care the cross-period performance change rather than the one-shot efficiency evaluation. Recently, Chou et al. (2016) developed a meta dynamic NDEA model to evaluate the meta-efficiency of 35 international airlines with the production and consumption processes from 2007 to 2009. They argued that output consumption is substantially different from output production since airline services cannot be stored. Omrani and Soltanzadeh (2016) proposed a relational dynamic NDEA model to measure the efficiencies of 8 airlines in Iran over 2010–2012 with considering the interaction between time periods and divisions (production and consumption divisions). However, there exists a diversity of definition on what carry-over activities should be used as inputs of a division and outputs of another division. Chou et al. (2016) assumed that net revenues and accident occurrence from a previous period are respectively an desirable input and an undesirable input to the consumption division in a subsequent period. Omrani and Soltanzadeh (2016) considered that the number of seats offered by the fleet as a carry-over activity in consumption division. However, fleet size and waypoint are capital assets and are beyond the control of airline operational managers (Mallikarjun, 2015). The additional fleet size and waypoint provide more options to the operations of production and service divisions, respectively. Fleet size represents the number of aircrafts available for providing the production of available seat kilometers (ASK) and freight available tone kilometers (FATK), while waypoint indicates the number of destinations available for maximum convenience to produce revenue passenger kilometers (RPK) and freight revenue tone kilometers (FRTK). A larger fleet size and a greater number of waypoint provide more production and service opportunities, respectively, while the larger fleet size brings the higher maintenance expenses, and an increase in waypoints increase the costs associated with cabin crew accommodation, maintenance and other facilities at the destinations. Since self-owned fleet size and waypoint are the capital assets and are carried to the next period, the number of fleet size and waypoints decided in current period will affect airlines’ operational strategy and ability to generate available capacity and passenger services in subsequent period. As such, the scales of fleet size and waypoints are the important carryover activities for deciding the dynamic operational performance of airline companies. In addition, although the dynamic NDEA can accommodate a range of periodic and divisional weights determined by management without necessarily compromising the baseline benchmark, there is still a problem with regard to whether such weights would affect the measurement of the overall operational efficiency, term efficiency and divisional efficiency or not. Sensitivity tests are attempted in order to better understand how dynamic NDEA estimates are likely to change, the efficiency scores of the various weight settings are then used to inspect whether the distribution of weights by yearly importance makes any difference on airlines’ operational performance in this paper. Chou et al. (2016) developed a meta dynamic NDEA model and Omrani and Soltanzadeh (2016) proposed a relational dynamic NDEA model to evaluate the efficiency of airline companies. They assessed the operational efficiency and its components of global airlines with the carry-over activities and linking activities among airlines’ internal divisions, but the impacts of external factors on the overall operational efficiency, term efficiency and divisional efficiency are less clear. Airline strategic alliances have become a popular alternative to mergers and acquisitions (Min and Joo, 2016). Wan et al. (2009) further identified several reasons for airlines to join alliances. First, alliances provide a way for airlines to expand their international networks, while circumventing the regulatory and legal barriers (Oum et al., 1996; Park, 1997). Second, airlines can obtain benefits from cooperation among airline partners, such as sharing facilities, maintenance costs, and joint marketing. Third, alliances may increase the partner airlines’ traffic. Fourth, alliances may build passenger loyalty due to more flexible schedules, shorter travel times, improved luggage handling, and shared frequent flyer programs. Park and Cho (1997), Oum et al. (2004) and Perezgonzalez and Lin (2010, 2011a, 2011b) demonstrated that strategic alliances had impacts on airlines’ performance, profitability and productivity. In addition, airline size may be correlated with airlines’ performance. Large firms could be more efficient, because they could use more specialized inputs, better allocate their resources, and obtain the advantages of economies of scale (Alvarez and Crespi, 2003). Assaf (2011) as well as Assaf and Josiassen (2011) further indicated that since the larger airlines usually had stronger economies of scale and greater flexible access to technology and innovation, these airlines had more productivity. In order to explore the impacts of alliances and size on operational performance, the bootstrapped truncated regression analysis is used in this paper. Findings can be useful to airlines as their management reference. To our best knowledge, this is the first study to explore the operational efficiency of 30 global airlines for the period from 2009 to 2012 by using the dynamic NDEA model based on the slack-based measure (SBM-DNDEA) with weighting scheme sensitivity tests, and investigate the determinants of the operational efficiency by applying the bootstrapped truncated regression model, simultaneously. 198

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The contributions of this paper to airline efficiency evaluation are threefold. First, we apply the SBM-DNDEA model to assess the dynamic operational efficiency of global airlines and take two site characteristics, fleet size and waypoint, into consideration. Unlike the previous studies, we treat self-owned fleet size and waypoint as carry-over items to model the airline operational process, which fit better into the production and service characteristics of airline operations. Second, we apply sensitivity tests to understand the impacts of different weights schemes on the operational performance of airlines. Third, using bootstrapped truncated regression, the effects of alliances and size on airlines’ dynamic efficiency are captured. Compared to the existing studies, we consider the intertemporal interactions and apply the dynamic efficiency as the dependent variable to understand the impacts of alliances and size on the individual divisional efficiencies and indicate that the impacts of individual alliances are different between different divisions. The remainder of this paper is organized as follows. In Section 2, we present the review of the literature on the airline industry’s performance evaluations. The methodology is described in Section 3. Data descriptions and empirical results are presented in Section 4. The conclusions and suggestions are offered in Section 5.

2. Literature review DEA is based on the efficiency measures proposed by Koopmans (1951) and Debreu (1951) and empirically applied by Farrell (1957), who proposed the concept of production frontier to measure efficiency and used linear programming model to calculate the deterministic non-parametric efficient frontier. The DEA method uses mathematical programming techniques to calculate the relative efficiency of the individual DMUs based on their inputs and outputs, without the need for prior assumptions on the production function. The CCR model, introduced by Charnes et al. (1978), mainly handles the efficiencies of DMUs based on the assumption of constant returns to scale, while the BCC model, introduced by Banker et al. (1984), deals with efficiencies based on the assumption of variable returns to scale. In the past, some studies have used DEA methods for aviation performance analysis. For example, Adler and Golany (2001) used principle component analysis (PCA) to extract common factors from the original, excessive evaluation variables, so that input and output data are downsized to restructure the DEA model for evaluation of an optimal aviation route network. Coelli et al. (2002) used DEA to evaluate the capacity utilization and profitability of 28 international airlines, and the findings revealed that under short-term maximum profits, the average profit gap was as large as 815 million dollars─30% of which was contributed by inefficient output allocation. Ray (2004) combined the direct distance function, which allows simultaneous evaluation of input-oriented and outputoriented features, and a super efficiency model to explore the overall efficiency of 28 airline companies. He took the number of employees, number of gallons of fuel consumed, maximum take-off weights and other operational items excluding the aforementioned ones as input items, and the passenger-kilometers and ton-kilometers as output items. Färe et al. (2007) extended the research topics by Alam and Sickles (2000) into an index of airlines' flight service quality, and then evaluated 10 American airlines' productivity for the period from 1987 to 1994 when the sky control was lifted, and the findings revealed that under the impact of schedule delays and huband-spoke networks, all 10 American airlines saw their productivity go down. Barbot et al. (2008) integrated DEA with total factor analysis to evaluate the productivity and efficiency of 49 global airlines under the background of emerging markets in 2005, resulting in deep insight into the key variables that affected the airlines' efficiency. Barros and Peypoch (2009) applied DEA to evaluate the efficiency of 27 European airlines from 2000 to 2005, and used the truncated regression model to analyze the impacts of population and network alliance on efficiency. Park et al. (2009) integrated a financial ratio method with DEA to understand the impact of internal operating leased expenses ratio on overall operational efficiency based on the data of 21 airline companies. Assaf and Josiassen (2009) used DEA to evaluate 15 major British airlines during the period from 2002 to 2007, to explore the impact on the British airlines brought about by the aviation industry's competition during those years. Lee and Worthington (2014) employed a bootstrap DEA truncated regression to assess the efficiency of 42 airlines in 2006 and investigate the impacts of ownership, status as a low-cost carrier, the number of departures and weight load on efficiency of airlines. All the above studies, however, have treated the operational processes of the airlines as a black box, by merely evaluating the overall operational efficiency between inputs and outputs. However, most airline companies comprise many divisions, each of which is inter-connecting and, as a result, to reflect the actual operational situations, the NDEA model is thus proposed to involve the divisional correlations in the evaluation of operational efficiency. The NDEA contains divisional correlations in the model to explore the impact of input allocation and intermediate outputs on the production process. The measurement of transportation efficiency has initially developed through the use of productivity indicators, such as average vehicle-miles per vehicle, revenue ton-miles per employee, and passengers per revenue vehicle hour. This type of indicators provides information on single aspects of production and consumption. It is not suitable for the multi-output and multi-input case (Hensher, 1992), because an increase in the productivity with respect to one input or output may result from a decrease in the productivity of other inputs or outputs. When we consider, for instance, the output of many transportation companies, we can observe that their production process is generally multi-dimensional, both from the input and the output side. There is a need, therefore, to use more advanced techniques that take into account the multi-dimensional nature of transportation production and consumption.1 After the concept of performance measurement combined input, output and consumption, named cost efficiency, service effectiveness and cost effectiveness, as proposed by Fielding et al. (1985), many studies have incorporated this concept into their NDEA methods in the exploration of correlations among divisions. Zhu (2011) divided the internal operation of an airline 1 Although Chen and McGinnis (2007) have shown the theoretical connection between output-input ratios and multiple output-input analysis, they also indicated that the conventional partial productivity metric was not a proper benchmark index for evaluating the system-based efficiency because it also depended on other effects.

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company into two phases─the first phase is of costs, and second of profits─and combined them with the DEA to explore the operational efficiency within the structure. Lu et al. (2012) classified airline efficiency into production efficiency and marketing efficiency, and used available seat miles (ASM) and available tone miles (ATM) as intermediate products. They also used truncated regression to explore the impact of corporate governance, and indicated that corporate governance had the significant influence on the performance of airlines. Yu (2012) categorized the overall operation of an airline company into production division and service division, and took the size of fleet, fuel expenses and number of employees as the input items along with the flights and seat-miles as output items to explore airline operational efficiency. Tavassoli et al. (2014) divided overall operational performance into technical efficiency and service effectiveness, and treated RPK as the intermediate output to connect two divisions in the exploration of the operational efficiency of 11 Iranian domestic airlines in 2010.2 Chang and Yu (2014a) categorized overall operational efficiency into production division and consumer division, and treated seat-kilometers as intermediate outputs to explore operational efficiency, and discovered that the production divisions of EasyJet, US Airways and Virgin Blue Airlines were inefficient and needed to reinforce their control of input utilization, while the service divisions of Jet2, Aer Lingus and JetBlue Airlines were inefficient and needed to reinforce marketing activities to boost business performance. Lozano and Gutiérrez (2014) divided the overall operation of an airline company into production process and sales process, and treated ASK and ATK as intermediate products to explore the operational efficiency of European airlines. Li et al. (2015) categorized the operation of an airline company into operations stage, services stage and sales stage to assess the overall efficiency and divisional efficiency of 22 international airlines and 27 US domestic airlines, respectively. Duygun et al. (2016) divided the internal operation of an airline company into two stages and considered the existence of shared inputs to analyze the performance of European airlines. Shao and Sun (2016) classified an air route service into allocation stage and transport stage, in which the transport stage was further divided into passenger transport sub-function and freight transport sub-function. They used the available seats and available tonnage as intermediate outputs to respectively connect the allocation stage and passenger transport sub-function as well as the allocation stage and freight transport sub-function in the exploration of efficiency of 477 China’s air routes in 2013. The evaluation of the NDEA efficiency of production can, then, be obtained through a multiplicity of indicators, which allows a clear-cut evaluation of the efficiency of a transportation company. Moreover, when comparing the values of the same indicator for different companies, the relevance of the comparison is not limited by the fact that quantities of output, multiples or submultiples of those achieved by any given company are necessarily technically attainable employing multiples or sub-multiples of the inputs used by that company. In, addition, Chou et al. (2016) proposed the meta SBM-DNDEA model to analyze the efficiency of 35 international airlines during 2007–2009. They divided an airline system into production division and consumption division, and treated ASK as intermediate products and accident occurrence and net revenues as carry-over items. Omrani and Soltanzadeh (2016) proposed a relational DNDEA model to evaluate the production and consumption efficiencies of 8 Iranian airlines during 2010–2012. They treated ASK, ATK and number of scheduled flights as intermediate products and the number of fleet seat as carry over item to generate passenger-kilometer perfumed and ton-kilometer perfumed. Since the future of airline planning is making airline going sustainable, airlines cannot only focus on the one-shot efficiency evaluation. Hence, the most challenging task faced by the growing literature on the assessment of efficiency of airline companies is perhaps the identification of the operational performance during periods, namely the dynamic performance changes. However, most of previous studies applied Malmquist productivity index or window analysis method to assess cross-period performance, and ignored the inter-temporal dynamics in the airline operational process. Only Chou et al. (2016) and Omrani and Soltanzadeh (2016) considered the carry-over items and developed DNDEA models to evaluate multi-period efficiency. In natures of airline industry, intertemporal features exist in airline operations. For example, the self-owned fleets of an airline can be carried over from one period to another. The main problem encountered here is the clear identification of the term efficiencies and of what connects the operational processes of airline companies between two consecutive terms. Therefore, to understand the actual operational efficiency of an airline company, it is imperative to grasp its long-term business variations. This is where the dynamic DEA comes to the scene. It adds the concept of time to acknowledge the time-factored impact on the operational efficiency of airlines. Therefore, this paper uses SBMDNDEA, proposed by Tone and Tsutsui (2014), to accommodate divisional connectivity and time-related correlations, so that the distinction of operational efficiencies among the airlines can be clearly distinguished. 3. Methodology In order to investigate the internal structure of a airline, this paper adopts SBM-DNDEA proposed by Tone and Tsutsui (2014) to construct a two divisions and multiple-period model based on the operational characteristics in the first stage. In the second stage, this paper uses the bootstrapped truncated regression model to explore the determinants of operational performance of airlines. 3.1. Estimation of dynamic operational performance To take the non-storable feature into consideration, Fielding et al. (1985) developed a framework of performance evaluation to measure three important dimensions of a transit system: cost efficiency, service effectiveness, and cost effectiveness. Cost efficiency 2 Most of previous studies applied two methods to evaluate effectiveness. One is to evaluate organizational effectiveness with respect to given goals and objectives. Another is to decompose a production process by using network analysis. Furthermore, Lee and Johnson (2014) defined a new effectiveness measure by using a demand-truncated production function. Lee and Johnson (2015) extended the way proposed by Lee and Johnson (2014) to assess profit effectiveness for airline companies and capture the sales effect by the concept of a Malmquist productivity decomposition. “Effectiveness” mentioned in this paper is evaluated by the second method – network analysis.

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Fig. 1. Performance concept proposed by Fielding et al. (1985).

describes how well inputs are used to produce service output. Service effectiveness presents how well the produced outputs achieve the service consumption. Cost effectiveness measures the relationship between inputs and consumed services which is the integration of cost efficiency and service effectiveness. Fig. 1 illustrates the concept of performance proposed by Fielding et al. (1985). This paper extends the transportation performance architecture proposed by Fielding et al. (1985) and categorizes the operational process of an airline company into two major components: production division and service division. According to the non-storable characteristics of aviation transportation outputs, the ASK and FATK are treated as intermediate outputs connecting the two divisions, and since the flight schedules are usually preset, the intermediate outputs are set as fixed (Yu and Chen, 2011). In addition, the airliners are divided into leased fleet and self-owned fleet, and the latter is fixed assets, which by nature will be transferred to the next term, thus it is set as a carry-over activity of the production division. The flight waypoints provide geographic positioning for aircraft, and are often used in the termination phase of a flight to its destination airport. The more waypoints are provided by airlines, the more destination airports are, and the more convenience will be brought to travelers. Thus, the more travelers will be attracted. In principle, airline companies do not easily delete their waypoints. The waypoints will be persisted from one period to another. This is a concept of setting waypoints as a carry-over activity of service division. Since airline companies can freely purchase or dispose of fleets and increase or decrease waypoints due to economic benefits, the carry-over activity are set as free-link. The conceptual structure of SBM-DNDEA in the airline industry is described in Fig. 2. Since non-oriented SBM models have the advantages of simultaneously and disproportionally handling excessive inputs or insufficient outputs, this paper uses the SBM-DNDEA proposed by Tone and Tsutsui (2014) to calculate divisional efficiencies, term efficiencies and overall efficiency. It is assumed that there are n DMUs (j = 1, … , n) over T terms (t = 1, … , T), and each DMU has two divisions, i.e. production and service divisions. Given that m, s, h, p and q are respectively the number of inputs, outputs, intermediate outputs, carry-over items in the production and service divisions. The input item is {x ijt ∊ R+} (i = 1, … , m; j = 1, … , n; t = 1, … , T), denoting its ith input of term t of DMUj , and the output item is {yrjt ∊ R+} (r = 1, … , s; j = 1, … , n; t = 1, … , T), denoting its rth output of term t of DMUj ); while {z fjt ∊ R+} (f = 1, … , h; j = 1, … , n; t = 1, … , T) denotes the fth intermediate output of term t of DMUj that connects the production and service divisions, {u aj(t ,t + 1) ∊ R+} (a = 1,…,p; j = 1,…,n; t = 1,…,T −1) denotes the ath carry-over item in the production division of DMUj from term t to t + 1, and {v bj(t ,t + 1) ∊ R+} (b = 1,…,q; j = 1,…,n; t = 1,…,T −1) denotes the bth carry-over item in the service division of DMUj from term t to t + 1. As a result, the overall operational efficiency θo∗ model of DMUo (o = 1,…,n) is shown as follows:

min

θo∗

(

t−

)

T m s 1 ∑t = 1 W tw1 ⎡1− m × ∑i = 1 xiot ⎤ io ⎦ ⎣ = t+ s T s 1 t 2 ∑t = 1 W w ⎡1 + s × ⎛∑r = 1 rot ⎞ ⎤ y ro ⎠ ⎦ ⎝ ⎣

(1)

s.t. n

x iot =

∑

x ijt λtj + siot−

i = 1,…,m; t = 1,…,T (1.1)

j=1

n

yrot =

∑

yrjt δ tj −srot+

r = 1,…,s; t = 1,…,T (1.2)

j=1

n t z fo =

∑

z fjt λtj

f = 1,…,h; t = 1,…,T (1.3)

j=1 n t z fo =

∑

z fjt δ tj

f = 1,…,h; t = 1,…,T (1.4)

j=1

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Fig. 2. The structure of DNDEA for an airline.

M.-M. Yu et al.

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M.-M. Yu et al. n (0,1) uao =

uaj(0,1) λ1j a = 1,…,p

∑

(1.5)

j=1 n (0,1) vbo =

vbj(0,1) δ1j

∑

b = 1,…,q (1.6)

j=1

n

n

∑

uaj(t ,t + 1) λtj =

j=1

uaj(t ,t + 1) λtj+ 1 a = 1,…,p; t = 1,…,T −1

∑

(1.7)

j=1 n

(t ,t + 1) uao =

∑

(t ,t + 1) uaj(t ,t + 1) λtj + sao

a = 1,…,p; t = 1,…,T −1 (1.8)

j=1

n

∑

n

vbj(t ,t + 1) δ tj =

j=1

vbj(t ,t + 1) δ tj + 1

∑

b = 1,…,q; t = 1,…,T −1 (1.9)

j=1

n (t ,t + 1) vbo =

∑

(t ,t + 1) vbj(t ,t + 1) δ tj + sbo

b = 1,…,q; t = 1,…,T −1 (1.10)

j=1 n

∑

λtj = 1 t = 1,…,T (1.11)

j=1

n

∑

δ tj = 1 t = 1,…,T (1.12)

j=1 (t ,t + 1) (t ,t + 1) siot−,srot+ ⩾ 0; sao ,sbo : free in sign

i = 1,…,m; r = 1,…,s; a = 1,…,p; b = 1,…,q; t = 1,…,T

(1.13)

λtj ,δ tj ⩾ 0 j = 1,…,n; t = 1,…,T

(1.14)

w1

where W t

andw 2

is the weight of term t, are the weights of the production and service divisions, respectively. The weights indicate T the relative importance of each term or division, and they can be normalized to that they add up to one, e. g., ∑t = 1 W t = 1, and w1 + w 2 = 1, where W t ,w1,w 2 ≥ 0 . λt and δ t are the intensity vectors of the production and service divisions, respectively. We assume that the linking activities between the production and service divisions are fixed by constraints (1.3) and (1.4), the initial conditions can be obtained by constraints (1.5) and (1.6), and the carry-over activities in the production division and service division act as the free link by constraints (1.7) and (1.8) and (1.9) and (1.10), respectively. Note that Model (1) is non-linear. It should be transformed into linear programming as shown in Appendix A. Since the scales of airline companies are different, this paper uses the DEA model with the assumption of variable returns to scale (Barbot et al., 2008). With this overall operational efficiency model, the definitions of term efficiency, divisional efficiencies and term-divisional efficiencies are shown as follows: (1) Term efficiency: the overall operational efficiency of term t 1

w1 ⎡1− m × ⎣ τo = 1 2 ⎡ w 1+ s × ⎣ t∗

t− m sio i=1 x t io

(∑

⎛∑rsc= 1 ⎝

) ⎤⎦

t+ sro

⎞⎤ ⎦

t yro ⎠

(2)

(2) Divisional efficiency i. Production efficiency T

α∗o =

∑ t=1

m

s t− ⎞ ⎤ 1 ⎡ ⎛ W t ⎢1− × ⎜∑ iot ⎟ ⎥ m x ⎝ i = 1 io ⎠ ⎦ ⎣

(3)

ii. Service efficiency

β∗o =

1 T ∑t = 1

W t ⎡1 + ⎣

1 s

s × ⎛∑r = 1 ⎝

t+ sro

⎞⎤ ⎦

t yro ⎠

(4) 203

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(3) Term-divisional efficiency i. Term-production efficiency: the production efficiency of term t m

s t− ⎞ ⎤ 1 ⎡ ⎛ αto∗ = ⎢1− × ⎜∑ iot ⎟ ⎥ m x ⎝ i = 1 io ⎠ ⎦ ⎣

(5)

ii. Term-service efficiency: the service efficiency of term t

βtoc∗ =

1 ⎡1 + ⎣

1 s

s × ⎛∑r = 1 ⎝

t+ sro

⎞⎤ ⎦

t yro ⎠

(6)

In evaluation of airlines’ overall operational performance, this paper looks into the importance of division-based internal performance as well as the time-based carry-over activities and their impacts on operational performance. Therefore, this paper not only captures the distinction of operational performance of the 30 airlines, but also reveals their production and service efficiencies. 3.2. Regression analysis of determinants of operational performance In the past, there have been many academic applications of Tobit regression analysis to the transportation industries (e.g., Oum and Yu, 1994; Malighetti et al., 2007; Bhadra, 2009). However, Simar and Wilson (2007) indicated DEA efficiency estimates were serially correlated, and then the use of Tobit regression was inappropriate. They proposed a truncated regression model to overcome the above problem. Following Simar and Wilson (2007), this paper adopts the bootstrapped truncated regression to investigate the effects of external factors on the OE, PE and SE of airlines. The three separate truncated regression model are formulated as follows: L

∗

τ tj = γ0 +

∑

γl κtlj + ε jt , j= 1,…,n;

t= 1,…,T

l= 1 L

ε tj ⩾ −γ0− ∑ γl κtlj ∼ N(0,σ 2ε )

(7)

l= 1

L

∗

αtj = ζ 0 +

∑

ζlκtlj + νjt , j= 1,…,n;

t= 1,…,T

l= 1 L

ν tj ⩾ −ζ 0− ∑ ζlκtlj ∼ N(0,σ 2ν)

(8)

l= 1 L

∗

βtj = ξ 0 +

∑

ξlκtlj + υjt , j= 1,…,n;

t= 1,…,T

l= 1 L

υtj ⩾ −ξ 0− ∑ ξlκtlj ∼ N(0,σ 2υ)

(9)

l= 1 ∗ τ tj ,

∗ αtj ,

∗ βtj

and are the scores of term where n, T and L are the number of DMUs, terms and exogenous variables, respectively; efficiency, term-production efficiency and term-service efficiency of the jth DMU in term t, respectively; γ0 , ζ 0 , and ξ 0 are the constant term of the regression for the term efficiency, term-production efficiency and term-service efficiency, respectively; γl , ζl , and ξl are used to describe the marginal effect of the lth exogenous variable on the term efficiency, term-production efficiency and term-service efficiency, respectively; κtlj is the lth exogenous variable of the jth DMU in term t, and this variable will impact the efficiency. The L

inequality in Eqs. (7)–(9) mean that ε , ν , and υ are distributed N(0,σ 2ε ) with left-truncation at −γ0− ∑l= 1 γl κtlj, N(0,σ 2ν) with leftL −ζ 0− ∑l= 1 ζlκtlj,

N(0,σ 2υ)

L −ξ 0− ∑l= 1 ξlκtlj,

and with left-truncation at respectively, because the estimated efficiency scores truncation at must be more than or equal to zero. In this paper, we adopt the single bootstrap procedure proposed by Simar and Wilson (2007) to obtain the estimated values of parameters. Taking term efficiency as example, the algorithm is presented as follows: ∗

Step 1. Using the Model (1) and the original input and output data, obtain the score of term efficiency τ tj , j = 1, … , n, t = 1, … , T. ε γ0 of γ0 , the estimate  Step 2. Using the maximum likelihood method to obtain the estimate  γl of γl , l = 1, … , L, and the estimate σ ∗ ∗ of σε in the truncated regression of τ tj on a set of exogenous variables, κ1jt,…,κtLj in Eq. (7) using the observations which τ tj > 0 . ε ∗)B 〉2000 Step 3. Looping steps 3.1–3.3 2000 times to obtain a set of bootstrap estimates A= 〈 (γ0 ∗,γ1 ∗,…,γL ∗,σ B= 1 . 2 t∗ t Step 3.1. For each observation excluding the observation which τ j ⩽ 0 , draw ε j from the N(0,σ ε ) distribution with left-truncation L

at −γ0− ∑l= 1 γl κtlj.

∗ L γ0 + ∑l= 1  γ1κtlj + ε tj . Step 3.2. Compute τ tj = 

∗ Step 3.3. Using the maximum likelihood method to estimate the truncated regression of τ tj on a set of exogenous variables, 204

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1 ∗,…,γL ∗,σ ε ∗) . κ1jt,…,κtLj to generate estimates (γ0 ∗,γ ε ) to construct the confidence intervals for γ0 , γ1,…, γL Step 4. Using the bootstrap values in A and the original estimates (γ0 ,γ1,…,γL ,σ and σε . 4. Empirical results 4.1. Data descriptions Due to the availability and accessibility of data, our sample consists of 30 airlines over the period 2009–2012. According to the Air Transport Yearly Monitor of the International Civil Aviation Organization (ICAO) in 2013, nine out of 30 airlines (Emirates Airlines, Delta Airlines, United Airlines, Air China, KLM Royal Dutch Airlines, British Airways, Lufthansa, American Airlines and Cathay Pacific Airways) are ranked in the top 15 airlines in 2012 by RPK. These 30 airlines come from European, American, Asian and African. Therefore, they are representative of global airlines to a certain extent. The data are collected from the corresponding annual reports of the airlines, the Civil Aeronautics Administration, Ministry of Transportation and Communications, R. O. C., and the Bureau of Transportation Statistics, U.S. Department of Transportation. Table 1 lists the inputs, intermediate outputs and final outputs adopted by previous studies. It can be seen that most input variables in these studies are related to fuel, labor (employee) and capital (fleet), intermediate output variables are related to available capacity. For output variables, RPK or revenue passenger miles is popularly selected. Based on the inputs and outputs adopted by the studies listed in Table 1, this paper takes the labor expenses, the size of the leased fleet and fuel expenses as the input variables. In addition, since the operation of an airline company also incurs other expenses, such as maintenance expenses and insurance expenses, other operational expenses are treated as the input variable. ASK and FATK are the intermediate output items, because they represent the supply capacity of an airline company. By the supply of ASK and FATK, an airline can provide the passenger service and cargo service. In order to show how many available seats and tonnage to be actually sold relative to its capacity, RPK and FRTK are treated as the output variables. We use RPK and FRTK rather than operational revenues, because operational revenues include a wide variety of service revenues, such as aircraft maintenance and reservation services for other airlines, consulting services, and hotel business, and these revenues are regardless of ASK and FATK. Finally, flight waypoints and size Table 1 Input and output variables for airlines’ performance with NDEA. Author

Inputs

Intermediate outputs

Outputs

Lu et al. (2010)

Employees Fuel consumed Seating capacity Flight equipment Maintain expense Ground property and equipment Cost per ASM Salaries per ASM Wages per ASM Benefits per ASM Fuel expense per ASM Fuel cost Personnel cost Aircraft cost Number of full-time employees on the payroll Fleet size Fuel consumption Production process: Fuel costs Wages and salaries Non-current assets Other operating costs Sales process: Selling costs Number of passenger planes Number of employees Number of cargo planes Number of aircrafts Quantity of pilots, cabin crew, mechanics, passenger and aircraft handlers, and other labor Quantity of supplies, outside services, and non-flight equipment Total number of flights Frequency Percentage of flights between the hubs Average load factor

ASM ATM

Revenue passenger miles Non-passenger revenue

Load factor Fleet size

Revenue passenger miles Passenger revenue

Number of flights Seat-miles

Passenger-miles Embarkation passenger

Number of destinations Available capacity

Revenue passenger miles

ASK ATK

RPK RTK

Passenger-plane-km Cargo-plane-km

Passenger-km Ton-km

RTK/load factor

RTK

Available seats Available tonnage

Passenger throughput Cargo and mail throughput

Zhu (2011)

Yu (2012)

Chang and Yu (2014a)

Lozano and Gutiérrez (2014)

Tavassoli et al. (2014)

Duygun et al. (2016)

Shao and Sun (2016)

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Table 2 Variables description. Item

Variable

Unit

Description

Inputs

Size of leased fleet Labor expenses Fuel expenses Other operational expenses

N/A Thousand dollars Thousand dollars Thousand dollars

Number of leased aircraft Total staff wages and benefits Total fuel expenses Other operational expenses excluding labor and fuel expenses

Outputs

RPK FRTK

Million kilometers Million kilometers

Number of revenue passengers multiplied by distance of flight segments Freight revenue weights (tons) multiplied by distance of flight segments (km)

Intermediate outputs

ASK FATK

Million kilometers Million kilometers

Available passenger seats multiplied by flight distance (km) Available freight weights (tons) multiplied by flight distance (km)

Carry-over activities

Size of self-owned fleet Waypoints

N/A N/A

Number of self-owned aircraft Defining regional flight routes or positioning specific geographic locations of regional aircraft routes

of the self-owned fleet are used to measure the carry-over activity variables. The relevant variables are listed in Table 2. Since some of the variables are monetary data, to consider the variations of currency values and inflation among different countries and different years, this paper takes the gross domestic product (GDP) deflator and purchasing power parity (PPP) set forth by the World Bank3 for currency conversion, in order to eliminate the currency and inflation issues (Oum and Yu, 1995). The descriptive statistics of input and output variables for the 30 airlines are shown in Table 3. 4.2. Dynamic network data envelopment analysis To begin with, this section examines whether the yearly weights affect the efficiency results, and then explores the trend of airlines’ operational efficiency for the period from 2009 to 2012. Finally, a Boston consulting group (BCG) matrix is used to look into the distribution of airlines in the four quadrants and explore where the differences of airlines’ performance lie. 4.2.1. Analysis of weights and sensitivity Analyzing the yearly weight changes allows us to understand the yearly impact on the efficiency values of DMUs. Bigger weights indicate larger contribution to the efficiency scores. The weight settings are listed in Table 4. The efficiency scores of the various weight settings are then used to inspect whether the distribution of weights by yearly importance makes any difference on airlines’ operational performance. The statistical test is done by the Wilcoxon signed ranked tests. The hypothesis is: H1. Weights distributed by the importance of the years and weights evenly distributed over the years make a difference on efficiency. On operational efficiency, term-production efficiency and term-service efficiency, if p< α = 0.05, where α indicates the significance level, it means that weights distributed by the importance of the years and weights evenly distributed over the years do make a significant difference on efficiency at the 5% significance level. Tables 5 and 6 show that the yearly weights distribution does make a difference on the airlines’ efficiencies. Taking the “Test 1” for instance, the p values of overall operational efficiency and the respective 2009 and 2012 term efficiencies are all less than 0.05, i.e. α = 0.05, and the 2009 production efficiency and the 2012 production and service efficiencies are also less than 0.05, i.e. α = 0.05, which indicates that uneven distribution of weights by yearly importance and even distribution over the years do make a difference for efficiency. The distribution of the weights does affect the evaluation of yearly operations of airlines. Therefore, by conceptualizing the notion of “the bigger the weight is, the more it contributes to efficiency”, and on the basis that more recent years have stronger impact on operational performance, this paper distributes the weights to the years from 2009 to 2012 as 0.1, 0.2, 0.3 and 0.4, respectively. 4.2.2. Efficiency analysis The average term and term-divisional efficiencies are shown in Fig. 3. Generally, the average term, term-production and termservice efficiencies of the 30 airlines from 2009 to 2012 are in a descending trend. The possible reason is that although the global economy gradually recovered from the economic crisis in 2008, the airline industry was hit by declining global air freight traffic during 2011–2012 due to the economic recession in Europe and rising fuel prices. In addition, Barros and Couto (2013) studied 23 European airlines for the period from 2000 to 2011, and their empirical results also showed a trend of descending efficiencies. When comparing the efficiency between the production and service divisions, the rate of decline of service efficiency is more than that of production efficiency. Airline companies should pay more attention to their sales operations. In addition, since the term efficiency is the product of the term-production efficiency and term-service efficiency, the term efficiency is decreasing more specifically when the efficiencies are simultaneously falling in the production and service divisions. However, many factors can affect the operational performance of airlines. With the SBM-DNDEA model, this paper evaluates the efficiencies of airlines’ internal divisions to further 3

The World Bank website: http://www.worldbank.org/.

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Table 3 Descriptive statistics of inputs and outputs. Variables

Minimum

Maximum

Mean

Std. dev.

Inputs Size of leased fleet Labor expenses Fuel expenses Other operational expenses

4 2169 2982 10,888

856 7,822,799 11,509,944 19,418,314

118.28 1792664.97 2710661.11 3534773.53

142.52 1924089.72 2569546.86 4‘84691.26

Outputs RPK FRTK

1847 152

353,134 882,003

84783.31 47215.70

83179.57 150374.02

Intermediate outputs ASK FATK

99 73

293,323 272,931

67860.46 17620.52

68086.66 44848.49

Carry-over activities Size of self-owned fleet Waypoints

3 18

677 375

‘46.74 135.41

176.61 88.16

Table 4 Weight settings. Test

2009

2010

2011

2012

Average 1 2 3 4

0.25 0.1 0.4 0.3 0.2

0.25 0.2 0.1 0.4 0.3

0.25 0.3 0.2 0.1 0.4

0.25 0.4 0.3 0.2 0.1

Table 5 Wilcoxon signed ranked tests for operational efficiencies. Test

Overall operational efficiency

2009 Term efficiency

2010 Term efficiency

2011 Term efficiency

2012 Term efficiency

1 2 3 4

0.008 0.837 0.015 0.211

0.028 0.005 0.889 0.012

0.433 0.001 0.036 0.142

0.285 0.925 0.005 0.003

0.008 0.026 0.286 0.013

Note: The values are p values. Table 6 Wilcoxon signed ranked tests for divisional efficiencies. Test

1 2 3 4

2009

2010

2011

2012

Production division

Service division

Production division

Service division

Production division

Service division

Production division

Service division

0.018 0.180 0.593 0.043

0.173 0.012 1.000 0.465

0.726 0.028 0.109 0.345

0.093 0.004 0.051 0.461

0.116 0.080 0.109 0.018

0.345 0.333 0.008 0.197

0.027 0.109 0.285 0.307

0.028 0.053 0.343 0.144

Note: The values are p values.

look into the distribution of airlines in the quadrants in order to find out where the distinction of operational efficiencies are. The production and service efficiencies of the airlines are shown in Fig. 4─the BCG matrix of divisional efficiencies. By taking the average efficiency scores of the production and service divisions as the center, the 30 airlines are divided into the four quadrants (Lu et al., 2012; Chang and Yu, 2014a). Air Mauritius, Emirates Airlines, Croatia Airlines, El Al Israel Airlines, Icelandair, Delta Airlines, United Airlines, All Nippon Airways and Qantas Airways are all doing excellent in their production and service divisions, with their efficiency values of 1, falling in the upper right corner of the first quadrant. Other airlines also having their efficiency scores falling in the first quadrant include Iberia Airlines, Hawaiian Airlines, Republic Airlines, Air China and Singapore Airlines, indicating that their operation and sales 207

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Fig. 3. Yearly trend of average term and term-divisional efficiencies.

Fig. 4. BCG matrix of divisional efficiencies.

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capabilities are above the average, creating a benchmark for other airlines, and they all need to maintain their operational efficiencies in order to stay competitive. Airlines having their efficiencies falling in the second quadrant include Aeroflot, KLM Royal Dutch Airlines, Alaska Airlines, LAN Airlines, and SkyWest Airlines, indicating that their production efficiencies are weaker than others. The ranks of Aeroflot, KLM Royal Dutch Airlines and LAN Airlines are higher in terms of labor cost per ASK, labor cost per FATK, fuel consumption per ASK and fuel consumption per FATK.4 The average labor cost per ASK of Alaska Airlines and SkyWest Airlines are ranked 14th and fourth in the 30 airlines, and the average labor cost per FATK of these two airlines are ranked 15th and 11th. Hence, in this quadrant, all airlines have a bigger waste in terms of the usage of labor, and some airlines don’t save their energy. Among these airlines, Aeroflot, KLM Royal Dutch Airlines and SkyWest Airlines show bigger efficiency gaps between their production and service divisions, and Aeroflot has production efficiency of merely 0.1479, the biggest deviation from the average of the 30 airlines. This indicates that these airlines are weaker in utilizing resources, and they should enhance management and control of inputs to avoid excessive inputs that cause waste of resources and inefficient performance. Locating in the third quadrant are Scandinavian Airlines, Turkish Airlines, EVA Airways and Malaysia Airlines, indicating that their production and service efficiencies are way behind the average. The average passenger load factor, labor cost per ASK and labor cost per FATK of Scandinavian Airlines are ranked 22th, ninth and 12th in the 30 airlines. The average passenger load factor, freight load factor and fuel consumption per ASK of Turkish Airlines are ranked 23th, 21th and 12th. The fuel consumption per ASK and fuel consumption per FATK of EVA Airways are ranked second and fourth. The average passenger load factor, fuel consumption per ASK and fuel consumption per FATK of Malaysia Airlines are ranked 24th, sixth and 12th. Hence, in this quadrant, most airlines have the disadvantages in the both production and service sides. Worst among them is Scandinavian Airlines, whose production and service efficiencies are merely 0.2208 and 0.2854, respectively. It is suggested that they review the strategies implemented in the past to find out the causes leading to the inefficient operations, and meanwhile take references from other airlines in order to reinforce their own operating strategies and marketing capabilities. Located in the fourth quadrant are British Airways, Finnair, Lufthansa, American Airlines, Cathay Pacific Airways, Garuda Indonesia and Thai Airways, whose service efficiencies are weaker than other airlines. Finnair, Garuda Indonesia and Thai Airways have the lower passenger and freight load factors. The average passenger load factors of these airlines are ranked 20th, 26th and 25th in the 30 airlines, and the average freight load factor of these airlines are ranked 18th, 28th and 16th. The average passenger load factors of British Airways and Lufthansa are ranked 19th and 17th. In addition, the average freight load factors of the airlines ranked first and second have a big difference from other airline. Even the average freight load factors of British Airways, Lufthansa, American Airlines, Cathay Pacific Airways are ranked the sixth, seventh, 22th and 11th, they have the relative lower freight load factors. British Airways, Cathay Pacific Airways and Garuda Indonesia show bigger efficiency gaps between their production and service divisions, and Garuda Indonesia has service efficiency of merely 0.4922, the biggest deviation from the average among the three. They may utilize marketing activities to boost their sales ability and improve their operational performance. 4.3. Truncated regression analysis This paper treats the efficiency results obtained from the first stage as the dependent variables, and uses airline alliance and airline size as the independent variables. In order to clarify the relationship between airline alliance (airline size) and efficiency, this paper add two control variables, GDP and the time effect, to control or eliminate the influences of region and time. The independent and control variables are illustrated as follows: Alliance. Three dummy variables are used to divide alliance types into SkyTeam, OneWorld, and Star Alliance. The dummy variable ST is equal to 1 for the airlines joining the SkyTeam alliances; otherwise, ST is equal to 0; the dummy variable OW is equal to 1 for the airlines joining the OneWorld alliance; otherwise, OW is equal to 0; the dummy SA is equal to 1 for the airlines joining the Star Alliance; otherwise SA is equal to 0. Wan et al. (2009) indicated that alliances could expand their international networks, obtain benefits from cooperation among airline partners, increase the partner airlines’ traffic, and build passenger loyalty. However, according to Perezgonzalez and Lin (2010, 2011a, 2011b), airlines joining alliances saw their net profits drop dramatically. Thus, the impacts of joining alliances on the operational performance of airlines are an empirical question. Assets. Airlines’ total assets is used as a proxy for airline size. When an airline gets more assets, it is supposed to possess more resources and become more capable of planning and execution of its operational strategies. Assaf and Josiassen (2009) also indicated that these airlines with larger size usually had stronger economies of scale. Therefore, the more total assets an airline possesses, the higher is the positive impact on its operational performance. In addition, the effect of assets involves the asset utilization. An increase in assets will cause two effects. One is that airlines may leverage their resource through economies of scale so as to increase efficiency. Another is that airlines require more resource with the increase in assets so as to decrease efficiency. The impact of total assets on efficiency may be non-linear due to the trade-offs in asset utilization. Thus, a variable of the square of total assets is added to investigate the above effect. GDP. GDP is the core index of the national income accounting, and also an important index for the measurement of economic status and development level of a country (region). This paper accommodates GDP into the bootstrapped truncated regression model, with the expectation that GDP growth will bring a positive impact on the operational performance of airlines. Time effect. Three year dummies are added to reflect the time effect. The dummy variable Year2009 is equal to 1 for the year 2009; 4

The higher the ranks of labor cost per ASK, labor cost per FATK, fuel consumption per ASK and fuel consumption per FATK are, the lower the inputs are saved.

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Table 7 Collinearity test result. Variables

Variance inflation factor

ST OW SA Asset GDP Year2009 Year2010 Year2011

1.150 1.308 1.303 1.037 1.055 1.519 1.517 1.500

otherwise, Year2009 is equal to 0; the dummy variable Year2010 is equal to 1 for the year 2010; otherwise, Year2010 is equal to 0; the dummy variable Year2011 is equal to 1 for the year 2011; otherwise, Year2011 is equal to 0. To investigate the effects of these factors on the OE, PE and SE of airlines, the three separate bootstrapped truncated regression model are formulated as follows: ∗

τ tj = γ0 + γ1 × STtj + γ2 × OWtj + γ3 × SAtj + γ4 × Assettj + γ5 × (Asset)2tj + γ6 × GDPtj + γ7 × Year2009t + γ8 × Year2010t + γ9 × Year2011t + ε jt

(10)

∗

αtj = ζ 0 + ζ 1 × STtj + ζ 2 × OWtj + ζ 3 × SAtj + ζ 4 × Assettj + ζ5 × (Asset)2tj + ζ 6 × GDPtj + ζ 7 × Year2009t + ζ 8 × Year2010t + ζ 9 × Year2011t + νjt

(11)

t∗

β j = ξ 0 + ξ1 × STtj + ξ 2 × OWtj + ξ 3 × SAtj + ξ 4 × Assettj + ξ5 × (Asset)2tj + ξ 6 × GDPtj + ξ 7 × Year2009t + ξ 8 × Year2010t + ξ 9 × Year2011t + υjt

(12)

Before using the bootstrapped truncated regression analysis to explore the impacts of external factors on the operational performance of 30 airlines, the problem of collinearity should be tested. We adopt the variance inflation factors’ (VIF) diagnostics proposed by Neter et al. (1985) to analyze collinearity. The result of the collinearity test is presented in Table 7. According to Lind et al. (2015), a VIF above 10 indicates higher correlation, and the independent variable should be removed from the analysis. The result shows that the VIFs of all variables are below 2, and there is no evidence of collinearity between variables in our regression models. We use NLOGIT 3.0 software to run the bootstrapped truncated regression model. The regression results of the separate efficiency are illustrated in Tables 8–10.5 The following are descriptions of the variables that have significant impacts on efficiencies. (1) Alliance The empirical results reveal that joining the SkyTeam alliance has a significantly negative impact on production efficiency. This indicates that joining the SkyTeam alliance can reduce the operational performance of airline companies. This finding is consistent with the empirical results concluded by Perezgonzalez and Lin (2010, 2011a, 2011b).6 The reduced operational performance can be the result of unsound internal integration within the alliances (Oum et al., 2004). Although strategic alliances can reduce the usage of inputs through resource pooling or sharing and joint activities, such as code-sharing, and joint governance, the organizational complexity of assigning tasks among partners cause ongoing coordination of activities and coordination costs. If alliance costs are greater than alliance benefits, joining alliance will cause loss. The solution should be to reinforce the internal integration and planning of the alliances, enhance resource sharing and reduce waste, so that overall operational performance can be boosted. (2) Assets The larger an airline’s total assets, the greater will be the positive impact on its overall operational efficiency and its production efficiency. This means an airline company with more assets possesses better resources for planning and developing its operational strategies, and is more capable of effectively distributing its input resources to boost sales capacities. In addition, the square of total assets has the negative impact on the overall operational efficiency and production efficiency, indicating that the benefit of economies of scale is gradually offset by an increase in the usage of inputs. When an airline company expands its size, it should pay 5 The robustness of the regression results is also examined by rerunning the regression results based on different weight schemes. The test results indicate the signs among different regression results are the same, and the significance is only changed in few independent variables. Due to lack of space, the results are not reported in this paper, but can be obtained from the authors upon request. 6 Perezgonzalez and Lin (2010, 2011a, 2011b) explore the impact of joining alliances on profitability while this paper examines the relationship between joining alliances and operational efficiency. However, Perezgonzalez and Lin (2010, 2011a, 2011b) treat profitability as a performance-related variable, and Schefczyk (1993) indicated the relationship between profitability and efficiency is positive. Hence, we apply the findings of Perezgonzalez and Lin (2010, 2011a, 2011b) to explain the results of this paper, but there is no denying that profitability can be affected by other financial conditions.

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Table 8 Regression results of overall operational efficiency. Variables

Constant ST OW SA Asset (Asset)2 GDP Year2009 Year2010 Year2011

Coefficient

Confidence interval

*

0.6640 −0.1370 −0.0457 −0.0690 8.1838* −0.0001* 0.7032 0.1210* 0.0755 0.0377

Lower bound

Upper bound

0.5603 −0.2943 −0.1624 −0.1788 3.1336 −2.63E−04 −0.0878 0.0146 −0.0283 −0.0719

0.7662 0.0231 0.0704 0.0450 13.2274 −0.0002 1.5075 0.2370 0.1880 0.1435

Note: * is significant at 5% confidence interval; total number of iterations = 2000. The unit of total assets is trillion, and the unit of GDP is hundred trillion. Table 9 Regression results of production efficiency. Variables

Constant ST OW SA Asset (Asset)2 GDP Year2009 Year2010 Year2011

Coefficient

Confidence interval

*

0.7005 −0.4018* 0.0624 −0.0665 11.1750* −0.0002* 0.1766 0.0810 0.0448 0.0198

Lower bound

Upper bound

0.5851 −0.5902 −0.0652 −0.1876 5.7237 −3.66E−04 −0.6861 −0.0360 −0.0687 −0.1010

0.8107 −0.2258 0.1900 0.0579 16.9471 −0.0003 1.0541 0.2092 0.1691 0.1361

Note: * is significant at 5% confidence interval; total number of iterations = 2000. The unit of total assets is trillion, and the unit of GDP is hundred trillion. Table 10 Regression results of service efficiency. Variables

Constant ST OW SA Asset (Asset)2 GDP Year2009 Year2010 Year2011

Coefficient

Confidence interval

*

0.6836 0.1062 −0.0938 −0.0650 4.4112 −6.81E−06 1.0924* 0.1230* 0.0787 0.0351

Lower bound

Upper bound

0.5634 −0.0733 −0.2270 −0.1913 −1.3955 −1.73E−05 0.1813 0.0010 −0.0402 −0.0910

0.8003 0.2899 0.0402 0.0662 10.2143 3.72E−06 2.0146 0.2574 0.2082 0.1569

Note: * is significant at 5% confidence interval; total number of iterations = 2000. The unit of total assets is trillion, and the unit of GDP is hundred trillion.

more attention to the trade-offs involving asset utilization. (3) GDP GDP volume has the positive impact on the service efficiency of airlines. Chang and Yu (2014b) indicated that the GDP was a good indicator of the potential demand for air transport services. An airline located in a country (region) with higher GDP volume faces the better demand conditions, thus it can achieves the higher performance.

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(4) Time effect The year 2009 has the positive impact on the overall operational efficiency and service efficiency of airlines. This means that holding all the exogenous variables fixed, on average, the operational efficiency and service efficiency in 2009 are higher than those in 2012. Since fuel prices gradually rise and air freight traffic gradually drops, the performance of airline finally decreases from 2009 to 2012, significantly. 5. Conclusions and suggestions The contribution of this paper is to combine the SBM-DNDEA model with the bootstrapped truncated regression for a better understanding of the variations of operational performance among international airlines. First, to develop a dynamic network production model that accounts for self-owned fleet size and waypoint as carry-over items and can be estimated using the dynamic NDEA model. Each of the airline companies is divided into production and service divisions, and the SBM-DNDEA model is used to explore the operational efficiency of 30 airlines for the period from 2009 to 2012. Sensitivity analysis is then used to explore the impact of yearly weights distribution on the operational efficiencies of the airlines. Finally, the bootstrapped truncated regression analysis is conducted to look into potential factors that may affect the operational performance of airlines. The sensitivity analysis reveals that the yearly weights distribution has significant impact on the operational efficiency of airlines. Therefore, it is necessary to take a management perspective in handling the yearly weights distribution and assess the importance of each year in advance so that the operational efficiency of each year can be accurately determined. In summary, the empirical results for the 30 airlines show that the average overall operational efficiencies in the four years are in a declining trend, in conformity with the empirical results obtained by Barros and Couto (2013). This paper also puts the 30 airlines in a BCG matrix to understand their efficiency distribution in the matrix. The results of bootstrapped truncated regression analysis show that joining the SkyTeam alliance has a significant negative impact on the operational performance of airlines, probably due to insufficient collaboration within this alliance. Therefore, it is suggested that this alliance should reinforce its internal integration and planning to enhance its resource sharing and reduce waste, so that the operational performance can be raised. Total assets have a significant positive impact on the operational performance of airlines, indicating that the bigger total assets an airline possesses, the more resources it will gain for disposal, thus it will be more capable of developing sound planning and development strategies to boost its overall operational performance. The square of total assets has a significant negative impact on the operational performance of airlines, indicating the benefit of economies of scale is gradually offset by an increase in the usage of inputs. GDP volume has a significant positive impact on the operational performance of airlines, meaning that higher potential demand can increase revenues, thus affecting the operational performance. In addition, during the sample period, the overall operational performance of airlines in 2009 is higher than that in 2012. Based on the results of this paper, the following suggestions are proposed: It is not easy to collect the operation data of airlines and, as a result, the past studies of airlines’ performance mostly feature short timeframes and inadequate data. In future studies, it is important to involve more years of data and more evaluated airlines so that the analysis can be more comprehensive and complete. The distribution of the weights does affect the evaluation of yearly operations of airlines. Future research might make the weight vector endogenous in the optimization process to choosing the weight vector in dynamic network models. On bootstrapped truncated regression analysis, it is noted that joining an alliance can result in airlines seeing their operational performance drop, and therefore adding some new variables that respond to the negative impact from joining an allianceis suggested, so that the results can be a more valuable reference for the aviation industry. Appendix A By using the “Charnes-Cooper transformation” (1962), Model (1) can be transformed from a fractional programming formulation into a linear programming structure. Let −1

s

T

φo =

⎧ s t+ ⎞ ⎤ ⎫ 1 ⎛ ⎡ W tw 2 ⎢1 + × ⎜∑ rot ⎟ ⎥ ∑ ⎨ t=1 ⎬ s y ⎝ r = 1 ro ⎠ ⎦ ⎭ ⎣ ⎩

(A.1)

Multiplying the numerator and the denominator in the objective function of Model (1) by this φo leaves its value unchanged. We then have T

min ρ∗o =

∑ t=1

m

rt− ⎞ ⎤ 1 ⎡ ⎛ W tw1 ⎢φo− × ⎜∑ iot ⎟ ⎥ m x ⎝ i = 1 io ⎠ ⎦ ⎣

(A.2)

s.t. T

∑ t=1

s

rt+ ⎞ ⎤ 1 ⎛ ⎡ W tw 2 ⎢φo + × ⎜∑ rot ⎟ ⎥ = 1 s y ⎝ r = 1 ro ⎠ ⎦ ⎣

(A.2.1)

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φo x iot =

x ijt ψtj + riot−

∑

i = 1,…,m; t = 1,…,T (A.2.2)

j=1 n

φO yrot =

yrjt μjt −rrot+

∑

r = 1,…,s; t = 1,…,T (A.2.3)

j=1

n t φO z fo =

∑

z fjt ψtj

f = 1,…,h; t = 1,…,

z fjt μjt

f = 1,…,h; t = 1,…,T

(A.2.4)

j=1 n t φO z fo =

∑

(A.2.5)

j=1 n (0,1) φO uao =

uaj(0,1) ψ1j

∑

a = 1,…, (A.2.6)

j=1 n (0,1) φO vbo =

∑

vbj(0,1) μj1

b = 1,…, (A.2.7)

j=1 n

n

∑

uaj(t ,t + 1) ψtj =

∑

j=1

uaj(t ,t + 1) ψtj+ 1 a = 1,…,p; t = 1,…,T −1

(A.2.8)

j=1

n (t ,t + 1) uao =

∑

(t ,t + 1) uaj(t ,t + 1) ψtj + rao

a = 1,…,p; t = 1,…,T −1 (A.2.9)

j=1 n

∑

n

vbj(t ,t + 1) μjt =

∑

j=1

vbj(t ,t + 1) μjt + 1

b = 1,…,q; t = 1,…,T −1 (A.2.10)

j=1 n

(t ,t + 1) vbo =

∑

(t ,t + 1) vbj(t ,t + 1) μjt + rbo

b = 1,…,q; t = 1,…,T −1 (A.2.11)

j=1 n

∑

ψtj = φO

t = 1,…,T

μjt = φO

t = 1,…,T

(A.2.12)

j=1

n

∑

(A.2.13)

j=1 (t ,t + 1) (t ,t + 1) riot−,rrot+ ⩾ 0;rao ,rbo : free in sign

i = 1,…,m; r = 1,…,s; a = 1,…,p; b = 1,…,q; t = 1,…,T

ψtj ,μjt

⩾ 0 j = 1,…,n; t = 1,…,T

rrot+

φo srot+, riot−

(t ,t + 1) φo siot−, rao

(A.2.14) (A.2.15)

(t ,t + 1) (t ,t + 1) φo sao , rbo

(t ,t + 1) t φo sbo ψj

φo λtj ,

μjt

φo δ tj ,

= = = = = ∀ r,i,a,b,j. By means of the execution of = where and Model (A.2), the optimal solutions of the overall operational efficiency and its components can be obtained.

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