A balanced scorecard envelopment approach to assess airlines' performance
| Pages | 123-143 |
| Published date | 28 January 2014 |
| Date | 28 January 2014 |
| DOI | https://doi.org/10.1108/IMDS-03-2013-0135 |
| Author | Wann-Yih Wu,Ying-Kai Liao |
| Subject Matter | Information & knowledge management,Information systems,Data management systems |
A balanced scorecard
envelopment approach to assess
airlines’ performance
Wann-Yih Wu
Business Administration, National Cheng Kung University, Tainan,
Taiwan and
Chinese Culture University, Taipei, Taiwan, and
Ying-Kai Liao
Business Administration, National Cheng Kung University, Tainan, Taiwan
Abstract
Purpose – This study purposed an integrated DEA-BSC model to evaluate the operational ef ficiency of
airlines. To adapt this model, 38 major airlines in the world were selected to assess their relative performance.
Design/methodology/approach – An empirical study is employed using a cross-sectional research
design. The operational and financial data of 38 leading airlines companies were collected from annual
reports and business reports. Specifically, this study integrated the concepts of balanced scorecard
(BSC) and data envelopment analysis (DEA) and incorporated seven leading variables and four
lagging variables from BSC to implement DEA.
Findings – By using the leading and lagging variables to implement DEA, this study not only
assessed the efficiency frontiers, input slack, output slacks, and benchmarking learning partners of
38 airlines, but also illustrated how leading indicators are related and influence lagging indicators.
In particular, the study results indicated that airlines with excellent performance in the efficient
frontiers tended to perform better in energy, capital, and other operating costs.
Research limitations/implications – This study presented a DEA-BSC model to integrate the
concepts of BSC into DEA. The empirical results showed that the model is more advanced than the
capabilities of individual DEA and BSC. This model could also eliminate the faults of each one. Due to
the cross-sectional research design of this research, future research should develop the longitudinal
study to identify the time series of the influences of leading factors on lagging factors.
Practical implications – This study offered an integrated model that incorporated the concepts of
BSC and DEA. The leading and lagging factors of BSC were adopted to the evaluation of operational
performance of airlines along with DEA. Therefore, BSC has served as the compliment of DEA. Using
the DEA-BSC results, such as the efficiency frontiers, the amount of slacks, and benchmark learning
partners, business executives could develop their improvement strategies.
Originality/value – Since none of previous studies have integrated BSC and DEA to assess the
operational efficiency of the airline industry, the results of this study could serve as a baseline for
further academic validations, the results could also be very useful for the executives of airline
companies to allocate their resources for further improvement.
Keywords Balanced scorecard,Data envelopment analysis, Airlineperformance, Bench marking
Paper type Research paper
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0263-5577.htm
The authors would like to thank Orawan Wannadee for providing valuable assistance on an
earlier version of this manuscript. Further, the authors would like to greatfully thank the editor
and two anonymous reviewers for providing constructive remarks and useful suggestions,
which have been instrumental in the development of his paper.
Received 22 March 2013
Revised 29 June 2013
10 July 2013
Accepted 12 July 2013
Industrial Management & Data
Systems
Vol. 114 No. 1, 2014
pp. 123-143
qEmerald Group Publishing Limited
0263-5577
DOI 10.1108/IMDS-03-2013-0135
A BSC
envelopment
approach
123
1. Introduction
As global air transport industry becomes increasingly competitive, most operating
airlines feel pressured and have to respond quickly in order to survive in the industry.
Historically, low input prices have been more important than productive efficiency in
determining cost competitiveness. Asia-Pacific carriers tend to be more competitive in
lower factor costs, even though they are, in general, less efficient than US and
European carriers (Oum and Yu, 1998). Unfortunately, the circumstance has changed
as airlines have increased their global sourcing and input prices in developing
countries have continued rising over time. Because input prices are beyond the control
of airlines, the only way to lead in this industry is to improve airlines efficiency
(Bjelicic, 2012).
Over the last few decades, the issue of performance evaluation has created a
significant attention. The economy indicators that researchers usually considered in
evaluating the overall performance of airlines could be obtained from either operational
measures or financial measures (Merkert and Morrell, 2012; Tsai et al., 2012; Hung and
Chen, 2013). Traditionally, most organ izations only looked at their financial
performance. Schefczyk (1993) explained the difficulty in using financial information
of international airlines since different accounting and taxation rules in various
countries may result in different impacts of leased assets on profit and balance-sheet
information. Scheraga (2004) investigated the structural drivers of operational
efficiency as well as the financial posture of airlines after the attacks of September 11,
2001. They found that relative operational efficiency did not inherently imply superior
financial mobility. Therefore, further validations on this issue are essential.
Among others, data envelopment analysis (DEA) and the balanced scorecard (BSC)
are two of the most important methods for performance evaluation, DEA (Charnes et al.,
1978) is a non-parametric technique based on the observed input-output data (or
decision making units (DMUs)) to identify the best practice units (efficiency frontiers)
and the inefficient units. While DEA has widely been adopted to evaluate the relative
efficiency among airlines, it still suffered from lacking of future view in which the
longitudinal variables (e.g. the records of continuous improvement) could not be
included in a single stage DEA study (Aryanezhad et al., 2011). Kaplan and Norton
(2007), as the founders of BSC, argued that firms should emphasize not only the
lagging factors (e.g. financial performance), but also the leading factors (e.g. customer
orientation, internal process improvement, and learning and growth). Without paying
attention to the leading factors, the lagging factors are doomed to be failed. Although
BSC has received wide acceptance from academics and practitioners, it was criticized
as having no formal implementation methodology, which may result in lack of
accountability (Fletcher and Smith, 2004). As a result, Aryanezhad et al. (2011), in a
case study of banking sector, proposed to integrated the concept of BSC and DEA by
adopting the indices of BSC into the input-output of DEA to increase the explanation
power of the model.
This study aims to measure the operational performance of the airline industry by
developing a DEA-BSC model. DEA has been applied in the evaluation of airline
performance (Sengupta, 1999; Barbot et al., 2008; Barros and Peypoch, 2009), most of
them follow the traditional input-output model which neglect the intermediate
measures or linking activities (Tone and Tsutsui, 2010, Lu et al., 2012). DEA is good at
estimating relative efficiency but poor at absolute efficiency. Specifically, previous
IMDS
114,1
124
To continue reading
Request your trial