Enhanced performance assessment of airlines with integrated balanced scorecard, network-based superefficiency DEA and PCA methods
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CitationAydın, U., Almula Karadayı, M., Ülengin, F. ve Ülengin, K. B. (2021). Enhanced performance assessment of airlines with integrated balanced scorecard, network-based superefficiency DEA and PCA methods. Contributions to Management Science içinde (225-247. ss.). Springer Science and Business Media Deutschland GmbH. https://dx.doi.org/10.1007/978-3-030-52406-7_9
In the last decade, due to the aggressively increasing competition in the airline industry, strategic decisions to improve airline performance have become crucial. However, evaluating airline efficiency is an extremely complex, multidimensional problem and requires the application of Multiple Criteria Decision-Making (MCDM) methods. This study evaluates the performance of 45 airline companies via combining the balanced scorecard (BSC) approach and the network-based superefficient data envelopment analysis (DEA). The proposed methodology incorporates finance, customers, internal processes, learning, and growth dimensions of BSC into the analysis in order to conduct a comprehensive assessment of airline companies from financial and nonfinancial perspectives of performance. Moreover, the eigenvector centrality concept is used to determine the airlines that should act as a role model (peer) for efficiency in each dimension of BSC. Rankings of airline companies in each dimension are also presented using the eigenvector centrality values. Additionally, in order to improve the discriminatory power of DEA, initially the principal component analysis (PCA) is conducted and based on the representation of the 14 variables by seven factors revealed from PCA, a compact model that integrates the four dimensions of the evaluation is obtained. Those factors are named according to their characteristics as Flight Capacity, Profitability, Profitability per Employee, Customer Satisfaction, Operational Profitability, Liquidity, and Operational Performance. Those key performance indicators are used in order to make overall performance evaluation and reveal the overall rankings. Finally, the significance of the ranking differences between the ranking based on each of the four dimensions and the overall ranking is tested by spearman rank correlation.