Incorporation of Q method into DEA cross-efficiency: A case study on airport assessment

Abstract

Though widely used, traditional Data Envelopment Analysis (DEA) methods have limitations when it comes to accurately ranking airport performance. The Cross-Efficiency Method (CEM), derived from DEA, addresses these limitations. However, recently, the conventional CEM has been considered for further improvements. Firstly, it assumes equal significance for all Decision Makers’ (DMs) viewpoints by using the arithmetic mean for overall cross-efficiency calculation, which is unrealistic. Secondly, it does not consider DMs’ viewpoints psychologically. Thirdly, it does not often consider achieving consensus through efficiency calculations. Finally, it includes all viewpoints for efficiency calculation, regardless of their relevance, which can bias the results. Thus, the primary objective of this study is to develop hybrid Q-based CEMs to address these shortcomings. Also, this study is the first to use the Q methodology to address the limitations of traditional CEM. The Q-based aggressive and benevolent CEMs, known as QACEM and QBCEM, provide policymakers with several advantages. Firstly, they allow for the exclusion of irrelevant viewpoints from the analysis. Secondly, they enable the calculation of each DM’s appropriate contribution. Thirdly, they capture DMs’ psychological preferences using the Q methodology. Lastly, they facilitate consensus-building by extracting group perspectives through factor analysis. The Q-based CEMs were utilized to assess the performance of 25 Iranian international airports and demonstrated their effectiveness. Selecting the optimal loading factors of 0.6 and 0.7, QACEM and QBCEM included the highest possible number of DM’s viewpoints, which were 24 and 25 respectively. The results indicate that airports BND, AWZ, and OMH demonstrated satisfactory performance.