The Impact of Airport Managerial Type and Airline Market Share on Airport Efficiency

This paper investigates the impact of airport managerial type and airline market structure on airport efficiency. It explores whether the market shares of the largest airlines differ depending on the managerial type of the airport. In this study, the efficiency scores for the sample airport are measured through DEA (Data Envelop Analysis), and the impacts of the airport managerial type and dominant carrier market share on airport efficiency are subsequently estimated through CEM (Coarsened Exact Matching). This paper concludes that group airports are more efficient than the standalone airports, and the market shares of the largest airlines have a positive impact on enhancing airport efficiency. In addition, the market shares of the largest airlines are found to be higher for group airports than for standalone airports. These findings can serve as practical guidelines for governments and airport authorities by suggesting that efficiency improves when multiple standalone airports are operated as a group through the M&A of airports or the establishment of airport operation agencies. While facing unprecedented challenges from the spread of COVID 19, this paper also suggests that an increase in airline market share through airport–airline cooperation has a positive impact on airport efficiency.

Evaluation of Production Efficiency by the Three-Stage Data Envelopment Analysis Model: Chinese Power Sector as the Study Case

Previous research on the evaluation of production efficiency cannot effectively respond influences caused by environmental effect and random error. Specifically, most of electric power companies in China suffer from low production efficiency and ineffective evaluation models. However, with the adoption of the proposed three-stage data envelop analysis (DEA) model, reasonable judgments on the investment decision and management level can be reached. Practical investigation has proved that it can help power companies make more objective judgment which would then bring about continuous improvement.

RANKING DECISION ALTERNATIVES BY INTEGRATED DEA, AHP AND GOWER PLOT TECHNIQUES

Data Envelop Analysis (DEA) and Analytic Hierarchy Process (AHP) are widely used methods in ranking decision alternatives. However, current DEA models are difficult to discriminate decision-making units through articulating the decision makers' preferences. While AHP and Gower plot models have to specify complete pairwise preferences without providing assisting information. This study develops an iterative method of ranking decision alternatives by integrating DEA, AHP and Gower plot techniques. The developed method first utilizes a modified DEA model to narrow the ranges of a decision maker's preferences. Then, the tentative ranks of the decision alternatives, computed by embedding the decision maker's preferences, are depicted via Gower plots to illustrate the cardinal and ordinal inconsistencies of these preferences. The decision maker then adjusts the preferences iteratively until the inconsistencies are within the tolerance.