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The connectivity provided by full-service network carriers under the umbrella of airline alliances is increasingly challenged by the services of Middle Eastern airlines via their own hubs, and the rise of new passenger strategies like self-connectivity. While these two developments can potentially benefit consumers with more services and lower fares, the rise of Middle East carriers has been met with opposition by EU and US airlines that call for increased protectionism. In addition, only a few airports in the world actively support self-connections. In this context, this study aims to investigate (1) the markets in which Middle East carriers exert a stronger dominance in terms of the number of passenger connections, (2) whether EU, US, or Asian hubs provide a competitive quality of connectivity in terms of travel time, and (3) whether a significant potential for self-connections is hidden at major airports worldwide. To that end, several datasets of passenger bookings (MIDT), airline schedules, and minimum connecting times between 2012 and 2015 are combined in a connections-building methodology that delivers six market-specific airport connectivity indicators for our benchmarking exercise. Our findings show that although European and some Asian hubs have lost traffic in global markets, they remain competitive from a quality perspective. US hubs have maintained their market share and competitive position. Finally, we identify the airports and airlines with the highest potential to provide self-connecting travel options, which can become an attractive new source of revenue for the parties involved.

When large samples are used to estimate airport efficiency, clustering is a necessary step before carrying out any benchmarking analysis. However, the existing literature has paid little attention to developing a robust methodology for airport classification, instead relying on ad hoc techniques. In order to address this issue, this paper aims to develop a new airport clustering procedure.

A frontier-based hierarchical clustering procedure is developed. An application to cost-efficiency benchmarking is presented using the cost function parameters available in the literature. A cross-section of worldwide airports is clustered according to the relevant outputs and input prices, with cost elasticities and factor shares serving as optimal variable weights.

The authors found 17 distinct airport clusters without any ad hoc input. Factors like the use of larger aircraft or the dominance of low-cost carriers are shown to improve cost performance in the airport industry.

The proposed method allows for a more precise identification of the efficiency benchmarks, which are characterized by a set of cophenetic distances to their “peers”. Furthermore, the resulting classification can also be used to benchmark other indicators linked to airport costs, such as aeronautical charges or service quality.

This paper contributed to airport clustering by providing the first discussion and application of optimal variable weighting. In regard to efficiency benchmarking, the paper aims to overcome the limitations of previous papers by defining a method that is not dependent on performance, but on technology, and that can be easily adapted to large airport datasets.