Finding service quality improvement opportunities across different typologies of public transit customers

Abstract

Existing approaches dealing with customer perception data have two fundamental challenges: heterogeneity of customer perceptions and simultaneous interrelationships between attitudes that explain customer behavior. This paper aims to provide practitioners with a methodology to address the twin challengers of service quality evaluation based on public transit user behavioral theory and advanced market segmentation. The original contributions of this paper are: the definition of customer typologies based on advanced customer segmentation with Latent Class Clustering; analysis of the effect of service quality perceptions on behavioral intentions within the behavioral theory framework that considers multiple attitudes simultaneously affecting customer intentions; identification of transit service improvement opportunities aimed at most customers as well as for specific customer typologies. Our research shows practitioners and researchers that specific needs and perceptions of advanced segmentations of customers can be identified and they may be as important as those shared by most customers. We applied our methodology to a light rail transit service in Seville, Spain. We measured the direct effect of LRT service quality on behavioral intentions, customer satisfaction and, in the case of some customers, the available transport alternatives. Other observed attitudes of customers were also indirectly related to behavioral intentions. We found common customer agreement about aspects of LRT service quality for tangible service equipment, accessibility, information, individual space and environmental pollution. Customers clearly showed different opinions relating to safety, customer services and availability.