The Role of Critical Incidents and Involvement in Transit Satisfaction and Loyalty

Abstract

We analyse the relationship between transit passengers’ satisfaction and loyalty. Understanding passengers’ behavioural intentions after experiencing a service is an essential task for transit managers. We use structural equation models (SEM) to explore the relationship among various satisfaction latent constructs. In particular, we introduce Loyalty, which represents the intent to recommend the service. We also introduce the concept of Critical Incidents (CI), i.e. closure of a transit line in the last three months (planned) or a service anomaly in the past month (unplanned), and hypothesise that CI negatively affects any attribute-specific satisfaction construct. We additionally model the concept of Involvement, measured as the intent to participate in future public transport (PT) marketing studies, and hypothesise that both Overall Satisfaction and Loyalty may affect this variable. Finally, we conduct an SEM Multi-Group Analysis (SEM-MGA), with the objective to determine whether heterogeneity is present in passengers’ satisfaction models, by incorporating users’ travel and demographic characteristics (i.e. gender, age, nationality, time of day, travel frequency, and trip purpose). Our findings show that CI significantly impact all attribute-specific satisfaction constructs, specially the unplanned events during the last month. We also find that Loyalty is influenced by Overall Satisfaction and also by specific satisfaction constructs. By comparing various SEM models, we find that service satisfaction constructs, such as speed and waiting time at the platform, are the most relevant towards Overall Satisfaction. The SEM-MGA serves as a tool to test for heterogeneity in the satisfaction models within user groups. We find differences in time of day, age, and travel frequency. Finally, we consider that the Involvement relationship needs further research. Our framework allows for more detailed policy-making in PT systems regarding heterogeneous subpopulations and more concrete policy variables, such as Critical Incidents.