Assessing commuters' behavioral intention to use Singapore Mass Rapid Transit (MRT): The role of service quality and commuters' satisfaction

Abstract

Commuting is an integral part of daily life for millions around the world, yet it often presents challenges related to service quality, commuter satisfaction, and behavioral intentions. With increasing urbanization and demand for efficient public transportation systems, understanding commuter choices becomes crucial for transport planners and policymakers. Singapore commuters rely on Mass Rapid Transit (MRT), which reduces travel time across different areas and enhances overall mobility. This study addressed a gap in understanding how service quality, customer satisfaction, and behavioral intention influence urban transit systems, particularly Singapore's MRT. The behavioral intentions of 601 commuters were examined through service quality and customer satisfaction variables. By integrating structural equation modeling and analytic hierarchy process, stakeholders must prioritize reliability, assurance, and tangibility in service quality variables. These three latent variables stemmed from six hypotheses significantly affecting customer satisfaction, resulting in positive behavioral intention. The integration of Structural Equation Modeling (SEM) and Analytic Hierarchy Process (AHP) offered a practical framework adaptable to global transit systems. The combined approach identified key service quality factors are reliability, assurance, and tangibility—as critical to influencing commuter behavior. Policy implications suggested enhancing Singapore's MRT system through ergonomically designed spaces or platforms, commuter-centered software or visualization monitor for real-time updates, optimized maintenance activities aligned with peak hours, and improved security protocols. These targeted strategies, grounded in empirical analysis, aimed to improve operational efficiency, commuter satisfaction, and system effectiveness. The framework served as a model for other urban transit networks seeking data-driven approaches to service enhancement and policy development.