A Structural Equation Modeling Approach to Understand User's Perceptions of Acceptance of Ride-Sharing Services in Dhaka City

TL;DR

This study develops a structural equation model to analyze users' perceptions and key factors influencing acceptance of ride-sharing services in Dhaka, highlighting safety and security as critical elements affecting user decisions.

Contribution

It introduces a validated SEM framework specifically tailored to assess ride-sharing acceptance in Dhaka, incorporating user perceptions and perceived risks.

Findings

Safety & security significantly influence service quality perception.

Account and personal information are key factors in ride-sharing acceptance.

Risk of conflict and accidents are perceived as low-impact variables.

Abstract

This research aims at building a multivariate statistical model for assessing users' perceptions of acceptance of ride-sharing services in Dhaka City. A structured questionnaire is developed based on the users' reported attitudes and perceived risks. A total of 350 normally distributed responses are collected from ride-sharing service users and stakeholders of Dhaka City. Respondents are interviewed to express their experience and opinions on ride-sharing services through the stated preference questionnaire. Structural Equation Modeling (SEM) is used to validate the research hypotheses. Statistical parameters and several trials are used to choose the best SEM. The responses are also analyzed using the Relative Importance Index (RII) method, validating the chosen SEM. Inside SEM, the quality of ride-sharing services is measured by two latent and eighteen observed variables. The latent variable 'safety & security' is more influential than 'service performance' on the overall quality of service index. Under 'safety & security' the other two variables, i.e., 'account information' and 'personal information' are found to be the most significant that impact the decision to share rides with others. In addition, 'risk of conflict' and 'possibility of accident' are identified using the perception model as the lowest contributing variables. Factor analysis reveals the suitability and reliability of the proposed SEM. Identifying the influential parameters in this will help the service providers understand and improve the quality of ride-sharing service for users.