Determinants of ride-splitting adoption in Ho Chi Minh City: The moderating role of carbon credit schemes

Abstract

A particular kind of shared ridesourcing service possessing the potential to drastically cut the GHG emissions is called ride-splitting. Nonetheless, the current rate of user adoption stays fairly modest. Implementing policies like carbon credit schemes, which provide incentives for minimizing emissions, hold great promise in encouraging ride splitting. The purpose of this study was to quantitatively evaluate factors influencing the ride splitting decision behaviors under a carbon credit scheme. The data was collected through Google Forms and filtered to ensure validity. First, descriptive statistics were presented. Then, the author group evaluated the measurement model by testing reliability and validity using Cronbach’s alpha, composite reliability (CR), average variance extracted (AVE), and discriminant validity with the Fornell-Larcker criterion and HTMT. Multicollinearity was checked with the VIF coefficient. To assess how well the dependent variables were explained, the author group looked at key metrics like R², f², and Q². Finally, we used bootstrapping with 5,000 subsamples to test our hypotheses with t-tests. The findings demonstrate ride fare, perceived usefulness and waiting time exert significant positive influence on trust, perceived risk negatively influences on trust in service. In addition, trust has a positive influence on ride splitting choice behavior of people