With the development of mobile communication technologies, it brings new implementations in intelligent transport system (ITS). Previous studies of ITS ignored the advanced services from service providers and especially taxi drivers who provide mobile payment service at front‐line towards passengers. This research combines the unified theory of acceptance and use technology 2 (UTAUT2) and the moderating variable of experience, perceived risk (PR), to investigate the key influence on the adoption intention of mobile payments for the taxi drivers. A total of 228 valid questionnaires were collected and subsequently analysed by using the structural equation modelling (SEM) approach and back‐propagation artificial neural network. The results demonstrate that performance expectancy (PE), effort expectancy (EE) facilitating conditions (FCs), and PR are significant with respect to the adoption intention. As there is limited UTAUT2‐based research conducted on mobile payments, this article is among the few that attempt to extend the model and study taxi drivers in Taiwan, who play both roles of business and customer. This finding provides the current situation of a new technology on taxi industry. Furthermore, it also provides useful information and direction for mobile stakeholders and ITS implementations to understand the rationale behind the use of service and how it leads to promoting acceptance.
Predicting adoption of mobile payments from the perspective of taxi drivers
IET Intelligent Transport Systems ; 13 , 7 ; 1116-1124
2019-07-01
9 pages
Article (Journal)
Electronic Resource
English
technology acceptance model , service providers , statistical analysis , structural equation modelling approach , mobile payment service , PR , mobile stakeholders , facilitating conditions , neural nets , mobile commerce , adoption intention , mobile payments , intelligent transport system , unified theory of acceptance and use technology 2 , traffic engineering computing , backpropagation artificial neural network , UTAUT2‐based research , taxi drivers , mobile communication technologies , taxi industry , effort expectancy , performance expectancy , intelligent transportation systems , transportation
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