Consumer acceptance of smart cards in public transportation - Khudaibergenova, Kamila (2017)
Smart card technology is certainly not new; it has been widely implemented in public transportation in many countries. It is considered as a permanent and efficient fare collection system, which allows to enhance planning and management activities of transport authorities and optimize vehicle supply in accordance with the passenger demand. Overall, smart cards are considered to improve user perceptions regarding public transportation, lead to changes in individuals’ travel behavior and, consequently, decrease amount of private vehicles in transit. However, successful implementation of new fare collection system largely depends on its wide user adoption. Therefore, this study focuses on identification of factors that influence user intention to adopt smart cards in public transit sector. Development of the research model was based on several technology acceptance theories: Theory of Reasoned Action (TRA), Technology Acceptance Model (TAM), Innovation Diffusion Theory (IDT) and Unified Theory of Acceptance and Use of Technology2 (UTAUT2). The proposed model incorporated perceived usefulness, perceived ease of use, relative advantage, social influence, habit, security, transparency and trust as major predictors of the new system acceptance. Age, gender, education, media exposure, computer self-efficacy, smartphone and Internet use were included as control variables. The study results showed that habit, economic profitability dimension of relative advantage and trust played predominant role in individuals’ intention to adopt smart cards. While, transparency favorably influenced user trust in vendor. Surprisingly, none of the TAM predictors influenced passengers’ intention to adopt the system. Effect of social influence and users’ security concerns were also insignificant. Out of seven control variables only smart card media exposure and smartphone use duration were found to be influencing behavioral intention. Overall, the proposed model explained 54% of variance in user intention to accept smart cards.