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IT CONTINUOUS USE IN MOBILE RIDESHARING APPLICATIONS: DRIVER PERSPECTIVE

Information and communication technologies have boosted a new culture of consumption, the so-called sharing economy. Sharing economy (SE) can be defined as a peer-to-peer (P2P) online marketplace where individuals (a peer-provider) connect with other individuals (a peer-consumer) through online platform for sharing, swapping, or lending resources. As an off shoot of SE, ridesharing mobile apps (RMA) such as Uber and Lyft have been introduced. RMA have achieved incredible growth and success in the taxi business. However, it is noticed that there are many campaigns against RMA that are organized by peer-drivers. This can mean that while peer-drivers’ satisfaction is low with RMA, they still continue use these apps. This is inconsistent with predictions from the extant theories in information systems (IS). The reason behind this inexplicable behaviour might be that, in RMA, how user attitudes formed and value experienced is fundamentally changed as more entities are involved. Ridesharing apps are experienced within the triple-interaction; peer-peer interaction, and peer-app interaction rather than double-interaction; user-app interaction as in the conventional IT in previous research. This new innovative context of IT has been neglected in current IT use research and IT theories. To address these gaps, this research plans to understand this phenomenon through incorporating not only past-and current-related factors (as in previous studies) but also future-related factors into the prediction of IT continuance models. This is because these IT are rapidly growing and individual expectations about future are changing fast. Based on the expectation-confirmation theory, the unified theory of acceptance and use of technology, and some constructs of psychological literatures, this study will focus on the effect of integrating future-related factors into IT continuance model on the interpretation of continuance intention in RMA. This aim will be achieved through conducting several quantitative studies (i.e. questionnaire and experiment) on the Uber app as a case study. The first study will develop a research framework and test it through collecting data from Uber drivers through questionnaires. Based on the results of the first study, other studies will be designed. It is hoped that this research will extend IS continuance models to include future aspects of user experience rather than only past and present experiences. In addition, the findings of investigating continuous use behaviour will add more understanding for user needs in the disruptive phenomenon of SE.