Behavioral Intention to Continue Using a Library Mobile App

Abstract

To meet the needs of today's library users, institutions are developing library mobile apps (LMAs), as their libraries are increasingly intelligent and rely on deep learning. This paper explores the influencing factors and differences in the perception of LMAs at different time points after a user has downloaded an LMA. A research model was constructed based on the technology acceptance model. A questionnaire was designed and distributed twice to LMA users with an interval of three months to collect dynamic data. The analysis was based on structural equation modeling. The empirical results show that the perceived ease of use, the perceived usefulness, the social influence, and the facilitating conditions affected the users' behavioral intention, but their impacts were different at different times. As the usage time increases, the technology acceptance model is still universal for understanding the user perception of LMA. In addition, two extended variables (social impact and convenience) also affect the user's behavior intention. User behavior is dynamic and changed over time. This study is important both theoretically and practically, as the results could be used to improve the service quality of LMAs and reduce the loss rate of users. Its findings may help the designers and developers of LMAs to optimize them from the perspective of a user and improve the service experience by providing a deeper understanding of the adoption behavior of information systems by LMA users.