Article Preview
TopIntroduction
Using mobile terminals, especially smart phones, is omnipresent worldwide and has become part of users’ routine life (Hajiheydar, Hernandez-Carrio, & Ashkani, 2018; Hossain, Muhammad, Abdul, Song, & Gupta, 2018). Mobile terminal users can acquire a variety of mobile applications (e.g., instant message and mobile payment) on a mobile terminal that meet their various needs (Hsiao, Chang, & Tang, 2016; Mäki & Kokko, 2017; Olaleye, Salo, Sanusi, & Okunoye, 2018; Yoon, Jeong, & Rolland, 2015). Moreover, a large number of mobile terminal users can facilitate the long-term development of mobile applications. Continuance usage of a mobile terminal (CUMT) not only benefits mobile terminal users but also mobile application providers (Hajiheydar et al., 2018; Hsiao & Chen, 2017). However, besides popular mobile applications, many mobile applications are inactive in mobile markets. Therefore, it is necessary to explore the mechanism of CUMT to explain and predict the behavior of mobile terminal users.
Continuance usage (CU) refers to the users’ continual use of information system (IS) over a long period of time after adoption (Bhattacherjee, 2001). Prior studies confirmed that CU is important for long-term success of IS or mobile applications and conducted their research from different perspectives. Some studies showed that CU is the result of rationed action, which is influenced by user cognitions such as beliefs, attitude, and intention (Hsu, Hsu, Wang, & Chang, 2016). Other studies asserted that CU correlates with habit that is unconscious (Limayem & Cheung, 2008). In addition, some studies such as unified theory of acceptance and use of technology (UTAUT) also supported the point that user’s behavior beliefs and subjective norm determinate user’s intention and behavior, and argued that both individual factor and social factor influence the use behavior (Montaño & Kasprzyk, 2008; Venkatesh & Morris, 2003). However, there are several gaps in previous studies of CU. (1) Although Ajzen and Dasgupta (2015) stated that there are conscious and unconscious processes in human behavior, few studies considered the integration of individual behavior nature that is associated with both reasoned action and unconscious behavior with social influence in order to develop a theoretical model that provides a comprehensive understanding of user behavior. (2) Although prior studies considered social influence as a moderator in different contexts (Chou & Sun, 2017; Shukla & Drennan, 2018), few studies explored the moderating effect of social influence to reveal the boundary condition in the mobile Internet context.
In the mobile Internet context, mobile terminal users can compare a mobile application with traditional or other mobile applications and then make a decision on whether they will continue using that mobile application (Hsu et al., 2016). In addition, when users repeatedly use mobile applications, they will form a habit of using mobile application with an unconscious awareness (B. Kim, 2012). Therefore, CUMT might be related to both rationed action and unconscious use. Furthermore, as social media move from the Internet context to the mobile Internet, the relationships among peoples become closer and communication is much more frequent than before. The impact of social network relationship (SNR) on the CUMT is important. Because the levels of SNR play different roles in the social groups, at different levels of SNR, users might be influenced differently (Chou & Sun, 2017).