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Based on the congruence between users’ continued usage intention towards IT and consumers’ repeat purchase decision making behavior, Bhattacherjee (2001a, 2001b) proposed expectation-confirmation model (ECM) to explain IT users’ intention to continue or discontinue to use an IS.
Ito et al. (2008) indicated that learners are greatly influenced by social interactions in multimedia learning environments. When learners perceive that their important referents think they should use GBL, they are likely to incorporate this idea into their own beliefs, simply because their friends are users of GBL.
Social influence profoundly affects user behavior (Ajzen, 1991; Lee, 2006), and thus users’ decision to adopt a specific IT may often be influenced by the suggestions of group members. I
TAM proposes that two particular beliefs, perceived usefulness and perceived ease of use, are the primary drivers of technology acceptance. Perceived ease of use is defined as “the degree to which a person believes that using a particular system would be free of physical and mental effort”, while perceived usefulness is defined as “the degree to which a person believes that
The technology acceptance model (TAM) (Davis, 1989) is the most frequently cited and influential model for explaining technology acceptance and adoption. Davis (1989) first introduced the TAM as a theoretical extension of the theory of reasoned action (TRA) (Fishbein & Ajzen, 1975).
The ECM posits that users’ perceived usefulness of IT has a positive effect on their intentions to continue IT usage. The confirmation of expectations suggests that users obtained the expected benefits through their usage of the focal IT, and this has a positive effect on their satisfaction.
Educational digital games, which typically require the use of logic, memory, problem-solving, and critical thinking skills, generate higher levels of student interest in the focal subject matters (Annetta, 2008).
Game-based learning (GBL) is as a system in which learners are immersed in a set of interactive components and challenging activities based on a series of clear goals, agreed rules and constraints, and such issues are often discussed in the context of educational technology (Salen & Zimmerman, 2004).
The expectation confirmation model (ECM) was developed by Bhattacherjee (2001) based on expectation confirmation theory (Oliver, 1980), and includes three dimensions of user intention to continue using certain technologies: perceived usefulness, confirmation of expectations and satisfaction.
Researchers have consistently demonstrated the beneficial effects of collaborative technologies on students’ online project learning task in the cross-disciplines domain knowledge.
People need to feel competent and autonomous to maintain their intrinsic motivation. Previous studies have shown that teachers’ perceived level of e-learning competence has positive effects on their perceived level of usefulness, ease of use, and intrinsic motivation (enjoyment)
The online learning platform could provide the new opportunities for those disadvantage students in rural area to learn and acquire knowledge without limitation of time and space. Online tutoring system provides a channel to delivering the educational resources to fulfill those students in needs (Huang, Liu, and Sung, 2011).
the e-tutoring system provided an e-learning platform that tutors and learners could communicate and interact with each other synchronously in real time without distance limitation.
As more and more e-tutors participated in the Government funded programs and projects for digital divide remediation, more researchers have devoted their efforts to explore the factors that would influence the success of e-tutoring.
Students in rural and remote areas fail to receive as much educational resources as students in the cities. The online learning platform could provide the new opportunities for those disadvantage students in rural area to learn and acquire knowledge without limitation of time and space.
Previous studies have adopted social presence as one of the most influential factors that would affect users’ continuance intention towards using internet related technologies, such as Twitter(Park & Lee, 2010), social networking services (Sun, Lin, Peng, and Barnes, 2014), as well as cloud services (Huang, 2016).
Davis et al. (1992) found that enjoyment, serves as the intrinsic motivation, and extrinsic motivation (usefulness) were the key determinants of behavioral intention to use computers.
Social influence refers to users’ attitudes, thoughts, or decisions influenced by others when they participate in a social system (Cheng, 2014; Huang, 2015).
Enjoyment, as the most self-determined form of intrinsic motivation, refers to a kind of human action that is motivated by interest, enjoyment, pleasure, and satisfaction generated by the activity itself (Ryan & Deci, 2000).