Using a cognition-motivation-control view to assess the adoption intention for Web-based learning
Introduction
The Internet has changed the higher education learning environment during the past decade (Ives & Jarvenpaa, 1996). For instance, students can now access academic papers via digital libraries, discuss projects with advisors by e-mail and attend Web-based courses at home. The application of Web technology in higher education has influenced learning behavior, such as by providing an effective learning environment that encourages more active participation, offering opportunities for responsive feedback and individual involvement, and promoting teamwork through collaborative learning (Gilliver, Randall, & Pok, 1998). The transformation from traditional classrooms to Web-based learning environments has changed learning styles and interactions between instructors and students (Agres, Edberg, & Igbaria, 1998). Notably, Web technology enables students to communicate electronically and attend courses online. Moreover, trainers can work in cyberspace to improve educational inputs, process, and outcomes (Wachter, Gupta, & Quaddus, 2000). To summarize, the growth of Web applications has made the Web an important educational medium (Siau, Nah, & Teng, 2002).
This study considers ‘Web-based learning’ to be a goal-directed behavior through which human agents acquire and share information or knowledge via the Web for enhancing personal capacity to take effective actions (Kim, 1993). Web-based learning thus is a goal-directed human behavior underlying community networks that sustain interaction among students, instructors, and Web-based systems (Hiltz & Wellman, 1997). The literature has identified the usefulness and ease of use of Web-based systems for learning (Ngai et al., in press, Selim, 2003). However, overemphasizing the effectiveness of Web-based systems may ignore the cognitive processes of learners in their adaptation learning behavior (Lee, 2001). As a result, the mechanism used by human agents to respond to Web-based learning environments was lacking and still needs to be addressed.
Unlike learning in traditional classrooms, Web-based learning presents learners with a new environment, one which they may either accept or reject depending on their adaptation of the learning process (Lee, 2001). Whether human agents can learn more effectively in Web-based learning environments compared to traditional classrooms depends on how the evolution of community networks can sustain collaboration, develop trust between students and instructors, and encourage active participation in student–instructor interaction (Bruckman, 2002, Hiltz and Turoff, 2002). Web-based learning on campus is considered an adaptation learning behavior with respect to cognitive interaction among students, instructors, and Web-based systems. Exploring perceptual processes of Web-based learning facilitates this study to develop a research model to examine what leads learners to be likely to accept Web-based systems for learning. The proposed research model is based on the learner perspective and will be helpful to educational institutions when they attempt to develop and deliver Web-based courses and encourage student participation in Web-based learning.
Section snippets
Research model
Belief–attitude–intention chains provide a useful theoretical basis for developing behavioral models to explain goal-directed human behavior (Fishbein & Ajzen, 1975), such as Web-based system adoption behavior. A well-known learning model based on the cognition-motivation view, namely social cognitive theory (SCT) as proposed by Bandura, 1977, Bandura, 1978, Bandura, 1982, Bandura, 1986, has been widely applied to explain goal-directed human behavior. Many previous studies have applied SCT to
Sample and data collection
Many undergraduate students frequently use websites to access course-related information, and their usage patterns can be divided into browsing, downloading and messaging (Teo, Lim, & Lai, 1997). Thus, this study focuses on surveying these usage patterns (behaviors) in Web-based learning. Undergraduate students who had enrolled in Web-based courses at a university were selected and invited to participate in this questionnaire survey. With the support of a Web-based system developed on campus,
Measurement model
Factor analysis with the VARIMAX rotation method was used to assess the factor loading of each item on different constructs. As shown in Table 2, five factors (constructs) with eigenvalues greater than 1.0 were extracted, and 75.77% of the cumulative variance was explained by the five constructs in this study. Furthermore, the factor loadings of the measurement items on their posited underlying constructs markedly exceeded 0.5, and the factor loadings of the items on other constructs (i.e.,
Discussion
Unlike Compeau and Higgins, 1995b, Compeau et al., 1999, the empirical results of this study revealed no direct effect of self-efficacy on individual attitudes towards Web-based learning. Particularly, self-efficacy was found to indirectly affect attitude and behavioral intention towards Web-based learning via personal outcome expectations and perceived behavioral control. The mechanism for this effect may be that most participants perceive the Web-based system to be a medium for supporting
Conclusions
Overall, this empirical study concludes that learner efficacy control and efficacy expectations guide their adaptation learning behavior on the Web. After the introduction of a Web-based system to learners, efficacy control produces a stronger influence on learning behavior than efficacy expectations do. Thus, this study concludes that the short-term effect dominates compared to the long-term effect in assessing learner adaptation of Web-based learning. Arguments of the research model based on
Limitations
Three limitations should be noted in interpreting the empirical findings of this exploratory study. First, the empirical findings were obtained by surveying undergraduate students with access to a Web-based system for academic learning during a four-week training program. Caution thus is necessary in applying the findings to assess adoption behavior for other commercial learning websites. The causal link between self-efficacy and personal outcome expectations is likely to be enlarged if using
Acknowledgements
The author would like to thank the two anonymous reviewers and the Co-Editor, Professor Underwood, for their valuable comments and suggestions on the early version of this paper.
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