Factors Affecting the Use of Web-based Learning for Universities in Thailand

Abstract

The purposes of this study were to determine the effects of the four influential factors, namely, performance expectancy, effort expectancy, social influence, and facilitating conditions and a moderator (university policies) on usage behavior and behavioral intention to use web-based learning system, and to explore how universities adjust their policies to increase the usage of web-based learning systems. Population in this study were students who studied in Faculty of Business Administration and Faculty of Science and Technology in list of universities by the Office of the Higher Education Commission of Thailand. And those 2 faculties had launched their web-based learning systems more than one year. Both qualitative and quantitative methods were employed; Unified Theory of Acceptance and Use of Technology (UTAUT) theory were introduced as the technology acceptance model. According to the quantitative method, data were collected from students by using questionnaire and analyzed with Structural Equation Model (SEM) while the convergent validity was measured by Confirmatory Factor Analysis. The value of factor loading used in the study was greater than 0.6. Concerning the qualitative method, data were collected from selected Dean or Associate Dean for Academic Affairs by in-depth interview and the data were analyzed to facilitate the results of quantitative research. Research findings were as follows. The relationship among performance expectancy, effort expectancy, social influence, facilitating conditions and usage behavior with the behavioral intention to use web-based learning had a model fit and regression weight significantly support the hypotheses (p<.05) for the science students. While results from both the social science and science students indicated that the performance expectancy had no relation to behavioral intention to use the web-based learning system. In addition, the influence of effort expectancy and social influence on behavioral intention to use web-based learning were varied by university policies, such that the effects were stronger in universities with high control policy. The influence of facilitating conditions on usage behavior was varied by university policies, such that the effects were stronger in universities with high control policy. Finally, instructors should inform their students that they could use web-based learning through website and they should have some channels on the systems in order to communicate with their students.