A Study of College Students’ Behaviors of UsingWeb2.0Technologies Based On the Decomposed Theory of Planned Behavior.

• Main Authors: Ching-Ting Huang, 黃敬婷
• Other Authors: Horng-Ji Lai
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/84903823026530798776

碩士 === 國立暨南國際大學 === 成人與繼續教育研究所 === 103 === The purpose of this study was to investigate college students’ behavioral intentions in using Web2.0 technologies for E-learning by. The study analyzing the patterns of students usingWeb2.0 technologies. The Decomposed Theory of Planned Behavior (DTPB ) was used to understand and to predict college students’ behavioral intentions. Furthermore, according to the results of the study, some relevant suggestions are offered for higher education institutions. The research employed a structural questionnaire that contained a demographic data sheet and Decomposed Theory of Planned Behavior (based on both overseas and domestic researches.). A total of 435 questionnaires were distributed to college students in Taiwan, 412 copies were returned, for a valid response rate of 89.7% . Statistical analyses were performed using SPSS 18.0 statistical software, with results including descriptive statistic percentage, means, standard, Pearson’s product-moment correlations and multiple regressions. The empirical results were as follows: 1. College students’ backgrounds in using Web2.0 technologies (1) The majority of college students who responded to the questionnaire had never heard of Web2.0 technologies. Most part of minorities had heard it and with little knowledge of it. (2) Average College students spent more time on surfing the Internet each day than aged above 12 in Taiwan. (3) The three major types of daily Internet usage reported by the students were searching or browsing for information, using social networks and watching videos online. (4) YouTube and social networks were the types of Web 2.0 technologies the students mainly reported using. (5) The main purposes reported by the students for using Web 2.0 technologies were social interactions and maintaining friendships. 2. Correlation analysis of college students utilizing Web2.0 technologies for E-learning. (1) The research results indicated that all the correlation coefficients were highly correlated (2) Peer influence and subjective norms stood the highest in canonical coefficient (.93); both “resources facilitating conditions and perceived behavioral control” and “technology facilitating conditions and perceived behavioral control” rated second with a value of 0.88. 3. Prediction Analysis of students’ applications of using web2.0 technologies (1) The DTPB could significantly predict students’ behaviors with regard to using web2.0 technologies. (2) Regression results confirmed that three factors, namely, perceived usefulness, ease of use, and compatibility, were significantly indicative of attitude toward learning, which regression coefficient beta values were 0.26, 0.41, and 0.21 respectively. Its whole explanatory power was 53%, adjusted by R². (3) Regression results confirmed that two factors, peer influence and superior’s influence, could predict subjective norms, with regression coefficient beta values of 0.19 and 0.39, respectively. Its whole explanatory power was 25%, adjusted by R². (4) Regression results confirmed each of the three factors, self-efficacy, resources facilitating conditions, technology facilitating conditions, which regression coefficient beta value were .40,.07, and .37 respectively, indicated that it had a significant effect on perceived behavioral control . Its whole explanatory power was 55% (adjusted R²). (5) Regression results confirmed that three factors, namely, attitude, subjective norm, and perceived behavioral control, which had regression coefficient beta values of .36, .09, and .50, respectively, had significant effects on behavioral intention., which whole explanatory power was 70% (adjusted R²). (6) According to the results of regression analysis, behavioral intention had greatest ability to predict DTPB.