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THE RELATIONSHIPS OF MOTIVATION FACTORS AND TECHNOLOGY USERS' ACCEPTANCE WITH ONLINE LEARNING EFFICIENCY DURING THE COVID-19 PANDEMIC SITUATION |
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| รหัสดีโอไอ | |
| Creator | Wachiranun Pum |
| Title | THE RELATIONSHIPS OF MOTIVATION FACTORS AND TECHNOLOGY USERS' ACCEPTANCE WITH ONLINE LEARNING EFFICIENCY DURING THE COVID-19 PANDEMIC SITUATION |
| Contributor | - |
| Publisher | สถาบันการจัดการปัญญาภิวัฒน์ |
| Publication Year | 2565 |
| Journal Title | วารสารปัญญาภิวัฒน์ |
| Journal Vol. | 14 |
| Journal No. | 3 |
| Page no. | 220-231 |
| Keyword | Motivation;Technology User Acceptance;Online Learning |
| URL Website | https://so05.tci-thaijo.org/index.php/pimjournal/index |
| Website title | วารสารปัญญาภิวัฒน์ |
| ISSN | ISSN 2651-1088 (ออนไลน์) |
| Abstract | This paper aims to examine the relationships of personal factors, motivation factors, perceived ease of use, and perceived usefulness with online learning efficiency during the COVID-19 pandemic situation. Firstly, an online questionnaire was used to collect data from the research sample consisting of 176 students obtained by using simple random sampling technique. The contents of the online questionnaires were to ascertain the influences of individual parameters such as gender, college years, and online learning equipment required for online education. Secondly, the data was analyzed with the use of descriptive statistics (frequency distribution, percentage, mean, and standard deviation). Then inferential statistics, namely, the t-test and F-test (one-way ANOVA) were used to determine group differences. Finally, Pearson correlation and stepwise multiple regression analysis were used to examine the relationships between variables to ascertain the factors affecting online learning performance.As a result, the perceived ease-of-use, responsibility, and benefit can predict online learning efficiency with the significance level of 0.05. The most predictable variables, in order, were the perceived ease-of-use (0.505), responsibility (0.244), and benefit (0.149), with a combined predictive power of 81.70%. Nevertheless, personal characteristics were shown to have little effect on online learning. |
