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EVALUATION OF THE SUCCESSFUL IMPLEMENTATION OF GOOGLE CLASSROOM ON EQUALITY EDUCATION IN PKBM USING THE TECHNOLOGY ACCEPTANCE MODEL APPROACH

Tarekah Hasanah(1),


(1) UIN Raden INtan
Corresponding Author

Abstract


This study aims to evaluate the success of Google Classroom implementation in non-formal education (PKBM) using the Technology Acceptance Model (TAM) approach. A quantitative survey method was employed with 76 PKBM students as respondents. The variables examined include Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Behavioral Intention (BI), and Actual System Use (ASU). Data were collected using a Likert-scale questionnaire and analyzed through SEM-PLS. The results indicate that PEOU significantly influences PU (β = 0.68; p < 0.001), while both PEOU and PU significantly affect BI (β = 0.41 and β = 0.52; p < 0.001). Furthermore, BI has the strongest influence on ASU (β = 0.73; p < 0.001). The coefficient of determination reveals that the model explains 64% of the variance in BI and 53% in ASU. These findings suggest that perceived ease of use and perceived usefulness are key determinants in enhancing users’ behavioral intention, which subsequently drives successful system implementation. This study contributes to the extension of TAM in non-formal education contexts and provides practical insights for technology-based learning implementation in PKBM.

Keywords


Technology Acceptance Model, Google Classroom, Non-formal Education, E-learning, Behavioral Intention

References


F. D. Davis, “Perceived usefulness, perceived ease of use, and user acceptance of information technology,” MIS Quarterly, vol. 13, no. 3, pp. 319–340, 1989.

V. Venkatesh and H. Bala, “Technology acceptance model 3 and a research agenda on interventions,” Decision Sciences, vol. 39, no. 2, pp. 273–315, 2008.

S. Y. Park, “An analysis of the technology acceptance model in understanding university students’ behavioral intention to use e-learning,” Educational Technology & Society, vol. 12, no. 3, pp. 150–162, 2009.

V. Venkatesh and F. D. Davis, “A theoretical extension of the technology acceptance model,” Management Science, vol. 46, no. 2, pp. 186–204, 2000.

S. Alharbi and S. Drew, “Using the technology acceptance model in understanding academics’ behavioural intention to use learning management systems,” International Journal of Advanced Computer Science and Applications, vol. 5, no. 1, pp. 143–155, 2014.

A. Tarhini, K. Hone, and X. Liu, “Factors affecting students’ acceptance of e-learning environments in developing countries,” International Journal of Information and Education Technology, vol. 3, no. 1, pp. 54–59, 2013.

M. Turner, B. Kitchenham, S. Brereton, S. Charters, and D. Budgen, “Does the technology acceptance model predict actual use?,” Information and Software Technology, vol. 52, no. 5, pp. 463–479, 2010.

B. Šumak, M. Heričko, and M. Pušnik, “A meta-analysis of e-learning technology acceptance,” Computers & Education, vol. 72, pp. 206–233, 2014.

W. M. Al-Rahmi et al., “Use of e-learning by university students in Malaysian higher educational institutions,” IEEE Access, vol. 6, pp. 14268–14276, 2018.

S. A. Salloum et al., “Factors affecting the adoption of e-learning systems in UAE,” Education and Information Technologies, vol. 24, pp. 509–530, 2019.

F. Abdullah and R. Ward, “Developing a general extended technology acceptance model for e-learning,” Computers in Human Behavior, vol. 56, pp. 238–256, 2016.


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DOI: 10.56327/jurnaltam.v17i1.1886

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