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ANALYSIS OF E-RAPORT IMPLEMENTATION ACCEPTANCE IN IMPROVING THE QUALITY OF ELEMENTARY SCHOOL EDUCATION USING THE TECHNOLOGY ACCEPTANCE MODEL APPROACH

Wahyu Febriana(1), Subandi Subandi(2),


(1) UIN Raden Intan Lampung
(2) UIN Raden Intan Lampung
Corresponding Author

Abstract


This study aims to analyze the acceptance of E-Raport system implementation in improving the quality of elementary education in Pringsewu Regency using the Technology Acceptance Model approach. The research employed a quantitative method with a survey design involving 56 respondents, consisting of 22 school operators and 34 elementary school teachers. Data were collected using a Likert-scale questionnaire measuring Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Attitude Toward Using (ATU), and Behavioral Intention to Use (BI). Data analysis was conducted using SEM-PLS to examine the relationships among variables. The results indicate that all variables fall into high categories, with mean values above 4.00. Hypothesis testing reveals that PEOU significantly influences PU, while both PEOU and PU significantly affect ATU. Furthermore, ATU has the strongest influence on BI. The R-square value of 0.66 indicates that the model explains 66% of the variance in behavioral intention to use E-Raport. These findings suggest that ease of use and perceived usefulness are key determinants in shaping user attitudes and intentions toward technology adoption. The study highlights the importance of developing user-friendly systems and enhancing user competencies to optimize E-Raport implementation in improving the quality of elementary education.

Keywords


E-Raport, Technology Acceptance Model, Technology Acceptance, Elementary Education, SEM-PLS

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.

Y. S. Wang, Y. M. Wang, H. H. Lin, and T. I. Tang, “Determinants of user acceptance of Internet banking: An empirical study,” International Journal of Service Industry Management, vol. 14, no. 5, pp. 501–519, 2003.

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.

M. Šumak, M. Heričko, and M. Pušnik, “A meta-analysis of e-learning technology acceptance: The role of user types and e-learning technology types,” Computers in Human Behavior, vol. 27, no. 6, pp. 2067–2077, 2011.

M. Al-Emran, V. Mezhuyev, and A. Kamaludin, “Technology Acceptance Model in M-learning context: A systematic review,” Computers & Education, vol. 125, pp. 389–412, 2018.

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

N. Salloum, M. Al-Emran, K. Shaalan, and A. Tarhini, “Factors affecting the E-learning acceptance: A case study from UAE,” Education and Information Technologies, vol. 24, pp. 509–530, 2019.

O. Isaac, A. Abdullah, T. Ramayah, and A. M. Mutahar, “Internet usage within government institutions in Yemen: An extended Technology Acceptance Model (TAM) with trust and perceived risk,” International Journal of Information Management, vol. 36, no. 6, pp. 1117–1132, 2016.

N. M. Y. Lee, “Understanding the acceptance of mobile learning: An extension of the Technology Acceptance Model,” Computers & Education, vol. 56, no. 2, pp. 490–498, 2011.

H. M. Alalwan, Y. K. Dwivedi, and N. P. Rana, “Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust,” International Journal of Information Management, vol. 37, no. 3, pp. 99–110, 2017.

Y. K. Dwivedi et al., “A generalised adoption model for services: A cross-country comparison of mobile health (m-health),” Government Information Quarterly, vol. 33, no. 1, pp. 174–187, 2016.

T. Teo, “Modelling the determinants of pre-service teachers’ perceived usefulness of e-learning,” Campus-Wide Information Systems, vol. 27, no. 5, pp. 294–306, 2010.

M. Chao, “Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT model,” Frontiers in Psychology, vol. 10, p. 1652, 2019.

S. Y. Yoon and K. J. Kim, “An empirical study of the adoption of e-government services: The role of trust and perceived risk,” Electronic Government, an International Journal, vol. 4, no. 2, pp. 168–185, 2007.

A. H. Al-Gahtani, “Empirical investigation of e-learning acceptance and assimilation: A structural equation model,” Applied Computing and Informatics, vol. 12, no. 1, pp. 27–50, 2016.

T. Zhou, “Understanding mobile Internet continuance usage from the perspectives of UTAUT and flow,” Information Development, vol. 27, no. 3, pp. 207–218, 2011.

P. Ifinedo, “An empirical analysis of factors influencing Internet/e-business technologies adoption by SMEs in Canada,” International Journal of Information Technology & Decision Making, vol. 10, no. 4, pp. 731–766, 2011.

T. Oliveira and M. F. Martins, “Understanding e-business adoption across industries in European countries,” Industrial Management & Data Systems, vol. 110, no. 9, pp. 1337–1354, 2010.

M. A. Yakubu and S. I. Dasuki, “Assessing eLearning systems success in Nigeria: An application of the DeLone and McLean Information Systems Success Model,” Journal of Information Technology Education: Research, vol. 17, pp. 183–203, 2018.


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DOI: 10.56327/jurnaltam.v17i1.1884

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