Assessing the Impact of Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions on Technology Adoption in Educational Institutions

Penulis

  • Nike Agustijania Sekolah Tinggi Ilmu Ekonomi Harapan Bangsa
  • Laura Lahindah Sekolah Tinggi Ilmu Ekonomi Harapan Bangsa

DOI:

https://doi.org/10.31334/yr16gq39

Kata Kunci:

Effort Expectancy; Facilitating Conditions; Behavioral Intention; Technology Adoption; Social Influence;

Abstrak

This study looks into the elements that influence the behavioral intention and user behavior regarding the Integrated Software System of Christian Education Foundation X among employees, teachers, and school leaders. The application of Structural Equation Modeling (SEM) in conjunction with a Partial Least Squares (PLS) methodology, we analyze the impact of various constructs, including Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions, on Behavioral Intention and User Behavior. The findings demonstrate that Performance Expectancy substantially improves Behavioral Intention, although Facilitating Conditions also considerably contribute to user engagement. Conversely, Effort Expectancy and Social Influence did not show significant effects on Behavioral Intention, suggesting that ease of use and peer influence may require further investigation to improve their impact. These findings underscore the importance of emphasizing performance benefits and providing organizational support to facilitate technology adoption in educational contexts. Practical implications for management strategies are discussed, along with recommendations for enhancing user experience and future research directions.

Referensi

Adirinekso, G. P., Purba, J. T., & Budiono, S. (2020). Measurement of performance, effort, social influence, facilitation, habit and hedonic motives toward pay later application intention: Indonesia evidence. Proceedings of the International Conference on Industrial Engineering and Operations Management, 59, 208–219.

Alam, M. M. D., Alam, M. Z., Rahman, S. A., & Taghizadeh, S. K. (2021). Factors influencing mHealth adoption and its impact on mental well-being during COVID-19 pandemic: A SEM-ANN approach. Journal of Biomedical Informatics, 116, 103722. https://doi.org/10.1016/j.jbi.2021.103722

Alghamdi, J., & Holland, C. (2020). A comparative analysis of policies, strategies and programmes for information and communication technology integration in education in the Kingdom of Saudi Arabia and the republic of Ireland. Education and Information Technologies, 25(6), 4721–4745. https://doi.org/10.1007/s10639-020-10169-5

Ambarwati, R., Harja, Y. D., & Thamrin, S. (2020). The Role of Facilitating Conditions and User Habits: A Case of Indonesian Online Learning Platform. The Journal of Asian Finance, Economics and Business, 7(10), 481–489. https://doi.org/10.13106/JAFEB.2020.VOL7.NO10.481

Asyari, Hoque, M. E., Hassan, M. K., Susanto, P., & Jannat, T. (2022). Millennial Generation ’ s Islamic Banking Behavioral Intention : The Moderating Role of Profit-Loss Sharing , Perceived Financial Risk , Knowledge of Riba , and Marketing Relationship. J. Risk Financial Manag, 15(590).

Chao, C. M. (2019). Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT model. Frontiers in Psychology, 10(JULY), 1652. https://doi.org/10.3389/fpsyg.2019.01652

Chen, T., Samaranayake, P., Cen, X. Y., Qi, M., & Lan, Y. C. (2022). The Impact of Online Reviews on Consumers’ Purchasing Decisions: Evidence From an Eye-Tracking Study. Frontiers in Psychology, 13(June). https://doi.org/10.3389/fpsyg.2022.865702

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13(3), 319–339. https://doi.org/10.2307/249008

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A Primer on Partial Least Squares Structural Equation Modeling. In Structural Equation Modeling (SEM) Lab (Edition: 3, Vol. 46, Issues 1–2). Sage Publishing. https://doi.org/10.1016/j.lrp.2013.01.002

Julyazti, N. F., Alfarisi, M. F., & Adrianto, F. (2023). Pengaruh Behavioral Intention dan User Behavioral terhadap Gender sebagai Variabel Moderasi. Jurnal Informatika Ekonomi Bisnis Vol., 5(1), 187–197. https://doi.org/10.37034/infeb.v5i1.220

Kranthi, A. K., & Ahmed, K. A. A. (2018). Determinants of smartwatch adoption among IT professionals – an extended UTAUT2 model for smartwatch enterprise. International Journal Enterprise Network Management, 9(3/4), 294–316.

Mahande, R. D., & Malago, J. D. (2019). An e-learning acceptance evaluation through utaut model in a postgraduate program. Journal of Educators Online, 16(2), 2. https://doi.org/10.9743/jeo.2019.16.2.7

Oktavianita, A. D. (2021). Pengaruh Faktor Model UTAUT (Unified Theory of Acceptance and Use of Technology) Terhadap Niat Generasi Milenial Dalam Menggunakan Mobile Banking di …. Jurnal Ekonomi Dan Bisnis (EK&BI), 4, 649–660. https://doi.org/10.37600/ekbi.v4i2.414

Shah, S. N. A., Khan, A. U., Khan, B. U., Khan, T., & Xuehe, Z. (2021). Framework for teachers’ acceptance of information and communication technology in Pakistan: Application of the extended UTAUT model. Journal of Public Affairs, 21(1), 1–11. https://doi.org/10.1002/pa.2090

Timbula, M. A., & Marvadi, C. (2023). Digital transformation: acceptance and use of technology among microfinance institutions in developing country: an application of UTAUT2 mode. International Journal of Information Technology (Singapore), 15(8), 4459–4468. https://doi.org/10.1007/s41870-023-01535-w

Venkatesh, V. (2000). Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model. Information Systems Research, 11(4), 342–365. https://doi.org/10.1287/isre.11.4.342.11872

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance Of Information Technology: Toward A Unified View1. MIS Quarterly Vol. 27 No. 3, Pp. 425-478/September 2003, 27(3), 425–478.

Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer Acceptance And Use Of Information Technology: Extending The Unified Theory Of Acceptance And Use Of Technology. MIS Quarterly Vol. 36 No. 1 Pp. 157-178/March 2012, 36(1), 157–178.

Zacharis, N. Z. (2016). Predicting Student Academic Performance In Blended Learning Using Artificial Neural Networks. International Journal of Artificial Intelligence and Applications (IJAIA), 7(5), 17–29. https://doi.org/10.5121/ijaia.2016.7502

Diterbitkan

2026-03-31

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