EXTENDING THE HOT-FIT MODEL WITH INFORMATION LITERACY TO EXPLAIN AI ADOPTION IN HIGH SCHOOLS
DOI:
https://doi.org/10.33480/jitk.v11i4.7270Keywords:
Artificial Intelligence, Adoption, HOT-Fit Model, Information Literacy, LearningAbstract
Artificial Intelligence (AI) is increasingly transforming educational practices by enabling more adaptive and personalized learning experiences in secondary schools. Nevertheless, previous applications of the Human–Organization–Technology Fit (HOT-Fit) model have given limited attention to the roles of information literacy and trust in influencing AI adoption. To address this gap, the present study expands the HOT-Fit framework by incorporating three additional constructs: information literacy, perceived validity, and perceived trust, in order to better explain AI readiness and adoption in educational settings. A quantitative approach was employed involving 316 senior high school students from Kuningan Regency, Indonesia. Data were gathered using a structured questionnaire based on a five-point Likert scale and analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS v3.0 to evaluate 29 proposed hypotheses. The findings revealed that 21 hypotheses were statistically supported. Information literacy demonstrated a strong positive effect on perceived trust (β = 0.708; p < 0.001), as well as on system use, organizational structure, environmental support, and user satisfaction. In addition, system quality significantly contributed to user satisfaction, whereas service quality affected both system use and satisfaction. Among all relationships, net benefit exerted the strongest effect on action to use (β = 0.547; p < 0.001). The R² results for several endogenous constructs were above 0.50, indicating acceptable explanatory capability of the proposed model. Practically, the findings offer implications for educators, policymakers, and system developers in designing AI-supported learning environments by emphasizing the enhancement of digital literacy, service support, and system effectiveness for sustainable AI integration in schools.
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References
[1] Felice. Alfieri and Christoforos. Spiliotopoulos, “ICT task force study : final report,” Publications Office of the European Union, 2023. doi: 10.2760/486253.
[2] Riska Aini Putri, “Pengaruh Teknologi dalam Perubahan Pembelajaran di Era Digital,” Journal of Computers and Digital Business, vol. 2, no. 3, pp. 105–111, Sep. 2023, doi: 10.56427/jcbd.v2i3.233.
[3] E. Bahçekapılı, “Predicting the secondary school students’ intention to use e-learning technologies,” Research in Learning Technology, vol. 31, Jan. 2023, doi: 10.25304/rlt.v31.2881.
[4] P. Dubey, R. L. Pradhan, and K. K. Sahu, “Underlying factors of student engagement to E-learning,” Journal of Research in Innovative Teaching and Learning, vol. 16, no. 1, pp. 17–36, Mar. 2023, doi: 10.1108/JRIT-09-2022-0058.
[5] T. Lubaba and Zulfi Zumala Dwi Andriani, “Utilizing English E-Learning: An Interesting Innovation In Modern Learning,” Darussalam English Journal (DEJ), vol. 5, no. 1, pp. 67–79, Jun. 2025, doi: 10.30739/dej.v5i1.3874.
[6] M. Ardiansyah, A. Fauzan, A. Setiawan, and U. Rokania, “Trend Penggunaan E-Learning Sekolah Menengah Kejuruan (SMK) Di Riau,” Jurnal Teknik Informatika dan Sistem Informasi, vol. 11, no. 2, 2024.
[7] R. Audiva, F. Rini, and I. Irsyadunas, “Implementasi E-Learning di Sekolah Menengah Kejuruan,” JTEV (Jurnal Teknik Elektro dan Vokasional), vol. 8, no. 1, p. 46, Jan. 2022, doi: 10.24036/jtev.v8i1.114664.
[8] W. Holmes and I. Tuomi, “State of the art and practice in AI in education,” Eur. J. Educ., vol. 57, no. 4, pp. 542–570, Dec. 2022, doi: 10.1111/ejed.12533.
[9] Lukman, R. Agustina, and R. Aisy, “Problematikan Penggunaan AI Untuk Pembelajran di Kalangan Mahasiswa,” Madaniyah, vol. 13, no. 2, Jun. 2023, doi: 10.58410/madaniyah.v13i2.826.
[10] S. Nemorin, A. Vlachidis, H. M. Ayerakwa, and P. Andriotis, “AI hyped? A horizon scan of discourse on artificial intelligence in education (AIED) and development,” Learn. Media Technol., vol. 48, no. 1, pp. 38–51, 2023, doi: 10.1080/17439884.2022.2095568.
[11] Rheinata Rhamadani Putri Supriadi, Sulistiyani, and M. Minan Chusni, “Inovasi pembelajaran berbasis teknologi Artificial Intelligence dalam Pendidikan di era industry 4.0 dan society 5.0,” JPSP: Jurnal Penelitian Sains dan Pendidikan, vol. 2, no. 2, pp. 192–197, 2022, doi: 10.23971/jpsp.v2i2.4036.
[12] Y. Shi and F. Peng, “A blended learning model based on smart learning environment to improve college students’ information literacy,” IEEE Access, vol. 10, no., pp. 89485–89498, 2022, doi: 10.1109/ACCESS.2022.3201105.
[13] F. Yusuf, “Information Technology Readiness and Acceptance Model for Social Media Adoption in Blended Learning: A Case Study in Higher Education Institutions in West Java, Indonesia,” Journal of Applied Data Sciences, vol. 5, no. 2, pp. 382–402, May 2024, doi: 10.47738/jads.v5i2.195.
[14] W. Li and / Au-Gsb E, “Factors Impacting Satisfaction with Blended Learning Among Private College Students in Mianyang, China,” 2024. [Online]. Available: http://www.assumptionjournal.au.edu/index.php/AU-GSB/index
[15] Y. Mei, S. Wang, S. Bu, and B. Deng, “Research on the Construction and Application of an AI Literacy Evaluation System for Vocational Undergraduate Students,” in Proceedings of The 2nd International Conference on Intelligent Education and Computer Technology, IECT 2025, Association for Computing Machinery, Inc, Nov. 2025, pp. 505–511. doi: 10.1145/3764206.3764284.
[16] Xiaoxue Song, “Innovative Research on Information Literacy Education in Higher Vocational Colleges in the Context of Generative AI,” The Frontiers of Society, Science and Technology, vol. 5, no. 15, 2023, doi: 10.25236/fsst.2023.051510.
[17] X. Chen, X. Xu, Y. J. Wu, and W. F. Pok, “Learners’ Continuous Use Intention of Blended Learning: TAM-SET Model,” Sustainability (Switzerland), vol. 14, no. 24, Dec. 2022, doi: 10.3390/su142416428.
[18] O. A. Alismaiel, J. Cifuentes-Faura, and W. M. Al-Rahmi, “Social Media Technologies Used for Education: An Empirical Study on TAM Model During the COVID-19 Pandemic,” Front. Educ. (Lausanne)., vol. 7, Apr. 2022, doi: 10.3389/feduc.2022.882831.
[19] C. Wang, J. Dai, K. Zhu, T. Yu, and X. Gu, “Understanding the Continuance Intention of College Students toward New E-Learning Spaces Based on an Integrated Model of the TAM and TTF,” Int. J. Hum. Comput. Interact., vol. 40, no. 24, pp. 8419–8432, Dec. 2024, doi: 10.1080/10447318.2023.2291609.
[20] H. Patil and S. Undale, “Willingness of university students to continue using e-Learning platforms after compelled adoption of technology: Test of an extended UTAUT model,” Educ. Inf. Technol. (Dordr)., vol. 28, no. 11, pp. 14943–14965, Nov. 2023, doi: 10.1007/s10639-023-11778-6.
[21] R. Marisa Putri and M. Aisyah, “Implementing the HOT-Fit method in Hospital Management Information Systems Evaluation,” in Proceeding International Conference on Accounting and Finance, InCAF 2024, 2024, pp. 25–36. Accessed: Jun. 02, 2026. [Online]. Available: https://journal.uii.ac.id/inCAF/article/view/33046
[22] E. E. Sala and A. P. Subriadi, “Hot-Fit Model to Measure the Effectiveness and Efficiency of Information System in Public Sector,” The Winners, vol. 23, no. 2, pp. 131–141, May 2023, doi: 10.21512/tw.v23i2.7423.
[23] R. W. Osok, J. P. P. Naibaho, and A. De Kweldju, “Evaluation of E-Learning System Implementation using Hot Fit Evaluation Model (Case Study of Papua University),” G-Tech: Jurnal Teknologi Terapan, vol. 9, no. 2, pp. 685–694, Apr. 2025, doi: 10.70609/gtech.v9i2.6601.
[24] S. Pohan et al., “Evaluation of Success Factors for Academic Information System Applications in Higher Education Using the Hot-Fit Model,” Journal of Advances in Information Systems and Technology, vol. 7, no. 1, pp. 15–30, Apr. 2025.
[25] G. K. Pradhana, G. R. Dantes, and D. G. H. Divayana, “Analysis of the Implementation of E-Learning in Melajah.id Using Human Organization Technology (HOT) Fit Model,” Journal of Computer Networks, Architecture and High Performance Computing, vol. 5, no. 2, pp. 780–794, Oct. 2023, doi: 10.47709/cnahpc.v5i2.2921.
[26] Joseph F. Hair Jr et al., “Classroom Companion: Business Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R AAWorkbook,” 2021. [Online]. Available: http://www.
[27] M. C. Ph. D. C. Prof. H. Imam Ghozali, Aplikasi Analisis Multivariate Dengan Program IBM SPSS 26 edisi 10, 10th ed. Universitas Dipponegoro, 2021.
[28] A. Santoso, “Rumus Slovin: Masalah Ukuran Sampel?,” vol. 4, no. 2, p. 2443, Oct. 2023, Accessed: Sep. 09, 2025. [Online]. Available: https://e-journal.usd.ac.id/index.php/suksma/article/view/6434
[29] M. Fan and O. C. Ukaegbu, “Information literacy and intention to adopt e-pharmacy: a study based on trust and the theory of reasoned action,” BMC Health Serv. Res., vol. 24, no. 1, Dec. 2024, doi: 10.1186/s12913-024-11301-8.
[30] H. Qin, Y. Zhu, Y. Jiang, S. Luo, and C. Huang, “Examining the impact of personalization and carefulness in AI-generated health advice: Trust, adoption, and insights in online healthcare consultations experiments,” Technol. Soc., vol. 79, Dec. 2024, doi: 10.1016/j.techsoc.2024.102726.
[31] S. C. Viontita and E. R. Mahendrawathi, “Evaluation of Surabaya population administration & civil registration systems using DeLone & McLean information system success model,” in Procedia Computer Science, Elsevier B.V., 2024, pp. 1154–1163. doi: 10.1016/j.procs.2024.03.111.
[32] B. Chimbo and L. Motsi, “The Effects of Electronic Health Records on Medical Error Reduction: Extension of the DeLone and McLean Information System Success Model,” JMIR Med. Inform., vol. 12, 2024, doi: https://doi.org/10.2196/54572.
[33] A. Gonçalves, J. Varajão, P. Moura Oliveira, and I. Moura, “Success Factors for Public Sector Information Systems Projects,” Digital Government: Research and Practice, vol. 6, no. 4, Dec. 2025, doi: 10.1145/3761819.
[34] H. Jo and Y. Bang, “Understanding continuance intention of enterprise resource planning (ERP): TOE, TAM, and IS success model,” Heliyon, vol. 9, no. 10, Oct. 2023, doi: 10.1016/j.heliyon.2023.e21019.
[35] J. F. Hair, G. T. M. Hult, C. M. Ringle, M. Sarstedt, N. P. Danks, and S. Ray, Classroom Companion: Business Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R. Springer, 2021. doi: 10.1007/978-3-030-80519-7.
[36] J. Atenas, L. Havemann, and C. Nerantzi, “Critical and creative pedagogies for artificial intelligence and data literacy: an epistemic data justice approach for academic practice,” Research in Learning Technology, vol. 32, 2024, doi: 10.25304/rlt.v32.3296.
[37] M. Folmeg, I. Fekete, and R. Koris, “Towards identifying the components of students’ AI literacy: An exploratory study based on Hungarian higher education students’ perceptions,” Journal of University Teaching and Learning Practice, no. 6, p. 21, Apr. 2024, doi: 10.53761/wzyrwj33.
[38] S. Bećirović, E. Polz, and I. Tinkel, “Exploring students’ AI literacy and its effects on their AI output quality, self-efficacy, and academic performance,” Smart Learning Environments, vol. 12, no. 1, Dec. 2025, doi: 10.1186/s40561-025-00384-3.
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