Analisis Perilaku Adopsi Generative Artificial Intelligence dalam Mendukung Pengambilan Ide dan Proses Kreatif Berdasarkan Model UTAUT
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Published
Mar 20, 2026
Abstract
Penelitian ini bertujuan untuk menganalisis faktor-faktor yang memengaruhi niat penggunaan berkelanjutan Generative Artificial Intelligence (GenAI) pada masyarakat Indonesia dalam mendukung pengambilan ide dan proses kreatif. Meskipun adopsi awal GenAI telah banyak diteliti, kajian yang berfokus pada keberlanjutan penggunaan teknologi ini dalam konteks aktivitas kreatif masih terbatas. Penelitian ini menggunakan pendekatan kuantitatif dengan desain eksplanatori. Model penelitian didasarkan pada Unified Theory of Acceptance and Use of Technology (UTAUT) yang dikembangkan dengan menambahkan variabel kualitas informasi dan kepuasan pengguna. Data dikumpulkan melalui penyebaran kuesioner kepada masyarakat Indonesia yang telah menggunakan GenAI dalam aktivitas pengambilan ide dan proses kreatif. Analisis data dilakukan menggunakan metode Partial Least Squares Structural Equation Modeling (PLS-SEM). Hasil penelitian menunjukkan bahwa konstruk-konstruk dalam model UTAUT serta variabel tambahan yang digunakan berpengaruh signifikan terhadap niat penggunaan berkelanjutan GenAI. Temuan ini mengindikasikan bahwa persepsi manfaat, kemudahan penggunaan, dukungan lingkungan, kualitas informasi, dan kepuasan pengguna berperan penting dalam mendorong keberlanjutan pemanfaatan GenAI pada aktivitas kreatif.
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How to Cite
Imang, A. A. and Roostika, R. R. R. (2026) “Analisis Perilaku Adopsi Generative Artificial Intelligence dalam Mendukung Pengambilan Ide dan Proses Kreatif Berdasarkan Model UTAUT”, Ranah Research : Journal of Multidisciplinary Research and Development, 8(2), pp. 1215-1224. doi: 10.38035/rrj.v8i2.2004.
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Sarker, P., Hughes, L., Malik, T., & Dwivedi, Y. K. (2025). Examining consumer adoption of social commerce: An extended META-UTAUT model. Technological Forecasting and Social Change, 212. https://doi.org/10.1016/j.techfore.2024.123956
Singh, S., & Paiva, J. (2025). The role of AI characteristics and their influence on higher education students’ continuance intention to use GenAI tools. Information Discovery and Delivery. https://doi.org/10.1108/idd-03-2025-0060
Singha, M. (2025). Detecting AI Hallucinations in Finance: An Information-Theoretic Method Cuts Hallucination Rate by 92%. http://arxiv.org/abs/2512.03107
Situmorang, B. Y., Rabbani, T. M., Dominica, V. A., Handayani, P. W., & Fitriani, H. (2025). Continuance intention to use telecommunication mobile applications in Indonesia based on mobile service quality theory. Social Sciences and Humanities Open, 11. https://doi.org/10.1016/j.ssaho.2025.101444
Almulla, M. A. (2024). Investigating influencing factors of learning satisfaction in AI ChatGPT for research: University students perspective. Heliyon, 10(11). https://doi.org/10.1016/j.heliyon.2024.e32220
Al-Qaysi, N., Al-Emran, M., Al-Sharafi, M. A., Iranmanesh, M., Ahmad, A., & Mahmoud, M. A. (2025). Determinants of ChatGPT Use and its Impact on Learning Performance: An Integrated Model of BRT and TPB. International Journal of Human-Computer Interaction, 41(9), 5462–5474. https://doi.org/10.1080/10447318.2024.2361210
Buriro, M., Sethar, W. A., & Parhya, A. (2024). The Future of Learning: Integrating ChatGPT in Pakistan’s Higher Education. Annals of Human and Social Sciences, 5(II). https://doi.org/10.35484/ahss.2024(5-ii)64
Camilleri, M. A. (2024). Factors affecting performance expectancy and intentions to use ChatGPT: Using SmartPLS to advance an information technology acceptance framework. Technological Forecasting and Social Change, 201. https://doi.org/10.1016/j.techfore.2024.123247
Chau, H. K. L., Ngo, T. T. A., Bui, C. T., & Tran, N. P. N. (2025). Human-AI interaction in E-Commerce: The impact of AI-powered customer service on user experience and decision-making. Computers in Human Behavior Reports, 19. https://doi.org/10.1016/j.chbr.2025.100725
Guest Writer. (2023, October 11). 3 Ways Indonesians Use Generative AI for Social Impact. ICTworks.
Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R. Springer International Publishing. https://doi.org/10.1007/978-3-030-80519-7
Jade, N.-B.-N., & Tseng, F.-M. (2025). Coinciding users’ goals: the mediating role of goal-congruent outcomes in predicting ChatGPT plus users’ continuance intention. Asia Pacific Journal of Marketing and Logistics, 1–18. https://doi.org/10.1108/APJML-07-2025-1417
Jantzen, L., Bottel, M., & Kempen, R. (2024). How to achieve trust, satisfaction, and acceptance in the interaction with AI through an AI Cockpit and a Transparency Interface? - A psychological framework. Procedia Computer Science, 246(C), 292–301. https://doi.org/10.1016/j.procs.2024.09.408
Joshi, H. (2025). Integrating trust and satisfaction into the UTAUT model to predict Chatbot adoption – A comparison between Gen-Z and Millennials. International Journal of Information Management Data Insights, 5(1). https://doi.org/10.1016/j.jjimei.2025.100332
Kim, H. J., Ahn, S., & Ye, S. (2025). Exploring AI assistant in luxury brands: How social presence and emotional appeal drive technology adoption. Journal of Retailing and Consumer Services, 87. https://doi.org/10.1016/j.jretconser.2025.104409
Kim, J. S., Kim, M., & Baek, T. H. (2025). Enhancing User Experience With a Generative AI Chatbot. International Journal of Human-Computer Interaction, 41(1), 651–663. https://doi.org/10.1080/10447318.2024.2311971
Kong, S. C., Yang, Y., & Hou, C. (2024). Examining teachers’ behavioural intention of using generative artificial intelligence tools for teaching and learning based on the extended technology acceptance model. Computers and Education: Artificial Intelligence, 7. https://doi.org/10.1016/j.caeai.2024.100328
Li, S., Han, R., Fu, T., Chen, M., & Zhang, Y. (2024). Tourists’ behavioural intentions to use ChatGPT for tour route planning: an extended TAM model including rational and emotional factors. Current Issues in Tourism. https://doi.org/10.1080/13683500.2024.2355563
Margono, H., Saud, M., & Falahat, M. (2024). Virtual Tutor, Digital Natives and AI: Analyzing the impact of ChatGPT on academia in Indonesia. Social Sciences and Humanities Open, 10. https://doi.org/10.1016/j.ssaho.2024.101069
Niu, B., & Mvondo, G. F. N. (2024). I Am ChatGPT, the ultimate AI Chatbot! Investigating the determinants of users’ loyalty and ethical usage concerns of ChatGPT. Journal of Retailing and Consumer Services, 76. https://doi.org/10.1016/j.jretconser.2023.103562
Pangestu, A., Setiawan, H., Afifah, N., & Purmono, B. B. (2025). Modeling Chatgpt Continuance Intention: The Role of Expectancy, Satisfaction, and Trust. Jurnal Ilmiah Manajemen, Bisnis Dan Kewirausahaan, 5(3), 55–74. https://doi.org/10.55606/jurimbik.v5i3.1164
Sarker, P., Hughes, L., Malik, T., & Dwivedi, Y. K. (2025). Examining consumer adoption of social commerce: An extended META-UTAUT model. Technological Forecasting and Social Change, 212. https://doi.org/10.1016/j.techfore.2024.123956
Singh, S., & Paiva, J. (2025). The role of AI characteristics and their influence on higher education students’ continuance intention to use GenAI tools. Information Discovery and Delivery. https://doi.org/10.1108/idd-03-2025-0060
Singha, M. (2025). Detecting AI Hallucinations in Finance: An Information-Theoretic Method Cuts Hallucination Rate by 92%. http://arxiv.org/abs/2512.03107
Situmorang, B. Y., Rabbani, T. M., Dominica, V. A., Handayani, P. W., & Fitriani, H. (2025). Continuance intention to use telecommunication mobile applications in Indonesia based on mobile service quality theory. Social Sciences and Humanities Open, 11. https://doi.org/10.1016/j.ssaho.2025.101444