Exploring the Role of Artificial Intelligence in Enhancing Environmental Health: UTAUT2 Analysis
Keywords:
Artificial Intelligence, Environmental Health, UTAUT2, Technology Acceptance IndonesiaAbstract
Environmental health is an important issue that affects the quality of human life. AI can provide innovative solutions to improve environmental health, but its acceptance and use is still low in many countries. This research explores the role of AI in improving environmental health using the UTAUT2 model. This study used an online survey of 500 respondents in big cities in Indonesia. The
results show that the factors that influence the intention and behavior of using AI for environmental health are expected performance, expected effort, facility conditions, social influence, affordable price, hedonic pleasure, and habits. This research provides theoretical and practical contributions to AI developers and providers, governments, and society.
References
[1] P. William, G. Ramu, L. Kansal, P. P. Patil, A. Alkhayyat, and A. K. Rao, “Artificial intelligence based air quality monitoring system with modernized environmental safety of sustainable development,” in 2023 3rd International Conference on Pervasive Computing and Social Networking (ICPCSN). IEEE, 2023, pp. 756–761.
[2] Q. Aini, K. D. Hartomo, H. D. Purnomo, A. Wahab, and U. Rahardja, “Factors on the utaut2 to identify the influence on the usage behavior of ai-based air quality,” in 2024 3rd International Conference on Creative Communication and Innovative Technology (ICCIT). IEEE, 2024, pp. 1–8.
[3] M. Garc´ıa de Blanes Sebasti´an, J. R. Sarmiento Guede, and A. Antonovica, “Application and extension of the utaut2 model for determining behavioral intention factors in use of the artificial intelligence virtual assistants,” Frontiers in Psychology, vol. 13, p. 993935, 2022.
[4] T.-H. Chu, C.-M. Chao, H.-H. Liu, and D.-F. Chen, “Developing an extended theory of utaut 2 model to explore factors influencing taiwanese consumer adoption of intelligent elevators,” Sage Open, vol. 12, no. 4, p. 21582440221142209, 2022.
[5] S. Mustafa, W. Zhang, S. Anwar, K. Jamil, and S. Rana, “An integrated model of utaut2 to understand consumers’ 5g technology acceptance using sem-ann approach,” Scientific Reports, vol. 12, no. 1, p. 20056, 2022.
[6] M. Alhwaiti, “Acceptance of artificial intelligence application in the post-covid era and its impact on faculty members’ occupational well-being and teaching self efficacy: a path analysis using the utaut 2 model,” Applied Artificial Intelligence, vol. 37, no. 1, p. 2175110, 2023.
[7] X.-Y. Xu, Y.-B. Hu, Y.-X. Gao, and Q.-D. Jia, “Exploring the recycled water acceptance based on the technological perspective of utaut2: a hybrid analytical approach,” Frontiers in Psychology, vol. 15, p. 1384635, 2024.
[8] M. M. H. Emon, F. Hassan, M. H. Nahid, and V. Rattanawiboonsom, “Predicting adoption intention of artificial intelligence,” 2023.
[9] Z. Thabet, S. Albashtawi, H. Ansari, M. Al-Emran, M. A. Al-Sharafi, and A. A. AlQudah, “Exploring the factors affecting telemedicine adoption by integrating utaut2 and is success model: a hybrid sem–ann approach,” IEEE Transactions on Engineering Management, vol. 71, pp. 8938–8950, 2023.
[10] G. N. Fathima, V. P. Rathod, P. Mishra et al., “Improving the adoption of artificial intelligence in library financial services: A comprehensive utaut2 framework with ai-specific variables.” Library of Progress-Library Science, Information Technology & Computer, vol. 44, no. 3, 2024.
[11] S. Grassini, M. L. Aasen, and A. Møgelvang, “Understanding university students’ acceptance of chatgpt: insights from the utaut2 model,” Applied Artificial Intelligence, vol. 38, no. 1, p. 2371168, 2024.
[12] G. Chen, J. Fan, and M. Azam, “Exploring artificial intelligence (ai) chatbots adoption among research scholars using unified theory of acceptance and use of technology (utaut),” Journal of Librarianship and Information Science, p. 09610006241269189, 2024.
13] L. Hu, H. Wang, and Y. Xin, “Factors influencing chinese pre-service teachers’ adoption of generative ai in teaching: an empirical study based on utaut2 and pls-sem,” Education and Information Technologies, pp. 1–23, 2025.
[14] E. M. Kparl and F. Iddris, “Factors influencing the adoption of chatgpt for innovation generation: a moderated analysis of innovation capability and gender using utaut2 framework,” Global Scientific and Academic Research Journal of Economics, Business and Management, vol. 3, no. 12, pp. 135–148, 2024.
[15] H. Tawfik, “Extending (utaut2) to examine the utilization of mobile food-sharing apps and online plat-forms for the reduction of household food waste,” Technological Sustainability, vol. 3, no. 4, pp. 392–415, 2024.
[16] M. Al-Emran, “Beyond technology acceptance: Development and evaluation of technology-environmental, economic, and social sustainability theory,” Technology in Society, vol. 75, p. 102383, 2023.
[17] O. Sergeeva, M. Zheltukhina, T. Shoustikova, L. Tukhvatullina, D. Dobrokhotov, and S. Kondrashev, “Understanding higher education students’ adoption of generative ai technologies: An empirical investigation using utaut2,” Contemporary Educational Technology, vol. 17, no. 2, 2025.
[18] Q. Aini, D. Manongga, U. Rahardja, I. Sembiring, and Y.-M. Li, “Understanding behavioral intention to use of air quality monitoring solutions with emphasis on technology readiness,” International Journal of Human–Computer Interaction, pp. 1–21, 2024.
[19] P. Wongras and T. Tanantong, “An extended utaut model for analyzing users’ acceptance factors for artificial intelligence adoption in human resource recruitment: A case study of thailand,” Education and Information Technologies, vol. 3, no. 7, pp. 13–27, 2023.
[20] S. Mukherjee, “A quantitative study using the utaut2 model to evaluate the behavioral intention to use telemedicine via iot-enabled devices by older adults in the chicago tri-state region,” Ph.D. dissertation, University of the Cumberlands, 2021.
[21] C. Ammi, G. Kondrateva, and P. Baudier, “Healthcare sustainability: the role of artificial intelligence acceptance by medical staff,” Journal of Innovation Economics & Management, vol. 44, no. 2, pp. 53–86, 2024.
[22] M. P. Frank and G. George, “Pilot study on adoption and usage of ai in food processing industry by utaut2,” in From Industry 4.0 to Industry 5.0: Mapping the Transitions. Springer, 2023, pp. 191–205.
[23] T. T. Wijaya, M. Su, Y. Cao, R. Weinhandl, and T. Houghton, “Examining chinese preservice mathematics teachers’ adoption of ai chatbots for learning: Unpacking perspectives through the utaut2 model,” Education and Information Technologies, pp. 1–29, 2024.
[24] S. B. GC, P. Bhandari, S. K. Gurung, E. Srivastava, D. Ojha, and B. R. Dhungana, “Examining the role of social influence, learning value and habit on students’ intention to use chatgpt: the moderating effect of information accuracy in the utaut2 model,” Cogent Education, vol. 11, no. 1, p. 2403287, 2024.
[25] H. Ghnaimeh, “Extending the extended unified theory of acceptance and use of technology (utaut2): The moderation role of information privacy concerns,” Ph.D. dissertation, The University of North Carolina at Charlotte, 2024.
[26] A. Bessadok and M. Hersi, “A structural equation model analysis of english for specific purposes students’ attitudes regarding computer-assisted language learning: Utaut2 model,” Library Hi Tech, vol. 43, no. 1, pp. 36–55, 2025.
[27] S. Yin, Y. Wang, Y. Liu, and S. Wang, “Exploring drivers of behavioral willingness to use clean energy to reduce environmental emissions in rural china: An extension of the utaut2 model,” Journal of Renewable and Sustainable Energy, vol. 16, no. 4, 2024.
[28] M. Lin and Y. Huang, “Research on user experience design strategy of digital aquarium based on utaut2 model,” in International Conference on Human-Computer Interaction. Springer, 2023, pp. 167–188.
[29] T. Tanantong and P. Wongras, “A utaut-based framework for analyzing users’ intention to adopt artificial intelligence in human resource recruitment: a case study of thailand,” Systems, vol. 12, no. 1, p. 28, 2024.
[30] Y. Zhu, Z. Zhao, J. Guo, Y. Wang, C. Zhang, J. Zheng, Z. Zou, and W. Liu, “Understanding use intention of mhealth applications based on the unified theory of acceptance and use of technology 2 (utaut-2) model in china,” International journal of environmental research and public health, vol. 20, no. 4, p. 3139, 2023.
[31] M. Farrukh, I. A. Shahzad, F. Meng, Y. Wu, and A. Raza, “Three decades of research in the technology analysis & strategic management: a bibliometrics analysis,” Technology Analysis & Strategic Management, vol. 33, no. 9, pp. 989–1005, 2021.
32] M. B. Amnas, M. Selvam, and S. Parayitam, “Fintech and financial inclusion: Exploring the mediating role of digital financial literacy and the moderating influence of perceived regulatory support,” Journal of Risk and Financial Management, vol. 17, no. 3, p. 108, 2024.
[33] A. J. Kusumo, Q. Aini, S. Millah, T. Nurhaeni, and L. Sunarya, “Exploring the user satisfaction of gamification in promoting savings among millennials,” in 2024 2nd International Conference on Technology Innovation and Its Applications (ICTIIA). IEEE, 2024, pp. 1–6.
[34] A. Emran and M. T. H. Rubel, “Big data analytics and ai-driven solutions for financial fraud detection: Techniques, applications, and challenges,” Innovatech Engineering Journal, vol. 1, no. 01, pp. 10–70 937, 2024.
[35] S. Khan, H. U. Khan, S. Nazir, B. Albahooth, and M. Arif, “Users sentiment analysis using artificial intelligence-based fintech data fusion in financial organizations,” Mobile Networks and Applications, vol. 29, no. 2, pp. 477–488, 2024.
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Copyright (c) 2025 Tri Pujiati, Harlis Setiyowati, Bhupes Rawat, Nuke Puji Lestari Santoso, Muhammad Ghifari Ilham
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