Agentic AI Adoption: Balancing Enthusiasm and Ethical Concerns An Exploratory Study

Authors

  • Nadia Azaria Information Technology, Faculty of Science and Technology, Sari Mulia University, Banjarmasin
  • Mambang Mambang Information Technology, Faculty of Science and Technology, Sari Mulia University, Banjarmasin
  • Finki Dona Marleny Informatics, Faculty of Engineering, University of Muhammadiyah, Banajarmasin
  • Trifebi Shina Sabrila Information Technology, Faculty of Science and Technology, Sari Mulia University, Banjarmasin

DOI:

https://doi.org/10.30871/jaic.v10i3.13070

Keywords:

Agentic AI, Adoption Perceptions, Exploratory Data Analysis, Human-AI Trust, Technology Ethics

Abstract

Agentic AI represents a significant paradigm shift in artificial intelligence, transitioning from passive command execution to autonomous goal pursuit. This exploratory study investigates user perceptions toward Agentic AI adoption in Indonesia, focusing on the balance between functional enthusiasm and ethical concerns. The 20 Likert-scale statements were developed based on the Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), and AI trust frameworks. These items cover key dimensions including perceived usefulness, ease of use, trust in autonomy, productivity enhancement, data privacy, loss of human control, algorithmic bias, and adoption intention. Utilizing Exploratory Data Analysis (EDA) supplemented with non-parametric tests on a convenience sample of 22 respondents predominantly tech-savvy young adults this study reveals high familiarity with AI tools (54.5% daily users). Respondents showed strong optimism regarding productivity and efficiency (Positive Aspects mean = 3.52), while maintaining notable ethical concerns (Concern Aspects mean = 3.64). The instrument demonstrated excellent reliability (Cronbach’s Alpha = 0.921 overall). Mann-Whitney U tests indicated significant gender differences on certain items, particularly bias concerns. Due to the small sample size and self-selection bias, findings should be interpreted cautiously. This study provides preliminary insights and highlights the need for human-centric design, transparent governance, and culturally appropriate regulations to support responsible Agentic AI adoption in Indonesia.

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References

[1] S. Hosseini and H. Seilani, “The role of agentic AI in shaping a smart future : A systematic review,” Array, vol. 26, no. December 2024, p. 100399, 2025, doi: 10.1016/j.array.2025.100399.

[2] U. Rehman, “The future role of artificial intelligence in energy management systems for smart cities : A systematic literature review of trends , gaps , and future direction,” Sustain. Comput. Informatics Syst., vol. 49, no. September 2025, p. 101249, 2026, doi: 10.1016/j.suscom.2025.101249.

[3] A. Hidayat, J. Wahyudi, F. D. Marleny, U. S. Mulia, and U. Muhammadiyah, “Explanatory Data Analysis to Evaluate Keyword Searches for Educational Videos on YouTube with a Machine Learning,” Sink. J. dan Penelit. Tek. Inform., vol. 7, no. 3, pp. 915–922, 2022, doi: 10.33395/sinkron.v7i3.11502.

[4] G. Priday, L. Sterling, and A. Livingstone, “Shaping the Future of Generative AI in Aged and Community Care : A P ractitioners ’ Perspective,” Procedia Comput. Sci., vol. 270, pp. 4615–4625, 2025, doi: 10.1016/j.procs.2025.09.587.

[5] E. Schmidt, C. Bersch, I. Rahwan, and M. Dong, “Computers in Human Behavior : Artificial Humans First interactions with generative chatbots shape local but not global sentiments about AI,” Comput. Hum. Behav. Artif. Humans J., vol. 6, no. August, 2025, doi: 10.1016/j.chbah.2025.100223.

[6] Y. Ji, C. Xinyi, C. Fang, B. Christian, T. Most, and H. Human, “Navigating the human-AI divide : Boundary work in the age of generative AI,” Comput. Hum. Behav. Artif. Humans, vol. 6, no. September, p. 100214, 2025, doi: 10.1016/j.chbah.2025.100214.

[7] Q. Wu, L. Chen, M. Chen, and Y. Huang, “Exploring the impact of artificial intelligence on business talent development in higher education : A systematic literature review and research agenda,” Int. J. Manag. Educ., vol. 24, no. 1, p. 101287, 2026, doi: 10.1016/j.ijme.2025.101287.

[8] Y. Ma, N. H. Zakaria, B. Al-haimi, and C. Wu, “Intelligent Systems with Applications Artificial intelligence-driven green innovation in packaging : A systematic review of adoption and diffusion challenges,” Intell. Syst. with Appl., vol. 28, no. September, pp. 1–22, 2025, doi: 10.1016/j.iswa.2025.200589.

[9] A. Vocino, “Technovation Mapping artificial intelligence research in the cultural and creative industries : A systematic bibliometric review,” Technovation, vol. 154, no. April, p. 103551, 2026, doi: 10.1016/j.technovation.2026.103551.

[10] M. Zhang, F. Yi, and D. Gursoy, “The effects of generative artificial intelligence on consumers in hospitality and tourism : A systematic review and future research directions,” Int. J. Hosp. Manag., vol. 133, no. September 2025, p. 104452, 2026, doi: 10.1016/j.ijhm.2025.104452.

[11] T. Thuy et al., “Understanding continuance intention toward the use of AI chatbots in customer service among generation Z in Vietnam,” Acta Psychol. (Amst)., vol. 259, no. June, p. 105468, 2025, doi: 10.1016/j.actpsy.2025.105468.

[12] J. Shao, L. Y. Su, Z. Gong, and M. Chen, “Computer Studies Conversational agents and charitable behavioral intentions : The roles of modality , communication style , and perceived anthropomorphism,” Int. J. Hum. - Comput. Stud., vol. 205, no. September, p. 103616, 2025, doi: 10.1016/j.ijhcs.2025.103616.

[13] K. Lin, M. Li, F. Lo, H. Huang, and K. Matsuno, “Adaptive learning with human factors and Artificial Intelligence : associations with training effectiveness in programming education,” Int. J. Ind. Ergon., vol. 110, no. September, p. 103834, 2025, doi: 10.1016/j.ergon.2025.103834.

[14] V. Rus, M. Cermak, and T. Fritzov, “Using relational graphs for exploratory analysis of network traf fi c data,” Forensic Sci. Int. Digit. Investig., vol. 45, 2023, doi: 10.1016/j.fsidi.2023.301563.

[15] O. Pons-valladares, “Beyond the human eye : Review of quantitative methods for evaluating facade visual perception,” J. Environ. Psychol., vol. 112, no. April, 2026, doi: 10.1016/j.jenvp.2026.103020.

[16] F. D. Marleny and M. Zulfadhilah, “Prediction of linear model on stunting prevalence with machine learning approach,” Bull. Electr. Eng. Informatics, vol. 12, no. 1, pp. 483–492, 2023, doi: 10.11591/eei.v12i1.4028.

[17] W. Febriani, S. E. Prastya, B. Sabella, and F. D. Marleny, “Analysis of the Impact of Violent Content on Social Media on Adolescent Cyberpsychology Using Support Vector Machine and Random Forest,” J. Appl. Informatics Comput., vol. 10, no. 1, pp. 707–711, 2026, doi: https://doi.org/10.30871/jaic.v10i1.11415.

[18] S. Kumi, R. K. Lomotey, R. Deters, S. Kumi, R. K. Lomotey, and R. Deters, “Governance-as-Code : Managing Agentic AI with a Distributed Dual Governance-as-Code : Managing Proxy Agentic Gateway AI with a Distributed Dual Proxy Gateway,” Procedia Comput. Sci., vol. 272, pp. 122–130, 2025, doi: 10.1016/j.procs.2025.10.187.

[19] G. Bhatnagar, “Modernization of enterprise payment infrastructure : A case study on LLM-assisted migration of legacy distributed systems,” Array, vol. 30, no. April, p. 100806, 2026, doi: 10.1016/j.array.2026.100806.

[20] T. Ameer and O. F. Valilai, “Predictive exploratory data analysis of shopfloor CNC machine operation through a machine learning model,” J. Open Innov. Technol. Mark. Complex., vol. 11, no. June, 2025, doi: 10.1016/j.joitmc.2025.100559.

[21] B. S. Byers, K. Stengele, and C. De Wolf, “Data carriers for circular construction supply chains : An exploratory quantitative analysis,” J. Clean. Prod., vol. 494, no. February, p. 145053, 2025, doi: 10.1016/j.jclepro.2025.145053.

[22] D. Byrne et al., “An exploratory graphical analysis of the Montgomery-Åsberg Depression Rating Scale pre- and post-treatment using pooled antidepressant trial secondary data,” J. Affect. Disord., vol. 368, no. August 2024, pp. 584–590, 2025, doi: 10.1016/j.jad.2024.09.087.

[23] D. L. Harkin, J. Little, and S. J. Mccullough, “What are the most salient visuoperceptual reading symptoms to identify visual stress in adults ? Using exploratory factor analysis to develop the Ulster visual stress questionnaire ☆,” Vision Res., vol. 235, no. September 2024, p. 108668, 2025, doi: 10.1016/j.visres.2025.108668.

[24] Y. Huang et al., “Deriving exploratory temperature-based phenological indicators under data-limited conditions : integrating ERA5 and citizen science ☆,” Ecol. Indic., vol. 181, no. September, 2025, doi: 10.1016/j.ecolind.2025.114484.

[25] Y. Shen, B. Benke, M. Ashtiani, M. Huang, and K. Simonen, “Exploratory Data Analysis of a North American Whole Building Life Cycle Assessment datasets,” Build. Environ., vol. 286, no. September, p. 113655, 2025, doi: 10.1016/j.buildenv.2025.113655.

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Published

2026-06-14

How to Cite

[1]
N. Azaria, M. Mambang, F. D. Marleny, and T. S. Sabrila, “Agentic AI Adoption: Balancing Enthusiasm and Ethical Concerns An Exploratory Study”, JAIC, vol. 10, no. 3, pp. 2680–2684, Jun. 2026.

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