Abstract
The study investigates the determinants of individual acceptance of agentic AI by integrating traditional technology acceptance constructs with agency-related characteristics of AI agents. While classical models such as UTAUT have been validated for traditional IT systems, emerging agentic AI, due to its autonomous performance and decision-making, requires a different theoretical lens. We develop and test an extended acceptance model incorporating perceived competence, decision autonomy, and performance autonomy alongside traditional determinants. A quantitative survey of AI users was conducted, and the collected data were analyzed using PLS‑SEM. The results demonstrate that all three agentic characteristics significantly shape performance expectancy, which, together with facilitating conditions and social influence, predicts intention to use AI agents. The findings highlight the need to reconceptualize acceptance frameworks for increasingly autonomous AI systems.
Paper Type
Short Paper
DOI
10.62036/ISD.2026.120
Determinants of Acceptance of Agentic AI: Integrating Traditional and Agentic Perspectives
The study investigates the determinants of individual acceptance of agentic AI by integrating traditional technology acceptance constructs with agency-related characteristics of AI agents. While classical models such as UTAUT have been validated for traditional IT systems, emerging agentic AI, due to its autonomous performance and decision-making, requires a different theoretical lens. We develop and test an extended acceptance model incorporating perceived competence, decision autonomy, and performance autonomy alongside traditional determinants. A quantitative survey of AI users was conducted, and the collected data were analyzed using PLS‑SEM. The results demonstrate that all three agentic characteristics significantly shape performance expectancy, which, together with facilitating conditions and social influence, predicts intention to use AI agents. The findings highlight the need to reconceptualize acceptance frameworks for increasingly autonomous AI systems.
Recommended Citation
Biernikowicz, A., Ashraf, R.U., Nowacka, A. & Oleś-Filiks, M.(2026). Determinants of Acceptance of Agentic AI: Integrating Traditional and Agentic Perspectives. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.120