The emergence of agentic artificial intelligence (AI) presents a novel frontier for cybersecurity research, yet its potential to simulate complex human behaviours in controlled environments remains underdeveloped. While extensive literature examines employee compliance with cybersecurity policies, it lacks leveraging agentic AI. To bridge this gap, this study implements a novel four-phase research design to identify the configurations predicting employee compliance intentions (CI) with institutional policies in the e-government sector. The proposed approach integrates agentic AI simulations, where AI agents emulate employee responses to multi-scenario vignettes. The study first employs a grounded theory approach, following the Gioia methodology, to code AI-generated qualitative data into theoretically grounded themes. Subsequently, it utilises AI agents to weight quantitative responses. The analysis reveals distinct behavioural archetypes (i.e. embracers, negotiators and resisters). Finally, it reports fuzzy-set qualitative comparative analysis (fsQCA) to move beyond net effects and uncover specific pathways of conditions that consistently lead to high CI. This work foregrounds agentic AI simulations as a pioneering tool for behavioural analysis. It offers a replicable methodology for investigating complex socio-technical phenomena and suggests new avenues for simulation-based inquiry. This research establishes a conceptual foundation for facilitating theory development and methodological innovation using agentic AI in simulation.
Mushtaq et al. (Mon,) studied this question.