Document Type : Original Article (Qualitative)
Authors
1
PhD in Business Administration, Department of Business Administration, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2
Master's student, Department of Psychology, Takestan Institute of Higher Education, Qazvin, Iran
3
Assistant Professor of Management Department, Takestan Institute of Higher Education, Qazvin, Iran.
4
Business Management Department, Payame Noor University, Tehran , Iran.
10.22034/jmep.2026.587845.1677
Abstract
The aim of the present study is to explain the paradigmatic model of "development of mutual trust between humans and systems" in executive agencies. This research is applied in terms of purpose and qualitative in terms of methodology with a data-based theory approach. The statistical population included experts, senior managers of information technology, and public administration specialists, who were conducted using a purposive sampling method (snowball), 17 semi-structured interviews until theoretical saturation was achieved. The data analysis process was carried out using three-stage coding (open, axial, and selective) with maxquda software. The findings show that the pivotal phenomenon of "development of mutual trust" is the result of the interaction of causal conditions (governance and legal requirements, complexity of administrative tasks, need for computational transparency), contextual conditions (digital literacy and user expertise, fear of substitution, ambiguity in the legal accountability of the system), and intervening factors (complexity of algorithm logic and fear of substitution). In the strategy phase, continuous training and empowerment, participatory design of systems plays a key role in reducing organizational uncertainties. The results of this model indicate that the deployment of intelligent systems in the public sector, beyond technical aspects, requires a mental re-creation of the role of machines in decision-making processes. By providing a comprehensive framework, this research offers operational solutions for enhancing social capital and national productivity through the transition to data-driven governance.
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