Social Capital Management

Social Capital Management

Designing a Data-Driven Model for the Transformation of Social Capital in Public Organizations under the Influence of Artificial Intelligence

Document Type : Research/Original/Regular

Authors
Mohsen Arefnezhad
Faezeh Hassanvand
Department of Public Administration, Faculty of Management and Economics, Lorestan University, Khorramabad, Iran
10.22059/jscm.2026.411058.2655
Abstract
Objective: In recent decades, digital transformation and the expansion of intelligent technologies, particularly artificial intelligence (AI), have presented unprecedented challenges and opportunities for public sector organizations. These changes have not only reshaped work processes and decision-making but also elevated the critical role of social capital—including networks, trust, and human interactions within organizations. This study was conducted with the aim of designing a data-driven model of social capital transformation in public organizations under the influence of artificial intelligence using the grounded theory approach.
Method: The study is applied in purpose, descriptive-survey in data collection, and qualitative in nature based on grounded theory. Semi-structured in-depth interviews were conducted with 14 experts in management and information technology within public organizations. Sampling was purposeful and theoretical, with participants recruited based on their specialized knowledge and professional experience relevant to the research topic, and data collection continued until theoretical saturation was achieved.
Results: Rapid changes in public sector environments and the expansion of intelligent technologies have highlighted the need for social capital transformation. The study showed that AI acts not only as a supportive tool but also as a shaping force for social structures, decision-making processes, and human interactions. Key causal factors include technological drivers (algorithmic governance, intelligent decision-support systems, algorithmic transparency, and accountability), cognitive-human drivers (perceived job threat, employees’ digital literacy, data-driven culture), and governance-institutional drivers (data governance and AI ethics, transformative leadership, alignment of values, and technology). Contextual factors, such as bureaucratic structure, organizational culture, prior social capital, and the macro digital governance environment, provide the necessary foundation for the interaction of technology and social capital. Furthermore, intervening variables—including digital maturity, external oversight and regulations, prior organizational trust, digital change management, and algorithmic bias—moderate the strength and direction of the effects of causal factors on the core phenomenon: social capital reconfiguration and techno-social convergence. Key strategies identified include digital empowerment of employees, transparent algorithmic governance, and fostering technological collaboration. The outcomes of this transformation include enhanced digital trust, restructured interaction networks, algorithmic justice, organizational cohesion, and public trust.
Conclusions: The findings indicate that, when properly implemented and aligned with organizational strategies, AI can serve as a sustainable organizational capability, strengthening social capital, restructuring it, and improving techno-social convergence in public organizations. This transformation enhances data-driven decision-making, organizational efficiency, resilience, and public trust, demonstrating that the intelligent integration of technology and social capital is a prerequisite for the success of public organizations in the digital era.
Keywords
Social capital
Artificial intelligence
Grounded theory
Public organizations
Subjects
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Social Capital Management
Volume 13, Issue 3
Autumn 2026
Pages 295-311