Digital Government Transformation Through Artificial Intelligence: The Mediating Role of Stakeholder Trust and Participation
This study explores how artificial intelligence utilization in digital government, through AI-enabled service automation and AI-based decision support, contributes to digital government transformation, emphasizing the mediating roles of stakeholder trust and stakeholder participation. Grounded in stakeholder theory and public value theory, the research aims to understand the relational mechanisms that connect technological innovation to institutional change. A quantitative research design was employed using survey data collected from 412 stakeholders, including citizens, civil society members, public employees, and private actors, who had interacted with AI-driven government services in Pakistan. Structural equation modeling was used to test a conceptual model involving direct and indirect effects. Results reveal that both AI-enabled service automation and AI-based decision support significantly enhance stakeholder trust and participation, which in turn positively influence digital government transformation. Stakeholder trust emerged as a stronger mediator than participation. The findings highlight the importance of ethical, transparent, and participatory AI integration in public administration. Theoretically, the study extends digital governance literature by validating behavioral mediators in technology-driven reform. Practically, it offers strategic insights for policymakers on how to enhance stakeholder engagement and legitimacy in AI-based public systems. Overall, the study emphasizes that successful digital transformation is not solely technological, but also deeply relational and participatory.
Authors
- Syed Asad Abbas Bokhari (ORCID: https://orcid.org/0000-0002-9510-0359)
- So Young Park (ORCID: https://orcid.org/0000-0003-2718-0518)
- Shahid Manzoor
Institutions
- Dakota State University (US)
- Daegu Catholic University (KR)
- Nazarbayev University (KZ)
Publication Details
- Journal
- Digital
- Published
- 2025-09-16
- DOI
- https://doi.org/10.3390/digital5030043
- Citations
- 2
- Primary Topic
- E-Government and Public Services
- Type
- article
- Field-Weighted Citation Impact
- 17.04