Navigating complexity in public digital transformation: adaptation process, knowledge sharing and innovation

Purpose This study aims to examine how public digital transformation (DT) is revealed as a complex adaptive process, focusing on the co-evolution between technological infrastructures and organizational structures in e-government initiatives. Drawing on complexity theory (CT) and the complex adaptive systems (CAS) perspective, the research seeks to understand how public administrations restructure their technological and human networks and the drivers of the adaptation process. Design/methodology/approach An interpretative qualitative study was conducted using a storytelling approach with 39 middle and senior public managers involved in DT projects. Narrative data were analyzed to identify the dynamic mechanisms shaping technology adaptation and organizational restructuring within public institutions. Findings The findings reveal that public DT operates as a complex socio-technical system characterized by continuous interaction between technological and human networks. Learning processes, communication patterns, innovation practices and knowledge-sharing and acquisition mechanisms emerge as critical attributes that enable adaptive capacity and process. The study highlights how dynamic capabilities foster fluid, self-reinforcing and evolutionary restructuring processes in public digital projects. Practical implications The paper provides operational insights for public decision-makers and managers seeking to promote a responsible and adaptive process of technology implementation. It proposes actionable levers to enhance learning ecosystems and strengthen collaborative capabilities within public administrations. Originality/value By conceptualizing public DT assimilated to a CAS, this study advances an understanding of technological and human subsystems dynamics in e-government projects and helps bridge CT and public sector DT research. Moreover, building on service ecosystem and open innovation literature, the authors argue that the proposed adaptation model emerges not only from interactions between organizational and technological subsystems but also from continuous exchanges among heterogeneous stakeholders within a multi-actor service ecosystem perspective.