Paper Number
ECIS2026-2085
Paper Type
CRP
Abstract
Public sector organizations are facing disruptions due to demographic shifts and higher citizen expectations for digital services. Artificial intelligence solutions can support process automation, improve services, and relieve employees of monotonous tasks. They are, however, also associated with risks. We argue that dynamic capability theory offers a fruitful perspective for deepening our understanding of how to leverage the potential of artificial intelligence while responding to associated uncertainties. Therefore, we conducted a multiple-case study in the public sector to examine the role of dynamic capabilities within this context. With our theoretically guided and empirically derived results, we make two contributions to information systems theory. First, we conceptualize dynamic capabilities as a means of mitigating artificial intelligence-related uncertainties and barriers to its appropriation, thereby broadening our understanding of dynamic capabilities in the age of disruptive digital technologies. Second, we propose microfoundations that contribute to the development and leverage of these dynamic capabilities.
Recommended Citation
Urban, Isabella; Baer, Manfred; and Plattfaut, Ralf, "Managing Artificial Intelligence In Public Sector Organizations: A Dynamic Capabilities Perspective" (2026). ECIS 2026 Proceedings. 11.
https://aisel.aisnet.org/ecis2026/govtrans/govtrans/11
Managing Artificial Intelligence In Public Sector Organizations: A Dynamic Capabilities Perspective
Public sector organizations are facing disruptions due to demographic shifts and higher citizen expectations for digital services. Artificial intelligence solutions can support process automation, improve services, and relieve employees of monotonous tasks. They are, however, also associated with risks. We argue that dynamic capability theory offers a fruitful perspective for deepening our understanding of how to leverage the potential of artificial intelligence while responding to associated uncertainties. Therefore, we conducted a multiple-case study in the public sector to examine the role of dynamic capabilities within this context. With our theoretically guided and empirically derived results, we make two contributions to information systems theory. First, we conceptualize dynamic capabilities as a means of mitigating artificial intelligence-related uncertainties and barriers to its appropriation, thereby broadening our understanding of dynamic capabilities in the age of disruptive digital technologies. Second, we propose microfoundations that contribute to the development and leverage of these dynamic capabilities.
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