Abstract/Summary

The economic and political climate expects public administration to do more with less. Artificial Intelligence (AI) technologies can add immense value towards achieving these goals. However, AI use is accompanied by negative externalities on the environment and already at-risk populations. Against this backdrop of increasing rhetoric of AI benefits and its associated harms, this study explains the AI adoption phenomenon in public administration both from outside-in and inside-out perspectives. The context of the study is Canadian public administration, and the scope is limited to machine learning and natural language processing. This thesis consists of four papers. The first paper is an exploratory literature review. Through a cross-case analysis of thirty AI implementations, a typology of AI use cases is developed. The second paper is a systematic literature review and identifies technological, organisational, and environmental factors that influence AI adoption in public administration. The third and fourth papers are mixed-methods studies that draw on a cross-sectional survey (n=277) and semi-structured interviews (n=39). The third paper is grounded in institutional and sensemaking theories and explains factors that affect the perceived benefits of AI use in public administration and how they operate. The fourth paper is grounded in the resource-based view (RBV) of the firms and explains what resources and capabilities enable AI adoption in public administration and how these capabilities are developed. The study contributes to both theory and practice. Theoretical contributions include an updated AI innovation process expanding the diffusion of innovation theory within the context of AI. The study demonstrates black-box assumptions of the institutional theory and RBV can be explained by enumerating underlying mechanisms. Practitioner contributions include guidelines on four AI capability development paths with associated risks and benefits and recommendations on assessing organisational and technological AI readiness, crossing the operationalisation chasm, and managing negative perceptions of AI.