Prioritizing the strategic impacts of Gen-AI adoption in public administrations: a multi-criteria analysis using AHP
Francesco Mercuri, Francesco Laviola, Giacinto ConiglioPurpose
This study investigates the strategic impact factors associated with the adoption of generative artificial intelligence (Gen-AI) in the public sector, with the aim of identifying and weighting the factors that public-sector actors consider most relevant across different institutional domains. In doing so, it interprets Gen-AI adoption not merely as a technological or organizational issue but as a strategic and institutional process in which competing public values must be balanced.
Design/methodology/approach
The study employs the analytic hierarchy process (AHP) to structure and prioritize seven strategic impact factors derived from a structured literature review. Primary data were collected in Italy through pairwise comparison questionnaires initially administered to 80 public-sector employees across four domains: healthcare, education, local government and public security. After consistency screening, 65 valid AHP responses were retained for the final analysis. Individual judgements were aggregated using the geometric mean, and consistency was assessed through standard AHP indices.
Findings
At the aggregate level, psychological barriers emerge as the most influential factor, followed by work performance and privacy and security, indicating that Gen-AI adoption in public administrations is shaped not only by expected utility but also by human and risk-related concerns. At the same time, the sector-level analysis reveals substantial heterogeneity in priorities. The relative salience of the seven factors varies across domains depending on institutional missions, governance logics and operational risk profiles, suggesting that public-sector Gen-AI adoption is strongly context-dependent.
Originality/value
This study contributes to the literature by extending prevailing technology-adoption perspectives through a public-value and sector-sensitive interpretation of Gen-AI adoption in public administrations. It shows that public actors do not evaluate adoption through a single efficiency logic but through a multidimensional prioritization of strategic concerns that differs across institutional contexts. Theoretically, the study integrates public value theory with the institutional logics perspective to explain why these priorities are sector-differentiated rather than uniform. Methodologically, the article demonstrates the value of AHP as a decision-analytic tool for comparing heterogeneous adoption priorities in the public sector and for informing more differentiated governance and implementation strategies.