ArticleBMC health services research2026
Employee perceptions of AI adoption across service domains in a Finnish public health and social care organization: a cross-sectional mixed-methods study.
Article in BMC health services research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundEmpirical evidence on how employees across different service domains within a single multi-sector public organization perceive AI adoption is limited. This study, therefore, examined differences in self-reported AI usage, perceived competence, training needs, barriers, and motivations across four service domains within a Finnish public wellbeing services organization responsible for health and social services.
methodsA cross-sectional, mixed-methods web-based survey was conducted among all employees of the organization in August-September 2025 (N = 437; response rate 10.3%; domain-level rates 6%-25%). The survey design and analytical lens drew on the Unified Theory of Acceptance and Use of Technology (UTAUT). Quantitative data were analyzed using descriptive statistics, chi-square tests, one-way ANOVA, and Spearman correlations. Open-ended responses were analyzed through reflexive thematic analysis.
resultsSelf-reported AI usage varied across four service domains, from 78% among strategy and shared services respondents to 28% among elderly and disability services respondents (χ²(3) = 59.81, p < .001, Cramér's V = 0.38). Perceived competence among AI users (M = 2.7/5) and motivation orientation did not differ across domains. Training need was high overall (M = 3.6/5), and the strongest bivariate association was between perceived competence and training need (ρ = -0.51). Among non-users, the highest-ranked barriers reflected facilitating conditions. Three of six qualitative themes converged on the same facilitating conditions UTAUT dimension.
conclusionsDomain-level variation in generative AI adoption was consistent with an interpretation that structural facilitating conditions may shape whether training-focused interventions can translate into AI use in multi-sector public organizations.
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