Public Values in Saudi Arabian Artificial Intelligence Policy Documents: A Mixed-Method Analysis Using NVivo and the Bannister & Connolly (2014) Framework
Abstract
Background: The integration of artificial intelligence (AI) into public sector governance generates complex value tensions that remain understudied in non-western institutional contexts. Objective: This study examines public values embedded in Saudi Arabian governmental AI policy documents, using the Bannister & Connolly (2014) framework as an analytical lens alongside qualitative content analysis conducted via NVivo 15. A purposive sample of nine official documents published between 2016 and 2024 was subjected to systematic matrix coding, generating 198 total coding references across three value categories. Two NVivo matrices were produced: a cross-tabulation matrix (Q3) yielding 222 pure diagonal references from 270 total, and a considerations-to-risks matrix (Q4) comprising 115 considerations and 54 risks (n = 169).
Duty-Oriented Values dominate at 43.7%, followed by Socially-Oriented Values (28.4%) and Service-Oriented Values (27.9%), with a single-reference margin separating the latter two. The Q4 matrix reveals that Socially-Oriented Values carry the highest relative tension (ratio 1.33:1), while Duty-Oriented Values record the highest absolute risk count. Cross-contextual comparison with Toll et al. (2020) reveals a notable structural similarity: both Saudi and Swedish AI policy documents exhibit an identical 43.7% dominance of duty/professionalism values, which may suggest an institutional logic in which governments frame transformative technologies primarily through compliance and control lenses. Crucially, Saudi documents acknowledge risks at 32% of Q4 references versus 9.6% in the Swedish corpus, embodying the “more nuanced view” advocated by Toll et al.
The term second-generation governance is used here in the sense developed by Janowski (2015) and elaborated by Bullock (2019). Janowski (2015) maps four stages of digital government evolution, distinguishing first-generation models, characterised by the digitisation of existing services, from later-generation models that integrate adaptive policy-making, contextual responsiveness, and transformational governance objectives. Bullock (2019) extends this stage-logic into the AI policy domain by tracking the analytical shift from compliance-and-control framings of artificial intelligence (first generation) to dual-track framings that combine promotional optimism with explicit regulatory caution (second generation). Saudi AI policy documents, as the present analysis demonstrates, exhibit this dual-track structure: a substantial proportion of references articulate developmental ambitions while another substantial proportion of references articulate risk-acknowledging regulatory positions, the two coexisting within the same policy texts. Saudi AI policy documents represent a second-generation governance model that balances promotional optimism with legislative caution. The findings contribute to the emerging literature on AI governance values in non-Western institutional contexts and offer a replicable methodological framework for comparative document analysis.
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