The research team, led by Qingyu Gao and colleagues, combined Hofstede’s cultural scores with the Government AI Readiness Index published by Oxford Insights, which evaluates countries across government, technology sector, and data and infrastructure pillars. Because cultural values are time-invariant over the study window, the authors employed a random effects panel model, a standard econometric approach for handling characteristics that do not change over time. The final sample covered 61 countries with complete data on all five cultural dimensions examined: power distance, individualism, uncertainty avoidance, masculinity, and long-term orientation. Control variables included internet usage, government expenditure, GDP per capita, population, and unemployment, with missing values imputed using a random forest algorithm.

Three cultural dimensions emerged as decisive. Masculinity, which reflects a society’s emphasis on competition, assertiveness, and achievement, was negatively associated with government AI adoption, with each unit increase predicting a 0.082 unit decrease in readiness. Uncertainty avoidance, the degree to which societies feel threatened by ambiguity and unpredictability, showed a similar inhibitory effect, reducing readiness by 0.076 units per unit increase. In contrast, long-term orientation, the tendency to prioritize future goals over immediate gratification, was the strongest positive predictor, adding 0.13 units of AI readiness per unit increase and reaching statistical significance at the 0.001 level. Power distance and individualism, by comparison, showed no significant effects.

The masculinity result is perhaps the most counterintuitive. Earlier research on e-government had often found masculinity to be a minor or statistically insignificant factor, and one might expect performance-driven, competitive cultures to leap at technologies promising measurable gains. But the authors argue that AI is fundamentally different from earlier digital upgrades. Successful AI adoption demands long-term investment, cross-domain collaboration, and ethical co-governance, qualities that sit uneasily with a cultural orientation toward immediate, controllable competitive wins. AI also carries risks of bias, inequity, and unintended social consequences, and a governance ethos grounded in cooperation and concern for citizen well-being, characteristic of feminine cultures, appears better suited to managing them.

Uncertainty avoidance operates through a more intuitive channel. AI is a radical innovation that raises ethical dilemmas, regulatory ambiguity, and value conflicts, so governments must tolerate experimentation and learn from trial and error. Societies that prize stability and rules tend to perceive such technologies as risky or disruptive, and their decision makers emphasize control and predictability over bold experimentation. The study’s heterogeneity analysis sharpened this picture: the negative effect of uncertainty avoidance was most pronounced in countries with high internet usage, where mature technological infrastructure lowers perceived reliance on AI while heightening concerns about its potential to disrupt existing security frameworks.

Beyond direct effects, the researchers probed a mediating mechanism using Baron and Kenny’s causal step method, supplemented by Sobel tests. Governance effectiveness, drawn from the World Bank’s Worldwide Governance Indicators and lagged by two years to capture temporal effects, proved to be a key pathway. Masculinity and uncertainty avoidance both significantly reduced governance effectiveness, while long-term orientation enhanced it. Governance effectiveness, in turn, strongly promoted AI adoption. Notably, it fully mediated the relationships between masculinity and AI adoption and between uncertainty avoidance and AI adoption, and partially mediated the long-term orientation pathway. In other words, culture does not simply push governments toward or away from AI; it shapes the institutional quality that makes AI integration feasible in the first place.

The heterogeneity analyses revealed that cultural influence is context-dependent in surprising ways. Stratifying countries by GDP per capita at a 20,000 dollar threshold, the team found that the negative effects of masculinity and uncertainty avoidance were stronger in high-income countries, while the positive effect of long-term orientation was concentrated in low-income countries, where resource constraints make long-term strategic planning essential for rationalizing AI investment. The authors suggest that wealthy nations with robust welfare systems and egalitarian gender roles may find traditional masculine norms of competition and control particularly detrimental, and that their sophisticated technological expectations generate heightened concerns about AI security and privacy.

Employment and connectivity conditions further modulated the picture. In high-unemployment countries, power distance showed a significant negative effect on AI adoption, plausibly because governments preoccupied with joblessness and political stability can use centralized authority to reject disruptive technologies. In low-unemployment countries, individualism and long-term orientation turned significantly positive, while uncertainty avoidance remained a brake. Meanwhile, in low internet usage countries, power distance and long-term orientation showed positive effects, suggesting that nations with limited technical capacity may actually benefit from centralized strategic planning and sustained long-term guidance to jumpstart AI development. A significant positive effect of long-term orientation on AI adoption was observed specifically in the cohort combining low income, low unemployment, and low internet usage.

Methodologically, the study reframes AI adoption as a dynamic, process-oriented phenomenon rather than a binary adopt-or-not decision. By using AI readiness as the dependent variable, the authors align with recent scholarship showing that governments that appear well positioned to adopt AI do not always succeed, and that adoption unfolds across initial, assessment, decision, management, and optimization stages. This operationalization matters because failed AI deployments in the public sector can produce bureaucratic dysfunction or even systemic collapse, as documented in cases such as automated debt recovery systems. Readiness, encompassing tangible resources, human capital, and intangible institutional capacity, captures the preparedness that determines whether AI initiatives take root or wither.

The authors acknowledge limitations. Hofstede’s framework, while widely validated, does not cover all countries, restricting generalizability, and its static scores cannot capture cultural evolution over time. Cross-national regressions also face omitted variable bias, and factors such as religion or legal traditions were not modeled. Still, the practical implications are considerable. Policymakers seeking to accelerate government AI adoption may need culturally adapted strategies: institutional designs that reorient competitive, short-term policy cultures toward long-term strategic development, and governance reforms that build the effectiveness through which cultural values ultimately flow. As AI reshapes public administration worldwide, this study suggests that the most important infrastructure may not be silicon, but shared values.

Subject of Research: The influence of Hofstede's national culture dimensions on government artificial intelligence adoption across countries

Article Title: Sailing with the cultural winds: the impact of National culture on government AI adoption

Article References: Gao, Q., Wang, S., Liang, Z., Wang, G., & Guo, L. (2025). Sailing with the cultural winds: the impact of National culture on government AI adoption. Global Public Policy and Governance, 5(3), 251-273. https://doi.org/10.1007/s43508-025-00122-y

Image Credits: AI Generated

DOI: 10.1007/s43508-025-00122-y

Keywords: artificial intelligence, government AI adoption, national culture, Hofstede, AI readiness, governance effectiveness, public sector innovation, uncertainty avoidance, long-term orientation, cross-national study, e-government, public policy

The distinction the authors draw between AI and earlier waves of digital government is technical as well as institutional. Previous generations of e-government technology primarily improved the efficiency, connectivity, and accessibility of administrative processes, digitising forms and speeding up workflows while leaving the underlying logic of human decision-making intact. AI, by contrast, increasingly performs the knowledge work of administration: it predicts, classifies, optimises, and generates scenario analyses that feed directly into judgements about rights, resources, and sanctions. The central analytical question, the review argues, is therefore no longer how governments digitalise administrative processes, but how administrative intelligence is governed and distributed across human and computational actors. In the authors’ framing, AI is best understood not as a discrete application but as a systems phenomenon whose consequences ripple across power structures, public values, institutions, decision-making, organisations, and frontline bureaucracy.

That systems perspective, drawing on the tradition of systems thinking associated with Barry Richmond and Donella Meadows, rejects the idea that AI’s effects can be assessed by examining any single agency or decision point in isolation. An algorithm deployed in one part of government depends on professional norms, institutional rules, data infrastructures, and frontline judgements that translate algorithmic outputs into real-world action. To capture these interdependencies, the editorial proposes a six-dimension framework covering the full administrative life cycle of AI. The framework moves from the strategic conditions under which governments acquire AI capacity, through the public values used to justify adoption, the institutions that authorise its use, the decisions officials make with it, and the organisations that implement it, to the street-level encounters where algorithms meet citizens. Each dimension, the authors insist, is a location where AI enters, circulates, and becomes institutionalised in government.

The first dimension, power structures, may be the most politically charged. The review argues that public administration scholarship has too often examined AI only after it arrives inside government, ignoring the political economy of the systems being procured. Governments frequently do not own the capabilities their AI depends on: digital infrastructure, specialist talent, computational resources, and research pipelines are concentrated in large technology firms. Drawing on scholarship on platform capitalism and technological sovereignty, the authors describe a fundamental asymmetry in technical capacity, epistemic authority, and regulatory knowledge between states and major tech companies. This dependence shapes what AI systems are available to governments, what officials can know about risk, bias, and explainability, and how much bargaining power agencies have when seeking access to proprietary source code or audit evidence. Supporting research in the special issue, using OpenAlex bibliometric data, finds that Big Tech’s presence in critical technology research is growing, particularly in AI, raising questions about who controls the knowledge on which public-sector AI increasingly rests.

Public value forms the second and normative core of the framework. The authors stress that in government, unlike the commercial sector, AI adoption cannot be justified by productivity or cost reduction alone; it must serve public purposes such as fairness, transparency, responsiveness, and trust. An algorithm that improves technical accuracy does not settle questions of proportionality or legitimacy, because public agencies must justify how decisions are reached and whose interests are served. A service that reduces processing time but increases exclusion or distrust cannot simply be counted as administrative improvement. Contributing papers in the collection develop this argument in detail: one proposes a value-chain approach for tracking where public value is created or lost across the stages of an AI-enabled service, while another maps 3,268 studies using BERTopic modelling and finds that after 2022, work on algorithmic fairness, ethics, and state-society relations has overtaken earlier emphases on efficiency and service delivery.

Institutions constitute the third dimension, translating public purposes into enforceable rules about who may use AI, under what conditions, and subject to which review. The editorial highlights a structural puzzle: most regulatory sandboxes were designed for private-sector innovation, where regulators supervise firms testing products, but in public-sector AI the state is simultaneously regulator, user, implementer, and accountable authority. One contributing paper adapts the sandbox model into an experimentalist governance framework for government AI, emphasising supervised testing, stakeholder participation, iterative learning, and institutional feedback. The authors connect this to Elinor Ostrom’s nested levels of institutional rules, showing how operational rules govern day-to-day AI use, collective-choice rules govern how those rules are revised, and constitutional-choice rules keep experimentation consistent with administrative law, procurement requirements, and rights protection. Institutions, in this account, do not merely permit or prohibit AI; they define the terms under which algorithmic systems can be tested, trusted, and treated as legitimate parts of public administration.

The framework’s fourth dimension turns to administrative decisions, where the editorial examines experimental evidence on whether public officials actually accept AI-assisted judgement, particularly in budgeting, a domain long understood as political rather than purely technical. The review contrasts two views of human-AI collaboration: augmentation, in which algorithms provide forecasts and rankings without displacing human responsibility, and de facto automation, in which officials defer to algorithmic outputs they lack the expertise or organisational backing to challenge. Extending Nathan Caplan’s two-communities theory, the authors suggest that technical experts, vendors, and data scientists may produce algorithmic knowledge that bureaucrats do not regard as usable or congruent with their problems, producing a decoupling between formal AI adoption and practical utilisation. Bureaucratic acceptance, they argue, is an overlooked condition for successful AI reform: officials must come to see algorithmic inputs as credible, legitimate, and compatible with the responsibilities attached to public office.

Organisations and street-level bureaucrats complete the framework. Implementation, the editorial stresses, is rarely smooth: public agencies inherit legacy systems, fragmented data arrangements, risk-averse cultures, and budget constraints, so AI reforms may look successful at launch yet lose momentum as maintenance costs rise and staff revert to familiar practices. Contributing research on AI-powered platform government shows that organisational inertia is not mere resistance but a mechanism through which prior investments and routines govern the pace and direction of AI use. At the frontline, drawing on Michael Lipsky’s theory of street-level bureaucracy, the authors argue that AI does not eliminate discretion but redistributes its materials, changing what officials see, how cases are categorised, and how departures from system recommendations must be justified. Ethnographic work in Finnish public organisations introduces the concept of the moral crumple zone: bureaucrats become accountability buffers who absorb blame for algorithmic failures while lacking meaningful control over the systems that shape their decisions. Experimental evidence from policing further finds that AI-supported enforcement can shift officers toward more formal, coercive styles, depending on the accountability mechanisms in place.

The editorial’s conclusion is stark in its ambition: AI represents a genuine paradigm shift rather than the latest phase of digital government, because it begins to change the nature of administration itself. If digital government changed the machinery of administration, the authors write, AI is displacing the human monopoly over administrative judgement. The review closes with a forward research agenda calling for scholarship to move from national AI policy to AI ecosystems, from AI ethics to AI legitimacy, from adoption to institutional capability, and from street-level discretion to behavioural adaptation. The decisive question for democratic governance, the authors argue, is no longer how governments digitalise administrative processes, but how they govern administrative systems in which computational actors increasingly participate alongside human ones, and whether citizens can still recognise, understand, and challenge the decisions that shape their lives.

Subject of Research: A systems framework for understanding how artificial intelligence enters, circulates, and becomes institutionalised across the public administration system.

Article Title: Artificial intelligence across the public administration system: public values, institutions, and bureaucrats in the algorithmic age

Article References: Cho, W., Fan, Z., & Zheng, Y. (2026). Artificial intelligence across the public administration system: public values, institutions, and bureaucrats in the algorithmic age. Global Public Policy and Governance, 6(2), 103-123. https://doi.org/10.1007/s43508-026-00151-1

Image Credits: AI Generated

DOI: 10.1007/s43508-026-00151-1

Keywords: artificial intelligence, public administration, public value, bureaucracy, street-level bureaucracy, algorithmic governance, technological sovereignty, regulatory sandboxes, digital government, administrative discretion, AI accountability, public sector innovation