AccScience Publishing / IJOSI / Online First / DOI: 10.6977/IJoSI.202610_10(X).026260169
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Institutional factors influencing national artificial intelligence development: The moderating role of social capital

Hyungjun Seo1 ,  Hyoungsuk Lee2*
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1 Department of Smart Governance and Policy, Graduate School, Inha University, Incheon , Republic of Korea
2 Department of Commerce and Finance, College of Economics and Commerce, Kookmin University, Seoul , Republic of Korea
Received: 26 June 2026 | Revised: 22 August 2026 | Accepted: 15 September 2026 | Published online: 21 October 2026
(This article belongs to the Special Issue Systematic Innovation and AI Integration)
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Abstract

Artificial intelligence (AI) levels can determine which countries lead the technological paradigm shift, suggesting that governments should invest in building a national foundation for AI development. This study aims to uncover institutional factors, specifically the independent variables (democracy, transparency, government effectiveness, quality regulation, and gender inequality) and a moderating variable (social capital), related to national AI levels. The objective is to provide policy recommendations for countries seeking to enhance their AI capabilities for national innovation. This study utilized the technology sector and data and infrastructure pillars from the government AI readiness index by Oxford Insights as measures of the national AI level. By classifying countries into Organization for Economic Cooperation and Development (OECD) and non-OECD categories using three years of data from 137 countries, a panel analysis was conducted to test the hypotheses. The panel regression analysis revealed that government effectiveness positively affects the technology sector pillar in the OECD country model, while democracy positively influences the data and infrastructure sector pillars in the non-OECD country model. Additionally, the panel moderating effect analysis demonstrated that social capital moderates the relationship between gender inequality and the data and infrastructure sector pillars through the interference effect in the OECD country model. Similarly, social capital moderates the relationship between transparency and the technology sector pillars through the interference effect in the non-OECD country model. Based on these findings, this study suggests that enhancing government effectiveness and democracy, along with understanding the dual nature of social capital, are crucial for improving national AI levels. These insights offer valuable policy implications.

Keywords
Artificial intelligence
Digital transformation
Digital government
E-government
Social capital
Funding
None.
Conflict of interest
The authors declare they have no competing interests.
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