EXPRESS: Toward Developing the Foundations of Global Artificial Intelligence Governance: Insights from Triple Stakeholder Perspectives and Reflexive Thematic Analysis

Amit Arora et al.

Journal of Public Policy and Marketing2026https://doi.org/10.1177/07439156261430023article
AJG 3ABDC A
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0.50

Abstract

This article proposes the building blocks of a global artificial intelligence governance (GAIG) framework based on a triple stakeholder approach that encompasses consumers, governments, and businesses. It develops GAIG’s building blocks through a synthesis of AI governance policies from the United States (US), the European Union (EU), and China using reflexive thematic analysis. The three key theoretical lenses applied are: a consumer-oriented value hierarchy framework, a government-oriented dynamic pyramid model, and a business-oriented privacy-by-design framework. The analysis reveals distinct regional approaches: the US emphasizes business-led AI innovation and flexible regulation, the EU prioritizes strict consumer protection and ethical standards, and China focuses on state-controlled AI development aligned with national interests. Despite differences in approaches, the findings suggest opportunities for convergence in GAIG, particularly in promoting ethical AI innovation, ensuring transparency, and safeguarding consumer rights. The resulting GAIG building blocks and future research avenues provide a comprehensive approach to AI governance, balancing the unique needs of consumers, governments, and companies while addressing diverse challenges and promoting ethical and responsible AI innovation. The study contributes to the understanding of international AI policy convergence and divergence, offering marketing and public policy insights for companies, marketers, policymakers, and researchers navigating the complex GAIG landscape.

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https://doi.org/https://doi.org/10.1177/07439156261430023

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@article{amit2026,
  title        = {{EXPRESS: Toward Developing the Foundations of Global Artificial Intelligence Governance: Insights from Triple Stakeholder Perspectives and Reflexive Thematic Analysis}},
  author       = {Amit Arora et al.},
  journal      = {Journal of Public Policy and Marketing},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1177/07439156261430023},
}

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Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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