Permanent record
RIR-2062 · Logged 13 July 2026
Theoretical Frameworks for Data-Intensive Economies and Digital Governance Policy Design
This paper proposes a theoretical model of the data economy, arguing that data sharing is essential to correct market failures in knowledge production.
In plain words
Data can be used by many people at once without running out, which makes it unlike most other resources. How might economic theory about data-heavy industries help governments write better rules for sharing data and keeping AI open?
Primary research question
How can theoretical economic models of data-intensive industries inform the design of digital governance and data-sharing policies?
Knowledge gap
What remains worth asking
Existing economic models may insufficiently account for the unique characteristics of data as a non-rivalrous input in AI-driven knowledge creation.
Potential contribution
Why it may matter
Provides a conceptual foundation for policymakers to address the societal challenges of data capitalism and AI transparency.
Academic placement
Academic fields and topic tags
Study frame: Macro-level data economy and digital governance frameworks. · Method signals: Theoretical conceptualization, Economic modeling
Possible study pathways
One question, different levels
Professional master’s / MBA
Strategic management of data assets and corporate governance in AI.
Doctoral
Developing new economic theories for the digital knowledge economy.
- originality
- Advanced
- methodology
- Advanced
- Data access
- Accessible
- ethics
- Moderate
Quality and audit
90/100 quality
Safeguard-based audit was not recorded under the prior standard.
- Highly theoretical approach.
- Applicable to both public policy and private sector strategy.
- Open-access scholarly source and DOI metadata verified
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Research Idea curation
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APA 7 source
de Pedraza, P., & Vollbracht, I. (2023). General theory of data, artificial intelligence and governance. Humanities and Social Sciences Communications, 10(1), Article 607. https://doi.org/10.1057/s41599-023-02096-w
Paper abstract and discussion context; AI-inferred direction
This is an AI-inferred direction. The source authors did not state it as a recommendation.
Open source ↗