Evidence (4114 claims)
Adoption
8570 claims
Productivity
7631 claims
Governance
6869 claims
Human-AI Collaboration
6491 claims
Org Design
4175 claims
Innovation
4114 claims
Labor Markets
3566 claims
Skills & Training
2966 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 758 | 199 | 100 | 900 | 2007 |
| Governance & Regulation | 826 | 400 | 191 | 122 | 1563 |
| Organizational Efficiency | 777 | 193 | 124 | 84 | 1189 |
| Technology Adoption Rate | 635 | 233 | 124 | 97 | 1098 |
| Research Productivity | 422 | 128 | 57 | 336 | 954 |
| Output Quality | 476 | 179 | 59 | 47 | 761 |
| Decision Quality | 328 | 177 | 81 | 47 | 640 |
| Firm Productivity | 435 | 57 | 88 | 20 | 606 |
| AI Safety & Ethics | 218 | 277 | 65 | 33 | 599 |
| Market Structure | 180 | 170 | 123 | 24 | 502 |
| Task Allocation | 213 | 64 | 72 | 33 | 387 |
| Skill Acquisition | 170 | 61 | 61 | 17 | 309 |
| Innovation Output | 203 | 27 | 43 | 18 | 292 |
| Employment Level | 105 | 54 | 107 | 13 | 281 |
| Fiscal & Macroeconomic | 131 | 69 | 43 | 26 | 276 |
| Consumer Welfare | 117 | 63 | 42 | 11 | 233 |
| Firm Revenue | 153 | 48 | 26 | 3 | 230 |
| Task Completion Time | 173 | 31 | 8 | 12 | 225 |
| Inequality Measures | 44 | 122 | 49 | 6 | 221 |
| Worker Satisfaction | 89 | 65 | 22 | 12 | 188 |
| Error Rate | 69 | 92 | 10 | 2 | 173 |
| Regulatory Compliance | 77 | 69 | 14 | 5 | 165 |
| Automation Exposure | 56 | 56 | 26 | 13 | 154 |
| Training Effectiveness | 94 | 21 | 13 | 19 | 149 |
| Wages & Compensation | 77 | 36 | 25 | 6 | 144 |
| Team Performance | 86 | 17 | 27 | 10 | 141 |
| Developer Productivity | 95 | 17 | 14 | 6 | 133 |
| Job Displacement | 12 | 80 | 20 | 1 | 113 |
| Hiring & Recruitment | 52 | 7 | 8 | 3 | 70 |
| Creative Output | 31 | 18 | 8 | 3 | 61 |
| Skill Obsolescence | 5 | 46 | 6 | 1 | 58 |
| Social Protection | 27 | 16 | 8 | 2 | 53 |
| Labor Share of Income | 17 | 19 | 17 | — | 53 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Innovation
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The realization of the positive effect of big data applications on markups depends on the synergistic support of various complementary resources.
Authors conclude—based on model analysis and empirical heterogeneity tests—that complementary resources (organizational, technological, environmental) are necessary for big data applications to translate into higher markups; details and sample sizes not provided in the summary.
Improving production efficiency is a key channel through which big data applications contribute to higher price markups.
Mechanism analysis in the paper identifies improved production efficiency as the second key channel linking big data applications to increased markups; supported by the heterogeneous firm model and empirical tests on firm-level data (sample size not reported).
Promotion of product innovation is a key channel through which big data applications contribute to higher price markups.
Mechanism analysis in the paper identifies product innovation as one of two key channels linking big data applications to increased markups; supported by the model and empirical mechanism tests using micro-level firm data (sample size not reported).
Big data applications significantly enhance firms' price markups.
Paper constructs a heterogeneous firm model with variable markups and conducts empirical tests using micro-level firm data; reported result states a significant positive effect of big data applications on firms' price markups. (Sample size not reported in the provided summary.)
Policy effects are stronger in municipalities with robust intellectual property protection and strong policy implementation capacity.
Heterogeneity analysis in the DID framework using measures/indices of IP protection and policy implementation capacity across the 283-city panel (2012–2023), showing larger estimated effects in cities with higher IP protection and stronger implementation capacity.
Policy effects of establishing big data pilot zones on urban economic resilience are more pronounced in megacities and large cities than in smaller cities.
Heterogeneity/subsample analysis within the DID framework on the 2012–2023 panel of 283 prefecture-level and above cities, comparing effects across city-size categories (megacities/large vs. others).
Mechanism analysis indicates the big data pilot zones primarily exert influence on urban economic resilience through talent aggregation and enterprise clustering pathways.
Channel/mechanism analysis reported in the paper using the same DID framework and city panel (2012–2023, 283 cities); the analysis identifies talent aggregation and enterprise clustering as mediating pathways.
The establishment of national big data comprehensive pilot zones significantly enhances urban economic resilience.
Natural experiment framework using a difference-in-differences (DID) approach on a panel of 283 prefecture-level and above cities from 2012 to 2023; reported statistical tests indicate a significant policy effect.
Strategic, forward-looking regulatory measures can improve market contestability in AI-driven sectors without undermining innovation incentives.
Inference from the paper's combined conceptual framework and empirical results showing that interventions (e.g., interoperability, data-access) mitigate exclusionary dynamics while the paper argues they can be designed to preserve innovation incentives.
Interoperability and data-access can alleviate the exclusionary effects of algorithmic advantage.
Empirical interaction/moderation analysis and conceptual/legal argumentation in the paper showing that measures improving interoperability and data access reduce the negative association between algorithmic advantage and market entry/contestability.
Elevated levels of algorithmic advantage are consistently linked to improved market concentration.
Empirical panel-data results from the paper's unbalanced sample of AI-intensive markets, with controls for firm size, capital intensity, R&D expenditure, and industry growth.
Exploitative innovation is directly associated with long-term competitive performance.
PLS-SEM analysis of survey data from 104 Portuguese B2B managers showing a significant direct path from exploitative innovation to performance.
Exploratory innovation's association with long-term competitive performance operates indirectly through GenAI adoption (mediation).
Survey of 104 Portuguese B2B managers and PLS-SEM showing a mediated pathway from exploratory innovation to performance via GenAI adoption in the estimated model.
GenAI adoption is positively associated with long-term competitive performance.
Survey data from 104 Portuguese B2B managers; association estimated via PLS-SEM in the study's structural model.
Ethical governance is the strongest organisational correlate of long-term competitive performance.
Survey data from 104 Portuguese B2B managers; analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM); reported as a comparative strength of model paths.
The study synthesizes interdisciplinary literature spanning health informatics, regulatory policy, ethical AI design, and healthcare economics to examine how governance structures can balance innovation with accountability.
Methodological statement in the paper describing the scope of the literature review and interdisciplinary synthesis (description of methods / scope).
The review contributes a unified conceptual model that clarifies relationships among governance, privacy assurance, and sustainable financing, offering guidance for designing resilient digital health systems that maintain ethical integrity, regulatory compliance, and economic viability.
Authors' stated contribution in the paper: a unified conceptual model produced by integrating findings across health informatics, policy, ethics, and economics literatures (conceptual synthesis).
Linking governance maturity with economic resilience provides a structured pathway for policymakers, healthcare institutions, and technology developers to operationalize responsible AI in healthcare environments.
Proposal in the paper connecting governance maturity levels (conceptual) to organizational economic resilience based on cross-disciplinary literature (theoretical linkage from review).
Financial sustainability of digital health systems can be supported through value-based healthcare models, cost optimization strategies, and scalable digital infrastructure that preserve compliance obligations.
Conceptual analysis and literature synthesis across healthcare economics and digital infrastructure studies presented in the review (literature review / conceptual proposal).
Privacy-by-design architectures, secure data interoperability, and compliance automation contribute to trust, institutional legitimacy, and long-term adoption of digital health solutions.
Synthesis of literature on privacy engineering, interoperability standards, and compliance technologies presented in the review (literature review; inferred causal linkages discussed).
The framework gives particular attention to algorithmic transparency, risk management, regulatory alignment, and lifecycle oversight of AI-enabled health systems operating under evolving privacy regulations (e.g., data protection laws and cross-border data governance standards).
Descriptive emphasis within the proposed framework, based on cited literatures in regulatory alignment and algorithmic governance (literature synthesis / conceptual emphasis).
This review develops a comprehensive conceptual framework that integrates AI governance principles, data privacy compliance mechanisms, and financially sustainable operational models within digital health ecosystems.
The paper's primary contribution is a proposed conceptual framework derived from synthesizing interdisciplinary literatures (conceptual framework produced by authors based on literature review).
The rapid expansion of digital health technologies driven by artificial intelligence has transformed healthcare delivery, clinical decision-making, and health data management.
Narrative synthesis in the review paper drawing on interdisciplinary literature in health informatics, clinical AI studies, and health data management (literature review / conceptual synthesis).
When used appropriately, LLMs are powerful tools that can expand the frontier of empirical economics.
Normative conclusion in the abstract based on the paper's proposed framework and discussion; presented as an overall benefit but not supported by empirical outcomes or quantified gains in the excerpt.
For estimation problems—automating the measurement of economic concepts for downstream analysis—valid downstream inference requires combining LLM outputs with a small validation sample to deliver consistent and precise estimates.
Methodological claim in the abstract advocating use of a small validation sample together with LLM outputs to achieve consistent/precise estimates; no empirical demonstration or sample-size specification provided in the excerpt.
The paper provides an econometric framework for realizing the potential of LLMs in two empirical uses: prediction problems and estimation problems.
Claim of contribution in the abstract describing a methodological framework (the excerpt reports the existence of the framework but does not detail empirical validation or sample sizes).
Researchers can now revisit old questions and tackle novel ones with rich data using LLMs.
Asserted in the paper's abstract as a consequence of LLM-enabled large-scale text analysis; no empirical demonstration or quantified case described in the excerpt.
Large language models (LLMs) enable researchers to analyze text at unprecedented scale and minimal cost.
Stated as an assertion in the paper's abstract/summary; based on the authors' framing of LLM capabilities (no empirical sample, experiment, or quantified result provided in the excerpt).
These empirical findings provide reference for global governments to optimise artificial intelligence policies for low-carbon urban development.
Paper conclusion interpreting results as policy-relevant and generalisable lessons for governments; based on observed positive association between NAIDPZ and urban GEE.
The impact of the NAIDPZ policy on urban GEE is positively moderated by government attention and public environmental attention.
Reported moderation analysis showing interaction effects between the treatment indicator and measures of government attention and public environmental attention within the DiD framework.
The composite NAIDPZ policy effect increases GEE mainly through promoting green technological innovation and optimising industrial structure.
Mechanism analysis reported in the paper (channel/mediation tests) showing that indicators of green technological innovation and industrial structure optimisation account for much of the policy effect on GEE.
The policy effect on GEE is stronger in inland cities, central-region cities, and non-resource-based cities.
Reported heterogeneity/subgroup analysis within the staggered DiD framework comparing effects across geographic regions (inland vs. others, central vs. others) and city types (non-resource-based vs. resource-based) in the 267-city sample.
The NAIDPZ policy significantly improves urban green economic efficiency (GEE).
Estimated treatment effect from staggered DiD on the 267-city panel (2007–2023) with reported statistical significance and multiple robustness checks mentioned.
Properly designed, agentic copyright offers a path toward scalable, fair, and legally meaningful copyright markets in the age of AI.
Synthesis and prescriptive claim grounded in the paper's conceptual framework; presented as an argument rather than as empirically demonstrated outcome.
AI should be understood not only as a source of disruption, but also as a governance tool capable of restoring market-based ordering in creative industries.
Normative conclusion of the paper based on theoretical reasoning and proposed governance mechanisms; no empirical tests provided.
Embedding normative constraints and monitoring functions into multi-agent architectures can align agent behavior with the underlying values of copyright law.
Conceptual proposal and argumentation in the paper; no experimental or field evidence offered.
The governance framework should emphasize ex ante and ex post coordination mechanisms capable of correcting agentic market failures before they crystallize into systemic harm.
Prescriptive policy/design recommendation grounded in the paper's conceptual analysis; no empirical validation provided.
A supervised multi-agent governance framework that integrates legal rules, technical protocols, and institutional oversight can address agentic market failures.
Framework development and prescriptive argumentation within the paper; proposed design rather than empirically validated solution.
Multi-agent ecosystems promise efficiency gains and reduced transaction costs in creative markets.
Theoretical claim and normative argument in the paper; no empirical measurement or sample provided to quantify efficiency gains.
The paper introduces 'agentic copyright', a model in which AI agents act on behalf of creators and users to negotiate access, attribution, and compensation for copyrighted works.
Conceptual proposal and definitional development within the paper; presented as a new model rather than as empirically validated intervention.
We illustrate a human-in-the-loop research methodology for LLMs to automatically classify and summarize research descriptions at scale.
Methodological description and application of a human-in-the-loop LLM workflow applied to the 58,746 NIH project descriptions, including labeling, model prompting, and human review steps as described in the paper.
AI research is concentrated in discovery, prediction, and data integration across disease domains.
Topic/semantic classification of AI-labelled project descriptions indicating topical concentration in discovery, prediction, and data integration areas across disease domains within the analyzed portfolio.
AI projects receive a 13.4% funding premium.
Comparison of funding amounts for AI-classified projects versus non-AI projects in the analyzed NIH portfolio (sample drawn from the 58,746 projects).
AI constitutes 15.9% of the NIH portfolio.
Classification of the 58,746 NIH project descriptions to identify projects using AI, yielding a reported share of 15.9%.
We present a comprehensive analysis of 58,746 NIH-funded biomedical research projects from 2025.
Enumeration and analysis of NIH project descriptions (n = 58,746) in 2025 using the paper's described dataset and methods.
The United States maintains superior resources by enforcing strict export controls on semiconductor chips, AI models, as well as outbound investments in these areas.
Stated as a comparative conclusion in the chapter; implies policy analysis of U.S. export-control regimes (e.g., controls on chips, models, outbound investment), but no specific datasets or sample sizes are given in the excerpt.
China's legal environment may offer certain advantage in terms of IP protection.
Asserted in the chapter as part of comparative analysis of IP regimes in the US and China; presented as a conclusion without reported empirical metrics in the excerpt.
China's legal environment may offer certain advantage in terms of access to training data.
Stated as an analytical conclusion in the chapter based on comparative legal/regulatory assessment of data regimes; no empirical sample or quantitative evidence reported in the provided excerpt.
Market-Bench provides a reproducible testbed for studying how LLMs interact in competitive markets.
Paper presents the benchmark design and logging mechanisms intended to enable reproducible experiments of multi-agent market interactions.
Market-Bench logs complete trajectories of bids, prices, slogans, sales, and balance-sheet states, enabling automatic evaluation with economic, operational, and semantic metrics.
Paper states that the benchmark captures full transaction trajectories and exposes economic/operational/semantic metrics for automatic evaluation.