Evidence (8542 claims)
Adoption
5831 claims
Productivity
5063 claims
Governance
4582 claims
Human-AI Collaboration
3625 claims
Labor Markets
2749 claims
Innovation
2704 claims
Org Design
2667 claims
Skills & Training
2126 claims
Inequality
1429 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 448 | 118 | 70 | 511 | 1163 |
| Governance & Regulation | 458 | 217 | 125 | 67 | 884 |
| Research Productivity | 274 | 103 | 35 | 303 | 720 |
| Organizational Efficiency | 444 | 106 | 78 | 43 | 675 |
| Technology Adoption Rate | 347 | 130 | 76 | 45 | 603 |
| Firm Productivity | 324 | 39 | 73 | 13 | 454 |
| Output Quality | 273 | 76 | 27 | 30 | 406 |
| AI Safety & Ethics | 122 | 188 | 46 | 27 | 385 |
| Market Structure | 119 | 134 | 86 | 14 | 358 |
| Decision Quality | 182 | 79 | 41 | 20 | 326 |
| Fiscal & Macroeconomic | 95 | 58 | 34 | 22 | 216 |
| Employment Level | 78 | 37 | 80 | 9 | 206 |
| Skill Acquisition | 104 | 37 | 41 | 9 | 191 |
| Innovation Output | 127 | 12 | 26 | 14 | 180 |
| Firm Revenue | 101 | 38 | 24 | — | 163 |
| Task Allocation | 95 | 18 | 36 | 8 | 159 |
| Consumer Welfare | 77 | 38 | 37 | 7 | 159 |
| Inequality Measures | 29 | 81 | 33 | 6 | 149 |
| Regulatory Compliance | 54 | 61 | 13 | 3 | 131 |
| Task Completion Time | 92 | 8 | 4 | 3 | 107 |
| Worker Satisfaction | 49 | 36 | 13 | 8 | 106 |
| Error Rate | 45 | 53 | 6 | — | 104 |
| Training Effectiveness | 60 | 13 | 12 | 16 | 102 |
| Wages & Compensation | 56 | 16 | 20 | 5 | 97 |
| Team Performance | 51 | 13 | 15 | 8 | 88 |
| Automation Exposure | 28 | 29 | 12 | 7 | 79 |
| Job Displacement | 7 | 45 | 13 | — | 65 |
| Hiring & Recruitment | 42 | 4 | 7 | 3 | 56 |
| Developer Productivity | 38 | 5 | 4 | 3 | 50 |
| Social Protection | 22 | 12 | 7 | 2 | 43 |
| Creative Output | 17 | 8 | 6 | 1 | 32 |
| Skill Obsolescence | 3 | 26 | 2 | — | 31 |
| Labor Share of Income | 12 | 7 | 10 | — | 29 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
The work offers a blueprint for converting the ideological potential of AI into implementable, regulator-compatible utilities in pharmaceutical science by synthesizing quantitative measures and practical measures.
Claim about the paper's contribution (blueprint). It is an author claim about the synthesis and guidance provided; the excerpt does not include empirical validation that following the blueprint yields successful implementation.
The paper proposes a systematized framework of integration that emphasizes creating high-impact pilot projects, in-the-wild testing, and ongoing monitoring of models in accordance with FDA, EMA, and EU AI Act guidance.
Described as the paper's proposed framework and recommendations for regulatory-aligned implementation. The excerpt indicates the proposal but does not present validation or empirical testing of the framework.
Grounded in the Resource-Based View (RBV), AI is conceptualized as a strategic intangible resource that can confer a competitive advantage when integrated with complementary capabilities.
Theoretical framing presented in the paper (RBV-based conceptualization); not an empirical finding but an explicit conceptual claim.
Firms with high AI adoption had an average profit growth rate of 9.5%, compared to 5.8% for low adopters.
Reported profit growth rates for high vs. low AI adoption groups from the questionnaire data (N=400); the paper gives the specific averages: 9.5% (high adopters) vs. 5.8% (low adopters).
O artigo discute implicações gerenciais e de políticas públicas para reduzir fricção, acelerar adoção responsável e orientar investimentos em produtividade e inclusão.
Seção de discussão mencionada no resumo abordando encargos gerenciais e políticas públicas; não há avaliação empírica de políticas no resumo.
O artigo entrega instrumentos replicáveis — a escala SCF-30, um checklist de governança mínima de IA e uma matriz 30-60-90 dias — para uso prático.
Afirmação explícita no resumo de que instrumentos replicáveis são disponibilizados; presunção de inclusão dos instrumentos no corpo do artigo.
AI significantly enhances firms' total factor productivity (TFP).
Empirical results from the multidimensional fixed-effects panel model applied to the 2007–2023 sample of agricultural A-share firms; statistical significance reported in the paper.
The model is disciplined using data from the Michigan Survey of Consumers and the Survey of Professional Forecasters, targeting key empirical moments.
Calibration/estimation strategy described in the paper: parameters are chosen to match moments from the Michigan Survey of Consumers and SPF (targeted empirical moments). Specific moments and calibration targets are reported in the paper.
I develop a search-and-matching model with sticky wages and endogenous separations.
Theoretical/model contribution: construction and analysis of a calibrated search-and-matching framework that incorporates wage stickiness and endogenous separation decisions.
Workers and firms face information frictions about the aggregate state of the economy (modeled explicitly).
Assumption and mechanism built into the paper's theoretical framework: a search-and-matching model with information frictions for both sides of the market (model specification).
Households form dispersed, backward-looking expectations about macroeconomic conditions.
Survey evidence from the Michigan Survey of Consumers showing dispersion in individual expectations and patterns consistent with backward-looking (slow/updating) belief formation about macro variables; exact sample sizes and empirical specifications are provided in the paper (not in the summary).
High-quality chatbots (96–100% accurate) improved caseworker accuracy by 27 percentage points.
Experimental result reported in paper: treatment with chatbots at 96–100% aggregate accuracy produced a 27 percentage-point increase in caseworker accuracy compared to control; based on the randomized experiment on the 770-question benchmark.
Caseworker performance significantly improves as chatbot quality improves.
Aggregated results from the randomized experiment show monotonic improvement in caseworker accuracy as the chatbot suggestion accuracy increases; paper states the improvement is statistically significant (specific p-values/statistical tests not provided in the excerpt).
AI-integrated fuel blending systems achieve very high precision, demonstrated by a coefficient of determination (R2) of 0.99 during validation.
Model validation results reported in the paper (fuel blending system validation, R2 = 0.99), indicating very high explanatory/ predictive fit compared to traditional models.
DARE posits that responsible AI deployment requires the simultaneous and integrated development of Digital readiness, Administrative governance, Resilience & ethics, and Economic equity.
Descriptive claim about the framework's components as reported in the abstract (conceptual proposition).
This paper introduces the DARE Framework, a holistic, four-dimensional model for national AI strategy and international cooperation.
Factual description of paper content in abstract — the framework is introduced by the authors (conceptual/model contribution).
ERM is an integrated, strategic framework that aligns risk management with corporate governance, objective setting, and performance management.
Conceptual descriptions and definitions of ERM drawn from existing ERM frameworks and literature reviewed in the article.
The authors curated a set of guidelines called the Incentive-Tuning Framework to aid researchers in designing effective incentive schemes for human–AI decision-making studies.
Authors' contribution described in the paper: development of a framework (framework content and evaluation details not provided in excerpt).
The intelligent scheduling model incorporates legal, contractual, skill-based, and preference-aware constraints to generate equitable and efficient rosters.
Methodological description of constraints encoded in the optimization model for scheduling; experimental validation of resulting rosters reported (conflict reduction and fairness metrics), but specific constraint formulations and datasets are not detailed in the excerpt.
The performance evaluation framework combines structured metrics (task completion, attendance, punctuality) with unstructured feedback (patient surveys, peer reviews) analyzed using natural language processing.
Methodological description in the paper of the performance evaluation module and use of NLP for unstructured feedback analysis; implementation details and dataset sizes not specified in the excerpt.
The proposed AI-driven HRM framework integrates forecasting, optimization, and performance evaluation to enhance workforce planning, staff scheduling, and continuous assessment.
Methodological contribution described in the paper: framework design with three core modules (demand forecasting, intelligent scheduling, performance evaluation); validated via experiments on synthetic and real hospital datasets (dataset sizes not specified in the text).
The Indian government believes that artificial intelligence (AI) will play an important role in India’s continued economic growth, both through its contribution to productivity in the private sector and through smarter and more data-led government.
Reported position in the paper based on review of government statements and policy documents (policy analysis/legal review). No empirical sample size applies; claim is descriptive of government belief.
The positive impact of generative AI on ESG performance is stronger in manufacturing firms, firms in eastern regions, and technology-intensive firms (relative to non-manufacturing, central/western regions, and non-technology-intensive firms).
Heterogeneity/subsample analysis on the panel of Chinese A-share firms (2012–2024) comparing effects across firm types, geographic regions, and technology intensity, showing larger estimated positive effects for manufacturing, eastern-region, and technology-intensive subsamples.
Sustainable innovation partially mediates the relationship between generative AI and corporate ESG performance improvement.
Mediation analysis conducted on the panel dataset (Chinese A-share firms, 2012–2024) indicating a partial mediating role for sustainable innovation measures between generative AI use and ESG performance.
The quality of information disclosure partially mediates the relationship between generative AI and corporate ESG performance improvement.
Mediation analysis (intermediary variable tests) performed on the same panel (Chinese A-share firms, 2012–2024) showing that information-disclosure quality accounts for part of the effect of generative AI on ESG outcomes.
Generative AI can effectively drive improvements in corporate ESG performance.
Empirical analysis using panel data of Chinese A-share listed firms covering 2012–2024; the paper reports an econometric panel-data model showing a positive effect of generative AI adoption/use on measured firm ESG performance.
The study extends human capital theory by integrating emotional and psychological dimensions into explanations of productivity and employment outcomes.
Theoretical contribution asserted by the authors based on their empirical findings linking emotional intelligence and psychological factors to economic outcomes; this is a conceptual extension rather than a statistical result.
Twelve testable hypotheses are proposed, with implications for agentic AI oversight and human-AI collaboration.
Paper statement that it proposes twelve testable hypotheses; verifiable by counting hypotheses in the paper.
Convolutional neural networks achieved 95.4% accuracy in identifying ulcers and hemorrhages.
Specific result reported from an included study using convolutional neural networks (accuracy = 95.4%) as cited in the review.
Technological innovation is the primary mediating mechanism through which NQPF affects supply chain efficiency, accounting for 84.6% of the effect.
Mediating-effect models applied to the 2012–2022 panel data (Shanghai and Shenzhen A-share listed firms) estimating mediation proportions; technological innovation mediation proportion reported as 84.6%.
New quality productivity forces (NQPF) significantly improve supply chain efficiency.
Empirical analysis using 2012–2022 panel data of Shanghai and Shenzhen A-share listed companies; results robust to robustness tests and reported as statistically significant in main regressions.
Persistent environmental state induces history sensitivity (dependence of long-run behavior on past trajectories and initial conditions) unless the overall system is globally contracting.
Formal theorem and proof showing that persistence of environmental variables creates non-autonomous/memory-dependent closed-loop behavior, and that only the special case of global contraction removes this history dependence (mathematical analysis of sensitivity to initial conditions).
Under dissipativity assumptions the induced closed-loop system admits a bounded forward-invariant region, guaranteeing viability of the dynamics without requiring global optimality.
A proven structural result (theorem) in the paper: mathematical proof using dissipativity hypotheses on components of the feedback architecture showing existence of a bounded forward-invariant set for the closed-loop dynamics. (The claim is theoretical; no empirical sample size.)
AI tools—ranging from machine learning algorithms in inventory management to natural language processing in customer engagement—are applied in micro‑enterprise contexts.
Descriptive synthesis from included articles reporting specific AI applications (ML for inventory management; NLP for customer engagement) across the reviewed literature.
Global efforts toward establishing shared norms and multilateral cooperation are underway through initiatives led by the United Nations, OECD, UNESCO, and G7.
Qualitative document review identifying initiatives and normative efforts by multilateral organizations (organizations named; specific initiatives referenced qualitatively but not enumerated as a dataset).
We demonstrate three distinct workflows across five environments.
Paper lists and evaluates five target environments and describes three workflows (direct translation, translation verified against existing performance implementations, and new environment creation). Sample size: five environments.
These trends (increased demand for complementary skills and decreased demand for substitutable skills) hold across geographies including the United States, United Kingdom, and Australia, demonstrating robustness.
Replication/comparison of results within the dataset’s country-specific subsamples (US, UK, Australia) drawn from nearly 30 million job postings collected between 2018 and 2024.
AI-intensive roles are significantly more likely to require complementary non-technical capabilities such as analytical thinking, resilience, and digital literacy.
Empirical analysis of a dataset of nearly 30 million job postings from the United States, the United Kingdom, and Australia between 2018 and 2024; roles classified as AI-intensive and skill mentions extracted from job postings to compare prevalence of non-technical capabilities.
Mainstreaming shared input and embracing climate-resilient management approaches are fundamental action items for building institutional resilience.
Paper conclusion lists these recommended action items based on its analysis of governance and sustainability linkages grounded in SDG and global governance literature; the summary does not indicate empirical testing of these recommendations.
The study builds and calibrates an integrated system dynamics model that connects demographics, labor supply, economic output, and public finance.
Method: development and calibration of a system dynamics model using official statistics for demographics, labor, output, and fiscal variables (model structure and calibration described in paper).
The paper ends with policy implications and recommends periodic evaluation and the integration of AI-related risks into financial governance.
Policy recommendations section in the paper advocating for periodic evaluation and AI-risk integration into financial governance (method: prescriptive/policy analysis based on review findings).
Specialized SDE services that require further study are grouped and highlighted.
Section of the paper grouping and highlighting specialized services for future research (method: expert-driven identification from review; no quantitative prioritization stated).
We introduce a concise conceptual model of a 'shadow' project for designing SDE products or services, detailing participant roles and project composition.
Presentation of a conceptual model within the paper (method: model construction and descriptive exposition; no empirical testing/sample).
The paper proposes a clear classification of criminally oriented products and services in the SDE.
Taxonomy/classification produced in the paper (method: conceptual taxonomy from literature and analysis; no quantitative validation reported).
We identify a structured set of labor‑market roles within the SDE model.
Analytical identification and description of roles within the paper (method: conceptual modeling and qualitative role-mapping; sample size N/A).
We propose an integrated definition of the shadow digital economy that synthesizes technical and economic definitions.
Conceptual analysis and literature synthesis in the paper that combines technical and economic definitions into a single integrated definition (method: review/synthesis; no numeric sample).
Regional peer effects of DT improve firms' resource allocation (RA), which in turn bolsters enterprise resilience (ER).
Mediation/ mechanism analysis on the 2013–2022 Chinese A-share manufacturing panel showing that RA mediates the relationship between regional peer DT and ER.
Industrial peer effects of DT enhance firms' innovation capability (IC), which in turn strengthens enterprise resilience (ER).
Mediation/ mechanism analysis on the same 2013–2022 Chinese A-share manufacturing panel showing that IC mediates the relationship between industrial peer DT and ER.
Digital transformation (DT) exhibits significant industrial and regional peer effects.
Empirical analysis using panel data of Chinese manufacturing enterprises listed on the Shanghai and Shenzhen A-share markets from 2013 to 2022; peer-effect regressions conducted within interlocking directorate networks (IDNs).
AI significantly enhances supplier stability in sports enterprises (SE).
Empirical estimation using a dual machine learning (DML) model on panel data of 45 Chinese listed sports enterprises (2012–2023); authors report a statistically significant positive effect of AI on supplier stability.