Evidence (6917 claims)
Adoption
8625 claims
Productivity
7686 claims
Governance
6917 claims
Human-AI Collaboration
6574 claims
Org Design
4189 claims
Innovation
4131 claims
Labor Markets
3588 claims
Skills & Training
2985 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 761 | 200 | 101 | 904 | 2020 |
| Governance & Regulation | 829 | 400 | 191 | 122 | 1566 |
| Organizational Efficiency | 784 | 193 | 125 | 84 | 1197 |
| Technology Adoption Rate | 637 | 236 | 124 | 97 | 1103 |
| Research Productivity | 431 | 131 | 58 | 340 | 972 |
| Output Quality | 481 | 183 | 59 | 47 | 770 |
| Decision Quality | 332 | 177 | 82 | 49 | 647 |
| Firm Productivity | 439 | 57 | 88 | 20 | 610 |
| AI Safety & Ethics | 218 | 279 | 66 | 33 | 602 |
| Market Structure | 181 | 170 | 123 | 24 | 503 |
| Task Allocation | 214 | 64 | 72 | 33 | 388 |
| Skill Acquisition | 174 | 62 | 62 | 17 | 315 |
| Innovation Output | 204 | 27 | 45 | 18 | 295 |
| Employment Level | 105 | 54 | 108 | 13 | 282 |
| Fiscal & Macroeconomic | 132 | 69 | 43 | 26 | 277 |
| Consumer Welfare | 117 | 63 | 42 | 11 | 233 |
| Firm Revenue | 154 | 48 | 26 | 3 | 231 |
| Task Completion Time | 173 | 31 | 8 | 12 | 225 |
| Inequality Measures | 44 | 123 | 50 | 6 | 223 |
| Worker Satisfaction | 89 | 65 | 22 | 12 | 188 |
| Error Rate | 71 | 92 | 10 | 2 | 175 |
| Regulatory Compliance | 77 | 69 | 14 | 5 | 165 |
| Automation Exposure | 58 | 56 | 26 | 13 | 156 |
| Training Effectiveness | 96 | 21 | 14 | 19 | 152 |
| Wages & Compensation | 77 | 37 | 25 | 6 | 145 |
| Team Performance | 86 | 17 | 27 | 10 | 141 |
| Developer Productivity | 95 | 17 | 14 | 6 | 133 |
| Job Displacement | 12 | 81 | 21 | 1 | 115 |
| Hiring & Recruitment | 52 | 7 | 8 | 3 | 70 |
| Creative Output | 32 | 20 | 8 | 3 | 64 |
| Skill Obsolescence | 5 | 47 | 6 | 1 | 59 |
| Social Protection | 28 | 16 | 8 | 2 | 54 |
| Labor Share of Income | 17 | 19 | 17 | — | 53 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Governance
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A path analysis was used to trace structural relationships between HR quality, effectiveness perceptions, and AI readiness.
Paper reports a path analysis linking composite HR quality indices, perceived HR effectiveness, and AI readiness measures; uses same survey sample.
A binary logistic regression modelling active AI adoption was estimated with McFadden R² = 0.032.
Reported logistic regression model fit (McFadden R² = 0.032) for AI adoption outcome using the survey data.
An OLS regression was estimated explaining perceived HR effectiveness with R² = 0.446.
Reported OLS model fit statistics in the paper (R-squared = 0.446); model explains perceived HR effectiveness using survey data.
Constructed and validated a composite index of external HR quality factors with Cronbach's α = 0.959.
Measurement validation reported in the paper; Cronbach's alpha reported for external HR factors.
Constructed and validated a composite index of internal HR quality factors with Cronbach's α = 0.924.
Measurement validation reported in the paper; Cronbach's alpha reported for internal HR factors.
A large-scale empirical survey of 12,562 public servants was conducted in June 2025 in Kazakhstan.
Statement in paper specifying survey sample and date; sample of public servants N = 12,562, June 2025.
We compare and benchmark strategy profiles adopted by open and proprietary state-of-the-art language models deployed in AgentSociety against best response.
Empirical benchmarking experiments comparing multiple language models' strategy profiles to best-response strategies (experimental evaluation / benchmarking).
Historically, the most visible high-end bugonomics was offense-priced because production-grade zero-days and exploit chains were expensive specialist outputs for governments, brokers, and offensive vendors.
Historical observation corroborated by reference to public exploit-market price anchors (market price data referenced; no specific figures included in the abstract).
The actual water footprint of a specific load varies dynamically with generation dispatch and network conditions.
Conceptual claim presented in the paper motivating the need for dynamic attribution (discussion/analysis rather than a reported empirical sample).
Water withdrawals associated with electricity consumption occur at generation sites and are virtually allocated to demand based on network power flows.
Conceptual statement about how water withdrawals are attributed to loads via network power flow accounting (methodological description in paper).
The analysis is structured across past, present, and future phases using an integrative socio-technical political economy framework and validated secondary sources (OECD, ILO, UNDP, WTO, WEF) alongside official Indian statistics and sector evidence.
Methodological claim stated in abstract describing the approach and data sources used in the paper (OECD, ILO, UNDP, WTO, WEF, MoSPI/NSO, PLFS, HCES, Reuters, Nasscom).
We analyzed over 1.5M assets and 128K agents in EvoMap.
Descriptive dataset statement in the paper reporting the scope of the empirical analysis (assets and agents counts).
There is a significant deficiency in India-centric qualitative investigations on human-AI collaboration in the IT sector.
Authors' review of peer-reviewed literature and secondary data concluding a gap in India-focused qualitative studies (literature gap analysis). No numeric count provided.
The study examines the impact of AI technologies on Uzbekistan's labor market transformation in the context of implementing the national strategy 'Digital Uzbekistan - 2030' and the Strategy for the Development of AI Technologies until 2030.
Framing and scope statement in the paper; analysis based on national strategy documents, statistical data, industry reviews, and regulatory legal documents.
The paper includes comparisons against accelerated baselines (reported experimental comparisons).
Statement in experimental section that comparisons to accelerated baselines were performed; specific baselines and results are in the paper.
The paper examines the legal implications of overusing export controls.
Statement of the paper's analytic scope and structure (description of content).
AI infrastructure decisions involve trade-offs across physical resource systems including energy, land, water, and labor.
Descriptive claim in the abstract and framing sections; supported by cited prior work on the economic, physical, and moral limits of AI development and by illustrative regional cases.
The evidence is used illustratively rather than as a full causal test.
Explicit methodological statement in the abstract describing the role of the evidence (coded comments and cases) as illustrative.
The article interprets stakeholder and regional positions as different ways of prioritizing the triad's frontiers.
Analysis of the coded public comments and illustrative regional cases used to map stakeholder/regional positions onto the Progress/Sustainability/Equity triad.
The article draws on a previously coded dataset of 10,068 public comments submitted to the 2025 U.S. AI Action Plan.
Empirical resource used in the paper; dataset size explicitly reported as 10,068 coded public comments.
This scoping review adhered to the PRISMA-ScR guidelines and encompassed 29 peer-reviewed empirical studies published from 2020 to 2025.
Methods statement in the paper (explicit methodological description).
The paper identifies five major research gaps and proposes future research directions in intelligent international marketing.
Author-reported outcome of the paper's systematic review and content analysis (2010–2025); descriptive claim about the paper's contributions.
Large language models are routinely used as automated evaluators (to review code, moderate content, or score outputs), often with many items passing through one conversation.
Background/introductory claim in the paper describing common practice; not an experimental result but contextual motivation.
Position of biased turns does not matter: five biased turns placed anywhere in a 50-turn history produce the same shift.
Follow-up experiment manipulating the positions of biased turns within 50-turn histories and observing equivalent bias magnitudes.
Bias does not grow with context length: 5 prior turns and 50 produce the same shift (Spearman |r| < 0.01; OLS slope p = 0.80).
Correlation and OLS analysis of bias magnitude versus context-length (number of prior turns) reported in the experiments.
We conducted 75,898 API calls to 11 models from 4 providers (OpenAI, Anthropic, Google, and four open-source models).
Descriptive statement of the experimental scope reported in the paper: total number of API calls and models/providers tested.
Agentic payments are distinct from traditional automated systems because they emphasise autonomy, contextual reasoning and adaptability.
Conceptual distinction asserted in the abstract (comparative analysis between agentic payments and traditional automated systems).
The paper examines operational logic, defining features and emerging use cases of agentic payments across retail, e-commerce and decentralised finance.
Stated scope in the abstract; analysis and case-study-driven review across specified sectors (retail, e-commerce, DeFi). No sample sizes reported.
Agentic payments refer to transactions initiated and completed by AI agents without direct human intervention.
Explicit definitional statement in the abstract (conceptual definition provided by the authors).
This inverse scaling does not appear on single-threshold metrics common in LLM forecasting benchmarks.
Comparative evaluation reported in the paper showing that single-threshold (binary) scoring metrics do not exhibit the inverse-scaling pattern observed with tail-inclusive distributional metrics (specific metrics and calculations not given in excerpt).
Domain knowledge does not reliably rescue calibration.
Experiments reported in the paper where domain-knowledge interventions (procedures or prompts incorporating domain knowledge) were applied and did not consistently improve forecast calibration (details not provided in excerpt).
Çalışmada yapay zekâ göstergesi olarak yapay zekâ patent sayıları (AI patent counts) kullanılmıştır.
Metodolojik açıklama: bağımlı değişken olarak AI patent sayıları kullanımı; veri: G8 ülkeleri + Türkiye, 2010-2020.
Following PRISMA 2020 guidelines, searches across Google Scholar, Web of Science, Scopus, ScienceDirect, and CNKI yielded 1,562 initial records, of which 21 studies published between 2019 and 2026 met inclusion criteria.
Methodological description of the systematic literature review reported in the paper: initial records = 1,562; included studies = 21; publication years 2019–2026.
Small and medium-sized enterprises (SMEs) constitute over 98.5% of businesses in many economies including China.
Descriptive statistic reported in the paper's background/intro; source of the statistic not specified within the summary provided.
This study analyzes developments through April 2026.
Explicit timeframe statement in the paper's summary/introduction.
Results remain robust across checks.
Robustness checks reported by the authors (unspecified in abstract) that do not overturn the main findings.
China's 14th Five Year Plan (FYP) is used as a quasi-natural experiment / strategic policy shock to study effects of AI washing.
Research design leverages the FYP announcement as an exogenous policy shock in a difference-in-differences framework (design claim; no sample size in abstract).
AI washing is identified as the residual between AI narrative intensity and patent output.
Constructed a firm-level AI washing proxy by regressing AI narrative intensity on patent output and using the residual; described as the study's measurement approach (no sample size reported in the abstract).
The paper's contribution is an evaluation and benchmark paradigm (discipline stability / trace-based evaluation), not a new optimizer or a universal claim about MARL.
Author statement in the abstract/summary clarifying the contribution is methodological (evaluation/benchmark) rather than proposing a new optimizer or making universal claims about multi-agent RL.
The convergence properties of the explore-then-exploit pricing pipeline can be characterized via a fluid-limit ordinary differential equation (ODE) analysis.
Analytical method used in the paper: fluid-limit ODE analysis applied to the multi-firm explore-then-exploit model to study convergence.
Firms following an explore-then-exploit pipeline randomize prices during an initial exploration phase, then estimate demand from their own historical data and set prices myopically thereafter; the estimation relies on a misspecified, monopoly-style model that omits competitors' prices.
Model specification and assumptions described in the paper (methodological setup).
A four-dimensional Flexibility Index is developed to assess reallocation authority, forecast cycles, AI integration, and transparency.
Methods section: construction of an index with four dimensions (reallocation authority, forecast cycles, AI integration, transparency).
The analysis draws on Form 10-K filings from Microsoft, Johnson & Johnson, Procter & Gamble, and ExxonMobil (2019–2023), alongside public sector data from the Open Budget Survey 2023, the OECD Budget Practices Database, and U.S. GAO oversight reports.
Methods/data section listing data sources and firm sample (four named firms, 2019–2023) and public datasets.
The paper constructs estimators for the own-adoption, spillover, and total effects and an inference procedure that allows for spatial dependence.
Presentation of concrete estimators and an inference procedure in the paper; the inference approach explicitly accommodates spatial dependence (methodological contribution).
Spillover effects are learned from never-treated units and evaluated for treated cohorts under the exposure distribution they face.
Methodological procedure in the paper: estimation of spillover effects using never-treated units as the source of variation, then applying those estimates to treated cohorts based on their observed exposure distributions.
Identification uses a prespecified summary of spillover exposure and parallel trends comparisons among units with the same exposure at the baseline and target dates.
Identification strategy articulated in the paper: assumption of a prespecified exposure summary and use of parallel trends comparisons conditional on equal exposure profiles at baseline and event dates.
For each treated cohort and event time, the framework separates the effect of own adoption, the spillover effect generated by other adopters, and the total effect under the realized rollout.
Analytical decomposition provided in the paper that defines separate estimands for (i) own-adoption effect, (ii) spillover effect from other adopters, and (iii) total realized effect for cohorts and event times.
The paper develops a difference-in-differences framework for staggered policy adoption when units can be affected by other units' adoption.
Theoretical development in the paper: presentation of a DID framework that explicitly allows units to be affected by other units' adoption (methodological derivation and formal description).
Sources were selected purposively through explicit inclusion and exclusion criteria tied to conceptual relevance, scholarly quality, and direct contribution to framework building; higher-order categories were retained only after iterative comparison across the four literature streams.
Author-reported sampling and analytic procedure for the integrative review.
Methodologically, the paper uses a structured integrative review combined with interpretive theory synthesis to connect literature on RegTech, sanctions compliance, institutional voids, supply chain governance, and algorithmic accountability.
Explicit methodological description in the paper (authors' stated approach).