Evidence (8066 claims)
Adoption
5586 claims
Productivity
4857 claims
Governance
4381 claims
Human-AI Collaboration
3417 claims
Labor Markets
2685 claims
Innovation
2581 claims
Org Design
2499 claims
Skills & Training
2031 claims
Inequality
1382 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 417 | 113 | 67 | 480 | 1091 |
| Governance & Regulation | 419 | 202 | 124 | 64 | 823 |
| Research Productivity | 261 | 100 | 34 | 303 | 703 |
| Organizational Efficiency | 406 | 96 | 71 | 40 | 616 |
| Technology Adoption Rate | 323 | 128 | 74 | 38 | 568 |
| Firm Productivity | 307 | 38 | 70 | 12 | 432 |
| Output Quality | 260 | 71 | 27 | 29 | 387 |
| AI Safety & Ethics | 118 | 179 | 45 | 24 | 368 |
| Market Structure | 107 | 128 | 85 | 14 | 339 |
| Decision Quality | 177 | 75 | 37 | 19 | 312 |
| Fiscal & Macroeconomic | 89 | 58 | 33 | 22 | 209 |
| Employment Level | 74 | 34 | 78 | 9 | 197 |
| Skill Acquisition | 98 | 36 | 40 | 9 | 183 |
| Innovation Output | 121 | 12 | 24 | 13 | 171 |
| Firm Revenue | 98 | 35 | 24 | — | 157 |
| Consumer Welfare | 73 | 31 | 37 | 7 | 148 |
| Task Allocation | 87 | 16 | 34 | 7 | 144 |
| Inequality Measures | 25 | 76 | 32 | 5 | 138 |
| Regulatory Compliance | 54 | 61 | 13 | 3 | 131 |
| Task Completion Time | 89 | 7 | 4 | 3 | 103 |
| Error Rate | 44 | 51 | 6 | — | 101 |
| Training Effectiveness | 58 | 12 | 12 | 16 | 99 |
| Worker Satisfaction | 47 | 33 | 11 | 7 | 98 |
| Wages & Compensation | 54 | 15 | 20 | 5 | 94 |
| Team Performance | 47 | 12 | 15 | 7 | 82 |
| Automation Exposure | 27 | 26 | 10 | 6 | 72 |
| Job Displacement | 6 | 39 | 13 | — | 58 |
| Hiring & Recruitment | 40 | 4 | 6 | 3 | 53 |
| Developer Productivity | 34 | 4 | 3 | 1 | 42 |
| Social Protection | 22 | 11 | 6 | 2 | 41 |
| Creative Output | 16 | 7 | 5 | 1 | 29 |
| Labor Share of Income | 12 | 6 | 9 | — | 27 |
| Skill Obsolescence | 3 | 20 | 2 | — | 25 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
Adoption of AI can shorten the procurement decision-making cycle by 21.3%.
Field survey data (n=326) analyzed (authors report a 21.3% reduction in procurement decision-making cycle associated with AI adoption); method described as questionnaire surveys and multiple linear regression.
Supplier AI capability positively drives AI adoption in procurement (β = 0.28, p < 0.01).
Same questionnaire survey (n=326) and multiple linear regression analysis; reported coefficient β=0.28 with p<0.01.
Perceived usefulness positively drives AI adoption in procurement (β = 0.32, p < 0.01).
Questionnaire survey of 326 procurement managers/supply chain managers in SMEs (Yangtze River Delta and Pearl River Delta) analyzed using multiple linear regression; reported coefficient β=0.32 with p<0.01.
The paper provides recommendations for designing strategic indicators to drive adoption, foster innovation, and objectively assess whether digital tools are delivering top-line impact.
Descriptive claim about the content of the perspective article (the authors state they provide these recommendations); the excerpt itself summarizes this contribution.
The shift from expert-driven computer-aided drug design (CADD) to semiautonomous AI necessitates a new framework of impact-oriented KPIs.
Stated by the EFMC2 community authors as a normative conclusion in the perspective piece; based on the characterisation of a technological shift rather than on presented empirical tests in the excerpt.
Harnessing AI's potential requires moving beyond measuring technical model performance (e.g., predictive accuracy) to measuring strategic impact.
Authors argue this as a conceptual requirement for realizing AI's benefits in R&D; presented as a recommendation rather than supported by quantified empirical evidence in the excerpt.
Preliminary analyses suggest that 'AI-native' companies may be outpacing traditional peers.
Explicitly stated in the paper as based on preliminary analyses; the excerpt provides no details on the analyses, metrics, or sample sizes.
The broad introduction of AI into the R&D landscape over the last years holds the promise to lift pharmaceutical R&D out of its productivity problem.
Framed as an expectation/promise in the paper; based on recent broad adoption trends of AI in R&D (no specific empirical evaluation or sample size reported in the excerpt).
The visualization preserved human control.
Reported result from the within-subjects experiment (N=32) indicating that using the visualization did not reduce human control/agency in the negotiation process.
In the same within-subjects experiment (N=32), the visualization improved efficiency.
Within-subjects experiment (N=32) reported in the paper; the authors state the visualization improved efficiency (likely measured as time, number of rounds, or steps to reach agreement).
In a within-subjects experiment (N=32), the uncertainty-based visualization improved human outcomes.
Within-subjects user experiment reported in the paper with N=32 participants comparing performance with and without the visualization.
We introduce a novel uncertainty-based visualization driven by Bayesian estimation of agreement probability that shows how the space of mutually acceptable agreements narrows as negotiation progresses, helping users identify promising options.
Design and implementation of a visualization technique described in the paper; the visualization is driven by Bayesian estimation of agreement probability and is presented as a tool to reveal the shrinking feasible agreement space during negotiation.
In this verifiable domain, simple arbitrage strategies generate net profit margins of up to 40%.
Empirical result from the SWE-bench case study comparing arbitrage strategy returns using GPT-5 mini and DeepSeek v3.2 (reported maximum net profit margin = 40%).
Generative AI can autonomously produce novel content, including text, images, models, and scenarios.
General technical/descriptive claim stated in the paper's background/introduction; not an empirically tested claim within the provided excerpt.
Generative AI facilitates the synthesis of structured and unstructured information from diverse sources, enabling managers to explore multiple decision pathways, identify potential risks, and optimize strategic choices.
Descriptive/functional claim made in the paper's introduction and conceptual framing; the empirical component (survey + SEM) is described generally but no specific measures or effect sizes for information synthesis or these capabilities are provided in the excerpt.
Generative AI augments human creativity by producing innovative solutions and scenario-planning alternatives that may not emerge through conventional analytical approaches.
Stated in the conceptual/argumentative portion of the paper; may be supported by survey items but no explicit empirical measure or effect size for creativity is provided in the provided text.
Decision quality and strategic agility positively influence organizational performance.
Reported SEM results from the paper linking the constructs (decision quality and strategic agility) to organizational performance using survey data from senior managers and AI adoption specialists; method = SmartPLS.
Generative AI adoption significantly enhances strategic agility.
Same empirical source as above: survey of senior managers/decision-makers/AI adoption specialists; tested via Structural Equation Modeling (SmartPLS) as reported in the paper.
Generative AI adoption significantly enhances decision quality.
Empirical analysis reported in the paper: survey data collected from senior managers, decision-makers, and AI adoption specialists across multiple industries; relationships assessed using Structural Equation Modeling (SmartPLS). No numeric sample size or effect estimate reported in the provided text.
Human-like presentations increased perceived usefulness and agency in certain tasks.
Experimental manipulation of the human-likeness of AI presentation in the study's three tasks; the abstract reports increased perceived usefulness and agency for human-like presentations in some tasks. No sample sizes, task specifics, or effect magnitudes reported in abstract.
A single dissent within a panel reduced pressure to conform.
Experimental manipulation of within-panel consensus (introducing a single dissent) in the study's three tasks; abstract reports that a single dissent lowered conformity pressure. No numerical data provided in abstract.
Accuracy improved for small panels relative to a single AI.
Reported experimental result from the paper's study: participants completed three tasks and received advice from AI panels; panel size was manipulated (small panels vs single AI). The abstract states this accuracy improvement for small panels. (Sample size and exact tasks not reported in abstract.)
The paper discusses a regulatory framework for token futures markets, providing a theoretical foundation and practical roadmap for the financialization of compute resources.
Policy/regulatory discussion and recommendations included in the paper; draws on comparisons to existing commodity regulation and futures markets.
The paper explores the feasibility of GPU compute futures as an alternative or complement to token futures.
Discussion/feasibility analysis in the paper (conceptual and comparative discussion; not presented as empirical field evidence).
Simulation results show that, under an application-layer demand explosion scenario, token futures can reduce enterprise compute cost volatility by 62%–78%.
Monte Carlo simulation results based on the constructed mean-reverting jump-diffusion stochastic process model; scenario described as 'application-layer demand explosion'. (No numerical sample size reported in the abstract.)
The authors propose a complete design for standardized token futures contracts, including the definition of a Standard Inference Token (SIT), contract specifications, settlement mechanisms, margin systems, and market-maker regimes.
Normative/proposal section of the paper specifying contract design components and market microstructure recommendations.
Tokens consumed by AI inference are evolving into a new type of commodity.
Conceptual/systematic analysis and argumentation presented in the paper (comparisons to established commodities and discussion of commodity attributes).
By enabling developers without initial capital to participate in the digital economy, RSI could unlock the 'latent jobs dividend' in low-income countries and help address local challenges in health, agriculture, and services.
Societal-impact argument in the paper linking the RSI model to potential employment gains and localized solutions; speculative extrapolation, no empirical employment estimates or pilot studies reported.
The RSI model could stimulate innovation in the ecosystem.
Argument based on lowered financial barriers and incentive structures from the paper's theoretical comparative analysis; no empirical measures of innovation provided.
The RSI model aligns stakeholder interests (platforms and developers).
Theoretical argument and incentive-alignment reasoning in the paper's comparative framework; no empirical validation presented.
A comparative analysis in the paper shows that the RSI model lowers entry barriers for developers.
Detailed comparative (theoretical) analysis within the paper contrasting existing models and RSI; no empirical trial, sample, or randomized test reported.
Generative AI platforms (Google AI Studio, OpenAI, Anthropic) provide infrastructures (APIs, models) that are transforming the application development ecosystem.
Statement in paper based on literature review and descriptive framing of current platforms; no empirical sample or quantitative test reported.
Policy should address not only the aftermath of AI labor displacement but also the competitive incentives that drive it.
Normative implication drawn from the model's findings; recommendation in the paper's conclusion based on theoretical results.
Only a Pigouvian automation tax can eliminate the excess automation in the model.
Theoretical welfare analysis demonstrating that a properly set Pigouvian tax that internalizes the demand externality restores the socially optimal level of automation in the model; analytical result, no empirical sample.
The paper proposes a dual-nudge governance architecture leveraging the DHDE to redistribute cross-prefectural flows and reduce economic leakage.
Policy/design proposal presented by the authors as an outcome of the DHDE adaptation and analysis (conceptual/proposed intervention).
The AI-driven decision support system achieves out-of-sample predictive performance of 68% (R^2 = 0.683).
Model performance metric reported in the paper (out-of-sample R^2 value); presumably from held-out validation or cross-validation on the datasets.
The AI-driven decision support system achieves in-sample explanatory power of 81% (R^2 = 0.810).
Model performance metric reported in the paper (in-sample R^2 value); derived from applying the DSS to the supplied datasets.
Financial digital intelligence enhances innovation by strengthening regional industry–university–research collaboration.
Authors report this channel from mechanism/mediation tests using the same empirical sample (5,731 observations, 2015–2022); specific measures of collaboration or identification strategy not provided in excerpt.
Financial digital intelligence enhances innovation by reducing transaction costs.
Mechanism analysis reported by authors on the same panel dataset (5,731 observations, 2015–2022); reduction in transaction costs is presented as a mediating channel (details of measurement/identification not included in excerpt).
Financial digital intelligence enhances innovation by improving corporate information disclosure.
Mechanism analysis reported in paper using same empirical sample (5,731 observations, 2015–2022); authors identify corporate information disclosure as a mediating channel (specific identification strategy not provided in excerpt).
Financial digital intelligence remarkably boosts the innovative development of strategic emerging industries.
Empirical analysis using panel data from 2015–2022 comprising 5,731 observations covering 789 listed companies and 114 prefecture-level cities in China (methods not specified in excerpt; presumably regression analysis on firm/city-level panel).
In production, the system received high satisfaction from both domain experts and developers, with all participants reporting full satisfaction with communication efficiency.
Post-deployment user feedback / satisfaction reports mentioned in paper (no numeric participant count provided).
The automated workflow saved an estimated 979 engineering hours.
Aggregate time-savings estimate reported in paper (derived from per-API time reduction × number of APIs).
The automated workflow reduces per-API development time from approximately 5 hours to under 7 minutes.
Time-per-API comparison reported in paper based on evaluation on spapi (comparison of manual vs automated per-API time).
The automated workflow achieves 93.7% F1 score.
Empirical evaluation on spapi (F1 reported); presumably computed over the evaluated API items/endpoints.
We address this gap through a graph-based workflow optimization approach that progressively replaces manual coordination with LLM-powered services, enabling incremental adoption without disrupting established practices.
Description of proposed method (graph-based workflow + LLM-powered services) and claim of design enabling incremental adoption; supported by subsequent case evaluation.
The work underscores the urgency of tangible actions aimed at closing the AI divide and allowing Africa to actively shape its AI future.
Concluding normative claim in the paper, supported by the paper's synthesis of identified infrastructural and policy barriers and the illustrative ACT tool.
We introduce the Africa AI Compute Tracker (ACT), an interactive map to monitor the availability of AI-ready HPC systems throughout the continent.
Paper reports development and introduction of the ACT tool; the claim is about the authors' own deliverable (an interactive map consolidating HPC availability data).
Sustainable AI adoption requires robust digital foundations through balanced access to compute, data, and the energy that makes it possible (the 'right enablers').
Normative claim grounded in the paper's stated quantitative and qualitative analysis and synthesis of official declarations; presented as a central conceptual conclusion.
Organizational size moderates the adoption–efficiency relationship such that larger firms realize proportionally greater efficiency gains from AI adoption.
Reported moderation effect in the PLS-PM analysis testing organizational size as a moderator of the relationship between AI adoption and recruitment efficiency metrics across sampled organizations.