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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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).
However, the exoplanet workflow is effectively tied with a strong combined-summary baseline, showing that decomposition does not always improve top-line performance.
Reported comparison between the coordinated workflow and a strong combined-summary baseline for exoplanet vetting indicating no meaningful improvement.
The study used established measurement scales to assess AI-driven learning culture, knowledge orchestration, organisational intelligence and innovation performance.
Methods: authors report use of established scales for AIDLC, KO, OI and IP in the questionnaire.
Structured questionnaires were distributed between March and October 2025 to employees involved in innovation, learning and project management roles in Karachi, Lahore and Islamabad.
Methods section description of data collection period, target respondent roles, and cities covered.
Most respondents held undergraduate or postgraduate degrees in computer science, engineering or business-related disciplines.
Sample demographic summary from the survey (N=348).
After screening the data, 348 valid responses were analyzed.
Structured questionnaires distributed March–October 2025 to employees in medium and large IT firms in Karachi, Lahore and Islamabad; screening produced 348 valid responses (sample description in methods).
Using large language models, we measure the AIO level of Chinese listed companies from 2010 to 2023.
Authors report constructing firm-level measures of artificial intelligence orientation (AIO) by applying large language models to corporate texts/disclosures for Chinese listed companies over the 2010–2023 period.
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.
Reported empirical values are transformed through transparent indicators such as relative growth, CAGR, growth multipliers, stock-flow ratios, concentration ratios, and HHI.
Methodological description and application in the paper listing these specific indicators used to summarize public data on AI investment, adoption, robots, compute, and labour-market reallocation.
The study uses a conceptual-empirical quantitative diagnostic design rather than a causal econometric model.
Explicit methodological statement in the paper describing the design choice and rejecting causal econometric modeling in favor of diagnostics using public institutional data and transparent indicators.
The agentic economy is not yet a completed global order, but its transition pressure is measurable enough to require a distinct economic vocabulary, reproducible diagnostics, and future sector-level measurement.
Synthesis of diagnostic indicators (AI investment/adoption trends, robot stock, compute-energy coupling, labour reallocation measures) showing measurable transition pressures; conclusion drawn from the conceptual-empirical diagnostic.
The study investigates the non-linear impact of AI on economic growth in 19 G20 countries (2005–2023) using the Generalized Method of Moments (GMM) with both linear and quadratic models.
Methodological description provided in the paper: panel dataset covering 19 G20 countries over 2005–2023 and estimation via GMM with linear and quadratic specifications.
This study used a three-wave lagged survey design with 381 valid matched employees from knowledge-intensive firms in China.
Methods statement in paper reporting study design and sample composition: three-wave lagged survey and 381 valid matched employee responses from knowledge-intensive Chinese firms.
This study conducts an empirical analysis using data on industrial robots from the International Federation of Robotics (IFR) and panel data from 14 sub-sectors of China's manufacturing industry.
Statement in paper describing data and methods: use of IFR robot data combined with panel data covering 14 manufacturing sub-sectors (panel regression framework implied).
Return forecasts are translated into long–short portfolios to assess economic performance.
Stated evaluation approach: conversion of predicted returns into long–short portfolios for economic/performance assessment.
The analysis is based on 30 market, liquidity, valuation, profitability, technical and risk factors and compares linear models, tree-based machine learning and deep learning architectures (including GRU, LSTM and Transformer) within a rolling-window forecasting framework.
Description of empirical design: use of 30 factor variables and explicit listing of model families (linear, tree-based, GRU, LSTM, Transformer) and use of a rolling-window forecasting setup.
We introduce the weighted evaluation index (WEI), a finance-specific performance metric that integrates prediction accuracy with market adaptability.
Methodological contribution stated in the paper: introduction of a new performance metric called WEI described as integrating accuracy and market adaptability.
We introduce the Diff-RMSE method for nonlinear factor identification.
Methodological contribution stated in the paper: introduction of a new method named 'Diff-RMSE' for identifying nonlinear factors.
The study uses A-share market data from 2013 to 2024 with equity and firm-characteristic data available from databases such as RESSET and CSMAR for more than 5,000 listed firms.
Empirical dataset description in the paper: time period 2013–2024, sources named (RESSET, CSMAR), and statement 'more than 5,000 listed firms'.
We audited 111 million references across 2.5 million papers in arXiv, bioRxiv, SSRN, and PubMed Central.
Direct data collection and audit described in the paper: dataset of 111,000,000 references from 2,500,000 papers across the four named preprint/repository sources.
Future research should test these findings across different institutional contexts, particularly European economies.
Paper's stated limitations and suggestions for future research.
The analysis employs fixed-effects models, U-tests, bootstrap mediation, and patent text similarity analysis.
Methods statement listing econometric and text-analytic techniques used in the paper.
The study uses a sample of 25,204 firm-year observations from Chinese A-share manufacturing companies (2010–2023).
Paper statement of sample and period; descriptive sample construction (firm-year observations = 25,204).
The empirical analysis is based on Chinese A–share listed firms observed from 2012 to 2024 and uses a difference‑in‑differences (DID) identification strategy.
Study description in the paper's methods/abstract specifying sample period (2012–2024), population (Chinese A–share listed firms), and methodology (DID).
These results are robust to alternative model specifications, including different lag lengths and forecast horizons.
Robustness checks reported in the paper: re-estimation of TVP-VAR with alternative lag lengths and forecast horizons producing consistent qualitative results.
The emergence of generative AI is not associated with a uniform increase in financial connectedness.
Empirical TVP-VAR analysis comparing connectedness measures before and after the emergence of generative AI (paper compares connectedness over the sample period and reports no uniform increase).
This study uses daily data from January 2021 to December 2025 to analyze spillover dynamics among AI-related equities, cryptocurrencies, and traditional financial assets within a time-varying parameter vector autoregression (TVP-VAR) framework.
Statement of data frequency and sample period plus description of methodology (TVP-VAR) in the paper; empirical analysis applied to specified asset groups.
Under standard smoothness and finite variance conditions, SGD is minimax optimal for finding stationary points measured by l2-norms, thereby fundamentally precluding any complexity gains for sign-based methods in standard settings.
Theoretical statement based on prior minimax optimality results for SGD under standard smoothness and finite-variance assumptions (as cited/used in the paper). No new experiment; relies on worst-case lower-bound theory.
The boundaries (critical thresholds) separating the tax regimes are derived from the workers' budget constraint.
Analytic derivation in the paper showing that constraints coming from the workers' budget constraint produce critical values of τ_ai and τ_f that determine transitions between the three regimes.
The model features quadratic self-amplification in both AI capability (λ A^2) and financial capital (γ_F K_f^2), coupled through investment flows.
Model specification and equations in the paper showing terms λ A^2 for AI capability growth and γ_F K_f^2 for financial capital growth, with explicit investment flow terms linking AI and financial capital.
The study uses a panel dataset of 35,347 firm-year observations from 2010 to 2023.
Reported sample description in the paper: panel dataset covering 2010–2023 with 35,347 firm-year observations.
Differences in perceived stylistic/aesthetic qualities do not translate into higher monetary valuation (i.e., stylistic preference differences do not increase willingness to pay).
BDM bidding behavior of N = 117 participants combined with rating data showing stylistic differences but no corresponding increases in bids.
There is no statistically significant relationship between perceived aesthetic quality and willingness to pay for LLM outputs.
Online experiment with N = 117 participants who evaluated model outputs, rated aesthetic quality, and submitted monetary bids using a Becker-DeGroot-Marschak (BDM) mechanism; statistical tests reported as not significant.
The study uses panel data of A-share listed energy-intensive firms from 2009 to 2021; measures corporate digital technology integration by counting frequency of digital-technology-related words in annual reports (text analysis); and evaluates low-carbon transformation using the LTFP method.
Methods and data description provided in the paper's abstract/summary: panel of A-share listed firms in energy-intensive industries (2009–2021); text analysis of annual reports for digital technology integration; LTFP method for low-carbon transformation measurement.
This paper focuses on five research questions about the historical pathways, leverage points, trajectory differences, alternative projects, and socio-technical programmes related to current dominant generative AI tools and possible AGI-adjacent development.
Explicit listing of the five research questions in the paper's introduction/aims; statement of scope and focus.
Data analysis combined quantitative analytics with qualitative sentiment analysis, while environmental impact data was collected through IoT sensors measuring energy consumption, waste generation, and carbon footprint metrics.
Methods description specifying mixed quantitative and qualitative analyses and IoT sensor measures.
The authors applied machine-learning models, natural language processing, sentiment scoring, predictive dashboards, and clustering techniques to map customer preferences, purchasing patterns, and green program participation.
Methods description listing analytical techniques used (ML, NLP, sentiment scoring, dashboards, clustering).
Data collection encompassed retail kiosks, shopping apps, home sensors, and wearables over twelve months.
Methods description in the chapter explicitly listing data sources and a twelve-month collection period.
The study employed stratified random sampling across urban shopping centers, suburban retail outlets, and online-to-offline hybrid stores in Nigeria to represent diverse consumer demographics and shopping behaviors.
Methods section description in the chapter stating use of stratified random sampling across specified retail contexts; no numeric sample counts given in the provided text.
Data analysis utilized regression modeling for performance correlations, time-series analysis for predictive maintenance patterns, and thematic analysis for qualitative interviews.
Paper methods: explicit listing of analytic techniques used (regression, time-series, thematic analysis).
Secondary data encompasses sustainability reports, carbon footprint assessments, and operational performance metrics.
Paper methods: explicit listing of secondary data sources (sustainability reports, carbon footprint assessments, operational metrics).
Blockchain transaction records spanning eighteen months across Nigeria were used as primary data.
Paper methods: explicit statement about 18 months of blockchain transaction records across Nigeria.
The study uses IoT sensor data from forty-five facilities.
Paper methods: explicit statement that IoT sensor data were collected from 45 facilities.
Primary data collection includes structured interviews with supply chain managers.
Paper methods section: primary data described as including structured interviews with supply chain managers (number of interviewees not specified).
The study uses mixed methods involving case studies from twelve multinational companies across the manufacturing, logistics, and retail sectors.
Paper statement of methods: explicit mention of mixed methods and case studies from 12 multinational companies across the three sectors.
The study constructs a tripartite evolutionary game framework composed of government regulators, leading computing power incumbents, and downstream AI innovators to analyze strategic interactions and derive evolutionarily stable strategies.
Methodological claim documented in the paper describing the model structure and analytic approach (method: formal model specification and ESS derivation).
Technologically advanced firms operating in hypercompetitive markets gain little from AI adoption, reflecting diminishing returns from capability saturation.
Cluster-specific results from the multidimensional heterogeneity analysis indicating small or negligible TFP effects for clusters identified as technologically advanced and highly competitive.
Existing literature has extensively examined general AI adoption but limited empirical evidence exists on how more autonomous, agent-like systems contribute to economic outcomes.
Literature review / positioning statement in the introduction of the paper.
The study uses panel data from the World Bank (World Development Indicators and Enterprise Surveys) and OECD AI indicators for the period 2015 to 2024.
Explicit statement of data sources and time period in the paper's methods section.
An AI Adoption Index was constructed using indicators of AI investment, business adoption, and innovation output as a proxy for diffusion of advanced AI capabilities (including agentic features).
Methodological description in the paper: index synthesis from OECD AI indicators and other measures of investment/adoption/innovation; exact index components and weighting described in methods (sample size not applicable).