Evidence (2320 claims)
Adoption
5227 claims
Productivity
4503 claims
Governance
4100 claims
Human-AI Collaboration
3062 claims
Labor Markets
2480 claims
Innovation
2320 claims
Org Design
2305 claims
Skills & Training
1920 claims
Inequality
1311 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 373 | 105 | 59 | 439 | 984 |
| Governance & Regulation | 366 | 172 | 115 | 55 | 718 |
| Research Productivity | 237 | 95 | 34 | 294 | 664 |
| Organizational Efficiency | 364 | 82 | 62 | 34 | 545 |
| Technology Adoption Rate | 293 | 118 | 66 | 30 | 511 |
| Firm Productivity | 274 | 33 | 68 | 10 | 390 |
| AI Safety & Ethics | 117 | 178 | 44 | 24 | 365 |
| Output Quality | 231 | 61 | 23 | 25 | 340 |
| Market Structure | 107 | 123 | 85 | 14 | 334 |
| Decision Quality | 158 | 68 | 33 | 17 | 279 |
| Fiscal & Macroeconomic | 75 | 52 | 32 | 21 | 187 |
| Employment Level | 70 | 32 | 74 | 8 | 186 |
| Skill Acquisition | 88 | 31 | 38 | 9 | 166 |
| Firm Revenue | 96 | 34 | 22 | — | 152 |
| Innovation Output | 105 | 12 | 21 | 11 | 150 |
| Consumer Welfare | 68 | 29 | 35 | 7 | 139 |
| Regulatory Compliance | 52 | 61 | 13 | 3 | 129 |
| Inequality Measures | 24 | 68 | 31 | 4 | 127 |
| Task Allocation | 71 | 10 | 29 | 6 | 116 |
| Worker Satisfaction | 46 | 38 | 12 | 9 | 105 |
| Error Rate | 42 | 47 | 6 | — | 95 |
| Training Effectiveness | 55 | 12 | 11 | 16 | 94 |
| Task Completion Time | 76 | 5 | 4 | 2 | 87 |
| Wages & Compensation | 46 | 13 | 19 | 5 | 83 |
| Team Performance | 44 | 9 | 15 | 7 | 76 |
| Hiring & Recruitment | 39 | 4 | 6 | 3 | 52 |
| Automation Exposure | 18 | 16 | 9 | 5 | 48 |
| Job Displacement | 5 | 29 | 12 | — | 46 |
| Social Protection | 19 | 8 | 6 | 1 | 34 |
| Developer Productivity | 27 | 2 | 3 | 1 | 33 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
| Creative Output | 15 | 5 | 3 | 1 | 24 |
| Skill Obsolescence | 3 | 18 | 2 | — | 23 |
| Labor Share of Income | 8 | 4 | 9 | — | 21 |
Innovation
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Introduces a new millisecond-resolution dataset of wireless channel and traffic-condition measurements from an operational 5G deployment.
Paper describes collection of operational 5G telemetry at millisecond sampling resolution; dataset is presented as a novel domain addition to TSFM pretraining corpora. Exact number of records/sessions not specified in the provided summary.
Under pathological label heterogeneity (mutually exclusive local labels) FederatedFactory restores CIFAR-10 classification accuracy from a collapsed baseline of 11.36% to 90.57%.
Empirical experiment reported on CIFAR-10 configured as a pathological heterogeneity stress test; paper reports baseline collapsed accuracy (11.36%) and FederatedFactory result (90.57%). (Specific sample sizes / client counts not provided in the summary.)
A single communication round of generative-module exchange suffices for clients to synthesize class-balanced datasets locally and align their training data.
Paper reports a single exchange of generative modules across clients (one communication round) and uses that to synthesize a globally class-balanced training set at each client; experiments (CIFAR-10, MedMNIST, ISIC2019) are run under this one-round regime.
All 13 surveyed generative systems report addressing syntactic validity (Layer 1).
For each of the 13 systems the review reports syntactic/parse/compile checks or token-level validity tests under Layer 1 in the systematic application of the evaluation framework.
Economists and researchers should measure organizational mediators (governance, mentoring practices, learning processes) alongside AI adoption and use empirical designs such as difference-in-differences with phased rollouts, randomized mentoring/training interventions, matched employer–employee panels, and IV exploiting exogenous shocks to innovation backing to identify causal effects.
Methodological recommendations and proposed empirical designs contained in the paper; no implementation or empirical results reported.
The integrated framework links multi-level outcomes: micro (individual skills, task performance), meso (team coordination, workflows), and macro (organizational strategy, innovation, productivity) effects to adaptive structuration processes and affordance actualization.
Framework specification and theoretical mapping across levels in the conceptual paper; no empirical validation or sample.
The paper develops a conceptual framework that integrates Adaptive Structuration Theory (AST) and Affordance Actualization Theory (AAT) to explain how effective human–AI collaboration can be structured within organizations.
Conceptual/theoretical synthesis and literature integration combining AST and AAT streams; no original empirical data or sample reported (theoretical development).
AI methods have improved molecular property prediction, protein structure modelling, ADME/Tox prediction, NLP-based extraction from literature, virtual screening, and generative chemistry, accelerating early-stage tasks.
Compilation of benchmarking results, method-comparison studies, and applied case studies cited in the paper across these specific application areas.
AI has materially improved efficiency, decision-making, and early-stage productivity in drug discovery, especially in hit discovery, property prediction, and protein modelling.
Synthesis of published benchmarking studies and industry case studies reported in the paper (e.g., improvements in virtual screening throughput, property-prediction benchmarks, and protein-structure prediction results such as those from folding competitions and tool evaluations).
Molecule operates a marketplace for decentralized clinical and preclinical assets, focusing on tokenizing drug assets and enabling investors to finance development.
Case-study description based on Molecule's public materials and marketplace listings; demonstrates platform design and transactions rather than long-term outcomes.
VitaDAO is a community-driven organization funding and acquiring IP for longevity-related research, emphasizing open science and community governance.
Detailed case-study description drawing on VitaDAO's public documentation, governance records, and whitepaper materials.
Research agenda priorities include: empirically quantifying the value of digital twins on R&D productivity; studying complementarities between AI tools and tacit sensory knowledge; measuring cultural translation costs; and analyzing market concentration risks from proprietary sensory models.
List of recommended empirical research directions derived from conceptual analysis and gap identification; no primary empirical work conducted within the paper itself.
The collection highlights resolving methodological challenges such as ecological validity, generalization across environments, and integrating domain knowledge rather than purely optimizing benchmarks.
Methodological-focus summary from the collection indicating emphasis on ecological validity, generalization, and domain-knowledge integration across multiple papers.
Early applications focused on automating straightforward, repetitive tasks (e.g., filtering blank camera‑trap images); current work aims for deeper integration with ecological questions.
Historical-arc observation drawn from the collection's examples and classifications of papers (descriptive review of prior vs. current papers in the collection).
The AI–ecology interface is maturing from simple, task‑automation proofs of concept into genuinely interdisciplinary work that advances both AI methods and ecological science.
Synthesis of the paper collection (mix of methodological, empirical, and translational papers) and the paper's summary of trends across those contributions (no single-sample experiment; claim based on cross-paper review).
AI-adopting firms increase R&D expenditures following adoption.
Firm financial data showing higher R&D spending for adopters relative to nonadopters in post-adoption periods using the diff-in-diff framework.
Post-adoption patents by AI adopters receive more citations than those of nonadopters.
Difference-in-differences estimates comparing citation counts per patent before and after AI installation versus nonadopters; patent citation data used as the dependent variable.
Firms that adopt AI subsequently increase patenting relative to nonadopters.
Firm-level analysis using a novel AI adoption measure based on timing of AI product installations and a stacked difference-in-differences design exploiting staggered adoption; dependent variable = firm patent counts (patenting rate). (Sample size and exact time period not specified in the provided text.)
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.
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.
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.
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.
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).
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).
General US employment for prime age workers (age 25–54) is currently high (~80%).
Paper cites a current employment rate of 80% for prime-age workers; likely based on national labor statistics though the exact data source and year are not specified in the excerpt.
The growth effect of AI exhibits industry heterogeneity: high‑tech manufacturing industries benefit more significantly.
Heterogeneity/subgroup regressions on the 2003–2017 Chinese industry panel showing larger estimated AI effects in high‑tech manufacturing sectors.
The positive effect of AI on industry growth increases over time.
Dynamic/DID analysis across the 2003–2017 panel showing that the estimated treatment effect grows larger in later periods.
The industry growth rate of the treatment group (industries with intensive AI application or high AI patent concentration) is significantly higher than that of the control group.
DID comparison between treatment and control industry groups in the China 2003–2017 panel, where treatment is defined by intensive AI application or AI patent concentration.
AI technology innovation has a significant positive impact on economic growth.
Industry panel data for Chinese industries from 2003 to 2017 analyzed using a differences-in-differences (DID) approach; main specification estimates effect of AI-related innovation on economic growth.
There is a significant positive direct relationship between Generative AI (GenAI) adoption and business performance.
PLS-SEM results from the cross-sectional survey (n = 312) showing a statistically significant positive path coefficient from GenAI adoption to business performance.
One-way ANOVA confirmed that observed improvements in yield, water use, WUE, and energy consumption were highly significant.
Statistical validation reported as one-way ANOVA with F and p values for wheat yield (F(1,18)=1335.66, p<0.001), water use (F(1,18)=15228.16, p<0.001), WUE (F(1,18)=13065.49, p<0.001), and energy consumption (F(1,18)=24312.67, p<0.001). Degrees of freedom imply 20 total observations (df between=1, df within=18).
Water-use efficiency (WUE) improved by 109% under AI-assisted irrigation (ANOVA F(1,18) = 13065.49, p < 0.001).
Reported WUE improvement percentage and one-way ANOVA treatment effect for WUE: F(1,18) = 13065.49, p < 0.001 from the field experiments.
AI-assisted irrigation decreased energy consumption by 30% (p < 0.001).
Field experiment results with one-way ANOVA showing treatment effect for energy consumption: F(1,18) = 24312.67, p < 0.001. Percentage change reported in the paper.
AI-assisted irrigation reduced water use by 36% (p < 0.001).
Field experiment results with one-way ANOVA showing treatment effect for water use: F(1,18) = 15228.16, p < 0.001. Percentage change reported directly in the paper.
AI-assisted irrigation increased wheat yield by 35% (p < 0.001).
Field experiment results with one-way ANOVA showing treatment effect for wheat yield: F(1,18) = 1335.66, p < 0.001. Percentage change reported directly in the paper.
State-owned enterprises and high-tech firms with robust digital infrastructure experience the largest productivity and innovation gains from AI adoption, indicating absorptive capacity matters.
Heterogeneity analysis on the same panel data comparing subgroups (state-owned vs. non-state-owned; high-tech vs. others; firms with stronger digital infrastructure), showing larger estimated AI effects in those subgroups.
Adoption of AI strengthens firms' innovation outcomes.
Same panel dataset (A-share-listed design firms, 2014–2023) with AI indicators derived from annual reports and patent texts; regression analyses linking AI indicator to innovation metrics (patent-related measures and/or firm-level innovation proxies referenced in the study).
Integrating AI technologies significantly enhances Total Factor Productivity (TFP) in design-oriented, project-based firms.
Panel regression analysis using firm-level panel data of A-share-listed design-oriented enterprises in China (2014–2023). AI exposure measured via an enterprise-level AI indicator constructed from NLP-based text analysis of annual reports and patents; TFP estimated at the firm level as the dependent variable. Robustness checks (e.g., Propensity Score Matching) reported.
The recommended IS research emphases include hybrid human–AI ensembles, situated validation, design principles for probabilistic systems, and adaptive governance.
Explicitly listed components of the authors' proposed research agenda in the discussion section of the paper, derived from synthesis of reviewed literature and conceptual analysis.
To bridge the misalignment, the paper proposes reorienting IS scholarship from analyzing impacts toward actively shaping the co-evolution of technical capabilities with organizational procedures, societal values, and regulatory institutions.
Authors' proposed research agenda and recommendations derived from the synthesis of the 28 reviewed studies and their socio-technical analysis.
The rapid growth of geospatial data and advances in artificial intelligence (AI) have driven GeoAI’s rise as a key paradigm in urban analytics.
Synthesis from the paper's literature review highlighting trends in data availability and AI capability; evidence likely based on counts of recent publications, reported applications, and domain examples (specific sample size or bibliometric measures not provided in the excerpt).