Evidence (8974 claims)
Search and filter individual claims pulled from the papers. Looking for a specific finding ("what's the effect on wages?"), you're in the right place. Want to compare whole outcome categories against each other instead? Use the Evidence Explorer.
The board below groups claims two ways: by broad theme (nine paper-level topics) and by outcome category (the 34 claim-level outcomes that the Explorer and Syntheses also use).
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Nine broad, paper-level topics. Click one to filter the claims below.
Adoption
10085 claims
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Productivity
8974 claims
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Governance
8062 claims
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Human-AI Collaboration
7749 claims
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Org Design
5057 claims
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Innovation
4896 claims
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Labor Markets
4088 claims
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Skills & Training
3372 claims
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Inequality
2377 claims
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Claims by outcome category
Counts by direction of finding. These are the same 34 outcome categories the Explorer compares and the Syntheses are written for. A linked row has a published synthesis.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 882 | 244 | 117 | 1097 | 2424 |
| Governance & Regulation | 1010 | 469 | 229 | 135 | 1875 |
| Organizational Efficiency | 977 | 235 | 149 | 90 | 1462 |
| Technology Adoption Rate | 781 | 299 | 143 | 128 | 1362 |
| Research Productivity | 506 | 155 | 74 | 363 | 1110 |
| Output Quality | 555 | 219 | 71 | 70 | 915 |
| Decision Quality | 395 | 200 | 95 | 54 | 751 |
| Firm Productivity | 523 | 67 | 101 | 27 | 724 |
| AI Safety & Ethics | 262 | 309 | 75 | 36 | 688 |
| Market Structure | 195 | 201 | 135 | 30 | 566 |
| Task Allocation | 248 | 77 | 96 | 38 | 464 |
| Innovation Output | 300 | 34 | 55 | 20 | 411 |
| Skill Acquisition | 207 | 75 | 65 | 21 | 368 |
| Employment Level | 138 | 67 | 119 | 24 | 350 |
| Fiscal & Macroeconomic | 156 | 80 | 53 | 33 | 329 |
| Task Completion Time | 211 | 38 | 13 | 16 | 280 |
| Firm Revenue | 183 | 52 | 29 | 5 | 270 |
| Consumer Welfare | 131 | 77 | 48 | 13 | 269 |
| Inequality Measures | 50 | 141 | 54 | 9 | 254 |
| Worker Satisfaction | 104 | 85 | 25 | 13 | 227 |
| Error Rate | 87 | 112 | 11 | 5 | 215 |
| Automation Exposure | 69 | 69 | 37 | 20 | 198 |
| Wages & Compensation | 102 | 49 | 31 | 11 | 193 |
| Team Performance | 115 | 30 | 30 | 11 | 187 |
| Regulatory Compliance | 88 | 74 | 17 | 7 | 186 |
| Training Effectiveness | 109 | 22 | 14 | 21 | 168 |
| Developer Productivity | 116 | 21 | 15 | 8 | 161 |
| Job Displacement | 12 | 92 | 26 | 1 | 131 |
| Hiring & Recruitment | 57 | 12 | 9 | 5 | 83 |
| Skill Obsolescence | 6 | 59 | 10 | 2 | 77 |
| Social Protection | 43 | 17 | 8 | 2 | 70 |
| Creative Output | 35 | 21 | 9 | 4 | 70 |
| Labor Share of Income | 18 | 23 | 17 | 1 | 59 |
| Worker Turnover | 15 | 16 | — | 4 | 35 |
| Industry | — | — | — | 1 | 1 |
Productivity
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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).
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 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.
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.
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.
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.
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).
Extending existing behavioral frameworks (e.g., TAM, JD–R, Organizational Trust) to the AI-augmented workplace constitutes a theoretical contribution of the paper.
Theoretical elaboration and integration presented in the paper; contribution characterized as an extension of pre-existing models to AI contexts (no quantitative validation described in the summary).
The paper proposes a five-phase strategic roadmap for phased organizational implementation that integrates HRM practice redesign, psychological support systems, and evidence-based governance mechanisms.
Prescriptive/strategic proposal based on the paper's theoretical synthesis and applied recommendations (roadmap described in the paper; summary contains no implementation trial data).
The paper develops a comprehensive, multi-dimensional organizational psychology framework for preparing the U.S. workforce for AI integration composed of six interdependent dimensions: human–AI symbiosis, trust and transparency, job redesign, AI-enabled recruitment and selection, learning and adaptation, and ethical AI governance.
Conceptual framework derived from theoretical integration (TAM, Human–AI Symbiosis Theory, JD–R Model, Organizational Trust Theory) and review of AI–HRM literature; framework construction is a theoretical contribution of the paper (no empirical validation reported in the summary).
Grid-Scale Battery Energy Storage Systems (GS-BESS) play a crucial role in modern power grids, addressing challenges related to integrating renewable energy sources (RESs), load balancing, peak shaving, voltage support, load shifting, frequency regulation, emergency response, and enhancing system stability.
Synthesis of prior literature reported in this systematic review (methodology: literature review following PRISMA guidelines). The excerpt does not specify the number or identity of primary studies summarized for this claim.
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.
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 weeder was equipped with a Raspberry Pi microcontroller and a camera module to detect crops and weeds in real-time, enabling autonomous operation.
Design description in the paper: hardware integration of Raspberry Pi and camera module for real-time detection (method: system design and implementation). No sample size or quantitative test data reported for detection accuracy in the provided summary.
AI adoption in Slovakia increased across all enterprise size classes between 2021 and 2024.
Analysis of harmonised Eurostat enterprise-level adoption indicators for 2021–2024 using descriptive statistics and dynamics-of-change methods, disaggregated by enterprise size class. (Sample: enterprises in Slovakia as reported in Eurostat; exact n not specified in the paper summary.)
Economic performance (presumably baseline economic indicators) has a positive effect on growth across all quantiles, with the effect strengthening at upper-tail quantiles (τ = 0.75–0.90).
MMQR results reported in the paper indicating positive coefficients for the economic performance variable at all quantiles, with larger coefficients/greater significance at τ = 0.75–0.90.
AI is often touted for its potential to revolutionize productivity.
Authors' observation about prevailing claims in public, industry, and academic discourse (qualitative observation; the excerpt does not cite specific sources).
The authors propose 'thick entertainment' as a framework for evaluating AI-generated cultural content — one that considers entertainment's role in meaning-making, identity formation, and social connection rather than simply minimizing harm.
Explicit conceptual proposal put forward by the authors in the paper (normative/framework contribution).
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 study contributes to theory by developing a human-grounded decision analytics perspective and to practice by providing practical advice to executives and analytics leaders.
Author-stated contributions based on the conceptual framework and practical recommendations included in the paper. No practitioner evaluation or citation analysis provided.
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).
The study reframes AI as an augmentation mechanism rather than a substitute for managerial judgment and extends organizational decision theory to account for socio-technical decision systems.
Theoretical contribution asserted by the paper based on its literature synthesis and conceptual development (claim about extension of theory rather than empirical test).
The paper develops an integrative conceptual framework that explains how human judgment, algorithmic intelligence, and organizational context interact to shape decision quality and organizational outcomes.
Author-constructed conceptual framework based on synthesized literature across decision sciences, management, and information systems (framework described as output of the meta-analysis; no empirical validation reported in abstract).
Digital–real integration and New Quality Productive Forces exhibit a significant bidirectional positive relationship (each variable positively and significantly promotes the other).
Empirical results from the GS3SLS spatial simultaneous equations model applied to the 30-province panel (2011–2022); paper reports statistically significant positive coefficients in both directions.
This research introduces the RL-FRB/US model which integrates the FRB/US macroeconomic model and a Proximal Policy Optimization (PPO) reinforcement learning agent with an active enhancement of a relocation mechanism for fiscal policy optimization.
Methodological description in the paper: construction of a hybrid model combining the FRB/US structural macro model with a PPO RL algorithm and an added 'active enhancement of relocation' mechanism for fiscal policy decision-making.
Curated (human-authored) Skills substantially improve agent task success on average (+16.2 percentage points).
Aggregate result reported over the SkillsBench benchmark: comparison of pass rates between baseline (no Skills) and curated-Skills conditions across the benchmark. SkillsBench comprises 86 tasks across 11 domains; evaluations used 7 agent–model configurations and 7,308 execution trajectories to compute pass rates and deltas.
Common AI applications in accounting include transaction automation, invoice processing, reconciliations, fraud detection, anomaly detection, automated financial reporting, and predictive forecasting.
Descriptive listing drawn from academic and industry sources/case studies summarized in the paper.
The positive AI → executive pay relationship is robust to endogeneity controls, including instrumental variable approaches, and to multiple robustness checks.
Instrumental variable analyses and a battery of robustness checks reported in the paper applied to the same A-share firm panel and baseline specifications; IV strategy and robustness test details provided in the methods section.