Evidence (4781 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
9875 claims
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Productivity
8807 claims
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Governance
7870 claims
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Human-AI Collaboration
7560 claims
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Org Design
4892 claims
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Innovation
4781 claims
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Labor Markets
4004 claims
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Skills & Training
3308 claims
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Inequality
2332 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 | 870 | 233 | 116 | 1066 | 2363 |
| Governance & Regulation | 976 | 451 | 218 | 133 | 1809 |
| Organizational Efficiency | 949 | 224 | 144 | 88 | 1416 |
| Technology Adoption Rate | 764 | 287 | 141 | 122 | 1325 |
| Research Productivity | 501 | 152 | 74 | 362 | 1101 |
| Output Quality | 542 | 216 | 69 | 69 | 896 |
| Decision Quality | 387 | 198 | 94 | 54 | 740 |
| Firm Productivity | 513 | 67 | 101 | 27 | 714 |
| AI Safety & Ethics | 249 | 303 | 73 | 36 | 667 |
| Market Structure | 190 | 192 | 134 | 27 | 548 |
| Task Allocation | 243 | 77 | 91 | 36 | 452 |
| Innovation Output | 291 | 33 | 55 | 20 | 401 |
| Skill Acquisition | 206 | 72 | 65 | 21 | 364 |
| Employment Level | 133 | 63 | 115 | 22 | 335 |
| Fiscal & Macroeconomic | 153 | 79 | 52 | 32 | 323 |
| Task Completion Time | 206 | 37 | 12 | 15 | 272 |
| Firm Revenue | 179 | 52 | 29 | 5 | 266 |
| Consumer Welfare | 130 | 76 | 47 | 13 | 266 |
| Inequality Measures | 48 | 137 | 51 | 6 | 242 |
| Worker Satisfaction | 101 | 81 | 25 | 13 | 220 |
| Error Rate | 84 | 110 | 11 | 5 | 210 |
| Wages & Compensation | 98 | 47 | 30 | 10 | 185 |
| Regulatory Compliance | 88 | 73 | 17 | 7 | 185 |
| Automation Exposure | 66 | 64 | 33 | 16 | 182 |
| Team Performance | 105 | 29 | 30 | 11 | 176 |
| Training Effectiveness | 109 | 22 | 14 | 21 | 168 |
| Developer Productivity | 114 | 21 | 14 | 8 | 158 |
| Job Displacement | 12 | 90 | 24 | 1 | 127 |
| Hiring & Recruitment | 57 | 9 | 9 | 5 | 80 |
| Skill Obsolescence | 6 | 56 | 9 | 1 | 72 |
| Social Protection | 43 | 17 | 8 | 2 | 70 |
| Creative Output | 35 | 21 | 9 | 4 | 70 |
| Labor Share of Income | 18 | 21 | 17 | 1 | 57 |
| Worker Turnover | 15 | 16 | — | 4 | 35 |
| Industry | — | — | — | 1 | 1 |
Innovation
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The positive effect of NQPF on supply chain efficiency is stronger in state-owned enterprises (SOEs) than in non-state firms.
Heterogeneity analysis by ownership type performed on the 2012–2022 A-share panel data showing larger coefficients/effects for SOEs.
NQPF affects supply chain efficiency via multiple mechanisms: technological innovation, management restructuring, and digital transformation.
Mechanism analysis using mediating-effect models and supplementary tests on the 2012–2022 A-share panel data identifying these specific mediators.
Population growth shows a significant positive effect on GDP growth across the countries in the sample.
Population growth entered as a regressor and reported significant positive association with GDP growth in the panel models (OLS, FE, Difference and System GMM); exact magnitude and significance levels not provided in the summary.
Government expenditure shows a significant positive effect on GDP growth across the countries in the sample.
Positive and statistically significant coefficients on government expenditure reported in the applied econometric models (OLS, FE, Difference and System GMM); government spending included as a control macroeconomic determinant (sample/time not specified).
Gross fixed capital formation (GFCF) has a significant positive effect on GDP growth across the countries in the sample.
Estimated positive and statistically significant coefficients on GFCF in the panel regressions (OLS, FE, Difference and System GMM); GFCF included as a macroeconomic determinant in the model (sample size/time period not provided).
The study presents a complementary linking theory that connects sustainability practice and reasoning to inform future discourse on sustainable e-commerce growth strategy in the dual carbon phase.
Theoretical/conceptual contribution described in the paper; this is a conceptual claim rather than an empirical finding.
Alongside concerns, AI proliferation may introduce new, positive affordances for military decision-making organizations.
Normative/analytical claim by the author based on argumentation; no empirical demonstration, experimental results, or case-study evidence is provided in the excerpt.
Military AI adoption is incentivized by competitive pressures and expanding national security needs.
Author assertion based on qualitative argumentation and literature-informed reasoning; no empirical study, dataset, or sample size reported in the text.
AI innovation produces significant positive spatial spillover effects on employment in neighboring cities, promoting expansion of their employment scale.
Spatial analysis (spatial econometric tests) on the 268 Chinese cities (2010–2023) indicating positive spillovers to neighboring cities' employment.
Temporally, AI innovation affects urban employment through both immediate and lagged effects, with the magnitude of these effects diminishing over time.
Temporal (lag) analysis in extended tests on the 268-city panel covering 2010–2023.
Governmental digital attention positively moderates the relationship between AI innovation and urban employment.
Moderation analysis using measures of governmental digital attention and AI innovation in the 268-city panel (2010–2023).
AI innovation indirectly promotes employment growth by enhancing urban economic density (mediation effect).
Mechanism (mediation) analysis conducted on the 268-city panel (2010–2023) showing economic density as an intermediary channel.
The positive employment effect of AI innovation is stronger in southern cities than in others.
Geographic heterogeneity analysis across 268 Chinese cities (2010–2023).
The positive employment effect of AI innovation is more pronounced in the tertiary sector.
Heterogeneity/sectoral analysis using the panel of 268 Chinese cities (2010–2023).
The positive employment effect of AI innovation is more pronounced in the secondary sector.
Heterogeneity/sectoral analysis using the same panel of 268 Chinese cities (2010–2023).
Overall, AI innovation has a positive effect on urban employment.
Empirical testing on a panel of 268 Chinese cities over the period 2010–2023 (integrated theoretical and empirical analysis).
Digital transformation enables manufacturing enterprises to navigate volatile and uncertain market environments, thereby achieving sustainable development.
Theoretical framing (institutional theory, enterprise resilience durability theory, strategic ecology) supported by empirical findings from the 2013–2022 Chinese A-share manufacturing sample linking DT, peer effects, and ER.
Regional peer effects are stronger for enterprises located in central cities.
Heterogeneity analysis by city centrality (location in central cities vs. non-central cities) in the 2013–2022 Chinese A-share manufacturing panel.
Regional peer effects are stronger for enterprises occupying central positions within interlocking directorate networks (IDNs).
Heterogeneity analysis by firm centrality within IDNs using the 2013–2022 A-share manufacturing dataset.
Industrial peer effects are stronger in highly competitive industries.
Heterogeneity analysis across industry competition levels in the 2013–2022 Chinese A-share manufacturing panel.
Industrial peer effects are more pronounced for enterprises in non-central positions within interlocking directorate networks (IDNs).
Heterogeneity analysis (subgroup analysis) by firm centrality within IDNs using the 2013–2022 A-share manufacturing sample.
Forward-looking, robust regulation is necessary to ensure the digital world remains a safe place for young people and to fully protect their rights, privacy, and well-being.
Prescriptive recommendation from the book's conclusions based on its comparative analysis of law, policy, and practice; the excerpt provides no empirical study or quantified analysis to directly validate this necessity.
Across the European Union, most youth use the internet daily and encounter digital environments from an early age.
Claim in the text; likely grounded in EU-wide survey data (e.g., Eurostat, EU Kids Online) measuring frequency of internet use among youth, but the excerpt gives no specific source, method, or sample size.
Children and young people are growing up more connected than any previous generation.
Asserted in the book summary; likely based on cross-cohort and population-level data on device ownership and internet access (e.g., national/EU surveys), but no specific study, dataset, method, or sample size is specified in the provided excerpt.
Federal funding for automation in specialty crops has been a focus of increased funding by both the US Department of Agriculture and the National Science Foundation, providing a path for innovators to produce automation and technology for nursery crops.
Statement in the paper about increased federal funding priorities (USDA and NSF); no specific program names, funding amounts, or timelines provided in the excerpt.
The percent of all tasks automated has increased approximately 15% over a 15-year period ending in 2021.
Comparison reported from a national labor survey (mid-2000s to 2021); exact survey methodology and sample size are not provided in the excerpt.
Use of the H-2A visa program has increased tremendously for the green industry in the past decade to help stop-gap the labor crisis.
Paper's statement about trend in H-2A program usage for the green industry; specific administrative data, years, or magnitudes not provided in the excerpt.
The main conclusions are reliable after various robustness tests.
Paper reports multiple robustness checks (unspecified in abstract) applied to the DID estimates using the 2003–2017 industry panel, which reportedly do not overturn the main findings.
The results support the 'capital‑technology complementarity' theory: AI combined with capital investment yields higher marginal returns, especially in capital‑intensive industries.
Empirical finding of larger marginal AI effects in capital‑intensive industries via interaction terms on the 2003–2017 Chinese industry panel; interpreted as evidence for capital‑technology complementarity.
Synergy between AI and R&D investment amplifies the growth effect of AI.
Interaction regressions in DID framework on the 2003–2017 panel showing that industries with higher R&D investment exhibit larger AI-related growth effects (positive AI × R&D interaction).
AI promotes economic growth through efficiency improvements and by driving innovation.
Mechanism tests reported in the paper (mediation/auxiliary analyses) using the 2003–2017 industry panel that link AI measures to productivity/efficiency indicators and innovation outcomes, which in turn relate to growth.
Capital‑intensive industries benefit more significantly from AI, with a higher marginal effect.
Heterogeneity analysis and interaction tests in the DID framework on the 2003–2017 panel; interaction of AI measures with capital intensity shows larger marginal effects for capital‑intensive industries.
Knowledge‑intensive service industries gain more significant growth benefits from AI than other services.
Subsample/heterogeneity analysis of service industries within the China 2003–2017 panel showing stronger AI effects for knowledge‑intensive services.
GenAI functions not just as a tool for cost reduction but as a strategic lever for growth, primarily through enhanced innovation, implying a need for sustained investment in technological infrastructure and workforce skills.
Interpretation of empirical findings: stronger mediating role of product innovation and positive direct effect on business performance; managerial/policy implications drawn in discussion section based on these results.
Technological competence, top management support, and competitive pressure are key drivers of GenAI adoption.
TOE/RBV-based predictor variables were tested in the PLS-SEM model; these constructs showed significant positive path coefficients to GenAI adoption in the survey data (n = 312).
Product innovation is a significant partial mediator of the relationship between GenAI adoption and business performance and exhibits a stronger mediating effect than operational efficiency.
Comparative mediation analysis in PLS-SEM reported significant indirect effects for both mediators, with the indirect effect size (or relative path coefficients) through product innovation larger than through operational efficiency (n = 312 survey responses).
Operational efficiency is a significant partial mediator of the relationship between GenAI adoption and business performance.
Mediation tests within the PLS-SEM framework using survey data (n = 312) showed significant indirect effect of GenAI adoption on business performance via operational efficiency, while a direct effect remained (partial mediation).
Integrating AI into irrigation substantially enhances productivity, economic returns, and sustainability outcomes for wheat production under semiarid conditions in Iraq.
Synthesis of field experiment results (yield, water use, energy, WUE), statistical significance (ANOVA results), economic evaluation (NPV, BCR, IRR), and sustainability indices reported in the paper.
Sensitivity analyses confirmed that investment profitability remained robust under adverse scenarios, including increased capital costs and reduced wheat prices.
Reported sensitivity analyses in the paper stating robustness of profitability under adverse scenarios; specific scenarios mentioned include increased capital costs and reduced wheat prices (details of scenario ranges not provided in the excerpt).
Sustainability indicators improved: Sustainability Efficiency Index (SEI) increased from 0.25 to 0.51.
Reported sustainability indices computed in the study showing SEI values before and after AI-assisted irrigation implementation.
Economic evaluation showed strong feasibility of AI-assisted irrigation: NPV = USD 18,121, BCR = 2.81, IRR = 30%, payback period = 3.65 years.
Cost–benefit analysis, net present value (NPV), benefit–cost ratio (BCR), and internal rate of return (IRR) reported in the paper as calculated from the field experiment outcomes and economic modeling.
To enable large-scale adoption of Material Passports, cohesive adoption strategies, unified standards, stakeholder collaboration, clear responsibilities, and regulatory support are needed.
Practical recommendations synthesized from the included studies and authors' discussion summarizing common requirements and enablers identified across the literature.
Digital tools have potential to address MP implementation challenges by improving cohesion, enabling dynamic updates, and enhancing interoperability.
Reported propositions and case examples in the literature included in the review suggesting digital solutions (e.g., digital platforms, DPPs, DBLs) as approaches to improve data cohesion, dynamic updating, and interoperability.
Material Passports (MPs) are crucial for bridging the data gap hindering CE adoption in the AEC industry.
Thematic findings across the included studies emphasizing MPs' role in providing material and product data; synthesis in the paper concluding MPs as a key instrument to address data scarcity.
AI should be framed as augmentation rather than substitution, implying organizations need to invest in workforce upskilling in AI literacy to prevent harmful displacement and to enable designers to act as 'co-pilots' or 'AI curators'.
Interpretive and normative conclusion based on observed productivity/innovation benefits and literature/theoretical discussion; no firm-level employment displacement metrics reported in the study.
Managers should prioritize Generative Design and Predictive Analytics and adopt a 'Data-First' strategy (digitize historical assets and build digital infrastructure) to realize AI-enabled efficiency and innovation gains in design projects.
Managerial recommendations derived from the empirical findings linking AI to productivity and innovation gains; prescriptive guidance rather than empirically tested interventions within the paper.
AI functions as a bridge between project management efficiency and creativity in design projects, enabling automation of routine workflows and freeing designers to focus on higher-value creative tasks.
Interpretation based on empirical findings (AI positively associated with TFP and innovation) and mechanism discussion; supported by text-analysis results and conceptual framing in the paper (no granular project-level workflow logs presented).
The research provides insight into Resource-Based View (RBV) and Dynamic Capabilities (DC) theory by showing that AI Adoption contributes to competitive advantage and sustainability-related firm performance.
Theoretical integration and empirical findings reported in the paper linking AI Adoption (measured in the 207-firm survey) to outcomes interpreted through RBV and DC frameworks.
AI Adoption creates a significant competitive advantage for companies, improving their success in creating entrepreneurial and technology-based firms.
Reported PLS-SEM findings from the 207-firm survey linking AI Adoption to competitive advantage and firm-level entrepreneurial/technology-based success (paper frames this within RBV and dynamic capabilities theory).
AI Adoption enables sustainable business models (holistic sustainability) and is associated with increased economic, environmental, and social performance.
PLS-SEM results from the 207-firm survey reportedly showing positive relationships between AI Adoption and measures of sustainable business models / economic, environmental, and social performance (paper links AI Adoption to holistic sustainability outcomes).