Evidence (14922 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).
Browse by theme
Nine broad, paper-level topics. Click one to filter the claims below.
Adoption
9047 claims
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Productivity
8066 claims
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Governance
7278 claims
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Human-AI Collaboration
6912 claims
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Org Design
4439 claims
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Innovation
4359 claims
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Labor Markets
3652 claims
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Skills & Training
3018 claims
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Inequality
2160 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 | 795 | 210 | 105 | 955 | 2131 |
| Governance & Regulation | 886 | 414 | 197 | 126 | 1654 |
| Organizational Efficiency | 826 | 204 | 129 | 87 | 1257 |
| Technology Adoption Rate | 681 | 259 | 128 | 110 | 1189 |
| Research Productivity | 464 | 138 | 65 | 349 | 1028 |
| Output Quality | 503 | 196 | 61 | 53 | 813 |
| Decision Quality | 351 | 180 | 84 | 51 | 673 |
| AI Safety & Ethics | 238 | 288 | 71 | 34 | 637 |
| Firm Productivity | 455 | 58 | 92 | 20 | 631 |
| Market Structure | 186 | 172 | 123 | 25 | 511 |
| Task Allocation | 222 | 70 | 76 | 34 | 407 |
| Innovation Output | 238 | 28 | 48 | 18 | 334 |
| Skill Acquisition | 177 | 62 | 62 | 17 | 318 |
| Employment Level | 107 | 57 | 108 | 13 | 287 |
| Fiscal & Macroeconomic | 135 | 72 | 44 | 26 | 284 |
| Firm Revenue | 172 | 50 | 28 | 5 | 256 |
| Consumer Welfare | 121 | 68 | 45 | 12 | 246 |
| Task Completion Time | 183 | 33 | 10 | 13 | 240 |
| Inequality Measures | 45 | 126 | 50 | 6 | 227 |
| Worker Satisfaction | 95 | 74 | 23 | 12 | 204 |
| Error Rate | 77 | 98 | 11 | 4 | 190 |
| Regulatory Compliance | 84 | 73 | 17 | 7 | 181 |
| Automation Exposure | 61 | 61 | 27 | 14 | 166 |
| Training Effectiveness | 98 | 21 | 14 | 19 | 154 |
| Wages & Compensation | 78 | 37 | 25 | 6 | 146 |
| Developer Productivity | 105 | 18 | 14 | 6 | 144 |
| Team Performance | 87 | 17 | 28 | 10 | 143 |
| Job Displacement | 12 | 83 | 23 | 1 | 119 |
| Hiring & Recruitment | 53 | 8 | 8 | 3 | 72 |
| Social Protection | 39 | 17 | 8 | 2 | 66 |
| Creative Output | 32 | 20 | 8 | 3 | 64 |
| Skill Obsolescence | 5 | 50 | 6 | 1 | 62 |
| Labor Share of Income | 17 | 20 | 17 | — | 54 |
| Worker Turnover | 15 | 15 | — | 3 | 33 |
| Industry | — | — | — | 1 | 1 |
Robotics adoption produces stronger regional linkages than traditional greenhouse farming.
Higher indirect and induced impacts (multipliers) identified by the IMPLAN 2022 I–O modeling for robotics-related investments compared with conventional greenhouse investments in the NWI scenarios.
Robotics adoption generates regional economic benefits for Northwest Indiana.
I–O impact estimates (direct, indirect, induced) produced with IMPLAN 2022 for the NWI region as part of Project TRAVERSE, showing positive effects on regional output, income, and employment.
Robotics and automation enhance productivity in greenhouse farming.
Inference from I–O modeling results and study discussion indicating efficiency/productivity gains associated with robotics adoption (IMPLAN 2022-based scenario analysis).
Robotics adoption yields higher multipliers for output, employment, labor income, and value added compared to traditional greenhouse farming.
Input–output (I–O) modeling using IMPLAN 2022 data for Northwest Indiana (NWI); scenario comparison of investments in greenhouse versus robotics sectors estimating direct, indirect, and induced impacts. (No field sample size reported; model-based estimates.)
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.
Continued investment in reskilling and education is essential for aligning workforce capabilities with market demand.
Interpretation and recommendation based on the paper's analysis of skill gaps from industry reports and workforce data; the abstract does not present empirical evaluation of reskilling programs or quantified return on investment.
Talent pools in tier-2 cities will become more significant sources of hires.
Workforce data and industry report analysis indicating geographic dispersion of jobs toward tier-2 cities; abstract omits concrete regional employment figures or sample sizes.
There will be a stronger emphasis on mid-career hires (relative to other career stages).
Findings drawn from industry reports and workforce data analyzed by the authors; the abstract does not specify counts, proportions, or sampling methodology.
Overall hiring in IT and allied digital domains will remain robust through 2026.
Projected hiring trends derived from industry reports and workforce data cited in the paper; abstract provides no numeric projections or sample details.
AI, cloud, and cybersecurity competencies will increasingly influence hiring decisions in the IT sector.
Analysis of industry reports and workforce data highlighting the growing importance of these competencies; no specific quantitative measures provided in the abstract.
There will be accelerated demand for digital and specialised tech roles in India's IT sector by 2026.
Projection and analysis based on industry reports and workforce data (paper states it draws on industry reports and workforce data). Specific datasets, sample sizes, and statistical methods are not specified in the abstract.
In the digital economy, effective use of AI is crucial for maintaining supply chain stability in sports enterprises.
Argument supported by application of systems theory and supply chain management theory and substantiated by the paper's empirical results from the DML analysis of 45 listed Chinese SEs (2012–2023).
Talent attraction is the primary mechanism through which AI affects supply chain stability in sports enterprises.
Mechanism/mediation analysis within the DML framework applied to the 45-firm panel (2012–2023), showing talent attraction mediates the AI → SCS relationship more strongly than other tested channels.
Individuals earn higher wages when their personality traits align with occupational demands.
Wage analyses showing higher pay for individuals whose Photo Big 5 trait profiles match the measured or inferred demands of their occupations, within the MBA LinkedIn sample.
Individuals systematically sort into occupations where their personality traits are valued.
Observed patterns of occupational choice and trait distributions across occupations in the LinkedIn sample, implying systematic sorting of individuals into occupations aligned with their Photo Big 5 profiles.
The Photo Big 5 predicts career advancement.
Analyses in the paper relating Photo Big 5 trait scores to indicators of career advancement (e.g., promotions, seniority) in the LinkedIn sample (n ≈ 96,000).
The Photo Big 5 predicts job transitions.
Analysis linking Photo Big 5 scores to observed job transitions (moves between jobs) among the MBA graduate sample (n ≈ 96,000).
The Photo Big 5 predicts compensation.
Statistical predictive analyses associating Photo Big 5 trait scores with compensation/wages in the LinkedIn sample of MBA graduates (n ≈ 96,000).
The Photo Big 5 predicts job matching.
Predictive analysis in the paper linking Photo Big 5 scores to measures of job matching/occupational fit in the LinkedIn graduate sample (n ≈ 96,000).
The Photo Big 5 predicts school rank.
Predictive analysis relating Photo Big 5 scores to school rank within the same LinkedIn/graduate sample (n ≈ 96,000); implied use of statistical models comparing trait scores to school rank.
The framework and roadmap offer actionable guidance for HRM practitioners, organizational leaders, and U.S. workforce policy stakeholders seeking to leverage AI for sustained competitive advantage.
Applied recommendations produced from the paper's conceptual synthesis; labeled as 'actionable guidance' in the summary (no outcome evaluation or pilot implementation results reported).
Economists have made great progress in explaining how to use AI within existing production functions, who benefits, and why.
Claim based on developments in the economics literature as represented in the reviewed books and related work (literature review/synthesis); method = qualitative synthesis of theoretical and empirical contributions; sample includes the 7 books and referenced economic studies within them.
These works offer valuable insights — AI as cheap prediction, architectural barriers to adoption, data as an economic asset, and implementation challenges.
Synthesis of recurring themes across the seven reviewed books (qualitative content analysis of book arguments and summaries); sample = 7 books.
By analyzing the latest developments in AI applications and BESS technologies, the review provides a comprehensive perspective on their synergistic potential to drive sustainability, cost-effectiveness, and energy systems reliability.
Synthesis claim from the review's analysis of recent literature; the excerpt does not quantify the extent or strength of synergy nor provide aggregated effect sizes.
Advanced dispatch strategies yield benefits including improved economic efficiency, reduced emissions, and enhanced grid resilience.
Synthesis of results reported in the reviewed studies regarding advanced dispatch and control strategies. The excerpt lacks specific experimental designs, case studies, or numerical results.
AI techniques including machine learning (ML), predictive modeling, optimization algorithms, deep learning (DL), and reinforcement learning (RL) improve operational efficiency and control precision in GS-BESS.
Surveyed applications of ML, DL, RL and optimization methods reported across the literature included in the systematic review. The excerpt does not provide counts of studies or quantitative performance improvements.
AI-based intelligent optimization enhances GS-BESS performance, with impacts on techno-economic outcomes, environmental impacts, and policy/regulatory considerations.
Aggregate findings synthesized from reviewed literature examining AI applications to GS-BESS (review methodology: PRISMA). The excerpt does not list individual study methods, sample sizes, or effect magnitudes.
A balance between technological advancement and human capital investment is critical for minimising disruptions and ensuring a smooth transition to AI-driven operations.
Presented as a central conclusion from combining theoretical and empirical findings in the mixed-method study; the summary does not include quantification or sector-specific validation.
Organisations that integrate transparent governance and employee participation into AI adoption strategies experience lower resistance and higher acceptance.
Empirical insight reported by the study based on its theoretical analysis and Scopus-derived evidence; specific case studies are referenced but details (number of organisations, sectors, measures of resistance/acceptance) are not provided in the summary.
AI increases demand for advanced technical skills.
Reported as a main finding based on a mixed-method approach combining theoretical analysis and empirical insights from an analysis of records in the 'AI-driven transformation' Scopus database. (No sample size, statistical tests, or specific metrics provided in the summary.)
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).