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 |
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).
LLMs are increasingly supporting decision-making across high-stakes domains, requiring critical reflection on the socio-technical factors that shape how humans and LLMs are assigned roles and interact during human-in-the-loop decision-making.
Background/positioning claim supported by cited literature and the authors' motivation for the work (trend observation). Specific empirical support is not detailed in the abstract.
The AI-based Wi‑Fi weeder is an effective, energy-efficient, and economically viable solution for automated weed control and has potential for precision agriculture applications.
Conclusion drawn by the authors based on laboratory Wi‑Fi tests, battery/motor evaluations, field trial metrics (weeding efficiency, field efficiency, useful work coefficient, time/energy ratio) and economic analysis. Specifics on replicates, statistical significance, and broader applicability are not provided in the summary.
The AI-based Wi‑Fi weeder reduces labor dependency.
Inference from autonomous operation capability and economic/profit metrics reported from field trials; no direct measurement of labor hours saved or comparative labor study provided in the summary.
Economic analysis showed an average profit gain of ₹68.5 per hour, demonstrating cost-effectiveness for small and medium-scale farmers.
Economic analysis reported in the paper produced an estimated profit gain of ₹68.5/hour. The underlying assumptions (labor costs, operating costs, scale, crop prices) and sample size/period are not provided in the summary.
The time/energy ratio was 72.1%, indicating efficient energy use.
Reported metric from field evaluation (time/energy ratio = 72.1%). Calculation details and measurement protocol not provided in the summary.
The useful work coefficient was 84.5%.
Value reported from field trials/evaluation metrics in the paper. The summary does not include how the coefficient was computed or the data supporting it.
Field efficiency of the system was 59.68% in field trials.
Field trials reported field efficiency = 59.68%. Details on sample size, field conditions, or calculation method (e.g., theoretical vs. effective field capacity) are not specified in the summary.
Field trials produced a weeding efficiency of 98.07%.
Field trials reported in the paper measured weeding efficiency and reported a value of 98.07%. The summary does not state the number of trials, treated area, crop type, weed species, or statistical variability.
Laboratory tests evaluated Wi‑Fi connectivity and showed effective communication up to 50 m.
Laboratory tests measuring Wi‑Fi connectivity range; summary reports effective operation up to 50 m. Sample size, test conditions (line-of-sight, interference) and measurement protocol not specified in the provided text.
Using a synthetic twin panel design, increased optimism about AI's societal impact raises GenAI use among young women from 13 percent to 33 percent, substantially narrowing the gender divide.
Causal-style analysis employing a synthetic twin panel design applied to the 2023–2024 UK survey data to estimate effect of changing optimism about AI's societal impact on GenAI use among young women; reported increase from 13% to 33%.
Some individual agents profit handsomely even when the population collectively experiences overload or competition.
Empirical distributional results reported in the paper showing that a subset of agents obtain substantially higher individual payoffs/rewards in the experiments.
Under resource scarcity, emergent tribe formation lessens the risk of dangerous system overload.
Empirical observations in the paper showing that agent grouping into opposing tribes reduces overall overload in scarce-resource conditions (supported by the paper's analyses).
Digital and AI technologies offer a pathway to enhanced efficiency, resilience, and competitiveness in maritime logistics.
Synthesis of literature and international reports included in the study's secondary data analysis (sources include Nigerian academic studies, NPA reports, policy documents, UNCTAD).
The integration of digital and artificial intelligence (AI) technologies is fundamentally reshaping global maritime logistics.
Secondary data synthesis of recent academic literature and international trade facilitation reports (e.g., UNCTAD) reported in the study; method: secondary data analysis (no primary data collection reported).
Technological progress has historically contributed to productivity and economic growth.
Asserted in the paper as a historical generalization within the conceptual analysis; no original empirical data or sample provided in this paper to quantify the effect.
The integration of Fuzzy BWM-PROMETHEE II-DEMATEL framework constitutes a novel methodological contribution and provides useful decision support for strategic planning and resource allocation.
Authors' methodological claim in the paper that combining these fuzzy MCDM techniques is novel and yields decision-support outputs; novelty and practical utility asserted but not externally validated in the provided summary.
Addressing High Initial Investment and Supply Chain Integration initially helps accelerate digital readiness and enhance transformation performance.
Inference/recommendation derived from the PROMETHEE II and DEMATEL results that mark these two factors as dominant causal drivers; no reported empirical intervention or longitudinal validation in the provided text.
Fuzzy BWM results highlight Customization, Flexible Production, Human–Machine Collaboration, and Cybersecurity as the most influential practices supporting I4.0 implementation.
Results reported from the paper's Fuzzy BWM analysis informed by literature survey and expert judgments. (Exact number/composition of experts and statistical details not provided in the supplied summary.)
AI tools (e.g., machine learning) can mitigate managers' information processing constraints and thereby help integrate tax planning with core business strategy (i.e., AI mitigates constraints to improve effective tax planning).
Inference from observed positive relationships between AI investment and tax effectiveness, stronger effects in complex/high-status-tax-function firms, and mediation through information quality and capital management in the 2010–2018 U.S. nontechnology firm sample.
AI improves tax effectiveness by enhancing internal capital management.
Mechanism/mediation tests showing AI investment is associated with improved internal capital management, which is associated with increased tax effectiveness.
AI improves tax effectiveness by enhancing internal information quality.
Mechanism/mediation tests in the empirical analysis showing AI investment is associated with higher internal information quality, which in turn is associated with greater tax effectiveness.
The positive association between AI investment and tax effectiveness is concentrated among firms where the tax function holds a higher status.
Moderation analysis in the sample indicating the AI–tax effectiveness effect is stronger when the tax function has higher organizational status.
The positive association between AI investment and tax effectiveness is concentrated among more complex firms.
Subgroup/moderation analyses in the same sample (U.S. nontechnology firms, 2010–2018) showing stronger AI–tax effectiveness relationship for firms classified as more complex.
Investment in AI-related human capital is positively associated with tax effectiveness.
Empirical analysis using a recently developed firm-year measure of investment in AI-related human capital for a broad sample of U.S. nontechnology firms between 2010 and 2018; reported positive association (regression-based).
In those same jurisdictions, hourly compensation for the remaining platform workers increased by 31%.
Post-reform wage analysis using platform transaction records and administrative wage data comparing pre- and post-policy periods in jurisdictions that adopted employee-classification requirements.
Top-decile gig earners achieve premium wages relative to comparable traditional employment.
Earnings distribution analysis from platform transaction records showing top-decile gig-worker wages exceed comparable traditional-employment wages (24-country sample).
AI-driven solutions enhance strategic decision-making in HRM.
Claimed by the authors following their literature synthesis and empirical work with HR professionals across IT firms (methodology described but specific decision-quality measures not provided in the summary).
AI-driven solutions improve accuracy in HR operations.
Stated in the paper based on the same literature review, data analysis, and empirical study with HR professionals from multiple IT companies (no numeric accuracy metrics or sample size provided in the summary).
AI-driven solutions enhance HR operations by improving efficiency.
Reported in the paper as a conclusion drawn from a literature review, data analysis, and an empirical study involving HR professionals from various IT firms (summary does not state sample size or exact measures).
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).
AI Adoption provides companies with opportunities for strategic renewal.
PLS-SEM analysis linking AI Adoption (measured in the survey of 207 entrepreneurial businesses) to strategic renewal/opportunity constructs reported as positive in the paper.
Competitive pressures are a significant positive factor contributing to a firm's decision to adopt AI.
PLS-SEM analysis of survey data from 207 entrepreneurial firms measuring competitive pressure and AI Adoption (paper reports a positive relationship).
Social influences are a significant positive factor contributing to a firm's decision to adopt AI.
PLS-SEM analysis of survey data from 207 entrepreneurial firms measuring social influence and AI Adoption (paper reports a positive relationship).
Facilitating conditions are a significant positive factor contributing to a firm's decision to adopt AI.
PLS-SEM analysis of survey data from 207 entrepreneurial firms measuring facilitating conditions and AI Adoption (paper reports a positive relationship).
AI features increase consumers' purchase intention for electronic products.
Reported relationship tested via SEM between AI feature constructs and the dependent variable 'purchase intention' in the structured questionnaire data (no sample size or statistical details provided in the summary).
AI features improve perceived decision-making support (i.e., ease of decision-making / simplification of product evaluation).
Reported empirical result from SEM analysis of questionnaire data measuring perceived decision-making support as a dependent variable.
AI features positively affect consumer trust.
Empirical finding reported from SEM analysis linking AI features to the dependent variable 'consumer trust' in the questionnaire data (specific effect sizes/p-values not provided in the summary).
AI-enabled features significantly enhance consumer confidence and satisfaction by simplifying product evaluations and increasing perceived usefulness.
Reported empirical result from this study based on a quantitative research design using a structured questionnaire and analyzed with Structural Equation Modeling (SEM). (Sample size and specific statistical values not reported in the summary.)