Evidence (4560 claims)
Adoption
5267 claims
Productivity
4560 claims
Governance
4137 claims
Human-AI Collaboration
3103 claims
Labor Markets
2506 claims
Innovation
2354 claims
Org Design
2340 claims
Skills & Training
1945 claims
Inequality
1322 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 378 | 106 | 59 | 455 | 1007 |
| Governance & Regulation | 379 | 176 | 116 | 58 | 739 |
| Research Productivity | 240 | 96 | 34 | 294 | 668 |
| Organizational Efficiency | 370 | 82 | 63 | 35 | 553 |
| Technology Adoption Rate | 296 | 118 | 66 | 29 | 513 |
| Firm Productivity | 277 | 34 | 68 | 10 | 394 |
| AI Safety & Ethics | 117 | 177 | 44 | 24 | 364 |
| Output Quality | 244 | 61 | 23 | 26 | 354 |
| Market Structure | 107 | 123 | 85 | 14 | 334 |
| Decision Quality | 168 | 74 | 37 | 19 | 301 |
| Fiscal & Macroeconomic | 75 | 52 | 32 | 21 | 187 |
| Employment Level | 70 | 32 | 74 | 8 | 186 |
| Skill Acquisition | 89 | 32 | 39 | 9 | 169 |
| Firm Revenue | 96 | 34 | 22 | — | 152 |
| Innovation Output | 106 | 12 | 21 | 11 | 151 |
| Consumer Welfare | 70 | 30 | 37 | 7 | 144 |
| Regulatory Compliance | 52 | 61 | 13 | 3 | 129 |
| Inequality Measures | 24 | 68 | 31 | 4 | 127 |
| Task Allocation | 75 | 11 | 29 | 6 | 121 |
| Training Effectiveness | 55 | 12 | 12 | 16 | 96 |
| Error Rate | 42 | 48 | 6 | — | 96 |
| Worker Satisfaction | 45 | 32 | 11 | 6 | 94 |
| Task Completion Time | 78 | 5 | 4 | 2 | 89 |
| Wages & Compensation | 46 | 13 | 19 | 5 | 83 |
| Team Performance | 44 | 9 | 15 | 7 | 76 |
| Hiring & Recruitment | 39 | 4 | 6 | 3 | 52 |
| Automation Exposure | 18 | 17 | 9 | 5 | 50 |
| Job Displacement | 5 | 31 | 12 | — | 48 |
| Social Protection | 21 | 10 | 6 | 2 | 39 |
| Developer Productivity | 29 | 3 | 3 | 1 | 36 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
| Skill Obsolescence | 3 | 19 | 2 | — | 24 |
| Creative Output | 15 | 5 | 3 | 1 | 24 |
| Labor Share of Income | 10 | 4 | 9 | — | 23 |
Productivity
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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.
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).
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).
Rigorous user evaluation can help develop systems that allow for effective and responsible human agency in veracity-assessment processes.
Interpretation and conclusion drawn from the study's findings showing differences in user behavior across support types and highlighting design implications for tooling that supports human verification.
User responsibility for assessing veracity is particularly critical in sectors that rely on on-demand, AI-generated data extraction, such as biomedical research and the legal sector.
Framing and motivation in the paper (domain argument citing the high-stakes nature of biomedical and legal information extraction). This is a contextual claim rather than a new empirical result from the study.
LLM explanations enable rapid veracity assessments.
Same controlled user study, where assessment speed (time-to-decision) was measured for the LLM-explanation condition and found to be fast relative to other conditions.
Passage retrieval offers a reasonable compromise between accuracy and speed, with judgments of veracity comparable to using the full source text.
Controlled user study comparing three types of supporting information (full source text, passage retrieval, LLM explanations). Outcome measures reported include veracity-judgment accuracy and time-to-assessment. (Sample size and statistical details not specified in the abstract; see paper for exact n and tests.)
The effects of AI, FinTech, financial evolution, governance quality and economic performance on monetary policy improve across higher quantiles.
Reported MMQR findings in the paper indicating that the estimated influence of these variables on monetary-policy-related outcomes (across quantiles) becomes stronger at higher quantiles.
Financial development shows increasing positive effects on growth at higher quantiles.
MMQR estimation in the paper reporting that the financial development variable's positive effect on growth increases at upper quantiles.
Financial technology (FinTech) shows increasing positive effects on growth at higher quantiles.
MMQR coefficient estimates for the FinTech variable across quantiles reported in the paper, showing rising positive effects toward higher τ values.
Tailoring AI explanations to individual users can improve human–AI team performance and provides insights into how personalization may enhance human-AI collaboration.
Synthesis of experimental findings across the two preregistered tasks: observed interactions between user characteristics and explanation types, and demonstration of complementarity in the geography task, form the basis for this general claim. (This is an inferential conclusion drawn from the experiments; full generalizability depends on task scope and replication.)
In the geography-guessing task, user characteristics interact with explanation types, and these interactions contribute to human–AI complementarity (the joint performance exceeds either alone).
Results from the preregistered geography-guessing experiment showing interaction effects between user characteristics and explanation types that lead to observed complementarity. (Exact effect sizes, statistical significance, and sample size not provided in the excerpt.)
We designed a geography-guessing task in which humans and AI possess complementary strengths.
Task design described in the paper intended to generate complementary error patterns between humans and the AI model (methodological claim based on experimental design). (Details on design specifics and validation not provided in the excerpt.)
AI has the potential to deliver predictive benefits for recruitment and retention.
Aggregated findings from empirical studies in the systematic review and supporting meta-analytic/qualitative evidence across the 85 publications that examine recruitment/retention applications.
The meta-analysis shows a small-to-moderate direct positive relationship between AI use and operational productivity (r = 0.28).
Quantitative meta-analysis reported in the paper; pooled effect size r = 0.28; heterogeneity I^2 = 74% (based on the meta-analytic sample drawn from the reviewed studies).
The study links digital technologies to evolving economic models, offering insights into how nations can leverage digital infrastructures to foster competitiveness, resilience, and sustainable growth.
Claim about the paper's contribution and policy-relevant insights; the abstract does not lay out the specific analytical framework, case comparisons, or empirical backing used to generate these policy prescriptions.