Evidence (5539 claims)
Adoption
5539 claims
Productivity
4793 claims
Governance
4333 claims
Human-AI Collaboration
3326 claims
Labor Markets
2657 claims
Innovation
2510 claims
Org Design
2469 claims
Skills & Training
2017 claims
Inequality
1378 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 402 | 112 | 67 | 480 | 1076 |
| Governance & Regulation | 402 | 192 | 122 | 62 | 790 |
| Research Productivity | 249 | 98 | 34 | 311 | 697 |
| Organizational Efficiency | 395 | 95 | 70 | 40 | 603 |
| Technology Adoption Rate | 321 | 126 | 73 | 39 | 564 |
| Firm Productivity | 306 | 39 | 70 | 12 | 432 |
| Output Quality | 256 | 66 | 25 | 28 | 375 |
| AI Safety & Ethics | 116 | 177 | 44 | 24 | 363 |
| Market Structure | 107 | 128 | 85 | 14 | 339 |
| Decision Quality | 177 | 76 | 38 | 20 | 315 |
| Fiscal & Macroeconomic | 89 | 58 | 33 | 22 | 209 |
| Employment Level | 77 | 34 | 80 | 9 | 202 |
| Skill Acquisition | 92 | 33 | 40 | 9 | 174 |
| Innovation Output | 120 | 12 | 23 | 12 | 168 |
| Firm Revenue | 98 | 34 | 22 | — | 154 |
| Consumer Welfare | 73 | 31 | 37 | 7 | 148 |
| Task Allocation | 84 | 16 | 33 | 7 | 140 |
| Inequality Measures | 25 | 77 | 32 | 5 | 139 |
| Regulatory Compliance | 54 | 63 | 13 | 3 | 133 |
| Error Rate | 44 | 51 | 6 | — | 101 |
| Task Completion Time | 88 | 5 | 4 | 3 | 100 |
| Training Effectiveness | 58 | 12 | 12 | 16 | 99 |
| Worker Satisfaction | 47 | 32 | 11 | 7 | 97 |
| Wages & Compensation | 53 | 15 | 20 | 5 | 93 |
| Team Performance | 47 | 12 | 15 | 7 | 82 |
| Automation Exposure | 24 | 22 | 9 | 6 | 62 |
| Job Displacement | 6 | 38 | 13 | — | 57 |
| Hiring & Recruitment | 41 | 4 | 6 | 3 | 54 |
| Developer Productivity | 34 | 4 | 3 | 1 | 42 |
| Social Protection | 22 | 10 | 6 | 2 | 40 |
| Creative Output | 16 | 7 | 5 | 1 | 29 |
| Labor Share of Income | 12 | 5 | 9 | — | 26 |
| Skill Obsolescence | 3 | 20 | 2 | — | 25 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
Adoption
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AI adoption shifts inventor composition within firms.
Analyses of inventor-level or inventor-aggregate characteristics before and after AI adoption showing changes in composition, using the staggered diff-in-diff approach.
Overall, AI adoption facilitates both refinement of existing knowledge (exploitation) and exploration of new technological domains (exploration).
Combined evidence: increases in exploitative-patent share (exploitation) together with increases in originality, generality and technological distance (exploration) using the stacked diff-in-diff approach.
Many of the fundamental advantages and challenges studied in distributed computing also arise in LLM teams.
Empirical and/or conceptual analysis reported by the authors mapping distributed computing phenomena to LLM-team behavior (the excerpt states this finding but does not include the experimental details or metrics).
Model transparency received 90% approval but still requires further refinement.
Stakeholder validation reporting a 90% approval rate for model transparency, while the authors note transparency needs additional work. (Summary does not specify transparency criteria or evaluation method.)
Ethical governance received 85% approval but requires further refinement.
Stakeholder validation results showing 85% approval for ethical governance aspects, with the paper noting the need for further refinement. (No details given on stakeholder composition or ethical framework used.)
The transformative potential of AI is not automatic but is contingent upon the presence of digital literacy, contextualized tools, and a supportive ecosystem.
Interpretation and synthesis of empirical findings showing conditional effects and mediators from the questionnaire data; presented as a substantive conclusion in the paper.
Factors identified as relevant to AI emergence/adoption include Technology Adoption Rate (AI1), Government Policies and Regulations (AI2), Labor Market Dynamics (AI3), Technological Advancements (AI4), Corporate Strategies (AI5), and Socio-cultural Factors (AI6).
Author-provided list of factors in the paper; no empirical quantification, weighting, or methodology for selecting these factors is given in the excerpt.
The maturity of an organization's data governance framework influences the success of AI and Big Data in lowering market uncertainty.
Findings from the qualitative case studies and overall analysis highlighting organizational data-governance maturity as a moderating factor (no standardized maturity measure or sample breakdown provided in the summary).
The stringency of the regulatory environment moderates how effectively AI and Big Data reduce market uncertainty.
Moderation identified via the study's analysis and case studies (specific regulatory measures and empirical tests not detailed in the summary).
The effectiveness of AI and Big Data in reducing market uncertainty is contingent upon industry type.
Observed variation across industries in the paper's qualitative case studies and analysis (the summary does not specify which industries or comparative sample sizes).
Technology adoption preferences correlate with structural role: central coordinators prefer predictive analytics while peripheral actors prioritize traceability systems.
Interview data tied to network positions produced reported preferences for types of technologies (predictive analytics vs. traceability systems) associated with different structural roles; analysis based on thematic coding and node-role mapping (sample details not in abstract).
Evidence on apprenticeship reforms indicates a shift toward higher-level qualifications and younger participants, while overall apprenticeship participation has declined.
Synthesis of reform evaluations and comparative studies on apprenticeship systems presented in the paper (summary does not identify which reforms/countries or provide participation statistics).
Participation in adult education and training has increased overall but remains uneven across age groups and skill levels.
Secondary data and comparative evidence cited in the paper showing rising adult learning participation with heterogeneity by age and skill level (no numerical breakdown provided in the summary).
Facilitated access to AI reconfigures startup roles, organizational structures, and decision routines.
Analytic findings from semi-structured interviews pointing to changes in role definitions, reporting lines, and decision-making routines after AI adoption (qualitative evidence; sample size not specified).
AI adoption generates different effects across different occupations.
Summary statement based on analysis of publicly available labor market data (occupational-level heterogeneity asserted but specific datasets, sample sizes, and methods not described).
AI is not an unprecedented disruption; its effects can be situated within established economic frameworks related to automation and task substitution.
Conceptual analysis comparing recent AI developments to historical automation and task-substitution frameworks; empirical grounding claimed via publicly available labor market and productivity data (details not provided).
AI has emerged as a transformative force that influences economic systems, institutional functions, and daily human behaviors.
Stated as an overarching observation in the paper (theoretical/interpretive claim); no empirical methods or sample sizes are reported in the abstract.
Firm learning raises the persistence of the economy's response to shocks but dampens volatility.
Quantitative model experiments: introducing firm learning into the calibrated model increases impulse-response persistence to shocks (higher persistence) while reducing the magnitude/variance of fluctuations (lower volatility) in simulated aggregate variables.
Three developer archetypes are present: Enthusiasts, Pragmatists, and Cautious.
Classification/typology derived from the study's survey data of 147 developers (e.g., cluster analysis or thematic grouping) identifying three distinct groups based on usage patterns, attitudes, and intent.
Improvements in caseworker accuracy level off as chatbot accuracy increases (an "AI underreliance plateau").
Observed pattern in experimental results: incremental gains in caseworker accuracy diminish at higher chatbot accuracies, described by authors as an 'AI underreliance plateau' (specific curves or thresholds not in the excerpt).
The rapid global proliferation of Artificial Intelligence (AI) has created a profound paradox: while promising unprecedented productivity gains, its current trajectory exacerbates labor market polarization, deepens inequality, and threatens to fracture the 20th-century social contract.
Asserted in abstract; no empirical methods, datasets, or sample sizes described in the abstract (presumably supported in paper by literature review/argumentation).
AI’s labor market impacts in the Philippines are not technologically predetermined; outcomes will depend on policy choices related to skills development, governance, social protection, and innovation.
Integrated conceptual framework in the paper linking AI capabilities, occupational structure, and institutional mediation, supported by the scenario analysis which shows divergent outcomes conditional on policy settings.
Observed AI adoption patterns in the Philippines to date are cautious, with limited job loss but growing task reconfiguration and emerging skills gaps.
Firm- and worker-level evidence on AI adoption (surveys/interviews and/or administrative firm adoption data described in the paper) documenting current adoption practices, reported job impacts, task changes, and reported skill shortages.
A significant share of Philippine employment is exposed to generative AI—particularly in service-sector and BPO-related occupations.
Occupational exposure analysis using Philippine labor force data (occupational employment shares and task-content measures) combined with task-level evidence on generative AI capabilities.
The benefits of ERM depend on the maturity of implementation and the extent to which risk management is embedded in organizational culture and daily decision-making, rather than being a formal compliance mechanism alone.
Synthesis of qualitative and quantitative findings across studies in the literature review indicating conditional effects based on implementation maturity and integration; primarily comparative or observational evidence summarized by the authors.
The actions of large employers in an occupation or industry affect local and national wages, employment and output.
Theoretical/empirical claim in the paper; excerpt does not supply empirical methods, identification, or sample sizes demonstrating these effects.
AI shows potential as an adjunct tool in acute GIB management but requires further validation to confirm its clinical utility.
Conclusion synthesizing review findings: high diagnostic metrics and workflow benefits but insufficient evidence on patient outcomes and safety.
AI enhances diagnostic accuracy and workflow efficiency but lacks robust evidence linking it to improved patient outcomes in acute GIB.
Synthesis in the discussion combining reported high diagnostic metrics and time savings with the paucity of studies reporting patient outcomes.
The effects of technology and policy on emissions vary by country due to differences in energy policy, energy market structure, regulatory frameworks, and implementation challenges.
Cross-country comparative analysis across China, the United States, and Germany reported in the paper; heterogeneity attributed to institutional and market differences (details of heterogeneity tests not provided in the summary).
Gender shapes the impact of social protection: program effects are mediated by gender norms and intra-household dynamics, and gender differences in opportunities, constraints, and preferences determine who can participate in and benefit from social protection.
Theoretical and literature-based assertion in the introduction; authors indicate program impacts are mediated by gender norms and household dynamics and will review evidence in the chapter (no specific empirical details in excerpt).
AI reshapes traditional power structures, challenges regulatory frameworks, and redefines global governance mechanisms.
Broad analytic claim supported by comparative policy analysis and qualitative document review; the paper frames this as an overarching conclusion without reporting quantitative indicators or case counts.
The geopolitics of AI constitutes not only a competition for technological supremacy but also a contest over the moral and institutional foundations of global governance.
Theoretical synthesis drawing on international relations theories (realism, liberal institutionalism, constructivism) and comparative policy analysis; presented as an interpretive conclusion rather than empirically quantified.
AI represents a new dimension of geopolitical power that influences how states project authority, regulate innovation, and negotiate global norms.
Argument based on comparative policy analysis and qualitative document review of state and multilateral policy documents (specific documents and number not enumerated in text).
Artificial intelligence (AI) has emerged as one of the most transformative forces shaping the 21st-century international order.
Conceptual claim supported by literature review and theoretical framing in the paper (no empirical sample or quantitative data reported).
Variations in prompt design influenced agents’ performance indicators, including response accuracy, task completion efficiency, coordination coherence, and error rates.
Experimental simulations with systematic variation of prompt designs and quantitative analysis of resulting performance indicators listed above. (Sample size, effect sizes, and statistical tests not specified in the provided excerpt.)
Knowledge democratization through AI may reduce educational inequality but may also exacerbate digital divides and erode universities' social mobility function.
Theoretical and socio-political analysis considering opposing effects; framed as a conditional/mixed outcome without empirical measurement reported in the paper.
AI displacement potential varies substantially across university functions.
Summary finding from the paper's comparative analysis of university functions; the paper provides ranked/percent estimates but does not report empirical sampling or statistical testing.
The impact of AI on supply chain stability in sports enterprises exhibits heterogeneity by enterprise type and profitability status.
Heterogeneity/subgroup analyses within the DML panel estimations (sample of 45 listed SEs, 2012–2023) showing differential AI effects across firm types and across firms with different profitability profiles.
The Photo Big 5 provides predictive power comparable to race, attractiveness, and educational background.
Comparative predictive-performance analyses reported in the paper that evaluate Photo Big 5 against observables such as race, measured attractiveness, and education background within the same sample.
There is significant variation in psychological readiness for AI across generational cohorts, industry sectors, and organizational maturity levels.
Aggregated findings from emerging AI–HRM empirical studies referenced in the paper (no specific study counts or sample sizes provided in the summary).
Harnessing the full potential and lifetime of GS-BESS requires intelligent operational strategies that balance technological performance, economic viability, and environmental sustainability.
Conclusion drawn from the systematic review of existing studies and frameworks (PRISMA-based literature synthesis). Specific empirical studies or quantitative sample sizes supporting trade-off analyses are not provided in the excerpt.
In a 2021 national labor survey, no single task was automated by more than 57% of respondents, compared with a maximum of 52% in the mid-2000s.
National labor survey results (mid-2000s vs 2021) as reported in the paper; survey details and sample size are not included in the excerpt.
The research landscape on MPs is recent, heterogeneous, and rapidly growing, with limited synergies with existing construction datasets.
Synthesis of publication timelines, topic diversity, and cross-references in the included studies; qualitative assessment reported in the paper noting limited integration with existing construction datasets.
Each category of AI trigger presents distinct avenues for value creation alongside significant risks.
Analytical argument in the paper discussing potential benefits and risks per trigger type. No empirical evaluation, case studies, or quantitative evidence reported here.
Generative artificial intelligence (GenAI) adoption is diffusing rapidly but its adoption is strikingly unequal.
Nationally representative UK survey data collected in 2023–2024 reporting adoption rates by subgroup; descriptive analysis of diffusion and disparities by demographic groups.
Labour productivity developments in Slovakia were characterised by substantial short-term volatility during the study period.
Dynamics-of-change analysis of Eurostat labour productivity measures for Slovakia over 2021–2024 (time-series behaviour examined; exact productivity metric and sample size not specified in the summary).
Within the context of Nigeria, the adoption of advanced digital and AI-driven logistics solutions presents both a critical opportunity and a complex challenge for the country's seaports.
Analysis of secondary data sources focusing on Nigeria: academic literature by Nigerian scholars, Nigerian Ports Authority (NPA) performance reports, and policy documents as synthesized in the study.
AI is transforming jobs that are technical in nature.
Asserted in the paper's conceptual discussion of dual impacts; presented without empirical measurement or reported sample data in this paper.
The study clarifies the interplay between perceived usefulness, trust, and ethical design, offering insights into responsible AI implementation to empower consumers.
Authors' reported contribution combining empirical SEM findings (linking perceived usefulness and trust to outcomes) with normative discussion on ethical design; specific empirical mediation/moderation tests not detailed in the summary.
Data maturity, ethical governance of algorithms, and industry type shape business performance in AI-augmented workflows.
Moderator/subgroup analyses and qualitative synthesis across the reviewed studies indicating these contextual factors influence outcomes; based on the 85-publication review.