Evidence (6917 claims)
Adoption
8625 claims
Productivity
7686 claims
Governance
6917 claims
Human-AI Collaboration
6574 claims
Org Design
4189 claims
Innovation
4131 claims
Labor Markets
3588 claims
Skills & Training
2985 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 761 | 200 | 101 | 904 | 2020 |
| Governance & Regulation | 829 | 400 | 191 | 122 | 1566 |
| Organizational Efficiency | 784 | 193 | 125 | 84 | 1197 |
| Technology Adoption Rate | 637 | 236 | 124 | 97 | 1103 |
| Research Productivity | 431 | 131 | 58 | 340 | 972 |
| Output Quality | 481 | 183 | 59 | 47 | 770 |
| Decision Quality | 332 | 177 | 82 | 49 | 647 |
| Firm Productivity | 439 | 57 | 88 | 20 | 610 |
| AI Safety & Ethics | 218 | 279 | 66 | 33 | 602 |
| Market Structure | 181 | 170 | 123 | 24 | 503 |
| Task Allocation | 214 | 64 | 72 | 33 | 388 |
| Skill Acquisition | 174 | 62 | 62 | 17 | 315 |
| Innovation Output | 204 | 27 | 45 | 18 | 295 |
| Employment Level | 105 | 54 | 108 | 13 | 282 |
| Fiscal & Macroeconomic | 132 | 69 | 43 | 26 | 277 |
| Consumer Welfare | 117 | 63 | 42 | 11 | 233 |
| Firm Revenue | 154 | 48 | 26 | 3 | 231 |
| Task Completion Time | 173 | 31 | 8 | 12 | 225 |
| Inequality Measures | 44 | 123 | 50 | 6 | 223 |
| Worker Satisfaction | 89 | 65 | 22 | 12 | 188 |
| Error Rate | 71 | 92 | 10 | 2 | 175 |
| Regulatory Compliance | 77 | 69 | 14 | 5 | 165 |
| Automation Exposure | 58 | 56 | 26 | 13 | 156 |
| Training Effectiveness | 96 | 21 | 14 | 19 | 152 |
| Wages & Compensation | 77 | 37 | 25 | 6 | 145 |
| Team Performance | 86 | 17 | 27 | 10 | 141 |
| Developer Productivity | 95 | 17 | 14 | 6 | 133 |
| Job Displacement | 12 | 81 | 21 | 1 | 115 |
| Hiring & Recruitment | 52 | 7 | 8 | 3 | 70 |
| Creative Output | 32 | 20 | 8 | 3 | 64 |
| Skill Obsolescence | 5 | 47 | 6 | 1 | 59 |
| Social Protection | 28 | 16 | 8 | 2 | 54 |
| Labor Share of Income | 17 | 19 | 17 | — | 53 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Governance
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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).
As AI becomes increasingly integrated into higher education, instructors and institutions face urgent questions about its implications for teaching, learning, scholarly practice, and for power, agency, and access.
Framing claim in the paper's introduction supported by literature context and reinforced by the study's analysis of practitioner (faculty) discussions on Reddit indicating concern/uncertainty. (The excerpt does not report survey or quantitative prevalence data on how widespread these concerns are.)
Through thematic content analysis, the study explores faculty perceptions, pedagogical tensions, and imaginative possibilities surrounding AI’s academic role.
Method stated by author: thematic content analysis of subreddit discussions to identify themes relating to faculty perceptions, pedagogical tensions, and imagined futures for AI in academia. (Exact number of themes, coding procedure, and sample size not provided 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).
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.
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.
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.
Exposure to information about the technology produced significant attitudinal change, even when it conflicted with participants' prior disposition or direct experience.
Information-exposure treatment within the same experimental design; attitudinal outcomes measured in the three-wave panel showed statistically significant change following information exposure, including among participants whose prior disposition or direct AI-as-boss experience would predict resistance.
Personal experience with an AI 'boss' affected workers' job performance.
Randomized experiment described in the paper: over 1,500 workers were randomly assigned to task supervision by either an AI or a human 'boss' (task content and valence also randomized), with job performance measured across a three-wave panel.
Selection of human-LLM interaction archetype can influence LLM outputs and decisions.
Findings from the evaluation across clinical diagnostic cases (empirical comparison of archetypes' effects on outputs and decisions). Specific experimental details and sample size are not provided in the abstract.
We evaluate these diverse archetypes across real-world clinical diagnostic cases to examine the potential effects of adopting distinct human-LLM archetypes on LLM outputs and decision outcomes.
Empirical evaluation described in the paper using real-world clinical diagnostic cases. Method: application of archetypes to clinical cases and comparison of resulting LLM outputs and decisions. Sample size and specific case details are not provided in the abstract.
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.
There is little existing knowledge about how the public perceives AI’s labor market impact and how those perceptions affect democratic attitudes and behaviors.
Literature gap claim motivating the study (based on authors' review of prior research; not empirically tested here).
Experts remain divided on whether AI will primarily displace human labor or generate new employment opportunities.
Statement based on prior literature and expert commentary cited in the paper (no new empirical test in this study).
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.
Approximately 35% of gig workers use platforms as primary income sources and have limited alternative opportunities.
Classification of worker role and opportunity measures from labor force surveys and administrative records across the 24 OECD countries; proportion of gig workers identified as relying primarily on platform income.
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.
Most moderators tested in the analyses have a considerable influence on the relationship between AI use and business performance.
Moderator analyses reported in the meta-analysis (unspecified number of moderators) across the sample of reviewed studies (n=85).
Digital transformation reshapes labor markets.
Paper asserts effects on labor markets (skills demand, employment patterns); the abstract lacks details on labor market data, sample sizes, or econometric analyses used to substantiate this claim.
AI, blockchain, and big data analytics affect productivity, investment strategies, labor markets, and regulatory frameworks.
Stated in the paper as impacts analyzed; the abstract does not specify the data, methods, or scope used to measure these impacts.
Digital transformation through artificial intelligence (AI), blockchain technology (BT), and big data (BD) analytics reconfigures economic mechanisms at both micro- and macroeconomic levels.
Paper-level analytic claim referencing impacts of AI, blockchain, and big data; detailed empirical methodology and sample information not described in the abstract.
This mainstream narrative about what AI is and what it can do is in tension with another emerging use case: entertainment.
Authors' conceptual argument contrasting dominant productivity-oriented narratives with observed/emerging entertainment uses; no quantified data in the excerpt.
A consistent finding is that implementation outcomes are determined by institutional conditions rather than algorithmic performance.
Synthesis across the 81 reviewed sources indicating recurring patterns where institutional factors (governance, reimbursement, workforce, regulations) drive implementation success more than raw algorithmic accuracy. Specific studies supporting this pattern are not named in the abstract.
The fast spread of artificial intelligence (AI) in U.S. organizations has radically altered the managerial decision-making process.
Statement based on a conceptual research design and integration of interdisciplinary literature (literature review). No empirical sample or quantitative data reported.
The increasing integration of artificial intelligence (AI) into organizational decision-making has fundamentally reshaped how managers analyze information, evaluate alternatives, and exercise judgment.
Synthesis of interdisciplinary literature presented in this conceptual meta-analysis; no primary empirical sample or quantitative effect sizes reported in the abstract (literature review basis).
In digital tourism, there is both substitution potential (virtual experiences, demand management) and rebound risks that may offset emissions reductions.
Sectoral case synthesized from peer-reviewed studies and reports on digital tourism and travel demand (review-level evidence; no single empirical sample size).
Sustainable infrastructure and energy-transition analyses must account for hydrogen value chains and the substantial energy footprint of digital systems (data centers and AI workloads).
Review of sectoral studies on hydrogen supply chains and studies estimating energy use of data centers and AI workloads (review synthesis; specific lifecycle analyses and energy-use studies referenced in paper).
The convergence of green finance and computing — especially automated ESG assessment — expands monitoring capacity but also amplifies measurement divergence and greenwashing risks.
Review of literature on automated ESG tools, sustainable finance, and computational assessment methods (synthesis of empirical and conceptual studies; no single sample size reported).
AI and digitalization are restructuring labor markets, producing wage polarization and rents, with outcomes mediated by labor-market institutions.
Review of labor-market literature on AI/digitalization effects (aggregate synthesis of empirical studies and theoretical papers; review does not report an aggregated sample size).
AI drives changes in economic growth.
The paper synthesizes theoretical and empirical arguments from the literature about AI's role for economic growth; the review itself does not present new growth accounting or causal estimates.
AI influences income and wage disparity.
Review discussion of research linking technological change and differential wage/income outcomes; no original econometric analysis or dataset presented in this paper.
AI adoption affects productivity levels.
Discussion and synthesis of existing economic literature on AI and productivity included in the review; the paper does not report primary empirical estimates or a quantified effect size.
By 2024Q2 the RL-FRB/US model produced a PCPI of 317.9 versus FRB/US model: 312.3 (reported as evidence of more effective inflation management).
Reported price index (PCPI) simulation outputs for 2024Q2 from the comparative model runs in the paper; the paper interprets the difference as improved inflation management.
Knowledge industries exhibit strong complementarities with AI but also face task-level automation (e.g., routine analysis) that changes job content.
Literature synthesis on AI adoption in knowledge sectors and task-based mapping showing both complementarities and partial task substitution.
Services show mixed effects: routine clerical and customer-service tasks are vulnerable, while personalized, creative, and relational services are less so.
Task-level synthesis of service-sector automation exposure studies and conceptual analysis of task complementarities in relational services.
Manufacturing faces high automation potential for routine production tasks but also opportunities in advanced manufacturing and robotics maintenance.
Cross-sectoral analysis and literature on automation in manufacturing; theoretical task mapping indicating routine task exposure and emergence of maintenance/advanced roles.
Wage polarization is likely: middle-skill wages will be compressed while high-skill wages rise; some low-skill service roles may persist or expand.
Synthesis of skill-biased technological change literature and task substitution/complementarity arguments; paper references empirical patterns of polarization in prior studies.
Firms with better data infrastructure and higher initial IT investment will adopt AI faster, potentially widening performance gaps across firms and industries.
Theory-informed assertion and literature synthesis; no empirical heterogeneity analysis is specified in the abstract.
Complementarity between AI and skilled accountants may raise wages for analytical roles while compressing demand for routine clerical roles, contributing to wage polarization.
Prediction grounded in economic theory and prior literature; the paper does not report direct wage-change estimates in the abstract.
AI will automate routine accounting tasks, reducing demand for low-skill bookkeeping work while increasing demand for higher-skilled roles (data interpretation, advising, oversight), creating occupational reallocation and upskilling needs.
Projection based on task-based labor economics literature and the paper's synthesis; not supported by specific longitudinal labor-market estimates in the abstract.
Treating privacy as non-tradeable (or tightly constrained trade) will change incentives for firms that monetize personal data, affecting the supply of training data for AI and the trajectory of AI development.
Policy-analytic inference drawing on market-incentive logic and descriptive accounts of firms’ data practices; no quantitative modeling of data supply or AI development provided.
Implication for AI economics: scholars should be alert to epistemic capture—funding, institutional incentives, and geopolitical context can shape which AI governance and market theories gain traction.
Analogy and inference from the historical Cold War case study applied to contemporary AI economics; conceptual argument rather than direct empirical test in AI context.
Europe, Japan, and South Korea occupy intermediate positions between China and the United States in terms of AI–robotics integration and actor composition.
Comparative country-level decomposition of patent series and actor-type shares (1980–2019) reported in the paper; metrics for integration and actor composition place these regions between the stronger China pattern and the more market-driven U.S. pattern.