Evidence (1920 claims)
Adoption
5227 claims
Productivity
4503 claims
Governance
4100 claims
Human-AI Collaboration
3062 claims
Labor Markets
2480 claims
Innovation
2320 claims
Org Design
2305 claims
Skills & Training
1920 claims
Inequality
1311 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 373 | 105 | 59 | 439 | 984 |
| Governance & Regulation | 366 | 172 | 115 | 55 | 718 |
| Research Productivity | 237 | 95 | 34 | 294 | 664 |
| Organizational Efficiency | 364 | 82 | 62 | 34 | 545 |
| Technology Adoption Rate | 293 | 118 | 66 | 30 | 511 |
| Firm Productivity | 274 | 33 | 68 | 10 | 390 |
| AI Safety & Ethics | 117 | 178 | 44 | 24 | 365 |
| Output Quality | 231 | 61 | 23 | 25 | 340 |
| Market Structure | 107 | 123 | 85 | 14 | 334 |
| Decision Quality | 158 | 68 | 33 | 17 | 279 |
| Fiscal & Macroeconomic | 75 | 52 | 32 | 21 | 187 |
| Employment Level | 70 | 32 | 74 | 8 | 186 |
| Skill Acquisition | 88 | 31 | 38 | 9 | 166 |
| Firm Revenue | 96 | 34 | 22 | — | 152 |
| Innovation Output | 105 | 12 | 21 | 11 | 150 |
| Consumer Welfare | 68 | 29 | 35 | 7 | 139 |
| Regulatory Compliance | 52 | 61 | 13 | 3 | 129 |
| Inequality Measures | 24 | 68 | 31 | 4 | 127 |
| Task Allocation | 71 | 10 | 29 | 6 | 116 |
| Worker Satisfaction | 46 | 38 | 12 | 9 | 105 |
| Error Rate | 42 | 47 | 6 | — | 95 |
| Training Effectiveness | 55 | 12 | 11 | 16 | 94 |
| Task Completion Time | 76 | 5 | 4 | 2 | 87 |
| Wages & Compensation | 46 | 13 | 19 | 5 | 83 |
| Team Performance | 44 | 9 | 15 | 7 | 76 |
| Hiring & Recruitment | 39 | 4 | 6 | 3 | 52 |
| Automation Exposure | 18 | 16 | 9 | 5 | 48 |
| Job Displacement | 5 | 29 | 12 | — | 46 |
| Social Protection | 19 | 8 | 6 | 1 | 34 |
| Developer Productivity | 27 | 2 | 3 | 1 | 33 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
| Creative Output | 15 | 5 | 3 | 1 | 24 |
| Skill Obsolescence | 3 | 18 | 2 | — | 23 |
| Labor Share of Income | 8 | 4 | 9 | — | 21 |
Skills Training
Remove filter
AI (including machine learning, generative AI, and NLP) is reshaping biomedical research and pharmaceutical R&D by creating distinct adoption archetypes within large pharmaceutical companies.
Editorial / conceptual synthesis using qualitative analysis and archetype classification based on cross-industry observations and illustrative examples; no systematic measurement or sample size reported.
Emerging technologies (AI, digital twins, computational rheology) can compress high-dimensional sensory/rheological spaces into actionable models, enabling faster iteration in R&D and altering how firms value R&D inputs.
Theoretical projection and literature-based argument about technological capabilities; illustrative scenarios offered; no empirical trials or measured productivity changes reported.
Occupational competence varies from 43.2% in high-tech to 9.7% in the public sector.
Sectoral analysis derived from the study's dataset (LinkedIn job adverts and/or Indeed salary information, 2022–2024) where 'occupational competence' was operationalized and measured across sectors to produce the cited percentages.
Programming experience cannot be fully substituted by Gemini.
Comparative results from the experimental conditions: although participants could use Gemini (free or paid), the observed benefit of programming experience on code security remained significant, indicating Gemini did not replicate or replace the effect of experience in the sample of 159 developers.
Human capital is no longer defined solely by formal education or accumulated experience; it increasingly takes the form of a multidimensional system in which cognitive abilities, digital competencies, social and communicative skills, and ethical awareness interact and reinforce one another.
Result of the paper's synthesis combining systemic analysis and comparative assessment of international practices; conceptual/qualitative evidence rather than quantified measurement across populations.
Ongoing digital transformation and the widespread adoption of artificial intelligence are reshaping the formation, structure, and practical use of human capital in modern economies.
Paper's core analytical conclusion based on systemic analysis, comparative assessment of international practices, and analytical generalization of organizational learning models; no primary quantitative sample size or experimental data reported.
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.
Organizations must reconceptualize AI implementation as a fundamental redesign of work systems requiring new competencies, governance structures, and attention to human cognitive limits.
Normative recommendation based on the paper's synthesis of organizational adaptation literature and reported negative outcomes of current AI deployments; no empirical test of this prescriptive claim provided in the excerpt.
Generative AI is not purely a job-destroying technology but a task-transforming force that reshapes skill requirements and occupational structures.
Synthesis of empirical studies and systematic reviews reported in the paper showing task reallocation, skill shifts, and occupational restructuring (study details not specified in excerpt).
There is a decline in mid‑skilled occupations, such as operations and management (O&M), accompanied by an increase in high‑skilled jobs that require skills in artificial intelligence (AI), data analytics, and engineering.
Reported pattern from the systematic literature review and recent studies/reports cited by the paper noting occupational declines in mid‑skilled O&M roles and rises in high‑skill technical roles; the summary does not specify which studies or their sample sizes.
With renewable energy (RE), particularly the scale of solar power expansion in India, the job scenario is changing.
Stated conclusion from the paper's systematic literature review drawing on recent reports and studies about RE/solar expansion in India; no primary data or sample size reported in the summary.
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.
These findings have important implications for understanding how political ideology may influence party members’ perspectives on AI in relation to labor markets, job losses, and regulation in OECD countries.
Interpretive implication drawn by the authors from their reported results (synthesis rather than a new empirical claim).
Political ideology shapes party members’ positions on AI education and training programs intended to assist workers in environments where AI is more prevalent.
Inferred finding stated by the authors based on content analysis of party member statements; the excerpt indicates the authors examined positions on AI education/training but does not provide specific results or metrics.
Political ideology significantly affects party members’ views on the need for government regulations to protect workers from labor market disruptions caused by AI.
Reported finding from the paper's content analysis of media interviews, speeches, and debates by party members in OECD countries (2016–2025); details on coding categories, inter-rater reliability, and quantitative significance measures are not included in the excerpt.
Political ideology significantly affects party members’ concerns regarding AI-related job losses.
Result reported by the authors based on content-analysis of party member comments and statements across OECD countries (2016–2025); specific analytic procedures, coding scheme, sample size, and statistical tests are not provided in the excerpt.
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).
Artificial intelligence (AI) has redefined what it means to perform, achieve and succeed.
Stated as a conceptual claim in the paper's purpose/introduction; supported by theoretical argument and literature synthesis (leadership theory, emotional intelligence research, AI ethics). No empirical sample, experiments, or quantitative data provided in the paper.
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).
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).
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.
AI alters job structures, workflow patterns, and human roles in decision-making processes.
Thematic content analysis of recent accredited journal literature as part of the qualitative library research (sources not enumerated).
AI is fundamentally transforming the workplace by creating new opportunities, intensifying challenges, and redefining professional skills.
Qualitative library research: systematic documentation and thematic content analysis of recent accredited journal sources (number of sources not specified).
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.)
AI is not simply replacing tasks or only requiring more AI developer skills; it may be transforming workforce skill requirements to favor human attributes that enhance collaboration with intelligent systems.
Synthesis of the three empirical findings above (higher prevalence of complementary non-technical skills in AI roles, wage premiums for those skills, and spillover increases in complementary-skill demand alongside decreases in substitutable skills) based on analysis of ~30 million job postings (2018–2024).
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).
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.
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.
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.
In the sentiment-analysis task, individual differences in user characteristics shape how users respond to AI explanations.
Results from the preregistered sentiment-analysis experiment reported in the paper indicating interaction effects between user characteristics and explanation types. (Exact sample size and statistical details not provided in the excerpt.)
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.
AI readiness emerged as both an opportunity and a source of uncertainty for workers.
Analyses of survey responses about AI readiness and perceptions showed mixed patterns—some respondents view AI competence as enabling optimism/advancement while others report uncertainty—based on the 5,000-worker and 501-employer data.
Smaller models augmented with curated Skills can match the performance of larger models without Skills (model–skill tradeoff).
Cross-size performance comparisons reported across seven agent–model configurations showing that certain smaller model + curated-Skill pairings achieve pass rates comparable to larger model baselines without Skills. Analysis uses the SkillsBench trajectories (7,308 total) to support tradeoff claims.