Evidence (7395 claims)
Adoption
7395 claims
Productivity
6507 claims
Governance
5921 claims
Human-AI Collaboration
5192 claims
Org Design
3497 claims
Innovation
3492 claims
Labor Markets
3231 claims
Skills & Training
2608 claims
Inequality
1842 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 609 | 159 | 77 | 738 | 1617 |
| Governance & Regulation | 671 | 334 | 160 | 99 | 1285 |
| Organizational Efficiency | 626 | 147 | 105 | 70 | 955 |
| Technology Adoption Rate | 502 | 176 | 98 | 78 | 861 |
| Research Productivity | 349 | 109 | 48 | 322 | 838 |
| Output Quality | 391 | 121 | 45 | 40 | 597 |
| Firm Productivity | 385 | 46 | 85 | 17 | 539 |
| Decision Quality | 277 | 145 | 63 | 34 | 526 |
| AI Safety & Ethics | 189 | 244 | 59 | 30 | 526 |
| Market Structure | 152 | 154 | 109 | 20 | 440 |
| Task Allocation | 158 | 50 | 56 | 26 | 295 |
| Innovation Output | 178 | 23 | 38 | 17 | 257 |
| Skill Acquisition | 137 | 52 | 50 | 13 | 252 |
| Fiscal & Macroeconomic | 120 | 64 | 38 | 23 | 252 |
| Employment Level | 93 | 46 | 96 | 12 | 249 |
| Firm Revenue | 130 | 43 | 26 | 3 | 202 |
| Consumer Welfare | 99 | 51 | 40 | 11 | 201 |
| Inequality Measures | 36 | 106 | 40 | 6 | 188 |
| Task Completion Time | 134 | 18 | 6 | 5 | 163 |
| Worker Satisfaction | 79 | 54 | 16 | 11 | 160 |
| Error Rate | 64 | 79 | 8 | 1 | 152 |
| Regulatory Compliance | 69 | 66 | 14 | 3 | 152 |
| Training Effectiveness | 82 | 16 | 13 | 18 | 131 |
| Wages & Compensation | 70 | 25 | 22 | 6 | 123 |
| Team Performance | 74 | 16 | 21 | 9 | 121 |
| Automation Exposure | 41 | 48 | 19 | 9 | 120 |
| Job Displacement | 11 | 71 | 16 | 1 | 99 |
| Developer Productivity | 71 | 14 | 9 | 3 | 98 |
| Hiring & Recruitment | 49 | 7 | 8 | 3 | 67 |
| Social Protection | 26 | 14 | 8 | 2 | 50 |
| Creative Output | 26 | 14 | 6 | 2 | 49 |
| Skill Obsolescence | 5 | 37 | 5 | 1 | 48 |
| Labor Share of Income | 12 | 13 | 12 | — | 37 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Adoption
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Upskilling, reskilling, cross-skilling, and learning initiatives are necessary mechanisms for organizations to prepare their workforce for GenAI-driven changes.
Derived from literature recherche and analysis of individual tasks across occupations within Erasmus+ projects, plus practitioner discussions; no sample sizes or outcome metrics specified.
Generative AI (GenAI) models are growing rapidly, changing job roles, and revolutionizing entire industries.
Stated by the authors based on a literature recherche (scope and search strategy not specified in abstract). No quantitative sample size or bibliometric details provided.
Policy priorities should include enforceable AI governance, life-cycle carbon accounting across hydrogen supply chains, and targeted SME capability policies to realize conditional synergies between digitalization and green transition.
Policy recommendations derived from the review of empirical and institutional literature (authorial proposal based on synthesized evidence; not an empirical test).
Digital tools can accelerate green innovation and emissions reductions when coupled with credible standards, auditability, clean power, and workforce capability building.
Synthesis of peer-reviewed research and authoritative institutional reports (review article); conditional-synergy thesis based on multiple empirical and policy studies cited in the review (no single primary sample size reported).
Evaluating employee performance has become increasingly important in order to align workforce capabilities with evolving technological demands.
Framed as an emphasis/argument in the study's rationale; not accompanied here by reported quantitative measures.
Artificial Intelligence (AI) has emerged as a powerful force shaping the modern economy, particularly within the Information Technology (IT) sector.
Stated as background context in the paper's introduction; supported by literature-style assertion rather than presented empirical results in this excerpt.
These findings highlight research opportunities for machine learning applications in finance and for the development of sentiment-based corporate disclosure analytics.
Interpretation by the authors based on identified gaps in the 42-study review (e.g., underused corporate-report sentiment, limited labeled data, geographic concentration, few deep-learning/end-to-end approaches).
Regression-based and other supervised learning approaches remain dominant.
Aggregated reporting from the 42-study review showing a prevalence of regression and supervised ML methods in the literature sample.
The reviewed studies rely on feature-engineered sentiment indices derived from lexicons or sentence-level classification.
Review synthesis noting frequent use of lexicon-based sentiment scoring and sentence-level classification to produce engineered sentiment features across the sampled studies.
Most studies focus on the U.S. stock market.
Findings from the review of 42 studies indicating a majority of the reviewed works concentrate on U.S. markets (geographic coding/synthesis across studies reported by the authors).
Machine learning methods have been widely used to predict stock prices using technical indicators and sentiment features, mostly extracted from social media and news.
Systematic review of the literature summarized in the paper (corpus of 42 studies published 2014–2025) reporting that many reviewed studies use ML to predict stock prices and that sentiment inputs commonly come from social media and news sources.
Partial adoption of artificial agents can still improve aggregate outcomes.
Mixed-population analysis and simulation results reported in the paper showing aggregate welfare improvements under partial adoption scenarios.
Unilateral entry of artificial-agent technology is feasible: adopters are not structurally penalized.
Analysis of mixed populations of adopters and non-adopters presented in the paper (mixed-population evolutionary analysis and simulations); exact parameter sweeps and sample sizes are not provided in the abstract.
Artificial agents can shift the learning dynamics to favour coordination outcomes.
Findings from evolutionary dynamics analysis and reinforcement learning experiments demonstrating changes in learning trajectories and equilibrium selection when artificial agents are present.
Introducing artificial agents that use globally observable signals increases coordination among agents.
Experimental results reported in the paper using reinforcement learning experiments and evolutionary-dynamics simulations with artificial agents that observe global signals (details of experimental setup and sample sizes are not specified in the abstract).
AI adoption raises ethical controversies that require public policy action to promote social equity and economic opportunity.
Synthesis of debates on AI ethics and policy from the literature; the paper provides normative recommendations rather than empirical measurement of policy impact.
Labor market regulatory frameworks should be updated in response to AI adoption.
Narrative review of regulatory issues and recommendations drawn from existing literature and policy debates; no empirical testing of specific regulatory interventions included.
Social safety net programs need changes to respond to AI-related labor market disruption.
Policy analysis and synthesis of prior proposals in the literature; the review presents arguments rather than new program evaluation data.
There is an urgent need for education and training policy to address AI-driven changes in the labor market.
Policy-focused literature review and the authors' policy recommendations based on synthesis of studies on skill demand shifts; no primary policy evaluation or randomized trial reported.
AI generates employment opportunities emerging from new technologies and innovation.
Narrative review of studies and examples in the literature cited by the paper; no new empirical measurement or sample provided in this review itself.
This study extends the technology–organisation–environment (TOE) theory by providing comprehensive empirical evidence of internal and external factors affecting BT adoption.
Use of the TOE framework to structure empirical analysis on 27,400 firm-year observations (2013–2021) linking technology (AI), organisation (corporate culture), and environment (market competition, government support, digital financial development) variables to BT adoption outcomes.
Environmental factors—market competition, government support, and the level of digital financial development across provinces—positively affect BT adoption.
Empirical tests using the 27,400 firm-year sample (2013–2021) incorporating provincial- and market-level environmental variables (market competition, measures of government support, and provincial digital financial development indices) alongside firm-level data and BT adoption coding from annual reports.
Externally oriented corporate cultures, specifically competition-oriented and creation-oriented cultures, positively affect BT adoption.
Same sample of 27,400 firm-year observations (2013–2021). Corporate culture indicators (competition- and creation-orientation) collected via Python web crawler from the management discussion & analysis (MD&A) sections of annual reports; BT adoption measured by manual annual report keyword search and content validation.
AI technology positively affects blockchain technology (BT) adoption.
Empirical analysis of 27,400 firm-year observations of Chinese A-share listed firms (2013–2021). AI technology measured using AI patent data collected via a Python web crawler from annual report MD&A sections and China National Knowledge Infrastructure (CNKI). BT adoption identified by manual search of annual reports for the keyword 'blockchain technology' and content assessment to confirm adoption status.
To alleviate adverse spatial spillovers, it is necessary to strengthen interactive development between digital–real integration and New Quality Productive Forces, foster interregional cooperation, and optimize resource allocation.
Policy recommendations derived from the paper's empirical findings (bidirectional positive relationship and negative spatial spillovers) — normative conclusion based on observed results.
The promotional effect of digital–real integration on New Quality Productive Forces is slightly stronger than the reverse effect (New Quality Productive Forces on digital–real integration).
Comparison of estimated coefficients from the GS3SLS spatial simultaneous equations model (paper reports the coefficient for integration→productive-forces is marginally larger than productive-forces→integration).
Focused, small Skills (2–3 modules) are more effective than comprehensive documentation-style Skills.
Experimental analysis comparing Skill granularity: authors report higher pass-rate gains for Skills composed of 2–3 focused modules versus larger, comprehensive documentation-style Skills within the SkillsBench experiments. (Details on exact sample counts per granularity condition are reported in the paper's Skill-design analyses.)
Policy interventions (lifelong learning, reskilling programs, active labor-market policies, social protection) are necessary to manage transitional unemployment and distributional effects.
Policy prescriptions based on theoretical framework and synthesis of prior policy evaluations; the paper recommends these approaches but does not present new impact estimates.
AI indirectly creates employment via platform ecosystems, new industries, and productivity-induced demand expansion.
Economic theory on demand-driven employment effects and literature synthesis of platform and productivity spillovers; cross-sectoral discussion rather than a new empirical estimate.
AI directly creates new occupations and tasks related to AI development, deployment, maintenance, and oversight.
Empirical and conceptual synthesis noting observed emergence of AI-specific roles in labor markets and task-based theory of job creation; no single quantified sample provided.
AI complements high-skill, technology-intensive roles, increasing demand for advanced cognitive, creative, and supervisory skills.
Task-complementarity argument from theory and empirical patterns in literature where technology raises demand for skilled workers; cross-sectoral examples cited conceptually.
Adoption of AI in accounting can raise firm-level productivity via faster close cycles, better control, and improved forecasting, potentially affecting profitability and investment decisions.
Theoretical and literature-based claim; the paper suggests mechanisms but does not present a specified empirical estimation in the abstract.
The paper advocates a complementary (augmenting) view of AI in accounting instead of a pure substitution view.
Argumentative conclusion based on synthesis of reviewed studies and theoretical considerations presented in the paper.
AI adoption changes accountants' roles from data entry and routine processing to analysis, interpretation, and strategic decision support.
Inferred from qualitative literature, surveys, and case studies discussed in the paper rather than from a specified empirical identification strategy.
Documented benefits of AI in accounting include increased efficiency, fewer manual errors, faster close cycles, improved report accuracy, and better fraud/irregularity detection.
Reported from literature and industry reports/case examples cited by the paper; the paper does not provide detailed sample sizes or econometric estimates in the abstract.
AI complements accountants rather than substituting them, raising productivity and shifting accountants' focus toward strategic financial management.
Argument based on literature review and qualitative interpretation of workflow changes (surveys/case studies likely); no randomized or quasi-experimental evidence reported in the abstract.
AI technologies (machine learning, robotic process automation, and advanced analytics) are materially improving accounting by automating repetitive tasks, reducing errors, detecting fraud, and providing predictive insights.
Stated as the paper's main finding and supported by cited literature and industry/case examples; the abstract does not specify an empirical design or sample for causal estimation.
Serious-game DSTs can reduce informational frictions by making model outputs (including AI-based recommendations) more interpretable and actionable, lowering barriers to adoption and improving translation of technical advice into economic behavior.
Conceptual synthesis and illustrative practice examples where visualization and interactivity improved understanding; empirical evidence is limited to qualitative user reports and small demonstrations.
Games can act as front-ends to underlying models and datasets or bridge multiple DSTs, improving interoperability and workflow fit for farmers.
Examples of prototypes and deployed tools that connected game interfaces to models/datasets or multiple DSTs; evidence is case-based and demonstrates feasibility rather than large-scale adoption.
Serious games can explicitly model economic outcomes alongside environmental metrics, showing how mitigation/adaptation actions affect enterprise resilience and income.
Prototype demonstrations and case studies that combined economic models with environmental outputs in game interfaces; economic outcome data in these examples are limited and typically short-term or simulated rather than long-term observed incomes.
Dynamic, scenario-based visual outputs in serious games help users understand trade-offs over time (for example, carbon sequestration versus yields).
Comparative demonstrations and workshop observations where scenario visualization was used to communicate temporal trade-offs; evaluation mostly via self-reported comprehension and qualitative feedback from participants.
Interactive, transparent simulations in games reduce skepticism by letting users explore assumptions and model behavior, thereby building trust in DST recommendations.
Qualitative interviews, user testing in workshops, comparative demonstrations where participants explored model assumptions and reported increased confidence; evidence primarily anecdotal and from small pilots.
Co-design through serious games facilitates participatory design with farmers and stakeholders, producing tools that better match on-farm decision contexts and preferences.
Reports from participatory workshops and co-design sessions, case studies of prototype development with farmer groups; evidence largely qualitative (user feedback, design iterations) and based on small-group engagements.
Serious games—interactive, simulation-based decision support tools—can materially increase farmer uptake of land-use decision support tools (DSTs) needed to meet global net zero targets by enabling co-design, building trust, visualizing outcomes, demonstrating profitability–environment links, and integrating with other tools.
Synthesis of literature and practice examples including case studies and deployed game prototypes used with farmer groups, participatory workshops, and qualitative interviews/surveys. Evidence is primarily from small-scale pilots and demonstrations rather than large randomized trials; sample sizes are heterogeneous and often small or not reported.
The paper's conceptual contribution challenges macro-centric crisis narratives by centering social mechanisms (support systems, peer benchmarking, institutional trust) as critical determinants of small-firm adaptation.
Theoretical framing (novel socially embedded analytical lens) combined with empirical results showing the importance of networks, identities, and normative motivations in explaining adaptation outcomes relative to macro-structural explanations.
AI governance for training should require content validation, transparency of model use, data minimisation, human accountability, and auditable logs to prevent hidden biases and exclusion.
Policy recommendation from conceptual risk analysis and best-practice governance principles; no field implementation or audit data provided.
Skills recognition should emphasize functional, employer‑usable verification and portability (e.g., micro‑credentials, QA/transparency instruments), not formal legal harmonisation.
Policy recommendation coming from conceptual analysis and review of transferable instrument layers (drawing from EU tools); no empirical comparison provided.
Mandatory pre-departure training in South–South labour corridors (examined via the Myanmar–Malaysia corridor) is a highly implementable, cross-level lever for improving regularity and rights-protecting mobility in contexts with limited enforcement and coordination capacity.
Conceptual analysis anchored in the Myanmar–Malaysia corridor using a structured desk review of policy/program materials, corridor process mapping, and governance gap analysis. No new causal field experiments or quantitative impact estimates reported.
AI adoption raises executives' human capital/market value, which contributes to higher compensation.
Mediation tests linking AI application to measures of executive human capital (skills/market value) and linking those measures to higher pay in the reported analyses.
AI adoption increases firm total factor productivity (TFP), and higher TFP is associated with higher executive compensation.
Mechanism analysis reporting that firms with higher AI application have higher estimated TFP, and TFP is positively related to executive pay (mediation tests on the sample).