Evidence (2506 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 |
Labor Markets
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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.
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 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).
Progressing from ChatGPT 3.5 to 4.0 produced three distinct effect scenarios across markets, which reinforce the paper's inflection point conjecture.
Empirical comparison/analysis of the effects associated with different ChatGPT versions (3.5 vs 4.0) on online labor markets; method implied to be similar DiD or temporal comparison. (Specific sample sizes and the definitions of the three scenarios are not provided in the abstract.)
The authors developed a Cournot competition model that identifies an inflection point for each market: before this point human workers benefit from AI enhancements; beyond this point human workers would be replaced.
Theoretical modeling via a Cournot competition framework constructed by the authors to characterize market dynamics and derive an inflection point; this is a model-based (analytical) result rather than an empirical estimate.
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.
Education systems, training/reskilling, labor market institutions, industrial policy, and social safety nets mediate the net employment outcomes of AI adoption.
Policy and institutional analysis grounded in labor economics theory; presented as a mediating mechanism in the synthesis rather than demonstrated with empirical causal estimates or sample-based intervention studies.
Knowledge industries exhibit significant complementarities as AI augments cognitive tasks, although some research and analytical roles may be automated.
Theory-based assessment of cognitive-task complementarity and substitution; synthesis rather than empirical occupational-level measurement or causal estimates provided in the paper.
In services, routine service tasks are vulnerable to AI, while high-contact and creative services are less vulnerable; digital platform services are likely to expand.
Task-level sectoral reasoning and qualitative examples in services; no empirical sectoral employment dataset or quantified vulnerability scores reported in the paper.
Manufacturing has strong automation potential but also opportunities in advanced manufacturing and maintenance/engineering roles.
Sector-specific analysis combining task vulnerability to automation with emergence of advanced manufacturing tasks; presented as theoretical/qualitative assessment rather than measured manufacturing employment trajectories from a stated sample.
Distributional effects will include wage polarization (rising returns to high-skill labor and pressure on middle-skill wages) and uneven regional impacts.
Application of SBTC and task-based wage theory to AI adoption; sectoral and regional heterogeneity discussed qualitatively. No new wage-distribution panel or cross-country regression evidence reported in the paper.
Short- to medium-run transitional unemployment, wage polarization, and sector- and country-level heterogeneity are likely.
Temporal-mismatch argument from task-based substitution and SBTC frameworks; sectoral assessment across manufacturing, services, knowledge industries. Evidence is theoretical/synthesized rather than from a stated empirical panel or cross-sectional dataset.
Net employment outcomes depend more on institutions and policy than on technology alone.
Comparative treatment of advanced versus developing economies and policy/institutional analysis; grounded in economic theory rather than primary empirical causal estimates (no sample sizes or identification strategies reported).
AI will substantially restructure labor markets.
Theory-driven sectoral analysis and task-based arguments (synthesis of labor economics frameworks). No primary empirical dataset or quantified cross-country sample reported in the paper.
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.
Generative AI can play a bounded, auditable role as multilingual, low‑bandwidth learning support, but must be governed to avoid digital gatekeeping and should be excluded from eligibility screening, risk scoring, or automated decision‑making.
Analytical assessment of AI's potential roles and risks in training delivery; governance prescriptions based on policy and risk reasoning rather than empirical AI evaluations in the corridor.
Proposition 3: Rights‑based effectiveness requires measurable capability outcomes and institutional follow‑through (beyond information transfer).
Normative and governance analysis based on gap mapping and the paper's empirical agenda; not tested with outcome data in this study.
Training can be treated as migration-governance infrastructure that functions simultaneously as a capability intervention (actionable navigation, contract comprehension, safe help‑seeking), a labour‑market signal when aligned with TVET/human-capital planning, and a potential gatekeeping node if access, assessment, and accountability are weak.
Conceptual reframing supported by policy analysis and governance gap mapping; no empirical validation provided in the paper.
The technological-form parameter (η1 vs. η0, i.e., proprietary vs. commodity) can independently flip the model across the inequality-increase/decrease boundary.
Model counterfactuals varying η1 versus η0 show that changing the degree of proprietary control over AI can move the calibrated model from one regime to the other.
At the calibrated baseline, the sign of the change in inequality (ΔGini) is determined mainly by one empirical moment (m6) together with the rent‑sharing elasticity ξ.
Results of the sensitivity decomposition and calibration reported in the paper indicating m6 and ξ primarily drive the sign of ΔGini in the baseline parameterization.
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.
AI can enable new revenue streams (platforms, personalized pricing, automation-as-a-service) and increase market concentration, producing 'winner-takes-most' dynamics that raise profit rates for leading adopters and compress margins for laggards.
Literature synthesis on platforms and winner-take-all effects applied to AI; conceptual argument without firm-level causal testing in the paper.
AI adoption exerts downward pressure on routine labor costs while raising capital and recurrent costs (R&D, computing infrastructure, data, cybersecurity); higher fixed and lower marginal costs favor scale and incumbents with access to data and capital.
Conceptual cost-structure analysis drawing on automation and platform literature; no microdata or empirical cost estimates presented.
AI is a Schumpeterian general-purpose technology that can increase aggregate productivity potential but will do so unevenly across firms and sectors, producing heterogeneous effects on profitability.
Theoretical application of general-purpose technology and Schumpeterian literature to AI; literature-based claims without original empirical validation in the paper.
Firms' profitability and sustainability are shaped both by technological adoption (which can raise productivity and market power) and by structural pressures (trade wars, labor relations, supply constraints) that can erode margins.
Synthesis of firm-level implications from innovation and political-economy literatures; no firm-level causal estimates presented in the paper.
Contemporary crises change firms' cost structures (logistics, inputs, financing) and revenue prospects (demand shifts, market access).
Interpretive synthesis of observed firm-level impacts from pandemic, inflation episodes, and geopolitical events reported in secondary literature (no primary firm-level panel used).
Supply-chain fragilities and trade conflicts (emphasized by Mandel) mediate distributional and macroeconomic outcomes during long waves and crises.
Qualitative historical interpretation and literature references on supply-chain disruptions and trade conflicts (no systematic empirical identification in the paper).
New technological waves—most notably artificial intelligence (AI) and the green transformation—act as Schumpeterian forces that can alter productivity, competition, and profitability.
Conceptual mapping of Schumpeterian innovation-cluster theory to contemporary technologies (literature synthesis; no firm-level causal estimates reported).
Contemporary shocks (COVID-19, global inflation, geopolitical tensions) interact with long-wave mechanisms to reshape firms' cost and revenue structures.
Interpretive application of the comparative framework to recent historical episodes and macro trends; qualitative evidence from literature on pandemic and recent shocks (no primary microdata presented).
Students use GenAI as a co-designer and idea generator, which modifies workflow, decision points, and evaluative practices in their design process.
Qualitative interview data from architecture students; thematic analysis surfaced accounts of GenAI being used for ideation, variant generation, and as a collaborative partner (N unspecified).
Collaboration between architecture students and generative AI reshapes creative cognition in the architectural design process through algorithmic thinking strategies.
Semi-structured interviews with architecture students (interview sample size not specified) analyzed via inductive thematic analysis; authors synthesize recurring themes linking GenAI use to changes in cognitive strategies.
Patients classified as high‑risk by CDRG‑RSF had higher TMB, lower NK and CD8+ T cell infiltration, and model‑predicted resistance to Erlotinib and Oxaliplatin but sensitivity to 5‑fluorouracil.
CDRG‑RSF study reported immune deconvolution and TMB comparisons across risk groups and used pharmacogenomic prediction methods to infer drug sensitivity/resistance patterns for high‑risk vs low‑risk groups.
Both DNNs and LASSO correlated well at the individual‑sample level, but linear models (LASSO) struggled to recover cross‑study DEA log2FCs despite good sample‑level fits.
Same cross‑omics comparative study: reported good sample‑level prediction correlations for both model classes, but DNNs more faithfully reproduced differential expression signals across independent studies while LASSO did not recover DEA log2FCs robustly.
The taxonomy clarifies where substitution versus complementarity are likely: AI-assisted tasks imply partial substitution of routine work; AI-augmented applications generate complementarities that increase demand for higher cognitive skills; AI-automated systems shift labor toward monitoring, exception handling, and governance.
Inference from mapping the three interaction levels to observed case features (n=4) and application of the Bolton et al. framework in cross-case synthesis.
AI-augmented systems support real-time medical tasks (e.g., decision support during procedures), amplifying human judgment and speed but raising required cognitive skills and changing training and coordination practices.
Findings from the case(s) labeled AI-augmented in the four-case qualitative sample and cross-case interpretive analysis using the service-innovation framework.