Evidence (4560 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 |
Productivity
Remove filter
Firms with high AI adoption reported superior decision-making quality compared to low adopters.
Survey comparisons of decision-making quality measures between AI adoption groups in the questionnaire data (N=400), reported as superior for high adopters.
Firms with high AI adoption reported significantly higher financial literacy scores compared to low adopters.
Comparison of financial literacy scores between high and low AI adoption groups derived from the structured questionnaire responses (sample N=400); described as 'significantly higher' in the paper.
There is a positive correlation between the level of AI adoption and key business outcomes.
Survey-based correlational analysis reported in the paper linking self-reported AI adoption level to business outcome measures across the sample of 400 respondents.
Holistic AI integration across supply chain functions yields greater performance benefits than isolated technological implementations.
Comparative analysis using survey and statistical methods (correlation/regression) on data from supply chain professionals; the summary reports superior outcomes for integrated (ecosystem-level) AI adoption versus isolated implementations, but does not provide the comparative metrics or sample breakdown.
AI-enabled performance management plays a mediating role that strengthens the linkage between strategic planning and operational outcomes.
Mediation analysis conducted on survey data from supply chain professionals (manufacturing and service sectors); the summary indicates a mediating effect of performance management but provides no mediation statistics (indirect effect size, confidence intervals) or sample size.
AI-enabled execution emerged as the strongest direct predictor of supply chain performance.
Regression analysis from the quantitative survey of supply chain professionals comparing AI-enabled planning, execution, and performance management as predictors of supply chain performance; specific coefficients, significance levels, and sample size are not reported in the excerpt.
AI integration significantly improved overall supply chain performance.
Quantitative study using data collected from supply chain professionals and analyzed with reliability testing, correlation, and regression methods; the provided text does not include sample size, p-values, or effect magnitudes.
AI integration significantly improved responsiveness (supply chain responsiveness).
Survey data from supply chain professionals across manufacturing and service sectors analyzed via correlation and regression analyses; the summary does not state sample size or numerical results.
AI integration significantly improved operational efficiency.
Quantitative survey of supply chain professionals (manufacturing and service sectors) with statistical analyses including reliability testing, correlation, and regression; specific sample size and effect sizes not provided in the summary.
AI integration significantly improved forecasting accuracy.
Quantitative survey of supply chain professionals (manufacturing and service sectors) analyzed using reliability testing and correlational/regression statistics; exact sample size and effect size not reported in the provided text.
The expanding use of AI is reshaping agricultural production systems and has emerged as a key driver of high-quality development in the sector.
Synthesis and interpretation of the paper’s empirical findings (significant AI effects on TFP, identified channels, and heterogeneous impacts) based on the listed-firm panel analysis.
Productivity gains from AI are more pronounced in regions facing higher natural risks.
Heterogeneity analysis in the paper that compares regions with differing natural-risk levels and finds stronger AI–TFP effects in higher-risk regions using the 2007–2023 panel of listed agricultural firms.
Productivity gains from AI are more pronounced among firms in their growth stage.
Heterogeneity analysis in the paper that splits the sample by firm life-cycle/stage and reports larger AI-associated TFP effects for firms classified as being in the growth stage.
AI fosters productivity growth by facilitating inter-firm resource sharing.
Mechanism analysis in the paper indicating a significant association between AI adoption and measures of inter-firm resource sharing, which in turn are associated with higher TFP in the panel sample.
AI fosters productivity growth mainly by optimizing labor structures.
Mechanism analysis reported in the paper linking AI adoption to measures of labor-structure optimization and finding that this channel is a significant contributor to TFP gains in the sample of listed agricultural firms.
The adoption of AI improves factor allocation efficiency and constitutes a critical economic foundation for efficiency-driven sustainable growth in agriculture by optimizing resource utilization and strengthening risk-management capacity.
Conceptual framing supported by the paper's empirical findings (panel data on agricultural firms listed on Shanghai and Shenzhen A-share markets, 2007–2023) that show AI raises total factor productivity (TFP) and stronger effects in higher natural-risk regions (interpreted as improved risk management).
The findings provide practical guidance for entrepreneurs on building adaptive, AI-integrated organizations by redefining hiring, decision processes, and learning practices.
Prescriptive recommendations derived from the interview analysis and observed patterns in the sample of entrepreneurs (qualitative grounding; specific examples or measured impacts not provided in the excerpt).
Hybrid decision architectures have emerged: startup-specific configurations where algorithmic reasoning and human judgment recursively interact to shape decisions, roles and routines.
Thematic synthesis of interview data identifying recurring patterns of human–AI recursive interaction in decision-related practices across the studied startups (qualitative evidence; no quantitative counts reported).
Entrepreneurs who founded startups after ChatGPT's release integrated AI into their post-release ventures.
Direct accounts from the subset of interviewees who founded startups after ChatGPT's release describing AI incorporation in those ventures (qualitative interview evidence; sample details not given).
AI is becoming embedded in the architecture of startups rather than serving only as a task-automation tool.
Interview data and qualitative analysis identifying patterns of AI integration across startup roles, routines and structures (derived from the same semi-structured interview sample; exact N not provided).
Facilitated access to AI following the release of ChatGPT is transforming how startups organize and make decisions.
Qualitative study using semi-structured interviews with entrepreneurs who founded startups both before and after ChatGPT's release and who integrated AI into their post-release ventures; thematic/qualitative analysis of interview data. (Sample size not reported in the provided excerpt.)
Education, reskilling, and institutional responses are important in shaping the economic outcomes of artificial intelligence.
Policy implication derived from the observed/modeled heterogenous effects of AI on occupations and productivity; presented as a normative recommendation rather than an empirically tested result in the provided text.
Productivity gains associated with AI may support long-term economic growth.
Reference to productivity data and growth theory linking productivity improvements to long-run growth; the paper states this as a potential outcome but does not provide quantified long-run estimates or empirical identification in the excerpt.
AI complements higher-skill labor.
Interpretation of labor market data patterns and theoretical task-complementarity arguments presented in the paper; empirical details (which datasets, estimation strategy, sample size) are not provided in the text excerpt.
Artificial intelligence is a skill-biased technological innovation.
Framing and argumentation in the paper situating AI within the skill-biased technical change literature; references to analyses of publicly available labor market and productivity data (sources, time periods, and sample sizes not specified in the text).
Firms' technical competencies amplify the positive effect of AI adoption on performance.
Moderation analysis in the PLS-SEM using the same 280-SME survey indicating a significant positive moderating role for technical/technical competency measures.
Firms' financial capacity amplifies the positive effect of AI adoption on performance.
Moderation analysis within the PLS-SEM on survey data from 280 Tunisian SMEs showing a significant positive moderating effect of financial strength on the AI adoption → performance link.
AI adoption significantly improves operational performance of Tunisian SMEs.
Same empirical dataset (n=280) and PLS-SEM analysis reporting a significant AI adoption → operational performance relationship.
AI adoption significantly improves financial performance of Tunisian SMEs.
Survey data from 280 Tunisian SMEs analyzed using partial least squares structural equation modeling (PLS-SEM); significance of the AI adoption → financial performance path reported in the model.
The future of AI must be guided by human-centered ethical principles, international cooperation, and strategic regulatory planning to ensure societal benefit and minimize systemic risks.
Concluding recommendation in the paper (normative/policy prescription); the abstract gives no empirical evidence or quantified analysis to demonstrate effectiveness of these measures.
Public governance is pivotal to ensuring equitable and accountable AI implementation.
Policy argument/conclusion presented in the paper; the abstract does not report empirical validation, case studies, or metrics supporting this causal claim.
Big Data Analytics and AI can improve audit accuracy and reduce costs.
Reported results from literature review and empirical analysis in the study; precise cost or accuracy metrics and sample information are not provided in the abstract.
Integrating BDA and AI within the Audit 5.0 framework represents a fundamental shift toward intelligent, adaptive, and value-driven auditing, while underscoring the need for enhanced auditor competencies and alignment with evolving regulatory and professional requirements.
Overall synthesis of literature and empirical results from the mixed-method study (systematic review + SEM-based empirical analysis in finance and technology sectors); phrased as a high-level conclusion.
There is a need for stronger governance, ethical frameworks, and targeted training to fully realize the benefits of digital auditing.
Conclusions drawn from the literature synthesis and empirical observations regarding challenges to implementing Audit 5.0; recommendation rather than a measured effect.
BDA and AI enable real-time and predictive risk assessment and enhanced fraud detection, expanding audit coverage beyond traditional sampling.
Synthesis of prior theoretical and empirical studies and the study's empirical analysis (SEM) focusing on risk assessment, anomaly detection, and continuous auditing in finance and technology sectors.
Investment in AI correlates with improved audit efficiency.
Reported empirical correlations from the study's analysis (SEM) combined with literature review; detailed metrics and sample information not included in the abstract.
Investment in AI correlates with reductions in audit restatements.
Empirical evidence cited in the study (SEM-based analysis across organizations in finance and technology); exact sample size and statistical coefficients not provided in the summary.
BDA and AI facilitate continuous auditing (real-time auditing).
Synthesis of prior literature and empirical analysis within Audit 5.0 framework; methods include systematic literature review and SEM on sectoral samples (finance and technology).
Digitalization (BDA and AI) improves audit productivity.
Empirical analysis (SEM) and literature synthesis focused on finance and technology organizations; empirical details (sample size, effect sizes) not given in the summary.
Audits supported by Big Data Analytics (BDA) and artificial intelligence (AI) significantly outperform traditional audit approaches.
Mixed-method research: systematic literature review plus empirical analysis using structural equation modeling (SEM) on organizations in the finance and technology sectors (sample size not reported in the provided text).
High current usage, breadth of application, frequent use of AI tools for testing, and ease of use correlate strongly with future intended adoption.
Correlational/regression analyses of survey variables (N=147) predicting respondents' stated future intention to increase AI tool use from measures of current usage, breadth of tool applications, frequency of testing-tool use, and perceived ease-of-use.
Developers report both productivity and quality gains from using AI tools.
Aggregate self-reported responses from 147 professional developers indicating perceived improvements in productivity and code quality associated with AI tool use.
There is no perceptual support for the Quality Paradox; PP is positively correlated with Perceived Code Quality (PQ) improvement.
Statistical analysis of survey measures (N=147) showing a positive correlation between respondents' Perceived Productivity scores and their Perceived Code Quality improvement scores; absence of evidence for a negative PP–quality relationship.
Frequent and broad AI tools use are the strongest correlates of both Perceived Productivity (PP) and quality, with frequency strongest.
Correlational analysis of self-reported survey responses from a sample of 147 professional developers measuring AI tool usage frequency and breadth and perceived outcomes (Perceived Productivity and Perceived Code Quality).
AI-influenced efficiency has a statistically significant but moderate positive impact on reducing the oil and gas trade deficit and on GDP growth.
Quantitative macroeconomic assessment (second hypothesis) reported in the paper indicating statistically significant, albeit moderate, positive effects of AI-driven efficiency on macro indicators (GDP growth and oil & gas trade balance).
AI-based real-time optimization in fuel blending surpasses traditional modeling approaches and reduces waste.
Comparative model validation and application evidence reported alongside the R2 = 0.99 result; qualitative statements that AI optimization outperforms traditional models and reduces material waste.
Predictive maintenance (PdM) systems powered by advanced AI methods ensure continuous operation and extend the life of critical hydrocarbon assets.
Qualitative and case-based evidence from described AI applications in the downstream sector within the mixed-methods study (examples of PdM deployments and reported operational outcomes).
AI adoption in the downstream petroleum sector is significantly positively correlated with improved operational efficiency.
Quantitative analysis within the mixed-methods study assessing immediate impact of AI on downstream operational efficiency (first hypothesis); reported as a statistically significant positive correlation (method described as quantitative assessment of operational metrics following AI adoption).
Through a comparative analysis of pioneering AI strategies in Rwanda, the United Kingdom, the United States, China, and Australia, this paper demonstrates how the DARE framework can serve as both a diagnostic tool to identify national gaps and a prescriptive blueprint for building a more equitable, human-centric automated future.
Reported method in abstract: comparative analysis of five countries (Rwanda, UK, US, China, Australia). The abstract claims demonstration but does not detail the analytic method, metrics, or sample beyond the five-country comparison.
AI promises unprecedented productivity gains.
Asserted in abstract; no empirical evidence or quantification provided in the abstract.