Evidence (2215 claims)
Adoption
5126 claims
Productivity
4409 claims
Governance
4049 claims
Human-AI Collaboration
2954 claims
Labor Markets
2432 claims
Org Design
2273 claims
Innovation
2215 claims
Skills & Training
1902 claims
Inequality
1286 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 369 | 105 | 58 | 432 | 972 |
| Governance & Regulation | 365 | 171 | 113 | 54 | 713 |
| Research Productivity | 229 | 95 | 33 | 294 | 655 |
| Organizational Efficiency | 354 | 82 | 58 | 34 | 531 |
| Technology Adoption Rate | 277 | 115 | 63 | 27 | 486 |
| Firm Productivity | 273 | 33 | 68 | 10 | 389 |
| AI Safety & Ethics | 112 | 177 | 43 | 24 | 358 |
| Output Quality | 228 | 61 | 23 | 25 | 337 |
| Market Structure | 105 | 118 | 81 | 14 | 323 |
| Decision Quality | 154 | 68 | 33 | 17 | 275 |
| Employment Level | 68 | 32 | 74 | 8 | 184 |
| Fiscal & Macroeconomic | 74 | 52 | 32 | 21 | 183 |
| Skill Acquisition | 85 | 31 | 38 | 9 | 163 |
| Firm Revenue | 96 | 30 | 22 | — | 148 |
| Innovation Output | 100 | 11 | 20 | 11 | 143 |
| Consumer Welfare | 66 | 29 | 35 | 7 | 137 |
| Regulatory Compliance | 51 | 61 | 13 | 3 | 128 |
| Inequality Measures | 24 | 66 | 31 | 4 | 125 |
| Task Allocation | 64 | 6 | 28 | 6 | 104 |
| Error Rate | 42 | 47 | 6 | — | 95 |
| Training Effectiveness | 55 | 12 | 10 | 16 | 93 |
| Worker Satisfaction | 42 | 32 | 11 | 6 | 91 |
| Task Completion Time | 71 | 5 | 3 | 1 | 80 |
| Wages & Compensation | 38 | 13 | 19 | 4 | 74 |
| Team Performance | 41 | 8 | 15 | 7 | 72 |
| Hiring & Recruitment | 39 | 4 | 6 | 3 | 52 |
| Automation Exposure | 17 | 15 | 9 | 5 | 46 |
| Job Displacement | 5 | 28 | 12 | — | 45 |
| Social Protection | 18 | 8 | 6 | 1 | 33 |
| Developer Productivity | 25 | 1 | 2 | 1 | 29 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
| Creative Output | 15 | 5 | 3 | 1 | 24 |
| Skill Obsolescence | 3 | 18 | 2 | — | 23 |
| Labor Share of Income | 7 | 4 | 9 | — | 20 |
Innovation
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AI accelerated cross-border payment processes.
Reported quantitative evaluation of AI adoption effects on operational efficiency components, with cross-border payment speed cited as an improved component (measurement details and sample size not specified).
AI integration significantly improved international trade efficiency.
Quantitative analysis evaluating relationships among AI adoption, operational efficiency variables, and international trade efficiency; the paper reports a statistically significant improvement (exact tests, p-values, and sample size not provided in the summary).
These AI formulation models reduced experimental workload by 30–50%.
Reported in the review as estimated reductions in experimental workload when using AI-driven formulation optimization. The excerpt lacks details on how workload was measured, which experiments were replaced or reduced, and sample sizes.
In formulation optimization, artificial neural networks, neuro-fuzzy systems, and hybrid model-based AI models have been able to predict dissolution profiles and critical quality attributes with accuracy rates of over 90%.
Reported model performance in formulation optimization studies summarized by the review. The excerpt does not include which specific studies, datasets, cross-validation protocols, or sample sizes produced >90% accuracy.
AI has reduced clinical trial duration by up to 59%.
Reported in the review as an observed maximum reduction in trial duration associated with AI-driven approaches. The excerpt omits details on which trials, therapeutic areas, trial phases, or sample sizes produced this figure.
AI has sped up compound screening by 1–2 years.
Presented in the review as a comparative reduction in time-to-screening attributed to AI methods. The excerpt does not provide the underlying studies, screening scope, or sample sizes.
AI-enabled platforms have cut the drug discovery pipeline timelines (compared with the traditional 4–6 years) down to 46 days.
Reported as an outcome of AI-enabled platforms in the review. The excerpt does not list the specific platform(s), individual study design(s), or sample sizes underlying the 46-day figure.
Artificial intelligence (AI) is transforming pharmaceutical research and development (R and D), and making measurable improvements in efficiency, precision, and cost-effectiveness in drug research and development.
Stated as a summary conclusion in the review based on cross-domain literature synthesis. Specific studies or quantitative meta-analytic methods and sample sizes are not provided in the excerpt.
The findings provide valuable insights for entrepreneurs, policymakers, and academic institutions to implement adaptive strategies for sustainable and inclusive entrepreneurial growth in the era of artificial intelligence.
Authors' implications/conclusions based on the study results (n=350; statistical analyses) recommending adaptive strategies targeted at stakeholders.
AI functions as a strategic enabler that reshapes entrepreneurial practices, labour dynamics, and innovation strategies.
Conclusion drawn from the study's quantitative findings (survey of 350, regression/SEM results) that linked AI adoption to changes in opportunity recognition, labour substitution, and innovation processes.
AI-driven innovation processes accelerated product development, improved operational efficiency, and supported experimentation, thereby strengthening entrepreneurial performance.
Survey data from 350 AI-adopting SMEs analyzed with regression and SEM showing positive associations between AI adoption and measures of product development speed, operational efficiency, experimentation, and overall entrepreneurial performance.
AI facilitated labour substitution by automating repetitive tasks, allowing human resources to focus on creative and analytical roles.
Responses from the same sample (n=350) of AI-adopting SME entrepreneurs/managers; descriptive statistics and inferential analyses (regression/SEM) linking AI adoption to increased automation and role reallocation.
AI adoption significantly enhanced opportunity recognition by enabling entrepreneurs to identify emerging market trends, assess risks, and make informed strategic decisions.
Quantitative survey of 350 entrepreneurs and managers of SMEs who had adopted AI; relationships tested using regression analysis and structural equation modelling (SEM) reported a significant positive effect of AI adoption on opportunity recognition.
AI-based ESG systems are increasingly applied to extract deeper sustainability signals from corporate disclosures, reports and external data sources.
Descriptive claim supported by cited literature and examples of AI applications in ESG analytics within the paper's background (references to recent AI/ESG studies). The summary does not quantify the rate of adoption.
Regression analysis revealed that AI-derived ESG scores were more strongly associated with excess returns than traditional ESG metrics.
Regression models estimating the association between ESG scores (AI-derived vs traditional) and excess returns. The summary does not specify the regression specification, control variables, sample size, time horizon, or statistical significance measures.
AI-driven high-ESG portfolios demonstrated lower downside-risk exposure and smaller maximum drawdowns during market stress, indicating stronger resilience.
Downside-risk and maximum drawdown metrics computed for AI-driven high-ESG portfolios versus comparator portfolios during periods of market stress (portfolio-level analysis). Specific stress period(s), sample size and statistical tests are not provided in the summary.
AI-enhanced high-ESG portfolios achieved higher mean returns and superior Sharpe ratios than both AI-based low-ESG portfolios and traditionally rated ESG portfolios.
Portfolio-level performance comparison reported in the study (mean returns and Sharpe ratios calculated for portfolios constructed using AI-driven ESG indicators versus portfolios using conventional ESG ratings). The summary does not report sample size, time period, market coverage, rebalancing frequency, or statistical significance levels.
AI and Big Data enable proactive risk management strategies that contribute to lowering market uncertainty.
Qualitative case studies and quantitative analysis indicating firms used AI/Big Data for proactive risk management; details on number of cases or measurement of 'proactive risk management' not provided in the summary.
The reduction in market uncertainty occurs through enhanced predictive modeling capabilities enabled by AI and Big Data.
Findings reported in the paper attributing improved predictive modeling (from quantitative analysis and case-study observations) as a mechanism for uncertainty reduction (no specific metrics or effect sizes provided in the summary).
Strategic integration of AI and Big Data can significantly reduce market uncertainty during periods of economic turbulence.
Mixed-methods study combining quantitative analysis of market data and qualitative case studies of firms implementing AI and Big Data solutions (specific sample size and statistical details not provided in the summary).
The study's findings provide strategic guidance for firms seeking long-term sustainable growth through reliance on generative AI to improve ESG performance.
Interpretation and managerial implications drawn from the empirical results of the panel analyses (2012–2024 Chinese A-share sample); presented as implications/recommendations in the paper's discussion section.
The positive impact of DDDM on international firm performance is amplified by state ownership.
Reported interaction/moderation result in the paper indicating that state ownership increases the strength of the DDDM–performance relationship (specific empirical details not provided in the excerpt).
The positive impact of DDDM on international firm performance is amplified by greater foreign shareholding.
Reported interaction/moderation finding in the paper showing that higher foreign shareholding enhances the positive DDDM–performance effect (detailed statistics and sample description not included in the excerpt).
The positive impact of DDDM on international firm performance is amplified by higher market competition.
Reported interaction/moderation result in the paper indicating that market competition strengthens the DDDM–performance relationship (specific interaction coefficients, significance levels, and sample details not provided in the excerpt).
DDDM positively relates to sustainability vision co-creation (future external).
Listed in the paper's framework as the future external dimension through which DDDM generates sustainable value and influences performance (empirical backing not specified in the excerpt).
DDDM positively relates to sustainability information disclosure (current external).
Identified as a current external mechanism in the paper's framework linking DDDM to improved international firm performance (supporting analyses not detailed in the excerpt).
DDDM positively relates to green innovation (future internal).
Included in the paper's framework as one of the four mechanisms through which DDDM creates sustainable value and affects firm performance (empirical support details not provided in the excerpt).
DDDM positively relates to pollution prevention (current internal) activities.
Part of the paper's framework and reported findings tying DDDM to the 'pollution prevention' dimension (empirical support details not included in the excerpt).
DDDM creates sustainable value for firms and thereby enhances international firm performance across four dimensions: pollution prevention (current internal), green innovation (future internal), sustainability information disclosure (current external), and sustainability vision co-creation (future external).
The paper presents a developed conceptual/framework explanation linking DDDM to sustainable value creation across the four specified dimensions; the excerpt does not specify whether these links are supported by mediation analysis or qualitative/theoretical argumentation.
Data-driven decision-making (DDDM) positively impacts international firm performance.
Empirical analysis reported in the paper in which DDDM is quantified using AI language models (BERT and ChatGLM2-6B) and related statistically to measures of international firm performance (details on sample size and statistical tests not provided in the excerpt).
Findings provide granular evidence to support differentiated regional and industrial policies aimed at strengthening supply chain resilience.
Policy implication derived from heterogeneity analyses (ownership, industry, region) on the 2012–2022 Shanghai and Shenzhen A-share dataset.
The paper empirically clarifies the previously opaque ('black-box') mediation role of technological innovation between NQPF and supply chain efficiency.
Use of mediating-effect models on 2012–2022 A-share panel data to quantify mediation (including reported mediation proportion of 84.6%).
This study develops a unified NQPF theoretical framework integrating digital, green, and talent dimensions.
Authors' stated theoretical integration in the paper, presenting a multi-dimensional NQPF framework combining digital, green, and talent elements.
NQPF’s positive impact on supply chain efficiency is stronger in Eastern China compared with other regions.
Regional heterogeneity analysis using the 2012–2022 A-share panel data showing larger estimated effects for firms located in Eastern China.
The positive effect of NQPF on supply chain efficiency is stronger in state-owned enterprises (SOEs) than in non-state firms.
Heterogeneity analysis by ownership type performed on the 2012–2022 A-share panel data showing larger coefficients/effects for SOEs.
NQPF affects supply chain efficiency via multiple mechanisms: technological innovation, management restructuring, and digital transformation.
Mechanism analysis using mediating-effect models and supplementary tests on the 2012–2022 A-share panel data identifying these specific mediators.
Population growth shows a significant positive effect on GDP growth across the countries in the sample.
Population growth entered as a regressor and reported significant positive association with GDP growth in the panel models (OLS, FE, Difference and System GMM); exact magnitude and significance levels not provided in the summary.
Government expenditure shows a significant positive effect on GDP growth across the countries in the sample.
Positive and statistically significant coefficients on government expenditure reported in the applied econometric models (OLS, FE, Difference and System GMM); government spending included as a control macroeconomic determinant (sample/time not specified).
Gross fixed capital formation (GFCF) has a significant positive effect on GDP growth across the countries in the sample.
Estimated positive and statistically significant coefficients on GFCF in the panel regressions (OLS, FE, Difference and System GMM); GFCF included as a macroeconomic determinant in the model (sample size/time period not provided).
The study presents a complementary linking theory that connects sustainability practice and reasoning to inform future discourse on sustainable e-commerce growth strategy in the dual carbon phase.
Theoretical/conceptual contribution described in the paper; this is a conceptual claim rather than an empirical finding.
Alongside concerns, AI proliferation may introduce new, positive affordances for military decision-making organizations.
Normative/analytical claim by the author based on argumentation; no empirical demonstration, experimental results, or case-study evidence is provided in the excerpt.
Military AI adoption is incentivized by competitive pressures and expanding national security needs.
Author assertion based on qualitative argumentation and literature-informed reasoning; no empirical study, dataset, or sample size reported in the text.
AI innovation produces significant positive spatial spillover effects on employment in neighboring cities, promoting expansion of their employment scale.
Spatial analysis (spatial econometric tests) on the 268 Chinese cities (2010–2023) indicating positive spillovers to neighboring cities' employment.
Temporally, AI innovation affects urban employment through both immediate and lagged effects, with the magnitude of these effects diminishing over time.
Temporal (lag) analysis in extended tests on the 268-city panel covering 2010–2023.
Governmental digital attention positively moderates the relationship between AI innovation and urban employment.
Moderation analysis using measures of governmental digital attention and AI innovation in the 268-city panel (2010–2023).
AI innovation indirectly promotes employment growth by enhancing urban economic density (mediation effect).
Mechanism (mediation) analysis conducted on the 268-city panel (2010–2023) showing economic density as an intermediary channel.
The positive employment effect of AI innovation is stronger in southern cities than in others.
Geographic heterogeneity analysis across 268 Chinese cities (2010–2023).
The positive employment effect of AI innovation is more pronounced in the tertiary sector.
Heterogeneity/sectoral analysis using the panel of 268 Chinese cities (2010–2023).
The positive employment effect of AI innovation is more pronounced in the secondary sector.
Heterogeneity/sectoral analysis using the same panel of 268 Chinese cities (2010–2023).
Overall, AI innovation has a positive effect on urban employment.
Empirical testing on a panel of 268 Chinese cities over the period 2010–2023 (integrated theoretical and empirical analysis).