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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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.
EASP offers a practical tradeoff between reasoning quality and latency by avoiding iterative LLM tool-calls at inference time while still producing grounded plans.
Methodological claim in the paper: Probe-then-Plan uses a lightweight probe to avoid heavy iterative LLM tool calls during serving; supported by design rationale and performance-focused evaluations (offline and online).
EASP has been successfully deployed in JD.com's AI-Search system.
Statement in the paper that EASP was deployed in JD.com's AI-Search system; presumably validated by internal deployment logs and online A/B testing reported.
Online A/B testing on JD.com demonstrates that EASP achieves substantial lifts in UCVR (user conversion rate) and GMV (gross merchandise volume).
Reported results from online A/B testing on JD.com referenced in the paper indicating lifts in UCVR and GMV (no numerical magnitudes provided in the abstract).
Extensive offline evaluations and online A/B testing on JD.com show that EASP significantly improves relevant recall.
Empirical claims in the paper citing extensive offline evaluations and online A/B testing on JD.com as the basis for observed improvements in relevant recall (specific datasets/sizes not reported in the abstract).
Environment-Aware Search Planning (EASP) resolves the blindness-latency dilemma in LLM-based e-commerce search by grounding planning in the real retrieval environment via a Probe-then-Plan mechanism.
Conceptual design and empirical evaluation described in the paper: introduces a lightweight Retrieval Probe to expose a retrieval snapshot and a Planner that diagnoses execution gaps and generates grounded search plans; supported by offline evaluations and online A/B testing on JD.com (section describing method and experiments).
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.
The study recommends multi-stakeholder collaborations (policymakers, financial institutions, entrepreneurs) to design inclusive AI solutions, bridge the digital skills gap, and foster an environment for equitable entrepreneurial growth.
Policy and practice recommendations drawn in the paper's conclusion based on empirical findings and interpretation of barriers.
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.
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 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).
A set of emerging methodological approaches—prompt-based experiments, synthetic population sampling, comparative-historical modeling, and ablation studies—map onto familiar social-scientific designs while operating at unprecedented scale.
Survey and mapping of methodological techniques presented in the paper; claim is a conceptual synthesis rather than a report of a particular dataset or experiment in the provided text.
Instruct-only and modular adaptation regimes constitute pragmatic compromises for behavioral research because they can preserve pretrained cultural regularities while allowing researchers to elicit targeted behaviors.
Methodological recommendation derived from comparing adaptation regimes (conceptual argument / review of adaptation strategies); no empirical comparison or sample sizes provided in the excerpt.
Trained on unprecedented volumes of human-produced text, LLMs encode large-scale regularities in how people argue, justify, narrate, and negotiate norms across social domains.
Inference based on known pretraining procedures for LLMs and the paper's theoretical account; no specific corpus size or empirical validation reported in the provided text.
There is a third, emerging ambition in AI research: using large language models (LLMs) as scientific instruments for studying human behavior, culture, and moral reasoning.
Argumentative proposal grounded in the paper's conceptual analysis and review of existing methodological work; framed as an emerging research program rather than demonstrated empirical fact.
Vocational graduates who undergo strong work-based training demonstrate competitive and sometimes superior long-term employment trajectories compared with other pathways.
Comparative empirical studies and secondary analyses referenced in the paper that link work-based vocational training to favorable long-term outcomes (the summary does not provide exact studies, effect sizes, or sample sizes).
Higher education graduates generally experience favorable employment outcomes.
Synthesis of prior empirical studies and secondary labor-market indicators cited in the paper indicating better employment prospects for higher education graduates (no specific effect sizes or sample n given in the summary).
There has been substantial growth in higher education attainment across the countries examined.
Descriptive results drawn from secondary data and comparative empirical studies documenting trends in higher education enrollment and attainment (paper does not report specific country list or sample sizes in the summary).
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.