Evidence (3492 claims)
Adoption
7395 claims
Productivity
6507 claims
Governance
5877 claims
Human-AI Collaboration
5157 claims
Innovation
3492 claims
Org Design
3470 claims
Labor Markets
3224 claims
Skills & Training
2608 claims
Inequality
1835 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 609 | 159 | 77 | 736 | 1615 |
| Governance & Regulation | 664 | 329 | 160 | 99 | 1273 |
| Organizational Efficiency | 624 | 143 | 105 | 70 | 949 |
| Technology Adoption Rate | 502 | 176 | 98 | 78 | 861 |
| Research Productivity | 348 | 109 | 48 | 322 | 836 |
| Output Quality | 391 | 120 | 44 | 40 | 595 |
| Firm Productivity | 385 | 46 | 85 | 17 | 539 |
| Decision Quality | 275 | 143 | 62 | 34 | 521 |
| AI Safety & Ethics | 183 | 241 | 59 | 30 | 517 |
| 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 | 105 | 40 | 6 | 187 |
| Task Completion Time | 134 | 18 | 6 | 5 | 163 |
| Worker Satisfaction | 79 | 54 | 16 | 11 | 160 |
| Error Rate | 64 | 78 | 8 | 1 | 151 |
| Regulatory Compliance | 69 | 64 | 14 | 3 | 150 |
| Training Effectiveness | 81 | 15 | 13 | 18 | 129 |
| 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 |
Innovation
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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).
Digital transformation enables manufacturing enterprises to navigate volatile and uncertain market environments, thereby achieving sustainable development.
Theoretical framing (institutional theory, enterprise resilience durability theory, strategic ecology) supported by empirical findings from the 2013–2022 Chinese A-share manufacturing sample linking DT, peer effects, and ER.
Regional peer effects are stronger for enterprises located in central cities.
Heterogeneity analysis by city centrality (location in central cities vs. non-central cities) in the 2013–2022 Chinese A-share manufacturing panel.
Regional peer effects are stronger for enterprises occupying central positions within interlocking directorate networks (IDNs).
Heterogeneity analysis by firm centrality within IDNs using the 2013–2022 A-share manufacturing dataset.
Industrial peer effects are stronger in highly competitive industries.
Heterogeneity analysis across industry competition levels in the 2013–2022 Chinese A-share manufacturing panel.
Industrial peer effects are more pronounced for enterprises in non-central positions within interlocking directorate networks (IDNs).
Heterogeneity analysis (subgroup analysis) by firm centrality within IDNs using the 2013–2022 A-share manufacturing sample.
Forward-looking, robust regulation is necessary to ensure the digital world remains a safe place for young people and to fully protect their rights, privacy, and well-being.
Prescriptive recommendation from the book's conclusions based on its comparative analysis of law, policy, and practice; the excerpt provides no empirical study or quantified analysis to directly validate this necessity.
Across the European Union, most youth use the internet daily and encounter digital environments from an early age.
Claim in the text; likely grounded in EU-wide survey data (e.g., Eurostat, EU Kids Online) measuring frequency of internet use among youth, but the excerpt gives no specific source, method, or sample size.
Children and young people are growing up more connected than any previous generation.
Asserted in the book summary; likely based on cross-cohort and population-level data on device ownership and internet access (e.g., national/EU surveys), but no specific study, dataset, method, or sample size is specified in the provided excerpt.
Federal funding for automation in specialty crops has been a focus of increased funding by both the US Department of Agriculture and the National Science Foundation, providing a path for innovators to produce automation and technology for nursery crops.
Statement in the paper about increased federal funding priorities (USDA and NSF); no specific program names, funding amounts, or timelines provided in the excerpt.
The percent of all tasks automated has increased approximately 15% over a 15-year period ending in 2021.
Comparison reported from a national labor survey (mid-2000s to 2021); exact survey methodology and sample size are not provided in the excerpt.
Use of the H-2A visa program has increased tremendously for the green industry in the past decade to help stop-gap the labor crisis.
Paper's statement about trend in H-2A program usage for the green industry; specific administrative data, years, or magnitudes not provided in the excerpt.
The main conclusions are reliable after various robustness tests.
Paper reports multiple robustness checks (unspecified in abstract) applied to the DID estimates using the 2003–2017 industry panel, which reportedly do not overturn the main findings.
The results support the 'capital‑technology complementarity' theory: AI combined with capital investment yields higher marginal returns, especially in capital‑intensive industries.
Empirical finding of larger marginal AI effects in capital‑intensive industries via interaction terms on the 2003–2017 Chinese industry panel; interpreted as evidence for capital‑technology complementarity.
Synergy between AI and R&D investment amplifies the growth effect of AI.
Interaction regressions in DID framework on the 2003–2017 panel showing that industries with higher R&D investment exhibit larger AI-related growth effects (positive AI × R&D interaction).
AI promotes economic growth through efficiency improvements and by driving innovation.
Mechanism tests reported in the paper (mediation/auxiliary analyses) using the 2003–2017 industry panel that link AI measures to productivity/efficiency indicators and innovation outcomes, which in turn relate to growth.
Capital‑intensive industries benefit more significantly from AI, with a higher marginal effect.
Heterogeneity analysis and interaction tests in the DID framework on the 2003–2017 panel; interaction of AI measures with capital intensity shows larger marginal effects for capital‑intensive industries.
Knowledge‑intensive service industries gain more significant growth benefits from AI than other services.
Subsample/heterogeneity analysis of service industries within the China 2003–2017 panel showing stronger AI effects for knowledge‑intensive services.
GenAI functions not just as a tool for cost reduction but as a strategic lever for growth, primarily through enhanced innovation, implying a need for sustained investment in technological infrastructure and workforce skills.
Interpretation of empirical findings: stronger mediating role of product innovation and positive direct effect on business performance; managerial/policy implications drawn in discussion section based on these results.
Technological competence, top management support, and competitive pressure are key drivers of GenAI adoption.
TOE/RBV-based predictor variables were tested in the PLS-SEM model; these constructs showed significant positive path coefficients to GenAI adoption in the survey data (n = 312).
Product innovation is a significant partial mediator of the relationship between GenAI adoption and business performance and exhibits a stronger mediating effect than operational efficiency.
Comparative mediation analysis in PLS-SEM reported significant indirect effects for both mediators, with the indirect effect size (or relative path coefficients) through product innovation larger than through operational efficiency (n = 312 survey responses).
Operational efficiency is a significant partial mediator of the relationship between GenAI adoption and business performance.
Mediation tests within the PLS-SEM framework using survey data (n = 312) showed significant indirect effect of GenAI adoption on business performance via operational efficiency, while a direct effect remained (partial mediation).
Integrating AI into irrigation substantially enhances productivity, economic returns, and sustainability outcomes for wheat production under semiarid conditions in Iraq.
Synthesis of field experiment results (yield, water use, energy, WUE), statistical significance (ANOVA results), economic evaluation (NPV, BCR, IRR), and sustainability indices reported in the paper.
Sensitivity analyses confirmed that investment profitability remained robust under adverse scenarios, including increased capital costs and reduced wheat prices.
Reported sensitivity analyses in the paper stating robustness of profitability under adverse scenarios; specific scenarios mentioned include increased capital costs and reduced wheat prices (details of scenario ranges not provided in the excerpt).