Evidence (13661 claims)
Adoption
8339 claims
Productivity
7479 claims
Governance
6715 claims
Human-AI Collaboration
6267 claims
Org Design
4098 claims
Innovation
3987 claims
Labor Markets
3488 claims
Skills & Training
2888 claims
Inequality
2016 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 740 | 192 | 95 | 871 | 1945 |
| Governance & Regulation | 796 | 388 | 185 | 119 | 1512 |
| Organizational Efficiency | 765 | 186 | 123 | 82 | 1166 |
| Technology Adoption Rate | 610 | 227 | 121 | 95 | 1061 |
| Research Productivity | 409 | 121 | 56 | 331 | 928 |
| Output Quality | 464 | 174 | 58 | 47 | 743 |
| Decision Quality | 318 | 173 | 75 | 42 | 615 |
| Firm Productivity | 432 | 55 | 88 | 20 | 601 |
| AI Safety & Ethics | 214 | 273 | 65 | 33 | 589 |
| Market Structure | 175 | 165 | 120 | 24 | 489 |
| Task Allocation | 206 | 64 | 70 | 31 | 376 |
| Skill Acquisition | 161 | 57 | 57 | 16 | 291 |
| Innovation Output | 201 | 27 | 41 | 18 | 288 |
| Fiscal & Macroeconomic | 130 | 69 | 43 | 26 | 275 |
| Employment Level | 104 | 50 | 105 | 13 | 274 |
| Consumer Welfare | 116 | 62 | 42 | 11 | 231 |
| Firm Revenue | 149 | 45 | 26 | 3 | 223 |
| Inequality Measures | 43 | 120 | 49 | 6 | 218 |
| Task Completion Time | 164 | 29 | 8 | 12 | 214 |
| Worker Satisfaction | 89 | 60 | 20 | 12 | 181 |
| Error Rate | 69 | 89 | 9 | 2 | 169 |
| Regulatory Compliance | 74 | 67 | 14 | 4 | 159 |
| Training Effectiveness | 91 | 19 | 13 | 19 | 144 |
| Wages & Compensation | 77 | 33 | 25 | 6 | 141 |
| Team Performance | 86 | 17 | 27 | 9 | 140 |
| Automation Exposure | 49 | 50 | 22 | 12 | 136 |
| Developer Productivity | 91 | 17 | 14 | 5 | 128 |
| Job Displacement | 12 | 80 | 19 | 1 | 112 |
| Hiring & Recruitment | 51 | 7 | 8 | 3 | 69 |
| Creative Output | 31 | 16 | 7 | 2 | 57 |
| Skill Obsolescence | 5 | 43 | 6 | 1 | 55 |
| Social Protection | 27 | 16 | 8 | 2 | 53 |
| Labor Share of Income | 17 | 17 | 17 | — | 51 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
When firms adopt AI as an augmentative tool rather than a replacement mechanism, it can raise worker productivity and contribute to job creation.
Literature review citing empirical examples and studies of AI augmentation that increased productivity and produced new job roles (empirical studies summarized).
Combining insights from multiple disciplines, the review contributes to broader discussions on creating AI-enabled work environments that are both innovative and gender-inclusive.
Stated as the paper's contribution and framing in the abstract; based on the paper's described interdisciplinary literature synthesis rather than new empirical findings.
Practical recommendations that improve gender-inclusive outcomes include reskilling, mentorship programs, bias-aware AI deployment, and inclusive organizational design.
Recommendations synthesized from the reviewed literature and policy analyses; the abstract does not indicate rigorous causal evaluations or quantification of the effectiveness of these interventions within the paper.
There exist successful initiatives, organizational strategies, and policy interventions that have enhanced women’s inclusion, career progression, and representation in emerging tech roles.
Paper reports examples from the reviewed literature and policy analyses that are characterized as 'successful initiatives'; the abstract does not list specific programs, evaluation designs, or sample sizes.
The study extends resource-based, knowledge-based, and dynamic capabilities perspectives by conceptualising competitive intelligence as a mediating dynamic capability that transforms AI-driven data into actionable strategic knowledge.
Theoretical/conceptual synthesis supported by the study's empirical results (quantitative n = 312; qualitative n = 28).
AI enhances sensing, analytics, and reporting capabilities, and these capabilities are embedded into strategic routines to produce strategic value only when integrated into CI processes and organisational routines.
Mixed-methods evidence: quantitative associations (n = 312) showing AI → CI → growth/sustainability plus qualitative interview evidence (n = 28) describing how AI-enabled sensing/analytics/reporting are embedded into routines.
Qualitative Gioia analysis of 28 semi-structured interviews identifies three aggregate dimensions: AI-enabled competitive intelligence, strategic decision making and growth, and sustainable value creation.
Qualitative data from 28 semi-structured interviews across manufacturing, financial services, telecommunications, and retail sectors; analysis using the Gioia methodology.
CI effectiveness partially mediates the relationship between AI capability and sustainability outcomes.
Mediation analysis reported from the quantitative survey sample (n = 312); mediation described as 'partial'. Exact indirect effect size not provided in summary.
CI effectiveness partially mediates the relationship between AI capability and corporate growth.
Mediation analysis reported from the quantitative survey sample (n = 312); mediation described as 'partial'. Exact indirect effect size not provided in summary.
CI effectiveness significantly predicts sustainability performance (β = 0.47, p < .001).
Quantitative survey (n = 312); reported standardized regression/path coefficient β = 0.47, p < .001.
CI effectiveness significantly predicts corporate growth (β = 0.51, p < .001).
Quantitative survey (n = 312); reported standardized regression/path coefficient β = 0.51, p < .001.
AI capability significantly predicts competitive intelligence (CI) effectiveness (β = 0.62, p < .001).
Quantitative survey (n = 312) of senior managers and strategy professionals from medium and large Zimbabwean firms; reported standardized regression/path coefficient β = 0.62, p < .001.
The research provides empirical evidence from an emerging economy (China) to comparative research on global AI governance.
Statement of contribution/implication in the paper noting that the empirical findings from Chinese A-share listed manufacturing firms contribute to comparative studies on AI governance internationally.
Enhancing the ESG performance of manufacturing enterprises represents a critical pathway for promoting high-quality economic development and achieving sustainable development goals.
Framing/background claim made in the paper's introduction/motivation; normative statement connecting ESG improvement to broader economic and sustainable development objectives (not presented as an empirical result within this study).
The Pilot Zone policy effects are more evident among non-labor-intensive enterprises.
Heterogeneity analysis by factor structure reported in the paper showing stronger policy impacts for firms classified as non-labor-intensive.
The Pilot Zone policy effects are more evident among non-capital-intensive enterprises.
Heterogeneity analysis by factor structure reported in the paper showing stronger policy impacts for firms classified as non-capital-intensive.
The policy effects are more evident among high-tech enterprises.
Heterogeneity analysis by firms' technological endowment/industry classification reported in the paper showing larger policy effects for high-tech manufacturing firms.
The policy effect on ESG performance is stronger for non-high-pollution enterprises than for high-pollution enterprises.
Heterogeneity analysis by pollution intensity reported in the paper (comparison between high-pollution and non-high-pollution manufacturing firms).
The Pilot Zone policy has a more pronounced enabling effect on ESG performance for non-state-owned enterprises compared with state-owned enterprises.
Heterogeneity analysis by ownership type reported in the paper (comparison between state-owned vs. non-state-owned A-share listed manufacturing firms under DID specification).
Operational efficiencies significantly moderate the policy effect, further amplifying the Pilot Zone policy's positive impact on ESG performance.
Reported moderation/heterogeneity analysis indicating that firms with higher operational efficiency experience stronger positive policy effects on ESG performance.
Enterprise resource allocation significantly moderates the policy effect, amplifying the enabling effect of the Pilot Zone policy on ESG performance.
Reported moderation/heterogeneity analysis showing interaction effects between measures of enterprise resource allocation and the Pilot Zone policy on ESG outcomes in the DID framework.
The policy enhances manufacturing enterprises' ESG performance by strengthening environmental compliance pressures (regulatory/compliance channel).
Mechanism analysis reported in the paper identifying increased environmental compliance pressure as a transmission channel linking the Pilot Zone policy to improved ESG performance.
The policy primarily enhances manufacturing enterprises' ESG performance by intensifying R&D expenditure intensity (R&D investment channel).
Mechanism analysis reported in the paper identifying R&D expenditure intensity as a transmission channel between the Pilot Zone policy and firm ESG performance (presumably mediation/interaction tests within DID framework).
The positive effect of the Pilot Zone policy on manufacturing firms' ESG performance is robust to parallel trends tests, placebo tests, and multiple robustness checks.
Reported application of common DID robustness diagnostics: parallel trends test, placebo tests, and additional robustness checks (details not provided in abstract). Same sample frame: A-share listed manufacturing firms, 2010–2023.
The Artificial Intelligence Innovation and Development Pilot Zone policy exerts a significant positive effect on manufacturing enterprises' ESG performance.
Empirical analysis using a multi-period difference-in-differences (DID) model leveraging the establishment of National New-Generation Artificial Intelligence Innovation and Development Pilot Zones as a quasi-natural experiment; sample: A-share listed manufacturing enterprises on the Shanghai and Shenzhen Stock Exchanges, 2010–2023. Robustness checks reported (parallel trends, placebo tests, multiple robustness checks).
This work contributes by integrating fragmented literature into a coherent, comparative perspective that offers actionable insights for researchers, policy makers, and industry stakeholders.
Author claim about the contribution of the review (self-assessment; no external validation reported in the abstract).
Findings highlight the growing importance of re-skilling and adaptive policy measures to mitigate employment risks associated with AI.
Policy recommendation derived from the review and synthesis of sectoral literature (no empirical evaluation of re-skilling program effectiveness provided in the abstract).
Knowledge-driven domains experience significant augmentation and skill shifts rather than displacement.
Reported synthesis from the systematic review comparing sectoral effects (qualitative statement; no quantified effect sizes or counts in the abstract).
Traditional software engineering artifacts can serve as effective control mechanisms in AI-assisted development.
Concluding claim in the abstract synthesizing the preliminary evaluation results; presented as the paper's implication/recommendation (based on the exploratory study noted).
Embedding machine-readable requirements and architectural artifacts reduces implementation drift.
Reported as a preliminary finding from the exploratory evaluation; the abstract claims a reduction in implementation drift when using Shift-Up artifacts versus unstructured approaches (no quantification provided).
This paper proposes Shift-Up, a framework that reinterprets established software engineering practices (executable requirements / BDD, C4 architectural modeling, and architecture decision records / ADRs) as structural guardrails for GenAI-native development.
Design-science research (DSR) artifact: the Shift-Up framework is presented as the paper's primary design contribution (description/proposal in the paper; no broad empirical validation in the abstract).
Generative AI (GenAI) is reshaping software engineering by shifting development from manual coding toward agent-driven implementation.
Stated as a high-level premise in the paper's introduction/abstract; presented as an observed trend motivating the research (no empirical sample or quantified measurement reported in the abstract).
The classical First Fundamental Theorem of Welfare Economics is recovered as the low-autonomy limit of the autonomy-qualified model.
Analytical result in the paper showing limiting case of the model yields the classical theorem (theoretical/mathematical derivation).
Using a minimal general-equilibrium model with autonomy-conditioned welfare, welfare-status assignment, delegation accounting, and verification institutions, we set out conditions for which autonomy-complete competitive equilibrium is autonomy-Pareto efficient.
Formal theoretical development and derivation in a minimal general-equilibrium model described in the paper (mathematical/modeling evidence; no empirical sample).
The First Fundamental Theorem ought to be subject to an autonomy qualification where the impact of changes in autonomy assumptions is incorporated.
Normative prescription based on the paper's conceptual critique and modeling agenda; supported by theoretical reasoning rather than empirical testing.
Government transfers become compelling when singularity-driven growth overwhelms deadweight costs.
Conditional policy conclusion stated in the abstract based on model comparison of welfare gains versus deadweight costs; no empirical calibration or data reported.
Market incompleteness creates a rationale for government transfers.
Normative/policy implication stated in the abstract, derived from the model's welfare comparisons; no empirical validation provided.
Because markets are incomplete -- investors cannot trade private AI capital -- AI stocks command a premium.
Theoretical result asserted in the paper's abstract, derived from the asset-pricing model under market incompleteness (no empirical data provided).
We develop an asset pricing model in which investors use AI stocks to hedge against an AI singularity that displaces their consumption.
Description of the paper's theoretical asset-pricing model and stated model mechanism in the abstract; no empirical test reported.
AI stocks trade at extraordinary valuations.
Explicit statement in the paper's abstract; no empirical data, sample, or statistical analysis reported.
Policy implications: there is a need for infrastructure support and interoperability standards to enable digitalization for resilient supply chains.
Authors' stated policy implications in the paper, derived from empirical findings on the role of digital technologies and visibility.
Practical implications: strategic digital investment should target visibility as a key intermediate performance goal.
Authors' stated practical implications based on empirical results showing visibility mediates digital technologies' effect on resilience.
Heterogeneity analyses reveal stronger effects of digital technologies on visibility and resilience in technology-intensive industries.
Reported heterogeneity/subgroup analyses in the paper (no subgroup sample sizes provided in the excerpt); methods include regression/SEM.
Heterogeneity analyses reveal stronger effects of digital technologies on visibility and resilience in high-complexity supply chains.
Reported heterogeneity/subgroup analyses in the paper (no subgroup sample sizes provided in the excerpt); methods include regression/SEM.
Supply chain visibility mediates 67.4% of the total effect of digital technologies on supply chain resilience (mediation = 67.4%; bootstrap CI [0.156, 0.253]; Sobel test Z = 8.745, p< .001).
Mediation analysis reported in the paper using bootstrapped confidence intervals and a Sobel test; sample of 742 firms.
Supply chain visibility significantly predicts supply chain resilience (= 0.486, p< .001).
SEM / regression coefficient reported in paper with p-value (< .001); sample of 742 firms.
Digital technologies (IoT, blockchain, AI, big data analytics, and cloud computing) exert a significant positive effect on supply chain resilience (= 0.298, p< .001).
Hierarchical regression and SEM results reported in the paper; sample of 742 firms; p-value reported (< .001).
Digital technologies (IoT, blockchain, AI, big data analytics, and cloud computing) exert a significant positive effect on supply chain visibility (= 0.412, p< .001).
Hierarchical regression and SEM results reported in the paper; sample of 742 firms; p-value reported (< .001).
The results demonstrate a 'less is more' pattern: simpler combination (memory + reflection) yields better performance than adding architectural complexity.
Authors' interpretation of the ablation study results showing that adding multiple extra mechanisms degraded performance compared to the memory+reflection configuration.
A nine-variant ablation reveals that memory and reflection together produce a 58% cumulative improvement over the stateless baseline.
Ablation study with nine variants on the sequential portfolio benchmark; authors report a 58% cumulative improvement when combining memory and reflection versus the stateless baseline.