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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Sustainability indicators improved: Sustainability Efficiency Index (SEI) increased from 0.25 to 0.51.
Reported sustainability indices computed in the study showing SEI values before and after AI-assisted irrigation implementation.
Economic evaluation showed strong feasibility of AI-assisted irrigation: NPV = USD 18,121, BCR = 2.81, IRR = 30%, payback period = 3.65 years.
Cost–benefit analysis, net present value (NPV), benefit–cost ratio (BCR), and internal rate of return (IRR) reported in the paper as calculated from the field experiment outcomes and economic modeling.
To enable large-scale adoption of Material Passports, cohesive adoption strategies, unified standards, stakeholder collaboration, clear responsibilities, and regulatory support are needed.
Practical recommendations synthesized from the included studies and authors' discussion summarizing common requirements and enablers identified across the literature.
Digital tools have potential to address MP implementation challenges by improving cohesion, enabling dynamic updates, and enhancing interoperability.
Reported propositions and case examples in the literature included in the review suggesting digital solutions (e.g., digital platforms, DPPs, DBLs) as approaches to improve data cohesion, dynamic updating, and interoperability.
Material Passports (MPs) are crucial for bridging the data gap hindering CE adoption in the AEC industry.
Thematic findings across the included studies emphasizing MPs' role in providing material and product data; synthesis in the paper concluding MPs as a key instrument to address data scarcity.
AI should be framed as augmentation rather than substitution, implying organizations need to invest in workforce upskilling in AI literacy to prevent harmful displacement and to enable designers to act as 'co-pilots' or 'AI curators'.
Interpretive and normative conclusion based on observed productivity/innovation benefits and literature/theoretical discussion; no firm-level employment displacement metrics reported in the study.
Managers should prioritize Generative Design and Predictive Analytics and adopt a 'Data-First' strategy (digitize historical assets and build digital infrastructure) to realize AI-enabled efficiency and innovation gains in design projects.
Managerial recommendations derived from the empirical findings linking AI to productivity and innovation gains; prescriptive guidance rather than empirically tested interventions within the paper.
AI functions as a bridge between project management efficiency and creativity in design projects, enabling automation of routine workflows and freeing designers to focus on higher-value creative tasks.
Interpretation based on empirical findings (AI positively associated with TFP and innovation) and mechanism discussion; supported by text-analysis results and conceptual framing in the paper (no granular project-level workflow logs presented).
The research provides insight into Resource-Based View (RBV) and Dynamic Capabilities (DC) theory by showing that AI Adoption contributes to competitive advantage and sustainability-related firm performance.
Theoretical integration and empirical findings reported in the paper linking AI Adoption (measured in the 207-firm survey) to outcomes interpreted through RBV and DC frameworks.
AI Adoption creates a significant competitive advantage for companies, improving their success in creating entrepreneurial and technology-based firms.
Reported PLS-SEM findings from the 207-firm survey linking AI Adoption to competitive advantage and firm-level entrepreneurial/technology-based success (paper frames this within RBV and dynamic capabilities theory).
AI Adoption enables sustainable business models (holistic sustainability) and is associated with increased economic, environmental, and social performance.
PLS-SEM results from the 207-firm survey reportedly showing positive relationships between AI Adoption and measures of sustainable business models / economic, environmental, and social performance (paper links AI Adoption to holistic sustainability outcomes).
AI Adoption provides companies with opportunities for strategic renewal.
PLS-SEM analysis linking AI Adoption (measured in the survey of 207 entrepreneurial businesses) to strategic renewal/opportunity constructs reported as positive in the paper.
Competitive pressures are a significant positive factor contributing to a firm's decision to adopt AI.
PLS-SEM analysis of survey data from 207 entrepreneurial firms measuring competitive pressure and AI Adoption (paper reports a positive relationship).
Social influences are a significant positive factor contributing to a firm's decision to adopt AI.
PLS-SEM analysis of survey data from 207 entrepreneurial firms measuring social influence and AI Adoption (paper reports a positive relationship).
Facilitating conditions are a significant positive factor contributing to a firm's decision to adopt AI.
PLS-SEM analysis of survey data from 207 entrepreneurial firms measuring facilitating conditions and AI Adoption (paper reports a positive relationship).
The study links digital technologies to evolving economic models, offering insights into how nations can leverage digital infrastructures to foster competitiveness, resilience, and sustainable growth.
Claim about the paper's contribution and policy-relevant insights; the abstract does not lay out the specific analytical framework, case comparisons, or empirical backing used to generate these policy prescriptions.
Digital transformation enhances efficiency and inclusion.
Reported as a finding in the paper; the abstract does not specify the empirical indicators, measurement approach, or samples used to establish efficiency and inclusion gains.
China’s digital economy framework demonstrates the role of state-led policies, technological innovation, and private sector dynamism in shaping one of the world’s most advanced digital ecosystems.
Paper includes a special focus on China (case analysis implied); the abstract does not provide the specific evidence, datasets, or case-study methodology supporting this claim.
The digital revolution has fundamentally reshaped global economic structures, driving a transition from traditional labor- and capital-intensive systems toward knowledge-, data-, and technology-driven models.
Assertion presented in the paper's analysis; specific empirical methods, data sources, and sample size are not provided in the abstract.
Generative AI (GenAI) offers transformative potential for productivity and innovation.
Synthesis of themes reported across the 28 reviewed papers (authors' thematic summary of literature highlighting potential productivity and innovation gains).
The review suggests future research to ensure that GeoAI advances are fair, transparent, and aligned with urban policy goals.
Recommendation and research agenda presented in the paper based on identified gaps and ethical/policy considerations from the literature review (formulative guidance rather than empirical proof).
There are opportunities to use GeoAI to enhance climate resilience, alleviate poverty, foster inclusive urban strategies, and develop better cities.
Prospective and applied examples synthesized in the review that illustrate possible applications of GeoAI for resilience, poverty alleviation, and inclusive planning (these are framed as opportunities; specific pilot studies or effect sizes are not provided in the excerpt).
Recent research highlights improvements in methodology, decision-making support, and impacts on resilience, social inclusion, and fair governance.
Aggregate claim from the review of recent research; supported by cited methodological advances and application studies showing decision-support impacts (the excerpt does not enumerate the studies or quantitative measures).
GeoAI methods support spatial planning, risk assessment, and policymaking in cities facing climate change, socio-economic disparities, and environmental challenges.
Review of applied GeoAI studies and case examples reported in the paper that demonstrate use in spatial planning, risk assessment, and policy support (specific studies and sample sizes not provided in the excerpt).
Policy priorities should include enforceable AI governance, life-cycle carbon accounting across hydrogen supply chains, and targeted SME capability policies to realize conditional synergies between digitalization and green transition.
Policy recommendations derived from the review of empirical and institutional literature (authorial proposal based on synthesized evidence; not an empirical test).
Digital tools can accelerate green innovation and emissions reductions when coupled with credible standards, auditability, clean power, and workforce capability building.
Synthesis of peer-reviewed research and authoritative institutional reports (review article); conditional-synergy thesis based on multiple empirical and policy studies cited in the review (no single primary sample size reported).
These findings highlight research opportunities for machine learning applications in finance and for the development of sentiment-based corporate disclosure analytics.
Interpretation by the authors based on identified gaps in the 42-study review (e.g., underused corporate-report sentiment, limited labeled data, geographic concentration, few deep-learning/end-to-end approaches).
Regression-based and other supervised learning approaches remain dominant.
Aggregated reporting from the 42-study review showing a prevalence of regression and supervised ML methods in the literature sample.
The reviewed studies rely on feature-engineered sentiment indices derived from lexicons or sentence-level classification.
Review synthesis noting frequent use of lexicon-based sentiment scoring and sentence-level classification to produce engineered sentiment features across the sampled studies.
Most studies focus on the U.S. stock market.
Findings from the review of 42 studies indicating a majority of the reviewed works concentrate on U.S. markets (geographic coding/synthesis across studies reported by the authors).
Machine learning methods have been widely used to predict stock prices using technical indicators and sentiment features, mostly extracted from social media and news.
Systematic review of the literature summarized in the paper (corpus of 42 studies published 2014–2025) reporting that many reviewed studies use ML to predict stock prices and that sentiment inputs commonly come from social media and news sources.
Visa recapture would reclaim approximately 339,000 unused visas from prior years, delivering immediate backlog relief under existing statutory authority.
Authors' calculation/estimate of cumulative unused employment-based visas available for recapture (presumably based on historical visa usage statistics from the Department of State); the excerpt does not show the year-by-year accounting or the assumptions used to reach 339,000.
Dependent exemption (excluding spouses and minor children from counting toward the annual cap) would ensure that all 140,000 visas are allocated to independently qualified principal workers rather than divided among family members.
Policy design claim; premise depends on current family-derivative usage of the cap and would require counting statistics (number of visas currently used by dependents) to quantify effect—those counts are not provided in the excerpt.
Increasing the annual employment-based visa ceiling would alleviate the overall shortage that persists regardless of allocation methods.
Logical/policy claim that raising the statutory cap increases supply; the excerpt does not include a quantitative elasticity, model, or simulation showing the required increase or magnitude of backlog reduction.
Phasing out the seven-percent per-country cap would gradually transition visa allocation from nationality-based limits to a demand-driven system, allowing applicants from high-demand countries to advance in the backlog without causing abrupt increases in wait times for those from low-demand countries.
Policy proposal with implied simulation/modeling rationale (demand-driven allocation); the excerpt does not provide a formal model, simulation parameters, or empirical test showing the gradual, non-disruptive transition.
This study extends the technology–organisation–environment (TOE) theory by providing comprehensive empirical evidence of internal and external factors affecting BT adoption.
Use of the TOE framework to structure empirical analysis on 27,400 firm-year observations (2013–2021) linking technology (AI), organisation (corporate culture), and environment (market competition, government support, digital financial development) variables to BT adoption outcomes.
Environmental factors—market competition, government support, and the level of digital financial development across provinces—positively affect BT adoption.
Empirical tests using the 27,400 firm-year sample (2013–2021) incorporating provincial- and market-level environmental variables (market competition, measures of government support, and provincial digital financial development indices) alongside firm-level data and BT adoption coding from annual reports.
Externally oriented corporate cultures, specifically competition-oriented and creation-oriented cultures, positively affect BT adoption.
Same sample of 27,400 firm-year observations (2013–2021). Corporate culture indicators (competition- and creation-orientation) collected via Python web crawler from the management discussion & analysis (MD&A) sections of annual reports; BT adoption measured by manual annual report keyword search and content validation.
AI technology positively affects blockchain technology (BT) adoption.
Empirical analysis of 27,400 firm-year observations of Chinese A-share listed firms (2013–2021). AI technology measured using AI patent data collected via a Python web crawler from annual report MD&A sections and China National Knowledge Infrastructure (CNKI). BT adoption identified by manual search of annual reports for the keyword 'blockchain technology' and content assessment to confirm adoption status.
To alleviate adverse spatial spillovers, it is necessary to strengthen interactive development between digital–real integration and New Quality Productive Forces, foster interregional cooperation, and optimize resource allocation.
Policy recommendations derived from the paper's empirical findings (bidirectional positive relationship and negative spatial spillovers) — normative conclusion based on observed results.
The promotional effect of digital–real integration on New Quality Productive Forces is slightly stronger than the reverse effect (New Quality Productive Forces on digital–real integration).
Comparison of estimated coefficients from the GS3SLS spatial simultaneous equations model (paper reports the coefficient for integration→productive-forces is marginally larger than productive-forces→integration).
Cost–benefit analyses in AI economics should internalize long-term, hard-to-quantify harms (autonomy loss, social trust erosion) rather than rely solely on market price signals.
Normative critique of standard welfare analysis with literature support from ethics and political philosophy; no empirical recalculation of cost–benefit models provided.
Investing in privacy-preserving AI methods (differential privacy, federated learning, synthetic data) and governance institutions is warranted as an alternative to atomized data markets.
Policy and technical recommendation based on literature on privacy-preserving techniques and governance models; paper does not present original technical evaluations or cost–benefit analyses.
Economists modeling AI markets should incorporate non-pecuniary harms, externalities, and moral constraints when assessing welfare, innovation trade-offs, and optimal policy.
Normative recommendation grounded in philosophical argument and critique of standard welfare frameworks; not supported by empirical methodological comparison in the paper.
The paper's conceptual contribution challenges macro-centric crisis narratives by centering social mechanisms (support systems, peer benchmarking, institutional trust) as critical determinants of small-firm adaptation.
Theoretical framing (novel socially embedded analytical lens) combined with empirical results showing the importance of networks, identities, and normative motivations in explaining adaptation outcomes relative to macro-structural explanations.
The rapid rise of AI-enhanced robotics since the 2010s signals a shift toward increased embedding of AI into hardware systems, accelerating cross-sector spillovers.
Interpretation based on observed acceleration in AI-enhanced robotics patents (patent filings 1980–2019) and the convergence patterns reported in the paper. This is an inference drawn from patenting trends rather than a directly measured measure of cross-sector spillovers.
Crises (pandemics, supply shocks) tend to accelerate digital and AI adoption, potentially shortening adjustment time to new technological regimes.
Interpretation of recent historical episodes (e.g., COVID-19) and diffusion literature; qualitative assertion without presented microeconometric identification.
AI and the green transformation function as modern long-wave drivers by improving operational efficiency, enabling new products and services, and reorganizing competitive hierarchies.
Conceptual argument linking general-purpose technology literature to observed/anticipated capabilities of AI and green tech; literature synthesis without original empirical tests.
Schumpeterian cycles are driven by clusters of technological innovations and entrepreneurial activity; AI and green technologies represent contemporary innovation clusters with strong potential for productive disruption.
Application of Schumpeterian theory to contemporary technology trends via literature synthesis and conceptual argument (no empirical quantification provided).
Integrating lived temporality into design and evaluation is necessary to preserve and enhance the qualitative aspects of human life in transhumanist transformation.
Normative/philosophical argument supported by literature synthesis and conceptual reasoning; no empirical demonstration (N/A).