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
Remove filter
The integration of digital and artificial intelligence (AI) technologies is fundamentally reshaping global maritime logistics.
Secondary data synthesis of recent academic literature and international trade facilitation reports (e.g., UNCTAD) reported in the study; method: secondary data analysis (no primary data collection reported).
Technological progress has historically contributed to productivity and economic growth.
Asserted in the paper as a historical generalization within the conceptual analysis; no original empirical data or sample provided in this paper to quantify the effect.
The integration of Fuzzy BWM-PROMETHEE II-DEMATEL framework constitutes a novel methodological contribution and provides useful decision support for strategic planning and resource allocation.
Authors' methodological claim in the paper that combining these fuzzy MCDM techniques is novel and yields decision-support outputs; novelty and practical utility asserted but not externally validated in the provided summary.
Addressing High Initial Investment and Supply Chain Integration initially helps accelerate digital readiness and enhance transformation performance.
Inference/recommendation derived from the PROMETHEE II and DEMATEL results that mark these two factors as dominant causal drivers; no reported empirical intervention or longitudinal validation in the provided text.
Fuzzy BWM results highlight Customization, Flexible Production, Human–Machine Collaboration, and Cybersecurity as the most influential practices supporting I4.0 implementation.
Results reported from the paper's Fuzzy BWM analysis informed by literature survey and expert judgments. (Exact number/composition of experts and statistical details not provided in the supplied summary.)
AI-driven solutions enhance strategic decision-making in HRM.
Claimed by the authors following their literature synthesis and empirical work with HR professionals across IT firms (methodology described but specific decision-quality measures not provided in the summary).
AI-driven solutions improve accuracy in HR operations.
Stated in the paper based on the same literature review, data analysis, and empirical study with HR professionals from multiple IT companies (no numeric accuracy metrics or sample size provided in the summary).
AI-driven solutions enhance HR operations by improving efficiency.
Reported in the paper as a conclusion drawn from a literature review, data analysis, and an empirical study involving HR professionals from various IT firms (summary does not state sample size or exact measures).
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).
AI features increase consumers' purchase intention for electronic products.
Reported relationship tested via SEM between AI feature constructs and the dependent variable 'purchase intention' in the structured questionnaire data (no sample size or statistical details provided in the summary).
AI features improve perceived decision-making support (i.e., ease of decision-making / simplification of product evaluation).
Reported empirical result from SEM analysis of questionnaire data measuring perceived decision-making support as a dependent variable.
AI features positively affect consumer trust.
Empirical finding reported from SEM analysis linking AI features to the dependent variable 'consumer trust' in the questionnaire data (specific effect sizes/p-values not provided in the summary).
AI-enabled features significantly enhance consumer confidence and satisfaction by simplifying product evaluations and increasing perceived usefulness.
Reported empirical result from this study based on a quantitative research design using a structured questionnaire and analyzed with Structural Equation Modeling (SEM). (Sample size and specific statistical values not reported in the summary.)
The proposed framework positions Medicaid procurement as a lever for climate action, health equity, and long-term system resilience.
Theoretical synthesis and policy argumentation drawing on Stakeholder Theory, TBL, and examples from literature and benchmarking (conceptual claim; no empirical outcome data demonstrating realized lever effects).
International benchmarking with the UK National Health Service (NHS) Net Zero strategy demonstrates feasibility and scalability of ESG-integrated procurement approaches.
Comparative case benchmarking using the NHS Net Zero strategy as an international exemplar (qualitative comparative analysis; single-case international comparison; no pilot or implementation data for Medicaid presented).
The paper synthesizes theoretical foundations, operational mechanisms, and policy instruments—particularly Section 1115 waivers—to propose a practical roadmap for embedding ESG principles into Medicaid procurement.
Policy analysis and literature synthesis combining theoretical discussion with review of policy tools (Section 1115 waivers singled out); the roadmap is a proposed construct in the paper, not empirically implemented.
Value-based procurement can and should be reconceptualized beyond cost containment to include environmental stewardship, social equity, and institutional accountability.
Argument based on literature review across healthcare procurement, ESG governance, and TBL; normative policy analysis rather than empirical testing.
This paper develops an ESG-integrated framework for greening the Medicaid supply chain, anchored in Stakeholder Theory and the Triple Bottom Line.
Conceptual framework development based on theoretical synthesis of Stakeholder Theory and Triple Bottom Line (TBL) and literature in sustainable supply chain management and ESG governance (method: literature-driven framework construction; no empirical validation reported).
The SDK provides interoperability via MCP and A2A.
Implementation and interoperability description in the paper claiming MCP and A2A support; can be verified in code and integration tests.
AESP enforces the invariant that agents are economically capable but never economically sovereign.
Formal design of the protocol and five enumerated mechanisms described in the paper (policy engine, human review, EIP-712 commitments, HKDF isolation, ACE-GF substrate). Enforcement claim derives from architectural guarantees rather than empirical validation in the abstract.
The Agent Economic Sovereignty Protocol (AESP) is a layered protocol that lets agents transact autonomously at machine speed on crypto-native infrastructure while remaining cryptographically bound to human-defined governance boundaries.
Protocol design and specification presented in the paper; implementation claimed (see TypeScript SDK). No runtime throughput/latency measurements reported in the abstract.
Rigorous user evaluation can help develop systems that allow for effective and responsible human agency in veracity-assessment processes.
Interpretation and conclusion drawn from the study's findings showing differences in user behavior across support types and highlighting design implications for tooling that supports human verification.
User responsibility for assessing veracity is particularly critical in sectors that rely on on-demand, AI-generated data extraction, such as biomedical research and the legal sector.
Framing and motivation in the paper (domain argument citing the high-stakes nature of biomedical and legal information extraction). This is a contextual claim rather than a new empirical result from the study.
LLM explanations enable rapid veracity assessments.
Same controlled user study, where assessment speed (time-to-decision) was measured for the LLM-explanation condition and found to be fast relative to other conditions.
Passage retrieval offers a reasonable compromise between accuracy and speed, with judgments of veracity comparable to using the full source text.
Controlled user study comparing three types of supporting information (full source text, passage retrieval, LLM explanations). Outcome measures reported include veracity-judgment accuracy and time-to-assessment. (Sample size and statistical details not specified in the abstract; see paper for exact n and tests.)
AI has the potential to deliver predictive benefits for recruitment and retention.
Aggregated findings from empirical studies in the systematic review and supporting meta-analytic/qualitative evidence across the 85 publications that examine recruitment/retention applications.
The meta-analysis shows a small-to-moderate direct positive relationship between AI use and operational productivity (r = 0.28).
Quantitative meta-analysis reported in the paper; pooled effect size r = 0.28; heterogeneity I^2 = 74% (based on the meta-analytic sample drawn from the reviewed studies).
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.
Emerging data suggest AI is already widely adopted for entertainment purposes — especially by young people — and represents a large potential source of revenue.
Reference to unspecified 'emerging data' (likely usage statistics or surveys) cited by the authors; the excerpt does not give the data source, methodology, or sample size.
Generative AI systems are predominantly designed, evaluated, and marketed as intelligent systems which will benefit society by augmenting or automating human cognitive labor, promising to increase personal, corporate, and macroeconomic productivity.
Authors' synthesis of mainstream discourse and industry positioning (marketing, research and product literature) as described in the paper; no specific sample size or empirical study reported in the excerpt.
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).
Short-term productivity gains are documented.
Findings from some of the 81 reviewed sources report short-term productivity improvements associated with Agentic AI or related interventions. The abstract does not quantify the gains or specify domains/settings.
Analytics can serve as the focal interpretive intercession between AI outputs and human decision-makers, facilitating transparency, accountability, and contextual decision-making.
Conceptual proposition drawn from interdisciplinary literature synthesis and the proposed framework. No empirical validation or measured outcomes presented.
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).
The workforce should be prepared for GenAI-driven changes through targeted skilling programs (upskilling, reskilling, cross-skilling).
Recommendation based on literature and the authors' analyses/discussions; no trial data or program evaluation metrics are reported in the abstract.
Using suitable approaches to skill development and committing to continuous learning within organizations, GenAI drives innovation, improves decision-making, and creates new growth opportunities.
Conclusion drawn from the paper's literature recherche, task analyses (including Erasmus+ projects), and discussions with trainers/educators. The abstract does not present controlled empirical evidence or quantified effect sizes for these outcomes.
GenAI supports skill-assessment tools that enable continuous, granular evaluations of employees’ abilities.
Supported by literature synthesis, analysis of occupational tasks (Erasmus+ projects), and practitioner discussions; no quantitative validation (e.g., accuracy, reliability, sample sizes) reported in the abstract.
GenAI supports learning and development by performing various tasks that influence the creation and interaction with content.
Claim based on reviewed literature and task analyses presented in the paper; specifics of experiments or deployment (e.g., tools used, participant counts) are not provided in the abstract.