Evidence (4892 claims)
Search and filter individual claims pulled from the papers. Looking for a specific finding ("what's the effect on wages?"), you're in the right place. Want to compare whole outcome categories against each other instead? Use the Evidence Explorer.
The board below groups claims two ways: by broad theme (nine paper-level topics) and by outcome category (the 34 claim-level outcomes that the Explorer and Syntheses also use).
Browse by theme
Nine broad, paper-level topics. Click one to filter the claims below.
Adoption
9875 claims
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Productivity
8807 claims
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Governance
7870 claims
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Human-AI Collaboration
7560 claims
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Org Design
4892 claims
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Innovation
4781 claims
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Labor Markets
4004 claims
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Skills & Training
3308 claims
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Inequality
2332 claims
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Claims by outcome category
Counts by direction of finding. These are the same 34 outcome categories the Explorer compares and the Syntheses are written for. A linked row has a published synthesis.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 870 | 233 | 116 | 1066 | 2363 |
| Governance & Regulation | 976 | 451 | 218 | 133 | 1809 |
| Organizational Efficiency | 949 | 224 | 144 | 88 | 1416 |
| Technology Adoption Rate | 764 | 287 | 141 | 122 | 1325 |
| Research Productivity | 501 | 152 | 74 | 362 | 1101 |
| Output Quality | 542 | 216 | 69 | 69 | 896 |
| Decision Quality | 387 | 198 | 94 | 54 | 740 |
| Firm Productivity | 513 | 67 | 101 | 27 | 714 |
| AI Safety & Ethics | 249 | 303 | 73 | 36 | 667 |
| Market Structure | 190 | 192 | 134 | 27 | 548 |
| Task Allocation | 243 | 77 | 91 | 36 | 452 |
| Innovation Output | 291 | 33 | 55 | 20 | 401 |
| Skill Acquisition | 206 | 72 | 65 | 21 | 364 |
| Employment Level | 133 | 63 | 115 | 22 | 335 |
| Fiscal & Macroeconomic | 153 | 79 | 52 | 32 | 323 |
| Task Completion Time | 206 | 37 | 12 | 15 | 272 |
| Firm Revenue | 179 | 52 | 29 | 5 | 266 |
| Consumer Welfare | 130 | 76 | 47 | 13 | 266 |
| Inequality Measures | 48 | 137 | 51 | 6 | 242 |
| Worker Satisfaction | 101 | 81 | 25 | 13 | 220 |
| Error Rate | 84 | 110 | 11 | 5 | 210 |
| Wages & Compensation | 98 | 47 | 30 | 10 | 185 |
| Regulatory Compliance | 88 | 73 | 17 | 7 | 185 |
| Automation Exposure | 66 | 64 | 33 | 16 | 182 |
| Team Performance | 105 | 29 | 30 | 11 | 176 |
| Training Effectiveness | 109 | 22 | 14 | 21 | 168 |
| Developer Productivity | 114 | 21 | 14 | 8 | 158 |
| Job Displacement | 12 | 90 | 24 | 1 | 127 |
| Hiring & Recruitment | 57 | 9 | 9 | 5 | 80 |
| Skill Obsolescence | 6 | 56 | 9 | 1 | 72 |
| Social Protection | 43 | 17 | 8 | 2 | 70 |
| Creative Output | 35 | 21 | 9 | 4 | 70 |
| Labor Share of Income | 18 | 21 | 17 | 1 | 57 |
| Worker Turnover | 15 | 16 | — | 4 | 35 |
| Industry | — | — | — | 1 | 1 |
Org Design
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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 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.
Tailoring AI explanations to individual users can improve human–AI team performance and provides insights into how personalization may enhance human-AI collaboration.
Synthesis of experimental findings across the two preregistered tasks: observed interactions between user characteristics and explanation types, and demonstration of complementarity in the geography task, form the basis for this general claim. (This is an inferential conclusion drawn from the experiments; full generalizability depends on task scope and replication.)
In the geography-guessing task, user characteristics interact with explanation types, and these interactions contribute to human–AI complementarity (the joint performance exceeds either alone).
Results from the preregistered geography-guessing experiment showing interaction effects between user characteristics and explanation types that lead to observed complementarity. (Exact effect sizes, statistical significance, and sample size not provided in the excerpt.)
We designed a geography-guessing task in which humans and AI possess complementary strengths.
Task design described in the paper intended to generate complementary error patterns between humans and the AI model (methodological claim based on experimental design). (Details on design specifics and validation not provided in the excerpt.)
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.
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 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.
Upskilling, reskilling, cross-skilling, and learning initiatives are necessary mechanisms for organizations to prepare their workforce for GenAI-driven changes.
Derived from literature recherche and analysis of individual tasks across occupations within Erasmus+ projects, plus practitioner discussions; no sample sizes or outcome metrics specified.
Generative AI (GenAI) models are growing rapidly, changing job roles, and revolutionizing entire industries.
Stated by the authors based on a literature recherche (scope and search strategy not specified in abstract). No quantitative sample size or bibliometric details provided.
From a practical perspective, the study highlights the importance of designing decision systems that leverage AI’s analytical strengths while preserving human oversight, responsibility, and strategic sense-making.
Practical recommendations derived from the paper's synthesis of literature and theoretical framework (prescriptive guidance; abstract contains no implementation data or outcome measures).
Advances in algorithmic intelligence have enabled organizations to augment human decision-making through data-driven insights, predictive analytics, and automated reasoning systems.
Claim derived from review of technological and applied research literature synthesized in the conceptual meta-analysis (no specific datasets or sample sizes reported in abstract).
Closing the gender gap in digital skill use at work will require more than increasing women’s participation in STEM education or occupations; workplace organisation, task allocation, progression pathways, and organisational practices also need attention.
Policy inference drawn from empirical finding that education, field of study and occupational controls explain only a minority of the gender gap in advanced digital task use in ESJS decompositions.
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.
Focused, small Skills (2–3 modules) are more effective than comprehensive documentation-style Skills.
Experimental analysis comparing Skill granularity: authors report higher pass-rate gains for Skills composed of 2–3 focused modules versus larger, comprehensive documentation-style Skills within the SkillsBench experiments. (Details on exact sample counts per granularity condition are reported in the paper's Skill-design analyses.)
Practical recommendation: include policy historians and political‑economy scholars in AI advisory bodies and require replication/open data for influential results to limit covert ideological influence.
Normative and institutional recommendations based on the historical case study showing interdisciplinary gaps and channels of influence; proposed remedies in the paper.
Practical recommendation: increase transparency and disclosure of funding, affiliations, and normative assumptions in AI economics research to make potential persuasion effects visible.
Policy recommendation derived from the case study's findings about how funding and institutional contexts shaped intellectual influence; prescriptive inference rather than empirical demonstration.
These anti‑democracy/anti‑market ideas gained legitimacy and wider influence through elite channels (notably Nobel laureates and canonical publications), increasing their influence on policy and public discourse.
Tracing dissemination pathways via publication venues, prestige of authors (including Nobel laureates), citation and institutional channels, and archival records indicating engagement with policy circles; qualitative inference from prominence of authors and outlets.
The paper's qualitative framework can be operationalized for economists into measurable constructs such as task-level time use, output quality metrics, billable hours, client satisfaction, wages, and employment composition.
Authors propose next steps and measurement opportunities; suggestion comes from translating interview-derived categories into empirical variables for future work.
Architectural education should integrate AI tool training and algorithmic thinking to align workforce skills with evolving task demands.
Authors' recommendation grounded in interview evidence that students are adopting algorithmic strategies and in the constructed conceptual framework; presented as pedagogical implication.
Algorithmic thinking strategies—procedural, iterative, and prompt-based reasoning—are central to how students engage with GenAI during co-design.
Inductive thematic analysis of student interviews identified recurring descriptions of procedural/iterative prompting and tool orchestration as core practices.
Firms and hospitals need differentiated investment and governance strategies by interaction level: integration and workflow redesign for AI-assisted; training and decision-support protocols for AI-augmented; process redesign, liability allocation, and oversight for AI-automated systems.
Prescriptive recommendations derived from cross-case findings (n=4) and the conceptual mapping to innovation management implications.
Different interaction levels produce heterogeneous productivity gains (throughput increases, faster/safer decisions, process cost reductions); economic evaluation should be level-specific.
Theoretical/generalization drawn from observed effects across the four qualitative cases and conceptual analysis linking interaction level to types of productivity gains.
Adoption of healthcare AI is better framed as an evolution toward 'Human+' professionals (complementarity) rather than wholesale replacement of clinicians.
Cross-case interpretive analysis of the four qualitative case studies and theoretical framing with Bolton et al. (2018); presented as the paper's core insight.
AI-automated solutions streamline end-to-end processes (e.g., automated reporting pipelines) while keeping humans in supervisory/exception roles, producing process reconfiguration and efficiency gains and shifting roles toward exception management and governance.
Observed characteristics of the AI-automated case(s) in the qualitative multiple case study (n=4) and synthesized in cross-case comparison.
AI-assisted applications automate highly repetitive tasks (e.g., triage routing, routine image preprocessing), producing increased service availability and throughput while freeing clinician time but requiring oversight and workflow integration.
Empirical observations from one or more of the four qualitative case studies illustrating AI-assisted use-cases; interpreted via the Bolton et al. framework and cross-case comparison.
Phased implementation with middleware/integration layers and hybrid architecture is recommended to balance control, customization, and security.
Paper's implementation recommendation derived from pilot experience and the architecture's trade-offs; recommendation rather than empirically validated strategy in the summary.
AI components (predictive cash-flow analytics, automated compliance checks, risk-scoring) improved automation and decision support within the financial framework.
Paper describes integration of AI for predictive analytics and automation and reports improved automation as a benefit in pilot validation. No quantitative accuracy metrics, model validation details, or sample sizes given in the summary.
Blockchain/decentralized ledger provided improved security and auditability of transactions (tamper-evident records and secure milestone payments).
Paper proposes blockchain for tamper-evident records and reports technical validation for blockchain components (immutability). Experimental deployment claims improved security/auditability, but the summary gives no incident counts or security metrics.
The framework produced enhanced cash-flow visibility and faster reconciliation.
Listed as a reported benefit in the paper's experimental validation and implied by the use of real-time cloud systems and AI-driven forecasting. Evidence in the summary is qualitative; no quantitative metrics for 'visibility' or 'reconciliation speed' were provided.
Regulatory compliance efficiency improved by 40% following the framework implementation.
Reported numeric improvement from the paper's experimental validation (pilot or before/after comparison). No details on how 'regulatory compliance efficiency' was operationalized or measured, nor sample size or statistical analysis, were provided.
Financial processing time was reduced by 87.5% after implementing the hybrid cloud financial framework.
Reported as a result from the paper's experimental validation (pilot deployments / pre/post benchmarking). The summary did not provide sample size, baseline definition, or measurement period.
A hybrid cloud financial framework—combining SaaS for core accounting, PaaS for customization, and Blockchain for secure transactions—substantially improves financial operations in the EPC industry.
Paper presents a proposed hybrid framework and reports experimental validation (described as pilot deployments / before–after comparisons). Specific methodological details (sample size, number of firms/projects, duration, statistical tests) are not reported in the summary.
Researchers should develop benchmark datasets and validated simulation testbeds (industry‑anonymized) to enable reproducible economic analysis.
Explicit research recommendation in the paper's implications and research agenda section.