Evidence (4333 claims)
Adoption
5539 claims
Productivity
4793 claims
Governance
4333 claims
Human-AI Collaboration
3326 claims
Labor Markets
2657 claims
Innovation
2510 claims
Org Design
2469 claims
Skills & Training
2017 claims
Inequality
1378 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 402 | 112 | 67 | 480 | 1076 |
| Governance & Regulation | 402 | 192 | 122 | 62 | 790 |
| Research Productivity | 249 | 98 | 34 | 311 | 697 |
| Organizational Efficiency | 395 | 95 | 70 | 40 | 603 |
| Technology Adoption Rate | 321 | 126 | 73 | 39 | 564 |
| Firm Productivity | 306 | 39 | 70 | 12 | 432 |
| Output Quality | 256 | 66 | 25 | 28 | 375 |
| AI Safety & Ethics | 116 | 177 | 44 | 24 | 363 |
| Market Structure | 107 | 128 | 85 | 14 | 339 |
| Decision Quality | 177 | 76 | 38 | 20 | 315 |
| Fiscal & Macroeconomic | 89 | 58 | 33 | 22 | 209 |
| Employment Level | 77 | 34 | 80 | 9 | 202 |
| Skill Acquisition | 92 | 33 | 40 | 9 | 174 |
| Innovation Output | 120 | 12 | 23 | 12 | 168 |
| Firm Revenue | 98 | 34 | 22 | — | 154 |
| Consumer Welfare | 73 | 31 | 37 | 7 | 148 |
| Task Allocation | 84 | 16 | 33 | 7 | 140 |
| Inequality Measures | 25 | 77 | 32 | 5 | 139 |
| Regulatory Compliance | 54 | 63 | 13 | 3 | 133 |
| Error Rate | 44 | 51 | 6 | — | 101 |
| Task Completion Time | 88 | 5 | 4 | 3 | 100 |
| Training Effectiveness | 58 | 12 | 12 | 16 | 99 |
| Worker Satisfaction | 47 | 32 | 11 | 7 | 97 |
| Wages & Compensation | 53 | 15 | 20 | 5 | 93 |
| Team Performance | 47 | 12 | 15 | 7 | 82 |
| Automation Exposure | 24 | 22 | 9 | 6 | 62 |
| Job Displacement | 6 | 38 | 13 | — | 57 |
| Hiring & Recruitment | 41 | 4 | 6 | 3 | 54 |
| Developer Productivity | 34 | 4 | 3 | 1 | 42 |
| Social Protection | 22 | 10 | 6 | 2 | 40 |
| Creative Output | 16 | 7 | 5 | 1 | 29 |
| Labor Share of Income | 12 | 5 | 9 | — | 26 |
| Skill Obsolescence | 3 | 20 | 2 | — | 25 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
Governance
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This paper introduces the DARE Framework, a holistic, four-dimensional model for national AI strategy and international cooperation.
Factual description of paper content in abstract — the framework is introduced by the authors (conceptual/model contribution).
ERM is an integrated, strategic framework that aligns risk management with corporate governance, objective setting, and performance management.
Conceptual descriptions and definitions of ERM drawn from existing ERM frameworks and literature reviewed in the article.
The Indian government believes that artificial intelligence (AI) will play an important role in India’s continued economic growth, both through its contribution to productivity in the private sector and through smarter and more data-led government.
Reported position in the paper based on review of government statements and policy documents (policy analysis/legal review). No empirical sample size applies; claim is descriptive of government belief.
The positive impact of generative AI on ESG performance is stronger in manufacturing firms, firms in eastern regions, and technology-intensive firms (relative to non-manufacturing, central/western regions, and non-technology-intensive firms).
Heterogeneity/subsample analysis on the panel of Chinese A-share firms (2012–2024) comparing effects across firm types, geographic regions, and technology intensity, showing larger estimated positive effects for manufacturing, eastern-region, and technology-intensive subsamples.
Sustainable innovation partially mediates the relationship between generative AI and corporate ESG performance improvement.
Mediation analysis conducted on the panel dataset (Chinese A-share firms, 2012–2024) indicating a partial mediating role for sustainable innovation measures between generative AI use and ESG performance.
The quality of information disclosure partially mediates the relationship between generative AI and corporate ESG performance improvement.
Mediation analysis (intermediary variable tests) performed on the same panel (Chinese A-share firms, 2012–2024) showing that information-disclosure quality accounts for part of the effect of generative AI on ESG outcomes.
Generative AI can effectively drive improvements in corporate ESG performance.
Empirical analysis using panel data of Chinese A-share listed firms covering 2012–2024; the paper reports an econometric panel-data model showing a positive effect of generative AI adoption/use on measured firm ESG performance.
Twelve testable hypotheses are proposed, with implications for agentic AI oversight and human-AI collaboration.
Paper statement that it proposes twelve testable hypotheses; verifiable by counting hypotheses in the paper.
AI tools—ranging from machine learning algorithms in inventory management to natural language processing in customer engagement—are applied in micro‑enterprise contexts.
Descriptive synthesis from included articles reporting specific AI applications (ML for inventory management; NLP for customer engagement) across the reviewed literature.
Global efforts toward establishing shared norms and multilateral cooperation are underway through initiatives led by the United Nations, OECD, UNESCO, and G7.
Qualitative document review identifying initiatives and normative efforts by multilateral organizations (organizations named; specific initiatives referenced qualitatively but not enumerated as a dataset).
Mainstreaming shared input and embracing climate-resilient management approaches are fundamental action items for building institutional resilience.
Paper conclusion lists these recommended action items based on its analysis of governance and sustainability linkages grounded in SDG and global governance literature; the summary does not indicate empirical testing of these recommendations.
The paper ends with policy implications and recommends periodic evaluation and the integration of AI-related risks into financial governance.
Policy recommendations section in the paper advocating for periodic evaluation and AI-risk integration into financial governance (method: prescriptive/policy analysis based on review findings).
Specialized SDE services that require further study are grouped and highlighted.
Section of the paper grouping and highlighting specialized services for future research (method: expert-driven identification from review; no quantitative prioritization stated).
We introduce a concise conceptual model of a 'shadow' project for designing SDE products or services, detailing participant roles and project composition.
Presentation of a conceptual model within the paper (method: model construction and descriptive exposition; no empirical testing/sample).
The paper proposes a clear classification of criminally oriented products and services in the SDE.
Taxonomy/classification produced in the paper (method: conceptual taxonomy from literature and analysis; no quantitative validation reported).
We identify a structured set of labor‑market roles within the SDE model.
Analytical identification and description of roles within the paper (method: conceptual modeling and qualitative role-mapping; sample size N/A).
We propose an integrated definition of the shadow digital economy that synthesizes technical and economic definitions.
Conceptual analysis and literature synthesis in the paper that combines technical and economic definitions into a single integrated definition (method: review/synthesis; no numeric sample).
Extending existing behavioral frameworks (e.g., TAM, JD–R, Organizational Trust) to the AI-augmented workplace constitutes a theoretical contribution of the paper.
Theoretical elaboration and integration presented in the paper; contribution characterized as an extension of pre-existing models to AI contexts (no quantitative validation described in the summary).
The paper proposes a five-phase strategic roadmap for phased organizational implementation that integrates HRM practice redesign, psychological support systems, and evidence-based governance mechanisms.
Prescriptive/strategic proposal based on the paper's theoretical synthesis and applied recommendations (roadmap described in the paper; summary contains no implementation trial data).
The paper develops a comprehensive, multi-dimensional organizational psychology framework for preparing the U.S. workforce for AI integration composed of six interdependent dimensions: human–AI symbiosis, trust and transparency, job redesign, AI-enabled recruitment and selection, learning and adaptation, and ethical AI governance.
Conceptual framework derived from theoretical integration (TAM, Human–AI Symbiosis Theory, JD–R Model, Organizational Trust Theory) and review of AI–HRM literature; framework construction is a theoretical contribution of the paper (no empirical validation reported in the summary).
Grid-Scale Battery Energy Storage Systems (GS-BESS) play a crucial role in modern power grids, addressing challenges related to integrating renewable energy sources (RESs), load balancing, peak shaving, voltage support, load shifting, frequency regulation, emergency response, and enhancing system stability.
Synthesis of prior literature reported in this systematic review (methodology: literature review following PRISMA guidelines). The excerpt does not specify the number or identity of primary studies summarized for this claim.
Platform work accounts for 12.8% of labor income for participants in the studied sample.
Earnings and income calculations using platform transaction records combined with labor force survey and administrative income data for the 24-country sample (2015–2025).
Platform-mediated gig work has grown to represent 4.2% of total employment across 24 OECD countries (2015–2025).
Aggregate analysis of administrative data, national labor force surveys, and platform transaction records covering 24 OECD countries over the 2015–2025 period.
Medicaid, as the largest public purchaser of healthcare services in the United States, occupies a strategic position to drive systemic change through its supply chain.
Descriptive evidence from publicly available statistics and literature on Medicaid's scale and purchasing role (cited policy/literature sources within the paper); conceptual argument linking purchasing scale to leverage in supply chains.
AESP is implemented as an open-source TypeScript SDK with 208 tests and ten modules.
Implementation claim in the paper: TypeScript SDK, 208 tests, ten modules; verifiable by inspecting the repository and test suite.
AESP is built on an ACE-GF-based cryptographic substrate.
Paper states ACE-GF is used as the cryptographic substrate; implementation referenced in SDK.
AESP employs HKDF-based context-isolated privacy with batched consolidation.
Cryptographic design described in the paper; HKDF-based isolation and batched consolidation listed as mechanisms.
AESP uses EIP-712 dual-signed commitments with escrow to bind agent actions to human consent.
Protocol description cites EIP-712 dual-signed commitments with escrow as a core mechanism; implementation stated in SDK.
AESP provides human-in-the-loop review with automatic, explicit, and biometric tiers.
Design specification in the paper describing three tiers of human review; implementation claimed in the SDK.
AESP includes a deterministic eight-check policy engine with tiered escalation.
Protocol specification and implementation details described in the paper; presence asserted in the SDK implementation.
Economic performance (presumably baseline economic indicators) has a positive effect on growth across all quantiles, with the effect strengthening at upper-tail quantiles (τ = 0.75–0.90).
MMQR results reported in the paper indicating positive coefficients for the economic performance variable at all quantiles, with larger coefficients/greater significance at τ = 0.75–0.90.
AI is often touted for its potential to revolutionize productivity.
Authors' observation about prevailing claims in public, industry, and academic discourse (qualitative observation; the excerpt does not cite specific sources).
The authors propose 'thick entertainment' as a framework for evaluating AI-generated cultural content — one that considers entertainment's role in meaning-making, identity formation, and social connection rather than simply minimizing harm.
Explicit conceptual proposal put forward by the authors in the paper (normative/framework contribution).
The recommended IS research emphases include hybrid human–AI ensembles, situated validation, design principles for probabilistic systems, and adaptive governance.
Explicitly listed components of the authors' proposed research agenda in the discussion section of the paper, derived from synthesis of reviewed literature and conceptual analysis.
To bridge the misalignment, the paper proposes reorienting IS scholarship from analyzing impacts toward actively shaping the co-evolution of technical capabilities with organizational procedures, societal values, and regulatory institutions.
Authors' proposed research agenda and recommendations derived from the synthesis of the 28 reviewed studies and their socio-technical analysis.
The study contributes to theory by developing a human-grounded decision analytics perspective and to practice by providing practical advice to executives and analytics leaders.
Author-stated contributions based on the conceptual framework and practical recommendations included in the paper. No practitioner evaluation or citation analysis provided.
The rapid growth of geospatial data and advances in artificial intelligence (AI) have driven GeoAI’s rise as a key paradigm in urban analytics.
Synthesis from the paper's literature review highlighting trends in data availability and AI capability; evidence likely based on counts of recent publications, reported applications, and domain examples (specific sample size or bibliometric measures not provided in the excerpt).
The study reframes AI as an augmentation mechanism rather than a substitute for managerial judgment and extends organizational decision theory to account for socio-technical decision systems.
Theoretical contribution asserted by the paper based on its literature synthesis and conceptual development (claim about extension of theory rather than empirical test).
The paper develops an integrative conceptual framework that explains how human judgment, algorithmic intelligence, and organizational context interact to shape decision quality and organizational outcomes.
Author-constructed conceptual framework based on synthesized literature across decision sciences, management, and information systems (framework described as output of the meta-analysis; no empirical validation reported in abstract).
We conducted a systematic review of 42 studies published between 2014 and 2025.
The paper's methods section reports selection and inclusion of 42 studies covering the period 2014–2025 (sample size = 42).
This research introduces the RL-FRB/US model which integrates the FRB/US macroeconomic model and a Proximal Policy Optimization (PPO) reinforcement learning agent with an active enhancement of a relocation mechanism for fiscal policy optimization.
Methodological description in the paper: construction of a hybrid model combining the FRB/US structural macro model with a PPO RL algorithm and an added 'active enhancement of relocation' mechanism for fiscal policy decision-making.
Common AI applications in accounting include transaction automation, invoice processing, reconciliations, fraud detection, anomaly detection, automated financial reporting, and predictive forecasting.
Descriptive listing drawn from academic and industry sources/case studies summarized in the paper.
The positive AI → executive pay relationship is robust to endogeneity controls, including instrumental variable approaches, and to multiple robustness checks.
Instrumental variable analyses and a battery of robustness checks reported in the paper applied to the same A-share firm panel and baseline specifications; IV strategy and robustness test details provided in the methods section.
Firm-level AI adoption raises executive compensation in Chinese A-share listed companies (2007–2023).
Baseline panel regressions on a panel of Chinese A-share listed firms (2007–2023) linking a firm-level AI application indicator to executive compensation, controlling for standard firm controls and fixed effects.
Structural breaks in patenting dynamics are concentrated after 2010, consistent with an inflection in AI diffusion and commercialization.
Application of structural-break detection methods to patent filing time series (1980–2019) across domains; reported concentration of detected breakpoints after 2010. (Paper reports timing and clustering of breaks; exact statistical tests not enumerated in the summary.)
Patenting in AI-enhanced robotics experienced a sharp acceleration beginning in the early 2010s.
Observed marked upturn in the AI-enhanced robotics patent time series from the early 2010s onward (patent filings 1980–2019). Structural break tests applied to the time series identify an acceleration concentrated after 2010.
A dynamic Occupational AI Exposure Score (OAIES) that uses LLMs plus occupational task data can estimate time-varying, task-level AI exposure for occupations and workers.
Paper describes a concrete construction algorithm (task decomposition from O*NET/task inventories, LLM-based capability mapping, augmentation vs automation weighting, diffusion/adoption dynamics, and calibration to observed employment/wage/gross-flow changes). This is a proposed method rather than an applied/validated implementation.
The paper issues a research agenda for economists: empirically develop instruments linking first‑person temporal reports with behavioral and neural proxies; theoretically incorporate subjective temporality into models of utility, human capital, attention economics, and platform competition; and evaluate policy accounting for temporal‑experience externalities.
Explicitly stated research agenda and methodological recommendations in the paper; no empirical follow‑up included.
Economists will need new empirical measures: validated instruments translating phenomenological constructs (e.g., Chronons) into observable proxies or composite indices for welfare and labor studies, facing standardization and comparability challenges.
Methodological recommendation and discussion in the paper; no empirical measure development or validation reported.
The paper proposes candidate mappings from subjective reports to neural/behavioral signatures (e.g., neural markers of attentional episodes, temporal binding windows) and suggests experimental paradigms to operationalize temporal units.
Methodological proposals and suggested experimental agendas in the paper; no implemented experiments or sample sizes reported.