Evidence (5539 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 |
Adoption
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AI significantly enhances supplier stability in sports enterprises (SE).
Empirical estimation using a dual machine learning (DML) model on panel data of 45 Chinese listed sports enterprises (2012–2023); authors report a statistically significant positive effect of AI on supplier stability.
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.
General US employment for prime age workers (age 25–54) is currently high (~80%).
Paper cites a current employment rate of 80% for prime-age workers; likely based on national labor statistics though the exact data source and year are not specified in the excerpt.
The growth effect of AI exhibits industry heterogeneity: high‑tech manufacturing industries benefit more significantly.
Heterogeneity/subgroup regressions on the 2003–2017 Chinese industry panel showing larger estimated AI effects in high‑tech manufacturing sectors.
The positive effect of AI on industry growth increases over time.
Dynamic/DID analysis across the 2003–2017 panel showing that the estimated treatment effect grows larger in later periods.
The industry growth rate of the treatment group (industries with intensive AI application or high AI patent concentration) is significantly higher than that of the control group.
DID comparison between treatment and control industry groups in the China 2003–2017 panel, where treatment is defined by intensive AI application or AI patent concentration.
AI technology innovation has a significant positive impact on economic growth.
Industry panel data for Chinese industries from 2003 to 2017 analyzed using a differences-in-differences (DID) approach; main specification estimates effect of AI-related innovation on economic growth.
There is a significant positive direct relationship between Generative AI (GenAI) adoption and business performance.
PLS-SEM results from the cross-sectional survey (n = 312) showing a statistically significant positive path coefficient from GenAI adoption to business performance.
One-way ANOVA confirmed that observed improvements in yield, water use, WUE, and energy consumption were highly significant.
Statistical validation reported as one-way ANOVA with F and p values for wheat yield (F(1,18)=1335.66, p<0.001), water use (F(1,18)=15228.16, p<0.001), WUE (F(1,18)=13065.49, p<0.001), and energy consumption (F(1,18)=24312.67, p<0.001). Degrees of freedom imply 20 total observations (df between=1, df within=18).
Water-use efficiency (WUE) improved by 109% under AI-assisted irrigation (ANOVA F(1,18) = 13065.49, p < 0.001).
Reported WUE improvement percentage and one-way ANOVA treatment effect for WUE: F(1,18) = 13065.49, p < 0.001 from the field experiments.
AI-assisted irrigation decreased energy consumption by 30% (p < 0.001).
Field experiment results with one-way ANOVA showing treatment effect for energy consumption: F(1,18) = 24312.67, p < 0.001. Percentage change reported in the paper.
AI-assisted irrigation reduced water use by 36% (p < 0.001).
Field experiment results with one-way ANOVA showing treatment effect for water use: F(1,18) = 15228.16, p < 0.001. Percentage change reported directly in the paper.
AI-assisted irrigation increased wheat yield by 35% (p < 0.001).
Field experiment results with one-way ANOVA showing treatment effect for wheat yield: F(1,18) = 1335.66, p < 0.001. Percentage change reported directly in the paper.
State-owned enterprises and high-tech firms with robust digital infrastructure experience the largest productivity and innovation gains from AI adoption, indicating absorptive capacity matters.
Heterogeneity analysis on the same panel data comparing subgroups (state-owned vs. non-state-owned; high-tech vs. others; firms with stronger digital infrastructure), showing larger estimated AI effects in those subgroups.
Adoption of AI strengthens firms' innovation outcomes.
Same panel dataset (A-share-listed design firms, 2014–2023) with AI indicators derived from annual reports and patent texts; regression analyses linking AI indicator to innovation metrics (patent-related measures and/or firm-level innovation proxies referenced in the study).
Integrating AI technologies significantly enhances Total Factor Productivity (TFP) in design-oriented, project-based firms.
Panel regression analysis using firm-level panel data of A-share-listed design-oriented enterprises in China (2014–2023). AI exposure measured via an enterprise-level AI indicator constructed from NLP-based text analysis of annual reports and patents; TFP estimated at the firm level as the dependent variable. Robustness checks (e.g., Propensity Score Matching) reported.
The weeder was equipped with a Raspberry Pi microcontroller and a camera module to detect crops and weeds in real-time, enabling autonomous operation.
Design description in the paper: hardware integration of Raspberry Pi and camera module for real-time detection (method: system design and implementation). No sample size or quantitative test data reported for detection accuracy in the provided summary.
AI adoption in Slovakia increased across all enterprise size classes between 2021 and 2024.
Analysis of harmonised Eurostat enterprise-level adoption indicators for 2021–2024 using descriptive statistics and dynamics-of-change methods, disaggregated by enterprise size class. (Sample: enterprises in Slovakia as reported in Eurostat; exact n not specified in the paper summary.)
The study moves beyond treating AI as a monolith by empirically investigating how distinct AI features jointly influence the consumer decision journey.
Methodological claim supported by the study's modeling of three specific AI feature constructs (recommendation engines, chatbots, comparison tools) and analyzing their joint effects via SEM on decision-related outcomes.
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.
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).
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).
Digital–real integration and New Quality Productive Forces exhibit a significant bidirectional positive relationship (each variable positively and significantly promotes the other).
Empirical results from the GS3SLS spatial simultaneous equations model applied to the 30-province panel (2011–2022); paper reports statistically significant positive coefficients in both directions.
Curated (human-authored) Skills substantially improve agent task success on average (+16.2 percentage points).
Aggregate result reported over the SkillsBench benchmark: comparison of pass rates between baseline (no Skills) and curated-Skills conditions across the benchmark. SkillsBench comprises 86 tasks across 11 domains; evaluations used 7 agent–model configurations and 7,308 execution trajectories to compute pass rates and deltas.
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.
Entrepreneurs' expectations about future opportunities were significantly shaped by interpersonal influence (peer effects).
Quantitative analysis linking measures of interpersonal/peer exposure among entrepreneurs to reported expectations about future opportunities; analysis conducted within the >27,000 respondent sample across 43 countries.
Crisis adaptation among small business owners during COVID-19 was driven less by macroeconomic structure and more by social embedding (social networks, peer influence, and collective identities).
Comparative quantitative analysis of a survey sample of over 27,000 individual entrepreneurs in 43 countries using a novel socially embedded framework (networks, collective identities, normative motivations); empirical tests comparing explanatory power of social mechanisms versus macro-structural factors for adaptation outcomes.
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.
The framework situates itself at the intersection of neurophenomenology, computational phenomenology, brain–computer interfaces, and human–AI teaming research.
Cross-disciplinary literature synthesis and conceptual mapping in the paper; descriptive claim with no empirical sampling (N/A).