Evidence (13870 claims)
Adoption
8467 claims
Productivity
7558 claims
Governance
6805 claims
Human-AI Collaboration
6363 claims
Org Design
4132 claims
Innovation
4065 claims
Labor Markets
3526 claims
Skills & Training
2945 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 749 | 196 | 98 | 892 | 1984 |
| Governance & Regulation | 817 | 394 | 188 | 121 | 1544 |
| Organizational Efficiency | 771 | 189 | 124 | 83 | 1177 |
| Technology Adoption Rate | 627 | 233 | 123 | 96 | 1088 |
| Research Productivity | 411 | 123 | 56 | 332 | 933 |
| Output Quality | 467 | 178 | 59 | 47 | 751 |
| Decision Quality | 320 | 174 | 75 | 42 | 618 |
| Firm Productivity | 435 | 55 | 88 | 20 | 604 |
| AI Safety & Ethics | 214 | 276 | 65 | 33 | 593 |
| Market Structure | 178 | 167 | 122 | 24 | 496 |
| Task Allocation | 207 | 64 | 71 | 32 | 379 |
| Skill Acquisition | 165 | 59 | 60 | 17 | 301 |
| Innovation Output | 203 | 27 | 43 | 18 | 292 |
| Employment Level | 105 | 52 | 107 | 13 | 279 |
| Fiscal & Macroeconomic | 131 | 69 | 43 | 26 | 276 |
| Consumer Welfare | 116 | 63 | 42 | 11 | 232 |
| Firm Revenue | 150 | 48 | 26 | 3 | 227 |
| Inequality Measures | 44 | 122 | 49 | 6 | 221 |
| Task Completion Time | 169 | 29 | 8 | 12 | 219 |
| Worker Satisfaction | 89 | 63 | 20 | 12 | 184 |
| Error Rate | 69 | 92 | 10 | 2 | 173 |
| Regulatory Compliance | 76 | 68 | 14 | 5 | 163 |
| Training Effectiveness | 93 | 21 | 13 | 19 | 148 |
| Wages & Compensation | 77 | 36 | 25 | 6 | 144 |
| Automation Exposure | 51 | 54 | 22 | 12 | 142 |
| Team Performance | 86 | 17 | 27 | 9 | 140 |
| Developer Productivity | 94 | 17 | 14 | 6 | 132 |
| Job Displacement | 12 | 80 | 20 | 1 | 113 |
| Hiring & Recruitment | 51 | 7 | 8 | 3 | 69 |
| Creative Output | 31 | 17 | 7 | 3 | 59 |
| Skill Obsolescence | 5 | 46 | 6 | 1 | 58 |
| Social Protection | 27 | 16 | 8 | 2 | 53 |
| Labor Share of Income | 17 | 17 | 17 | — | 51 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
AI methods improve risk management (managing risk) in sustainable finance.
Claim synthesized from literature reviewed on AI applications in climate risk analytics and risk modeling; no numerical sample details provided in the excerpt.
AI methods improve portfolio management (managing portfolio) in sustainable finance contexts.
Asserted by the review as part of the assessment of AI effectiveness for managing portfolios and risk in sustainable investing; no quantitative sample size or effect estimate reported in the excerpt.
AI methods (including machine learning, natural language processing, predictive analytics) improve ESG measurement.
Paper claims this as a conclusion from its review of studies applying AI techniques to ESG scoring and analytics; no primary sample sizes or effect estimates presented in the excerpt.
AI facilitates the real-time tracking of environmental and social risks.
Claim reported in the paper as a synthesized finding from reviewed literature on AI applications in sustainability and climate/ESG analytics; no numeric sample size provided.
AI drastically enhances the ESG performance analysis, sustainable investment plan, and transparency of the companies.
Statement in the paper summarizing results from a literature review of studies on AI/ML, NLP, predictive analytics, and sustainability reporting (systematic review synthesis). No specific primary study sample size reported in the excerpt.
Efficient conversion of R&D into technological barriers is key to avoiding the 'AI trap'; new energy vehicle firms should prioritize R&D efficiency, translate innovation into stable returns, and maintain sound financial conditions.
Paper's conclusion/recommendation derived from empirical findings (2013–2023 sample) linking R&D conversion/patent transformation and intelligent equipment output to reduced financial risk from AI dependence.
Strong knowledge or intelligent equipment output and effective patent transformation mitigate the financial risks associated with AI dependence.
Moderation and heterogeneity tests reported in the paper using the same sample (listed NEV and automobile manufacturers, 2013–2023) indicate these factors reduce the adverse effect of AI dependence on financial safety.
Robustness checks using clustered standard errors confirm the stability of all key coefficients.
Abstract states robustness checks were performed using clustered standard errors and that these confirm stability of key coefficients (no additional statistics reported in abstract).
Time effects are pronounced, with positive and significant shifts in 2020 (+7.02) and 2022 (+8.10) relative to the baseline year, reflecting acceleration of digital public administration in the post-pandemic period.
Reported time-effect coefficients in the panel specification (years relative to baseline). Abstract gives +7.02 for 2020 and +8.10 for 2022. No p-values shown in abstract but described as positive and significant.
Random effects (RE) models show a positive cross-country correlation between AI readiness and e-government development, with a coefficient of 0.35 (p < 0.001).
RE model reported in abstract for AI readiness (presumably GAIRI) vs EGDI. Reported RE coefficient = 0.35 (p < 0.001). Sample for GAIRI–EGDI reported as 170 countries (2020–2024).
Random effects (RE) models show a positive cross-country correlation between the AI Vibrancy Score and e-government development, with a coefficient of 2.55 (p < 0.001).
RE model reported in abstract for the AIVS–EGDI relationship. Sample for AIVS–EGDI reported as 36 countries (2018–2022). RE coefficient reported = 2.55 (p < 0.001).
Within-country improvements in AI readiness (Government AI Readiness Index) are positively and robustly associated with higher levels of e-government development, with the FE estimate equal to 0.17 (p < 0.001).
Panel data analysis using fixed effects (FE) on the GAIRI–EGDI sample (Government AI Readiness Index vs E-Government Development Index). Reported FE coefficient = 0.17 with p < 0.001. Sample referred to in abstract for GAIRI–EGDI: 170 countries (2020–2024).
Cheaper search improves learning and consumer surplus.
Analytical results from the paper's theoretical model of agentic two-sided markets; steady-state characterization of dynamics under varying search cost parameters. No empirical sample or experimental data reported.
The dataset, contexts, annotations, and evaluation harness are released publicly.
Paper states that dataset, contexts, annotations, and evaluation harness are released publicly (release / open-source claim).
A structured 2,000-token diff-with-summary prompt outperforms a 2,500-token full-context prompt (enriched with execution context, behaviour mapping, and test signatures) across all 8 models.
Direct prompt/context-size comparison across the 8 models on SWE-PRBench; reported that the 2,000-token diff-with-summary prompt yields better performance than the 2,500-token full-context prompt with extra enrichments.
The LLM-as-judge framework used for evaluation is validated at kappa = 0.75.
Inter-judge validation reported in paper (agreement metric kappa reported as 0.75). Specific validation sample size not stated in the excerpt.
Pull requests are drawn from active open-source repositories, filtered from 700 candidates using a Repository Quality Score.
Dataset curation procedure reported: initial pool of 700 candidate repositories/PRs filtered by a Repository Quality Score to produce the final benchmark.
We introduce SWE-PRBench, a benchmark of 350 pull requests with human-annotated ground truth for evaluating AI code review quality.
Dataset construction described in paper: benchmark contains 350 pull requests with human annotations. Pull requests drawn from active open-source repositories and filtered from 700 candidates using a Repository Quality Score.
The paper concludes by articulating expected outcomes for management practice and proposes a research agenda calling for future mixed-methods validation of the framework.
Stated conclusion and explicit call for mixed-methods validation; no validation results provided in this paper.
The review derives constructs, hypothesized links among them, and governance implications for managing and institutionalizing workplace AI.
Paper reports that reviewed sources were used to derive constructs and governance implications; this is a conceptual derivation rather than empirical testing.
The framework and synthesis can be used to diagnose patterns of disengagement and pilot-to-production failure in corporate AI initiatives.
Proposed analytical structure derived from literature synthesis and conceptual mapping; intended as a diagnostic tool but not empirically validated within this paper.
The paper integrates adoption frameworks (TAM and TOE) with evidence on human-AI interaction to produce a scaling-oriented conceptual framework for diagnosing disengagement and pilot-to-production failures.
Comparative conceptual analysis and framework building based on reviewed literature; no new empirical validation reported.
Integrating technological, human, and organizational capabilities is important to maximize the benefits of AI in smart manufacturing.
Conclusion based on thematic patterns in interviews, observations, and document analysis from purposively sampled supply chain and production professionals; identified as an implementation implication.
Firms adopting AI-driven forecasting and inventory strategies can achieve higher operational agility, better strategic resource alignment, and maintain a competitive advantage in dynamic manufacturing contexts.
Synthesis and implications drawn from thematic analysis of interviews, site visits, and documents from purposively sampled industry practitioners; presented as study conclusions rather than quantitatively tested outcomes.
AI supports sustainability initiatives within manufacturing operations.
Thematic analysis of practitioner interviews and organizational documentation where respondents linked AI-based forecasting/inventory optimization to sustainability outcomes (e.g., waste reduction).
AI improves supply chain coordination among partners and internal functions.
Interview and document-based thematic findings from purposively sampled supply chain managers and industry experts reporting enhanced coordination following AI adoption.
AI contributes to operational resilience in manufacturing supply chains.
Qualitative evidence from interviews and organizational documents indicating that AI-enabled forecasting and inventory controls improve firms' ability to adapt to disruptions; thematic analysis produced resilience as a reported benefit.
Organizational readiness, skilled personnel, data quality, and robust technological infrastructure are critical factors influencing AI effectiveness.
Recurring themes identified via thematic analysis of semi-structured interviews with supply chain and production professionals, corroborated by observational site visits and organizational documents from purposive sample.
AI reduces excess inventory levels in manufacturing firms.
Thematic findings from interviews, site visits, and documents from industry experts and practitioners who reported decreased excess inventory following AI-driven forecasting and inventory optimization.
AI reduces stockouts in manufacturing supply chains.
Practitioner accounts and organizational document evidence from purposive qualitative sampling and thematic analysis indicating fewer stockouts associated with AI-driven forecasting and inventory controls.
AI adoption reduces operational inefficiencies in manufacturing processes.
Thematic analysis of qualitative data (semi-structured interviews, site observations, organizational documents) from purposively sampled industry practitioners reporting reductions in inefficiencies after AI implementation.
AI supports proactive decision-making among supply chain and production stakeholders.
Qualitative reports from interviews and document review with supply chain managers, production planners, and industry experts; thematic analysis identified proactive decision-making as a theme associated with AI use.
AI enables adaptive inventory management in manufacturing operations.
Findings from thematic analysis of semi-structured interviews with supply chain managers, production planners, and industry experts, plus observational site visits and organizational documents (purposive sampling).
AI technologies enhance forecasting accuracy in smart manufacturing.
Qualitative evidence from purposive sample of supply chain managers, production planners, and industry experts gathered via semi-structured interviews, observational site visits, and organizational documents; analyzed using thematic analysis.
Endogenous structural break analysis identifies 2007 as the break year for AI introduction in India.
Empirical analysis reported in the paper using an endogenous structural break test applied to relevant time-series data (paper states 2007 was identified as the break year).
A shift in preference towards non-traded AI services exacerbates income inequality among previously homogeneous workers in the non-traded sector (model finding).
Results from the paper's Finite Change General Equilibrium (theoretical) model which introduces AI as a shock in the non-traded sector and analyzes effects via price adjustments.
Artificial intelligence (AI) induced services are a reality in India and other developing countries.
Statement in paper citing existence/emergence of AI-powered services (examples given: Windows Live, AI ride-hailing apps such as Ola and Uber); descriptive assertion rather than quantified empirical analysis in the paper.
Our dataset is available at https://guide-bench.github.io.
Paper's statement providing a URL for dataset access.
Graphical User Interface (GUI) agents have the potential to assist users in interacting with complex software (e.g., PowerPoint, Photoshop).
Motivating claim in the paper's introduction/abstract, based on prior work and the authors' argument about potential application domains.
Providing user context significantly improved the performance, raising help prediction by up to 50.2pp.
Experimental comparison reported in the paper showing differences in Help Prediction performance with and without provided user context; reported improvement magnitude of up to 50.2 percentage points.
GUIDE defines three tasks - (i) Behavior State Detection, (ii) Intent Prediction, and (iii) Help Prediction that test a model's ability to recognize behavior state, reason about goals, and decide when and how to help.
Paper's benchmark/task definitions describing three evaluation tasks and their goals.
GUIDE consists of 67.5 hours of screen recordings from 120 novice user demonstrations with think-aloud narrations, across 10 software.
Paper's dataset description: dataset construction of screen recordings, number of demonstrations, duration, participant expertise (novice), and inclusion of think-aloud narrations across 10 software.
Geographical, cultural, and institutional proximities facilitate collaboration in the AI industry.
SAOM inclusion of dyadic proximity covariates in the longitudinal patent-collaboration model (2013–2024) with reported positive effects for geographic, cultural, and institutional proximity on tie formation.
Organizations with higher innovativeness attract more collaborative partners.
SAOM results linking organizational innovativeness (measured via patenting/innovation indicators) to greater degree (number of collaborative partners) in longitudinal patent data (2013–2024).
Universities and research institutions play a more central role in driving network evolution than firms.
SAOM analysis of patent-collaboration network trajectories (2013–2024) showing higher centrality/greater influence of universities and research institutions relative to firms in the modeled network evolution.
Endogenous structural effects — specifically transitivity and preferential attachment — actively shape tie formation in China’s AI industry collaboration network.
Empirical SAOM results on longitudinal patent collaboration data (2013–2024) testing endogenous network effects (transitivity, preferential attachment) on tie formation.
Collaboration networks play a crucial role in fostering innovation within the artificial intelligence (AI) industry.
Statement supported by analysis of longitudinal patent collaboration data (2013–2024) using a stochastic actor-oriented model (SAOM) integrating structural effects, organizational attributes, and dyadic proximities.
Overall, the results support the view that stable, deployable sentiment indicators require careful reconstruction, not only better classifiers.
Synthesis/conclusion drawn from the paper's empirical evaluations and proposed methods.
This three-week lead-lag is a structural regularity more informative than any single correlation coefficient.
Interpretation/claim based on empirical comparisons within the paper stating that the persistent lead-lag pattern provides more structural information than single correlation metrics.
The key empirical finding is a three-week lead lag pattern between reconstructed sentiment and price that persists across all tested pipeline configurations and aggregation regimes.
Empirical result reported in the paper: observed lead-lag relationship (three-week lead) between reconstructed sentiment and stock price across multiple pipeline/aggregation settings; no numerical sample size or statistical estimates provided in the abstract.