Evidence (8974 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).
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Nine broad, paper-level topics. Click one to filter the claims below.
Adoption
10085 claims
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Productivity
8974 claims
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Governance
8062 claims
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Human-AI Collaboration
7749 claims
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Org Design
5057 claims
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Innovation
4896 claims
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Labor Markets
4088 claims
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Skills & Training
3372 claims
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Inequality
2377 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 | 882 | 244 | 117 | 1097 | 2424 |
| Governance & Regulation | 1010 | 469 | 229 | 135 | 1875 |
| Organizational Efficiency | 977 | 235 | 149 | 90 | 1462 |
| Technology Adoption Rate | 781 | 299 | 143 | 128 | 1362 |
| Research Productivity | 506 | 155 | 74 | 363 | 1110 |
| Output Quality | 555 | 219 | 71 | 70 | 915 |
| Decision Quality | 395 | 200 | 95 | 54 | 751 |
| Firm Productivity | 523 | 67 | 101 | 27 | 724 |
| AI Safety & Ethics | 262 | 309 | 75 | 36 | 688 |
| Market Structure | 195 | 201 | 135 | 30 | 566 |
| Task Allocation | 248 | 77 | 96 | 38 | 464 |
| Innovation Output | 300 | 34 | 55 | 20 | 411 |
| Skill Acquisition | 207 | 75 | 65 | 21 | 368 |
| Employment Level | 138 | 67 | 119 | 24 | 350 |
| Fiscal & Macroeconomic | 156 | 80 | 53 | 33 | 329 |
| Task Completion Time | 211 | 38 | 13 | 16 | 280 |
| Firm Revenue | 183 | 52 | 29 | 5 | 270 |
| Consumer Welfare | 131 | 77 | 48 | 13 | 269 |
| Inequality Measures | 50 | 141 | 54 | 9 | 254 |
| Worker Satisfaction | 104 | 85 | 25 | 13 | 227 |
| Error Rate | 87 | 112 | 11 | 5 | 215 |
| Automation Exposure | 69 | 69 | 37 | 20 | 198 |
| Wages & Compensation | 102 | 49 | 31 | 11 | 193 |
| Team Performance | 115 | 30 | 30 | 11 | 187 |
| Regulatory Compliance | 88 | 74 | 17 | 7 | 186 |
| Training Effectiveness | 109 | 22 | 14 | 21 | 168 |
| Developer Productivity | 116 | 21 | 15 | 8 | 161 |
| Job Displacement | 12 | 92 | 26 | 1 | 131 |
| Hiring & Recruitment | 57 | 12 | 9 | 5 | 83 |
| Skill Obsolescence | 6 | 59 | 10 | 2 | 77 |
| Social Protection | 43 | 17 | 8 | 2 | 70 |
| Creative Output | 35 | 21 | 9 | 4 | 70 |
| Labor Share of Income | 18 | 23 | 17 | 1 | 59 |
| Worker Turnover | 15 | 16 | — | 4 | 35 |
| Industry | — | — | — | 1 | 1 |
Productivity
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Leaders should fund training coverage and design (not just headline hours), equip non-specialists to interpret model outputs, pair performance artefacts with participatory routines, and treat explainability as a usability requirement to achieve durable, auditable value in safety-critical energy contexts.
Prescriptive recommendation based on a 'field-tested playbook' synthesised from the multi-case qualitative study (interviews, surveys, documents). The claim is drawn from authors' interpretation of cross-case patterns rather than causal inference. (Sample size not reported.)
Structured upskilling and precise recourse mechanisms are associated with higher confidence, productivity, and clearer sustainability pathways.
Observed association in multi-case qualitative data: interviews, staff/manager surveys, and policy documents; triangulated through thematic coding and cross-case synthesis. (Sample size not reported.)
A tight workflow fit that minimises cognitive overhead at the decision point accelerates legitimate use and strengthens links to emissions monitoring and predictive-maintenance outcomes.
Synthesised from interviews, Likert-scale surveys of technical staff and managers, and internal workflow/policy documents across multiple cases in the energy sector. (Sample size not reported.)
Communicative governance — e.g. model cards, bias tests, validation reports, and explicit appeal rights — earns trust, curbs shadow workarounds, and improves safety culture.
Reported from thematic coding of interviews, surveys of staff and managers, and documentary evidence across multiple cases; triangulation claimed. (Sample size not reported.)
Broad-based capability building beyond specialist teams prevents benefits from concentrating in expert enclaves and reduces brittle scale.
Derived from cross-case thematic synthesis of interviews, Likert surveys of mid-level managers and technical staff, and internal policy/strategy document analysis (multi-case qualitative evidence). (Sample size not reported.)
Three reinforcing levers shape adoption outcomes: (1) broad-based capability building beyond specialist teams, (2) communicative governance that couples transparency with contestability, and (3) a tight workflow fit that minimises cognitive overhead at the decision point.
Qualitative, multi-case design triangulating a semi-structured interview with a senior manager, Likert-scale surveys of mid-level managers and technical staff, and analysis of internal policies and strategy documents; thematic coding with intercoder reliability and cross-case synthesis. (Sample size not reported.)
The framework demonstrates how digital intelligence can enhance supply chain resilience while supporting, rather than replacing, human decision-making (human-centric/planner-centered decision support).
Framework design emphasizes human-centric decision support; field deployment reported to be planner-centered (paper claims support rather than replacement of human decision-making).
The results indicate that upstream textile SMEs can leverage publicly visible e-commerce signals to enhance production planning responsiveness, minimize inventory exposure and dye-lot disruptions, and strengthen resilience to demand uncertainty through planner-centered digital decision support.
Synthesis claim based on model results, validation of comment volume as sales proxy, Monte Carlo-based production guidance, decision dashboard design, and the 12-month field study outcomes.
This research extends the C2M paradigm from downstream retail contexts to upstream textile SMEs and proposes an integrated and operationally feasible intelligence framework for resource-constrained manufacturers.
Conceptual claim supported by the methodological development, large-scale e-commerce data modeling, and a field deployment at one SME reported in paper.
In the same 12-month field study, implementation resulted in a 16% increase in capacity utilization.
Field deployment measurements reported in paper for one Taiwanese dyeing SME over 12 months.
In the same 12-month field study, implementation resulted in a 31% decrease in dye lot changeovers.
Field deployment measurements reported in paper for one Taiwanese dyeing SME over 12 months.
In a 12-month field study at a Taiwanese dyeing SME, implementation resulted in a 28% reduction in inventory value.
Field deployment and before-after (or intervention) measurement reported in paper over 12 months at one Taiwanese dyeing SME.
Forecasts were translated into production guidance using Monte Carlo simulation and a decision dashboard.
Description of operationalization methods in paper: Monte Carlo simulation and a planner-facing decision dashboard used to convert forecasts into production guidance.
Consumer comment volume was validated as a proxy for sales activity, facilitating demand estimation.
Validation analysis reported in paper linking consumer comment volume to sales activity (methodological validation; specific statistical details not provided in abstract).
A Neural Boosted Tree model with entity embeddings for textile attributes was constructed and achieved a mean R2 of 0.921 in cross-validation, surpassing benchmark methods.
Model training and cross-validation reported in paper using the e-commerce dataset; comparison to benchmark methods reported (specific benchmarks not listed in abstract).
The framework incorporates ethically compliant acquisition of consumer demand signals, semantic translation of unstructured market data into textile engineering attributes, machine-learning-based demand forecasting, and human-centric decision support.
Description of framework components and design choices presented in paper (methodological/architectural claim).
This study develops and validates a customer-to-manufacturer (C2M) intelligence framework that enables data-driven production planning using publicly available e-commerce data.
Methodological development described in paper; validation based on ML modeling using e-commerce data and a 12-month field deployment at one Taiwanese dyeing SME.
Future work improving geometric fidelity, data efficiency, and integrated XAI workflows will lead to more accurate and faster 3D molecular prediction and generation and ensure transparent, reliable guidance in drug design.
Forward-looking recommendations and projections in the review; presented as hoped-for research directions rather than empirically demonstrated outcomes.
The authors propose an integrated Q-BioFusion framework that synergizes quantum computing, autonomous experimentation, and generative models to address systemic R&D constraints.
Proposed conceptual framework within the paper; no experimental implementation, benchmarking, or sample sizes reported in the provided text.
Explainable AI (XAI) methods support transparent validation and trustworthy guidance during computer simulation in drug design.
Argument in the review advocating XAI for transparency and validation; no empirical validation or metrics provided in the provided text.
Data scarcity in biological assays can be mitigated via Few-Shot Learning and meta-learning approaches.
Review recommendation and discussion of methodological approaches to data-scarcity problems; no empirical evidence, datasets, or success rates provided in the provided text.
De novo molecular design is being applied using biological foundation models and flow-matching generative architectures.
Review describes practical applications and method classes in de novo design; no experimental results or sample sizes are reported in the provided text.
The performance of AI models in chemoinformatics is intrinsically linked to the quality of molecular representation.
Conceptual and literature-based argument presented in the review emphasizing representational choice as a key determinant of model performance; no benchmarking details given in the provided text.
AI can predict pharmacodynamic (PD) and toxicological effects significantly earlier in the drug discovery process.
Review claim asserting earlier prediction capability via AI models; no empirical metrics, study sizes, or quantified timing improvements given in the provided text.
AI technology, by simulating complex biological systems, has accelerated the innovation of the entire drug discovery pipeline.
Claim made in the review, supported by synthesized examples and cited AI applications across the pipeline (no original empirical evaluation or quantified acceleration provided in the provided text).
Customer satisfaction positively influences (increases) the intention to continue using the e-business service.
Regression analysis on the same Bosnian-Herzegovinian survey data; the paper summary explicitly states customer satisfaction positively affects intention to continue use. Sample size not reported in the provided text.
Perceived operational efficiency enabled by AI statistically significantly increases customer satisfaction in e-business.
Survey data from Bosnia and Herzegovina analyzed using regression; the summary reports operational efficiency is a statistically significant positive predictor of customer satisfaction. Sample size not reported in the provided text.
Perceived convenience (priročnost) supported by AI statistically significantly increases customer satisfaction in e-business.
Same survey data from Bosnia and Herzegovina analyzed with regression analysis; paper summary reports perceived convenience is statistically significantly associated with higher customer satisfaction. Sample size not reported in the provided text.
Perceived personalization supported by AI statistically significantly increases customer satisfaction in e-business.
Survey data collected in Bosnia and Herzegovina analyzed with regression analysis to estimate impact of AI-supported functionalities on customer satisfaction; paper summary states the effect is statistically significant. Sample size not reported in the provided text.
Managers should view AI as a strategic tool to enhance SCR (not only as cost-saving), and focus on optimizing resource allocation, increasing R&D investment, and enhancing organizational agility to amplify AI's resilience effects.
Authors' practical recommendations derived from empirical findings and mechanism analysis.
The paper provides empirical evidence that policy tools such as the National AI Innovation and Application Pioneer Zone can help enhance industrial and supply chain security (i.e., SCR).
Analysis was based on the policy of the National AI Innovation and Application Pioneer Zone and authors state their results provide empirical evidence supportive of such policies.
AI's impact on SCR is more significant in enterprises with lower levels of pollution.
Heterogeneity analysis reported by the authors that splits sample by pollution level.
AI's impact on SCR is more significant in private enterprises (versus non-private).
Heterogeneity analysis by ownership type reported in the paper.
AI's impact on SCR is more significant in large-scale enterprises.
Heterogeneity analysis across firm-size categories reported by the authors.
Enterprise agility significantly moderates the AI–SCR relationship: AI's positive effect on SCR is more pronounced in firms with higher agility.
Moderation analysis reported in the paper (moderation models applied to firm-level data).
AI boosts SCR by promoting continuous technological innovation.
Mediation analysis in the paper indicates continuous technological innovation (e.g., R&D/innovation indicators) is a channel through which AI enhances resilience.
AI mainly boosts SCR by improving total factor productivity (TFP).
Mechanism (mediation) analysis reported in the paper using firm-level data; authors identify TFP improvement as a key mediating channel.
The positive effect of AI on SCR holds after multiple robustness checks.
Authors state that the main conclusion remains valid after conducting multiple unspecified robustness checks on the empirical sample (multi-period DID).
AI significantly enhances supply chain resilience (SCR) in manufacturing firms.
Empirical analysis of A-share listed manufacturing companies (2011–2023) using a multi-period difference-in-differences (DID) model; authors report the finding and state it remains after robustness checks.
Prediction intervals are a more suitable evaluation format than point estimates for numerical forecasting because they require scale awareness, internal consistency across confidence levels, and calibration over a continuum of outcomes.
Conceptual/analytical argument presented in the paper explaining why prediction intervals better capture uncertainty and testability for continuous numerical forecasting (no empirical proof provided in the excerpt).
Technology-driven recruitment has emerged as a strategic imperative for organizations seeking competitive advantage in talent acquisition.
Argumentative/interpretive claim in the paper's introduction and discussion, supported by survey findings (N=150) indicating perceived strategic importance.
The paper proposes the Technology-Enabled Recruitment Optimization Framework (TEROF), a structured implementation model designed to guide organizations through the phased adoption of recruitment technology.
Paper synthesizes its empirical findings into a named framework (TEROF) described in the discussion/conclusions; based on combined survey (N=150) and case-study analysis (4 organizations).
Video interview platforms improved recruiter productivity by 41%.
Reported quantitative finding from the study's survey (N=150) and corroborating case study observations.
AI-powered resume screening reduced initial shortlisting time by 64%.
Reported quantitative result in the paper derived from the survey of HR professionals (N=150) and illustrated in case studies.
Integrated technology-driven recruitment produced a 52% reduction in cost-per-hire relative to traditional methods.
Reported quantitative finding from the study's survey (N=150) and supporting case studies (4 organizations).
Adoption of integrated recruitment technology yielded a 45% improvement in candidate quality as measured by first-year performance ratings.
Reported quantitative result from the survey (N=150) and case study evidence using first-year performance ratings as the quality metric.
Organizations adopting integrated technology-driven recruitment platforms experienced an average reduction in time-to-hire of 38%.
Reported quantitative finding based on the paper's mixed-methods analysis (survey of 150 HR professionals and corroborating qualitative case studies of 4 organizations).
We introduce DELEGATE-52 to study the readiness of AI systems in delegated workflows; DELEGATE-52 simulates long delegated workflows that require in-depth document editing across 52 professional domains (e.g., coding, crystallography, and music notation).
Paper describes creation of a benchmark/dataset called DELEGATE-52 covering 52 professional domains and designed to simulate long delegated document-editing workflows.
Our paper contributes to the emerging discourse on AI overreliance and provides an understanding of the appropriate degree of reliance as essential to developers making the most of these powerful technologies.
Authors' claimed contribution based on synthesis of themes from twenty-two interviews and presentation of the reliance-control framework.
The reliance-control framework can be used to recommend future research to explore different control levels supported by current and emergent LLM-driven tools.
Paper explicitly uses the framework to motivate and recommend directions for future research; based on qualitative interview findings (n=22) and authors' synthesis.