Evidence (13827 claims)
Adoption
8454 claims
Productivity
7544 claims
Governance
6789 claims
Human-AI Collaboration
6327 claims
Org Design
4126 claims
Innovation
4058 claims
Labor Markets
3520 claims
Skills & Training
2924 claims
Inequality
2057 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 749 | 195 | 97 | 889 | 1979 |
| Governance & Regulation | 815 | 391 | 188 | 121 | 1539 |
| Organizational Efficiency | 771 | 189 | 124 | 83 | 1177 |
| Technology Adoption Rate | 624 | 233 | 123 | 96 | 1084 |
| Research Productivity | 410 | 121 | 56 | 331 | 929 |
| Output Quality | 466 | 177 | 59 | 47 | 749 |
| 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 | 166 | 122 | 24 | 495 |
| Task Allocation | 206 | 64 | 70 | 31 | 376 |
| Skill Acquisition | 165 | 57 | 60 | 17 | 299 |
| Innovation Output | 201 | 27 | 41 | 18 | 288 |
| Employment Level | 105 | 51 | 107 | 13 | 278 |
| Fiscal & Macroeconomic | 131 | 69 | 43 | 26 | 276 |
| Consumer Welfare | 116 | 63 | 42 | 11 | 232 |
| Firm Revenue | 149 | 46 | 26 | 3 | 224 |
| Inequality Measures | 44 | 122 | 49 | 6 | 221 |
| Task Completion Time | 169 | 29 | 8 | 12 | 219 |
| Worker Satisfaction | 89 | 61 | 20 | 12 | 182 |
| Error Rate | 69 | 91 | 10 | 2 | 172 |
| Regulatory Compliance | 76 | 68 | 14 | 5 | 163 |
| Training Effectiveness | 92 | 19 | 13 | 19 | 145 |
| 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 |
| Skill Obsolescence | 5 | 45 | 6 | 1 | 57 |
| Creative Output | 31 | 16 | 7 | 2 | 57 |
| Social Protection | 27 | 16 | 8 | 2 | 53 |
| Labor Share of Income | 17 | 17 | 17 | — | 51 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
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.
The paper introduces a novel posted-price procurement model with coverage objectives for studying platform procurement of human input.
Methodological contribution declared in the paper: presentation of a new formal model (posted-price procurement with coverage objectives).
A small coalition of targeted low-cost workers who commit to a price floor forces the platform's total spending to change from logarithmic to linear in M.
Theoretical analysis within the model showing that when a targeted subset of low-cost workers commit to a minimum price, the asymptotic scaling of platform spending increases from logarithmic (in M) to linear (in M); proof-based, no empirical sample.
A research-degree-student survey showed high performance ratings across information reliability, theoretical depth and logical rigor, with pronounced ceiling effects on a 7-point scale, despite all participants already being frontier-model users.
Authors report results from a survey of research-degree students evaluating the scholar-bots on specified dimensions (information reliability, theoretical depth, logical rigor) using a 7-point scale and note ceiling effects; participants reportedly were experienced model users.
Recovered panel scores placed Scholar A between 7.9 and 8.9/10 and Scholar B between 8.5 and 8.9/10 under multi-turn debate conditions.
Paper reports numeric panel scores (ranges) for the two scholar-bots in multi-turn debate scenarios; scores are presented as recovered panel evaluations.
Appointment-level recommendations placed both bots at or above Senior Lecturer level in the Australian university system.
Authors state that appointment-level syntheses from assessors recommended both scholar-bots at or above the Senior Lecturer rank (Australian system); based on the experts' syntheses.
Across the preserved expert record, all review and supervision reports judged the outputs benchmark-attaining.
Authors report that the preserved set of expert review and supervision reports (from the three assessors) rated scholar-bot outputs as attaining the benchmark standards used for assessment.
The scholar-bots were deployed across doctoral supervision, peer review, lecturing and panel-style academic exchange.
Authors report deployment of the generated scholar-bots in multiple academic task contexts (doctoral supervision, peer review, lecturing, panel debates); reported as part of evaluation protocol.
We converted those systems into structured inference-time constraints for a large language model.
Authors describe a pipeline that transforms the extracted scholar reasoning artefacts into inference-time constraints applied to a LLM; presented as part of methods for the two scholar cases.
We extracted the scholarly reasoning systems of two internationally prominent humanities and social science scholars from their published corpora alone.
Authors report an extraction procedure applied to the published corpora of two named scholars; claim is descriptive of dataset and method (n=2).
From synthesis of results, we suggest three practices that focus on preserving agency in software engineering for coding, learning, and mentorship, especially as AI grows increasingly autonomous.
Authors' prescriptive recommendations derived from the paper's qualitative synthesis; presented as proposed practices rather than empirically tested interventions.
Seniors leverage pre-AI foundational instincts to steer modern tools and possess valuable perspectives for mentoring juniors in their early AI-encouraged career development.
Qualitative accounts from senior participants in the Delphi/ACTA process and blind reviews showing seniors reference pre-AI practices and see mentoring value.
Juniors enter as AI‑natives, seniors adapted mid‑career.
Authors' synthesis from a three-phase mixed-methods study: ACTA combined with a Delphi process (5 seniors), an AI-assisted debugging task (10 juniors), and blind reviews of junior prompt histories by 5 additional seniors.
Advancing meaningful fairness or accountability in AI requires: (1) recognizing when and how decoys serve as a distraction, and (2) grappling directly with the material political economy of the Project of AI.
Normative prescription based on the paper's conceptual analysis and literature synthesis; recommended two-part approach rather than empirically validated intervention. No sample size or experimental validation provided.
Policy proposals including universal basic income, portable benefits, retraining programs, and AI taxation are viable mechanisms to manage the socio-economic transition associated with AI, and the paper assesses these proposals.
Paper states it evaluates these policy proposals drawing on empirical studies, reports, and historical analysis; the abstract does not report empirical tests or effectiveness estimates for these policies.
The distributional consequences of AI adoption will be shaped primarily by institutional factors—including labor market regulation, education policy, and corporate governance structures—rather than by the technology itself.
Argument based on a literature review drawing on recent empirical studies, industry reports, and historical analyses of past technological transitions; no new empirical estimate or sample size provided in the abstract.
AI differs from previous automation technologies in its capacity to perform cognitive and creative tasks.
Paper's conceptual claim supported by references to recent empirical studies and industry reports on generative AI and large language models; no specific sample size or quantified effect reported in the abstract.
This study uncovers digital diffusion dynamics and provides theoretical foundations for policymaking.
Paper's concluding statement claiming contributions to understanding diffusion dynamics and policy relevance, based on the analyses (main paths, ERGM, heterogeneity).
In the inter-organizational network, only technological diversity (not proximity) promotes main path formation, indicating knowledge recombination drives micro-level trajectories.
ERGM applied to inter-organizational layer: significant positive coefficient for diversity, non-significant (or not positive) coefficient for proximity; interpretation linking to recombination-driven micro-level diffusion.
ERGM results show that combination opportunities (knowledge recombination potential) consistently promote the formation of main diffusion paths across network layers.
ERGM analysis reporting a positive, significant coefficient for a variable representing combination opportunities or recombination potential.
ERGM results show that technological collaboration value consistently promotes the formation of main diffusion paths across network layers.
Exponential Random Graph Models (ERGM) applied to the multilayer networks; reported positive, significant association between measures of technological collaboration value and presence/formation of main paths.
Geographical technology diffusion networks exhibit a 'core–periphery' structure.
Network analysis of the geographical technology diffusion layer indicating a core–periphery topology across regions.
Inter-organizational diffusion paths center on key universities.
Main path analysis and network mapping of the inter-organizational technology diffusion network showing centrality/positioning of universities in the identified paths.
The patent citation network analysis identifies 14 main paths spanning from core technologies like image recognition to enabling applications.
Main path analysis applied to the patent citation network derived from the patent dataset (2000–2024); result reported as identification of 14 main paths and their topical coverage (e.g., image recognition to applications).
Using patent data of China’s manufacturing digital technologies from 2000–2024, this study constructs a multilayer network comprising patent citation networks, inter-organizational technology diffusion networks, and geographical technology diffusion networks.
Methods reported in the paper: patent dataset covering China's manufacturing digital technologies (years 2000–2024); network construction producing three layers (patent citation, inter-organizational diffusion, geographical diffusion).
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).