Evidence (8625 claims)
Adoption
8625 claims
Productivity
7686 claims
Governance
6917 claims
Human-AI Collaboration
6574 claims
Org Design
4189 claims
Innovation
4131 claims
Labor Markets
3588 claims
Skills & Training
2985 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 761 | 200 | 101 | 904 | 2020 |
| Governance & Regulation | 829 | 400 | 191 | 122 | 1566 |
| Organizational Efficiency | 784 | 193 | 125 | 84 | 1197 |
| Technology Adoption Rate | 637 | 236 | 124 | 97 | 1103 |
| Research Productivity | 431 | 131 | 58 | 340 | 972 |
| Output Quality | 481 | 183 | 59 | 47 | 770 |
| Decision Quality | 332 | 177 | 82 | 49 | 647 |
| Firm Productivity | 439 | 57 | 88 | 20 | 610 |
| AI Safety & Ethics | 218 | 279 | 66 | 33 | 602 |
| Market Structure | 181 | 170 | 123 | 24 | 503 |
| Task Allocation | 214 | 64 | 72 | 33 | 388 |
| Skill Acquisition | 174 | 62 | 62 | 17 | 315 |
| Innovation Output | 204 | 27 | 45 | 18 | 295 |
| Employment Level | 105 | 54 | 108 | 13 | 282 |
| Fiscal & Macroeconomic | 132 | 69 | 43 | 26 | 277 |
| Consumer Welfare | 117 | 63 | 42 | 11 | 233 |
| Firm Revenue | 154 | 48 | 26 | 3 | 231 |
| Task Completion Time | 173 | 31 | 8 | 12 | 225 |
| Inequality Measures | 44 | 123 | 50 | 6 | 223 |
| Worker Satisfaction | 89 | 65 | 22 | 12 | 188 |
| Error Rate | 71 | 92 | 10 | 2 | 175 |
| Regulatory Compliance | 77 | 69 | 14 | 5 | 165 |
| Automation Exposure | 58 | 56 | 26 | 13 | 156 |
| Training Effectiveness | 96 | 21 | 14 | 19 | 152 |
| Wages & Compensation | 77 | 37 | 25 | 6 | 145 |
| Team Performance | 86 | 17 | 27 | 10 | 141 |
| Developer Productivity | 95 | 17 | 14 | 6 | 133 |
| Job Displacement | 12 | 81 | 21 | 1 | 115 |
| Hiring & Recruitment | 52 | 7 | 8 | 3 | 70 |
| Creative Output | 32 | 20 | 8 | 3 | 64 |
| Skill Obsolescence | 5 | 47 | 6 | 1 | 59 |
| Social Protection | 28 | 16 | 8 | 2 | 54 |
| Labor Share of Income | 17 | 19 | 17 | — | 53 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Adoption
Remove filter
Mediation and moderation models are leveraged to explore how AI enhances resilience via resource allocation optimization, productivity, and technological innovation, and how conditional factors (e.g., agility) affect these links.
Authors state they used mediation and moderation models on firm-level data to test mechanisms and conditional effects.
The study uses data on A-share listed manufacturing companies from 2011 to 2023 and applies a multi-period difference-in-differences (DID) model to assess AI's impact on SCR.
Methods description provided in the paper summary: sample timeframe and econometric approach explicitly stated.
In the patent citation network, neither technological diversity nor technological proximity shows a significant impact on main path formation.
Layer-specific ERGM results for the patent citation network reporting non-significant coefficients for variables measuring technological diversity and technological proximity.
Technology-driven recruitment encompasses Applicant Tracking Systems (ATS), AI-powered screening, video-based interviews, gamified assessments, and data analytics.
Conceptual description in the paper's introduction/background defining the scope of 'technology-driven recruitment'.
The study employed a mixed-methods research design combining a quantitative survey of 150 HR professionals and recruiters across manufacturing, IT, banking, and education sectors with qualitative case study analysis of four organizations in Chhatrapati Sambhajinagar.
Explicit methodological statement in the paper: quantitative survey (N=150) across specified sectors + qualitative case studies of 4 organizations in Chhatrapati Sambhajinagar.
The study used a mixed-method approach, combining qualitative and quantitative analysis of multiple case studies involving AI applications such as computer vision, robotics, and predictive analytics.
Authors report study design as mixed-method (qualitative + quantitative) applied to multiple case studies examining AI applications (computer vision, robotics, predictive analytics). No numeric sample size reported in the summary.
The paper analyses the complex interactive relationships among job seekers, recruitment platforms, and enterprises on the basis of the classic theory of incomplete information games.
Methodological description in abstract stating the use of incomplete information game theory to model interactions among stakeholders.
Mainstream recruitment algorithms are taken as the core research object and the multidimensional specific manifestations and internal generation mechanisms of group prejudices in algorithm screening are systematically investigated.
Methodological claim in the paper describing the study's scope and analytic focus (systematic investigation of manifestations and internal mechanisms); no empirical detail provided in abstract.
Existing academic research focuses primarily on macrolevel governance paths of algorithmic discrimination, with relatively insufficient in-depth exploration of the microlevel game logic of job seekers and the construction of systematic adaptation strategies.
Paper's literature review/positioning statement claiming a gap in the literature (macro focus vs. microlevel adaptation under-explored); no systematic literature-mapping statistics provided in abstract.
Future research should prioritize longitudinal and comparative studies to bridge the gap between experimental promise and practical application.
Authors' stated research agenda/recommendation in the review's conclusion.
Findings were synthesized narratively due to methodological heterogeneity.
Methods/results statement in the review explaining narrative synthesis choice because of heterogeneity among included studies.
Risk of bias was assessed using the ROBINS-I tool.
Methods statement in the review specifying ROBINS-I for risk-of-bias assessment.
The review followed PRISMA guidelines.
Methods statement in the paper indicating PRISMA adherence.
After screening, 10 studies met the inclusion criteria.
PRISMA-style screening result reported in the review (records screened and included).
A comprehensive search across Scopus, Web of Science, IEEE Xplore, and ScienceDirect yielded 260 records.
Systematic search following PRISMA guidelines reported in the paper; databases searched explicitly listed.
The review focuses on the 2020–2025 period for studies of AI application in financial auditing.
Stated scope/timeframe of literature included in the review.
Article selection was conducted using the Scopus (Q1–Q4) and Sinta (1–2) databases based on predefined inclusion and exclusion criteria, resulting in a final sample of 15 articles.
Stated data sources and selection procedure in the Methods section; final sample size explicitly reported as 15.
This study employs a Systematic Literature Review (SLR) method following the PRISMA 2020 protocol.
Stated methodology in the paper: explicit use of SLR and PRISMA 2020 protocol.
The study analyzes Chinese A-share listed companies in core digital economy industries from 2015 to 2024 using a panel fixed‑effects regression model.
Study design and methods statement describing the sample frame (A-share listed firms in core digital economy industries, 2015–2024) and the use of panel fixed‑effects regression.
A total of 160 peer-reviewed articles met the inclusion criteria for the review.
Direct numerical summary reported in the abstract (number of articles meeting inclusion criteria).
This study conducted a systematic review of articles published in Web of Science and Scopus up to December 2025, following established methodological guidelines.
Explicit statement in abstract describing the study method (systematic review), data sources (Web of Science and Scopus), and time cutoff (December 2025).
We surveyed 860 Microsoft developers to understand where they want AI support, and where they want it to stay out.
Primary empirical method reported in the paper (survey) with sample size explicitly stated as 860 Microsoft developers.
Developers spend roughly one-tenth of their workday writing code.
Statement reported in the paper (abstract). No sample-size or measurement method for this specific statistic provided in the abstract.
The global onset of Industry 4.0 and Artificial Intelligence (AI) necessitates a re-evaluation of employment forecasts for Nagpur's medium enterprises.
Interpretive/prescriptive claim based on the paper's framing of technological change (Industry 4.0/AI) and implications for employment forecasting; no empirical sample size or quantitative backing provided in the excerpt.
Medium-scale industries in zones like Butibori and Hingna have traditionally been labor-intensive.
Descriptive statement in the paper about the nature of current industries in Nagpur/MIDC; no sample size or quantitative data reported in the excerpt.
The full model, including all 11 analytical tabs, is made publicly available to facilitate replication and independent sensitivity testing.
Paper states that the full model and all 11 analytical tabs are publicly available.
A sensitivity analysis shows that the high-skill capture rate and the pace of friction decay are the two parameters with the greatest influence on the aggregate result.
Paper reports results of a sensitivity analysis identifying parameter importance; explicitly names high-skill capture rate and friction decay pace as most influential.
AI coverage scores are sourced from Massenkoff and McCrory (2026) and mapped to NAICS industries using employment-weighted averages derived from BLS Occupational Employment and Wage Statistics data for 2023.
Citation to Massenkoff and McCrory (2026) for theoretical LLM task coverage across SOC groups and explicit statement that mapping used employment-weighted averages from BLS OES 2023.
The core formula multiplies six inputs: base GDP, labor share, AI coverage, productivity gain percentage, adjusted adoption rate, and a skill-weighted capture rate.
Model specification in the paper describing the multiplicative core formula and listing the six inputs.
A motivation–resistance theoretical framework helps study AI knowledge stickiness, where 'motivation' captures within-city diffusion potential and 'resistance' captures frictions preventing knowledge transfer across cities and inducing local lock-in.
Conceptual/theoretical contribution presented in the paper defining the motivation–resistance framework and interpretable constructs (motivation and resistance) for explaining stickiness.
The study uses a city-year panel of AI patent applications combined with urban statistics for the years 2014–2023 and estimates relationships using a two-way fixed-effects model.
Methodological description in the paper specifying data sources (AI patent applications, urban statistics), temporal coverage (2014–2023) and econometric approach (two-way fixed-effects).
The two case firms demonstrated contrasting approaches to implementing AI in recruitment.
Findings and case descriptions comparing the two firms' AI recruitment strategies and levels of implementation (n = 2 firms; interviews with 22 participants).
The research contributes by shifting focus to under-researched non-Western workplace settings, particularly technologically advancing Middle Eastern economies like Qatar.
Paper's stated contribution and scope: focus on Qatari organisations and Middle Eastern context.
Four key themes emerged from the data: (1) process optimisation through AI integration, (2) subjectivity in AI-powered recruitment, (3) recruitment strategies in the age of AI, and (4) strategic investments in AI.
Findings: thematic analysis identified these four themes from interview data (n = 22) across the two case firms.
Thematic analysis was used to identify patterns and relationships within the interview data.
Methods: analysis section reporting use of thematic analysis framework.
Data were collected through semi-structured interviews with twenty-two participants across various organisational roles and hierarchical levels.
Methods: semi-structured interviews reported with total participants n = 22 across roles/levels.
The research investigated two prominent Qatari firms with contrasting AI recruitment implementation approaches.
Methods / case selection: two firms were selected and contrasted on their AI recruitment approaches (number of firms = 2).
The study employed an interpretivist philosophy and a case study design.
Methods section: explicitly states interpretivist philosophy and case study design.
A digital–intelligent integration index was constructed using entropy weighting and a coupling coordination model.
Methodological description in the paper: index construction via entropy weighting combined with a coupling coordination model.
The study uses panel data from 30 provincial-level regions in China covering 2014–2023 to analyze the relationship between digital–intelligent integration and carbon intensity.
Panel dataset described as 30 provincial-level regions, years 2014–2023; index construction and empirical analysis reported in the paper.
The paper provides statistics on the agreement rates between different measures of AI exposure.
Descriptive/statistical comparison of multiple AI-exposure measures (e.g., different O*NET-based metrics) reporting agreement rates.
The authors validate their industry-level control variable by examining historical examples of occupations that experienced either occupation-specific or industry-level shocks.
Validation exercise using historical case studies/examples comparing known occupation-specific and industry-level shocks to assess the control variable's performance.
There is a significant research gap in comparative understanding of generative AI's impact across developed and developing economies; differences in infrastructure, labour markets, and skill distributions may lead to uneven outcomes.
Review observation that the included literature lacks sufficient comparative studies across country-development contexts (explicitly noted as a gap in the paper).
This systematic literature review synthesised findings from 40 empirical and conceptual studies published between 2020 and 2025 using the PRISMA framework (search across Google Scholar and Dimensions.ai), yielding 3,252 database records plus 8 hand-searched studies, of which 40 met the inclusion criteria.
PRISMA-style structured literature search reported in the paper: database search (Google Scholar, Dimensions.ai) returning 3,252 records, 8 hand-searched records, 40 studies meeting inclusion.
This study uses Partial Least Squares Structural Equation Modeling (PLS-SEM) on 350 survey responses to examine the effects of AI adoption, regulatory clarity, digital infrastructure readiness, and cross-border data governance quality on international trade performance, with compliance effectiveness as a mediating mechanism.
Methodological description in the paper: PLS-SEM analysis on a survey sample of 350 responses (sample size explicitly reported).
Empirical evidence remains limited on how AI deployment and institutional conditions jointly influence compliance effectiveness and international trade performance.
Statement of research gap based on the paper's literature review and motivation for the study.
The selected studies originated mainly from Peru, Colombia, Chile, and Ecuador.
Geographic provenance reported for the 27 included studies (country distribution summarized in results).
After screening, 27 studies were selected for inclusion in the review.
PRISMA-style screening and eligibility process reported in the methods/results, yielding 27 included studies.
The initial search returned 276,302 records.
Reported search yield from the Scopus query described in the methods.
A systematic search was conducted in the Scopus database following PRISMA 2020 guidelines for articles published between 2021 and 2025 using Boolean operators related to AI and decision-making.
Methodological description in the paper stating adherence to PRISMA 2020 and the search strategy (Scopus, 2021–2025).