Evidence (7953 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 |
This is the first study to compare human–human and human–AI collaboration outcomes for temporary virtual tasks from employees’ perspective in an applied service-industry context.
Author-stated novelty claim in the paper (based on study design: online experiment with retail employees examining temporary, virtual teamwork).
Measuring AI's contribution to productivity and coordination effects will be challenging; new metrics (e.g., coordination time per task, error/rework rates attributable to communication lapses) are required.
Conceptual argument and recommended measurement agenda in the paper; no empirical testing of proposed metrics provided.
Many early-stage AI advances have not translated into higher Phase II/III success rates.
Synthesis of reported outcomes and failures from industry experience; no new systematic statistical analysis provided.
After roughly a decade of adoption in large biopharma, AI has not yet changed late-stage (Phase II/III) clinical success rates.
Qualitative assessment of industrywide experience and reported outcomes; statement based on narrative review rather than systematic, long-run quantitative analysis or causal estimates.
Three primary adoption archetypes in large pharma are (1) partnership-driven acceleration, (2) culture-centric transformation, and (3) production-first democratization.
Conceptual classification in the editorial derived from trends and illustrative examples rather than empirical survey or sampling; no quantitative validation provided.
This paper systematically studies the Impact Mechanism of artificial intelligence on the Globalized Division of Labor and reveals the Structural Transformation under Technology Substitution and Data Elements Dual-wheel Drive through Literature Review and Theoretical Analysis.
Methodological claim: supported by the paper's literature review and theoretical analysis; no quantitative sample or empirical design indicated for this specific conclusion in the excerpt.
Existing research largely focuses on general computer literacy and lacks precise measurement of the economic returns to specific vocational digital skills.
Paper's literature review and motivating statements (qualitative assessment of prior studies; no quantitative meta-analysis reported in the excerpt).
AI adoption is not associated with significant changes in operating costs.
Analysis of operating costs in firm financials showing no significant post-adoption change for adopters relative to nonadopters.
The innovation effects of AI adoption are not concentrated among larger firms, financially unconstrained firms, or high-tech firms.
Heterogeneity tests across firm size, financial constraint status, and industry technology intensity showing no concentration of effects in these groups (as reported in the paper).
We did not observe significant differences between using Gemini (free or paid) and not using Gemini in terms of secure software development.
Statistical comparison of code-security outcomes across the three experimental groups (no AI, free Gemini, paid Gemini) in the n = 159 participant sample; the paper reports no statistically significant group differences.
Workers prefer systems that are straightforward, tolerant, and practical.
Survey responses from workers collected in the study on the representative sample of tasks (171) and possibly summarized/scaled via LMs.
Developers report emphasizing politeness, strictness, and imagination in system design.
Survey responses from developers collected as part of the study on the representative sample of tasks (171) and possibly summarized/scaled via LMs.
Prior work has mapped which workplace tasks are exposed to AI, but less is known about whether workers perceive these tasks as meaningful or as busywork.
Statement referencing prior literature (background motivation) in the paper; no new data provided for this claim within the excerpt.
SWE-Skills-Bench is the first requirement-driven benchmark that isolates the marginal utility of agent skills in real-world software engineering (SWE).
Authors present a new benchmark designed to evaluate marginal utility of skills; benchmark pairs skills with repositories and requirement documents and is described as requirement-driven and focused on isolating marginal utility.
Collaborative ability is distinct from individual problem-solving ability.
Model-based estimates from the Bayesian IRT framework that separately parameterize collaborative ability and individual problem-solving ability, with results indicating they are separable constructs (analysis on n = 667 benchmark data).
A complexity-aware routing mechanism selectively activates planning for complex queries, ensuring optimal resource allocation during online serving.
Method description in the paper explaining adaptive online serving and complexity-aware routing; evaluated in serving experiments.
Early evidence from nationally representative datasets shows limited aggregate wage and employment changes following GenAI's emergence.
Empirical analyses referenced in the paper that use nationally representative population-level datasets (specific datasets and sample sizes not provided in the excerpt).
The information wedge vanishes precisely when signals are exogenous to controls, thereby delineating when strategic belief manipulation matters.
Analytical condition in the paper: shows V^i_t = 0 if and only if the signal-generating process does not depend on agents' controls; uses this equivalence to identify boundary between endogenous and exogenous-signal regimes.
There is a gap in the existing literature regarding empirical evidence about the relationship between AI/Big Data use and market uncertainty during economic downturns.
Paper motivates the study by citing this gap based on its literature review (the summary does not list the reviewed works or systematic review method).
AI has not yet significantly promoted university–industry collaborative R&D capabilities.
Mechanism analysis in the paper testing the university–industry collaborative R&D channel and reporting no statistically significant effect of AI adoption on that capability in the sample.
The studied construction supply chain network exhibits moderate density, reported as 0.591.
Network-level metric (density = 0.591) reported in the results; derived from the constructed network based on coded interview interactions (network size and sampling details not provided in abstract).
Purposive and snowball sampling produced semi-structured interview data that span all major construction supply chain roles.
Sampling approach stated in the paper: purposive and snowball sampling for interviews; claim that interviews 'span all major supply chain roles' (number of interviews and role breakdown not reported in the abstract).
LLMs can be understood as condensates of human symbolic behavior—compressed, generative representations that render patterns of collective discourse computationally accessible.
Theoretical framing and conceptual argument provided by the authors; presented as an interpretive model rather than an empirically tested assertion in the excerpt.
This study empirically tests a theoretically acknowledged but rarely tested relationship (AI adoption → performance conditional on structural constraints) in an emerging-economy setting.
Literature gap claim supported by the authors' review and execution of an empirical test using survey data from 280 Tunisian SMEs and PLS-SEM.
Institutional conditions do not exert a significant moderating influence on the relationship between AI adoption and firm performance in this sample.
PLS-SEM moderation tests on the 280 Tunisian SMEs found the institutional-environment moderator to be non-significant.
Empirically, many markets are concentrated and characterized by large, dominant employers.
Empirical assertion in the paper; the excerpt does not provide the datasets, measures of concentration (e.g., HHI), sample sizes, or citations supporting this statement.
Key limitations in the literature include methodological heterogeneity, scarce safety data, and a focus on non-acute settings.
Authors' appraisal of the included studies as reported in the discussion section.
Unemployment does not exert a statistically significant impact on GDP growth in the employed model.
Unemployment included among the macroeconomic determinants in the panel regressions but reported as statistically insignificant (no effect) in the provided summary; methods cited include OLS, FE, Difference and System GMM (sample details not included).
Robust methodology (panel VAR and DID) was used to assess the impact of technology and public policy interventions on emissions reductions.
Methods stated in the paper (panel VAR and difference-in-differences); robustness is claimed by the authors based on using these established econometric approaches, though formal robustness checks are not detailed in the summary.
Previous studies have identified language barriers as impediments to labor market engagement but empirical information assessing both policy reductions and the relative efficacy of professional, AI-assisted, and hybrid translation methods is scarce.
Paper's literature review claim that existing literature documents language barriers but lacks comparative empirical evaluations of policy reductions and multiple translation models; asserted as motivation for current study.
Translation verified against existing performance implementations achieves throughput parity with MJX (1.04x) for HalfCheetah JAX.
Benchmarking HalfCheetah implemented in the translated backend versus MJX, reporting a 1.04x throughput ratio (approximate parity).
Levers such as raising taxes, reforming pensions, boosting productivity interact with each other through feedback loops and time delays that are not yet well understood.
Literature and model motivation stated in the paper; the integrated model is built to capture such interactions and delays.
These efficiency and cost gains are achieved while maintaining accuracy parity with the matched hierarchical baseline.
Paper states accuracy parity was maintained in the empirical evaluation comparing the proposed framework to the matched hierarchical baseline on the 2,847-query testbed.
Logistics efficiency does not mediate (fails to fulfill) the anticipated role in transmitting AI's effects to supply chain stability.
Mechanism/mediation tests in the DML analysis on the 45 Chinese listed SEs (2012–2023) indicate no significant mediation via logistics efficiency.
The Photo Big 5 is only weakly correlated with cognitive measures such as test scores.
Correlation/associational analysis between Photo Big 5 trait scores and cognitive measures (e.g., test scores) reported for the MBA graduate sample.
The short‑term effect of AI on labor‑intensive industries is weak.
Short‑run/dynamic subgroup analysis in the China 2003–2017 panel indicating minimal or weak immediate growth effects for labor‑intensive sectors.
The article clarifies theoretical relationships and gaps between Material Passports, Digital Product Passports, and Digital Building Logbooks.
Theoretical analysis and synthesis section of the SLR where the authors compare concepts and identify overlaps and gaps among MPs, DPPs, and DBLs.
Personal experience with an AI 'boss' did not affect workers' attitudes on using AI in public decision making.
Same randomized design (N > 1,500) with attitudinal measures collected across a three-wave panel; comparison between AI-assigned and human-assigned participants showed no measurable effect on attitudes about AI in public decision making.
Correlation and illustrative regression results confirm the absence of an immediate statistical relationship between AI adoption and productivity at the aggregate level.
Both correlation analysis and an illustrative regression model applied to Eurostat aggregate data for 2021–2024; regression presented as illustrative (not necessarily causal); model specification details and robustness checks not given in the summary.
Labour productivity did not show a stable association with AI diffusion in Slovakia over the analysed period.
Correlation analysis between AI adoption indicators and labour productivity measures for Slovakia using harmonised Eurostat data (2021–2024); detailed coefficient estimates and significance levels not provided in the summary.
Diverse decision-making AI from different developers will commonly compete for finite shared resources in everyday devices (examples: charging slots, relay bandwidth, traffic priority).
Motivating background statement in the paper (observational/argumentative; examples drawn from real-world deployment contexts rather than reported experiment data).
There is an arithmetic crossover point between these regimes: it occurs where opposing tribes that form spontaneously first fit inside the available capacity.
Mathematical analysis in the paper deriving a capacity-based threshold (crossover) marked by whether spontaneously formed opposing tribes can be accommodated by available capacity.
When resources are abundant, the same ingredients (model diversity, individual RL, tribe formation) drive system overload to near zero.
Empirical and mathematical results in the paper showing that abundance of resources reduces overload to near zero under the same agent-population conditions.
The study presents an advanced systematic ranking of I4.0 adoption barriers in the Thai automotive industry.
Paper outputs a ranked list of barriers produced by the integrated Fuzzy BWM-PROMETHEE II-DEMATEL framework; full ranked list and quantitative ranks not included in the supplied summary.
Median hourly compensation for gig workers, after accounting for expenses and unpaid time, averages $14.20.
Earnings analysis using platform transaction records adjusted for reported expenses and estimated unpaid labor time; comparative baseline drawn from labor force and administrative wage data (24 countries, 2015–2025).
The study explores the influence of AI on HRM practice specifically within top IT companies.
Scope statement in the paper: empirical study involved HR professionals from various (described as top) IT firms. The summary does not supply the list of companies or sampling criteria.
Top management support does not have a direct influence on AI Adoption in the sampled firms.
PLS-SEM results from the 207-firm survey showing a non-significant direct path from top management support to AI Adoption (as reported in the paper).
Effort expectancy does not have a direct influence on AI Adoption in the sampled firms.
PLS-SEM results from the 207-firm survey showing a non-significant direct path from effort expectancy to AI Adoption (as reported in the paper).
This study developed a unified framework that integrates technology acceptance and trust-based perspectives.
Conceptual/methodological claim in the paper: authors report constructing an integrated framework based on literature and their empirical testing.
The paper contributes to both theory and policy by reconceptualizing procurement value and offering an actionable roadmap for embedding ESG principles in public healthcare procurement.
Scholarly contribution claimed via literature synthesis and framework/roadmap creation; contribution is normative and conceptual rather than empirically validated.