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
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External environmental pressures did not show a significant role in the adoption process.
PLS-SEM results from the survey (n=110) reportedly found no significant effect of environmental/external pressures on AI adoption.
Data analysis involved Smart PLS-SEM, which facilitated reliability and validity assessment along with hypothesis evaluation.
Paper reports using SmartPLS for Partial Least Squares Structural Equation Modeling to assess reliability, validity, and test hypotheses.
The investigation was guided by the Technology-Organization-Environment (TOE) framework combined with innovation characteristics from Diffusion of Innovation theory.
Paper states theoretical frameworks used to design variables and hypotheses: TOE plus DOI innovation characteristics.
A total of 110 valid responses were collected through an organized online survey using purposive sampling.
Reported sample description in the paper: online survey, purposive sampling, resulting in 110 valid responses.
The explanatory interface has no significant impact on situational trust.
Trust measured in different forms (situational, learned, cognitive, emotional) in the RCT; authors report no significant effect of explanatory interface on situational trust (N=120).
Under the sequential AI-assisted decision-making paradigm, the explanatory interface has no significant effect on immediate task performance.
Same randomized controlled experiment; authors report no significant effect of explanatory interface on immediate task performance in the sequential paradigm (N=120 total).
The study was a randomized controlled experiment with 120 pre-service teachers.
Randomized controlled experiment reported in the paper; sample described as 120 pre-service teachers.
The study uses a panel of 283 prefecture-level and above cities from 2012 to 2023 and a difference-in-differences (DID) identification strategy exploiting the establishment of national big data comprehensive pilot zones as a natural experiment.
Methodological description in the paper: sample composition (283 prefecture-level+ cities), time span (2012–2023), and the DID/natural experiment design to estimate policy effects.
Exploratory innovation does not show a significant direct association with long-term competitive performance.
PLS-SEM results from the survey of 104 Portuguese B2B managers reporting a non-significant direct path from exploratory innovation to performance.
Data were analyzed using partial least squares structural equation modeling (PLS-SEM) implemented in SmartPLS 4.
Methods section statement in paper indicating use of PLS-SEM and SmartPLS 4 for data analysis.
The empirical analysis is based on a questionnaire survey administered to 324 respondents from Romanian organizations operating in IT, services, industry, and public administration.
Questionnaire survey described in paper; sample size explicitly stated as 324 respondents from Romanian organizations across IT, services, industry, and public administration.
ARS's implementation can be found at https://github.com/t54-labs/AgenticRiskStandard.
Link to code repository provided in the abstract (factual statement pointing to implementation).
As AI systems evolve into autonomous agents deployed in open environments and increasingly connected to payments or assets, the operational meaning of trust shifts to end-to-end outcomes: whether an agent completes tasks, follows user intent, and avoids failures that cause material or psychological harm.
Conceptual/argumentative claim presented in the paper (no empirical sample reported in the abstract).
Prior work on trustworthy AI emphasizes model-internal properties such as bias mitigation, adversarial robustness, and interpretability.
Summary statement about existing literature (no empirical data or sample reported in the abstract; asserted by authors as background).
On document intelligence (DocILE), our Code Factory variant matches Direct LLM on key field extraction (KILE: 80.0%).
Empirical evaluation reported on DocILE dataset of 5,680 invoices; KILE metric reported at 80.0%.
We evaluate on two task types: function-calling (BFCL, n=400) and document intelligence (DocILE, n=5,680 invoices).
Statement in paper specifying dataset/task types and sample sizes used in evaluation.
All four models converge to similar skill profiles (3.6-point spread), suggesting that text-based automation feasibility may be more skill-dependent than model-dependent.
Comparison across 4 LLMs (LLaMA 3.3 70B, Mistral Large, Qwen 2.5 72B, Gemini 2.5 Flash) with reported 3.6-point spread in skill-profile SAFI scores.
Explicit 'Sponsored' labels do not significantly reduce persuasion.
Experimental comparison including conditions with explicit 'Sponsored' labels; authors report no significant reduction in persuasion when labels were present (from the preregistered experiments).
A fifth of all products were randomly designated as sponsored and promoted in different ways.
Paper description of experimental manipulation: 20% of products (a fifth) were randomly designated as sponsored in the catalog.
We conducted two preregistered experiments with N = 2,012 participants.
Statement of experimental design in the paper (two preregistered experiments) with total sample size reported as N = 2,012.
A pre-registered experiment evaluates this thesis in a commons production economy -- where agents share a finite resource pool and collaboratively produce value -- at 50-1,000 agent scale.
Paper states that a pre-registered experiment is planned/described; the experiment context (commons production economy) and planned scale (50-1,000 agents) are specified in the excerpt. No experimental outcomes or effect estimates are reported here.
We instantiate SoP in AgentCity on an EVM-compatible layer-2 blockchain (L2) with a three-tier contract hierarchy (foundational, meta, and operational).
Reported implementation/instantiation described in the paper (system implementation claim). The paper states the platform (AgentCity) and technical details (EVM-compatible L2, three-tier contracts).
In this architecture, smart contracts are the law itself -- the actual legislative output that agents produce and that governs their behavior.
Architectural/design claim in the paper describing conceptual role of smart contracts within SoP; presented as an intended property of the system.
Agents discover, transact with, and delegate to agents owned by other parties without centralized oversight.
Asserted behavior pattern of autonomous agents in the paper's motivation; presented as descriptive claim rather than supported by a reported experiment or dataset in the excerpt.
Autonomous AI agents are beginning to operate across organizational boundaries on the open internet.
Stated as an empirical observation in the paper's introduction/introduction-level motivation; no specific dataset or sample described in the text excerpt.
The empirical basis of the study is industry data from the Bureau of National Statistics of the Republic of Kazakhstan for 2020–2024.
Statement in the paper specifying the data source and years used for calibration of the model.
The study's methodological framework integrates the Bass model of innovation diffusion, an expanded production function with endogenous technological progress and the task-oriented Acemoglu–Restrepo approach, plus a multi-criteria system of industry prioritisation.
Description of the paper's modelling approach in the methods section; model components identified explicitly in the paper.
We conducted a systematic review and bibliometric analysis of 627 articles.
Statement in abstract reporting a systematic review and bibliometric analysis; sample size explicitly given as 627 articles.
This study uses data from 743 listed enterprises in China’s strategic emerging industries from 2014 to 2023 and employs mediation and moderation (interaction) tests to examine mechanisms (digital-green synergy, information asymmetry, financing constraints) and the moderating role of AI applications.
Statement of data and methods in the paper: panel of 743 listed firms (2014–2023); empirical strategy includes mediation analyses and moderation (interaction) tests.
This paper has been accepted at PEARC 2026.
Statement in the paper indicating conference acceptance.
The University's GIS Center Ecological Archive (849 curated datasets) serves as a single-agent baseline deployment of EnviSmart.
Reported deployment dataset count provided in the paper: 849 curated datasets used as a single-agent baseline.
The study employed a mixed-methods approach: a quantitative survey of 150 leading Nigerian firms across finance, tech, and manufacturing, complemented by qualitative analysis of government policy and workforce interviews.
Methodological statement in the paper explicitly describing sample and methods (quantitative survey n=150; qualitative policy and interviews).
The experiment used stratified randomization across 32 strata with 255 treatment firms and 260 control firms; baseline characteristics are well balanced across groups.
Experimental design description: stratification by geography, traction score, and baseline AI use; reporting of allocation counts and balance tests in Table 2.
Attrition from the accelerator was low (1.6%, eight ventures) and balanced across treatment and control.
Program enrollment and retention records for the 515 firms in the randomized accelerator; 8 firms attrited.
The gains from treatment are broad-based: there are no significant differential effects by baseline firm performance or founder technical background.
Heterogeneity/subgroup analyses in the randomized sample (515 firms) comparing treatment effects across strata defined by baseline traction and founder technical background.
Treated firms' demand for labor remains unchanged.
RCT with 515 firms; firms reported labor demand/changes, comparison between treatment and control groups showed no significant change.
AIGC and HGC exhibit distinct creation behaviors and consumption behaviors.
Descriptive comparisons in the longitudinal dataset showing differences in production rates, content volumes, and consumption patterns between AIGC and HGC.
The paper uses a comprehensive longitudinal dataset comprising tens of millions of users from a leading Chinese video-sharing platform.
Statement in paper summarizing data source: a longitudinal dataset covering 'tens of millions of users' from a major Chinese video-sharing platform; used for descriptive and comparative analyses of creation and consumption behavior.
Increasing reasoning effort (low, medium, high) provides no consistent benefit to estimation performance.
Controlled variation of each model's reasoning effort (low/medium/high) while asking them to produce 95% credible intervals for population statistics.
These chats were committed to public repositories as part of routine development, capturing in-the-wild behavior.
Data collection method: analysis of chat transcripts that were committed to public repositories (authors state collected from repos and describe them as routine commits).
We analyze 74,998 developer messages from 11,579 chat sessions across 1,300 repositories and 899 developers using Cursor and GitHub Copilot.
Reported dataset counts in the paper (message, session, repository, developer counts) drawn from public commit histories of chats.
We evaluate APEX across three baselines and six scenarios using sample sizes 2–4x larger than initial experiments (N=20–40 per scenario).
Experimental design statement in the paper (three baselines, six scenarios, reported N range of 20–40 per scenario).
The HTTP 402 protocol treats payment as a first-class protocol event, but most implementations rely on cryptocurrency rails.
Descriptive claim in the paper about the state of HTTP 402 and common implementations (literature/implementation survey-style claim in paper).
Green innovation does not yet significantly reduce carbon inequality.
Empirical results from the provincial panel analysis (2003–2021) showing that measures of green innovation are not associated with a statistically significant reduction in carbon inequality.
This paper employs a staggered difference-in-differences (DID) model using data from Chinese A-share listed manufacturing companies from 2012 to 2023 and uses the National Artificial Intelligence Innovative Application Pioneer Zone (AIIAPZ) policy as a quasi-natural experiment.
Staggered DID empirical design; sample described as Chinese A-share listed manufacturing firms, 2012–2023; AIIAPZ policy used as treatment assignment (quasi-natural experiment).
Big data analytics and blockchain technologies show no significant correlations with exports to specific destinations (multivariate probit result).
Multivariate probit model of destination-specific export decisions showing non-significant coefficients for big data analytics and blockchain across destinations (sample size not reported in prompt).
Adopting blockchain technologies does not have a statistically significant effect on a firm's likelihood of exporting (probit model result).
Probit regression analysis showing non-significant coefficient for blockchain adoption (sample size not reported in prompt).
Adopting big data analytics does not have a statistically significant effect on a firm's likelihood of exporting (probit model result).
Probit regression analysis showing non-significant coefficient for big data analytics adoption (sample size not reported in prompt).
We introduce the Agentic Task Exposure (ATE) score, a composite measure computed algorithmically from O*NET task data using calibrated adoption parameters (not a regression estimate), incorporating AI capability scores, workflow coverage factors, and logistic adoption velocity.
Methodological description in the paper; algorithmic construction from O*NET task data with specified calibrated adoption parameters and components (AI capability scores, workflow coverage, logistic adoption).
Code authoring and review are only a small part of the larger software engineering process; the resulting code must also be maintained and updated over time.
Conceptual/argumentative claim presented in the paper to motivate longitudinal analysis (not presented as an empirical estimate from the dataset).