Evidence (3308 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).
Browse by theme
Nine broad, paper-level topics. Click one to filter the claims below.
Adoption
9875 claims
Filter claims →
Productivity
8807 claims
Filter claims →
Governance
7870 claims
Filter claims →
Human-AI Collaboration
7560 claims
Filter claims →
Org Design
4892 claims
Filter claims →
Innovation
4781 claims
Filter claims →
Labor Markets
4004 claims
Filter claims →
Skills & Training
3308 claims
Filtered →
Inequality
2332 claims
Filter claims →
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 | 870 | 233 | 116 | 1066 | 2363 |
| Governance & Regulation | 976 | 451 | 218 | 133 | 1809 |
| Organizational Efficiency | 949 | 224 | 144 | 88 | 1416 |
| Technology Adoption Rate | 764 | 287 | 141 | 122 | 1325 |
| Research Productivity | 501 | 152 | 74 | 362 | 1101 |
| Output Quality | 542 | 216 | 69 | 69 | 896 |
| Decision Quality | 387 | 198 | 94 | 54 | 740 |
| Firm Productivity | 513 | 67 | 101 | 27 | 714 |
| AI Safety & Ethics | 249 | 303 | 73 | 36 | 667 |
| Market Structure | 190 | 192 | 134 | 27 | 548 |
| Task Allocation | 243 | 77 | 91 | 36 | 452 |
| Innovation Output | 291 | 33 | 55 | 20 | 401 |
| Skill Acquisition | 206 | 72 | 65 | 21 | 364 |
| Employment Level | 133 | 63 | 115 | 22 | 335 |
| Fiscal & Macroeconomic | 153 | 79 | 52 | 32 | 323 |
| Task Completion Time | 206 | 37 | 12 | 15 | 272 |
| Firm Revenue | 179 | 52 | 29 | 5 | 266 |
| Consumer Welfare | 130 | 76 | 47 | 13 | 266 |
| Inequality Measures | 48 | 137 | 51 | 6 | 242 |
| Worker Satisfaction | 101 | 81 | 25 | 13 | 220 |
| Error Rate | 84 | 110 | 11 | 5 | 210 |
| Wages & Compensation | 98 | 47 | 30 | 10 | 185 |
| Regulatory Compliance | 88 | 73 | 17 | 7 | 185 |
| Automation Exposure | 66 | 64 | 33 | 16 | 182 |
| Team Performance | 105 | 29 | 30 | 11 | 176 |
| Training Effectiveness | 109 | 22 | 14 | 21 | 168 |
| Developer Productivity | 114 | 21 | 14 | 8 | 158 |
| Job Displacement | 12 | 90 | 24 | 1 | 127 |
| Hiring & Recruitment | 57 | 9 | 9 | 5 | 80 |
| Skill Obsolescence | 6 | 56 | 9 | 1 | 72 |
| Social Protection | 43 | 17 | 8 | 2 | 70 |
| Creative Output | 35 | 21 | 9 | 4 | 70 |
| Labor Share of Income | 18 | 21 | 17 | 1 | 57 |
| Worker Turnover | 15 | 16 | — | 4 | 35 |
| Industry | — | — | — | 1 | 1 |
Skills Training
Remove filter
The article introduces a novel Bayesian Item Response Theory framework that quantifies human–AI synergy by separately estimating individual ability, collaborative ability, and AI model capability while controlling for task difficulty.
Methodological contribution described in the paper: development and application of a Bayesian Item Response Theory model that includes separate parameters for individual ability, collaborative ability, AI model capability, and task difficulty (method section of the paper).
A quantitative methodology was employed, utilizing a structured questionnaire administered to 400 small business owners.
Explicit methodological statement in the paper: structured questionnaire survey with sample size N=400 small business owners.
This research conducts a critical analysis of the ethical implications of artificial intelligence in terms of job displacement during the fifth industrial revolution.
Author-declared methodology: a literature-based critical analysis drawing on novel studies and the existing body of literature; no further methodological details (e.g., inclusion criteria, databases searched) provided in the excerpt.
This study analyzes comments and statements from party members in OECD countries from 2016 to 2025 through content analysis, examining media interviews, speeches, and debates.
Description of the study's data and method: content analysis of party member comments and statements drawn from media interviews, speeches, and debates across OECD countries over the 2016–2025 period (sample size and selection details not reported in the excerpt).
The study contributes to the literature by integrating evidence across higher education, vocational training, and lifelong learning to emphasize the need for balanced policy approaches to skill formation.
Stated contribution in the paper: cross-pathway synthesis of existing empirical evidence and secondary data (methods described as comparative synthesis; no primary empirical contribution reported in the summary).
The study uses secondary data and comparative evidence from prior empirical studies to analyze relationships between higher education, vocational education, and lifelong learning.
Stated methodology in the paper: analysis of secondary data and synthesis of prior empirical/comparative studies (no primary data collection; no sample sizes reported).
Drawing on leadership theory, emotional intelligence research and AI ethics informs the proposed framework.
Methodological/design statement in the paper describing its intellectual grounding; indicates literature-based synthesis rather than primary data collection.
Chatbot suggestions were artificially varied in aggregate accuracy across treatment conditions from low (53%) to high (100%).
Paper describes experimental manipulation of chatbot suggestion accuracy with aggregate accuracies ranging from 53% to 100%; manipulation method (how suggestions were generated or sampled) described in methods (not fully detailed in excerpt).
Caseworkers in the control condition (no chatbot suggestions) had a mean accuracy of 49%.
Reported experimental outcome: mean accuracy for control group = 49%; based on the randomized experiment using the 770-question benchmark.
We conducted a randomized experiment with caseworkers recruited from nonprofit outreach organizations in Los Angeles.
Paper describes a randomized experiment recruiting caseworkers from nonprofit outreach organizations in Los Angeles; sample size and recruitment details not given in the excerpt.
The benchmark questions have corresponding expert-verified answers.
Paper states benchmark questions have expert-verified answers; verification method and number/credentials of experts not specified in the excerpt.
We created a 770-question multiple-choice benchmark dataset of difficult, but realistic questions that a caseworker might receive.
Paper reports creation of a benchmark dataset containing 770 multiple-choice questions described as difficult and realistic; questions and dataset construction described in methods (no sample-of-questions or external validation details provided in the excerpt).
The study's conclusions draw on three complementary evidence bases: (a) task-level evidence on what generative AI can already do in practice; (b) occupational exposure and complementarity analysis using Philippine labor force data; and (c) firm- and worker-level evidence on AI adoption.
Description of methods and data sources in the paper: task-level capability testing/assessment, analysis of national labor force/occupation data for exposure/complementarity, and firm/worker surveys or qualitative adoption evidence.
The review focuses on AI applications within small‑scale business environments, with a special focus on women‑owned micro firms in Jaipur, India.
Scope and aim articulated in the paper; geographic and demographic focus explicitly stated by the authors.
The systematic review follows PRISMA 2020 guidelines.
Methodological statement in the paper indicating adherence to PRISMA 2020 for the review process.
After screening and eligibility filtering, 55 open‑access journal articles were included for in‑depth analysis.
PRISMA‑guided screening and eligibility process reported in the review; final included sample explicitly stated as 55 open‑access journal articles.
A Scopus search identified 265 records using keywords related to women’s entrepreneurship and AI.
Systematic literature search reported in the paper following PRISMA 2020; search executed in Scopus with specified keywords; initial yield stated as 265 records.
Viable transition pathways are operationally defined in this study as sharing at least 3 skills and achieving at least 50% skill transfer.
Methodological definition stated in the paper used to determine whether a job-to-job transition is considered viable.
We identified 4,534 feasible transitions between jobs in the dataset.
Count of feasible job-to-job transition pairs found in the knowledge graph analysis (4,534 transitions reported).
We constructed and validated a knowledge graph of 9,978 Egyptian job postings, 19,766 skill activities, and 84,346 job-skill relationships with a 0.74% error rate.
Empirical construction and validation of a knowledge graph using a dataset of 9,978 job postings, 19,766 distinct skill/activity nodes, and 84,346 job–skill edges; reported overall error rate 0.74% (validation method not detailed in the excerpt).
The study employs an input–output (I–O) modeling framework using IMPLAN 2022 data to estimate direct, indirect, and induced impacts of investments in greenhouse and robotics sectors for Northwest Indiana as part of Project TRAVERSE.
Explicit methodological statement in the paper: use of IMPLAN 2022 I–O model; geographic scope NWI; linkage to EDA Project TRAVERSE.
We extract the Big 5 personality traits from facial images of 96,000 MBA graduates using advances in AI and LinkedIn microdata.
Methodological claim reported in the paper: AI-based model applied to facial images linked to LinkedIn microdata for a sample of 96,000 MBA graduates; extraction yields 'Photo Big 5' trait scores.
The study is limited by the scope of available industry data and the generalisability of case study findings.
Explicit limitation reported in the paper summary stating constraints related to industry data availability and generalisability of case studies.
The research adopts a mixed-method approach, combining theoretical analysis with empirical insights, and uses data gathered from the 'AI-driven transformation' Scopus database.
Explicit methodological statement in the paper summary: mixed-method design and Scopus database as the data source. (No further methodological details or sample counts provided in the summary.)
Future research could strengthen causal identification by exploiting exogenous policy shocks rather than relying solely on matching methods like PSM.
Authors' methodological suggestion for future work, based on limitations of current causal inference strategy (PSM and observational panel regression).
Propensity Score Matching (PSM) and other robustness checks were used to mitigate selection bias and support the causal interpretation of AI's effects.
Paper reports use of Propensity Score Matching in robustness analyses on the panel of A-share-listed design firms (2014–2023).
The paper operationalizes firm-level AI exposure by constructing an AI lexicon via natural language processing and applying text analysis to annual reports and patents to generate enterprise-level AI indicators.
Described methodology: NLP to generate an AI lexicon and text-analysis of annual reports and patents to build AI measures for each listed design enterprise in the 2014–2023 panel.
A composite index capturing concerns about mental health, privacy, climate impact, and labor market disruption was constructed to measure societal risk perceptions of AI.
Author-constructed composite index derived from survey items on mental health, privacy, climate, and labor market disruption concerns in the 2023–2024 UK survey.
The analysis is framed through the integrated lens of the Technology-Organization-Environment (TOE) framework and Institutional Theory to provide a multi-faceted understanding of adoption dynamics.
Stated theoretical framing and analytical approach in the study (methodological claim).
The research synthesizes evidence from a wide array of sources, including recent academic literature by Nigerian scholars, NPA official performance reports, policy documents, and international trade facilitation reports (e.g., UNCTAD).
Explicit description of data sources in the study methodology; method: secondary data synthesis (no sample size applicable).
This study investigates the current state of adoption, the prevailing barriers, and the resultant performance outcomes of digital and AI-driven logistics within Nigeria’s maritime supply chain.
Stated study aim and scope; method: rigorous secondary data analysis drawing on multiple documentary sources (Nigerian academic literature, NPA reports, policy documents, UNCTAD).
This study uses a conceptual and analytical approach to examine the impact of AI and automation on work.
Stated methodology in the paper's abstract/introduction: methodological description that the study is conceptual and analytical; no empirical sample or quantitative data reported.
The study integrates Fuzzy Best Worst Method (BWM), PROMETHEE II, and DEMATEL (Fuzzy BWM-PROMETHEE II-DEMATEL) as a three-stage MCDM framework for prioritization and causal analysis of barriers.
Methodology explicitly described in paper: literature survey + expert knowledge feeding into integrated Fuzzy BWM, PROMETHEE II, and Fuzzy DEMATEL analyses.
This study investigates the barriers to the adoption of Industry 4.0 (I4.0) in the Thai automotive industry to inform firms and policymakers.
Stated research aim in paper; approach based on literature survey and expert knowledge; three-stage multi-criteria decision-making (MCDM) model used. (Sample size of experts / respondents not specified in the provided text.)
The paper's findings are based on a combination of literature review, data analysis, and an empirical study involving HR professionals.
Methodological description given in the paper's summary (no further methodological details, sample size, instruments, or statistical methods provided in the summary).
We conducted preregistered experiments in two tasks (a sentiment-analysis task and a geography-guessing task) to study whether user characteristics influence the effectiveness of AI explanations.
Preregistered experimental studies described in the paper; two distinct tasks (sentiment-analysis and geography-guessing). (Sample sizes and additional procedural details are not provided in the excerpt.)
The framework is depicted across organization areas with primary focus on strategic management and workforce decision-making and secondary focus on finance, operations, and marketing.
Descriptive claim based on the conceptual framework and its mapping to organizational domains within the paper. No empirical application or case studies reported.
This paper outlines a Human–AI Collaborative Decision Analytics Framework integrating five overlapping layers: data, AI analytics, business analytics interpretation, human judgment, and feedback learning.
Presentation of a conceptual framework developed by the authors (conceptual/modeling contribution). No empirical validation reported.
The results presented in the paper are based on a literature recherche, an analysis of individual tasks across different occupations (conducted within Erasmus+ projects), and discussions with trainers/educators.
Methodological statement from the paper; indicates the types of evidence used. The abstract does not provide numbers for analyzed tasks, the number of occupations, details of Erasmus+ projects, or counts of trainers/educators consulted.
The paper identifies key research gaps and proposes a future research agenda focused on human–AI interaction, organizational governance, and ethical accountability.
Conclusions/recommendations from the conceptual meta-analysis (paper-generated research agenda; no empirical testing reported in abstract).
This study presents a conceptual meta-analysis of interdisciplinary literature on AI-augmented decision-making in organizations.
Methodological statement of the paper (the paper itself is a conceptual meta-analysis); no primary empirical sample reported in the abstract.
Research has insufficiently modeled joint distributional outcomes and environmental performance, and lacks integrated evaluation of AI-enabled sustainable finance under heterogeneous disclosure regimes.
Review-level identification of methodological gaps across the surveyed literature (authors' synthesis of existing studies and their limitations).
There is a shortage of long-horizon causal evidence on non-linear coupling between digitalization and decarbonization, limiting robust policy inference.
Meta-level assessment in the review noting gaps in existing empirical literature (review authors' synthesis of the field; claim about research availability rather than primary data).
Competency mapping involves identifying and aligning the critical skills, knowledge, and abilities required for specific job roles.
Definition provided in the paper (conceptual).
A stratified random sampling method was employed to select a representative sample of 500 IT employees, based on a pilot study constituting 0.50 percent of the total population.
Sampling description provided in the methods section: stratified random sampling, sample size = 500, pilot study size referenced as 0.50% of population.
The study analyzes data from the period 2021 to 2023 using Multiple Regression Analysis as the principal analytical technique.
Methods statement provided in the paper (timeframe and analytical method).
The primary objective of this research is to examine the impact of AI adoption on competency mapping practices in the IT sector.
Explicitly stated research objective in the paper.
A Job Digital Intensity Index (JDII) was constructed to capture how digitally intensive jobs are overall, based on the range of digital tasks performed.
Methodological construction described in the report using ESJS digital task items to form a composite JDII.
The 2024 University of Phoenix Career Optimism Index® is a nationally representative survey of 5,000 U.S. workers and 501 employers.
Descriptive/methodological statement in the paper: a nationally representative cross-sectional survey (University of Phoenix Career Optimism Index®) with sample sizes of 5,000 U.S. workers and 501 employers.
Deterministic automated verifiers provide objective pass/fail checks for task success.
Methods section: verifiers are deterministic and automated, enabling objective evaluation of whether an agent's trajectory accomplished the task.