Evidence (2608 claims)
Adoption
7395 claims
Productivity
6507 claims
Governance
5877 claims
Human-AI Collaboration
5157 claims
Innovation
3492 claims
Org Design
3470 claims
Labor Markets
3224 claims
Skills & Training
2608 claims
Inequality
1835 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 609 | 159 | 77 | 736 | 1615 |
| Governance & Regulation | 664 | 329 | 160 | 99 | 1273 |
| Organizational Efficiency | 624 | 143 | 105 | 70 | 949 |
| Technology Adoption Rate | 502 | 176 | 98 | 78 | 861 |
| Research Productivity | 348 | 109 | 48 | 322 | 836 |
| Output Quality | 391 | 120 | 44 | 40 | 595 |
| Firm Productivity | 385 | 46 | 85 | 17 | 539 |
| Decision Quality | 275 | 143 | 62 | 34 | 521 |
| AI Safety & Ethics | 183 | 241 | 59 | 30 | 517 |
| Market Structure | 152 | 154 | 109 | 20 | 440 |
| Task Allocation | 158 | 50 | 56 | 26 | 295 |
| Innovation Output | 178 | 23 | 38 | 17 | 257 |
| Skill Acquisition | 137 | 52 | 50 | 13 | 252 |
| Fiscal & Macroeconomic | 120 | 64 | 38 | 23 | 252 |
| Employment Level | 93 | 46 | 96 | 12 | 249 |
| Firm Revenue | 130 | 43 | 26 | 3 | 202 |
| Consumer Welfare | 99 | 51 | 40 | 11 | 201 |
| Inequality Measures | 36 | 105 | 40 | 6 | 187 |
| Task Completion Time | 134 | 18 | 6 | 5 | 163 |
| Worker Satisfaction | 79 | 54 | 16 | 11 | 160 |
| Error Rate | 64 | 78 | 8 | 1 | 151 |
| Regulatory Compliance | 69 | 64 | 14 | 3 | 150 |
| Training Effectiveness | 81 | 15 | 13 | 18 | 129 |
| Wages & Compensation | 70 | 25 | 22 | 6 | 123 |
| Team Performance | 74 | 16 | 21 | 9 | 121 |
| Automation Exposure | 41 | 48 | 19 | 9 | 120 |
| Job Displacement | 11 | 71 | 16 | 1 | 99 |
| Developer Productivity | 71 | 14 | 9 | 3 | 98 |
| Hiring & Recruitment | 49 | 7 | 8 | 3 | 67 |
| Social Protection | 26 | 14 | 8 | 2 | 50 |
| Creative Output | 26 | 14 | 6 | 2 | 49 |
| Skill Obsolescence | 5 | 37 | 5 | 1 | 48 |
| Labor Share of Income | 12 | 13 | 12 | — | 37 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Skills Training
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This study proposes a framework for evaluating platform ecosystems by their long-term effects on human capital formation and institutional resilience.
Methodological contribution claimed by the paper (development of an evaluative framework); presented as part of the paper's contributions rather than an empirical finding.
Under three scenarios (optimistic: 2028-2035; base: 2035-2045; pessimistic: 2045-2060), we specify disconfirmation criteria that would weaken the thesis if observed.
Scenario analysis and specification of disconfirmation criteria by the authors; methodological claim about forecasting structure rather than empirical result.
Converging evidence from history, philosophy, neuroscience, technology, organizational studies, and cultural analysis supports this thesis.
Authors' multidisciplinary literature review and synthesis across the named fields (method: qualitative review); no single empirical dataset or sample size given.
We introduce 'instrumental dissolution' -- loss of institutional-default status while persisting in specialist niches.
Conceptual/theoretical contribution defined by the authors and illustrated via cross-disciplinary examples; no empirical validation sample reported.
Typing's dominance was instrumental, not cognitively necessary.
Argumentative/historical analysis presented in the paper; synthesis of historical and philosophical literature (no empirical sample or experiment reported).
Participants were retested individually on the programming tasks after a retention interval of one week.
Statement in abstract describing follow-up retest procedure (one-week retention interval, individual retest).
Participants were incentivized by bonus compensation to balance performance with understanding.
Paper description of participant incentives in methods/abstract; compensation scheme used during experiment.
We conducted a controlled pair programming study with 22 participants who wrote Python code under time pressure in teams of two and individually with GitHub Copilot for 20 minutes each.
Statement of study design in the paper's methods/abstract; controlled pair programming experiment with 22 participants, 20-minute tasks in both conditions (human teammate and Copilot).
Algorithmic accuracy alone does not determine value; legitimacy and uptake hinge on people's and process readiness.
Thematic conclusion drawn from interviews, Likert surveys, and document analysis across cases indicating non-technical factors strongly influence uptake despite algorithmic performance metrics. (Sample size not reported.)
We contribute junior–senior accounts on their usage of agentic AI through a three-phase mixed-methods study: ACTA combined with a Delphi process with 5 seniors, an AI-assisted debugging task with 10 juniors, and blind reviews of junior prompt histories by 5 more seniors.
Authors' methodological description of the study design and participant counts as reported in the paper.
The article examines the socioeconomic implications of AI-driven automation through the lens of political economy and labor sociology.
Methodological statement in the paper indicating theoretical framing and disciplinary approaches; no empirical sample reported in the abstract.
The review is a focused qualitative evidence synthesis and the proposed governance model is an evidence-informed conceptual framework that warrants future empirical validation.
Authors' explicit framing of the review approach and caveat calling for empirical validation of the proposed model.
Given the focused Title/Abstract/Keywords query and the small, heterogeneous corpus, the findings are interpreted as a scoped evidence map rather than an exhaustive census of all AI-and-work research.
Authors' explicit limitation statement referencing the search strategy (title/abstract/keywords focus), small number of included studies (n=19), and heterogeneity of studies.
Nineteen studies met the eligibility criteria and were analyzed using qualitative thematic synthesis.
Reported result of the screening/eligibility process in the review: final included sample = 19 peer-reviewed articles; analysis method stated as qualitative thematic synthesis.
We conducted a systematic review guided by PRISMA 2020, searching Scopus and Web of Science (Title/Abstract/Keywords) for English-language journal articles published between 2015 and 2025.
Methods reported in the paper: PRISMA 2020-guided systematic review; databases searched explicitly named (Scopus, Web of Science); query fields (Title/Abstract/Keywords); language and date restrictions stated (English, 2015–2025).
This paper treats pilots of supply chain innovation and application as a quasi-natural experiment and employs a difference-in-differences method to identify causal effects of supply chain digitalization.
Methodological description in the paper: sample of A-share listed companies (Shanghai and Shenzhen) 2013–2022; DID estimation using policy pilots as exogenous variation.
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 LLM fallacy is situated within existing literature on automation bias, cognitive offloading, and human–AI collaboration, but is distinguished as a form of attributional distortion specific to AI-mediated workflows.
Conceptual positioning and literature synthesis in the paper; claim is analytic rather than empirically tested in the abstract.
Less attention has been given to how LLM usage reshapes users' perceptions of their own capabilities.
Literature gap claim from the paper's review of prior research on model reliability, hallucination, and trust calibration; no quantitative synthesis or meta-analysis reported.
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.
We conducted a year-long longitudinal study of AI use in a high-stakes workplace among cancer specialists.
Methodological statement in the paper indicating a year-long longitudinal empirical study with cancer specialists (no sample size or detailed methods reported in 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.
All participants had access to the same AI tool; the experiment varied only the structure surrounding its use (behavioral vs cognitive scaffolding vs unstructured).
Experimental design description in the paper: common AI tool provided to all participants; randomization/assignment varied only the scaffolding around AI use.
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.
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.
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.
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).
This study uses semi-structured interviews with 10 practitioners to examine perceptions of collaborating with human versus AI teammates.
Methods statement in the paper: semi-structured interviews; sample size explicitly reported as 10 practitioners.
The study is based on a qualitative analysis of recent academic literature, comparative analysis of sector-specific applications of Big Data technologies, and synthesis of empirical findings from international studies using a systemic and structural analysis approach.
Methodological statement within the paper describing data sources and analytic approach; not an empirical claim about outcomes.
The research documents a transition in the literature (2013–2025) from early 'risk-of-automation' evaluations toward task-based and firm-level econometric models.
Literature review/synthesis across the 2013–2025 body of research as described in the paper.
Society 5.0 and Industry 5.0 call for human-centric technology integration, but the concept lacks an operational definition that can be measured, optimized, or evaluated at the firm level.
Motivating claim grounded in literature gap analysis presented in the paper (argument that normative frameworks lack formal, operational metrics at firm level).
We propose the Workplace Augmentation Design Index (WADI), a 36-item theory-grounded instrument for diagnosing human-centricity at the firm level.
Instrument design/proposal presented in the paper (36 items mapped to the five workplace-design dimensions); no validation sample reported in the abstract.
We conducted a PRISMA-guided systematic review of 120 papers (screened from 6,096 records) to map the evidence base for each workplace-design dimension.
Systematic literature review using PRISMA protocol; final sample = 120 papers; initial records screened = 6,096.
Existing models of human-AI complementarity treat the augmentation function phi(D) as exogenous and thus ignore that two firms with identical technology investments can achieve radically different augmentation outcomes depending on workplace organization.
Argument based on literature review of prior models (the paper contrasts its approach with existing complementarity models). No new empirical sample reported for this specific claim.
Metode penelitian yang digunakan adalah penelitian hukum normatif dengan pendekatan perundang-undangan, konseptual, dan komparatif, didukung oleh analisis literatur dari jurnal nasional terindeks SINTA dan jurnal internasional bereputasi.
Pernyataan metode yang jelas tercantum dalam abstrak/metodologi makalah.
Penelitian menilai kecukupan perlindungan hukum yang tersedia bagi pekerja terdampak PHK akibat adopsi AI.
Pernyataan tujuan penelitian dan pendekatan analitis (normatif, komparatif) yang didukung oleh tinjauan literatur pada jurnal-jurnal terpilih.
Penelitian ini bertujuan menganalisis bagaimana Undang-Undang Cipta Kerja dan peraturan turunannya mengklasifikasikan dan menjustifikasi Pemutusan Hubungan Kerja (PHK) akibat adopsi AI.
Pernyataan tujuan penelitian yang tercantum di bagian metodologi/pendahuluan; pendekatan peraturan-perundang-undangan dalam penelitian hukum normatif.
At the macroeconomic level, Kazakhstan's state programs (e.g., 'Digital Kazakhstan' and the Industrial and Innovation Development Program) and international indices (WIPO Global Innovation Index, OECD digital assessments, IMF data) are used to evaluate and position Kazakhstan within the global digital economy.
Macro-level analysis using national programs and international indices described in the article to assess Kazakhstan's digital economy standing.
We ran a behavioral experiment (N = 200) in which participants predicted the AI's correctness across four AI calibration conditions: standard, overconfidence, underconfidence, and a counterintuitive "reverse confidence" mapping.
Reported experimental design and sample size in the paper (behavioral experiment with N = 200; four experimental conditions).