Evidence (2215 claims)
Adoption
5126 claims
Productivity
4409 claims
Governance
4049 claims
Human-AI Collaboration
2954 claims
Labor Markets
2432 claims
Org Design
2273 claims
Innovation
2215 claims
Skills & Training
1902 claims
Inequality
1286 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 369 | 105 | 58 | 432 | 972 |
| Governance & Regulation | 365 | 171 | 113 | 54 | 713 |
| Research Productivity | 229 | 95 | 33 | 294 | 655 |
| Organizational Efficiency | 354 | 82 | 58 | 34 | 531 |
| Technology Adoption Rate | 277 | 115 | 63 | 27 | 486 |
| Firm Productivity | 273 | 33 | 68 | 10 | 389 |
| AI Safety & Ethics | 112 | 177 | 43 | 24 | 358 |
| Output Quality | 228 | 61 | 23 | 25 | 337 |
| Market Structure | 105 | 118 | 81 | 14 | 323 |
| Decision Quality | 154 | 68 | 33 | 17 | 275 |
| Employment Level | 68 | 32 | 74 | 8 | 184 |
| Fiscal & Macroeconomic | 74 | 52 | 32 | 21 | 183 |
| Skill Acquisition | 85 | 31 | 38 | 9 | 163 |
| Firm Revenue | 96 | 30 | 22 | — | 148 |
| Innovation Output | 100 | 11 | 20 | 11 | 143 |
| Consumer Welfare | 66 | 29 | 35 | 7 | 137 |
| Regulatory Compliance | 51 | 61 | 13 | 3 | 128 |
| Inequality Measures | 24 | 66 | 31 | 4 | 125 |
| Task Allocation | 64 | 6 | 28 | 6 | 104 |
| Error Rate | 42 | 47 | 6 | — | 95 |
| Training Effectiveness | 55 | 12 | 10 | 16 | 93 |
| Worker Satisfaction | 42 | 32 | 11 | 6 | 91 |
| Task Completion Time | 71 | 5 | 3 | 1 | 80 |
| Wages & Compensation | 38 | 13 | 19 | 4 | 74 |
| Team Performance | 41 | 8 | 15 | 7 | 72 |
| Hiring & Recruitment | 39 | 4 | 6 | 3 | 52 |
| Automation Exposure | 17 | 15 | 9 | 5 | 46 |
| Job Displacement | 5 | 28 | 12 | — | 45 |
| Social Protection | 18 | 8 | 6 | 1 | 33 |
| Developer Productivity | 25 | 1 | 2 | 1 | 29 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
| Creative Output | 15 | 5 | 3 | 1 | 24 |
| Skill Obsolescence | 3 | 18 | 2 | — | 23 |
| Labor Share of Income | 7 | 4 | 9 | — | 20 |
Innovation
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Temporal mapping and citation networks reveal distinct technology maturity patterns, which are visualised using S-curve and hype cycle models.
Paper describes use of temporal mapping and citation network analysis and visualization via S-curve and hype cycle models; methodological description without quantitative sample-size details.
Technologies such as AI-driven healthcare, quantum communication, hydrogen energy, and smart educational AI are identified as key domains of convergence.
Paper reports these domains were identified via the applied analytic framework and multi-source data triangulation; no numeric counts/sample sizes provided.
The study applies advanced techniques such as LDA topic modelling, BERT-based clustering, and co-citation analysis to detect innovation trajectories.
Paper states these specific analytic techniques were applied (method description).
The research leverages large AI models and multi-source data—including global patent databases (WIPO, USPTO, Lens.org), scientific literature corpora, and industry intelligence platforms (CB Insights, Qichacha).
Paper statement of data sources and use of large AI models; methodological description (no sample sizes reported).
Empirical findings demonstrate that digitalization significantly boosts efficiency and competitiveness of industrial production.
Correlation and regression analyses reported in the study linking digitalization measures to indicators of efficiency and competitiveness across levels of analysis.
Digital technologies (automation, IIoT, ERP systems, AI applications) reduce nonproductive costs, increase per-worker output, and improve the cost-efficiency of production in Kazakhstani enterprises.
Case studies and real examples from named enterprises (Asia Auto, Karaganda Foundry and Engineering Plant, Eurasian Resources Group) presented in the article.
The number of employees and working time have a positive but limited effect on labor productivity.
Results from the study's correlation and regression analysis comparing labor input measures (employee count and working time) with productivity outcomes.
Digitalization is the key driver of labor productivity growth in Kazakhstan.
Empirical correlation and regression analysis reported in the study across enterprise, industry, and national economy levels.
A stylized-facts analysis using OECD and World Bank indicators shows that economies with higher digital capacity, greater R&D intensity, and stronger institutions exhibit superior productivity and growth performance.
Stylized-facts (cross-country) analysis based on OECD and World Bank indicators; descriptive correlations reported in the paper (sample of countries not enumerated in the provided summary).
AI adoption stimulates institutional innovation, which in turn increases total factor productivity (TFP) and supports sustainable economic growth.
Theoretical mediation claim developed in the paper (integration of Schumpeterian growth theory with institutional economics); supported conceptually and argued with stylized-facts analysis but not presented as causally identified empirical estimates.
AI improves governance quality.
Argument within the conceptual framework linking AI capabilities (information processing, monitoring) to improved governance; stated qualitatively in the paper rather than supported by causal empirical tests.
AI lowers transaction costs.
Paper's conceptual/theoretical framework that characterizes AI as lowering transaction costs through improved information and coordination; no quantitative causal estimate reported.
AI reduces information asymmetries.
Theoretical/conceptual argument in the paper framing AI as a general-purpose technology that improves information flows; supported by the paper's conceptual framework (no experimental or causal identification reported).
AI-enabled competitive advantages are more likely to be achieved by innovation platforms than by transaction platforms.
Comparative finding reported from the fsQCA analysis on Chinese listed platform enterprises; the paper explicitly states innovation platforms are more likely to attain AI-enabled competitive advantages than transaction platforms. No sample breakdown by platform type provided in the abstract.
The AI-enabled combinations produce competitive advantages through three paths: AI internalization, AI leverage, and AI collaboration.
Causal/pathway interpretation from fsQCA solutions on the panel of Chinese listed platform enterprises as described in the paper (abstract reports three named paths). No quantitative effect sizes provided in the excerpt.
AI-enabled competitive advantages emerge from three types of configurations: the situated AI dominance type, the situated AI subsidiary type, and the collaborative drive type.
Configurations identified by fsQCA on the panel data; the paper reports three distinct solution/configuration types leading to competitive advantage. Details on case membership and calibration thresholds are not provided in the abstract.
AI technology innovation and recasting AI are necessary conditions for platform enterprises to establish competitive advantages.
Result from necessity analysis within the fsQCA applied to the panel of Chinese listed platform enterprises (paper reports these two conditions as necessary). Specific sample size and statistical measures not provided in the abstract.
This study draws on panel data from Chinese listed platform enterprises and employs fuzzy-set Qualitative Comparative Analysis (fsQCA).
The paper states it uses panel data from Chinese listed platform enterprises and applies fsQCA as its analytic method (methodological statement in abstract). Sample size not reported in the provided text.
The contribution is a falsifiable architectural thesis, a clear threat model, and a set of experimentally testable hypotheses for future work on distillation resistance, alignment, and model governance.
Theoretical contribution claim: the paper proposes hypotheses and a threat model intended to be testable in future empirical work; no experiments in the paper itself are reported.
Embedded shopping AI functions less as a substitute for conventional search than as a complementary interface for exploratory product discovery in e-commerce.
Synthesis of empirical regularities (demographic adoption patterns, timing in journey, interleaving behavior, high share of exploratory/attraction queries) from the descriptive analysis of Ctrip/Wendao usage data.
Consumers disproportionately use the assistant for exploratory, hard-to-keyword tasks: attraction queries account for 42% of observed chat requests.
Intent classification of chat requests in the dataset; reported share of chat requests labeled as 'attraction' (42%).
Among journeys containing both chat and search, the most common pattern is interleaving, with users moving back and forth between the two modalities.
Pattern/sequence analysis of journeys that include both chat and search events, counting and comparing patterns (e.g., interleaving versus strict ordering).
AI chat appears in the same broad phase of the purchase journey as traditional search and well before order placement.
Sequence/timestamp analysis of user journeys in platform logs showing the relative timing of chat, search, and order placement within journeys.
Adoption of the embedded shopping AI is highest among older consumers, female users, and highly engaged existing users, reversing the younger, male-dominated profile commonly documented for general-purpose AI tools.
Descriptive demographic analysis of adoption rates across users in the Ctrip dataset (user-level adoption comparisons by age, gender, and prior engagement). Sample drawn from the 31 million users in the platform logs.
Grok attracts users primarily for its content policy.
Survey items asking users for reasons they use each platform; reported attribution of content policy as primary reason for Grok (overall N=388).
DeepSeek attracts users primarily through word-of-mouth.
Survey items asking users for reasons they use each platform; reported attribution of word-of-mouth as primary reason for DeepSeek (overall N=388).
Claude attracts users primarily for answer quality.
Survey items asking users for reasons they use each platform; reported attribution of answer quality as primary reason for Claude (overall N=388).
ChatGPT attracts users primarily for its interface.
Survey items asking users for reasons they use each platform; reported attribution of interface as primary reason for ChatGPT (overall N=388).
Over 80% of users use two or more platforms (i.e., multi-platform usage is common).
Survey self-reports aggregated across respondents (paper reports 'over 80%'); overall sample N=388.
We conducted a cross-platform survey of 388 active AI chat users comparing satisfaction, adoption drivers, use case performance, and qualitative frustrations across seven major platforms: ChatGPT, Claude, Gemini, DeepSeek, Grok, Mistral, and Llama.
Cross-sectional online survey described in the paper; sample size reported as 388 users; seven named platforms explicitly listed.
Robustness tests confirm that the core conclusions about IRs improving urban energy resilience and the identified mechanisms/moderators are highly reliable.
Multiple robustness checks reported by the authors (unspecified in the abstract) applied to the DML estimates on the 280-city panel (2009–2023).
Science expenditure (SE) positively moderates the promoting effect of IRs on urban energy resilience; the interaction term coefficient is significantly positive.
Moderation analysis reported in the paper using interaction terms between IRs and science expenditure in the DML framework on the 280-city panel (2009–2023); reported statistically significant positive interaction coefficient.
Environmental regulation (ER) positively moderates the promoting effect of IRs on urban energy resilience; the interaction term coefficient is significantly positive.
Moderation analysis reported in the paper using interaction terms between IRs and environmental regulation in the DML framework on the 280-city panel (2009–2023); reported statistically significant positive interaction coefficient.
Green technology innovation is a main mediating path through which IRs improve urban energy resilience.
Mediation/transmission mechanism analysis reported in the paper based on the DML approach applied to the 280-city panel (2009–2023).
Industrial structure upgrading is a main mediating path through which IRs improve urban energy resilience.
Mediation/transmission mechanism analysis reported in the paper based on the same DML framework and the 280-city panel (2009–2023).
Industrial robots (IRs) significantly promote the improvement of urban energy resilience (UER).
Empirical analysis using Double Machine Learning (DML) on a panel of 280 prefecture-level and above Chinese cities from 2009 to 2023; various robustness tests reported.
The best designs often do not originate from top-ranked ILP candidates, indicating that global optimization exposes improvements missed by sub-kernel search.
Analysis comparing origins of the best final designs vs. their ILP ranking, reported across the benchmark set (12).
Larger gains on harder benchmarks: streamcluster exceeds 20× and kmeans reaches approximately 10×.
Per-benchmark empirical results reported for streamcluster and kmeans in the evaluation.
Scaling from 1 to 10 agents yields a mean 8.27× speedup over baseline.
Empirical evaluation across the reported benchmark set comparing performance with 1 agent versus 10 agents; mean speedup stated in the results.
We evaluate the approach on 12 kernels from HLS-Eval and Rodinia-HLS using Claude Code (Opus 4.5/4.6) with AMD Vitis HLS.
Experimental setup described in the paper reporting evaluation on 12 kernels drawn from HLS-Eval and Rodinia-HLS, using Claude Code (Opus 4.5/4.6) and AMD Vitis HLS.
In Stage 2, the pipeline launches N expert agents over the top ILP solutions, each exploring cross-function optimizations such as pragma recombination, loop fusion, and memory restructuring that are not captured by sub-kernel decomposition.
Method section describing Stage 2 which runs multiple expert agents exploring cross-function optimizations on top ILP solutions.
In Stage 1, the pipeline decomposes a design into sub-kernels, independently optimizes each using pragma and code-level transformations, and formulates an Integer Linear Program (ILP) to assemble globally promising configurations under an area constraint.
Method section describing Stage 1 decomposition, per-sub-kernel optimization and ILP assembly under an area constraint.
We introduce an agent factory, a two-stage pipeline that constructs and coordinates multiple autonomous optimization agents.
Method description in the paper describing the design and implementation of the two-stage 'agent factory' pipeline.
Deployment validation across 43 classrooms demonstrated an 18x efficiency gain in the assessment workflow.
Field deployment described in the paper: system was validated across 43 classrooms and an efficiency gain of 18x in the assessment workflow is reported.
Interaction2Eval achieves up to 88% agreement with human expert judgments.
Reported evaluation results comparing Interaction2Eval outputs to human expert annotations (rubric-based judgments) on the dataset.
Interaction2Eval, an LLM-based framework, addresses domain-specific challenges (child speech recognition, Mandarin homophone disambiguation, rubric-based reasoning).
Methodological description in the paper: a specialized LLM-based pipeline designed to handle listed domain challenges; presented as the approach used to extract structured quality indicators.
TEPE-TCI-370h is the first large-scale dataset of naturalistic teacher-child interactions in Chinese preschools (370 hours, 105 classrooms) with standardized ECQRS-EC and SSTEW annotations.
Authors' dataset construction and description: 370 hours of recorded interactions from 105 classrooms, annotated with ECQRS-EC and SSTEW rubrics as reported in the paper.
The dataset provides a reproducible and scalable foundation for research on technological diffusion, regional digitalisation, and industry-level transformation, and can be readily extended to future years or adapted to other countries.
Text asserts reproducibility, scalability, and extendability of the dataset and methods for future years and other countries.
By providing indicators for two benchmark years, the dataset supports the study of how AI adoption evolves across the Spanish business landscape.
Text highlights the availability of indicators for 2023 and 2025 and claims this supports temporal study of adoption evolution.
This multi-dimensional structure enables users to explore territorial patterns, sectoral differences, and size-related disparities in the uptake of AI.
Text claims that the dataset's dimensions make it possible to explore spatial (territorial), sectoral, and size-related patterns in AI uptake.