Evidence (4131 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 |
Innovation
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Artificial intelligence significantly promotes the growth of new quality productive forces in new energy vehicle firms.
Panel data analysis of new energy vehicle firms in the Yangtze River Delta from 2001 to 2023; firm-level indicators of artificial intelligence and new quality productive forces constructed; regression estimation showing a significant positive effect.
The effect of BDTA on improving CEE is more significant in enterprises with low market concentration.
Heterogeneity/subsample analysis on the listed manufacturing firm data (2010–2023) showing larger BDTA→CEE effects in firms operating in markets with lower concentration.
The effect of BDTA on improving CEE is more significant in high-tech enterprises.
Heterogeneity/subsample analysis reported on listed manufacturing firms (2010–2023) indicating stronger BDTA→CEE effects among high-tech enterprises.
The effect of BDTA on improving CEE is more significant in non-state-owned enterprises.
Heterogeneity analysis (subsample analysis) reported by authors using the 2010–2023 listed manufacturing firm sample, showing stronger BDTA→CEE effects in non-state-owned firms compared to state-owned firms.
BDTA improves CEE of manufacturing enterprises by enhancing internal control quality.
Theoretical channel analysis and empirical mediation/ mechanism tests on listed manufacturing firms (2010–2023) showing internal control quality is a mediator in the BDTA→CEE link.
BDTA improves CEE of manufacturing enterprises by fostering green innovation.
Theoretical channel analysis plus empirical mediation/ mechanism tests using the same sample (listed Chinese manufacturing firms 2010–2023) that show green innovation mediates the BDTA→CEE relationship.
Big data technology application (BDTA) can improve carbon emission efficiency (CEE) of manufacturing enterprises.
Empirical panel regression analysis on listed companies in China's manufacturing industry from 2010 to 2023; authors report baseline regressions showing a positive relationship between BDTA and CEE.
Together, these measures can properly establish a behavioral‑regulation model for brain‑privacy protection.
Concluding synthesis in the paper arguing that combined measures would yield the proposed regulatory model (normative conclusion without empirical validation).
Implement a 'pre‑market regulatory sandbox + post‑market tracking' regime to manage product risks.
Prescriptive policy design proposed in the paper (conceptual recommendation; no empirical pilot data reported).
Establish a compliance filing‑review mechanism for BCI privacy policies.
Policy recommendation in the paper proposing a procedural compliance mechanism (normative proposal without empirical testing).
Apply the principles of lawfulness, legitimacy, necessity and good‑faith to all brain‑privacy processing.
Policy recommendation formulated in the paper (prescriptive legal proposal; no empirical evaluation included).
A behavioral‑regulation model better reflects the multi‑interest, non‑exclusive nature of brain privacy and balances risk control with innovation.
Normative policy argument and conceptual comparison of regulatory models presented in the paper (theoretical, not empirically tested).
The study advances an integrative framework of sustainable AI governance emphasizing regulatory adaptability, institutional coordination, and ethical oversight as mechanisms for aligning AI innovation with long-term financial stability and sustainability objectives, and offers policy-relevant guidance for regulators and financial institutions.
Study conclusion reported in the abstract describing the proposed integrative framework and its policy relevance; based on the study's comparative mixed-methods analysis.
AI-enabled financial innovation is associated with improvements in risk assessment capabilities.
Comparative institutional analysis and integration of secondary quantitative indicators with qualitative documentary evidence across China, the United States, and the United Kingdom (2022–2025) as described in the abstract.
AI-enabled financial innovation is associated with improvements in ESG integration.
Same comparative mixed-methods approach across China, the United States, and the United Kingdom (2022–2025) using secondary quantitative indicators and qualitative documentary evidence, reported in the abstract.
AI-enabled financial innovation is associated with improvements in market efficiency.
Comparative mixed-methods analysis (comparative institutional analysis) across leading financial systems in China, the United States, and the United Kingdom (2022–2025), integrating secondary quantitative indicators with qualitative documentary evidence as reported in the study abstract.
Aggregators and niche specialists employ more open governance and sourcing logics that foster innovation, specialization, and ecosystem diversity.
Presented as a comparative finding from the taxonomy and qualitative examination of non-hyperscaler ML platform providers; supports drawn from conceptual analysis and examples in the paper rather than quantitative measures (no sample size reported in abstract).
The concordance has many relevant applications in research and policy analyses of innovation.
Claim about the utility and applicability of the concordance stated by the authors; no enumeration of specific applications or empirical demonstrations included in excerpt.
The concordance can be used to track the diffusion of patented technologies at the technology, firm, region, or country level.
Stated intended applications of the concordance in the paper; excerpt does not present empirical case studies or performance metrics.
We develop, validate and share a novel concordance between technology classes in patent records and market classes in trademark records.
Primary methodological contribution reported by the authors (development, validation, and sharing of a concordance); excerpt does not include validation method details or sample size.
Patent and trademark data can be combined to link given technologies to specific markets.
Conceptual/methodological claim in paper proposing combination of patent and trademark records to map technologies to markets; excerpt does not include empirical validation details.
Trademark filings that accompany the market introduction of new goods and services are a data source that can reveal the market introduction of technologies.
Descriptive claim in paper noting trademarks as a complementary data source to patents; no sample size or validation details in excerpt.
Patent data is the preferred source of information for tracking technological change.
Statement in paper (introductory claim); no empirical sample or method reported in excerpt.
We discuss implications for Information Systems (IS) design and propose future field evaluations.
Paper includes a discussion section outlining IS design implications and suggestions for future empirical/field work.
The approach preserves statistical rigour, traceability, and nuanced Persevere/Iterate decisions when accelerating experimentation.
Reported outcomes of controlled simulations and description of system design that enforces statistical procedures and logging; stated in manuscript as findings.
Logs render capabilities observable at the feature level, turning 'agentic AI' into a disciplined experimentation infrastructure rather than a generic assistant.
Implementation logs and descriptions from the Node.js instantiation reported in the paper; qualitative claim about observability and traceability at the feature level.
The Multi Agent System reduces time-to-validated-learning by roughly an order of magnitude while preserving statistical rigour, traceability, and nuanced Persevere/Iterate decisions.
Results from the controlled simulations reported in the paper (comparison between agentic multi-agent system and manual B-M-L cycles).
Controlled simulations compare agentic and manual B-M-L cycles on feature ideas.
Reported controlled simulation experiments in the paper comparing agentic (multi-agent) and manual B-M-L cycles; methodological description present in manuscript.
We instantiate them in a Node.js package instrumenting a production-grade SaaS codebase.
Implementation artifact reported in the paper (Node.js package) and description of instrumentation on a production-grade SaaS codebase.
Drawing on the Dynamic Capabilities View, we derive fifteen meta-requirements and thirty-three design principles (consolidated into seven goal-directed groups) for sensing, seizing, reconfiguring, orchestration, and governance.
Design-theory derivation reported in the paper (counts of meta-requirements and design principles are stated in the manuscript).
We propose a multi-agent artefact that operationalises the Build–Measure–Learn (B-M-L) cycle as a closed-loop control system.
Design science study described in the paper; conceptual derivation and artifact instantiation (Node.js package) reported in the manuscript.
The positive impact of AI application on enterprise innovation efficiency is stronger in labor-intensive firms.
Heterogeneity/subsample analysis using the 2012–2023 A-share panel indicating larger AI effects for labor-intensive firms.
The positive impact of AI application on enterprise innovation efficiency is stronger in asset-intensive firms.
Heterogeneity/subsample analysis on 2012–2023 A-share firm panel showing larger estimated AI effects for firms characterized as asset-intensive.
The positive impact of AI application on enterprise innovation efficiency is stronger in state-owned enterprises.
Heterogeneity/subsample analysis using firm ownership status in the 2012–2023 A-share panel showing larger effects for state-owned enterprises.
The positive impact of AI application on enterprise innovation efficiency is stronger in firms located in central and western regions of China.
Heterogeneity/subsample analysis on the 2012–2023 panel of Shanghai and Shenzhen A-share listed firms showing larger estimated effects for firms in central and western regions.
The effect of AI application on enterprise innovation efficiency is mediated by enterprise ESG performance.
Mediation analysis on Shanghai and Shenzhen A-share listed firms (2012–2023) demonstrating a significant mediating role for ESG performance.
The effect of AI application on enterprise innovation efficiency is mediated by the enterprise's data factor utilization level.
Mediation analysis (empirical) using the 2012–2023 A-share firm panel showing significant mediating effects of data factor utilization.
The effect of AI application on enterprise innovation efficiency is mediated by improvements in enterprise "new-quality productivity".
Mediation analysis (empirical) on the 2012–2023 panel of Shanghai and Shenzhen A-share listed firms showing a significant mediating role for new-quality productivity.
AI application can significantly improve enterprise innovation efficiency.
Empirical analysis of Shanghai and Shenzhen A-share listed enterprises using panel data from 2012–2023; baseline regressions showing a significant positive relationship between AI application measures and enterprise innovation efficiency.
Our project website, including the leaderboard, dataset, and code, is available at https://dong7313.github.io/muse-benchmark/.
Statement in abstract and provided URL pointing to project artifacts.
Together, MUSE provides a realistic benchmark and evaluation framework for advancing Text-to-CAD from geometric generation toward true engineering design.
Paper's stated contribution and intended purpose (abstract) and provision of dataset/benchmark artifacts via project website.
To enable scalable evaluation, we use a rubric-based visual language model (VLM) judge and validate its reliability through human annotation.
Method and validation claim in abstract stating use of rubric-based VLM and validation against human annotations.
The final stage uses design-specific rubrics to assess functionality, manufacturability, and assemblability, moving beyond shape matching toward practical design quality.
Paper's description of the benchmark's evaluation rubric and intended assessment criteria (abstract).
MUSE pairs practical design instances with structured Design Specifications and evaluates generated models through a three-stage protocol: code check, geometric check, and design-intent alignment.
Methodological description in abstract indicating dataset pairing and three-stage evaluation protocol.
We introduce MUSE, a Text-to-CAD benchmark focused on complex, editable boundary representation (B-Rep) assemblies.
Paper contribution / dataset creation described in abstract; supported by project website and accompanying dataset/code.
Multimodal contrastive learning enables generative AI to output images that closely align with text prompts.
Stated as background/technical premise in the paper (based on prior work on multimodal contrastive learning; no experiment details provided in the abstract).
Human-subject experiments further validate the commercial effectiveness of the utility-aware method.
Reported human-subject experiments in the paper that are said to validate commercial effectiveness (details such as sample size, design, and metrics are not provided in the abstract).
In downstream applications on Amazon and Airbnb, product images generated and edited by our method outperform state-of-the-art models in increasing demand and preserving fidelity, while maintaining text-image consistency.
Empirical evaluation on downstream applications using Amazon and Airbnb datasets / deployments reported in the paper (experiments comparing their method to state-of-the-art models; exact sample sizes and metrics not provided in the abstract).
The effect arises from a shift in the learned image-text representation space toward demand-driven visual cues, which we validate through a theoretical bound on the proposed objective.
Theoretical analysis presented in the paper claiming a bound that links the utility-aware objective to representation shifts toward demand-relevant features.
Optimizing this utility-aware objective guides generation toward images that are both semantically coherent and demand-enhancing.
Claim supported in the paper by a theoretical bound and by downstream empirical evaluation (described in the abstract; experiments on marketplace data referenced).