Evidence (5539 claims)
Adoption
5539 claims
Productivity
4793 claims
Governance
4333 claims
Human-AI Collaboration
3326 claims
Labor Markets
2657 claims
Innovation
2510 claims
Org Design
2469 claims
Skills & Training
2017 claims
Inequality
1378 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 402 | 112 | 67 | 480 | 1076 |
| Governance & Regulation | 402 | 192 | 122 | 62 | 790 |
| Research Productivity | 249 | 98 | 34 | 311 | 697 |
| Organizational Efficiency | 395 | 95 | 70 | 40 | 603 |
| Technology Adoption Rate | 321 | 126 | 73 | 39 | 564 |
| Firm Productivity | 306 | 39 | 70 | 12 | 432 |
| Output Quality | 256 | 66 | 25 | 28 | 375 |
| AI Safety & Ethics | 116 | 177 | 44 | 24 | 363 |
| Market Structure | 107 | 128 | 85 | 14 | 339 |
| Decision Quality | 177 | 76 | 38 | 20 | 315 |
| Fiscal & Macroeconomic | 89 | 58 | 33 | 22 | 209 |
| Employment Level | 77 | 34 | 80 | 9 | 202 |
| Skill Acquisition | 92 | 33 | 40 | 9 | 174 |
| Innovation Output | 120 | 12 | 23 | 12 | 168 |
| Firm Revenue | 98 | 34 | 22 | — | 154 |
| Consumer Welfare | 73 | 31 | 37 | 7 | 148 |
| Task Allocation | 84 | 16 | 33 | 7 | 140 |
| Inequality Measures | 25 | 77 | 32 | 5 | 139 |
| Regulatory Compliance | 54 | 63 | 13 | 3 | 133 |
| Error Rate | 44 | 51 | 6 | — | 101 |
| Task Completion Time | 88 | 5 | 4 | 3 | 100 |
| Training Effectiveness | 58 | 12 | 12 | 16 | 99 |
| Worker Satisfaction | 47 | 32 | 11 | 7 | 97 |
| Wages & Compensation | 53 | 15 | 20 | 5 | 93 |
| Team Performance | 47 | 12 | 15 | 7 | 82 |
| Automation Exposure | 24 | 22 | 9 | 6 | 62 |
| Job Displacement | 6 | 38 | 13 | — | 57 |
| Hiring & Recruitment | 41 | 4 | 6 | 3 | 54 |
| Developer Productivity | 34 | 4 | 3 | 1 | 42 |
| Social Protection | 22 | 10 | 6 | 2 | 40 |
| Creative Output | 16 | 7 | 5 | 1 | 29 |
| Labor Share of Income | 12 | 5 | 9 | — | 26 |
| Skill Obsolescence | 3 | 20 | 2 | — | 25 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
Adoption
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Stable energy policy, continuous economic growth, and improved global integration are significant for promoting AI development in the United States.
Policy implication drawn from empirical associations found using WQR/WQC on US quarterly data (2013Q1–2024Q4), where renewable energy, growth, trade openness, and globalisation positively associate with AI investment and energy policy uncertainty exhibits nonlinear effects.
Wavelet Quantile Regression (WQR) and Wavelet Quantile Correlation (WQC) effectively capture distributional asymmetries and time–frequency dynamics in the relationships between macro/policy determinants and AI investment.
Methodological claim supported by the paper's use of WQR and WQC on the 2013Q1–2024Q4 US quarterly dataset; results are reported across quantiles and scales (as stated).
Globalisation positively influences AI investment in the United States.
Empirical analysis using WQR and WQC on US quarterly data from 2013Q1 to 2024Q4 (48 quarters).
Trade openness positively influences AI investment in the United States.
Empirical analysis using WQR and WQC on US quarterly data from 2013Q1 to 2024Q4 (48 quarters).
Economic growth positively influences AI investment in the United States.
Empirical analysis using WQR and WQC on US quarterly data from 2013Q1 to 2024Q4 (48 quarters).
Renewable energy consumption positively influences AI investment in the United States.
Empirical analysis using Wavelet Quantile Regression (WQR) and Wavelet Quantile Correlation (WQC) on US quarterly data from 2013Q1 to 2024Q4 (48 quarters).
AlphaFold represents an 'oracle' breakthrough in AI for scientific discovery.
Cited as an example of an algorithmic breakthrough that changed a specific scientific subtask (protein structure prediction). The paper frames AlphaFold as a milestone in the history reviewed; no new experimental data presented.
The resulting policy matrix includes R&D funding, regulatory sandboxes, public procurement incentives, and tax relief, tailored to each stage of technological evolution.
Paper presents a policy matrix produced by the study listing these instruments mapped to maturity stages; no quantitative evaluation of impact reported in text provided.
To validate and prioritise policy instruments, Delphi rounds with domain experts and Analytic Hierarchy Process (AHP) weighting are employed.
Paper reports use of Delphi method and AHP for validation and prioritization; methodological description without reported number of experts or rounds.
A technology maturity classification categorises innovations into emerging, developing, and mature stages, forming the basis for strategic policy matching.
Paper defines a maturity classification (emerging/developing/mature) and indicates it is used to match policy instruments; categorical description provided, no quantitative validation details in text provided.
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.
These systems are now being widely used to produce software, conduct business activities, and automate everyday personal tasks.
Authors' statement describing observed applications and uses (policy/legal analysis; specific empirical data or sample size not provided in excerpt).
AI agents have entered the mainstream.
Authors' declarative statement based on their review of recent developments and observed uptake (policy/legal analysis in the paper). No empirical sample size reported in excerpt.
Opportunities arising from cyborg workflows include hyper-personalized narratives, democratized production, and ethical augmentation of underrepresented voices.
Forward-looking/interpretive claim in the paper describing potential benefits and opportunities; conceptual rather than empirically demonstrated in the excerpt.
Scalability is addressed via edge computing to support cyborg workflows.
Design/architectural claim in the paper mentioning edge computing as a scalability mechanism; no deployment-scale measurements reported in the excerpt.
The proposed workflows include robust bias mitigation strategies.
Paper asserts bias mitigation approaches are included and demonstrated in case studies; no quantitative fairness metrics or evaluation details provided in the excerpt.
Cyborg workflows produce enhanced creative output via iterative human–AI refinement.
Qualitative claim supported by case studies and examples presented in the paper (no quantitative creativity metrics or sample sizes reported in the excerpt).
Empirical evaluations validate 25-60% improvements in key metrics.
Paper states empirical evaluation results with a 25–60% improvement range; specific metrics, methods, and sample sizes are not provided in the excerpt.
Case studies in content generation, news curation, and immersive production demonstrate efficiency gains of up to 3x in throughput.
Reported results from unspecified case studies described in the paper; numeric claim provided but case study sample sizes and methodological details are not reported in the excerpt.
The paper proposes a comprehensive framework encompassing modular architectures, hybrid protocols, and real-time collaboration interfaces informed by cognitive science, AI engineering, and media studies.
Architectural and methodological proposal described in the paper (the claim is descriptive of the proposed system; no quantitative evaluation of the framework components provided).
Cyborg workflows fuse human judgment with agentic AI autonomous systems capable of goal-directed planning and execution.
Conceptual description and framework proposed in the paper (no empirical sample or trial details 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.
RL-based AVs improve average fuel efficiency by about 1.86% at lower speeds (below 50 km/h) compared to the IDM.
Macroscopic-level fuel efficiency comparison between RL-based AV model and IDM in simulation, stratified by speed (<50 km/h). Number of simulation runs not stated.
RL-based AVs improve average fuel efficiency by about 28.98% at higher speeds (above 50 km/h) compared to the IDM.
Macroscopic-level fuel efficiency comparison between RL-based AV model and IDM in simulation, stratified by speed (>50 km/h). Number of simulation runs not stated.
Transitioning from fully human-driven to fully RL-controlled traffic can increase road capacity by approximately 7.52%.
Macroscopic simulation experiments producing Fundamental Diagrams comparing fully human-driven traffic to fully RL-controlled traffic. Exact number of simulation scenarios or replicates not provided in the claim text.
This study implements a Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to control AVs and trains it using the NGSIM highway dataset to enable realistic interaction with human-driven vehicles.
Methodological description in the paper: implementation of TD3 and training on the NGSIM dataset. Dataset referenced but no numeric sample size reported in the claim text.
Economies and organizations that prioritize adaptability, workforce transformation, and real-time decision-making capabilities are better positioned to sustain growth under volatile conditions.
Claim based on the paper's cross-cutting analysis of global indicators and the conceptual AEPM framework; the excerpt does not provide a quantified causal estimate, experimental evidence, or sample size supporting this assertion.
AEPM is structured around five core pillars—energy resilience, supply chain flexibility, human capital adaptability, financial sustainability, and AI-enabled decision systems—which together provide a comprehensive approach to managing uncertainty and enabling dynamic responses to structural disruptions.
Conceptual design of the AEPM presented in the paper; described as a multidimensional framework combining these five pillars. No empirical validation or quantified impact measures reported in the excerpt.
The paper proposes shifting from forecasting-centric economic management to an adaptive preparedness paradigm and introduces the Adaptive Economic Preparedness Model (AEPM), a multi-dimensional framework designed to enhance resilience at both organizational and national levels.
Presentation of a conceptual model (AEPM) in the paper structured around five pillars; this is a proposed framework rather than an empirically validated intervention (no evaluation sample or randomized test reported in the excerpt).
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).