Evidence (13870 claims)
Adoption
8467 claims
Productivity
7558 claims
Governance
6805 claims
Human-AI Collaboration
6363 claims
Org Design
4132 claims
Innovation
4065 claims
Labor Markets
3526 claims
Skills & Training
2945 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 749 | 196 | 98 | 892 | 1984 |
| Governance & Regulation | 817 | 394 | 188 | 121 | 1544 |
| Organizational Efficiency | 771 | 189 | 124 | 83 | 1177 |
| Technology Adoption Rate | 627 | 233 | 123 | 96 | 1088 |
| Research Productivity | 411 | 123 | 56 | 332 | 933 |
| Output Quality | 467 | 178 | 59 | 47 | 751 |
| Decision Quality | 320 | 174 | 75 | 42 | 618 |
| Firm Productivity | 435 | 55 | 88 | 20 | 604 |
| AI Safety & Ethics | 214 | 276 | 65 | 33 | 593 |
| Market Structure | 178 | 167 | 122 | 24 | 496 |
| Task Allocation | 207 | 64 | 71 | 32 | 379 |
| Skill Acquisition | 165 | 59 | 60 | 17 | 301 |
| Innovation Output | 203 | 27 | 43 | 18 | 292 |
| Employment Level | 105 | 52 | 107 | 13 | 279 |
| Fiscal & Macroeconomic | 131 | 69 | 43 | 26 | 276 |
| Consumer Welfare | 116 | 63 | 42 | 11 | 232 |
| Firm Revenue | 150 | 48 | 26 | 3 | 227 |
| Inequality Measures | 44 | 122 | 49 | 6 | 221 |
| Task Completion Time | 169 | 29 | 8 | 12 | 219 |
| Worker Satisfaction | 89 | 63 | 20 | 12 | 184 |
| Error Rate | 69 | 92 | 10 | 2 | 173 |
| Regulatory Compliance | 76 | 68 | 14 | 5 | 163 |
| Training Effectiveness | 93 | 21 | 13 | 19 | 148 |
| Wages & Compensation | 77 | 36 | 25 | 6 | 144 |
| Automation Exposure | 51 | 54 | 22 | 12 | 142 |
| Team Performance | 86 | 17 | 27 | 9 | 140 |
| Developer Productivity | 94 | 17 | 14 | 6 | 132 |
| Job Displacement | 12 | 80 | 20 | 1 | 113 |
| Hiring & Recruitment | 51 | 7 | 8 | 3 | 69 |
| Creative Output | 31 | 17 | 7 | 3 | 59 |
| Skill Obsolescence | 5 | 46 | 6 | 1 | 58 |
| Social Protection | 27 | 16 | 8 | 2 | 53 |
| Labor Share of Income | 17 | 17 | 17 | — | 51 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
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.
The study developed and validated a new AI Job Crafting Scale.
Authors created and psychometrically validated an AI Job Crafting Scale within the multi-source, multi-wave study sample (287 employee–leader dyads); scale development and validation procedures reported.
Work autonomy strengthens the positive impact of AI approach job crafting on work meaningfulness (positive moderation).
Moderation analysis in the multi-wave, multi-source survey of 287 employee–leader dyads showing a significant interaction between AI approach job crafting and work autonomy predicting higher work meaningfulness.
The positive effect of AI approach job crafting on career-relevant outcomes (career satisfaction and performance) operates via increased work meaningfulness (mediation).
Mediation analysis conducted on multi-wave, multi-source survey data from 287 employee–leader dyads using measures of AI approach job crafting, work meaningfulness, and career outcomes.
AI approach job crafting positively predicts employee performance.
Multi-source, multi-wave survey of 287 employee–leader dyads in China; performance likely assessed via leader ratings in the dyadic design and linked to employee-reported AI approach job crafting.
AI approach job crafting positively predicts career satisfaction.
Multi-source, multi-wave survey of 287 employee–leader dyads in China using the newly developed AI Job Crafting Scale; statistical analysis linking employee-reported AI approach job crafting to career satisfaction (proximal professional indicator).
Investments in education and training are crucial for mitigating AI-induced employment disruptions and enhancing workforce adaptability.
Policy recommendation drawn from the paper's empirical findings (PLS-SEM, n = 351) and discussion.
Job displacement intensifies the demand for new skills, highlighting the need for reskilling and upskilling initiatives.
Finding reported from the study's PLS-SEM analysis of survey responses (n = 351).
AI has also fostered employment growth in emerging industries.
Empirical finding reported from the study's analysis of survey data (PLS-SEM, n = 351).
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).
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.
The authors call for shifting evaluation and assurance from tool qualification toward workflow qualification to achieve trustworthy Physical AI.
Normative recommendation based on the paper's theoretical analysis (policy/recommendation; no empirical sample reported).
The paper derives non-degradation conditions that characterize shadow-resistant workflows for AI-assisted safety analysis.
Analytic derivations and formal criteria presented in the paper (theoretical result; no empirical validation/sample size reported).
The paper formalizes four canonical human–AI collaboration structures and derives closed-form performance bounds for them.
Theoretical/mathematical derivations and models in the paper (no empirical verification/sample size reported).
A five-dimensional competence framework captures safety competence via domain knowledge, standards expertise, operational experience, contextual understanding, and judgment.
Theoretical contribution: paper defines and formalizes a five-dimension framework (no empirical validation/sample size reported).
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.
To facilitate adoption of our evaluation framework, we detail our testing protocols and make relevant materials publicly available.
Statement in paper that testing protocols and materials are documented and released publicly (paper claims to provide materials).
We assess an AI model with 10,101 participants spanning interactions in three AI use domains (public policy, finance, and health) and three locales (US, UK, and India).
Reported sample size and study design details stated in abstract: N = 10,101; three domains and three locales specified.
This paper introduces a framework for evaluating harmful AI manipulation via context-specific human-AI interaction studies.
Paper describes a proposed evaluation framework (methodological contribution); claimed in abstract/introduction as new contribution. No numeric sample required for the claim itself.
The result is evidence-based triggers that replace calendar schedules and make governance auditable.
Claimed outcome of applying the decision-theoretic framework in the paper (argumentative; no empirical deployment or case-study evidence reported in the summary).
The paper provides a decision-theoretic framework for retraining policies.
Explicit claim about the paper's contribution; the article presents a framework (conceptual/methodological exposition).
The retraining decision is a cost minimization problem with a threshold that falls out of your loss function.
Decision-theoretic derivation presented in the paper (analytical/theoretical reasoning; no empirical validation reported).