Evidence (4793 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 |
Productivity
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AI-assisted irrigation reduced water use by 36% (p < 0.001).
Field experiment results with one-way ANOVA showing treatment effect for water use: F(1,18) = 15228.16, p < 0.001. Percentage change reported directly in the paper.
AI-assisted irrigation increased wheat yield by 35% (p < 0.001).
Field experiment results with one-way ANOVA showing treatment effect for wheat yield: F(1,18) = 1335.66, p < 0.001. Percentage change reported directly in the paper.
State-owned enterprises and high-tech firms with robust digital infrastructure experience the largest productivity and innovation gains from AI adoption, indicating absorptive capacity matters.
Heterogeneity analysis on the same panel data comparing subgroups (state-owned vs. non-state-owned; high-tech vs. others; firms with stronger digital infrastructure), showing larger estimated AI effects in those subgroups.
Adoption of AI strengthens firms' innovation outcomes.
Same panel dataset (A-share-listed design firms, 2014–2023) with AI indicators derived from annual reports and patent texts; regression analyses linking AI indicator to innovation metrics (patent-related measures and/or firm-level innovation proxies referenced in the study).
Integrating AI technologies significantly enhances Total Factor Productivity (TFP) in design-oriented, project-based firms.
Panel regression analysis using firm-level panel data of A-share-listed design-oriented enterprises in China (2014–2023). AI exposure measured via an enterprise-level AI indicator constructed from NLP-based text analysis of annual reports and patents; TFP estimated at the firm level as the dependent variable. Robustness checks (e.g., Propensity Score Matching) reported.
The weeder was equipped with a Raspberry Pi microcontroller and a camera module to detect crops and weeds in real-time, enabling autonomous operation.
Design description in the paper: hardware integration of Raspberry Pi and camera module for real-time detection (method: system design and implementation). No sample size or quantitative test data reported for detection accuracy in the provided summary.
AI adoption in Slovakia increased across all enterprise size classes between 2021 and 2024.
Analysis of harmonised Eurostat enterprise-level adoption indicators for 2021–2024 using descriptive statistics and dynamics-of-change methods, disaggregated by enterprise size class. (Sample: enterprises in Slovakia as reported in Eurostat; exact n not specified in the paper summary.)
Economic performance (presumably baseline economic indicators) has a positive effect on growth across all quantiles, with the effect strengthening at upper-tail quantiles (τ = 0.75–0.90).
MMQR results reported in the paper indicating positive coefficients for the economic performance variable at all quantiles, with larger coefficients/greater significance at τ = 0.75–0.90.
AI is often touted for its potential to revolutionize productivity.
Authors' observation about prevailing claims in public, industry, and academic discourse (qualitative observation; the excerpt does not cite specific sources).
The authors propose 'thick entertainment' as a framework for evaluating AI-generated cultural content — one that considers entertainment's role in meaning-making, identity formation, and social connection rather than simply minimizing harm.
Explicit conceptual proposal put forward by the authors in the paper (normative/framework contribution).
The recommended IS research emphases include hybrid human–AI ensembles, situated validation, design principles for probabilistic systems, and adaptive governance.
Explicitly listed components of the authors' proposed research agenda in the discussion section of the paper, derived from synthesis of reviewed literature and conceptual analysis.
To bridge the misalignment, the paper proposes reorienting IS scholarship from analyzing impacts toward actively shaping the co-evolution of technical capabilities with organizational procedures, societal values, and regulatory institutions.
Authors' proposed research agenda and recommendations derived from the synthesis of the 28 reviewed studies and their socio-technical analysis.
The study contributes to theory by developing a human-grounded decision analytics perspective and to practice by providing practical advice to executives and analytics leaders.
Author-stated contributions based on the conceptual framework and practical recommendations included in the paper. No practitioner evaluation or citation analysis provided.
The rapid growth of geospatial data and advances in artificial intelligence (AI) have driven GeoAI’s rise as a key paradigm in urban analytics.
Synthesis from the paper's literature review highlighting trends in data availability and AI capability; evidence likely based on counts of recent publications, reported applications, and domain examples (specific sample size or bibliometric measures not provided in the excerpt).
The study reframes AI as an augmentation mechanism rather than a substitute for managerial judgment and extends organizational decision theory to account for socio-technical decision systems.
Theoretical contribution asserted by the paper based on its literature synthesis and conceptual development (claim about extension of theory rather than empirical test).
The paper develops an integrative conceptual framework that explains how human judgment, algorithmic intelligence, and organizational context interact to shape decision quality and organizational outcomes.
Author-constructed conceptual framework based on synthesized literature across decision sciences, management, and information systems (framework described as output of the meta-analysis; no empirical validation reported in abstract).
Digital–real integration and New Quality Productive Forces exhibit a significant bidirectional positive relationship (each variable positively and significantly promotes the other).
Empirical results from the GS3SLS spatial simultaneous equations model applied to the 30-province panel (2011–2022); paper reports statistically significant positive coefficients in both directions.
This research introduces the RL-FRB/US model which integrates the FRB/US macroeconomic model and a Proximal Policy Optimization (PPO) reinforcement learning agent with an active enhancement of a relocation mechanism for fiscal policy optimization.
Methodological description in the paper: construction of a hybrid model combining the FRB/US structural macro model with a PPO RL algorithm and an added 'active enhancement of relocation' mechanism for fiscal policy decision-making.
Curated (human-authored) Skills substantially improve agent task success on average (+16.2 percentage points).
Aggregate result reported over the SkillsBench benchmark: comparison of pass rates between baseline (no Skills) and curated-Skills conditions across the benchmark. SkillsBench comprises 86 tasks across 11 domains; evaluations used 7 agent–model configurations and 7,308 execution trajectories to compute pass rates and deltas.
Common AI applications in accounting include transaction automation, invoice processing, reconciliations, fraud detection, anomaly detection, automated financial reporting, and predictive forecasting.
Descriptive listing drawn from academic and industry sources/case studies summarized in the paper.
The positive AI → executive pay relationship is robust to endogeneity controls, including instrumental variable approaches, and to multiple robustness checks.
Instrumental variable analyses and a battery of robustness checks reported in the paper applied to the same A-share firm panel and baseline specifications; IV strategy and robustness test details provided in the methods section.
Firm-level AI adoption raises executive compensation in Chinese A-share listed companies (2007–2023).
Baseline panel regressions on a panel of Chinese A-share listed firms (2007–2023) linking a firm-level AI application indicator to executive compensation, controlling for standard firm controls and fixed effects.
From interview-based evidence the authors constructed a conceptual framework that integrates empirical insights with existing theories to explain how human–AI interaction alters design cognition.
Synthesis of qualitative interview findings with literature on creative cognition and design thinking; framework presented as an output of the study (framework construction described in paper).
The paper issues a research agenda for economists: empirically develop instruments linking first‑person temporal reports with behavioral and neural proxies; theoretically incorporate subjective temporality into models of utility, human capital, attention economics, and platform competition; and evaluate policy accounting for temporal‑experience externalities.
Explicitly stated research agenda and methodological recommendations in the paper; no empirical follow‑up included.
Economists will need new empirical measures: validated instruments translating phenomenological constructs (e.g., Chronons) into observable proxies or composite indices for welfare and labor studies, facing standardization and comparability challenges.
Methodological recommendation and discussion in the paper; no empirical measure development or validation reported.
The paper proposes candidate mappings from subjective reports to neural/behavioral signatures (e.g., neural markers of attentional episodes, temporal binding windows) and suggests experimental paradigms to operationalize temporal units.
Methodological proposals and suggested experimental agendas in the paper; no implemented experiments or sample sizes reported.
The framework situates itself at the intersection of neurophenomenology, computational phenomenology, brain–computer interfaces, and human–AI teaming research.
Cross-disciplinary literature synthesis and conceptual mapping in the paper; descriptive claim with no empirical sampling (N/A).
The paper introduces symbolic operators—Chronons, Hexachronons, Metachronos—as theoretical units intended to bridge first-person phenomenology of temporal experience with third‑person neurotechnology descriptions.
Theoretical proposal and definitional introduction within the paper (conceptual development); no experimental validation or sample (N/A).
XChronos is a philosophical-epistemological framework arguing that transhumanism must place subjective temporality (lived time, presence, attention, meaning) at the center of design and evaluation.
Conceptual/philosophical analysis and literature synthesis presented in the paper; no empirical sample or dataset (N/A).
A Random Survival Forest built on curated cancer‑death‑related genes (CDRG‑RSF) achieved the best long‑term prognostic performance among 14 tested ML algorithms for pancreatic cancer, with 3‑ and 5‑year AUCs > 0.7.
Comparison of 14 ML survival algorithms on curated prognostic genes; Random Survival Forest (CDRG‑RSF) reported superior 3‑ and 5‑year AUCs exceeding 0.7 (exact sample sizes/cohort details not provided in summary).
Experimental knockdown of PSME3 reduced proliferation and invasion and increased apoptosis in LUAD cells, implicating the PI3K/AKT/Bcl‑2 pathway as a mediator.
Functional assays (gene knockdown experiments) reported in the PIGRS study showing decreased proliferation/invasion and increased apoptosis after PSME3 knockdown, with pathway analyses implicating PI3K/AKT/Bcl‑2.
Deep neural networks (DNNs) better captured cross‑study differential expression (DEA) signals when predicting miRNA from mRNA than sparse linear models (LASSO); for HIV the cross‑study log2 fold‑change (log2FC) correlation was approximately R ≈ 0.59 for the DNN approach.
Analysis on seven paired viral infection datasets (including WNV and HIV); compared DNNs vs. LASSO for mRNA→miRNA prediction; reported cross‑study log2FC correlation R ≈ 0.59 for HIV for the DNNs. Methods included differential expression signal recovery across studies.
An AI‑powered pipeline (EPheClass) produced a parsimonious saliva microbiome classifier for periodontal disease with AUC = 0.973 using 13 features.
EPheClass pipeline using ensemble ML (kNN, RF, SVM, XGBoost, MLP), centred log‑ratio (CLR) transform and Recursive Feature Elimination (RFE); reported performance AUC = 0.973 for periodontal disease model with 13 features (sample size not specified in summary).
About 78% of the included studies document productivity increases related to digital transformation initiatives.
Quantitative summary across the 145 included studies indicating the proportion reporting productivity gains (~78%).
A systematic review of 145 empirical studies (published 2020–2025) finds a consistent positive association between digital transformation and work productivity.
Systematic review following PRISMA 2020 of 145 included empirical studies identified and screened from searches (see Methods); inclusion period 2020–2025; productivity outcomes extracted from each study.
A PaaS layer enables industry-specific customization (complex contract logic, milestone handling, multi-entity consolidation).
Paper's architectural proposal; described as the role of PaaS in the hybrid framework. This is a design claim, not a measured outcome in the summary.
A SaaS layer should provide standardized accounting, invoicing, and reporting workflows for the EPC industry.
Architectural proposition in the paper: design recommendation rather than an empirically isolated test. The claim is descriptive of the proposed architecture.
Core supply‑chain management challenges targeted by simulation are production layout, product strategy, and managing volume and variety.
Survey and critique of simulation applications presented in the paper; conceptual taxonomy of application areas.
The paper proposes a 'manufacturing operation tree'—an organizationally structured framework—to guide development of more realistic, validated, and industry‑relevant simulation models.
Conceptual/modeling output in the paper (diagram and explanation of the manufacturing operation tree); theoretical development rather than empirical testing.
Econometric and causal-inference tools (difference-in-differences, instrumental variables, randomized encouragement designs) are needed to estimate long-term effects of personalized robot interventions.
Recommended methodological agenda for AI economists in the paper; no applied causal studies presented.
Research and deployment will require new datasets: longitudinal multimodal interaction logs, user preference surveys, simulated user populations, and ethically annotated datasets for fairness and safety evaluation.
Data & Methods recommendations based on identified empirical needs; no dataset release or analysis in this paper.
Measuring welfare impact of personalized robots requires going beyond engagement to include non-market outcomes such as well-being, autonomy, and mental health.
Methodological recommendation in the implications and evaluation sections; no empirical measures provided.
A/B testing and longitudinal field studies are necessary for real-world validation of robot personalization, and metrics should include welfare-oriented outcomes (well-being, trust) in addition to engagement.
Recommended evaluation strategy drawing from HRI and RS experimental standards; no field trials reported in this work.
Prior to live trials, offline RS evaluation metrics (precision/recall, NDCG), counterfactual/off-policy estimators, and simulated users should be used to validate personalization policies.
Methodological recommendation based on RS evaluation practices; no empirical comparison with live trials in robots presented.
Contextual bandits and counterfactual/off-policy learning can enable safe exploration and off-policy evaluation when adapting robot interactions from logged data.
Methodological synthesis referencing contextual bandit and counterfactual learning techniques from RS and causal inference; no robotic implementation experiments reported.
Sequence-aware recommenders (RNNs, Transformers, Markov/session-based models) are suitable for modeling session dynamics and short-term preference shifts in robot interactions.
Survey of sequence/temporal RS models and their typical use cases; conceptual recommendation only.
RS tooling covers long-term user profiles, short-term/session signals, context-awareness, multi-objective ranking, and evaluation methods suited for personalization at scale.
Review of recommender-systems methods and tooling in the literature; conceptual synthesis without empirical new data.
Recommender systems are specialized in representing, predicting, and ranking user preferences across time and contexts (e.g., collaborative filtering, content-based models, sequential/session models).
Established RS literature surveyed and cited as the basis for the claim; conceptual argument, no new experiments.
Digital trade development raises city-level house prices in China in a robust, linear manner.
City-level panel regressions using a constructed digital trade index (entropy-TOPSIS aggregation of multiple indicators). Authors report tests for nonlinearity (none found) and multiple robustness checks. Sample: Chinese cities (years and exact sample size not specified in the summary).
Breakthroughs in structure prediction arise from end‑to‑end deep models that combine evolutionary information (MSAs, coevolutionary signals), geometric constraints and equivariant architectures, and large‑scale pretraining on sequence databases.
Paper describes methodological components: end‑to‑end architectures using MSAs, SE(3)/E(3)-equivariant layers, transformer‑based pretraining on UniRef/UniProt/metagenomic catalogs; no quantitative ablation studies are provided in the text.