Evidence (7953 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 |
The paper calls for subsequent quantitative validation (using task-based, matched employer-employee, and provider-level panel data) to estimate causal impacts on productivity, health outcomes, wages, and employment composition across the three interaction levels.
Stated research agenda and measurement recommendations in the paper's discussion section.
The study is qualitative and small-sample (four case) and therefore interpretive and illustrative rather than statistically generalizable.
Explicit methodological statement in the paper: design = qualitative multiple case study, sample = four AI healthcare applications.
The study identifies a three-level taxonomy of human–AI interaction in healthcare: AI-assisted, AI-augmented, and AI-automated.
Conceptual taxonomy derived from multiple qualitative case studies (n=4) using cross-case comparison and Bolton et al. (2018)'s three-dimensional service-innovation framework.
Few longitudinal or randomized studies were found, which limits the evidence base for causal claims about digital transformation's effect on productivity.
Review recorded a limited number of longitudinal analyses and quasi-experimental designs among the 145 studies; randomized studies were scarce or absent.
Measurement heterogeneity across studies includes self-reported productivity, output-per-worker metrics, and process efficiency indicators.
Extraction of productivity indicators from included studies (detailed in Methods/Extraction fields) showed multiple distinct measurement approaches.
There is a lack of standardized instruments and inconsistent controls for confounding factors across studies, limiting causal inference about the effect of digital transformation on productivity.
Review extraction documented varied instruments/measures and inconsistent adjustment for confounders across the included studies; few randomized or robust longitudinal designs were found.
Heterogeneous definitions of 'digital transformation' and a variety of productivity measurement approaches prevented a formal quantitative meta-analysis.
Extraction found wide variation in how digital transformation and productivity were defined and measured across the 145 studies (self-reported productivity, output per worker, process efficiency metrics, etc.), leading authors to forgo meta-analysis.
535 records were identified across Scopus, Web of Science, ScienceDirect, IEEE Xplore, and Google Scholar, of which 145 met PRISMA 2020 inclusion criteria.
Search and screening procedure documented in the review: initial database searches yielded 535 records → duplicates removed → screening → full-text evaluation → 145 included studies.
Non-probability sampling and self-reported measures limit claims about prevalence and causality; cross-sectional design cannot capture dynamics of skill acquisition over time.
Study limitations explicitly reported by authors: non-probability sampling, self-reported measures, and cross-sectional design.
There are few large-scale randomized controlled trials (RCTs) showing direct patient outcome improvements from GenAI CDS; high-quality real-world and longitudinal studies are limited but essential.
Evidence-maturity statement in the paper summarizing the literature; the paper explicitly notes scarcity of large RCTs and longitudinal evaluations.
The paper's empirical scope is primarily conceptual/theoretical and literature‑based rather than an empirical case study or large‑scale data experiment; it emphasizes the need for future empirical validation.
Explicit methodological description within the paper stating reliance on literature review and conceptual development; absence of empirical sample or case study.
Randomized or quasi-experimental evaluations of digital-ID rollouts, subsidy programs for fintech adoption, or sandboxed regulatory innovations can identify causal impacts on inclusion and growth.
Methodological recommendation proposing experimental and quasi-experimental designs to obtain causal inference; no implementation results reported in the paper summary.
AI economists should prioritize measuring how AI-driven services affect access, default rates, transaction costs, and market structure, disaggregated across income groups and regions.
Methodological recommendation in the 'Implications for AI Economics' section; suggested measurement priorities rather than an empirical finding.
There is a need for economic analysis of data governance regimes, model transparency requirements, algorithmic auditability, and incentives for responsible AI adoption in finance.
Methodological and policy recommendation based on identified gaps in the literature and regulatory practice; this is a stated research/policy need in the paper rather than an empirical claim requiring sample evidence.
Typical evaluation metrics reported are accuracy, precision, recall, F1-score, AUC, detection rate, false positive rate, latency, and computational cost.
Survey of evaluation practices in reviewed papers listing the metrics authors commonly report.
Emerging approaches in the literature include federated learning, online/streaming learning, and transfer learning for cross-device generalization.
Trend analysis across recent papers indicating adoption of federated and continual learning paradigms and transfer-learning techniques.
Unsupervised and semi-supervised methods (clustering, one-class classifiers, autoencoder-based anomaly detectors) are commonly employed to handle unlabeled/anomalous IoT traffic.
Synthesis of studies using anomaly-detection paradigms and unsupervised techniques reported in the reviewed papers.
Deep learning approaches used include CNNs, RNNs/LSTMs for sequence/traffic analysis, and autoencoders for anomaly detection.
Surveyed literature and taxonomy noting multiple studies that apply convolutional and recurrent architectures and autoencoders to network/traffic data.
Common ML approaches reported for IoT IDS include supervised models (random forest, SVM, gradient boosting, neural networks).
Taxonomy and literature synthesis showing frequent use of classical supervised classifiers in surveyed papers and experiments.
Empirical research suggestion: recommended outcome variables for future empirical work include productivity (TFP), profitability, exports, employment composition, and process innovation rates; explanatory variables include AI adoption intensity, strategic alignment indices, leadership commitment surveys, sensing activities, and institutional support measures.
Explicit research agenda and measurement suggestions provided in the paper based on the framework and gaps identified in the 72‑article review.
Scope & limits: the paper is a literature synthesis (no new primary empirical data), has a geographical emphasis on Ibero‑America, and covers literature up to 2024 (may omit post‑2024 developments).
Explicit limitations and scope noted in the paper (no primary data; regional emphasis; time window).
Methodological approach: the paper uses a structured narrative literature review following Torraco (2016) and Juntunen & Lehenkari (2021), analyzing a corpus of 72 articles from 2015–2024 via thematic synthesis and systematic coding.
Explicit methodological statement in the paper specifying approach, corpus size (72 articles), time window (2015–2024), and analytic techniques (thematic synthesis and coding).
The framework yields eight empirically testable propositions linking capability development to firm outcomes (the paper explicitly lists eight propositions including P1–P3 and five additional linked propositions).
Explicit claim in the reviewed paper: framework includes eight testable propositions; propositions are theoretical and untested empirically within the paper.
This work is a conceptual framework and design proposal synthesizing methods from recommender systems and HRI rather than a report of novel empirical experiments.
Explicit statement in the Data & Methods section of the paper.
The review followed PRISMA guidelines and included 30 scholarly articles retrieved from Scopus, published between 2020 and 2025, selected using pre-specified inclusion criteria.
Methods section of the paper reporting the SLR protocol, database, time window, and number of included studies.
The study is primarily diagnostic and prescriptive rather than empirical: no explicit empirical dataset, causal identification strategy, or statistical estimation is reported.
Methods section of the paper explicitly characterizes the work as conceptual, systems-oriented, and not reporting empirical evaluation data.
The urban AI index is constructed via text-mining techniques to capture city-level AI capability/intensity.
Methodological description: authors report using text-mining to build a city-level AI capability/intensity index (details of sources and text-mining procedure not provided in the summary).
The digital trade index is constructed using the entropy-TOPSIS method (multi-indicator aggregation).
Methodological description: digital trade index aggregation via entropy-TOPSIS reported by authors.
The study's empirical identification relies on longitudinal variation with city fixed effects and time effects, plus non-linear/threshold identification via polynomial (DE^2) terms and threshold-regression using green-technology-innovation as the threshold variable.
Description of empirical strategy in the paper: panel fixed-effects models (controlling for time-invariant city heterogeneity and common time shocks), mediating-effect models for channel tests, and threshold-regression models for regime-dependent effects, applied to the 278-city 2011–2022 panel.
Research recommendation: invest in longer-run, rigorous impact evaluations (RCTs, panel studies) and system-level assessments to capture spillovers and sustainability outcomes.
Authors' stated research agenda based on identified methodological gaps (limited long-term and system-level evidence) in the review.
There is variation in study design and quality in the evidence base (RCTs, quasi-experimental studies, observational case studies, pilots).
Methodological caveats noted by the authors summarizing the diversity of designs reported across reviewed studies.
The review used a structured literature review with thematic synthesis and a comparative effect-size analysis to quantify ranges for yield, cost, and efficiency outcomes.
Authors' description of review approach and analytical methods in the Data & Methods section.
The evidence base reviewed comprises more than 60 peer-reviewed articles and institutional reports from 2020–2025, primarily focusing on Sub-Saharan Africa.
Statement in the paper's Data & Methods section describing the scope and composition of the review sample.
Effect sizes and impacts vary substantially across contexts—by crop, farm size, and institutional setting.
Comparative synthesis across studies showing heterogeneity in reported outcomes and authors' methodological caveats highlighting context dependence.
Technologies assessed in the review include predictive analytics, digital advisory systems, smart irrigation, pest/disease detection, and precision fertilization.
Descriptive synthesis of the types of AI and digital technologies evaluated across the >60 reviewed articles and reports (2020–2025).
These quantitative performance figures come from case‑level, high‑performer pilots and should not be treated as typical industry benchmarks.
Authors' caveat based on the composition of evidence in the review (skew towards pilots and selected advanced implementations; limited longitudinal/multi‑project empirical studies).
Inter‑rater reliability for the study selection/encoding was Cohen’s κ = 0.83 (substantial agreement).
Reported inter‑rater reliability statistic from the review's quality control step (Cohen's kappa = 0.83).
The review screened 463 Scopus records (2018–2026) and selected 160 peer‑reviewed studies using a PRISMA‑guided process.
Systematic literature review described in paper: Scopus search (2018–2026), PRISMA screening and eligibility filtering; initial n=463, final n=160.
The abstract does not report the study sample size, sectoral scope, or country/context—limiting assessment of external validity and generalizability.
Observation of reporting in the paper's abstract (absence of sample size, sectoral/country context information in the abstract as provided).
The study used a two-stage mixed-methods design: a qualitative exploratory phase to surface determinants of trust and inertia, followed by a quantitative phase to validate the conceptual framework.
Methods description in the paper: explicit two-stage mixed-methods approach (qualitative then quantitative) used to identify and test determinants of initial trust and inertia toward GAICS.
Kebumen UNESCO Global Geopark is used as a practical context to ground the framework; its ecological/cultural assets and emergent digital presence make it a suitable case for studying emerging destinations balancing innovation with authenticity.
Paper provides Kebumen Geopark as the illustrative case study/context for the conceptual framework; no systematic case-study data reported.
Operationalization suggestions: social proof via ratings, reviews, UGC volume and valence; behavioral proxies include bookings and inquiries as outcomes.
Paper explicitly lists social-proof indicators and behavioral proxies as part of recommended empirical approaches (digital-trace and platform data).
Operationalization suggestions: sustainability communication via message clarity, perceived authenticity, and specificity of eco-actions.
Operationalization guidance in the paper for measuring sustainability messaging in experiments/surveys.
Operationalization suggestions: AI personalization via perceived relevance, transparency, and perceived fairness of recommendations.
Operationalization guidance in the paper; proposed as latent construct indicators for future SEM or experiments.
Operationalization suggestions: digital experience quality via usability, information richness, responsiveness, multi-channel integration.
Operationalization guidance provided in the paper's methods suggestions; intended for future empirical measurement.
Recommended empirical follow-ups include Structural Equation Modeling (SEM), experimental tests (lab/field/online), quasi-experimental causal-inference methods (DiD, IVs, RD), comparative/regional designs, and analysis of digital-trace/platform data (clickstreams, recommendation logs, bookings, UGC).
Methodological recommendations explicitly listed in the Data & Methods and Research Agenda sections of the paper; no primary empirical work conducted.
The framework produces ten testable propositions mapping hypothesized direct and mediated links among constructs and specifying contingencies for future empirical testing.
Explicit statement in the paper that the framework yields ten testable propositions; no empirical validation reported.
Experimental structure determination (X‑ray, NMR, cryo‑EM) remains the gold standard but is slow, costly, and low‑throughput.
Paper explicitly states experimental methods are 'gold standard' and characterizes them as slow, costly, low‑throughput; the PDB is cited as the source of structural ground truth.
The authors did not perform primary empirical validation or simulation of TVR‑Sec across real VR deployments.
Methods and limitations section explicitly state no original empirical experiments or simulations were conducted; analysis is conceptual and qualitative.
The paper's scope comprised a comparative literature review and conceptual integration of 31 peer‑reviewed studies published between 2023 and 2025.
Authors' methods description specifying sample size and publication window: 31 peer‑reviewed studies (2023–2025).