Evidence (14055 claims)
Adoption
8570 claims
Productivity
7631 claims
Governance
6869 claims
Human-AI Collaboration
6491 claims
Org Design
4175 claims
Innovation
4114 claims
Labor Markets
3566 claims
Skills & Training
2966 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 758 | 199 | 100 | 900 | 2007 |
| Governance & Regulation | 826 | 400 | 191 | 122 | 1563 |
| Organizational Efficiency | 777 | 193 | 124 | 84 | 1189 |
| Technology Adoption Rate | 635 | 233 | 124 | 97 | 1098 |
| Research Productivity | 422 | 128 | 57 | 336 | 954 |
| Output Quality | 476 | 179 | 59 | 47 | 761 |
| Decision Quality | 328 | 177 | 81 | 47 | 640 |
| Firm Productivity | 435 | 57 | 88 | 20 | 606 |
| AI Safety & Ethics | 218 | 277 | 65 | 33 | 599 |
| Market Structure | 180 | 170 | 123 | 24 | 502 |
| Task Allocation | 213 | 64 | 72 | 33 | 387 |
| Skill Acquisition | 170 | 61 | 61 | 17 | 309 |
| Innovation Output | 203 | 27 | 43 | 18 | 292 |
| Employment Level | 105 | 54 | 107 | 13 | 281 |
| Fiscal & Macroeconomic | 131 | 69 | 43 | 26 | 276 |
| Consumer Welfare | 117 | 63 | 42 | 11 | 233 |
| Firm Revenue | 153 | 48 | 26 | 3 | 230 |
| Task Completion Time | 173 | 31 | 8 | 12 | 225 |
| Inequality Measures | 44 | 122 | 49 | 6 | 221 |
| Worker Satisfaction | 89 | 65 | 22 | 12 | 188 |
| Error Rate | 69 | 92 | 10 | 2 | 173 |
| Regulatory Compliance | 77 | 69 | 14 | 5 | 165 |
| Automation Exposure | 56 | 56 | 26 | 13 | 154 |
| Training Effectiveness | 94 | 21 | 13 | 19 | 149 |
| Wages & Compensation | 77 | 36 | 25 | 6 | 144 |
| Team Performance | 86 | 17 | 27 | 10 | 141 |
| Developer Productivity | 95 | 17 | 14 | 6 | 133 |
| Job Displacement | 12 | 80 | 20 | 1 | 113 |
| Hiring & Recruitment | 52 | 7 | 8 | 3 | 70 |
| Creative Output | 31 | 18 | 8 | 3 | 61 |
| Skill Obsolescence | 5 | 46 | 6 | 1 | 58 |
| Social Protection | 27 | 16 | 8 | 2 | 53 |
| Labor Share of Income | 17 | 19 | 17 | — | 53 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
AI can enable new revenue streams (platforms, personalized pricing, automation-as-a-service) and increase market concentration, producing 'winner-takes-most' dynamics that raise profit rates for leading adopters and compress margins for laggards.
Literature synthesis on platforms and winner-take-all effects applied to AI; conceptual argument without firm-level causal testing in the paper.
AI adoption exerts downward pressure on routine labor costs while raising capital and recurrent costs (R&D, computing infrastructure, data, cybersecurity); higher fixed and lower marginal costs favor scale and incumbents with access to data and capital.
Conceptual cost-structure analysis drawing on automation and platform literature; no microdata or empirical cost estimates presented.
AI is a Schumpeterian general-purpose technology that can increase aggregate productivity potential but will do so unevenly across firms and sectors, producing heterogeneous effects on profitability.
Theoretical application of general-purpose technology and Schumpeterian literature to AI; literature-based claims without original empirical validation in the paper.
Firms' profitability and sustainability are shaped both by technological adoption (which can raise productivity and market power) and by structural pressures (trade wars, labor relations, supply constraints) that can erode margins.
Synthesis of firm-level implications from innovation and political-economy literatures; no firm-level causal estimates presented in the paper.
Contemporary crises change firms' cost structures (logistics, inputs, financing) and revenue prospects (demand shifts, market access).
Interpretive synthesis of observed firm-level impacts from pandemic, inflation episodes, and geopolitical events reported in secondary literature (no primary firm-level panel used).
Supply-chain fragilities and trade conflicts (emphasized by Mandel) mediate distributional and macroeconomic outcomes during long waves and crises.
Qualitative historical interpretation and literature references on supply-chain disruptions and trade conflicts (no systematic empirical identification in the paper).
New technological waves—most notably artificial intelligence (AI) and the green transformation—act as Schumpeterian forces that can alter productivity, competition, and profitability.
Conceptual mapping of Schumpeterian innovation-cluster theory to contemporary technologies (literature synthesis; no firm-level causal estimates reported).
Contemporary shocks (COVID-19, global inflation, geopolitical tensions) interact with long-wave mechanisms to reshape firms' cost and revenue structures.
Interpretive application of the comparative framework to recent historical episodes and macro trends; qualitative evidence from literature on pandemic and recent shocks (no primary microdata presented).
Students use GenAI as a co-designer and idea generator, which modifies workflow, decision points, and evaluative practices in their design process.
Qualitative interview data from architecture students; thematic analysis surfaced accounts of GenAI being used for ideation, variant generation, and as a collaborative partner (N unspecified).
Collaboration between architecture students and generative AI reshapes creative cognition in the architectural design process through algorithmic thinking strategies.
Semi-structured interviews with architecture students (interview sample size not specified) analyzed via inductive thematic analysis; authors synthesize recurring themes linking GenAI use to changes in cognitive strategies.
Patients classified as high‑risk by CDRG‑RSF had higher TMB, lower NK and CD8+ T cell infiltration, and model‑predicted resistance to Erlotinib and Oxaliplatin but sensitivity to 5‑fluorouracil.
CDRG‑RSF study reported immune deconvolution and TMB comparisons across risk groups and used pharmacogenomic prediction methods to infer drug sensitivity/resistance patterns for high‑risk vs low‑risk groups.
Both DNNs and LASSO correlated well at the individual‑sample level, but linear models (LASSO) struggled to recover cross‑study DEA log2FCs despite good sample‑level fits.
Same cross‑omics comparative study: reported good sample‑level prediction correlations for both model classes, but DNNs more faithfully reproduced differential expression signals across independent studies while LASSO did not recover DEA log2FCs robustly.
Fidelity gains from prompt engineering, model selection, or participant/environment modeling have been limited and context-dependent.
Synthesis of studies that tested prompt/model/participant modeling interventions and reported mixed or modest fidelity improvements; aggregated conclusion in the review.
Defender returns depend critically on attacker rationality and information-processing; economic/security models should incorporate strategic heterogeneity and bounded rationality for accurate valuation.
Computational sensitivity analyses varying attacker rationality/modeling assumptions with reported impact on metrics (simulations; details of attacker models and number of runs not specified).
Computational results highlight tradeoffs among decoy realism, defender budget, and attacker rationality (attacker model), affecting deception value.
Simulated parameter sweeps varying decoy realism, budget levels, and attacker rationality with reported sensitivity analyses (computational experiments; exact experimental grid not specified).
Heterogeneous program design and outcome measurement limit purchasers' ability to identify high‑value AI education offerings, creating a market opportunity but also risk.
Observed variability in program length, setting, content focus, target audience, and evaluation methods across the 27 included programs as reported in the review.
The predominant focus on entry‑level trainees suggests future workforce increases in basic AI literacy but leaves current mid‑career clinicians undertrained, potentially slowing adoption and creating heterogeneous skill premiums.
Distribution of target audiences and career stages in the 27 programs (56% entry‑to‑practice; many targeted students/early practitioners) and interpretation in the paper about labor market implications.
Compliance costs and audit requirements create regulatory barriers to entry but also incentives for standardized metadata and interoperable systems; policy can encourage open standards to reduce lock-in.
Policy analysis and recommendation in paper (theoretical); no regulatory cost quantification provided.
Algorithmic lesson planning, automated audits, and data-driven competency mapping are natural targets for AI augmentation and can reduce recurring resource burdens but require quality-labelled data, strong governance, and transparency.
Paper's discussion of AI complementarity (conceptual); no implementation trials or performance metrics presented.
The taxonomy clarifies where substitution versus complementarity are likely: AI-assisted tasks imply partial substitution of routine work; AI-augmented applications generate complementarities that increase demand for higher cognitive skills; AI-automated systems shift labor toward monitoring, exception handling, and governance.
Inference from mapping the three interaction levels to observed case features (n=4) and application of the Bolton et al. framework in cross-case synthesis.
AI-augmented systems support real-time medical tasks (e.g., decision support during procedures), amplifying human judgment and speed but raising required cognitive skills and changing training and coordination practices.
Findings from the case(s) labeled AI-augmented in the four-case qualitative sample and cross-case interpretive analysis using the service-innovation framework.
Returns to AI and digital investments are heterogeneous across firms and industries, implying adoption barriers and varied productivity impacts.
Across the 145 studies, reported effect sizes and qualitative findings vary by firm characteristics, industry sector, and technology readiness, as summarized in the review.
Impacts of digital transformation on productivity vary substantially by moderators such as digital competencies, organizational culture, leadership, and technology readiness.
Multiple included studies identified these factors as moderators/mediators in their empirical analyses; moderator effects were synthesized in the review.
Levels of familiarity and use of AI tools vary widely by role, discipline, and region.
Quantitative survey items (Likert-scale, multiple-choice) measuring familiarity and use of AI tools; subgroup comparisons (role, discipline, region) using descriptive statistics; thematic support from open-ended responses.
There are large disparities in AI engagement and preparedness across roles (students vs. educators), academic disciplines, and world regions.
Descriptive statistics from the survey comparing subgroups by role, discipline, and region; sample of >600 respondents; measures include self-reported awareness, familiarity, use, and confidence mapped to UNESCO competency frameworks.
DeFi components could enable automated milestone disbursement instruments but face regulatory and counterparty risk barriers.
Paper mentions DeFi as a potential disbursement automation mechanism and notes regulatory/counterparty risk; this is a conditional, context-dependent claim without pilot evidence for large-scale DeFi use.
Task-based labor effects: GenAI will substitute routine tasks (documentation, triage) and complement complex decision-making; net employment effects are ambiguous and vary by role.
Task-based model of labor and early observational/pilot studies; the paper highlights heterogeneity by specialty and role, but presents no comprehensive empirical employment-impact studies.
GenAI can reduce clinician time per case (productivity gains) but may increase utilization (more tests/treatments) if it lowers thresholds for intervention or aligns with revenue incentives.
Economic reasoning supported by early empirical and simulation work; the paper notes the possibility based on task substitution and induced demand literature; direct causal empirical evidence from large-scale deployments is limited.
AI-enabled credit scoring and dynamic pricing can expand access but also entrench algorithmic bias, affecting distributional outcomes.
Literature synthesis and conceptual discussion calling for research to evaluate distributional impacts; examples of mechanisms (credit scoring, dynamic pricing) cited but no empirical bias quantification provided in the summary.
The benefits of digital financial ecosystems are strongest where supporting infrastructure (broadband, identity systems, payment rails) and enabling policies exist.
Comparative case-study synthesis and descriptive comparisons across national/regional implementations showing conditional variation by infrastructure and policy context; no standardized cross-country regression evidence presented in the summary.
High-quality labeled IoT traffic is scarce and valuable, and data-sharing mechanisms (federated learning coalitions, data marketplaces) could emerge but require privacy and legal frameworks.
Survey notes about dataset scarcity and potential economic models for data sharing; recommendation that privacy/legal frameworks are prerequisites.
There is a strong commercial opportunity for deployable ML-IDS tailored to IoT and edge deployments, but development and operational costs (data collection, compression, privacy, pipelines) are substantial.
Economic implications and market analysis drawn from the survey: unmet deployment needs, scarce labeled data, and additional engineering requirements imply market demand and higher costs.
Heterogeneous returns: returns to AI will vary across SMEs due to differences in managerial capabilities and local institutional contexts; targeting complementary capabilities may be more cost‑effective than uniform subsidies for hardware/software.
Theoretical conclusion drawn from integrating RBV, dynamic capabilities, and institutional theory across reviewed studies; supported by cited heterogeneity in the literature.
Improved personalization via RS techniques can increase consumer surplus by better matching robot behaviors to user needs, but it also creates the potential for finer-grained price or content discrimination if monetized.
Economic reasoning and implications section; conceptual analysis without empirical measurement.
Sector-specific characteristics (regulation, competition intensity, product tangibility) shape the feasibility and design of VBP systems.
Thematic cluster from the SLR where sectoral factors were repeatedly cited as influencing VBP design across included studies.
Implementation challenges and pricing dynamics differ between B2B and B2C settings.
SLR thematic coding that separated findings and implementation considerations for B2B versus B2C contexts within the included literature.
Technology and AI are increasingly integrated into pricing processes, but this integration is uneven across contexts and the literature.
Thematic cluster from the SLR indicating growing but uneven mentions and treatments of technology/AI across included studies.
The relationship between IR and IWE is nonlinear — marginal effects vary with the level of robotization or other moderating factors (threshold/diminishing or accelerating returns).
Nonlinearity/threshold analysis reported in the paper (models testing nonlinear functional forms or interaction/threshold terms), showing varying marginal effects of IR on IWE across levels of IR or moderators.
The pollution‑reduction effect of IR operates primarily through higher technical (R&D/technology) expenditure.
Mechanism/mediation tests showing IR is positively associated with provincial technical/R&D expenditure, and that technical expenditure is linked to lower IWE; stepwise regressions used to establish the mediating channel.
The pollution‑reduction effect of IR operates primarily through increased green innovation (measured by green patents).
Mechanism (mediation/stepwise) regressions: IR positively predicts green patenting at the provincial level, and inclusion of green patents in IWE regressions attenuates the IR effect, consistent with mediation.
Green-technology innovation acts as a threshold moderator: DE produces direct carbon-reduction effects (reducing PCE) only after green-technology innovation exceeds a critical threshold; below that threshold DE does not reduce PCE.
Threshold-regression models (panel threshold estimation) using a measured index of green-technology innovation as the threshold variable on the 278-city panel (2011–2022). Results show different coefficient regimes for DE on PCE depending on whether green-innovation is below/above the estimated threshold.
The digital economy (DE) exhibits a U-shaped relationship with carbon emission efficiency (CEE): at early stages of DE development CEE worsens (declines) with DE, but beyond a certain DE level CEE improves as DE expands further.
Panel fixed-effects regressions using the same sample of 278 cities (2011–2022) with DE and DE^2 terms; the estimated coefficients on DE and DE^2 are statistically significant and imply a U-shaped relationship.
The digital economy (DE) exhibits an inverted-U relationship with per capita carbon emissions (PCE): at low levels of DE, PCE initially rises with DE, but after a turning point further DE expansion is associated with falling PCE.
Panel fixed-effects regressions on a balanced panel of 278 Chinese prefecture-level cities observed annually from 2011–2022. Models include DE and DE^2 terms; coefficients on DE and DE^2 are statistically significant in the pattern consistent with an inverted-U and a turning point is estimated from those coefficients.
Evidence of labour reallocation within rural economies following AI-driven productivity changes was observed in the reviewed literature.
Reported findings across several reviewed studies noting shifts in labour allocation and task composition on farms and in related value-chain activities.
Paper‑based regulatory environments slow DT diffusion; digitised compliance and standardised data schemas can accelerate adoption and enable AI‑driven oversight.
Findings in the review noting regulatory friction and proposed solutions; supported by case evidence where digitisation of compliance facilitated digital workflows.
DT adoption is a socio‑technical transformation that requires governance, standards, collaborative delivery models, and workforce capability building — not just technology deployment.
Conceptual synthesis and cross‑study recommendations in the reviewed literature emphasizing organizational, contractual, and governance changes alongside technology.
Both initial trust and inertia have statistically significant effects on GAICS adoption decisions.
Inferential statistical tests reported in the quantitative phase indicating significant pathways from initial trust and from inertia to adoption outcome (exact effect sizes and sample size not provided in the abstract).
Organizations’ adoption of Generative AI–enabled CRM systems (GAICS) is driven by initial trust and inertia.
Quantitative inferential analysis in the study's second phase testing the conceptual model (paper reports statistically significant relationships between initial trust, inertia, and GAICS adoption). Sample size and sector/country scope not reported in the abstract.
Better predictive models can shrink asymmetric‑information wedges and potentially reduce interest spreads for high‑quality but thin‑file borrowers; however, model errors or biased features can systematically exclude certain groups.
Conceptual analysis of model performance, bias risk, and implications for pricing; supported by literature on algorithmic bias and selective case evidence but not empirical causal tests within the paper.
Blockchain applications (tokenization, smart contracts) have potential for transparent, programmable financing and lower transaction costs but remain nascent and face legal and market adoption barriers.
Qualitative synthesis of emerging blockchain use cases and legal/regulatory analysis; characterization is forward‑looking and based on current maturity levels rather than empirical measurement of outcomes.