Evidence (2469 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 |
Org Design
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Existing research on AI-driven decision-making remains fragmented and often framed through substitution-oriented narratives that position AI as a replacement for human judgment.
Assessment based on the author's interdisciplinary literature synthesis (conceptual meta-analysis); descriptive evaluation of research framing rather than new empirical testing.
The AI productivity paradox reflects organizational constraints rather than technological failure.
Synthesis of the theoretical productivity funnel and empirical findings from firm-level data across Serbia, Croatia, Czechia, and Romania indicating conditional (not universal) productivity effects of AI.
Measurable productivity gains remain modest for firms lacking standardized processes and management systems.
Empirical comparisons within the firm-level dataset showing smaller productivity gains among firms characterized as lacking standardized processes/management systems (organizational readiness measures).
Within this framework, we identify a complementarity trap: firms lacking organizational readiness become stuck in the funnel, unable to convert AI diffusion into productivity gains.
Theoretical argument supplemented by empirical analysis using firm-level data from a subset of Central and Eastern European economies and AI diffusion indicators (countries named: Serbia, Croatia, Czechia, Romania).
Occupational sorting explains a somewhat larger share of the gender gap in Ireland than in other European countries, but a substantial portion remains unexplained, pointing to possible unobserved structural, cultural or organisational factors specific to the Irish labour market.
Decomposition analysis for Ireland using ESJS data showing occupation contributes more to the explained component in Ireland than on average, while the unexplained residual remains large.
Gender gaps are larger and less well explained by observable characteristics among younger cohorts (aged under 35), implying under-representation of women in advanced digital roles is emerging early in careers.
Age-cohort subgroup regressions and decomposition analyses on ESJS data comparing explained/unexplained gaps for workers aged under 35 versus older cohorts.
Gender disparities widen significantly at the very upper end of the distribution of digital job intensity — a 'digital glass ceiling' — while lower and middle levels show more modest differences.
Distributional analysis of the Job Digital Intensity Index (JDII), constructed from ESJS digital task items, showing larger gender gaps at the upper tail of the JDII distribution.
BT adoption reduces the level of earnings management practice.
Additional empirical tests on the same sample (27,400 firm-years, 2013–2021) comparing firms' earnings management measures before/after or between adopters and non-adopters of BT (earnings management measured by standard accrual-based metrics—details in paper).
The inability of models to reliably self-author useful Skills implies that models typically cannot produce the procedural knowledge they would benefit from consuming.
Interpretation based on the empirical finding that self-generated Skills provided no average benefit; inferred conclusion about model-authored procedural content quality. The paper's claim is supported by the comparative experimental results but the inference about broader capabilities is derived from those results rather than a direct separate measurement.
In some tasks, curated Skills worsened performance: 16 of 84 tasks showed negative deltas.
Per-task delta analysis reported in the paper: authors report 16 tasks with negative deltas where curated Skills reduced pass rate. (Note: the paper elsewhere reports 86 tasks in the benchmark; the negative-task count is reported as 16 of 84 in the paper's per-task summary.)
The emergence and promotion of these theories acted as a 'Trojan horse' of ideological persuasion: technically framed economic scholarship advanced political messages that ran counter to the expected normative defense of markets and democracy.
Interpretive synthesis from archival and textual analysis showing alignment between the technical content of certain economic arguments and political narratives; analysis of institutional and funding contexts that plausibly facilitated persuasive deployment.
A strand of influential 20th‑century Western economic theory concluded that democracy and market institutions are dysfunctional.
Case‑study historical and textual analysis of Cold War‑era economic literature and influential works (including canonical publications and writings by prominent economists); close reading of papers/books and contemporaneous debates as reconstructed from archival and publication materials.
Policy levers matter: increasing openness/shared ownership of AI, strengthening rent-sharing (higher ξ), and reducing concentration of complementary assets (antitrust, data portability) can reduce the probability that AI widens aggregate inequality.
Model counterfactuals and policy experiments in the calibrated framework that vary ownership/access parameters, ξ, and asset concentration to show distributional outcomes shift accordingly.
Workforce adoption barriers and the need for reskilling are obstacles to implementing the hybrid cloud financial framework.
Paper identifies workforce/reskilling challenges in its discussion; no empirical measurement of training needs or adoption rates provided in the summary.
On-premise ERPs create delays in reporting, security vulnerabilities, and regulatory/compliance inefficiencies for EPC firms.
Paper asserts these as problems motivating the hybrid approach. The summary provides no empirical comparison metrics between on-premise and cloud systems.
Current simulation practice is insufficiently integrated with enabling technologies (digital twins, data analytics, AI/ML) and with relevant government policy constraints.
Synthesis of literature and gap analysis in the paper; assertions are conceptual and not empirically tested within the paper.
Current simulation practice has limited strategic orientation, often focusing more on tactical and operational questions than on firm strategy.
Literature review and analysis in the paper highlighting the emphasis in existing studies on tactical/operational problems.
Current simulation practice lacks contextualization to firm‑ and industry‑specific realities.
Findings from the paper's literature review and critique sections; no new empirical measurement provided.
Current manufacturing and supply‑chain simulation practices are insufficiently contextualized, strategically focused, or integrated with modern technologies and policy considerations.
Literature review and critique of existing simulation practice presented in the paper; no original empirical data or case studies.
ML-based IDS models are vulnerable to adversarial examples, poisoning attacks, and evasion techniques, raising security and robustness concerns.
Survey references and synthesis of works discussing/adapting adversarial attacks and poisoning against ML models in network/IoT contexts.
Heterogeneity of devices, protocols, and feature sets complicates generalization of IDS models across different IoT environments.
Literature reports limited cross-device generalization and difficulties transferring models between device types; survey highlights heterogeneity as a major barrier.
Practical constraints — device heterogeneity, resource limits, dataset shortcomings, and ML pipeline pitfalls — prevent many research models from reaching operational use.
Thematic analysis across surveyed studies highlighting recurring barriers: heterogeneous device/protocol stacks, limited compute/memory on edge devices, dataset limitations, and methodological pitfalls.
Value-based pricing remains underdeveloped in practice because theory and empirical evidence are fragmented and sparse.
Synthesis from the SLR showing fragmented theoretical approaches and empirical gaps across the 30 included studies; authors' interpretation in discussion.
Data governance, privacy, and cybersecurity risks can create negative externalities and raise adoption costs, requiring governance frameworks that affect social welfare outcomes.
Recurring risk themes across reviewed papers (conceptual analyses, case reports) that highlight governance and cybersecurity concerns associated with DT data.
Principal barriers to DT adoption include paper‑based or legacy regulatory/compliance processes that slow digitisation.
Findings from reviewed studies noting regulatory and compliance processes as impediments to digital handover and automated workflows.
Principal barriers to DT adoption include misaligned stakeholder incentives and fragmented project delivery models.
Synthesis of conceptual and case literature describing contractual and incentive misalignments that impede lifecycle data continuity.
Principal barriers to DT adoption include low digital maturity and uneven capabilities across supply chains.
Recurring observations in the literature review about heterogeneous digital skills and maturity across firms in the supply chain.
Principal barriers to DT adoption include data quality and continuity problems at handover.
Thematic synthesis across reviewed literature reporting frequent issues with data quality and handover continuity between project phases.
Principal barriers to DT adoption include interoperability gaps and lack of standards.
Thematic findings from qualitative synthesis of the 160 reviewed studies (recurring theme across conceptual papers, case studies and pilots).
ANN analysis ranks need-for-human-interaction barriers as the most important predictor of GAICS adoption outcome.
ANN feature-importance analysis reported in the paper that ranks predictors for adoption outcome and finds the human-interaction barrier as the top predictor; paper abstract does not include details on ANN implementation or sample characteristics.
Harms from manipulation, harassment, and de‑anonymizing biometric data create negative social externalities (mental health impacts, discrimination); without regulation, platforms may under‑invest in protective measures.
Synthesis of harms and economic externality reasoning from the reviewed studies; claim is theoretical and policy‑oriented rather than empirically quantified in the paper.
Ongoing operational costs for safe multi‑user VR services (model updates, policy tuning, user support, human moderators) raise marginal costs relative to less‑protected services.
Qualitative cost components identified in the literature and by the authors; no empirical cost accounting or per‑unit estimates provided.
Implementing TVR‑Sec requires upfront investments in secure hardware, AI monitoring engines, and moderation infrastructure, increasing entry costs for new VR platforms and favoring incumbents or well‑capitalized entrants.
Authors' economic analysis based on component cost categories identified across the reviewed literature; no quantitative cost estimates provided.
At the organizational scale, AI adoption is constrained and shaped by compliance requirements, formal policies, and prevailing norms.
Participants' accounts in workshops (n=15) noting compliance and policy considerations; thematic analysis classified these as organizational-level constraints.
Rapid coherence decay with thread depth suggests collective problem solving or consensus formation among these agents will be shallow and brittle.
Embedding-based coherence metrics demonstrating fast decline in similarity with increasing thread depth across the dataset; inferential claim about effects on deliberation and consensus processes.
Low emotional alignment and frequent affective redirection indicate human emotional contagion models may not apply to AI-agent interaction, which could produce unstable or counterintuitive coordination dynamics.
Emotion-classification results showing 32.7% mean self-alignment and 33% fear→joy response rate; theoretical interpretation comparing these patterns to human emotional contagion expectations.
Ritualized signaling could create apparent activity (volume, buzz) without substantive informational content, opening avenues for manipulation or mispriced assets.
Observed high rates of patterned/formulaic replies and concentrated non-informational activity patterns in Moltbook; inferential reasoning about how signal amplification without content could affect market perception and asset pricing.
High prevalence of formulaic comments (≈56%+) implies large volumes of low-information signaling that can degrade signal-to-noise ratio in information environments, harming price discovery and liquidity forecasting.
Empirical observation of >56% formulaic comments via lexical-pattern analysis, combined with theoretical inference about information quality and market microstructure (argument linking high low-information reply volume to degraded signal-to-noise).
Instability of agent rankings across configurations makes procurement and deployment decisions based on narrow benchmarks risky; firms should evaluate agents under their own scaffolds, datasets, and workflows before committing.
Empirical finding of ranking instability across models, scaffolds, and datasets; methodological recommendation derived from that instability.
Claims that AI will imminently replace human auditors are overstated; real-world economic benefits are more likely to come from complementary automation (breadth + triage) rather than full substitution.
Interpretation based on empirical failures in end-to-end exploitation, instability across configurations, and scaffold sensitivity observed in this study.
Detection and exploitation rankings are unstable: rankings shift across model configurations, tasks, and datasets, so results are not robust to evaluation choices.
Observed variability in detection/exploitation rankings across the expanded matrix of models, scaffolds, and datasets in the study's experiments.
Standardized platforms and benchmarks may create network effects and lock-in around dominant hardware–software stacks; antitrust and standards policy will matter to preserve competition.
Workshop participants' market-structure analysis and policy discussion included in the summary recommendations (NSF workshop, Sept 26–27, 2024).
Key technical and organizational risks include model brittleness, privacy and IP concerns in code generation (training-data provenance), and increased governance and QA burdens.
Literature review highlighting known risks and survey responses reporting practitioner concerns; no quantified incident rates provided.
Practitioners report barriers to adoption including integration costs, lack of trust/explainability, poor data quality, and skills gaps.
Thematic analysis / coding of open-ended survey responses and literature review identifying common adoption barriers; survey sample size not specified.
Prior work often conflates feedback source and feedback model; this study isolates them through controlled experiments.
Authors' literature review and the paper's experimental design explicitly constructed to disentangle source and model effects.
QCSC systems are capital- and skill-intensive, favoring well-resourced incumbents (large tech firms, national labs, major pharma/materials companies), potentially increasing concentration in compute-enabled domains.
Economic and industry-structure reasoning based on anticipated capital costs, specialized skills required, and comparison to existing capital-intensive compute infrastructures; no empirical market-share data.
Recent quantum advantage demonstrations for quantum-system simulation show utility, but practical applied research requires hybrid workflows that neither QPUs nor classical HPC can efficiently execute alone.
Review and synthesis of published quantum-simulation demonstrations and known performance/scaling limits of classical HPC; qualitative analysis of hybrid algorithm requirements; no new experiments.
Under realistic limitations (distribution shift, very large prompt inventories, or severe cold starts), DPS’s realized rollout savings and performance gains may be reduced.
Authors list these scenarios as potential limitations and caveats in the Discussion/Limitations section; no quantification provided in the summary.
Expect diminishing returns from AI investments if parallel investments in organizational change and data governance are not made.
Synthesis of case evidence and theoretical argument: instances where additional AI investment produced limited marginal benefit absent organizational complements.
Legacy systems and siloed organizational structures produce persistent forecasting inaccuracies, operational disconnects, and constrained responsiveness.
Cross-case interview narratives documenting continued forecasting issues and operational misalignment in firms with legacy IT and functional silos.