Evidence (4175 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 |
Org Design
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AI integration into resort-to-force decision-making organizations raises important concerns.
Conceptual claim discussed by the author; the paper does not present empirical data, incident analyses, or quantified risk assessments supporting this claim within the provided excerpt.
Governing the complexity introduced by military AI integration is urgent but currently lacks clear precedents.
Authorative claim grounded in argumentation and review-style reasoning; no systematic review or empirical mapping of precedents is provided in the text.
We can expect increased organizational complexity in military decision-making institutions as AI proliferates.
Theoretical inference presented by the author; no empirical methods or measurements (e.g., complexity metrics, case studies, or sample sizes) are reported.
When positives are rare, the prevalence effect induces systematic cognitive biases that inflate misses and can propagate through the AI lifecycle via biased training labels.
Analysis of prior experimental evidence cited and discussed in the paper (literature review / synthesis). Specific prior studies and their methods are analyzed in the paper (sample sizes and individual study details not provided in the supplied excerpt).
Many core university functions can now be achieved through AI-powered alternatives, potentially rendering conventional models obsolete for many learners.
Analytical assessment by the authors, without reported empirical testing or quantified methodology; based on review of AI capabilities and extrapolation.
Universities' core value proposition is challenged and potentially displaced by AI technologies as they alter how knowledge is accessed, created, and validated.
Authors' analytical argument drawing on technological, economic, and social drivers; presented as synthesis rather than empirical proof (no sample size or empirical method reported).
Traditional IT service hiring will be displaced by expansion of product-focused roles and Global Capability Centres (GCCs).
Synthesis of industry reports and workforce data indicating shifts in hiring patterns; the abstract does not report sample sizes or exact metrics.
Psychological barriers — specifically algorithm aversion, AI-induced job insecurity, technostress, and diminished occupational identity — impede effective AI integration across U.S. industries.
Literature synthesis of empirical and theoretical work in AI–HRM and organizational psychology cited in the paper (summary does not report primary-study sample sizes).
Workforce psychological readiness, rather than technological capability alone, constitutes the critical bottleneck in organizational AI adoption.
Synthesis of emerging empirical AI–HRM research and theoretical integration (paper reports 'findings' from this synthesis; no primary-sample-size details provided in the summary).
The integration of AI into U.S. workplaces represents a profound organizational psychology challenge that extends well beyond mere technology adoption.
Conceptual/theoretical argument based on literature synthesis; draws on established theories (Technology Acceptance Model, Human–AI Symbiosis Theory, Job Demands–Resources Model, Organizational Trust Theory) and cited empirical AI–HRM studies (no specific sample sizes or primary data reported in the summary).
What remains needed is rigorous advice to policymakers concerned about rapid increases in labor churn, scientific development, labor–capital shifts, or existential risk.
Normative conclusion drawn by the author from gaps identified in the seven-book review (qualitative assessment of unmet policy-relevant analysis); sample = 7 books.
The reviewed works offer little guidance regarding the transformative scenarios considered plausible by many AI researchers.
Author's evaluative judgment based on the content and emphases of the seven books (qualitative gap analysis); sample = 7 books.
AI heightens job insecurity, particularly in organisations lacking structured reskilling programs.
Stated finding derived from the mixed-method study and Scopus database analysis; framed with a conditional modifier pointing to organisations without structured reskilling programs. (Summary does not provide sample size, effect sizes, or statistical significance.)
The stability and patience that define long-term investors can breed strategic inertia.
Introductory assertion in the paper (conceptual observation). The paper does not present empirical data or sample analysis to substantiate this causal claim in the provided excerpt.
Conventional thinking often frames AI uncritically as just a tool for efficiency, which is a narrow perspective that overlooks AI's transformative role.
Critical/theoretical argument presented in the paper (conceptual observation). No empirical data, sample, or statistical analysis reported to support this claim.
In abundant-resource conditions, emergent tribe formation slightly increases system overload (i.e., makes the near-zero overload slightly worse).
Empirical observations reported in the paper indicating a modest increase in overload when tribes form under abundant resources.
When resources are scarce, AI model diversity and reinforcement learning increase dangerous system overload.
Empirical results from the paper's AI-agent population experiments (simulations/real-agent trials) combined with mathematical analysis indicating increased overload under scarcity when model diversity and individual RL are present.
Combined analysis using Fuzzy PROMETHEE II and DEMATEL identifies High Initial Investment and Supply Chain Integration as critical barriers and dominant causal drivers that influence other dependent barriers.
Findings come from the integrated PROMETHEE II ranking and DEMATEL causal-mapping analyses based on expert input and literature review; detailed sample size and numerical results not provided in the summary.
There are challenges to adopting AI in HRM within IT firms.
Identified through the literature review and the empirical study involving HR professionals; the summary notes challenges but does not enumerate or quantify them.
We lack frameworks for articulating how cultural outputs might be actively beneficial.
Authors' identification of a gap in evaluation theory and practice (conceptual analysis); no systematic literature review details provided in the excerpt.
Current AI evaluation practices show a critical asymmetry: while AI assessments rigorously measure both benefits and harms of intelligence, they focus almost exclusively on cultural harms.
Authors' review/ critique of existing evaluation frameworks and metrics (qualitative analysis in the paper); the excerpt does not list the reviewed studies or their number.
The field of AI is unprepared to measure or respond to how the proliferation of entertaining AI-generated content will impact society.
Authors' assessment of current evaluation practices and frameworks (qualitative analysis presented in the paper); no empirical metrics or sample sizes provided in the excerpt.
Current literature has primarily focused on automation-based views of decision support and lacks insight into systematic human–AI coordination aided by analytics.
Literature review and conceptual critique within the paper. No systematic mapping study or bibliometric counts reported.
Most organizations have difficulties converting algorithmic results into sustainable managerial decisions due to low levels of trust, lack of explanation, and poor integration between AI systems and human judgment.
Synthesis of existing literature presented in the conceptual paper (literature review). No empirical study or sample provided to quantify 'most organizations.'
AI adoption has augmented complexity, uncertainty in decision-making, and accountability stresses for managers.
Claim supported by conceptual argument and literature integration (qualitative synthesis). No empirical sample size or quantitative testing reported.
Traditional methods for assessing and developing employees' skills often fail to provide real-time feedback.
Statement supported by literature review cited by the authors; the abstract does not provide empirical comparisons, metrics, or sample sizes.
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.