Evidence (3062 claims)
Adoption
5227 claims
Productivity
4503 claims
Governance
4100 claims
Human-AI Collaboration
3062 claims
Labor Markets
2480 claims
Innovation
2320 claims
Org Design
2305 claims
Skills & Training
1920 claims
Inequality
1311 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 373 | 105 | 59 | 439 | 984 |
| Governance & Regulation | 366 | 172 | 115 | 55 | 718 |
| Research Productivity | 237 | 95 | 34 | 294 | 664 |
| Organizational Efficiency | 364 | 82 | 62 | 34 | 545 |
| Technology Adoption Rate | 293 | 118 | 66 | 30 | 511 |
| Firm Productivity | 274 | 33 | 68 | 10 | 390 |
| AI Safety & Ethics | 117 | 178 | 44 | 24 | 365 |
| Output Quality | 231 | 61 | 23 | 25 | 340 |
| Market Structure | 107 | 123 | 85 | 14 | 334 |
| Decision Quality | 158 | 68 | 33 | 17 | 279 |
| Fiscal & Macroeconomic | 75 | 52 | 32 | 21 | 187 |
| Employment Level | 70 | 32 | 74 | 8 | 186 |
| Skill Acquisition | 88 | 31 | 38 | 9 | 166 |
| Firm Revenue | 96 | 34 | 22 | — | 152 |
| Innovation Output | 105 | 12 | 21 | 11 | 150 |
| Consumer Welfare | 68 | 29 | 35 | 7 | 139 |
| Regulatory Compliance | 52 | 61 | 13 | 3 | 129 |
| Inequality Measures | 24 | 68 | 31 | 4 | 127 |
| Task Allocation | 71 | 10 | 29 | 6 | 116 |
| Worker Satisfaction | 46 | 38 | 12 | 9 | 105 |
| Error Rate | 42 | 47 | 6 | — | 95 |
| Training Effectiveness | 55 | 12 | 11 | 16 | 94 |
| Task Completion Time | 76 | 5 | 4 | 2 | 87 |
| Wages & Compensation | 46 | 13 | 19 | 5 | 83 |
| Team Performance | 44 | 9 | 15 | 7 | 76 |
| Hiring & Recruitment | 39 | 4 | 6 | 3 | 52 |
| Automation Exposure | 18 | 16 | 9 | 5 | 48 |
| Job Displacement | 5 | 29 | 12 | — | 46 |
| Social Protection | 19 | 8 | 6 | 1 | 34 |
| Developer Productivity | 27 | 2 | 3 | 1 | 33 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
| Creative Output | 15 | 5 | 3 | 1 | 24 |
| Skill Obsolescence | 3 | 18 | 2 | — | 23 |
| Labor Share of Income | 8 | 4 | 9 | — | 21 |
Human Ai Collab
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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).
Current research in this area has a primary focus on methodology and computer science rather than applied occupational health questions.
Authors' synthesis from the review of existing studies (the paper reports that reviewed studies emphasize methodological and computer science aspects; exact counts or proportions not provided in the excerpt).
The application of machine learning in occupational mental health research remains in its preliminary stages.
Claim stated by the paper based on the authors' literature review of the field (review methodology referenced in the paper; number of studies or specific inclusion criteria not provided in the provided 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).
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.)
Reliance on H-2A has limitations, including requirements to provide housing and training and higher mandated wages compared with local seasonal help.
Paper's qualitative assessment of H-2A program constraints; no empirical measures or comparative wage data provided in the excerpt.
Declining US birth rates may not alleviate the nursery labor problem in the coming decades.
Projection/interpretation based on demographic trend (declining birth rates) noted in the paper; no demographic model or quantitative projection provided in the excerpt.
Despite high overall employment (80% for ages 25–54), nurseries reported they were prevented from hiring new workers due to high wages and unqualified workers.
Reported responses from nurseries (survey/industry responses) referenced in the paper; sample size and survey details not provided in the excerpt.
The US nursery industry faces a labor deficit.
Statement in the paper based on industry reporting; specific methodology or sample size not provided in the excerpt.
Selection of a human-LLM archetype brings important risks and considerations for the designers of human-AI decision-making systems.
Analytic discussion and synthesis of evaluation results and literature review; tradeoffs surfaced in the paper (e.g., decision control, social hierarchies, cognitive forcing strategies, information requirements).
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.
There are ethical concerns surrounding AI and automation including algorithmic decision-making, workforce exclusion, and inequality in access to reskilling opportunities.
Raised as an ethical analysis within the paper's conceptual framework; no empirical study, surveys, or quantified measures of these ethical issues are reported in this paper.
AI is eliminating repeated (routine) jobs.
Stated as part of the paper's argument about AI's dual impact; supported by conceptual analysis rather than new empirical evidence in this manuscript (no sample size or empirical method reported).
Artificial intelligence and automation are reshaping jobs, transforming them from a steady source of income to a dynamic process highly influenced by technology, flexibility, and uncertainty.
Central analytical claim made in the paper based on conceptual reasoning; the paper does not report empirical measures, datasets, or sample sizes to support the transformation quantitatively.
AI and automation pose significant challenges to employment stability, skill relevance, and human dignity.
Claim presented within the paper's conceptual and analytical discussion of AI's dual impacts; no empirical study, sample size, or quantitative measures provided in this paper.
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.
Information processing constraints hinder managers' ability to effectively integrate tax planning and core business strategies (i.e., processing constraints hinder effective tax planning).
The paper reports novel empirical evidence consistent with this theoretical claim based on observed associations and tests linking AI, information quality, capital management, and tax effectiveness in the 2010–2018 sample.
Concerns about privacy risks, overreliance on technology, and decision fatigue continue to shape consumer trust and adoption of AI features.
Reported qualitative/quantitative findings from the questionnaire and analysis indicating these concerns emerged as factors affecting trust and adoption (specific measurement items and effect sizes not reported in the summary).
LLM explanations foster inappropriate reliance and trust on the data-extraction AI: participants were less likely to detect errors when provided with LLM explanations.
User study measuring error-detection rates and trust/reliance indicators across conditions (full text, passage retrieval, LLM explanations). The LLM-explanation condition showed lower error-detection and greater reliance/trust compared to other conditions.
AI use also poses risks, including systemic discrimination, privacy invasion, and commodification of talent.
Qualitative synthesis and documented instances in the reviewed literature (n=85) reporting discriminatory outcomes, privacy concerns, and labor commodification effects associated with algorithmic HR tools.
Qualitative synthesis reveals a 'gray zone' in labor relations and a 'black box' in algorithmic data processing, both exposing businesses to procedural injustice risks.
Thematic/qualitative synthesis of findings from the reviewed literature (n=85) highlighting issues of labor relations and algorithmic opacity leading to procedural fairness concerns.
Interpreting the literature through a socio-technical lens reveals a persistent misalignment between GenAI's fast-evolving technical subsystem and the slower-adapting social subsystem.
Authors' conceptual interpretation of the reviewed studies (28 papers) using socio-technical theory to integrate technical and social themes from the literature.
Evidence strength is inversely correlated with intervention complexity.
Cross-domain synthesis reported in the paper that formalises an inverse evidence–complexity relationship based on the reviewed literature. The abstract does not quantify the correlation or list the domains/intervention types used to derive it.
Per-capita elderly care costs running 3–5 times those of working-age cohorts.
Cost comparisons reported in sources included in the 81-paper review. The abstract reports a 3–5x multiple but does not specify which cost categories, countries, or methodological adjustments were used.
Conventional policy instruments have failed to resolve pressures that include severe long-term care workforce shortfalls across leading ageing economies.
Synthesis of findings from the structured narrative review of 81 sources (2020–2025) indicating persistent workforce shortfalls. The abstract does not provide quantitative workforce shortfall magnitudes or country-specific data.
Demographic ageing is projected to reduce annual GDP growth by 0.3–1.2 percentage points by 2035.
Projection estimates referenced in the review literature (2020–2025). The abstract reports the 0.3–1.2 p.p. range but does not specify which models or studies generated these projections.
Ageing-related expenditure already absorbs up to 18% of GDP in the most affected economies.
Spending estimates drawn from the reviewed literature (2020–2025). The paper states 'up to 18% of GDP' for the most affected economies but does not list which economies or the original data sources in the abstract.
Advanced economies face a compounding demographic crisis: populations aged 65 and over will reach 30–40% in several nations by 2050.
Demographic projection claims cited in the paper's background literature (sources from the structured narrative review). No specific datasets or country-by-country breakdown provided in the abstract.
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.
The findings constitute a cautionary case for the effectiveness of LLM use in strategic decision-making.
Authors' interpretation based on the experimental results: representational changes occurred with LLM use but did not translate into improved strategic foresight, combined with observed increases in overload and decreases in ownership.
LLM use reduces psychological ownership (additional analyses).
Reported follow-up/additional analyses from the experiment showing a statistically significant decrease in psychological ownership measures for participants using LLMs.
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 IT sector is currently witnessing significant workforce restructuring, including employee layoffs, necessitating a critical reassessment of existing competency mapping frameworks.
Asserted in the paper as a motivating observation; no specific layoffs data or statistics provided in the excerpt.
More experienced translators appear more likely to exit the market after ChatGPT’s launch than less experienced translators.
Heterogeneous (subgroup) analysis by experience level within the translation market reported in the paper; evidence presumably from DiD estimates of exit/participation rates across experience levels. (Exact sample sizes and exit definitions not provided in the abstract.)
Following ChatGPT’s launch, some online labor markets experienced displacement effects characterized by reduced work volume and earnings, exemplified by the translation & localization OLM.
Empirical analysis using a Difference-in-Differences (DiD) design on online labor market (OLM) data; the abstract identifies translation & localization OLM as an example. (Sample size and exact data window not specified in the abstract.)
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).
Limited reskilling opportunities and ambiguity surrounding career progression were linked to reduced confidence in future career prospects.
Survey correlations in the national sample indicating that respondents reporting limited reskilling access and ambiguous progression reported lower confidence in their future career prospects.
The findings raise ethical concerns about using such models in sensitive selection processes and highlight the need for transparency and fairness in digital labour markets.
Interpretive/concluding claim based on the observed adjective-based gendering and the broader literature on algorithmic fairness; recommendation rather than direct empirical result.
Gendered linguistic patterns emerged in the adjectives attributed to female and male candidates: GPT-5 tended to associate women with emotional and empathetic traits and men with strategic and analytical traits.
Empirical/qualitative analysis of the adjectives and descriptive language in GPT-5's outputs for the 24 simulated profiles; categories reported (emotional/empathetic vs strategic/analytical).