Evidence (5539 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 |
Adoption
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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.)
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.
Regulatory uncertainty is a significant barrier to GenAI adoption.
Regulatory uncertainty included as an environmental/TOE variable in the PLS-SEM model showed a significant negative association with GenAI adoption in the survey results (n = 312).
There are significant implementation challenges for Material Passports, particularly for existing buildings.
Aggregate findings from included studies highlighting technical, data-collection, legacy-information, and workflow barriers when applying MPs to existing building stock.
Circular economy (CE) adoption in the Architecture, Engineering, and Construction (AEC) industry is hampered by data scarcity.
Synthesis of included literature and authors' framing in the introduction and analysis sections indicating repeated identification of data scarcity as a barrier to CE adoption in AEC.
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.
Gendered perceptions of AI's social and ethical consequences, rather than access or capability, are the primary drivers of unequal GenAI adoption.
Comparative model results from the 2023–2024 nationally representative UK survey showing perceptions (societal-risk index) have greater explanatory/predictive power than measures of access (e.g., device/internet access) or capability (digital literacy, education).
Intersectional analyses show the largest gender disparities in GenAI use arise among younger, digitally fluent individuals with high societal risk concerns, where gender gaps in personal use exceed 45 percentage points.
Subgroup (intersectional) analysis of the nationally representative 2023–2024 UK survey data stratified by age, digital fluency, and societal-risk concern levels; reported gender gap >45 percentage points in specified subgroup.
The societal-risk concerns index ranks among the strongest predictors of GenAI adoption for women across all age groups, surpassing digital literacy and education for young women.
Multivariable models and predictor ranking using the 2023–2024 UK survey data showing relative predictive strength of the concerns index versus measures of digital literacy and education, with subgroup (age × gender) comparisons.
The societal-risk concerns index explains between 9 and 18 percent of the variation in GenAI adoption.
Regression/statistical models using the composite concerns index as a predictor of GenAI adoption in the nationally representative 2023–2024 UK survey; reported explained variation (9–18%).
Women adopt GenAI less often than men because they perceive its societal risks differently.
Statistical analysis linking a constructed composite societal-risk concerns index (mental health, privacy, climate impact, labor market disruption) to GenAI adoption, using the UK 2023–2024 survey; models compare explanatory power of perceptions versus access/capability variables.
Women adopt GenAI substantially less often than men.
Analysis of the 2023–2024 nationally representative UK survey data comparing personal use/adoption rates by gender.
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.
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.
Performance expectancy is a negative factor related to the company's decision to adopt AI (attributed to initial implementation challenges reducing perceived ease of use).
PLS-SEM analysis of survey data from 207 firms; the paper reports a negative association between performance expectancy and AI Adoption and offers a rationale about 'reality check' and initial implementation difficulties.
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.
Digital transformation raises challenges related to privacy, inequality, and regulatory scrutiny.
Identified as a key challenge in the paper; the abstract provides no details on how privacy concerns, inequality measures, or regulatory incidents were documented or quantified.
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.
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.
Skills mismatch and SME adoption constraints constitute a binding bottleneck for inclusive digital–green upgrading.
Synthesis of studies on skills, firm capabilities, and SME adoption of digital and green technologies (review-level evidence; no single dataset or sample size provided).
Absent complementary institutions and infrastructure, digitalization may increase electricity demand, widen inequality, and incentivize strategic disclosure (greenwashing).
Literature review drawing on empirical studies of energy consumption from digital systems, labor-market studies, and analyses of ESG disclosure practices (review-level synthesis; no single sample size reported).
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.