Evidence (35 claims)
Search and filter individual claims pulled from the papers. Looking for a specific finding ("what's the effect on wages?"), you're in the right place. Want to compare whole outcome categories against each other instead? Use the Evidence Explorer.
The board below groups claims two ways: by broad theme (nine paper-level topics) and by outcome category (the 34 claim-level outcomes that the Explorer and Syntheses also use).
Browse by theme
Nine broad, paper-level topics. Click one to filter the claims below.
Adoption
10085 claims
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Productivity
8974 claims
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Governance
8062 claims
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Human-AI Collaboration
7749 claims
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Org Design
5057 claims
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Innovation
4896 claims
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Labor Markets
4088 claims
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Skills & Training
3372 claims
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Inequality
2377 claims
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Claims by outcome category
Counts by direction of finding. These are the same 34 outcome categories the Explorer compares and the Syntheses are written for. A linked row has a published synthesis.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 882 | 244 | 117 | 1097 | 2424 |
| Governance & Regulation | 1010 | 469 | 229 | 135 | 1875 |
| Organizational Efficiency | 977 | 235 | 149 | 90 | 1462 |
| Technology Adoption Rate | 781 | 299 | 143 | 128 | 1362 |
| Research Productivity | 506 | 155 | 74 | 363 | 1110 |
| Output Quality | 555 | 219 | 71 | 70 | 915 |
| Decision Quality | 395 | 200 | 95 | 54 | 751 |
| Firm Productivity | 523 | 67 | 101 | 27 | 724 |
| AI Safety & Ethics | 262 | 309 | 75 | 36 | 688 |
| Market Structure | 195 | 201 | 135 | 30 | 566 |
| Task Allocation | 248 | 77 | 96 | 38 | 464 |
| Innovation Output | 300 | 34 | 55 | 20 | 411 |
| Skill Acquisition | 207 | 75 | 65 | 21 | 368 |
| Employment Level | 138 | 67 | 119 | 24 | 350 |
| Fiscal & Macroeconomic | 156 | 80 | 53 | 33 | 329 |
| Task Completion Time | 211 | 38 | 13 | 16 | 280 |
| Firm Revenue | 183 | 52 | 29 | 5 | 270 |
| Consumer Welfare | 131 | 77 | 48 | 13 | 269 |
| Inequality Measures | 50 | 141 | 54 | 9 | 254 |
| Worker Satisfaction | 104 | 85 | 25 | 13 | 227 |
| Error Rate | 87 | 112 | 11 | 5 | 215 |
| Automation Exposure | 69 | 69 | 37 | 20 | 198 |
| Wages & Compensation | 102 | 49 | 31 | 11 | 193 |
| Team Performance | 115 | 30 | 30 | 11 | 187 |
| Regulatory Compliance | 88 | 74 | 17 | 7 | 186 |
| Training Effectiveness | 109 | 22 | 14 | 21 | 168 |
| Developer Productivity | 116 | 21 | 15 | 8 | 161 |
| Job Displacement | 12 | 92 | 26 | 1 | 131 |
| Hiring & Recruitment | 57 | 12 | 9 | 5 | 83 |
| Skill Obsolescence | 6 | 59 | 10 | 2 | 77 |
| Social Protection | 43 | 17 | 8 | 2 | 70 |
| Creative Output | 35 | 21 | 9 | 4 | 70 |
| Labor Share of Income | 18 | 23 | 17 | 1 | 59 |
| Worker Turnover | 15 | 16 | — | 4 | 35 |
| Industry | — | — | — | 1 | 1 |
These factors (surveillance anxiety, loss of autonomy, deskilling) negatively affect worker well-being and contribute to turnover.
Secondary data literature review of peer-reviewed research and industry evidence published 2022–2026 (method: secondary data review / synthesis). The paper synthesizes prior empirical and theoretical studies but does not report an original sample size.
Incumbent workers in more robot-exposed industries are unlikely to transition outside manufacturing over 2014-2021.
Longitudinal worker-level analysis of the 2014 manufacturing cohort through 2021 showing low rates of transition out of manufacturing for workers in higher-exposure industries (administrative employer-employee data).
Secure attachment further moderated the indirect effect of organizational AI adoption on employees' turnover intentions via identity threat (i.e., it attenuated the mediated effect).
Moderated mediation (conditional indirect effect) analysis reported on three-wave survey data of 312 employees; secure attachment reported to weaken the indirect AI adoption → identity threat → turnover intentions pathway.
The study identifies specific retention issues including rigid work practices, a predominantly masculine culture, and occurrences of bullying and harassment.
Findings from thematic analysis of 23 interviews using NVivo 13; participants' accounts raised these specific themes as retention-related issues.
Workplace stress is associated with lower employee retention.
PLS-SEM analysis on a cross-sectional survey of N = 350 pharmaceutical workers in Karnataka, India (purposive sampling). Reported direct path: Stress → Retention, β = 0.321, p < 0.001. (Note: the paper interprets this as stress reducing retention; sign/coding conventions of the variables are not detailed in the summary.)
There is sizable attrition in the pipeline from applicant admission through to direct employment of AI graduates, indicating leakages at multiple stages (application → admission → graduation → employment).
Quantification of human-resource losses across pipeline stages using the monitoring dataset for the 191 institutions; descriptive counts/percentages of entrants, admitted students, graduates, and those directly employed in AI roles (pipeline loss metrics reported in paper).
Onboarding and retention are unchanged after adoption.
Difference-in-differences estimates comparing onboarding and retention metrics between adopting projects and matched non-adopting controls; reported as no significant change post-adoption.
The study shifts retention analysis from descriptive correlations and surveys toward actionable, employee-level predictions and policy evaluation.
Combination of objective HR records (IBM dataset), predictive modeling (logistic regression), calibration, XAI tools (SHAP, LIME), and counterfactual policy simulations to evaluate intervention effects at individual and aggregate levels.
Local explainability (SHAP and LIME) can identify employee-specific intervention levers for targeted retention actions.
Use of SHAP and LIME for local explanations of individual predictions; counterfactual simulations applied at the employee level to estimate impact of feature changes on that employee's calibrated attrition probability.
Anxious attachment further moderated the indirect effect of organizational AI adoption on employees' turnover intentions via identity threat (i.e., it strengthened the mediated effect).
Moderated mediation (conditional indirect effect) analysis reported on three-wave survey data of 312 employees; anxious attachment reported to strengthen the indirect AI adoption → identity threat → turnover intentions pathway.
Identity threat is positively associated with employees' turnover intentions.
Reported positive relationship between identity threat and turnover intentions in the three-wave survey analyses (N=312), as part of the mediation model.
Identity threat mediated the relationship between organizational AI adoption and employees' turnover intentions.
Mediation analysis reported on three-wave survey data of 312 employees (longitudinal/self-report); the paper states identity threat served as a mediator between organizational AI adoption and turnover intentions.
By employing ANT, the research underscores the strategic potential of digital technologies in addressing systemic challenges within the construction sector and offers practical strategies for firms to improve retention by focusing on respect, support, and perceived value.
Interpretation and recommendations derived from the socio-technical framework and thematic interview findings (23 interviews); policy/practice suggestions are proposed in the paper.
Higher job performance is positively associated with greater employee retention.
PLS-SEM analysis, N = 350. Reported direct path: Performance → Retention, β = 0.348, p < 0.001.
Enhanced gross‑flows estimation using longitudinal microdata can better track transitions (job-to-job, upskilling, unemployment spells) and measure occupational churn and reallocation.
Established econometric practice cited in paper; recommendation to use panel/admin microdata (CPS longitudinal supplements, LEHD/LODES, UI records); no new empirical results but aligns with standard methods.
Counterfactual simulations show that modest salary increases have a smaller effect on predicted attrition than eliminating overtime (in this dataset and model).
Comparative counterfactual experiments run on the calibrated logistic model: simulations altering salary vs. altering overtime feature; reported that overtime elimination outperforms modest pay increases in retained headcount and probability reductions (exact salary-change amounts and comparative numbers not given in the summary).
In the dataset used, eliminating overtime could potentially retain about 31 employees — a larger effect than modest salary increases.
Aggregated counterfactual simulation on the IBM HR Analytics dataset: after setting overtime to zero for applicable records, the model-predicted net retained headcount ≈ 31; compared to simulations of modest salary increases which yielded smaller retained headcount (exact salary-change magnitude and headcount numbers not provided).
Eliminating overtime could lower predicted attrition probability by 17.35% for affected employees (per the model's counterfactual simulation).
Counterfactual policy simulation using the calibrated logistic model on the IBM HR Analytics dataset: set overtime feature to zero for affected employees and compute change in each employee's calibrated attrition probability; reported average reduction = 17.35%.
AI-justified layoffs are driven more by managerial short-termism and misaligned executive incentives than by immediate technological necessity.
Interdisciplinary conceptual synthesis drawing on labor-economics theory, organizational behavior literature linking executive compensation/short-termism to layoffs, and selected prior empirical studies; no new firm-level causal identification or large-scale dataset provided.
Extensive AI use correlates with increased turnover intention among employees.
Paper reports correlations observed in recent large-scale surveys and organizational research; the excerpt does not provide correlation coefficients, sample sizes, or control variables.
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.)
Displacement often occurs faster than job creation and worker reallocation, producing transitional unemployment and skills gaps.
Temporal-mismatch argument based on historical patterns of technological adoption and task-based substitution theory; paper synthesizes prior theoretical work rather than presenting new time-series microdata or measured reallocation speeds.
Creation of new jobs often lags displacement, producing transitional unemployment and reallocation frictions in the short- to medium-term.
Dynamic/task-based theoretical framing and synthesis of empirical evidence on technology adoption episodes showing delayed job creation relative to displacement.
An interpretable logistic-regression model, calibrated with isotonic regression, produces well-calibrated, individual-level attrition probabilities suitable for policy simulation.
Modeling pipeline: logistic regression for prediction, isotonic regression for calibration; authors report strong predictive performance and well-calibrated probabilities (specific performance metrics not included in the provided summary).
"Augmented Intelligence" models, which combine human contextual judgment with algorithmic precision, reduce attrition by 22% compared with complete automation.
Reported comparative result in the paper's analysis (paper claims comparative attrition rates between augmented and fully automated approaches; exact data source not explicitly tied to one of the stated samples in the abstract).
Main drivers of attrition identified by the model are overtime, business-travel frequency, and promotion opportunities (each having higher influence than salary).
Feature importance analyses using permutation importance and aggregated SHAP values on the fitted logistic-regression model trained on the IBM HR Analytics dataset.
Non-monetary workplace factors (excessive overtime, frequent business travel, limited promotion opportunities) are stronger predictors of individual attrition risk than salary.
Interpretable logistic-regression model trained on the IBM HR Analytics dataset; global importance assessed using aggregated SHAP values and permutation importance to rank predictors. (Exact sample size and numeric importance ranks not provided in the summary.)
Hybrid translation models produced approximately 20% higher retention rates relative to conventional methods.
Reported comparative retention-rate analysis from the study's quantitative dataset (survey of 150 LEP immigrants and placement/retention tracking) analyzed in SPSS v28.
The Photo Big 5 predicts job transitions.
Analysis linking Photo Big 5 scores to observed job transitions (moves between jobs) among the MBA graduate sample (n ≈ 96,000).
Workers who reported clear career pathways, internal mobility, and opportunities to apply newly acquired skills demonstrated higher optimism and stronger retention intentions.
Subgroup analyses within the 5,000-worker survey showing that respondents reporting clear career pathways, internal mobility, and opportunities to apply new skills had higher career optimism scores and greater self-reported retention intentions.
Estimating micro-level gross flows at occupation × industry × geography × demographic granularity (and at higher frequency) will better capture transitions such as reemployment paths, upskilling, and churn.
Proposal to use CPS, LEHD/LODES, JOLTS, administrative unemployment records and firm panels to estimate high-resolution flows. No empirical estimates or sample-size specifics provided.
AI-enabled upskilling and AI-guided procedures weaken the negative effect of workplace stress on employee retention (AI moderates the stress→retention link).
Moderation test in PLS-SEM on N = 350. Reported moderator effect (AI × Stress → Retention): β = 0.078, p < 0.005 (interpreted as a buffering/weakening effect of AI interventions on the stress→retention relationship).
Predictive analytics enhances workforce resilience by forecasting turnover, absenteeism, and skill gaps.
Theme extracted from multiple included studies that report or evaluate predictive models for turnover, absenteeism, and skills forecasting (synthesis across reviewed literature).
Job insecurity rises when FDI is short‑term, footloose, or concentrated in capital‑intensive extractive projects.
Conceptual arguments and empirical examples in the review linking investment temporariness and capital intensity to higher job instability; empirical evidence less comprehensive and context-specific.