Evidence (3103 claims)
Adoption
5267 claims
Productivity
4560 claims
Governance
4137 claims
Human-AI Collaboration
3103 claims
Labor Markets
2506 claims
Innovation
2354 claims
Org Design
2340 claims
Skills & Training
1945 claims
Inequality
1322 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 378 | 106 | 59 | 455 | 1007 |
| Governance & Regulation | 379 | 176 | 116 | 58 | 739 |
| Research Productivity | 240 | 96 | 34 | 294 | 668 |
| Organizational Efficiency | 370 | 82 | 63 | 35 | 553 |
| Technology Adoption Rate | 296 | 118 | 66 | 29 | 513 |
| Firm Productivity | 277 | 34 | 68 | 10 | 394 |
| AI Safety & Ethics | 117 | 177 | 44 | 24 | 364 |
| Output Quality | 244 | 61 | 23 | 26 | 354 |
| Market Structure | 107 | 123 | 85 | 14 | 334 |
| Decision Quality | 168 | 74 | 37 | 19 | 301 |
| Fiscal & Macroeconomic | 75 | 52 | 32 | 21 | 187 |
| Employment Level | 70 | 32 | 74 | 8 | 186 |
| Skill Acquisition | 89 | 32 | 39 | 9 | 169 |
| Firm Revenue | 96 | 34 | 22 | — | 152 |
| Innovation Output | 106 | 12 | 21 | 11 | 151 |
| Consumer Welfare | 70 | 30 | 37 | 7 | 144 |
| Regulatory Compliance | 52 | 61 | 13 | 3 | 129 |
| Inequality Measures | 24 | 68 | 31 | 4 | 127 |
| Task Allocation | 75 | 11 | 29 | 6 | 121 |
| Training Effectiveness | 55 | 12 | 12 | 16 | 96 |
| Error Rate | 42 | 48 | 6 | — | 96 |
| Worker Satisfaction | 45 | 32 | 11 | 6 | 94 |
| Task Completion Time | 78 | 5 | 4 | 2 | 89 |
| Wages & Compensation | 46 | 13 | 19 | 5 | 83 |
| Team Performance | 44 | 9 | 15 | 7 | 76 |
| Hiring & Recruitment | 39 | 4 | 6 | 3 | 52 |
| Automation Exposure | 18 | 17 | 9 | 5 | 50 |
| Job Displacement | 5 | 31 | 12 | — | 48 |
| Social Protection | 21 | 10 | 6 | 2 | 39 |
| Developer Productivity | 29 | 3 | 3 | 1 | 36 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
| Skill Obsolescence | 3 | 19 | 2 | — | 24 |
| Creative Output | 15 | 5 | 3 | 1 | 24 |
| Labor Share of Income | 10 | 4 | 9 | — | 23 |
Human Ai Collab
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Harnessing AI's potential requires moving beyond measuring technical model performance (e.g., predictive accuracy) to measuring strategic impact.
Authors argue this as a conceptual requirement for realizing AI's benefits in R&D; presented as a recommendation rather than supported by quantified empirical evidence in the excerpt.
Preliminary analyses suggest that 'AI-native' companies may be outpacing traditional peers.
Explicitly stated in the paper as based on preliminary analyses; the excerpt provides no details on the analyses, metrics, or sample sizes.
The broad introduction of AI into the R&D landscape over the last years holds the promise to lift pharmaceutical R&D out of its productivity problem.
Framed as an expectation/promise in the paper; based on recent broad adoption trends of AI in R&D (no specific empirical evaluation or sample size reported in the excerpt).
The visualization preserved human control.
Reported result from the within-subjects experiment (N=32) indicating that using the visualization did not reduce human control/agency in the negotiation process.
In the same within-subjects experiment (N=32), the visualization improved efficiency.
Within-subjects experiment (N=32) reported in the paper; the authors state the visualization improved efficiency (likely measured as time, number of rounds, or steps to reach agreement).
In a within-subjects experiment (N=32), the uncertainty-based visualization improved human outcomes.
Within-subjects user experiment reported in the paper with N=32 participants comparing performance with and without the visualization.
We introduce a novel uncertainty-based visualization driven by Bayesian estimation of agreement probability that shows how the space of mutually acceptable agreements narrows as negotiation progresses, helping users identify promising options.
Design and implementation of a visualization technique described in the paper; the visualization is driven by Bayesian estimation of agreement probability and is presented as a tool to reveal the shrinking feasible agreement space during negotiation.
Generative AI can autonomously produce novel content, including text, images, models, and scenarios.
General technical/descriptive claim stated in the paper's background/introduction; not an empirically tested claim within the provided excerpt.
Generative AI facilitates the synthesis of structured and unstructured information from diverse sources, enabling managers to explore multiple decision pathways, identify potential risks, and optimize strategic choices.
Descriptive/functional claim made in the paper's introduction and conceptual framing; the empirical component (survey + SEM) is described generally but no specific measures or effect sizes for information synthesis or these capabilities are provided in the excerpt.
Generative AI augments human creativity by producing innovative solutions and scenario-planning alternatives that may not emerge through conventional analytical approaches.
Stated in the conceptual/argumentative portion of the paper; may be supported by survey items but no explicit empirical measure or effect size for creativity is provided in the provided text.
Decision quality and strategic agility positively influence organizational performance.
Reported SEM results from the paper linking the constructs (decision quality and strategic agility) to organizational performance using survey data from senior managers and AI adoption specialists; method = SmartPLS.
Generative AI adoption significantly enhances strategic agility.
Same empirical source as above: survey of senior managers/decision-makers/AI adoption specialists; tested via Structural Equation Modeling (SmartPLS) as reported in the paper.
Generative AI adoption significantly enhances decision quality.
Empirical analysis reported in the paper: survey data collected from senior managers, decision-makers, and AI adoption specialists across multiple industries; relationships assessed using Structural Equation Modeling (SmartPLS). No numeric sample size or effect estimate reported in the provided text.
Human-like presentations increased perceived usefulness and agency in certain tasks.
Experimental manipulation of the human-likeness of AI presentation in the study's three tasks; the abstract reports increased perceived usefulness and agency for human-like presentations in some tasks. No sample sizes, task specifics, or effect magnitudes reported in abstract.
A single dissent within a panel reduced pressure to conform.
Experimental manipulation of within-panel consensus (introducing a single dissent) in the study's three tasks; abstract reports that a single dissent lowered conformity pressure. No numerical data provided in abstract.
Accuracy improved for small panels relative to a single AI.
Reported experimental result from the paper's study: participants completed three tasks and received advice from AI panels; panel size was manipulated (small panels vs single AI). The abstract states this accuracy improvement for small panels. (Sample size and exact tasks not reported in abstract.)
In production, the system received high satisfaction from both domain experts and developers, with all participants reporting full satisfaction with communication efficiency.
Post-deployment user feedback / satisfaction reports mentioned in paper (no numeric participant count provided).
The automated workflow saved an estimated 979 engineering hours.
Aggregate time-savings estimate reported in paper (derived from per-API time reduction × number of APIs).
The automated workflow reduces per-API development time from approximately 5 hours to under 7 minutes.
Time-per-API comparison reported in paper based on evaluation on spapi (comparison of manual vs automated per-API time).
The automated workflow achieves 93.7% F1 score.
Empirical evaluation on spapi (F1 reported); presumably computed over the evaluated API items/endpoints.
We address this gap through a graph-based workflow optimization approach that progressively replaces manual coordination with LLM-powered services, enabling incremental adoption without disrupting established practices.
Description of proposed method (graph-based workflow + LLM-powered services) and claim of design enabling incremental adoption; supported by subsequent case evaluation.
Organizational size moderates the adoption–efficiency relationship such that larger firms realize proportionally greater efficiency gains from AI adoption.
Reported moderation effect in the PLS-PM analysis testing organizational size as a moderator of the relationship between AI adoption and recruitment efficiency metrics across sampled organizations.
Procedural fairness perceptions positively predict employee experience outcomes, including organizational commitment, job satisfaction, and employer trust.
PLS-PM paths from procedural fairness perceptions to employee experience measures (organizational commitment, job satisfaction, employer trust) using survey data from HR professionals' reports.
Algorithmic transparency is a strong predictor of procedural fairness perceptions.
PLS-PM results linking measured algorithmic transparency to procedural fairness perceptions in the survey data (n=523 respondents).
AI adoption is positively associated with improvements in quality-of-hire.
PLS-PM association between AI adoption and reported quality-of-hire improvement from HR respondents across sampled organizations.
AI adoption is positively associated with reductions in cost-per-hire.
PLS-PM association between AI adoption and cost-per-hire reduction reported in the survey (firm-level outcomes across sampled organizations).
AI adoption is positively associated with reductions in time-to-hire (recruitment time).
PLS-PM association between AI adoption and recruitment efficiency metrics reported in the survey (firm-level outcomes across sampled organizations).
Top management support and HR digital readiness are both positively associated with organizational AI adoption, with top management support demonstrating greater explanatory power.
PLS-PM tests of organizational antecedents predicting organizational AI adoption using survey responses aggregated to organization level (184 organizations referenced).
Perceived usefulness and perceived ease of use significantly predict AI adoption intention, with perceived usefulness exhibiting a stronger effect.
PLS-PM results on relationships between TAM constructs (perceived usefulness, perceived ease of use) and adoption intention using survey data (n=523).
A large portion of the interactive activities' AI market value (26%) involves transferring information.
Descriptive subcategory statistic: within interactive activities, authors report 26% of market value pertains to information transfer tasks.
Interactive activities (which include both information-based and physical activities) account for 48% of AI market value.
Descriptive aggregate: authors define an 'interactive' category spanning info and physical activities and report it holds 48% of AI market value.
A substantial portion of AI market value (36%) is used in activities that involve creating information.
Descriptive aggregate: subcategory within information-based activities—authors report 36% of market value allocated to 'creating information'.
Most of the AI market value is used in information-based activities (72%).
Descriptive aggregate: authors categorize activities into information-based vs physical and report that 72% of estimated AI market value maps to information-based activities.
There is a highly uneven distribution of AI market value across activities: the top 1.6% of activities account for over 60% of AI market value.
Descriptive statistical result from mapping estimated AI market values to the ~20K activities; authors report concentration metrics (top 1.6% share >60%).
We use the data about AI software and robotic systems to generate graphical displays of how the estimated units and market values of all worldwide AI systems used today are distributed across the work activities that these systems help perform.
Analytic/mapping procedure: authors combine classifications of software (13,275) and robots (20.8M) with market-value estimates to create visual distributions across activities.
We classify a worldwide tally of 20.8 million robotic systems using the developed work-activity ontology.
Empirical classification/counting: authors report mapping 20.8 million robotic systems worldwide to the activity ontology.
We classify descriptions of 13,275 AI software applications using the developed work-activity ontology.
Empirical classification: authors state they mapped 13,275 AI software application descriptions to the ontology.
We disaggregate and then substantially reorganize the approximately 20K activities in the US Department of Labor's O*NET occupational database to produce a comprehensive ontology of work activities.
Methodological: authors report transforming the O*NET activity taxonomy (~20,000 activity-level records) by disaggregation and reorganization into a new ontology.
Organizations can design more effective recruitment strategies by signaling AI adoption to increase attractiveness to prospective applicants.
Practical implication drawn from the combined experimental findings (Study 1 N = 145; Study 2 N = 240; total N = 385) showing AI-adoption signals increase organizational attractiveness via perceived innovation ability, particularly for applicants with high AI self-efficacy.
Conceptualizing AI adoption as an organizational signal extends signaling theory to the context of technology-infused recruitment.
Theoretical argumentation in the paper, supported by the two experimental studies (Study 1 and Study 2) that test signaling mechanisms in recruitment contexts.
The positive indirect effect of AI-adoption signals on organizational attractiveness via perceived innovation ability is stronger for job seekers with high AI self-efficacy (Study 2 moderated mediation).
Study 2: moderated mediation model showing AI self-efficacy moderates the mediated relationship; sample size N = 240; participants were active job seekers.
Perceived innovation ability mediates the positive association between AI-adoption signals and organizational attractiveness (Study 2).
Study 2: moderated mediation analysis in an experiment recruiting active job seekers; sample size N = 240; mediation of AI-signal -> perceived innovation ability -> organizational attractiveness was validated.
AI-adoption signals are significantly positively associated with organizational attractiveness (Study 1).
Study 1: scenario-based experiment comparing AI-adoption signal vs no-signal conditions; sample size N = 145.
Human-replacing technologies have a strategic role in enhancing industrial productivity and ensuring the long-term resilience of Ukraine’s mining and metallurgical sector amid workforce shortages and structural labour-market changes due to war and demographic decline.
Integrated sectoral assessment in the paper combining current context (workforce shortages, structural changes), literature on technology-driven productivity/resilience, and industry-specific considerations; presented as a high-level conclusion.
Integrating ergonomic assessments and human–systems–interaction approaches into automation projects is important to prevent cognitive overload, occupational stress and operational risks for control‑room operators.
Recommendation and emphasis in the paper, supported by references to ergonomics and human-factors literature; presented as a preventive/mitigative approach rather than a quantified empirical result for the sector.
Successful technological modernization requires continuous investment in human capital, reskilling and the development of digital and engineering competencies.
Policy/recommendation based on the paper's synthesis of the sector analysis and literature on skill requirements and technology adoption; not presented as an original empirical estimate in the summary.
Higher robot density is associated with productivity gains, particularly in low-robotized sectors such as Ukraine’s mining and metallurgical industry.
Empirical evidence cited from international and industry-specific studies reviewed in the paper (literature review/meta-analytic style evidence); no Ukraine-specific causal estimate with sample size reported in the summary.
Human-replacing technologies also have an indirect impact on productivity by increasing total factor productivity (TFP).
Analytical argumentation in the paper supported by references to empirical studies showing TFP effects of automation/digitalization; literature synthesis rather than a new econometric estimate presented for Ukraine.
Human-replacing technologies (mechanization, automation, robotization, digitalization and AI-augmentation) make a direct contribution to labour productivity growth in Ukraine's mining and metallurgical sector.
Sectoral analysis and synthesis in the paper drawing on empirical international and industry-specific studies; literature review of productivity impacts of mechanization/automation/robotization/digitalization/AI in industrial contexts.
Across 378 hardware validated experiments, concise human-expert skills with structured expert knowledge enable near-perfect success rates across platforms.
Reported experimental results: 378 hardware-validated experiments across platforms comparing agent configurations; finding reported that human-expert skills produce near-perfect success rates (no numeric success rate provided in excerpt).