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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AIGC creators achieve aggregate engagement comparable to HGC creators by producing content at high volume (a 'scale-over-preference' dynamic).
Analysis of creation and engagement patterns in the dataset showing that AIGC creators compensate for lower per-item engagement by higher production volume, yielding comparable aggregate engagement levels to HGC creators.
Consumers show a marked preference for Human-Generated Content (HGC) over Artificial Intelligence-Generated Content (AIGC).
Comparative analysis of consumption behavior in the longitudinal dataset; the paper reports consumption metrics that indicate higher consumer preference for HGC versus AIGC (e.g., relative engagement per item).
Increasing the strictness of algorithmic control paradoxically increases the evolutionary fitness of coordinated resistance (e.g., coordinated log-offs).
Results from the EGT model and simulations showing fitness/payoff changes for coordinated resistance strategies as platform surveillance strictness parameter increases; model-only (no empirical N reported).
The future of transformative transformer-based AI is fundamentally many, not one.
Concluding synthesis and normative prediction based on the paper's theoretical arguments and literature synthesis; no empirical data or quantified projection provided in the excerpt.
Developing diverse AI teams addresses critics' concerns that current models are constrained by past data and lack the creative insight required for innovation.
Argumentative claim drawing on conceptual critique of current models and the proposed remedy of diverse AI teams; supported by referenced disciplinary literatures but no empirical validation provided in the excerpt.
Having a diverse team broadens the search for solutions, delays premature consensus, and allows for the pursuit of unconventional approaches.
Theoretical/argumentative claim referencing literature in complex systems and organizational behavior as support; no quantitative evidence or sample reported in the excerpt.
Deep intellectual breakthroughs should be expected to come from epistemically diverse groups of AI agents working together rather than singular superintelligent agents.
Predictive/theoretical claim motivated by referenced research and formal results in complex systems, organizational behavior, and philosophy of science; no empirical experiment or sample size given in the excerpt.
We should abandon the individual approach if we're hoping for AI to support groundbreaking innovation and scientific discovery.
Normative prescription based on theoretical argument and synthesis of literature from complex systems, organizational behavior, and philosophy of science; no empirical trial or quantified evaluation reported in the excerpt.
AI innovation achieves corporate low-carbon development by reorienting investment toward green assets.
Mechanism analysis reported in the paper (mediation/path analysis) using the same 21,428 firm-year observations; investment reorientation toward green assets identified as a mediation path.
AI innovation achieves corporate low-carbon development by upgrading emission-reducing production processes.
Mechanism analysis reported in the paper (mediation/path analysis) on the 21,428 firm-year sample; production-process upgrades identified as a mediation path.
AI innovation achieves corporate low-carbon development by optimizing low-carbon organizational governance.
Mechanism analysis reported in the paper (mediation/path analysis) using the same sample of 21,428 firm-year observations; paper identifies organizational governance optimization as one of three mediation paths.
With further development, this approach may exceed traditional methods regarding risk accuracy and help drive innovation in the insurance industry.
Forward-looking claim by the authors extrapolating from current prototype results and potential improvements; no empirical evidence provided that it already exceeds traditional methods.
ARQuest shows great potential to improve user satisfaction and streamline insurance processes.
Interpretation based on experimental findings (fewer questions, user preference) and the proposed framework; forward-looking claim rather than a fully established empirical result.
Adaptive versions were preferred by users for their more fluid and engaging experience.
User preference reported from the experiments (qualitative/user feedback or preference metric); specific measures and sample size not provided in excerpt.
Adaptive versions powered by GPT models required fewer questions.
Experimental result reported in paper comparing question counts between adaptive GPT-powered questionnaires and traditional questionnaires; no numeric counts or sample sizes provided in the excerpt.
Techniques such as social media image analysis, geographic data categorization, and Retrieval Augmented Generation (RAG) are used to extract meaningful user insights and guide targeted follow-up questions.
Described methods/techniques used within the ARQuest system implementation in the paper.
The ARQuest framework introduces a new approach to underwriting by using Large Language Models (LLMs) and alternative data sources to create personalized and adaptive questionnaires.
Methodological contribution described in the paper (framework design); description of components and intended function rather than a quantified outcome.
Only interventions that reshape risk allocation can plausibly shift stable system-level behaviour.
Argument based on the paper's game-theoretic reasoning and stylised example (theoretical claim; no empirical testing reported in the abstract).
Artificial intelligence (AI) is widely promoted as a promising technological response to healthcare capacity and productivity pressures.
Author assertion in the paper's introduction/abstract, based on literature/policy discourse (no empirical sample or quantitative analysis reported in the abstract).
Improvements in operational resilience enhance firms' capacity for sustainable development.
Further analysis in the paper showing a positive relationship between OR improvements and indicators of firms' sustainable development capacity.
The enabling effect of AI on operational resilience is more pronounced for capital-intensive enterprises.
Heterogeneity/subsample analysis showing larger AI effects on OR for capital-intensive firms.
The enabling effect of AI on operational resilience is more pronounced for technology-intensive enterprises.
Heterogeneity/subsample tests reported in the paper indicating stronger AI effects on OR for technology-intensive firms.
The enabling effect of AI on operational resilience is more pronounced for enterprises in the growth stage.
Heterogeneity/subsample analysis showing larger AI-induced OR gains among firms classified as in the growth stage.
The enabling effect of AI on operational resilience is more pronounced for enterprises located in the coastal eastern region.
Heterogeneity/subsample analysis reported in the paper showing larger AI effects for firms in the coastal eastern region compared to other regions.
AI promotes operational resilience by optimizing supply chain allocation performance.
Mechanism tests in the paper linking AI adoption to improved supply chain allocation/performance metrics, which are associated with higher OR.
Application of AI significantly enhances corporate operational resilience (OR).
Staggered DID estimation exploiting AIIAPZ policy as quasi-natural experiment on Chinese A-share listed manufacturing firms (2012–2023); main regression results reported as significant.
Effective collaboration with AI for software engineering (SE) tasks may benefit from functional design rather than replicating human SEI traits, thereby redefining collaboration as functional alignment.
Authors' conclusion and recommendation derived from qualitative interview evidence (10 practitioners) and the proposed concept of functional equivalents.
The authors introduce the concept of 'functional equivalents': technical capabilities (internal cognition, contextual intelligence, adaptive learning, and collaborative intelligence) that achieve collaborative outcomes comparable to human SEI attributes.
Conceptual contribution proposed by the authors based on interview findings and theoretical argumentation (no quantitative validation reported).
Socio-emotional intelligence (SEI) enhances collaboration among human teammates.
Stated as background in the paper (no primary data from this study provided to support the claim).
Results may be applied in the development of financial institution strategies, regulatory frameworks, risk management systems and professional training programmes.
Applied implications drawn from the literature synthesis and comparative analysis; presented as potential uses rather than empirically validated interventions.
Significant changes in human resource needs are occurring, with growing demand for analysts and specialists combining financial and technological competencies.
Conclusion from literature review and synthesis of international studies on labour demand in finance under Big Data/AI adoption; no original labour-market survey included.
Big Data and AI technologies significantly improve efficiency, risk assessment accuracy, fraud detection and financial inclusion.
The paper reports results from a qualitative analysis of recent academic literature, comparative analysis of sector-specific applications, and synthesis of empirical findings from international studies; no primary sample size reported.
Secondary empirical evidence from Colombia's EDIT manufacturing survey (N=6,799 firms) shows that management practice quality amplifies the return to technology investment (interaction coefficient 0.304, p<0.01).
Secondary empirical analysis of EDIT manufacturing survey data; sample size reported as N = 6,799 firms; regression interaction term reported as coefficient 0.304 with p < 0.01.
We endogenize the augmentation function as phi(D, W), where W is a five-dimensional workplace design vector (AI interface design, decision authority allocation, task orchestration, learning loop architecture, psychosocial work environment), and prove that human-centric design is profit-maximizing when the workforce's augmentable cognitive capital exceeds a critical threshold.
Theoretical model and formal proof presented in the paper (analytical derivation of phi(D,W) and threshold condition).
The ManagerWorker two-agent pipeline (expensive text-only manager + cheaper worker with repo access) can substitute expensive execution by using expensive reasoning in the manager and cheaper execution in the worker.
System design description plus empirical results on 200 SWE-bench Lite instances showing parity in success rates between a strong-manager/weak-worker pipeline and a strong single agent while using fewer strong-model tokens.
A minimal review-only manager loop adds only 2 percentage points over the baseline, whereas structured exploration and planning by the manager add 11 percentage points, demonstrating that active direction (not mere reviewing) produces most of the benefit.
Ablation-style comparison of pipeline variants on the 200-instance SWE-bench Lite evaluation: review-only manager loop versus manager with structured exploration and planning; reported improvements in percentage points.
A strong manager directing a weak worker achieves a 62% success rate on software-engineering tasks, matching a strong single agent which achieves 60%, while using a fraction of the strong-model token usage.
Empirical evaluation on 200 instances from SWE-bench Lite across five pipeline configurations and model pairings; measured task success rates and token usage for manager-worker pipelines versus single-agent baselines.
Overall, the HCT is a robust, accurate, and transparent alternative to the AI-as-advisor approach, offering a simple mechanism to tap into the wisdom of hybrid crowds.
Overall conclusion drawn from the empirical comparisons across datasets and analyses described in the paper (summary statement in abstract).
Using signal detection theory, the paper finds that the HCT outperforms the AI-as-advisor approach because people cannot discriminate well enough between correct and incorrect AI advice.
Analysis in the paper applying signal detection theory to the empirical results (as stated in abstract).
The HCT also performed better in almost all cases in which the AI offered an explanation of its judgment.
Empirical results on the subset of four datasets with AI explanations (abstract reports HCT performed better in 'almost all' of these cases).
The HCT outperformed the AI-as-advisor approach in all datasets.
Empirical comparisons reported across the 10 datasets (statement in abstract that HCT 'outperformed' in all datasets). Specific performance metrics not provided in abstract.
The results (conceptual/model results) support corporate GenAI policies, leadership development programs, and HR assessment of leader readiness for GenAI-enabled delegation and communication.
Practical implications and recommendations section arguing policy and HR applications based on the conceptual model.
The article introduces an EI-driven trust-calibration framework as an explanatory mechanism showing when generative AI improves leadership effectiveness and when it amplifies managerial errors.
Novel theoretical framework developed in the paper synthesizing EI, trust calibration, and psychological safety to explain boundary conditions of AI in leadership.
The paper provides an operationalization toolkit including measures: GenAI use intensity; delegation quality indices (clarity, boundaries, success criteria); communication quality indices (empathy, tone, transparency); psychological safety markers; and behavioral trust-calibration measures.
Operationalization section in the paper listing suggested indices and markers for empirical measurement.
As a follow-up validation path, the paper proposes a two-wave time-lag design and 180° assessment (leader + subordinates) to reduce common-method bias.
Methodological proposal in the paper describing longitudinal and multi-rater validation approaches.
The paper proposes a 'Package B' rapid empirical design: a randomized online experiment manipulating access to generative AI in core managerial tasks (decision, delegation, team communication), combined with EI measurement and trust-calibration indicators.
Methodology section proposing the rapid randomized online experiment design as the primary empirical test.
Emotional intelligence strengthens the positive impact of generative AI on managerial outcomes when trust is properly calibrated and psychological safety is maintained.
Conceptual model and integrative argument combining EI, trust-calibration, and psychological safety; supported by proposed empirical test design.
The paper conceptualizes human–AI leadership as an integrated managerial competence.
Conceptual modeling presented in the paper integrating EI theory, psychological safety, and trust calibration (theoretical synthesis).
Large language model (LLM) use can improve observable output and short-term task performance.
Paper synthesizes empirical findings from human–AI interaction studies, learning-research experiments, and model-evaluation work indicating improved produced outputs and short-term task performance when humans use LLMs; no single pooled sample size or unified effect estimate is reported in the paper.
These empirical insights provide actionable guidelines advocating dynamically routed architectures that adapt their collaborative structures to real-time task complexity.
Authors' recommendation derived from reported empirical findings comparing architectures under varying time budgets and task complexities (prescriptive claim based on study results).