Evidence (4114 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 |
Innovation
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Generative Artificial Intelligence (GenAI) is reshaping organisational creativity by emulating cognitive processes traditionally associated with human innovation.
Paper's theoretical argument and literature-grounded conceptual claims (conceptual analysis / literature review); no empirical sample or quantitative data reported.
The framework, all collected signals, scoring outputs, and evaluation harness are released under CC BY 4.0.
Statement of data and code release policy in the paper.
AgentPulse surfaces deployment signal absent from benchmarks; it is a methodology, not a ground-truth ranking.
Conceptual and empirical argument in the paper supported by the analyses described (correlations with external adoption proxies and divergence from benchmark-only rankings).
The Benchmark+Sentiment sub-composite correlates with VS Code installs (ρ_s=0.44, p<0.05), reported as illustrative given that only 11 of 35 agents have non-zero installs.
Spearman correlation between Benchmark+Sentiment sub-composite and VS Code installs on the 35-agent sample, with a caveat that installs are non-zero for only 11 agents; reported correlation and p-value.
The Benchmark+Sentiment sub-composite predicts Stack Overflow question volume (ρ_s=0.49, p<0.01) in the circularity-controlled test (n=35).
Circularity-controlled Spearman correlation between Benchmark+Sentiment sub-composite and Stack Overflow question volume on 35 agents; reported correlation and p-value.
A circularity-controlled test (n=35) shows the Benchmark+Sentiment sub-composite, which contains no GitHub-derived signals, predicts external adoption proxies it does not aggregate: GitHub stars (ρ_s=0.52, p<0.01).
Circularity-controlled correlation test (Spearman) between Benchmark+Sentiment sub-composite and GitHub stars on a 35-agent sample; reported Spearman correlation and p-value.
We introduce AgentPulse, a continuous evaluation framework scoring 50 agents across 10 workload categories along four factors (Benchmark Performance, Adoption Signals, Community Sentiment, and Ecosystem Health) aggregated from 18 real-time signals across GitHub, package registries, IDE marketplaces, social platforms, and benchmark leaderboards.
Methodological description in the paper; reported sample of 50 agents and use of 18 signals from enumerated sources.
We also provide empirical evidence to support our theoretical predictions.
Empirical analysis reported in the paper (details not given in the abstract regarding method, dataset, or sample size).
More skewed reward structures (favoring top-ranked contestants) can elicit more desirable contest outcomes.
Comparative-statics/theoretical analysis of the contest model showing how varying reward skewness alters equilibrium effort allocations and resulting contest outcomes.
We establish the existence of a symmetric monotone pure strategy equilibrium in this competition game.
Analytical game-theoretic model of a generic machine learning contest with contestants choosing creative vs mechanistic effort; existence proven theoretically (mathematical proof within the paper).
The framework shifts manual harness engineering into automated harness engineering, and takes one step further — automating the design of the automation itself.
Conceptual claim about the scope/implication of the proposed framework stated in the paper; the excerpt contains no empirical measures, experiments, or sample sizes to verify the claim.
The Meta-Evolution Loop optimizes the evolution protocol Λ across diverse tasks, learning a protocol Λ^(best) that enables rapid harness convergence on any new task — so that adapting an agent to a novel domain requires no human harness engineering at all.
Strong methodological claim and intended outcome stated in the paper (formalization and algorithms promised); no empirical validation, benchmarks, or sample sizes given in the excerpt to substantiate the universality or 'no human' guarantee.
The Harness Evolution Loop optimizes a worker agent's harness H for a single task: a Worker Agent W_H executes the task, an Evaluator Agent V adversarially diagnoses failures and scores performance, and an Evolution Agent E modifies the harness based on the full history of prior attempts.
Description of the proposed algorithmic component/architecture in the paper (conceptual specification); no empirical results or sample size provided in the excerpt.
We present a two-level framework that automates this process.
Methodological claim: the paper proposes a two-level framework (Harness Evolution Loop and Meta-Evolution Loop) and states it in the text; no experimental validation or sample size reported in the excerpt.
The presence of a Chief Information Officer (CIO) strengthens the influence of both the peer group and the peer leader on a focal firm’s AI adoption, with the influence of the peer leader being more pronounced when a CIO is present.
Subgroup/interaction analysis in fixed-effects regression models on panel data of publicly listed Chinese firms (2012–2023), comparing firms with and without a CIO.
Industry digital maturity enhances (strengthens) the impact of the peer group on a focal firm’s AI adoption.
Interaction/heterogeneity analysis in fixed-effects regression models on panel data of publicly listed Chinese firms (2012–2023), using an industry digital maturity moderator.
The influence of the peer group on a focal firm’s AI adoption is stronger than the influence of the peer leader.
Comparative estimates from fixed-effects regression models using panel data of publicly listed Chinese firms (2012–2023); tests comparing coefficients/magnitudes for peer group vs. peer leader effects.
The AI adoption level of the peer leader (the most advanced AI adopter among industry peers) positively influences the focal firm’s AI adoption level.
Panel dataset of publicly listed Chinese firms (2012–2023); fixed-effects regression models estimating effect of peer leader AI adoption on focal firm AI adoption.
The AI adoption levels of the peer group positively influence the focal firm’s AI adoption level.
Panel dataset of publicly listed Chinese firms (2012–2023); fixed-effects regression models estimating effect of peer group AI adoption on focal firm AI adoption.
Moving beyond traditional theories of the firm rooted in human bounded rationality is necessary because algorithmic decision-making changes the basis of strategic choice and governance.
Theoretical assertion in the paper's argument; presented as a reason for advancing the concept of algorithmic enterprises, grounded in conceptual critique rather than empirical testing in the abstract.
The paper contributes to scholarship on digital capitalism by proposing a redefinition of firm boundaries, strategy formation, and value creation in the age of intelligent systems.
Normative/theoretical claim presented as the paper's intellectual contribution; based on conceptual analysis and literature synthesis rather than empirical validation in the abstract.
Algorithmic decision-making enables new forms of strategic optimization, real-time adaptability, and predictive governance.
Paper asserts this as a normative/theoretical benefit of algorithmic decision-making, derived from conceptual analysis and synthesis of prior work; no empirical test reported in abstract.
Intelligent management systems (IMS) play a central role in shaping organizational strategy, operations, and governance within algorithmic enterprises.
Explicit theoretical claim in the paper; supported by conceptual framework and literature integration rather than reported empirical measurement.
The rapid advancement of AI, ML, and data-driven decision systems has fundamentally transformed the nature of firms and their strategic orientation globally, leading to the evolution of 'algorithmic enterprises'.
Stated as a central premise in the paper's conceptual argument; based on interdisciplinary synthesis of literature (economics, management, digital governance). No empirical sample or original data reported in the abstract.
The study extends resource-based, knowledge-based, and dynamic capabilities perspectives by conceptualising competitive intelligence as a mediating dynamic capability that transforms AI-driven data into actionable strategic knowledge.
Theoretical/conceptual synthesis supported by the study's empirical results (quantitative n = 312; qualitative n = 28).
AI enhances sensing, analytics, and reporting capabilities, and these capabilities are embedded into strategic routines to produce strategic value only when integrated into CI processes and organisational routines.
Mixed-methods evidence: quantitative associations (n = 312) showing AI → CI → growth/sustainability plus qualitative interview evidence (n = 28) describing how AI-enabled sensing/analytics/reporting are embedded into routines.
Qualitative Gioia analysis of 28 semi-structured interviews identifies three aggregate dimensions: AI-enabled competitive intelligence, strategic decision making and growth, and sustainable value creation.
Qualitative data from 28 semi-structured interviews across manufacturing, financial services, telecommunications, and retail sectors; analysis using the Gioia methodology.
CI effectiveness partially mediates the relationship between AI capability and sustainability outcomes.
Mediation analysis reported from the quantitative survey sample (n = 312); mediation described as 'partial'. Exact indirect effect size not provided in summary.
CI effectiveness partially mediates the relationship between AI capability and corporate growth.
Mediation analysis reported from the quantitative survey sample (n = 312); mediation described as 'partial'. Exact indirect effect size not provided in summary.
CI effectiveness significantly predicts sustainability performance (β = 0.47, p < .001).
Quantitative survey (n = 312); reported standardized regression/path coefficient β = 0.47, p < .001.
CI effectiveness significantly predicts corporate growth (β = 0.51, p < .001).
Quantitative survey (n = 312); reported standardized regression/path coefficient β = 0.51, p < .001.
AI capability significantly predicts competitive intelligence (CI) effectiveness (β = 0.62, p < .001).
Quantitative survey (n = 312) of senior managers and strategy professionals from medium and large Zimbabwean firms; reported standardized regression/path coefficient β = 0.62, p < .001.
The research provides empirical evidence from an emerging economy (China) to comparative research on global AI governance.
Statement of contribution/implication in the paper noting that the empirical findings from Chinese A-share listed manufacturing firms contribute to comparative studies on AI governance internationally.
Enhancing the ESG performance of manufacturing enterprises represents a critical pathway for promoting high-quality economic development and achieving sustainable development goals.
Framing/background claim made in the paper's introduction/motivation; normative statement connecting ESG improvement to broader economic and sustainable development objectives (not presented as an empirical result within this study).
The Pilot Zone policy effects are more evident among non-labor-intensive enterprises.
Heterogeneity analysis by factor structure reported in the paper showing stronger policy impacts for firms classified as non-labor-intensive.
The Pilot Zone policy effects are more evident among non-capital-intensive enterprises.
Heterogeneity analysis by factor structure reported in the paper showing stronger policy impacts for firms classified as non-capital-intensive.
The policy effects are more evident among high-tech enterprises.
Heterogeneity analysis by firms' technological endowment/industry classification reported in the paper showing larger policy effects for high-tech manufacturing firms.
The policy effect on ESG performance is stronger for non-high-pollution enterprises than for high-pollution enterprises.
Heterogeneity analysis by pollution intensity reported in the paper (comparison between high-pollution and non-high-pollution manufacturing firms).
The Pilot Zone policy has a more pronounced enabling effect on ESG performance for non-state-owned enterprises compared with state-owned enterprises.
Heterogeneity analysis by ownership type reported in the paper (comparison between state-owned vs. non-state-owned A-share listed manufacturing firms under DID specification).
Operational efficiencies significantly moderate the policy effect, further amplifying the Pilot Zone policy's positive impact on ESG performance.
Reported moderation/heterogeneity analysis indicating that firms with higher operational efficiency experience stronger positive policy effects on ESG performance.
Enterprise resource allocation significantly moderates the policy effect, amplifying the enabling effect of the Pilot Zone policy on ESG performance.
Reported moderation/heterogeneity analysis showing interaction effects between measures of enterprise resource allocation and the Pilot Zone policy on ESG outcomes in the DID framework.
The policy enhances manufacturing enterprises' ESG performance by strengthening environmental compliance pressures (regulatory/compliance channel).
Mechanism analysis reported in the paper identifying increased environmental compliance pressure as a transmission channel linking the Pilot Zone policy to improved ESG performance.
The policy primarily enhances manufacturing enterprises' ESG performance by intensifying R&D expenditure intensity (R&D investment channel).
Mechanism analysis reported in the paper identifying R&D expenditure intensity as a transmission channel between the Pilot Zone policy and firm ESG performance (presumably mediation/interaction tests within DID framework).
The positive effect of the Pilot Zone policy on manufacturing firms' ESG performance is robust to parallel trends tests, placebo tests, and multiple robustness checks.
Reported application of common DID robustness diagnostics: parallel trends test, placebo tests, and additional robustness checks (details not provided in abstract). Same sample frame: A-share listed manufacturing firms, 2010–2023.
The Artificial Intelligence Innovation and Development Pilot Zone policy exerts a significant positive effect on manufacturing enterprises' ESG performance.
Empirical analysis using a multi-period difference-in-differences (DID) model leveraging the establishment of National New-Generation Artificial Intelligence Innovation and Development Pilot Zones as a quasi-natural experiment; sample: A-share listed manufacturing enterprises on the Shanghai and Shenzhen Stock Exchanges, 2010–2023. Robustness checks reported (parallel trends, placebo tests, multiple robustness checks).
Government transfers become compelling when singularity-driven growth overwhelms deadweight costs.
Conditional policy conclusion stated in the abstract based on model comparison of welfare gains versus deadweight costs; no empirical calibration or data reported.
Market incompleteness creates a rationale for government transfers.
Normative/policy implication stated in the abstract, derived from the model's welfare comparisons; no empirical validation provided.
Because markets are incomplete -- investors cannot trade private AI capital -- AI stocks command a premium.
Theoretical result asserted in the paper's abstract, derived from the asset-pricing model under market incompleteness (no empirical data provided).
We develop an asset pricing model in which investors use AI stocks to hedge against an AI singularity that displaces their consumption.
Description of the paper's theoretical asset-pricing model and stated model mechanism in the abstract; no empirical test reported.
AI stocks trade at extraordinary valuations.
Explicit statement in the paper's abstract; no empirical data, sample, or statistical analysis reported.