Evidence (7953 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 |
AI integration significantly improved responsiveness (supply chain responsiveness).
Survey data from supply chain professionals across manufacturing and service sectors analyzed via correlation and regression analyses; the summary does not state sample size or numerical results.
AI integration significantly improved operational efficiency.
Quantitative survey of supply chain professionals (manufacturing and service sectors) with statistical analyses including reliability testing, correlation, and regression; specific sample size and effect sizes not provided in the summary.
AI integration significantly improved forecasting accuracy.
Quantitative survey of supply chain professionals (manufacturing and service sectors) analyzed using reliability testing and correlational/regression statistics; exact sample size and effect size not reported in the provided text.
The approach provides a closed-form mapping from information primitives to equilibrium outcomes.
Paper presents explicit formulas relating primitives (noise processes/Brownian shocks, signal-generation parameters, payoff matrices) to equilibrium objects (strategies, beliefs kernels, information wedge, and resulting payoffs).
The characterization yields an explicit information wedge V^i_t — a deterministic Volterra process — that prices the marginal value of shifting opponents' posteriors.
Derived closed-form expression in the paper: defines V^i_t as a deterministic Volterra-type process arising from the fixed-point solution; interprets it as the marginal value (price) of changing opponents' posterior beliefs.
This collapse reduces Nash equilibrium to a deterministic fixed point with no truncation and no large-population limit required.
Analytical reduction presented in the paper: after representing beliefs by deterministic kernels, the equilibrium conditions are expressed as a deterministic fixed-point problem solvable without approximations like truncating the belief hierarchy or taking N→∞.
Conditioning on primitive Brownian shocks (a dynamic analogue of Harsanyi's common-prior construction) collapses the infinite belief hierarchy onto deterministic two-time kernels.
Methodological derivation in the paper: change of conditioning variable from physical state to primitive Brownian shocks yields deterministic two-time kernel representation of agents' beliefs (i.e., belief dynamics become deterministic kernels rather than stochastic hierarchies).
We provide the first exact equilibrium characterization of finite-player continuous-time LQG games with endogenous signals.
Paper's constructive solution: derives an exact equilibrium by conditioning on primitive Brownian shocks and mapping the game to a deterministic fixed point; applies to finite number of players in continuous time with linear-quadratic-Gaussian structure and signals that depend on controls.
AI and Big Data enable proactive risk management strategies that contribute to lowering market uncertainty.
Qualitative case studies and quantitative analysis indicating firms used AI/Big Data for proactive risk management; details on number of cases or measurement of 'proactive risk management' not provided in the summary.
The reduction in market uncertainty occurs through enhanced predictive modeling capabilities enabled by AI and Big Data.
Findings reported in the paper attributing improved predictive modeling (from quantitative analysis and case-study observations) as a mechanism for uncertainty reduction (no specific metrics or effect sizes provided in the summary).
Strategic integration of AI and Big Data can significantly reduce market uncertainty during periods of economic turbulence.
Mixed-methods study combining quantitative analysis of market data and qualitative case studies of firms implementing AI and Big Data solutions (specific sample size and statistical details not provided in the summary).
The expanding use of AI is reshaping agricultural production systems and has emerged as a key driver of high-quality development in the sector.
Synthesis and interpretation of the paper’s empirical findings (significant AI effects on TFP, identified channels, and heterogeneous impacts) based on the listed-firm panel analysis.
Productivity gains from AI are more pronounced in regions facing higher natural risks.
Heterogeneity analysis in the paper that compares regions with differing natural-risk levels and finds stronger AI–TFP effects in higher-risk regions using the 2007–2023 panel of listed agricultural firms.
Productivity gains from AI are more pronounced among firms in their growth stage.
Heterogeneity analysis in the paper that splits the sample by firm life-cycle/stage and reports larger AI-associated TFP effects for firms classified as being in the growth stage.
AI fosters productivity growth by facilitating inter-firm resource sharing.
Mechanism analysis in the paper indicating a significant association between AI adoption and measures of inter-firm resource sharing, which in turn are associated with higher TFP in the panel sample.
AI fosters productivity growth mainly by optimizing labor structures.
Mechanism analysis reported in the paper linking AI adoption to measures of labor-structure optimization and finding that this channel is a significant contributor to TFP gains in the sample of listed agricultural firms.
The adoption of AI improves factor allocation efficiency and constitutes a critical economic foundation for efficiency-driven sustainable growth in agriculture by optimizing resource utilization and strengthening risk-management capacity.
Conceptual framing supported by the paper's empirical findings (panel data on agricultural firms listed on Shanghai and Shenzhen A-share markets, 2007–2023) that show AI raises total factor productivity (TFP) and stronger effects in higher natural-risk regions (interpreted as improved risk management).
A set of emerging methodological approaches—prompt-based experiments, synthetic population sampling, comparative-historical modeling, and ablation studies—map onto familiar social-scientific designs while operating at unprecedented scale.
Survey and mapping of methodological techniques presented in the paper; claim is a conceptual synthesis rather than a report of a particular dataset or experiment in the provided text.
Instruct-only and modular adaptation regimes constitute pragmatic compromises for behavioral research because they can preserve pretrained cultural regularities while allowing researchers to elicit targeted behaviors.
Methodological recommendation derived from comparing adaptation regimes (conceptual argument / review of adaptation strategies); no empirical comparison or sample sizes provided in the excerpt.
Trained on unprecedented volumes of human-produced text, LLMs encode large-scale regularities in how people argue, justify, narrate, and negotiate norms across social domains.
Inference based on known pretraining procedures for LLMs and the paper's theoretical account; no specific corpus size or empirical validation reported in the provided text.
There is a third, emerging ambition in AI research: using large language models (LLMs) as scientific instruments for studying human behavior, culture, and moral reasoning.
Argumentative proposal grounded in the paper's conceptual analysis and review of existing methodological work; framed as an emerging research program rather than demonstrated empirical fact.
Vocational graduates who undergo strong work-based training demonstrate competitive and sometimes superior long-term employment trajectories compared with other pathways.
Comparative empirical studies and secondary analyses referenced in the paper that link work-based vocational training to favorable long-term outcomes (the summary does not provide exact studies, effect sizes, or sample sizes).
Higher education graduates generally experience favorable employment outcomes.
Synthesis of prior empirical studies and secondary labor-market indicators cited in the paper indicating better employment prospects for higher education graduates (no specific effect sizes or sample n given in the summary).
There has been substantial growth in higher education attainment across the countries examined.
Descriptive results drawn from secondary data and comparative empirical studies documenting trends in higher education enrollment and attainment (paper does not report specific country list or sample sizes in the summary).
The findings provide practical guidance for entrepreneurs on building adaptive, AI-integrated organizations by redefining hiring, decision processes, and learning practices.
Prescriptive recommendations derived from the interview analysis and observed patterns in the sample of entrepreneurs (qualitative grounding; specific examples or measured impacts not provided in the excerpt).
Hybrid decision architectures have emerged: startup-specific configurations where algorithmic reasoning and human judgment recursively interact to shape decisions, roles and routines.
Thematic synthesis of interview data identifying recurring patterns of human–AI recursive interaction in decision-related practices across the studied startups (qualitative evidence; no quantitative counts reported).
Entrepreneurs who founded startups after ChatGPT's release integrated AI into their post-release ventures.
Direct accounts from the subset of interviewees who founded startups after ChatGPT's release describing AI incorporation in those ventures (qualitative interview evidence; sample details not given).
AI is becoming embedded in the architecture of startups rather than serving only as a task-automation tool.
Interview data and qualitative analysis identifying patterns of AI integration across startup roles, routines and structures (derived from the same semi-structured interview sample; exact N not provided).
Facilitated access to AI following the release of ChatGPT is transforming how startups organize and make decisions.
Qualitative study using semi-structured interviews with entrepreneurs who founded startups both before and after ChatGPT's release and who integrated AI into their post-release ventures; thematic/qualitative analysis of interview data. (Sample size not reported in the provided excerpt.)
Perceived autonomy enhances the positive effect of perceived algorithmic standardized guidance on riders' outcomes (i.e., strengthens the beneficial impact on mental health and reduction in risky riding via work pressure).
Interaction/moderation effects tested via SEM on 466 Chinese food delivery riders; results reported that perceived autonomy amplifies the beneficial pathways from standardized guidance.
Perceived autonomy mitigates (buffers) the negative effect of perceived algorithmic tracking evaluation on risky riding behavior (i.e., reduces the tendency toward risky riding driven by tracking evaluation via work pressure).
Moderation analysis within SEM using sample of 466 Chinese delivery riders with bootstrapped tests for interaction effects between tracking evaluation and perceived autonomy.
Perceived autonomy mitigates (buffers) the negative effect of perceived algorithmic tracking evaluation on riders' outcomes (i.e., reduces the adverse impact on mental health and risky riding via work pressure).
Moderation tested in SEM on data from 466 Chinese food delivery riders; interaction effects reported indicating perceived autonomy weakens the negative pathways from tracking evaluation.
Perceived algorithmic standardized guidance improves food delivery riders' mental health by reducing work pressure.
466 Chinese food delivery riders; SEM and bootstrapping testing mediation (standardized guidance -> work pressure -> mental health) within JD-R framework.
Perceived algorithmic behavioral constraint promotes risky riding behavior among food delivery riders through increased work pressure.
Data from 466 Chinese food delivery riders; mediation tested using SEM and bootstrapping showing behavioral constraint -> work pressure -> risky riding behavior.
Perceived algorithmic tracking evaluation promotes risky riding behavior among food delivery riders through increased work pressure.
Survey data from 466 Chinese food delivery riders; SEM and bootstrapping used to test mediation (tracking evaluation -> work pressure -> risky riding behavior).
Algorithms now surpass human capability in processing speed, pattern recognition and data-driven decision-making.
Asserted in the paper's opening claims as a general factual premise; grounded in the paper's literature grounding but no original empirical tests or sample reported.
Research on large language models (LLMs) has increased especially after the release of ChatGPT.
Temporal/topic-prevalence analysis in the corpus indicating a rise in LLM-related topic weight following the ChatGPT release date.
There is significant research concentration on AI applications in supply chains, labor markets, and large language models (LLMs).
Topic-modeling results showing relatively high prevalence of topics labeled as supply chains, labor markets, and LLMs in the >4,600-paper corpus.
Education, reskilling, and institutional responses are important in shaping the economic outcomes of artificial intelligence.
Policy implication derived from the observed/modeled heterogenous effects of AI on occupations and productivity; presented as a normative recommendation rather than an empirically tested result in the provided text.
Productivity gains associated with AI may support long-term economic growth.
Reference to productivity data and growth theory linking productivity improvements to long-run growth; the paper states this as a potential outcome but does not provide quantified long-run estimates or empirical identification in the excerpt.
AI complements higher-skill labor.
Interpretation of labor market data patterns and theoretical task-complementarity arguments presented in the paper; empirical details (which datasets, estimation strategy, sample size) are not provided in the text excerpt.
Artificial intelligence is a skill-biased technological innovation.
Framing and argumentation in the paper situating AI within the skill-biased technical change literature; references to analyses of publicly available labor market and productivity data (sources, time periods, and sample sizes not specified in the text).
Firms' technical competencies amplify the positive effect of AI adoption on performance.
Moderation analysis in the PLS-SEM using the same 280-SME survey indicating a significant positive moderating role for technical/technical competency measures.
Firms' financial capacity amplifies the positive effect of AI adoption on performance.
Moderation analysis within the PLS-SEM on survey data from 280 Tunisian SMEs showing a significant positive moderating effect of financial strength on the AI adoption → performance link.
AI adoption significantly improves operational performance of Tunisian SMEs.
Same empirical dataset (n=280) and PLS-SEM analysis reporting a significant AI adoption → operational performance relationship.
AI adoption significantly improves financial performance of Tunisian SMEs.
Survey data from 280 Tunisian SMEs analyzed using partial least squares structural equation modeling (PLS-SEM); significance of the AI adoption → financial performance path reported in the model.
The future of AI must be guided by human-centered ethical principles, international cooperation, and strategic regulatory planning to ensure societal benefit and minimize systemic risks.
Concluding recommendation in the paper (normative/policy prescription); the abstract gives no empirical evidence or quantified analysis to demonstrate effectiveness of these measures.
Public governance is pivotal to ensuring equitable and accountable AI implementation.
Policy argument/conclusion presented in the paper; the abstract does not report empirical validation, case studies, or metrics supporting this causal claim.
Big Data Analytics and AI can improve audit accuracy and reduce costs.
Reported results from literature review and empirical analysis in the study; precise cost or accuracy metrics and sample information are not provided in the abstract.
Integrating BDA and AI within the Audit 5.0 framework represents a fundamental shift toward intelligent, adaptive, and value-driven auditing, while underscoring the need for enhanced auditor competencies and alignment with evolving regulatory and professional requirements.
Overall synthesis of literature and empirical results from the mixed-method study (systematic review + SEM-based empirical analysis in finance and technology sectors); phrased as a high-level conclusion.