Evidence (3470 claims)
Adoption
7395 claims
Productivity
6507 claims
Governance
5877 claims
Human-AI Collaboration
5157 claims
Innovation
3492 claims
Org Design
3470 claims
Labor Markets
3224 claims
Skills & Training
2608 claims
Inequality
1835 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 609 | 159 | 77 | 736 | 1615 |
| Governance & Regulation | 664 | 329 | 160 | 99 | 1273 |
| Organizational Efficiency | 624 | 143 | 105 | 70 | 949 |
| Technology Adoption Rate | 502 | 176 | 98 | 78 | 861 |
| Research Productivity | 348 | 109 | 48 | 322 | 836 |
| Output Quality | 391 | 120 | 44 | 40 | 595 |
| Firm Productivity | 385 | 46 | 85 | 17 | 539 |
| Decision Quality | 275 | 143 | 62 | 34 | 521 |
| AI Safety & Ethics | 183 | 241 | 59 | 30 | 517 |
| Market Structure | 152 | 154 | 109 | 20 | 440 |
| Task Allocation | 158 | 50 | 56 | 26 | 295 |
| Innovation Output | 178 | 23 | 38 | 17 | 257 |
| Skill Acquisition | 137 | 52 | 50 | 13 | 252 |
| Fiscal & Macroeconomic | 120 | 64 | 38 | 23 | 252 |
| Employment Level | 93 | 46 | 96 | 12 | 249 |
| Firm Revenue | 130 | 43 | 26 | 3 | 202 |
| Consumer Welfare | 99 | 51 | 40 | 11 | 201 |
| Inequality Measures | 36 | 105 | 40 | 6 | 187 |
| Task Completion Time | 134 | 18 | 6 | 5 | 163 |
| Worker Satisfaction | 79 | 54 | 16 | 11 | 160 |
| Error Rate | 64 | 78 | 8 | 1 | 151 |
| Regulatory Compliance | 69 | 64 | 14 | 3 | 150 |
| Training Effectiveness | 81 | 15 | 13 | 18 | 129 |
| Wages & Compensation | 70 | 25 | 22 | 6 | 123 |
| Team Performance | 74 | 16 | 21 | 9 | 121 |
| Automation Exposure | 41 | 48 | 19 | 9 | 120 |
| Job Displacement | 11 | 71 | 16 | 1 | 99 |
| Developer Productivity | 71 | 14 | 9 | 3 | 98 |
| Hiring & Recruitment | 49 | 7 | 8 | 3 | 67 |
| Social Protection | 26 | 14 | 8 | 2 | 50 |
| Creative Output | 26 | 14 | 6 | 2 | 49 |
| Skill Obsolescence | 5 | 37 | 5 | 1 | 48 |
| Labor Share of Income | 12 | 13 | 12 | — | 37 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Org Design
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Research priorities include empirical testing and simulation of ISB-based control systems, cost–benefit analysis of proactive versus reactive AI governance, and distributional impact assessments.
Explicit research agenda proposed by the author (conceptual recommendation), not empirical results.
This work is conceptual/theoretical and reports no original empirical dataset; it explicitly calls for mixed-methods empirical validation (case studies, field experiments, longitudinal studies), measurement development, and multi-level data collection.
Explicit methodological statement in the paper describing its nature as a theoretical synthesis and listing empirical needs; no empirical sample provided.
Empirical strategy: the main identification strategy uses panel regressions with quadratic AI specification and interaction terms, controlling for firm covariates, employing fixed effects and robustness checks (alternative measures, sub-samples).
Methods section description: panel regressions including AI and AI^2, interactions for moderators, controls, fixed effects, and robustness analyses reported in the paper.
Data/sample claim: the empirical analysis uses a panel of 2,575 Chinese listed firms observed from 2013 to 2023.
Paper-stated sample description (panel dataset covering 2013–2023, N = 2,575 firms).
The paper recommends an empirical research agenda including field experiments comparing teams with and without AI mediation, structural models of labor supply and wages under reduced language frictions, microdata analysis of adopters, and measurement studies for coordination costs and mediated-action reliability.
Explicit recommendations and research agenda stated in the paper; this is a descriptive claim about the paper's content rather than an empirical finding.
The paper's primary approach is conceptual/theoretical development and agenda-setting; it does not report large-scale empirical or experimental data.
Explicit methods statement in the paper: synthesis, illustrative examples, framework development; absence of reported empirical sample or experiments.
The study's empirical base consists of 40 semi-structured interviews with cross-industry project practitioners in the UK, analyzed using thematic qualitative methods.
Stated data and methods in the paper: sample size (40), interview method, cross-industry sampling, and thematic analysis.
Limitation: Implementation heterogeneity — the costs and feasibility of the recommended HR changes vary by context and may affect generalisability.
Explicit limitation acknowledged in the paper; drawn from theoretical reasoning about contextual heterogeneity and practitioner variability.
Limitation: The framework is conceptual and requires empirical validation across sectors, firm sizes and AI‑intensity levels.
Explicit limitation acknowledged by the authors; based on the paper's method (theoretical synthesis, no original data).
The paper generates empirically testable propositions (e.g., how leader practices affect AI adoption speed, task reallocation, productivity, error rates, employee well‑being and turnover) and suggests natural‑experiment settings for evaluation.
Stated methodological output of the conceptual synthesis; the paper lists candidate empirical tests and research opportunities but contains no original empirical tests.
Typical methods used are deep learning for property prediction and representation learning, protein-structure modelling tools, generative models for de novo design, NLP for knowledge extraction, and ADME/Tox in silico models integrated with traditional computational chemistry.
Methodological survey in the paper listing these approaches and examples of their application.
Commonly used data types in AI-driven drug discovery include biochemical/binding assay data, protein structural data, HTS results, ADME/Tox and PK datasets, omics/phenotypic readouts, and scientific literature/patents.
Cataloguing of data sources used across studies and company pipelines described in the paper.
AI became widely adopted in pharmaceutical discovery during the 2010s, driven by greater compute, larger datasets, and advances in deep learning.
Historical overview and trend analysis in the paper referencing increased compute availability, growth in public and proprietary datasets, and the rise of deep-learning publications and tools over the 2010s.
Current evidence is illustrative rather than systematic; there is a lack of long-run, quantitative measures of AI’s effect on late-stage clinical outcomes in the literature reviewed.
Explicit methodological statement in the paper: study is an expert/opinion synthesis and narrative review with no new causal econometric estimates or primary experimental data.
The paper identifies three core mechanisms underlying calibrated trust and complementarity: (1) calibrated trust balancing reliance and oversight, (2) complementarity–trust interaction for optimal performance, and (3) dynamic feedback loops producing reinforcing learning cycles.
Explicit identification of mechanisms claimed in the paper's synthesis; this is a descriptive claim about the paper's content rather than an empirical finding—no sample or empirical test reported in the abstract.
The authors surveyed workers and developers on a representative sample of 171 tasks and used language models (LMs) to scale ratings to 10,131 computer-assisted tasks across all U.S. occupations.
Study methodology reported in the paper: surveys of 'workers and developers' on 171 tasks, plus LM-based scaling to 10,131 tasks (coverage claims across U.S. occupations).
The study uses a game-theoretic model involving a foundation model provider and two competing downstream firms to analyze how policy interventions affect consumer surplus in the AI supply chain.
Methodological description in the paper: a formal game-theoretic model with one upstream provider and two downstream competing firms; equilibrium analysis and comparative statics are performed on model outcomes (prices, qualities, profits, consumer surplus).
Foi realizada etnografia organizacional orientada ao SCF, com roteiro e triangulação de evidências.
Método qualitativo divulgado no resumo: etnografia organizacional com roteiro e triangulação; o resumo não fornece número de organizações, duração ou amostragem.
Foi construído e validado um instrumento psicométrico (escala SCF-30) e calculado um índice 0–100, com modelagem por Equações Estruturais (SEM) e testes de confiabilidade/validade.
Descrição metodológica explícita no resumo: construção e validação da escala SCF-30, uso de SEM e testes de confiabilidade e validade. O resumo não detalha estatísticas, amostra ou resultados numéricos.
O SCF é operacionalizado por três vetores centrais: Percepção de Complexidade (PC), Aversão ao Risco Institucional (AR) e Inércia Cultural (IC).
Estrutura conceitual e operacional apresentada no artigo; especificação explícita dos três vetores como componentes do construto SCF.
Degree, betweenness, and eigenvector centrality metrics were used to identify structural vulnerabilities and leverage points in the construction supply chain network.
Paper reports calculation of degree, betweenness, and eigenvector centrality to outline vulnerabilities; specific metrics and interpretations are reported (e.g., degree centrality value for brokers).
Thematic coding translated reported interactions into nodes and edges of a complex network and grouped challenges into thematic categories.
Methods described: thematic coding applied to interview data to create network structure and to generate challenge categories (six main categories, 16 open codes reported).
This study combines empirical, semi-structured interviews with social network analytics to map construction supply chain relationships and vulnerabilities.
Methods reported in the paper: use of semi-structured interviews plus social network analysis (thematic coding to create nodes/edges, calculation of network metrics). Sample size not specified in the abstract.
Extensive experiments were conducted using both synthetic and real hospital datasets to evaluate the framework.
Statement in the paper indicating experiments on synthetic and real datasets; exact sizes, sources, and composition of these datasets are not provided in the excerpt.
Coordination is treated as a structural property of the coupled dynamics (agents + incentives + persistent environment) rather than as the solution to a centralized global optimization objective or purely agent-centric learning problem.
Conceptual framing supported by the formal dynamical model and theorems showing properties of the closed-loop dynamics that do not rely on an underlying global objective.
The persistent environment component of the model stores accumulated coordination signals, and a distributed incentive field transmits those signals locally to adaptive agents, which update their states in response.
Model construction and definitions in the paper describing (i) an environmental state variable with persistent dynamics that accumulates signals, (ii) a spatially/distributed incentive field mapping environmental memory to local agent inputs, and (iii) adaptive update rules for agents.
The paper formalizes agents, incentives, and the environment as a recursively closed feedback architecture (i.e., a coupled dynamical system in which agents adapt to incentive signals that themselves depend on a persistent environmental memory produced by agent actions).
Mathematical model and definitions presented in the paper (formal system specification of agent states, incentive field, and persistent environment; no empirical data).
In a field experiment on the DiagnosUs medical crowdsourcing platform, the authors held the true prevalence in the unlabeled stream fixed at 20% (blasts) while varying the prevalence of positives in the gold-standard feedback stream (20% vs. 50%) and the response interface (binary labels vs. elicited probabilities).
Field experiment conducted on the DiagnosUs platform with experimental manipulations: (i) true prevalence in unlabeled stream fixed at 20% blasts, (ii) feedback-stream prevalence manipulated to 20% vs 50%, (iii) response interface manipulated between binary labels and elicited probabilities. (Sample size and number of workers not specified in the provided excerpt.)
The framework was evaluated on 2,847 queries across 15 task categories.
Paper reports an evaluation dataset consisting of 2,847 queries spanning 15 task categories; used as the sample for reported empirical results.
Non-text processing paths use SLM-assisted modality decomposition.
Paper reports that non-text queries are decomposed using SLM-assisted modality decomposition; described as the non-text routing approach in the framework.
For text-only queries, the framework uses learned routing via RouteLLM.
Paper states text-only routing is handled by a learned model named RouteLLM; presented as part of the system architecture.
A central Supervisor dynamically decomposes user queries, delegates subtasks to modality-appropriate tools (e.g., object detection, OCR, speech transcription), and synthesizes results through adaptive routing strategies rather than predetermined decision trees.
Methodological description in the paper of a Supervisor component that performs dynamic decomposition, delegation to modality-appropriate tools (examples given), and adaptive routing; supported by the framework's implementation details.
We present an agentic AI framework for autonomous multimodal query processing that coordinates specialized tools across text, image, audio, video, and document modalities.
Paper describes the framework design and components (Supervisor, modality-specific tools) and states support for text, image, audio, video, and document modalities; no external benchmark cited for this capability beyond the paper's own implementation.
The essay reviews seven books from the past dozen years by social scientists examining the economic impact of artificial intelligence (AI).
Qualitative book-review performed by the author; sample size explicitly stated as seven books published within the last ~12 years; method = synthesis/assessment of those seven books.
The study is limited by the scope of available industry data and the generalisability of case study findings.
Explicit limitation reported in the paper summary stating constraints related to industry data availability and generalisability of case studies.
The research adopts a mixed-method approach, combining theoretical analysis with empirical insights, and uses data gathered from the 'AI-driven transformation' Scopus database.
Explicit methodological statement in the paper summary: mixed-method design and Scopus database as the data source. (No further methodological details or sample counts provided in the summary.)
The experimental sample underlying the statistical tests comprised 20 observations (implied by ANOVA degrees of freedom: df between = 1, df within = 18).
Interpretation of the reported one-way ANOVA degrees of freedom (F(1,18) for multiple outcomes) indicating total N = 20 observations.
Field experiments at the Al‐Ra'id Research Station in Baghdad during the 2025 season compared conventional diesel‐based irrigation with AI‐assisted irrigation using soil moisture sensors, IoT controllers, and predictive weather algorithms.
Reported field experiment design in the paper (Al‐Ra'id Research Station, Baghdad, 2025 season) specifying two treatments: conventional diesel irrigation vs AI-assisted irrigation using soil moisture sensors, IoT controllers, and predictive weather algorithms.
By integrating dynamic capabilities theory with a micro foundations perspective, the study proposes a conditional model that reframes the essential challenge from technology adoption to organizational adaptation.
Model/theory construction presented in the paper (conceptual integration). This is a methodological/theoretical claim about the paper's contribution; no empirical validation provided.
This study identifies three types of AI triggers that target routines, cognitive frameworks, and resource allocation.
Proposed taxonomy / typology presented in the paper (theoretical classification). The claim is descriptive of the paper's contribution rather than empirically validated.
The study treats AI-agent populations as a system in which four key variables governing collective behaviour can be independently toggled: nature (innate LLM diversity), nurture (individual reinforcement learning), culture (emergent tribe formation), and resource scarcity.
Study design described in the paper (experimental setup allowing independent manipulation of the four variables: model diversity, individual RL, emergent tribe formation, and resource scarcity).
The study integrates Fuzzy Best Worst Method (BWM), PROMETHEE II, and DEMATEL (Fuzzy BWM-PROMETHEE II-DEMATEL) as a three-stage MCDM framework for prioritization and causal analysis of barriers.
Methodology explicitly described in paper: literature survey + expert knowledge feeding into integrated Fuzzy BWM, PROMETHEE II, and Fuzzy DEMATEL analyses.
This study investigates the barriers to the adoption of Industry 4.0 (I4.0) in the Thai automotive industry to inform firms and policymakers.
Stated research aim in paper; approach based on literature survey and expert knowledge; three-stage multi-criteria decision-making (MCDM) model used. (Sample size of experts / respondents not specified in the provided text.)
The paper's findings are based on a combination of literature review, data analysis, and an empirical study involving HR professionals.
Methodological description given in the paper's summary (no further methodological details, sample size, instruments, or statistical methods provided in the summary).
The study draws extensively on contemporary literature in sustainable supply chain management, healthcare procurement, and ESG governance.
Methodological claim about the paper's research approach: literature review/synthesis across the cited domains (bibliographic evidence within the paper).
A complete evaluation methodology is specified, including baselines and an ablation design.
Paper claims to specify evaluation methodology with baselines and ablation; details presumably in the methods section.
The paper formalizes two testable hypotheses on security coverage and latency overhead.
Explicit statement in the paper that two testable hypotheses are formalized (security coverage and latency overhead); no experimental results shown in the abstract.
We conducted preregistered experiments in two tasks (a sentiment-analysis task and a geography-guessing task) to study whether user characteristics influence the effectiveness of AI explanations.
Preregistered experimental studies described in the paper; two distinct tasks (sentiment-analysis and geography-guessing). (Sample sizes and additional procedural details are not provided in the excerpt.)
The framework is depicted across organization areas with primary focus on strategic management and workforce decision-making and secondary focus on finance, operations, and marketing.
Descriptive claim based on the conceptual framework and its mapping to organizational domains within the paper. No empirical application or case studies reported.
This paper outlines a Human–AI Collaborative Decision Analytics Framework integrating five overlapping layers: data, AI analytics, business analytics interpretation, human judgment, and feedback learning.
Presentation of a conceptual framework developed by the authors (conceptual/modeling contribution). No empirical validation reported.