Evidence (2954 claims)
Adoption
5126 claims
Productivity
4409 claims
Governance
4049 claims
Human-AI Collaboration
2954 claims
Labor Markets
2432 claims
Org Design
2273 claims
Innovation
2215 claims
Skills & Training
1902 claims
Inequality
1286 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 369 | 105 | 58 | 432 | 972 |
| Governance & Regulation | 365 | 171 | 113 | 54 | 713 |
| Research Productivity | 229 | 95 | 33 | 294 | 655 |
| Organizational Efficiency | 354 | 82 | 58 | 34 | 531 |
| Technology Adoption Rate | 277 | 115 | 63 | 27 | 486 |
| Firm Productivity | 273 | 33 | 68 | 10 | 389 |
| AI Safety & Ethics | 112 | 177 | 43 | 24 | 358 |
| Output Quality | 228 | 61 | 23 | 25 | 337 |
| Market Structure | 105 | 118 | 81 | 14 | 323 |
| Decision Quality | 154 | 68 | 33 | 17 | 275 |
| Employment Level | 68 | 32 | 74 | 8 | 184 |
| Fiscal & Macroeconomic | 74 | 52 | 32 | 21 | 183 |
| Skill Acquisition | 85 | 31 | 38 | 9 | 163 |
| Firm Revenue | 96 | 30 | 22 | — | 148 |
| Innovation Output | 100 | 11 | 20 | 11 | 143 |
| Consumer Welfare | 66 | 29 | 35 | 7 | 137 |
| Regulatory Compliance | 51 | 61 | 13 | 3 | 128 |
| Inequality Measures | 24 | 66 | 31 | 4 | 125 |
| Task Allocation | 64 | 6 | 28 | 6 | 104 |
| Error Rate | 42 | 47 | 6 | — | 95 |
| Training Effectiveness | 55 | 12 | 10 | 16 | 93 |
| Worker Satisfaction | 42 | 32 | 11 | 6 | 91 |
| Task Completion Time | 71 | 5 | 3 | 1 | 80 |
| Wages & Compensation | 38 | 13 | 19 | 4 | 74 |
| Team Performance | 41 | 8 | 15 | 7 | 72 |
| Hiring & Recruitment | 39 | 4 | 6 | 3 | 52 |
| Automation Exposure | 17 | 15 | 9 | 5 | 46 |
| Job Displacement | 5 | 28 | 12 | — | 45 |
| Social Protection | 18 | 8 | 6 | 1 | 33 |
| Developer Productivity | 25 | 1 | 2 | 1 | 29 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
| Creative Output | 15 | 5 | 3 | 1 | 24 |
| Skill Obsolescence | 3 | 18 | 2 | — | 23 |
| Labor Share of Income | 7 | 4 | 9 | — | 20 |
Human Ai Collab
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Future research could strengthen causal identification by exploiting exogenous policy shocks rather than relying solely on matching methods like PSM.
Authors' methodological suggestion for future work, based on limitations of current causal inference strategy (PSM and observational panel regression).
Propensity Score Matching (PSM) and other robustness checks were used to mitigate selection bias and support the causal interpretation of AI's effects.
Paper reports use of Propensity Score Matching in robustness analyses on the panel of A-share-listed design firms (2014–2023).
The paper operationalizes firm-level AI exposure by constructing an AI lexicon via natural language processing and applying text analysis to annual reports and patents to generate enterprise-level AI indicators.
Described methodology: NLP to generate an AI lexicon and text-analysis of annual reports and patents to build AI measures for each listed design enterprise in the 2014–2023 panel.
The study tracked participants in a three-wave panel totaling over 1,500 workers.
Abstract reporting a three-wave panel design and a sample size of over 1,500 workers.
Task content and valence were randomized in the experiment.
Methodological statement in the abstract that task assignments, including their content and valence, were randomized across participants.
The paper presents relevant tradeoffs and design choices across human-LLM archetypes, including decision control, social hierarchies, cognitive forcing strategies, and information requirements.
Qualitative analysis and discussion in the paper synthesizing insights from the literature review and empirical evaluation. Method: thematic synthesis and design analysis. Sample size: based on the review of 113 papers and the clinical-case evaluation (details in full text).
We describe 17 human-LLM archetypes derived from a scoping literature review and thematic analysis of 113 LLM-supported decision-making papers.
Scoping literature review and thematic analysis method; corpus size = 113 LLM-supported decision-making papers (as reported in the paper).
The paper introduces the concept of human-LLM archetypes, defined as re-occurring socio-technical interaction patterns that structure the roles of humans and LLMs in collaborative decision-making.
Conceptual contribution presented in the paper (definition and framing). Method: theoretical/conceptual description in the manuscript. Sample size: not applicable.
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.
This study uses a conceptual and analytical approach to examine the impact of AI and automation on work.
Stated methodology in the paper's abstract/introduction: methodological description that the study is conceptual and analytical; no empirical sample or quantitative data reported.
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 study uses a recently developed firm-year measure of investment in AI-related human capital, applied to a broad sample of U.S. nontechnology firms between 2010 and 2018.
Methodological statement in the abstract describing the independent variable and the sample years and population (U.S. nontechnology firms, 2010–2018).
Data were collected using a structured questionnaire and analyzed using Structural Equation Modeling (SEM).
Explicit methodological statement in the paper's summary.
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 paper empirically analyzes the algorithm-automated versus human decision-making debate using the AST and STS theoretical lenses.
Theoretical analysis and empirical synthesis across the reviewed studies (n=85), explicitly stated use of AST and STS frameworks to interpret findings.
To address the duality of benefits and harms, the paper proposes a dynamic Human-in-the-Loop (HITL) model that reconciles algorithmic determinism with normative HRM demands.
Conceptual/theoretical contribution presented in the paper (proposed HITL model based on synthesis of findings and theory).
There is substantial heterogeneity in effects (I^2 = 74%), indicating variability across studies.
Meta-analytic heterogeneity statistic reported in the paper (I^2 = 74%).
This study analyzes 28 papers (secondary studies and research agendas) published since 2023.
Systematic literature review conducted by the authors of secondary studies and research agendas; sample size explicitly reported as 28 papers; timeframe specified as 'since 2023'.
Three contributions are presented: the Agentic AI Framework (AAF 3.0); a cross-domain synthesis formalising the inverse evidence–complexity relationship; and a phased sociotechnical roadmap integrating governance sequencing, reimbursement reform, and equity safeguards.
Descriptive claim about the paper's outputs. These contributions are stated in the abstract as the study's deliverables based on the narrative review and synthesis of 81 sources.
Agentic AI is defined as autonomous, goal-directed systems capable of multi-step workflow coordination.
Definition provided by the authors within the paper (conceptual framing used for the review).
This structured narrative review of 81 sources (2020–2025) evaluates whether Agentic AI ... can support structural adaptation in ageing health systems.
Methodological statement in the paper: the study is a structured narrative review of 81 sources from 2020–2025.
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.
The results presented in the paper are based on a literature recherche, an analysis of individual tasks across different occupations (conducted within Erasmus+ projects), and discussions with trainers/educators.
Methodological statement from the paper; indicates the types of evidence used. The abstract does not provide numbers for analyzed tasks, the number of occupations, details of Erasmus+ projects, or counts of trainers/educators consulted.
Neither time constraints nor LLM use significantly change strategic foresight in the startup evaluation task.
Null findings reported from the same experimental comparisons in the 2 × 2 design (N = 348): no statistically significant effects of time constraints or LLM use on the strategic foresight outcome.
The study employed a 2 × 2 experimental design manipulating time constraints and LLM use.
Explicitly reported experimental design in the paper: two factors (time constraints, LLM use) crossed to form four conditions in the startup evaluation task.
The study used a sample of N = 348 participants.
Reported sample size in the paper's experimental study (startup evaluation task); participants across the 2 × 2 experimental design totaled 348.
The paper identifies key research gaps and proposes a future research agenda focused on human–AI interaction, organizational governance, and ethical accountability.
Conclusions/recommendations from the conceptual meta-analysis (paper-generated research agenda; no empirical testing reported in abstract).
This study presents a conceptual meta-analysis of interdisciplinary literature on AI-augmented decision-making in organizations.
Methodological statement of the paper (the paper itself is a conceptual meta-analysis); no primary empirical sample reported in the abstract.
Competency mapping involves identifying and aligning the critical skills, knowledge, and abilities required for specific job roles.
Definition provided in the paper (conceptual).
A stratified random sampling method was employed to select a representative sample of 500 IT employees, based on a pilot study constituting 0.50 percent of the total population.
Sampling description provided in the methods section: stratified random sampling, sample size = 500, pilot study size referenced as 0.50% of population.
The study analyzes data from the period 2021 to 2023 using Multiple Regression Analysis as the principal analytical technique.
Methods statement provided in the paper (timeframe and analytical method).
The primary objective of this research is to examine the impact of AI adoption on competency mapping practices in the IT sector.
Explicitly stated research objective in the paper.
The study employs the Difference-in-Differences (DiD) method to estimate AI impacts on online labor markets over time.
Methodological statement in the abstract specifying the use of Difference-in-Differences for empirical identification; implementation details (controls, parallel trends checks, sample size) are not given in the abstract.
The 2024 University of Phoenix Career Optimism Index® is a nationally representative survey of 5,000 U.S. workers and 501 employers.
Descriptive/methodological statement in the paper: a nationally representative cross-sectional survey (University of Phoenix Career Optimism Index®) with sample sizes of 5,000 U.S. workers and 501 employers.
Deterministic automated verifiers provide objective pass/fail checks for task success.
Methods section: verifiers are deterministic and automated, enabling objective evaluation of whether an agent's trajectory accomplished the task.
Scale of experiments: seven agent–model configurations and 7,308 execution trajectories were used to compute pass rates and deltas.
Reported experimental scale in Methods: 7 agent–model configurations and a total of 7,308 agent execution traces collected and analyzed across tasks/conditions.
Each task was evaluated under three conditions: (1) no Skills, (2) curated (human-authored) Skills, and (3) self-authored (model-generated) Skills.
Experimental protocol described in Methods: three-arm evaluation per task across the SkillsBench benchmark.
SkillsBench benchmark: evaluates 86 tasks spanning 11 domains with deterministic, automated verifiers.
Dataset and benchmark description in the paper: SkillsBench contains 86 tasks across 11 domains and uses deterministic pass/fail verifiers for objective evaluation.
Research agenda: there is a need for causal studies on AI’s impact on accounting labor demand and firm performance, analyses of distributional effects across firm sizes and industries, and evaluation of regulatory frameworks for reliable, interpretable AI in financial reporting.
Author-stated research priorities drawn from gaps identified in the literature review; not an empirical finding.
Policy implications include workforce retraining, standards for AI auditability and transparency, and regulation balancing innovation and controls (privacy, fraud prevention).
Policy recommendations based on identified risks and barriers discussed in the paper rather than empirical policy evaluation.
For stronger causal evidence, recommended empirical methods include difference-in-differences on adopting firms vs. controls, matched samples, and randomized pilots for particular tools, supplemented by qualitative interviews.
Methodological recommendations stated in the paper (not an empirical finding); no implementation/sample reported in the abstract.
Actionable research priorities include running larger-scale field trials linking game use to observed land-use and economic outcomes, developing validation protocols for game-backed models against empirical on-farm data, studying heterogeneity of impacts, and designing incentive mechanisms that leverage game-demonstrated profitability co-benefits.
Synthesis-driven recommendations based on identified evidence gaps—specifically the predominance of small-scale/qualitative studies and lack of long-term/causal evidence.
Rigorous economic evaluation (RCTs, quasi-experiments) is needed to quantify how game-enhanced DSTs affect investment, land-use choices, emissions outcomes, and farm incomes.
Chapter recommendation grounded in observed gaps: the literature lacks sufficiently rigorous causal impact evaluations; current evidence is largely qualitative or observational.
The paper's evidence is policy‑oriented, qualitative and analytical; it does not report causal estimates from new field data and produces testable propositions and an empirical agenda instead.
Explicit methods statement in the paper: structured desk review, corridor process mapping, governance gap analysis; absence of field experiments or causal quantitative analysis.
No new laboratory measurements or datasets are reported in the paper; the approach is methodological and conceptual rather than empirical.
Methods section and explicit statements within the paper noting absence of new data; verifiable by reading the paper.