Evidence (13870 claims)
Adoption
8467 claims
Productivity
7558 claims
Governance
6805 claims
Human-AI Collaboration
6363 claims
Org Design
4132 claims
Innovation
4065 claims
Labor Markets
3526 claims
Skills & Training
2945 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 749 | 196 | 98 | 892 | 1984 |
| Governance & Regulation | 817 | 394 | 188 | 121 | 1544 |
| Organizational Efficiency | 771 | 189 | 124 | 83 | 1177 |
| Technology Adoption Rate | 627 | 233 | 123 | 96 | 1088 |
| Research Productivity | 411 | 123 | 56 | 332 | 933 |
| Output Quality | 467 | 178 | 59 | 47 | 751 |
| Decision Quality | 320 | 174 | 75 | 42 | 618 |
| Firm Productivity | 435 | 55 | 88 | 20 | 604 |
| AI Safety & Ethics | 214 | 276 | 65 | 33 | 593 |
| Market Structure | 178 | 167 | 122 | 24 | 496 |
| Task Allocation | 207 | 64 | 71 | 32 | 379 |
| Skill Acquisition | 165 | 59 | 60 | 17 | 301 |
| Innovation Output | 203 | 27 | 43 | 18 | 292 |
| Employment Level | 105 | 52 | 107 | 13 | 279 |
| Fiscal & Macroeconomic | 131 | 69 | 43 | 26 | 276 |
| Consumer Welfare | 116 | 63 | 42 | 11 | 232 |
| Firm Revenue | 150 | 48 | 26 | 3 | 227 |
| Inequality Measures | 44 | 122 | 49 | 6 | 221 |
| Task Completion Time | 169 | 29 | 8 | 12 | 219 |
| Worker Satisfaction | 89 | 63 | 20 | 12 | 184 |
| Error Rate | 69 | 92 | 10 | 2 | 173 |
| Regulatory Compliance | 76 | 68 | 14 | 5 | 163 |
| Training Effectiveness | 93 | 21 | 13 | 19 | 148 |
| Wages & Compensation | 77 | 36 | 25 | 6 | 144 |
| Automation Exposure | 51 | 54 | 22 | 12 | 142 |
| Team Performance | 86 | 17 | 27 | 9 | 140 |
| Developer Productivity | 94 | 17 | 14 | 6 | 132 |
| Job Displacement | 12 | 80 | 20 | 1 | 113 |
| Hiring & Recruitment | 51 | 7 | 8 | 3 | 69 |
| Creative Output | 31 | 17 | 7 | 3 | 59 |
| Skill Obsolescence | 5 | 46 | 6 | 1 | 58 |
| Social Protection | 27 | 16 | 8 | 2 | 53 |
| Labor Share of Income | 17 | 17 | 17 | — | 51 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
The analysis is limited to OECD economies and monthly aggregate data, which constrains generalizability.
Study design: monthly panel of 38 OECD economies from 2000–2024 as stated in paper; author-reported limitation.
Digital trade alone is not statistically significant in affecting CO2 emissions (β = −0.030).
Same fixed-effects econometric specification on the monthly panel of 38 OECD economies (2000–2024); coefficient reported but not statistically significant.
Hierarchical regression analysis and bootstrapping methods were employed for empirical testing.
Methods section explicitly states use of hierarchical regression and bootstrapping for empirical tests on the survey data.
The study used a three-wave longitudinal survey design collecting matched data from 497 employees.
Methods section states a three-wave longitudinal survey and reports matched data from 497 employees.
We evaluate 20 state-of-the-art LLMs on their ability to predict empirically supported causal directions.
Experimental evaluation: 20 LLMs tested on the benchmark (10,490 triplets, including 1,056 contested instances) to predict empirically verified causal signs.
From 10,490 causal triplets (treatment-outcome pairs with empirically verified effect directions) derived from top-tier economics and finance journals, we identify 1,056 ideology-contested instances.
Construction/extension of the EconCausal benchmark by selecting 10,490 causal triplets from top-tier economics and finance journals and labeling 1,056 as ideology-contested (intervention- vs market-oriented divergence).
The paper contributes by sharpening the concept of management accounting decision quality, distinguishing GenAI from broader digital transformation, and offering a cautious process model grounded in documentary case evidence from leading Chinese manufacturers.
Author-stated contribution in the paper: conceptual refinement and process model based on the three-case documentary analysis.
Because the evidence is drawn primarily from external disclosures rather than direct internal observation, the claims should be read as interpretive analytical inferences rather than as definitive causal proof.
Author's own limitation statement about data sources (external corporate disclosures) and inferential scope.
The study adopts an interpretive multiple-case design and analyzes three major Chinese manufacturing firms - Midea Group, Haier Smart Home, and Dongfang Electric - using official annual and semi-annual reports, corporate disclosures, and recent AI-and-accounting literature.
Explicit methodological statement in the paper: interpretive multiple-case design; data sources listed as official annual and semi-annual reports, corporate disclosures, and literature; sample consists of three named firms.
The governance of open-weight artificial intelligence (AI) models has been framed as a binary choice: openness as risk, restriction as safety.
Literature and policy framing review presented in the paper (conceptual/argumentative analysis).
This is an exploratory and qualitative state-of-practice study grounded in over 30 interviews across four stakeholder groups (large enterprises, small/medium firms, AI developers, and CAD/CAM/CAE vendors).
Methodological statement in the paper describing study design and sample composition.
Key breakthroughs needed include integration with traditional engineering tools and data types, robust verification frameworks, and improved spatial and physical reasoning.
Interviewee-identified requirements compiled from over 30 interviews; stakeholders repeatedly pinpoint integration, verification, and spatial/physical reasoning as priority technical advances.
We conduct a controlled experiment where AI agents trade in a prediction market after receiving private signals, measuring information aggregation by the log error of the last price.
Statement of experimental design and measurement approach in the paper: laboratory-style controlled experiment, private signals given to agents, log error of last price used to quantify aggregation.
Allowing strategic prompting does not affect information aggregation.
Experimental manipulation that included strategic prompting of AI agents prior to trading; aggregation measured by log error of last price; observed no effect.
Changing the initial price does not affect information aggregation.
Experimental condition varying the initial market price and measuring resulting aggregation performance (log error of last price); reported no effect.
Changing the duration of the market does not affect information aggregation.
Experimental manipulation of market duration in the trading experiment; measured aggregation (log error of last price) across durations and found no effect.
Allowing cheap talk communication does not affect information aggregation.
Experimental condition comparing markets with and without cheap talk communication; aggregation measured by log error of the last price; reported no effect.
The study analyzes AI policies issued by provincial-level governments in China using a policy instrument framework and fuzzy-set qualitative comparative analysis (fsQCA).
Methods statement in the paper describing dataset (provincial-level AI policy documents), theoretical framing (policy instrument framework), and analytic method (fsQCA).
Five major themes emerged from the review: (1) Machine Learning for Credit Risk Assessment and Financial Inclusion; (2) Deep Learning and Neural Networks for Market Prediction and Volatility Forecasting; (3) Natural Language Processing and Sentiment Analysis for Decision Support; (4) AI-Based Fraud Detection and Operational Risk Management; and (5) Explainable AI, Regulatory Technology, and Governance Frameworks.
Thematic synthesis of the 64 retained studies reported in results; explicit listing of five themes in the paper's Results section.
We conducted a scoping review across four major databases (SciSpace, Google Scholar, ArXiv) covering publications from 2019 to 2025 and retained 64 unique studies after deduplication and screening.
Methods section: Arksey and O'Malley framework (enhanced by Levac et al.), explicit database search (SciSpace, Google Scholar, ArXiv), timeframe stated (2019–2025), and reported final sample of 64 studies after deduplication and screening.
The goal is not to identify causal effects, but to document stylized facts about how technology changes the scale of asset management work.
Author's stated research objective in the paper's summary/introduction (explicitly notes descriptive, not causal, intent).
Using a small panel of representative firms, we compare changes in AUM per employee, revenue per employee, and operating expense intensity over time.
Stated empirical approach: analysis of a small panel of representative firms comparing three metrics (AUM/employee, revenue/employee, operating expense intensity) over time. The excerpt notes panel is 'small' but gives no numeric sample size or firm list.
This project studies how much labor is required to manage capital across those waves by tracking a simple productivity measure: assets under management per employee.
Stated research design: longitudinal tracking of assets under management (AUM) per employee as the primary productivity measure; described in the paper's methods/summary. No numeric sample size provided in the excerpt.
Financial firms have gone through three major technological waves: computerization in the 1980s and 1990s, the rise of indexing and passive investing in the 2000s and 2010s, and the AI and automation wave from roughly 2015 to the present.
Author's historical categorization stated in the paper's introduction/summary (time periods specified). No sample or empirical test reported in the excerpt.
As AI reduces the costs of ideation, synthesis, and search, the central bottlenecks of science increasingly shift toward coordination, adjudication, validation, and adaptive steering.
Argumentative/trend claim presented in the paper as motivation for PIM; no empirical time-series or quantitative analysis provided in the paper itself.
The paper formalises crowdsourced R&D and hackathon-type architectures as operational search forms and links these to Causal Problem Modelling (CPM) and the Causal Theoretical Twin Architecture (CTTA).
Conceptual mapping and theoretical linkage between existing crowdsourcing/hackathon models and CPM/CTTA within the PIM framework (theoretical exposition; no empirical mapping or measurement reported).
PIM proceeds through causal problem decomposition, distributed search, real-time evidential updating, contribution traceability, staged validation, and dynamic reprioritisation of candidate solution pathways.
Procedural description of the PIM methodology and its constituent stages in the paper (methodological/theoretical exposition; no experimental implementation reported).
PIM is designed for problem spaces characterised by causal heterogeneity, partial observability, nonlinear interaction, long feedback delays, and distributed expertise.
Methodological design specification within the paper describing the target problem-space features for which PIM is intended (conceptual specification; no empirical testing).
This paper formalises extensions of crowdsourced R&D and hackathon-based research into a general methodology called Probabilistic Innovation Methodology (PIM).
The paper presents a conceptual/theoretical formalisation and names the resulting methodology PIM (no empirical study or sample reported).
The paper includes a companion video demonstrating the approach: https://youtu.be/55Q3lq1fINs.
Statement in paper providing link to companion video.
The physical robot scenario used a 7-DOF robot arm to validate the approach.
Experimental setup description in paper specifying hardware used (7-DOF robot arm).
Prior research typically considers task-level and motion-level adaptation in isolation (task-level methods ignore spatial interference; motion-level methods ignore broader task context).
Literature summary/related work section asserting the separation of prior task-level and motion-level approaches.
RAPIDDS models an individual's spatial behavior (motion paths) and temporal behavior (time required to complete tasks) over multiple cycles.
Description of modeling approach in the paper (method details describing spatial and temporal individual models over multi-cycle interactions).
This paper introduces RAPIDDS, a framework that unifies task-level and motion-level adaptation for human-robot teaming.
Methodological contribution described in paper (framework design and implementation).
This study proposes a framework for evaluating platform ecosystems by their long-term effects on human capital formation and institutional resilience.
Methodological contribution claimed by the paper (development of an evaluative framework); presented as part of the paper's contributions rather than an empirical finding.
The empirical analysis covers MENA economies over the period 2010–2023.
Paper explicitly states the temporal and geographic scope: MENA economies, 2010–2023.
The study employs a dynamic panel data approach using the System Generalized Method of Moments (System GMM) estimator to address endogeneity, unobserved heterogeneity, and persistence effects.
Methods statement in the paper describing the use of System GMM for panel data covering MENA economies over 2010–2023.
Endogeneity in estimating AI's effects was controlled using a two-way fixed effects (TWFE) model and Propensity Score Matching (PSM).
Methodological claim reported in the study about the identification strategy used to estimate causal effects of AI adoption.
The timing of AI adoption was identified through a multi-step, contextually validated text analysis of DART business reports.
Descriptive/methodological statement in the study describing how adoption dates were extracted from firms' regulatory/business reports (DART) via a validated text-analytic procedure.
The average effect of AI adoption on market value (Tobin's Q) was not statistically significant across all firms.
TWFE and PSM estimates on KOSDAQ-listed firms (2018–2025) reporting firm-level Tobin's Q before and after identified AI-adoption timing.
No statistically significant change was observed in return on assets (ROA) following AI adoption.
Same empirical setting as above (KOSDAQ firms 2018–2025) using TWFE and PSM to estimate causal effects of AI adoption on ROA.
Four propositions formalize the gradient, cascade compounding, delegation-depth effects, and extension sufficiency, establishing boundary conditions for the framework's valid operating envelope.
Theoretical/formal propositions presented in the paper that articulate limits and conditions for the framework's applicability.
The framework is analytically assessed for transferability across four decision system architectures.
Paper reports an analytic (cross-architecture) assessment comparing framework applicability across four named decision system architectures.
A formal welfare framework, analogous to the Nordhaus optimal patent life, characterises the trade-offs and yields testable predictions.
Proposal of a formal theoretical framework by the authors (analogy to Nordhaus); presented as a modeling approach rather than as an implemented empirical model in the excerpt.
CRediT contributions, funding acknowledgements and AI disclosure statements illustrate the annulus lifecycle.
Empirical examples/case illustrations cited by the authors to demonstrate how different metadata types move through the annulus; no systematic empirical analysis or sample size provided in the excerpt.
By analogy with the efficient market hypothesis, the width of the innovation annulus measures production inefficiency, set by the interplay of friction and demand.
Theoretical analogy and conceptual mapping presented in the paper; no empirical calibration or measurement of 'width' reported in the excerpt.
The innovation annulus is a permanent, functional feature of the ecosystem -- not a pathology to eliminate.
Normative/descriptive assertion by the authors based on their theoretical framing; no empirical longitudinal evidence provided in the excerpt.
We introduce the innovation annulus: the zone between freely available structured data and the advancing frontier of commercially refined knowledge products.
Definition/construct introduced by the authors as part of their conceptual framework; no empirical validation shown in the excerpt.
The real tension in scholarly knowledge infrastructure lies between the persistent cost of producing and refining structured metadata under deep technological friction, and the differentiated demands distinct communities place on data quality, focus and granularity.
Theoretical/analytical argument in the paper; presented as the central descriptive diagnosis rather than supported by empirical measurement in the excerpt.
A shift-share design finds no detectable effect of early adoption on worker-reported technology-related task restructuring.
Causal-style shift-share analysis using the 2024 EWCS exposure measures to estimate effects of early generative AI adoption on worker-reported changes in technology-related task content; sample >36,600 workers; result reported as no detectable effect.