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
Remove filter
Autonomy is characterised through a four-dimensional information-theoretic profile (epistemic, executive, evaluative, social).
Paper defines autonomy as a 4-dimensional information-theoretic profile (conceptual/mathematical definition within the formal model).
A life insurance system integrated into an industry partner mobile app was tested in two experiments.
Paper reports two experiments running the ARQuest-enabled life insurance system inside a partner mobile app; experimental setup is stated though sample sizes are not provided in the excerpt.
The paper's formalism shows that prompt/system messages shape distributions over possible execution paths (indirect control) but do not evaluate actual partial paths at runtime.
Formal mapping in the paper that treats prompts as shaping prior over paths; conceptual argument and illustrative examples.
Through a thematic review of existing research, the authors identified recurring themes about incentive schemes: their components, how researchers manipulate them, and their impact on research outcomes.
Authors' stated method and findings: thematic review (the scope/number of reviewed papers not specified in excerpt).
A critical aspect of conducting human–AI decision-making studies is the role of participants, often recruited through crowdsourcing platforms.
Claim based on the authors' thematic literature review noting participant sourcing practices (specific studies and counts not given in excerpt).
Researchers conduct empirical studies investigating how humans use AI assistance for decision-making and how this collaboration impacts results.
Statement summarizing the research landscape; supported implicitly by the authors' thematic review of existing empirical studies (number of studies not specified in excerpt).
Returns to AI are heterogeneous across firms; estimating treatment effects requires attention to selection, complementarities, and dynamic adoption pipelines.
Methodological argument referencing treatment-effect literature and observed firm heterogeneity; supported by conceptual examples rather than a single empirical treatment-effect estimate.
Sources were selected purposively through explicit inclusion and exclusion criteria tied to conceptual relevance, scholarly quality, and direct contribution to framework building; higher-order categories were retained only after iterative comparison across the four literature streams.
Author-reported sampling and analytic procedure for the integrative review.
Methodologically, the paper uses a structured integrative review combined with interpretive theory synthesis to connect literature on RegTech, sanctions compliance, institutional voids, supply chain governance, and algorithmic accountability.
Explicit methodological description in the paper (authors' stated approach).
Existing studies on regulatory technology mainly present it as a firm-level compliance tool, giving little attention to its role in shaping coordination across wider enterprise ecosystems in post-conflict and sanctions-affected settings.
Review finding based on purposive selection and comparison of literature on RegTech and related fields (method: structured integrative review and interpretive theory synthesis).
AI deployment has limited effects on retrial rates.
Same randomized field experiment; retrial rates (repeat customer contacts) were measured and reported as showing limited/no substantive change under AI deployment.
Five structural characteristics define the Metis AI zone: consequential irreversibility, relational irreducibility, normative open texture, adversarial co-evolution, and accountability anchoring.
Theoretical specification and definition of five characteristics grounded in social science, philosophy, and humanitarian practice; no empirical prevalence or measurement reported.
The dominant discourse on AI limitations frames the boundary of AI capability as a divide between digital tasks (where AI excels) and physical tasks (where embodiment is required).
Statement in paper framing prevailing discourse; conceptual observation rather than empirical test (literature critique). No sample size reported.
The study used a qualitative interpretivist research design drawing on semistructured interviews with 28 managers and professionals from 12 organizations across technology, finance and knowledge-intensive service sectors in Europe and Asia, using thematic and interpretive analysis supported by organizational document review.
Methodology statement from the paper (explicit description of sample, sectors, regions and analytic approach).
AI should be conceptualized as a co-evolving organizational capability rather than a deterministic technology.
Argument developed from interpretive analysis of interview data (n=28), literature engagement and organizational document review.
The study develops an emergent framework of AI–human co-adaptation comprising three interrelated dimensions: technological alignment, cognitive calibration and ethical anchoring.
Framework derived from thematic/interpretive analysis of interview data (n=28) and supporting organizational documents.
The paper introduces the concept of 'augmented work agency' as a multi-level, interpretive form of human agency in algorithmically mediated environments.
Conceptual development within the paper grounded in literature review and qualitative interview data (28 participants) and organizational document review.
The analysis proceeded through within-case coding and cross-case pattern matching across five dimensions: intelligence source, AI mechanism, decision domain, economic implication, and boundary condition.
Method section describing coding and analytical procedures applied to the archival corpus across the four cases.
The empirical corpus comprises annual reports, 10-K filings, earnings releases, and official corporate materials published mainly between 2024 and 2026, complemented by recent peer-reviewed literature.
Paper's data description listing document types and time window for archival evidence; number of documents not enumerated.
The study adopts a qualitative comparative multiple-case design using four theoretically sampled cases: Walmart, Unilever, Sprinklr, and DoubleVerify.
Methodological statement in the paper describing case selection and study design.
The dominant paradigm for AI agents is an "on-the-fly" loop in which agents synthesize plans and execute actions within seconds or minutes in response to user prompts.
Statement in paper presenting a characterization of current AI agent design; conceptual/observational claim with no empirical data or sample reported.
We thematically analysed twelve semi-structured interviews with SME owners and managers conducted in early 2025 using Atlas.ti, yielding 19 codes grouped into six categories.
Methods statement in the paper describing qualitative sample and analysis procedures.
We examine the interplay between AI adoption, social capital formation, workforce dynamics, and sustainable development in Eastern Macedonia and Thrace (EMT), one of the EU's least developed regions.
Study context and scope as stated in the paper; empirical work conducted in EMT.
Research has concentrated on advanced urban economies, leaving the implications of AI for peripheral small and medium-sized enterprises (SMEs) operating under weak human capital, thin digital infrastructure, and constrained social capital — underexplored.
Statement in the paper contrasting existing research focus (advanced urban economies) with a lack of attention to peripheral SMEs; no empirical sample size for this bibliographic claim reported in the excerpt.
Under the Brier score specifically, with type-independent inflation cost, the second-best welfare equals the first-best welfare (welfare equivalence).
Analytical result/proof specialized to the Brier score and the assumption of type-independent inflation costs; comparative welfare analysis in the model.
The synthesis covers research and practitioner guidance from the years 2023–2025.
Methods statement specifying the temporal scope of sources used for the synthesis.
This paper synthesizes recent research and practitioner guidance (2023–2025) to develop a practical model for designing human–AI collaboration in the financial reporting function (controllership).
Methods section declaration describing scope and approach (literature/practitioner guidance synthesis covering 2023–2025).
We conducted a controlled experiment comparing traditional task-splitting methods with AI-assisted approaches using GitLab Duo.
Methodological statement in the paper reporting a controlled experiment using GitLab Duo; sample size not stated in the provided summary.
The study uses a panel dataset of 35,347 firm-year observations from 2010 to 2023.
Reported sample description in the paper: panel dataset covering 2010–2023 with 35,347 firm-year observations.
This paper focuses on five research questions about the historical pathways, leverage points, trajectory differences, alternative projects, and socio-technical programmes related to current dominant generative AI tools and possible AGI-adjacent development.
Explicit listing of the five research questions in the paper's introduction/aims; statement of scope and focus.
Data analysis utilized regression modeling for performance correlations, time-series analysis for predictive maintenance patterns, and thematic analysis for qualitative interviews.
Paper methods: explicit listing of analytic techniques used (regression, time-series, thematic analysis).
Secondary data encompasses sustainability reports, carbon footprint assessments, and operational performance metrics.
Paper methods: explicit listing of secondary data sources (sustainability reports, carbon footprint assessments, operational metrics).
Blockchain transaction records spanning eighteen months across Nigeria were used as primary data.
Paper methods: explicit statement about 18 months of blockchain transaction records across Nigeria.
The study uses IoT sensor data from forty-five facilities.
Paper methods: explicit statement that IoT sensor data were collected from 45 facilities.
Primary data collection includes structured interviews with supply chain managers.
Paper methods section: primary data described as including structured interviews with supply chain managers (number of interviewees not specified).
The study uses mixed methods involving case studies from twelve multinational companies across the manufacturing, logistics, and retail sectors.
Paper statement of methods: explicit mention of mixed methods and case studies from 12 multinational companies across the three sectors.
For over a century, the electric grid has relied on a single statistical assumption: load diversity, the principle that the uncorrelated demands of millions of small consumers produce a smooth, predictable aggregate.
Statement and historical framing presented by the paper as background context; no empirical time series or citations provided in the excerpt.
The study constructs a tripartite evolutionary game framework composed of government regulators, leading computing power incumbents, and downstream AI innovators to analyze strategic interactions and derive evolutionarily stable strategies.
Methodological claim documented in the paper describing the model structure and analytic approach (method: formal model specification and ESS derivation).
The paper evaluates 'Spec Kit' and 'TDAD' as instantiations of the SGM via a four-month pilot study.
Empirical pilot evaluation reported in the paper; duration specified as four months. Sample size or number of teams/participants in pilot not specified in the summary.
The paper identifies two amplifying mechanisms for PRP: the code review bottleneck and the context window constraint.
Theoretical argumentation in the paper naming two mechanisms that amplify the PRP phenomenon (qualitative explanation).
The paper formally defines PRP with three moderating variables: task abstraction, codebase maturity, and developer experience.
Theoretical/formal definition presented in the paper identifying three moderators; claim is descriptive of the paper's conceptual model.
This paper conducted a multivocal literature review of 67 sources spanning 2022–2026.
Statement of method in the paper describing the literature review (count of sources = 67).
Telemetry across 10,000+ developers shows flat delivery metrics (no improvement in delivery outcomes) despite changes in PR and review behavior.
Observational telemetry across >10,000 developers reported in the paper; described result is no meaningful change in delivery metrics (e.g., delivery throughput, lead time) despite increases in PRs and longer reviews.
A qualitative design was adopted, drawing on 34 semi-structured interviews with project managers across five UK industries.
Qualitative study methods reported in the paper: 34 semi-structured interviews with project managers sampled across five UK industries; Gioia-informed thematic analysis.
The literature review employs the PRISMA model to screen, identify, and synthesize available literature on AI, Machine Learning and Deep Learning in promoting managerial productivity and task efficiency.
Methodological statement in the paper's abstract (explicitly states use of PRISMA for screening and synthesis).
The paper traces near-term evolutionary trajectories for digital proto-life through three narratives: Lamarck (self-modifying coding agents), Remora (resource-seeking companion chatbots), and Mycelium (DAO-LLC trading bots).
Methodological statement in the abstract: exploratory scenario method with three specified narrative scenarios; descriptive rather than empirical.
The paper develops a typology of enterprise applications by their sensitivity to AI-induced shifts in make-or-buy economics.
Paper's stated contribution (conceptual typology based on analysis of application categories and AI sensitivity).
This paper adopts a conceptual research approach, combining transaction cost economics and the resource-based view with an assessment of current AI capabilities, to systematically re-evaluate the factors underlying the make-or-buy decision.
Paper's stated methodology and theoretical framing (methodological claim about the paper itself).
At this stage, AI adoption in Israel does not result in widespread layoffs; its primary impact lies in restructuring the labor market through a slowdown in recruitment, changes in job composition, and the emergence of new AI-related roles.
Empirical claim reported in the paper; the excerpt does not specify datasets, time periods, or sample sizes supporting this observation.
The analysis employs rigorous econometric methods including difference-in-differences estimation and propensity score matching to control for confounding variables across industry (NAICS 2-digit), firm size, geographic location, occupation-level characteristics, and macroeconomic conditions.
Methodological description in the paper specifying DiD and propensity score matching and listed covariates/controls.