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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We contribute junior–senior accounts on their usage of agentic AI through a three-phase mixed-methods study: ACTA combined with a Delphi process with 5 seniors, an AI-assisted debugging task with 10 juniors, and blind reviews of junior prompt histories by 5 more seniors.
Authors' methodological description of the study design and participant counts as reported in the paper.
Technology-driven recruitment encompasses Applicant Tracking Systems (ATS), AI-powered screening, video-based interviews, gamified assessments, and data analytics.
Conceptual description in the paper's introduction/background defining the scope of 'technology-driven recruitment'.
The study employed a mixed-methods research design combining a quantitative survey of 150 HR professionals and recruiters across manufacturing, IT, banking, and education sectors with qualitative case study analysis of four organizations in Chhatrapati Sambhajinagar.
Explicit methodological statement in the paper: quantitative survey (N=150) across specified sectors + qualitative case studies of 4 organizations in Chhatrapati Sambhajinagar.
The study used a mixed-method approach, combining qualitative and quantitative analysis of multiple case studies involving AI applications such as computer vision, robotics, and predictive analytics.
Authors report study design as mixed-method (qualitative + quantitative) applied to multiple case studies examining AI applications (computer vision, robotics, predictive analytics). No numeric sample size reported in the summary.
Prior research often treats AI presence as binary, framing it either as a hidden tool or a visible teammate.
Literature-summary claim asserted by the authors (literature review / conceptual critique). No quantitative evidence reported in the abstract.
A total of 160 peer-reviewed articles met the inclusion criteria for the review.
Direct numerical summary reported in the abstract (number of articles meeting inclusion criteria).
This study conducted a systematic review of articles published in Web of Science and Scopus up to December 2025, following established methodological guidelines.
Explicit statement in abstract describing the study method (systematic review), data sources (Web of Science and Scopus), and time cutoff (December 2025).
We surveyed 860 Microsoft developers to understand where they want AI support, and where they want it to stay out.
Primary empirical method reported in the paper (survey) with sample size explicitly stated as 860 Microsoft developers.
Developers spend roughly one-tenth of their workday writing code.
Statement reported in the paper (abstract). No sample-size or measurement method for this specific statistic provided in the abstract.
We examine 12 tasks across two practical settings: an AI consultancy providing solutions to business problems and an AI software team developing software products.
Description of experimental design and sample reported in the paper (method section): 12 tasks, two practical settings.
The two case firms demonstrated contrasting approaches to implementing AI in recruitment.
Findings and case descriptions comparing the two firms' AI recruitment strategies and levels of implementation (n = 2 firms; interviews with 22 participants).
The research contributes by shifting focus to under-researched non-Western workplace settings, particularly technologically advancing Middle Eastern economies like Qatar.
Paper's stated contribution and scope: focus on Qatari organisations and Middle Eastern context.
Four key themes emerged from the data: (1) process optimisation through AI integration, (2) subjectivity in AI-powered recruitment, (3) recruitment strategies in the age of AI, and (4) strategic investments in AI.
Findings: thematic analysis identified these four themes from interview data (n = 22) across the two case firms.
Thematic analysis was used to identify patterns and relationships within the interview data.
Methods: analysis section reporting use of thematic analysis framework.
Data were collected through semi-structured interviews with twenty-two participants across various organisational roles and hierarchical levels.
Methods: semi-structured interviews reported with total participants n = 22 across roles/levels.
The research investigated two prominent Qatari firms with contrasting AI recruitment implementation approaches.
Methods / case selection: two firms were selected and contrasted on their AI recruitment approaches (number of firms = 2).
The study employed an interpretivist philosophy and a case study design.
Methods section: explicitly states interpretivist philosophy and case study design.
Collaboration among content creators can be modeled as a multi-agent stochastic linear bandit problem with a transferable utility (TU) cooperative game formulation, where a coalition's value equals the negative sum of its members' cumulative regrets.
Methodological/modeling claim: the paper defines a multi-agent stochastic linear bandit and maps coalition value to negative sum of cumulative regrets as the TU game payoff function.
All participants had access to the same AI tool; the experiment varied only the structure surrounding its use (behavioral vs cognitive scaffolding vs unstructured).
Experimental design description in the paper: common AI tool provided to all participants; randomization/assignment varied only the scaffolding around AI use.
These results are observational and reflect a single-operator dataset without controlled comparison.
Author statement in the paper describing study limitations.
External environmental pressures did not show a significant role in the adoption process.
PLS-SEM results from the survey (n=110) reportedly found no significant effect of environmental/external pressures on AI adoption.
Data analysis involved Smart PLS-SEM, which facilitated reliability and validity assessment along with hypothesis evaluation.
Paper reports using SmartPLS for Partial Least Squares Structural Equation Modeling to assess reliability, validity, and test hypotheses.
The investigation was guided by the Technology-Organization-Environment (TOE) framework combined with innovation characteristics from Diffusion of Innovation theory.
Paper states theoretical frameworks used to design variables and hypotheses: TOE plus DOI innovation characteristics.
A total of 110 valid responses were collected through an organized online survey using purposive sampling.
Reported sample description in the paper: online survey, purposive sampling, resulting in 110 valid responses.
Exploratory innovation does not show a significant direct association with long-term competitive performance.
PLS-SEM results from the survey of 104 Portuguese B2B managers reporting a non-significant direct path from exploratory innovation to performance.
We validate this principle through a controlled experiment on log format token economy across four conditions (human-readable, structured, compressed, and tool-assisted compressed).
Controlled experiment described in the paper comparing four log-format conditions (human-readable, structured, compressed, tool-assisted compressed); exact sample size not reported in the abstract.
For six decades, software engineering principles have been optimized for a single consumer: the human developer.
Historical/position claim asserted in the paper (conceptual/literature-based argument), no empirical sample or quantitative test reported.
Through a causal decomposition that automates one side of agent communication, we separate cooperation failures from competence failures, tracing their origins through agent reasoning analysis.
Method described in the paper: causal decomposition approach that automates one side of communication and analyzes agent reasoning to attribute failures (methodological claim; abstract mentions the approach but gives no sample size or quantitative metrics there).
Capability does not predict cooperation.
Comparative experimental results reported in the paper showing different models with different capability levels achieving substantially different collective cooperation outcomes (specifically comparing OpenAI o3 and o3-mini).
We build a multi-agent setup designed to study cooperative behavior in a frictionless environment, removing all strategic complexity from cooperation.
Methodological description in the paper: design and implementation of a multi-agent experimental setup intended to remove strategic complexity (no sample size or quantitative detail reported in the abstract).
A pre-registered experiment evaluates this thesis in a commons production economy -- where agents share a finite resource pool and collaboratively produce value -- at 50-1,000 agent scale.
Paper states that a pre-registered experiment is planned/described; the experiment context (commons production economy) and planned scale (50-1,000 agents) are specified in the excerpt. No experimental outcomes or effect estimates are reported here.
We instantiate SoP in AgentCity on an EVM-compatible layer-2 blockchain (L2) with a three-tier contract hierarchy (foundational, meta, and operational).
Reported implementation/instantiation described in the paper (system implementation claim). The paper states the platform (AgentCity) and technical details (EVM-compatible L2, three-tier contracts).
In this architecture, smart contracts are the law itself -- the actual legislative output that agents produce and that governs their behavior.
Architectural/design claim in the paper describing conceptual role of smart contracts within SoP; presented as an intended property of the system.
Agents discover, transact with, and delegate to agents owned by other parties without centralized oversight.
Asserted behavior pattern of autonomous agents in the paper's motivation; presented as descriptive claim rather than supported by a reported experiment or dataset in the excerpt.
Autonomous AI agents are beginning to operate across organizational boundaries on the open internet.
Stated as an empirical observation in the paper's introduction/introduction-level motivation; no specific dataset or sample described in the text excerpt.
The divergence in collective outputs is not driven by participants abandoning AI, but by how participants use it.
Behavioral/usage data from the RCT indicating continued AI use across incentive conditions and differing usage patterns (no sample size or quantitative metrics provided in excerpt).
We conducted a systematic review and bibliometric analysis of 627 articles.
Statement in abstract reporting a systematic review and bibliometric analysis; sample size explicitly given as 627 articles.
The governance calibration problem — balancing control with the autonomy that gives agentic AI its value — emerges as the STS joint optimization challenge: governance must simultaneously enable and constrain autonomous operation.
Authors' synthesis and theoretical claim based on STS analysis and identified tensions between autonomy benefits and control needs in the literature.
Agentic AI transformation barriers constitute an interdependent sociotechnical system rather than isolated obstacles.
Interpretive conclusion drawn from STS mapping and cross-barrier interaction analysis across the reviewed literature.
Governance serves as the social subsystem's primary mechanism for managing the technical subsystem.
Interpretation from STS analysis in the review: authors identify governance as the key social mechanism constraining/enabling technical subsystem behavior.
STS mapping based on root-cause analysis revealed that 12 barriers originate in the technical subsystem and 17 in the social subsystem.
Authors' STS mapping of the 29 barriers to subsystem origins (technical vs. social) as derived from their root-cause analysis of the coded literature.
Twenty-nine barriers were identified and classified into five dimensions: technological (7), organizational (7), human (6), governance and regulatory (4), and economic (5).
Results of inductive coding of the 30-source literature corpus yielding 29 distinct barriers and reported counts per dimension.
Sociotechnical Systems (STS) theory was applied as an interpretive lens to map dimensions onto social and technical subsystems and analyze cross-subsystem interactions.
Self-reported analytic approach: application of STS theory to the coded barriers to map origins and interactions across subsystems.
Barriers were identified inductively through open and axial coding.
Self-reported qualitative method: inductive thematic analysis using open and axial coding on the literature corpus.
A critical narrative literature review of 30 sources (2019–2026) was conducted.
Self-reported study method: critical narrative literature review; sample_size = 30 sources published between 2019 and 2026.
The experiment used stratified randomization across 32 strata with 255 treatment firms and 260 control firms; baseline characteristics are well balanced across groups.
Experimental design description: stratification by geography, traction score, and baseline AI use; reporting of allocation counts and balance tests in Table 2.
Attrition from the accelerator was low (1.6%, eight ventures) and balanced across treatment and control.
Program enrollment and retention records for the 515 firms in the randomized accelerator; 8 firms attrited.
The gains from treatment are broad-based: there are no significant differential effects by baseline firm performance or founder technical background.
Heterogeneity/subgroup analyses in the randomized sample (515 firms) comparing treatment effects across strata defined by baseline traction and founder technical background.
Treated firms' demand for labor remains unchanged.
RCT with 515 firms; firms reported labor demand/changes, comparison between treatment and control groups showed no significant change.
AIGC and HGC exhibit distinct creation behaviors and consumption behaviors.
Descriptive comparisons in the longitudinal dataset showing differences in production rates, content volumes, and consumption patterns between AIGC and HGC.