Evidence (3062 claims)
Adoption
5227 claims
Productivity
4503 claims
Governance
4100 claims
Human-AI Collaboration
3062 claims
Labor Markets
2480 claims
Innovation
2320 claims
Org Design
2305 claims
Skills & Training
1920 claims
Inequality
1311 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 373 | 105 | 59 | 439 | 984 |
| Governance & Regulation | 366 | 172 | 115 | 55 | 718 |
| Research Productivity | 237 | 95 | 34 | 294 | 664 |
| Organizational Efficiency | 364 | 82 | 62 | 34 | 545 |
| Technology Adoption Rate | 293 | 118 | 66 | 30 | 511 |
| Firm Productivity | 274 | 33 | 68 | 10 | 390 |
| AI Safety & Ethics | 117 | 178 | 44 | 24 | 365 |
| Output Quality | 231 | 61 | 23 | 25 | 340 |
| Market Structure | 107 | 123 | 85 | 14 | 334 |
| Decision Quality | 158 | 68 | 33 | 17 | 279 |
| Fiscal & Macroeconomic | 75 | 52 | 32 | 21 | 187 |
| Employment Level | 70 | 32 | 74 | 8 | 186 |
| Skill Acquisition | 88 | 31 | 38 | 9 | 166 |
| Firm Revenue | 96 | 34 | 22 | — | 152 |
| Innovation Output | 105 | 12 | 21 | 11 | 150 |
| Consumer Welfare | 68 | 29 | 35 | 7 | 139 |
| Regulatory Compliance | 52 | 61 | 13 | 3 | 129 |
| Inequality Measures | 24 | 68 | 31 | 4 | 127 |
| Task Allocation | 71 | 10 | 29 | 6 | 116 |
| Worker Satisfaction | 46 | 38 | 12 | 9 | 105 |
| Error Rate | 42 | 47 | 6 | — | 95 |
| Training Effectiveness | 55 | 12 | 11 | 16 | 94 |
| Task Completion Time | 76 | 5 | 4 | 2 | 87 |
| Wages & Compensation | 46 | 13 | 19 | 5 | 83 |
| Team Performance | 44 | 9 | 15 | 7 | 76 |
| Hiring & Recruitment | 39 | 4 | 6 | 3 | 52 |
| Automation Exposure | 18 | 16 | 9 | 5 | 48 |
| Job Displacement | 5 | 29 | 12 | — | 46 |
| Social Protection | 19 | 8 | 6 | 1 | 34 |
| Developer Productivity | 27 | 2 | 3 | 1 | 33 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
| Creative Output | 15 | 5 | 3 | 1 | 24 |
| Skill Obsolescence | 3 | 18 | 2 | — | 23 |
| Labor Share of Income | 8 | 4 | 9 | — | 21 |
Human Ai Collab
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Caseworker performance significantly improves as chatbot quality improves.
Aggregated results from the randomized experiment show monotonic improvement in caseworker accuracy as the chatbot suggestion accuracy increases; paper states the improvement is statistically significant (specific p-values/statistical tests not provided in the excerpt).
The authors curated a set of guidelines called the Incentive-Tuning Framework to aid researchers in designing effective incentive schemes for human–AI decision-making studies.
Authors' contribution described in the paper: development of a framework (framework content and evaluation details not provided in excerpt).
The intelligent scheduling model incorporates legal, contractual, skill-based, and preference-aware constraints to generate equitable and efficient rosters.
Methodological description of constraints encoded in the optimization model for scheduling; experimental validation of resulting rosters reported (conflict reduction and fairness metrics), but specific constraint formulations and datasets are not detailed in the excerpt.
The performance evaluation framework combines structured metrics (task completion, attendance, punctuality) with unstructured feedback (patient surveys, peer reviews) analyzed using natural language processing.
Methodological description in the paper of the performance evaluation module and use of NLP for unstructured feedback analysis; implementation details and dataset sizes not specified in the excerpt.
The proposed AI-driven HRM framework integrates forecasting, optimization, and performance evaluation to enhance workforce planning, staff scheduling, and continuous assessment.
Methodological contribution described in the paper: framework design with three core modules (demand forecasting, intelligent scheduling, performance evaluation); validated via experiments on synthetic and real hospital datasets (dataset sizes not specified in the text).
The study extends human capital theory by integrating emotional and psychological dimensions into explanations of productivity and employment outcomes.
Theoretical contribution asserted by the authors based on their empirical findings linking emotional intelligence and psychological factors to economic outcomes; this is a conceptual extension rather than a statistical result.
Twelve testable hypotheses are proposed, with implications for agentic AI oversight and human-AI collaboration.
Paper statement that it proposes twelve testable hypotheses; verifiable by counting hypotheses in the paper.
Persistent environmental state induces history sensitivity (dependence of long-run behavior on past trajectories and initial conditions) unless the overall system is globally contracting.
Formal theorem and proof showing that persistence of environmental variables creates non-autonomous/memory-dependent closed-loop behavior, and that only the special case of global contraction removes this history dependence (mathematical analysis of sensitivity to initial conditions).
Under dissipativity assumptions the induced closed-loop system admits a bounded forward-invariant region, guaranteeing viability of the dynamics without requiring global optimality.
A proven structural result (theorem) in the paper: mathematical proof using dissipativity hypotheses on components of the feedback architecture showing existence of a bounded forward-invariant set for the closed-loop dynamics. (The claim is theoretical; no empirical sample size.)
We demonstrate three distinct workflows across five environments.
Paper lists and evaluates five target environments and describes three workflows (direct translation, translation verified against existing performance implementations, and new environment creation). Sample size: five environments.
These trends (increased demand for complementary skills and decreased demand for substitutable skills) hold across geographies including the United States, United Kingdom, and Australia, demonstrating robustness.
Replication/comparison of results within the dataset’s country-specific subsamples (US, UK, Australia) drawn from nearly 30 million job postings collected between 2018 and 2024.
AI-intensive roles are significantly more likely to require complementary non-technical capabilities such as analytical thinking, resilience, and digital literacy.
Empirical analysis of a dataset of nearly 30 million job postings from the United States, the United Kingdom, and Australia between 2018 and 2024; roles classified as AI-intensive and skill mentions extracted from job postings to compare prevalence of non-technical capabilities.
Mainstreaming shared input and embracing climate-resilient management approaches are fundamental action items for building institutional resilience.
Paper conclusion lists these recommended action items based on its analysis of governance and sustainability linkages grounded in SDG and global governance literature; the summary does not indicate empirical testing of these recommendations.
AI significantly enhances supplier stability in sports enterprises (SE).
Empirical estimation using a dual machine learning (DML) model on panel data of 45 Chinese listed sports enterprises (2012–2023); authors report a statistically significant positive effect of AI on supplier stability.
Extending existing behavioral frameworks (e.g., TAM, JD–R, Organizational Trust) to the AI-augmented workplace constitutes a theoretical contribution of the paper.
Theoretical elaboration and integration presented in the paper; contribution characterized as an extension of pre-existing models to AI contexts (no quantitative validation described in the summary).
The paper proposes a five-phase strategic roadmap for phased organizational implementation that integrates HRM practice redesign, psychological support systems, and evidence-based governance mechanisms.
Prescriptive/strategic proposal based on the paper's theoretical synthesis and applied recommendations (roadmap described in the paper; summary contains no implementation trial data).
The paper develops a comprehensive, multi-dimensional organizational psychology framework for preparing the U.S. workforce for AI integration composed of six interdependent dimensions: human–AI symbiosis, trust and transparency, job redesign, AI-enabled recruitment and selection, learning and adaptation, and ethical AI governance.
Conceptual framework derived from theoretical integration (TAM, Human–AI Symbiosis Theory, JD–R Model, Organizational Trust Theory) and review of AI–HRM literature; framework construction is a theoretical contribution of the paper (no empirical validation reported in the summary).
General US employment for prime age workers (age 25–54) is currently high (~80%).
Paper cites a current employment rate of 80% for prime-age workers; likely based on national labor statistics though the exact data source and year are not specified in the excerpt.
State-owned enterprises and high-tech firms with robust digital infrastructure experience the largest productivity and innovation gains from AI adoption, indicating absorptive capacity matters.
Heterogeneity analysis on the same panel data comparing subgroups (state-owned vs. non-state-owned; high-tech vs. others; firms with stronger digital infrastructure), showing larger estimated AI effects in those subgroups.
Adoption of AI strengthens firms' innovation outcomes.
Same panel dataset (A-share-listed design firms, 2014–2023) with AI indicators derived from annual reports and patent texts; regression analyses linking AI indicator to innovation metrics (patent-related measures and/or firm-level innovation proxies referenced in the study).
Integrating AI technologies significantly enhances Total Factor Productivity (TFP) in design-oriented, project-based firms.
Panel regression analysis using firm-level panel data of A-share-listed design-oriented enterprises in China (2014–2023). AI exposure measured via an enterprise-level AI indicator constructed from NLP-based text analysis of annual reports and patents; TFP estimated at the firm level as the dependent variable. Robustness checks (e.g., Propensity Score Matching) reported.
The study moves beyond treating AI as a monolith by empirically investigating how distinct AI features jointly influence the consumer decision journey.
Methodological claim supported by the study's modeling of three specific AI feature constructs (recommendation engines, chatbots, comparison tools) and analyzing their joint effects via SEM on decision-related outcomes.
AESP is implemented as an open-source TypeScript SDK with 208 tests and ten modules.
Implementation claim in the paper: TypeScript SDK, 208 tests, ten modules; verifiable by inspecting the repository and test suite.
AESP is built on an ACE-GF-based cryptographic substrate.
Paper states ACE-GF is used as the cryptographic substrate; implementation referenced in SDK.
AESP employs HKDF-based context-isolated privacy with batched consolidation.
Cryptographic design described in the paper; HKDF-based isolation and batched consolidation listed as mechanisms.
AESP uses EIP-712 dual-signed commitments with escrow to bind agent actions to human consent.
Protocol description cites EIP-712 dual-signed commitments with escrow as a core mechanism; implementation stated in SDK.
AESP provides human-in-the-loop review with automatic, explicit, and biometric tiers.
Design specification in the paper describing three tiers of human review; implementation claimed in the SDK.
AESP includes a deterministic eight-check policy engine with tiered escalation.
Protocol specification and implementation details described in the paper; presence asserted in the SDK implementation.
The recommended IS research emphases include hybrid human–AI ensembles, situated validation, design principles for probabilistic systems, and adaptive governance.
Explicitly listed components of the authors' proposed research agenda in the discussion section of the paper, derived from synthesis of reviewed literature and conceptual analysis.
To bridge the misalignment, the paper proposes reorienting IS scholarship from analyzing impacts toward actively shaping the co-evolution of technical capabilities with organizational procedures, societal values, and regulatory institutions.
Authors' proposed research agenda and recommendations derived from the synthesis of the 28 reviewed studies and their socio-technical analysis.
The study contributes to theory by developing a human-grounded decision analytics perspective and to practice by providing practical advice to executives and analytics leaders.
Author-stated contributions based on the conceptual framework and practical recommendations included in the paper. No practitioner evaluation or citation analysis provided.
The study reframes AI as an augmentation mechanism rather than a substitute for managerial judgment and extends organizational decision theory to account for socio-technical decision systems.
Theoretical contribution asserted by the paper based on its literature synthesis and conceptual development (claim about extension of theory rather than empirical test).
The paper develops an integrative conceptual framework that explains how human judgment, algorithmic intelligence, and organizational context interact to shape decision quality and organizational outcomes.
Author-constructed conceptual framework based on synthesized literature across decision sciences, management, and information systems (framework described as output of the meta-analysis; no empirical validation reported in abstract).
The model was prompted to suggest jobs to 24 simulated candidate profiles balanced in terms of gender, age, experience and professional field.
Methods reported in the paper: experimental prompting of GPT-5 with N=24 simulated profiles, balanced across specified attributes.
This study evaluates how a state-of-the-art generative model (GPT-5) suggests occupations based on gender and work experience background for under-35-year-old Italian graduates.
Study design described in the paper: targeted population (under-35 Italian graduates), model used (GPT-5) and evaluation focus (occupation suggestions).
Curated (human-authored) Skills substantially improve agent task success on average (+16.2 percentage points).
Aggregate result reported over the SkillsBench benchmark: comparison of pass rates between baseline (no Skills) and curated-Skills conditions across the benchmark. SkillsBench comprises 86 tasks across 11 domains; evaluations used 7 agent–model configurations and 7,308 execution trajectories to compute pass rates and deltas.
Common AI applications in accounting include transaction automation, invoice processing, reconciliations, fraud detection, anomaly detection, automated financial reporting, and predictive forecasting.
Descriptive listing drawn from academic and industry sources/case studies summarized in the paper.
From interview-based evidence the authors constructed a conceptual framework that integrates empirical insights with existing theories to explain how human–AI interaction alters design cognition.
Synthesis of qualitative interview findings with literature on creative cognition and design thinking; framework presented as an output of the study (framework construction described in paper).
The paper issues a research agenda for economists: empirically develop instruments linking first‑person temporal reports with behavioral and neural proxies; theoretically incorporate subjective temporality into models of utility, human capital, attention economics, and platform competition; and evaluate policy accounting for temporal‑experience externalities.
Explicitly stated research agenda and methodological recommendations in the paper; no empirical follow‑up included.
Economists will need new empirical measures: validated instruments translating phenomenological constructs (e.g., Chronons) into observable proxies or composite indices for welfare and labor studies, facing standardization and comparability challenges.
Methodological recommendation and discussion in the paper; no empirical measure development or validation reported.
The paper proposes candidate mappings from subjective reports to neural/behavioral signatures (e.g., neural markers of attentional episodes, temporal binding windows) and suggests experimental paradigms to operationalize temporal units.
Methodological proposals and suggested experimental agendas in the paper; no implemented experiments or sample sizes reported.
The framework situates itself at the intersection of neurophenomenology, computational phenomenology, brain–computer interfaces, and human–AI teaming research.
Cross-disciplinary literature synthesis and conceptual mapping in the paper; descriptive claim with no empirical sampling (N/A).
The paper introduces symbolic operators—Chronons, Hexachronons, Metachronos—as theoretical units intended to bridge first-person phenomenology of temporal experience with third‑person neurotechnology descriptions.
Theoretical proposal and definitional introduction within the paper (conceptual development); no experimental validation or sample (N/A).
XChronos is a philosophical-epistemological framework arguing that transhumanism must place subjective temporality (lived time, presence, attention, meaning) at the center of design and evaluation.
Conceptual/philosophical analysis and literature synthesis presented in the paper; no empirical sample or dataset (N/A).
Recommendation: Treat synthetic participants as heuristic tools (supplemental roles) rather than replacements; use hybrid designs, validate against held-out human samples, pre-register synthetic-data usage, and adopt transparency and reproducibility practices (document prompts, model versions, seeds, fine-tuning).
Authors' recommendations drawn from the systematic review of 182 studies and the identified failure modes and risks.
A systematic review of 27 evaluated AI education/training programs for the healthcare workforce was conducted following PRISMA guidance and a PROSPERO-registered protocol.
Systematic review design reported in the paper: PRISMA-guided review, protocol registered in PROSPERO, searches of five databases (PubMed, Scopus, CINAHL, Embase, ERIC) on 20 Aug 2024; 27 programs met inclusion criteria.
Higher job performance is positively associated with greater employee retention.
PLS-SEM analysis, N = 350. Reported direct path: Performance → Retention, β = 0.348, p < 0.001.
About 78% of the included studies document productivity increases related to digital transformation initiatives.
Quantitative summary across the 145 included studies indicating the proportion reporting productivity gains (~78%).
A systematic review of 145 empirical studies (published 2020–2025) finds a consistent positive association between digital transformation and work productivity.
Systematic review following PRISMA 2020 of 145 included empirical studies identified and screened from searches (see Methods); inclusion period 2020–2025; productivity outcomes extracted from each study.
Core supply‑chain management challenges targeted by simulation are production layout, product strategy, and managing volume and variety.
Survey and critique of simulation applications presented in the paper; conceptual taxonomy of application areas.