Evidence (7953 claims)
Adoption
5539 claims
Productivity
4793 claims
Governance
4333 claims
Human-AI Collaboration
3326 claims
Labor Markets
2657 claims
Innovation
2510 claims
Org Design
2469 claims
Skills & Training
2017 claims
Inequality
1378 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 402 | 112 | 67 | 480 | 1076 |
| Governance & Regulation | 402 | 192 | 122 | 62 | 790 |
| Research Productivity | 249 | 98 | 34 | 311 | 697 |
| Organizational Efficiency | 395 | 95 | 70 | 40 | 603 |
| Technology Adoption Rate | 321 | 126 | 73 | 39 | 564 |
| Firm Productivity | 306 | 39 | 70 | 12 | 432 |
| Output Quality | 256 | 66 | 25 | 28 | 375 |
| AI Safety & Ethics | 116 | 177 | 44 | 24 | 363 |
| Market Structure | 107 | 128 | 85 | 14 | 339 |
| Decision Quality | 177 | 76 | 38 | 20 | 315 |
| Fiscal & Macroeconomic | 89 | 58 | 33 | 22 | 209 |
| Employment Level | 77 | 34 | 80 | 9 | 202 |
| Skill Acquisition | 92 | 33 | 40 | 9 | 174 |
| Innovation Output | 120 | 12 | 23 | 12 | 168 |
| Firm Revenue | 98 | 34 | 22 | — | 154 |
| Consumer Welfare | 73 | 31 | 37 | 7 | 148 |
| Task Allocation | 84 | 16 | 33 | 7 | 140 |
| Inequality Measures | 25 | 77 | 32 | 5 | 139 |
| Regulatory Compliance | 54 | 63 | 13 | 3 | 133 |
| Error Rate | 44 | 51 | 6 | — | 101 |
| Task Completion Time | 88 | 5 | 4 | 3 | 100 |
| Training Effectiveness | 58 | 12 | 12 | 16 | 99 |
| Worker Satisfaction | 47 | 32 | 11 | 7 | 97 |
| Wages & Compensation | 53 | 15 | 20 | 5 | 93 |
| Team Performance | 47 | 12 | 15 | 7 | 82 |
| Automation Exposure | 24 | 22 | 9 | 6 | 62 |
| Job Displacement | 6 | 38 | 13 | — | 57 |
| Hiring & Recruitment | 41 | 4 | 6 | 3 | 54 |
| Developer Productivity | 34 | 4 | 3 | 1 | 42 |
| Social Protection | 22 | 10 | 6 | 2 | 40 |
| Creative Output | 16 | 7 | 5 | 1 | 29 |
| Labor Share of Income | 12 | 5 | 9 | — | 26 |
| Skill Obsolescence | 3 | 20 | 2 | — | 25 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
The emergence of HACCAs will create a demand shock for defensive cyber tools and services (AI-based detection, incident response, resilience engineering), accelerating R&D and capital allocation into defensive AI.
Market-impact scenario analysis and industry inference about defensive responses to heightened threats (qualitative forecasting).
Main drivers of attrition identified by the model are overtime, business-travel frequency, and promotion opportunities (each having higher influence than salary).
Feature importance analyses using permutation importance and aggregated SHAP values on the fitted logistic-regression model trained on the IBM HR Analytics dataset.
Non-monetary workplace factors (excessive overtime, frequent business travel, limited promotion opportunities) are stronger predictors of individual attrition risk than salary.
Interpretable logistic-regression model trained on the IBM HR Analytics dataset; global importance assessed using aggregated SHAP values and permutation importance to rank predictors. (Exact sample size and numeric importance ranks not provided in the summary.)
Generative AI functions as a socio‑technical intermediary that facilitates interpretation, coordination, and decision support rather than merely automating discrete tasks.
Thematic analysis and co‑word linkage between terms related to interpretative work, coordination, and decision‑support and technical GenAI terms within the corpus.
The literature indicates a managerial shift away from hierarchical command‑and‑control toward guide‑and‑collaborate paradigms, where managers curate, guide, and coordinate AI‑augmented teams rather than micro‑manage tasks.
Synthesis of themes from the 212‑paper corpus (co‑word and thematic analyses) showing recurrent managerial/behavioural concepts such as autonomy, coordination, and decision‑support tied to GenAI discussions.
Higher educational attainment is positively associated with greater willingness to keep working before retirement.
Multivariate regression analysis of the cross-sectional survey (n=889) using education level as a key explanatory variable.
Male gender is positively associated with higher willingness to remain employed before retirement.
Multivariate regression on the survey sample (n=889) including gender as an explanatory variable, controlling for demographic and socioeconomic covariates.
Trust is a principal demand driver for AI-enabled marketing among Generation Z — higher trust substantially raises adoption intention and thereby accelerates diffusion.
Interpretation/implication drawn from the large standardized path coefficients (Trust → Adoption Intention β = 0.718) and mediation results in the SEM on n = 450 Gen Z respondents.
Adoption intention partially mediates the relationship between trust and brand loyalty (indirect effect Trust → Adoption → Loyalty: standardized β ≈ 0.390, p = 0.001).
Cross-sectional survey (n = 450); mediation tested within SEM framework; reported indirect standardized effect ≈ 0.390 with p = 0.001.
Economic models of firm behavior and market microstructure should incorporate endogenous, adaptive segmentation processes and faster feedback loops enabled by human–AI systems; ABS and large‑scale interaction data can be used to calibrate such models.
Methodological recommendation grounded in the study's mixed‑methods findings (ABS experiments and 150M interaction dataset) and observed differences between autopoietic and traditional STP regimes.
Canvas Design Principles mitigate algorithmic myopia (overfitting to historical patterns) and improve adaptability and resource efficiency.
Set of design principles proposed in the paper and evaluated through agent‑based simulation scenarios and analyses of the large behavioral dataset. Specific experimental details and quantitative effect sizes for these principles are not detailed in the summary.
Reconceptualizing STP as an autopoietic (self‑organizing) system enables continuous human–AI co‑creation and yields better outcomes in unstable markets than traditional, process‑based STP.
Conceptual argument grounded in 6‑month lab ethnography (n = 23), design and deployment of the Algorithmic Canvas in that lab context, and validation via large behavioral dataset analyses and agent‑based simulations.
Algorithmic co‑creation methods detect substantial market fluctuations about 5.8× better than traditional approaches.
Computational analysis of large behavioral dataset (150 million customer interactions) and comparative performance evaluation in empirically grounded agent‑based simulations. The detection metric and statistical significance details are not provided in the summary.
The autopoietic model shortens strategic planning cycle length by approximately 90%.
Observed/recorded time‑to‑update or strategy revision metrics gathered via Algorithmic Canvas usage and lab ethnography (6‑month lab ethnography inside a Fortune 500 company, n = 23). Exact measurement protocol and whether reduction measured in live firms, simulations, or system logs is not fully detailed in the summary.
Design and policy interventions that encourage active human contributions (e.g., draft-first workflows, co-creation interfaces, training) can help preserve worker agency and mitigate psychological costs.
Recommendation based on experimental evidence that Active-collaboration preserved psychological outcomes relative to passive use; presented as policy/design prescription rather than directly tested intervention at scale.
A complementary real-world survey (N = 270) across diverse tasks reproduced the experimental pattern, suggesting external validity beyond the lab writing tasks.
Cross-sectional survey of N = 270 respondents reporting on their AI use across multiple task types; reported patterns consistent with the experiment (passive use associated with lower efficacy/ownership/meaningfulness; active collaborative use did not).
Standardized data schemas and interoperable protocols reduce transaction costs and increase returns on AI investments; public-good components (shared taxonomies, open benchmarks) will accelerate innovation in DPP ecosystems.
Policy/economic recommendation synthesized from empirical observations about interoperability needs (survey and qualitative inputs) and economic reasoning; not directly measured as an outcome in the study.
Different consumer segments imply different AI-driven engagement strategies: targeted personalization and recommender systems for 'aware' consumers, and default, nudging, and tangible-benefit signals for 'unaware' consumers.
Derived from k‑means segmentation results and implication discussion linking consumer cluster characteristics to appropriate AI/UX interventions; segmentation is empirical, the AI-prescription is inferential.
DPPs generate high-quality, structured product and lifecycle data that are non-rivalrous and highly reusable, raising firm-level incentives to invest in AI models (forecasting, optimization, provenance verification) that exploit this data to capture value across production, secondary markets, and services.
Economic/technical implication drawn from the empirical characterization of DPP data and stakeholder interviews; this is an inferential claim linking DPP data properties to incentives for AI investment rather than a directly measured outcome in the surveys.
Practical DPP deployment must combine standards, governance, and user-centric design to unlock circular-economy benefits.
Inference from empirical mapping of barriers/drivers (survey and qualitative stakeholder input) and multivariate analyses showing interplay of technical and organizational factors; sample sizes not reported.
DPPs should be seen as both technical data platforms and participatory tools that enable collaborative value creation and responsible consumption (thus supporting SDG 12: responsible consumption and production).
Conceptual interpretation synthesized from empirical findings (surveys + multivariate analyses) and theoretical framing in the paper; empirical grounding via stakeholder responses but largely a conceptual contribution.
Successful DPP adoption requires matching technical functionalities (data granularity, interoperability, user interfaces) with firm-level readiness and strategies to engage different consumer segments.
Logistic regression and PCA mapping relationships among DPP features, organizational practices and consumer profiles arising from the two online surveys and mixed-method analysis; sample sizes not reported.
DPPs facilitate knowledge sharing and open innovation across firms, embedding sustainability and knowledge management into operational practice.
Qualitative and survey responses from industry stakeholders in the two sectors; analyses reported include mapping of cross‑firm knowledge exchange and organizational practices (methods: mixed methods, logistic regression/PCA); sample sizes not reported.
DPPs enhance transparency and traceability across complex supply chains, enabling material circularity and more resilient sourcing decisions.
Survey-based evidence and multivariate analyses (PCA, logistic regression) from stakeholders in Italian fashion and cosmetics indicating perceived/observed links between DPP functionalities (data granularity, interoperability) and traceability/circularity outcomes; sample sizes not reported.
Digital Product Passports (DPPs) function as a socio-technical, cognitive infrastructure that, when DPP technical capabilities are aligned with organizational readiness and consumer engagement, materially support circularity (raw-material reuse), supply-chain resilience, and cross-firm knowledge exchange, thereby turning sustainability from a compliance burden into a source of innovation and value in fashion and cosmetics.
Mixed-methods empirical study in Italian fashion and cosmetics using two online surveys, PCA and logistic regression to map relationships among technical features, organizational practices and consumer profiles; sample sizes not reported in the summary.
Economists and AI practitioners will need capacity-building in Earth-system knowledge to ensure models capture cumulative and systemic environmental risks rather than only firm-level signals.
Recommendation based on gap analysis between current disciplinary skills and systemic-environmental modeling needs; no survey or training-efficacy data offered.
There is a need for standards for data provenance, auditability, and adversarial robustness to prevent greenwashing and model manipulation.
Policy recommendation grounded in conceptual risk analysis; no technical standard proposals or threat-model evaluations provided.
Open environmental disclosure data supports reproducible empirical research in AI economics (causal inference, counterfactuals, macro-financial modeling) on effects of regulation and capital flows on environmental outcomes.
Logical argument about data availability enabling reproducible research; no empirical examples or reproducibility metrics provided.
More reliable environmental disclosures enable algorithmic investors and market models to price externalities more accurately and to implement sustainability-aligned strategies at scale.
Conceptual argument about improved information enabling market mechanisms; no empirical market-impact study included.
Open data facilitates automated, lower-cost reporting tools (NLP extraction, sensor/IoT integration, ETL pipelines) that reduce administrative burden and increase reporting frequency and timeliness.
Conceptual claim linking open standardized data to automation potential; no implemented cases or cost estimates provided.
Improved, standardized environmental disclosures improve training data quality for predictive models, reducing measurement error and bias.
Theoretical claim about data quality effects on model performance; no empirical evaluation provided.
Better, standardized, open environmental data unlocks AI/ML opportunities, enabling scalable models for firm- and system-level environmental risk assessment, scenario analysis, stress testing, and portfolio optimization.
Conceptual implications and use-case enumeration; no empirical model-building or benchmarking presented.
Applying assurance standards and regulatory oversight analogous to financial reporting will improve environmental data quality.
Normative recommendation; argument by analogy to financial assurance practices; no empirical assessment included.
Standardization (common taxonomies, units, definitions) and machine-readability are necessary to ensure comparability of environmental disclosures.
Methodological recommendation based on conceptual analysis of data interoperability issues; no empirical demonstration provided.
Treating environmental data with the same rigor as financial data (governance, standardization, auditing) would markedly improve investor, regulator, and public agency ability to assess environmental pressures, hold firms accountable, and align capital with sustainability objectives.
Conceptual causal claim argued from analogy to financial reporting; no empirical testing provided in the paper.
Corporate sustainability reporting is a powerful lever for changing corporate behavior; improving it can influence investment flows and corporate practice.
Normative/conceptual claim supported by literature synthesis and policy reasoning rather than new empirical testing.
The 2018 Supply Chain Innovation and Application Pilot Program can be used as a quasi‑natural experiment (treatment) to identify causal effects of SCD on firm outcomes.
Difference-in-differences design comparing treated (pilot-designated) versus control firms pre/post-2018; treatment defined by designation as pilot enterprise under the 2018 program.
The SCD → green innovation effects are larger for large firms (by firm size).
Heterogeneity analysis splitting sample by firm size (large vs small) with results indicating stronger SCD effects on green innovation for larger firms.
The SCD → green innovation effects are larger for state‑owned enterprises (SOEs).
Heterogeneity analysis by ownership type (SOE vs non‑SOE) showing larger and more significant coefficients for SOEs in the SCD effect on green innovation.
Carbon information disclosure (CID) is a key mediating channel: SCD increases the likelihood and quality of CID, which in turn promotes substantive green innovation.
Mediation analysis using observed CID indicators (likelihood/quality of carbon disclosure) in a causal pathway framework; SCD raised CID metrics in first-stage regressions and CID was positively associated with subsequent substantive green innovation in mediation tests.
Practical recommendation: incorporate uncertainty quantification (e.g., confidence intervals, Bayesian approaches) for ESG features in economic and ML models to reflect disclosure unreliability.
Applied recommendation in the implications section based on observed noise and manipulation risk in ESG data; not empirically tested in this review.
Market design and regulation should standardize ESG reporting and require audit/assurance, and AI can be used to monitor compliance at scale and target audits.
Policy recommendation synthesized from literature citing heterogeneity in ESG reporting and benefits of standardization; the paper presents this as an implication rather than reporting new empirical evidence.
Regulatory intervention and standardized ESG reporting/assurance are urgently required to mitigate misuse and information asymmetries.
Normative conclusion drawn from synthesis of empirical findings on disclosure heterogeneity, manipulation risk, and stakeholder harms; supported by cited calls in the literature but not empirically tested in this paper.
Strong ESG practices can reduce a firm's cost of capital (for equity and/or debt).
Synthesis of previous empirical studies linking higher ESG scores/disclosure to lower perceived risk and lower cost of equity/bond yields; literature review (secondary analysis), sample sizes and methods vary across cited studies.
ESG information can enhance long‑term firm value.
Qualitative synthesis of peer‑reviewed empirical studies in the literature review that report positive associations between stronger ESG practices and measures of firm valuation (e.g., Tobin's Q, market value). Evidence drawn from multiple prior studies with varied samples and methodologies; no new primary data in this paper.
Effective teams tend to evolve from ad-hoc interpretive methods toward systematic evaluation by (a) formalizing prompts/tests, (b) instrumenting outputs, (c) mapping failure modes to remediation paths, and (d) creating organizational decision rules.
Pattern observed in the qualitative coding of interviews where participants described trajectories or steps their teams took to formalize evaluation.
Successful teams close the results-actionability gap by systematizing interpretive practices and creating clearer pathways from evaluation signals to product changes.
Interview accounts and cross-case analysis showing some teams adopting formalization steps (e.g., standardized prompts/tests, instrumentation, remediation mappings) that participants described as enabling action.
Policy responses (active labor-market interventions, reskilling, lifelong learning, social insurance, redistribution) are needed to manage transitional inequality caused by AI-driven structural shifts in labor demand.
Policy implication drawn from reviewed empirical and theoretical literature on labor-market transitions and distributional impacts; presented as a recommendation without new empirical evaluation in this paper.
Economists should refine methods to measure AI adoption and incorporate AI-driven productivity gains into growth accounting while accounting for measurement challenges (quality change, task reallocation).
Methodological recommendation based on the review's identification of measurement difficulties in the existing empirical literature; the paper itself provides conceptual guidance rather than new measurement results.
AI has materially increased operational efficiency and productivity in industry, changing production processes and firm organization.
Qualitative integration of prior empirical studies and firm-level case studies cited in the literature review (industry analyses, adoption case examples); the paper itself does not provide new quantitative estimates or causal identification.