Evidence (35258 claims)
Search and filter individual claims pulled from the papers. Looking for a specific finding ("what's the effect on wages?"), you're in the right place. Want to compare whole outcome categories against each other instead? Use the Evidence Explorer.
The board below groups claims two ways: by broad theme (nine paper-level topics) and by outcome category (the 34 claim-level outcomes that the Explorer and Syntheses also use).
Browse by theme
Nine broad, paper-level topics. Click one to filter the claims below.
Adoption
20058 claims
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Productivity
17184 claims
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Governance
16099 claims
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Human-AI Collaboration
16034 claims
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Innovation
10501 claims
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Org Design
10496 claims
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Labor Markets
6444 claims
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Skills & Training
5385 claims
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Inequality
4148 claims
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Claims by outcome category
Counts by direction of finding. These are the same 34 outcome categories the Explorer compares and the Syntheses are written for. A linked row has a published synthesis.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 1820 | 479 | 278 | 1820 | 4588 |
| Organizational Efficiency | 2711 | 616 | 401 | 173 | 3922 |
| Governance & Regulation | 2075 | 886 | 459 | 246 | 3714 |
| Technology Adoption Rate | 1467 | 530 | 258 | 206 | 2488 |
| Decision Quality | 1281 | 496 | 289 | 152 | 2228 |
| Output Quality | 1227 | 447 | 207 | 138 | 2025 |
| AI Safety & Ethics | 634 | 754 | 207 | 83 | 1688 |
| Research Productivity | 826 | 241 | 114 | 422 | 1624 |
| Firm Productivity | 1052 | 154 | 163 | 66 | 1441 |
| Task Allocation | 685 | 211 | 331 | 99 | 1335 |
| Market Structure | 433 | 423 | 242 | 46 | 1150 |
| Innovation Output | 639 | 91 | 105 | 34 | 871 |
| Task Completion Time | 476 | 113 | 43 | 36 | 672 |
| Firm Revenue | 445 | 126 | 58 | 25 | 656 |
| Skill Acquisition | 364 | 119 | 109 | 34 | 626 |
| Consumer Welfare | 288 | 167 | 104 | 31 | 592 |
| Employment Level | 214 | 140 | 174 | 50 | 582 |
| Error Rate | 230 | 251 | 35 | 16 | 535 |
| Fiscal & Macroeconomic | 268 | 136 | 71 | 50 | 532 |
| Inequality Measures | 100 | 307 | 96 | 12 | 515 |
| Worker Satisfaction | 221 | 173 | 60 | 30 | 484 |
| Automation Exposure | 155 | 138 | 65 | 36 | 398 |
| Regulatory Compliance | 171 | 120 | 30 | 13 | 335 |
| Developer Productivity | 222 | 58 | 27 | 13 | 321 |
| Team Performance | 188 | 56 | 50 | 24 | 320 |
| Wages & Compensation | 146 | 104 | 46 | 16 | 312 |
| Training Effectiveness | 207 | 41 | 21 | 26 | 298 |
| Job Displacement | 23 | 153 | 52 | 4 | 232 |
| Hiring & Recruitment | 102 | 57 | 30 | 11 | 202 |
| Skill Obsolescence | 16 | 102 | 24 | 6 | 148 |
| Creative Output | 71 | 42 | 23 | 6 | 143 |
| Social Protection | 57 | 30 | 11 | 3 | 101 |
| Labor Share of Income | 29 | 42 | 24 | 2 | 97 |
| Worker Turnover | 43 | 29 | 6 | 4 | 82 |
| Industry | — | — | — | 1 | 1 |
Science has a positive local effect on co-located digital and artistic activity while exerting a negative regional backwash effect that draws creative capacity away from neighbouring districts.
Spatial Durbin model estimating within-district and between-district effects for digital technology, science, and arts segments.
Creative agglomeration in Slovakia is conditional on reaching segment-specific density thresholds and is shaped by asymmetric cross-district spillovers and industrial legacy.
District-level analysis using segment-specific location quotients, population density, manufacturing specialization, a spatial Durbin model, random-forest simulations, and breakpoint tests.
No single capability-sourcing route dominates under all conditions; the headline results depend on the corresponding model mechanisms.
Mechanism-knockout experiments that switch acquisition friction, absorption, open-weight cost reductions, and substitution on or off.
Waiting is relatively safe for a firm whose existing product cannot be replaced by the emerging technology, but it causes substantial value loss for a firm whose product is directly substitutable by that technology.
Agent-based model with demand-side substitution eroding the legacy revenue of non-adapters according to product exposure; stated as Proposition P4.
A one-time reduction in the cost of building generative-AI capability revives the build route primarily before the leading design has stabilized; after the dominant design is established, the effect is substantially weaker.
Agent-based model experiment varying the timing of an open-weight-style reduction in build costs relative to dominant-design formation; stated as Proposition P3.
The route by which incumbents enter generative AI depends on contractibility: firms tend to partner when the capability can be rented through an API and tend to absorb when the capability is too tacit to rent.
History-friendly agent-based simulation of incumbent sourcing choices across build, partner, acquire, absorb, and wait routes; stated as Proposition P1 and reported as a headline finding.
The paper distinguishes substantive peer competition from symbolic peer competition by measuring the former with peers’ real digital-transformation investments and the latter with peers’ disclosures or announcements.
Operationalization of the two peer-competition measures in the empirical analysis.
The effects of peer-driven digital transformation vary across industries and market structures.
Reported heterogeneity and supplementary analyses using industry and market-structure splits.
Educational data should be treated as an economic asset with potential negative externalities, including privacy harms and surveillance, requiring data-governance and property-rights analysis.
Normative political-economy implication drawn from the essay's analysis of platformisation and datafication; this is a proposed analytical framework rather than an empirically estimated result.
The platformisation and datafication of education are reshaping educational markets, data governance, and pedagogical practices while introducing new power asymmetries based on surveillance and data monetization.
Conceptual analysis of EdTech and platform actors, drawing on existing scholarship and contemporary developments in technology and education.
Movement toward digital work is pathway-dependent rather than uniform across career transitions.
The study constructs consecutive same-person job transitions and calculates changes in job-title digitalization scores between source and destination jobs, summarizing these by occupational groups.
Funding mandates, international collaboration, journal prestige, and disciplinary norms are associated with systematic differences in Creative Commons license selection.
The study models categorical CC-license choice using multinomial logistic regression and examines funding-policy strength, international collaboration, subject category, and journal impact-factor percentile among 122,085 open-access articles.
Employee resistance to AI adoption is driven by fears of job displacement and skill obsolescence, and the paper argues that training programmes and transparent change management can help overcome this resistance.
Thematic synthesis and practical recommendations in the systematic literature review; the supplied text reports no employee survey sample or estimated intervention effect.
Data ownership and algorithmic control influence who receives economic rents from AI-driven value creation.
Qualitative case studies and literature synthesis focused on data rights, data dividends, ownership, and algorithmic governance.
Automation causes employment disruption in some occupations, while the net employment effect varies by sector, skill composition, and institutional context.
Synthesis of empirical studies within the review, with heterogeneity reported across sectors, worker skill compositions, and institutional settings.
Platform regulations such as transparency requirements, auditability, and algorithmic impact assessments can change the incentives and equilibrium outcomes associated with algorithmic design in news markets.
Theoretical policy implication; the article proposes evaluating such effects but does not estimate them.
High-quality journalism has public-good characteristics, while algorithmic amplification of low-quality content can generate negative externalities.
Normative and economic framing in the discussion of externalities and public goods; no welfare estimate or empirical comparison is provided.
Algorithmic ranking and recommendation can allocate attention in ways that create distributional externalities for news producers and affect demand for different types of content.
Theoretical implication for platform economics and attention markets; the article does not quantify the externalities or estimate effects.
Accountability for public knowledge is distributed across journalists, platforms, and regulators rather than being assigned to a single actor.
Theoretical account of accountability within the contested epistemic space; no observed cases or measured accountability outcomes are reported.
Editorial choices and algorithmic amplification jointly shape what audiences see, while editorial standards, algorithmic signals, and regulatory legitimacy jointly shape what audiences trust.
Conceptual decomposition of the framework into visibility and credibility mechanisms; not supported by an empirical sample.
The intersection of editorial, algorithmic, and regulatory logics forms a contested epistemic space in which visibility, credibility, and accountability are negotiated and may be stabilized or destabilized.
Theoretical framework identifying the intersection and its effects on public knowledge; no empirical validation is presented.
Journalism's epistemic authority emerges from the interaction of editorial, algorithmic, and regulatory logics rather than being produced solely within newsrooms.
Conceptual synthesis in the article's main finding; no empirical sample or causal test is reported.
Visibility-enabled metrics such as typing activity, response times, and screen time may improve measurement precision while also encouraging gaming and performative labor that inflate measured productivity without corresponding improvements in output quality.
Conceptual implication for AI economics and productivity measurement; the paper does not report a causal test or quantify the gap between measured productivity and output quality.
Digital visibility has dual and divergent effects: it can increase felt recognition and relational connection while also creating performative pressures that weaken authentic belonging.
Conceptual theorization grounded in relational cohesion theory and sociomaterial perspectives; supported by literature synthesis rather than primary empirical data.
Twitter-derived signals and Google Trends signals provide complementary information for apparel demand forecasting; neither signal source uniformly dominates the other when used alone.
Pairwise head-to-head comparisons of signal sets, in which Twitter alone and Google Trends alone each won approximately 51%–57% of matchups.
Short-run disruption includes job churn and wage compression for affected groups, while long-run outcomes depend on reskilling, capital re-allocation, and institutions.
Asserted in the supplied example contribution; no longitudinal employment, wage, or reskilling evidence is provided.
Regions with higher human capital and adoption capacity capture more productivity gains, while disadvantaged regions face stagnation.
Presented as a regional heterogeneity claim; no regional panel, productivity measure, or comparative estimate is supplied.
AI substitutes for routine cognitive and manual tasks, shifting worker duties toward nonroutinized, interpersonal, and creative tasks.
Presented as a task-based displacement claim; no task-level dataset or estimates are supplied.
Net employment effects are modest short-run losses, with potential long-run gains if complementary skill investment and policy support occur.
Asserted in the supplied example contribution; the text provides no employment panel, identification strategy results, or quantified estimates.
High-skill cognitive tasks and complementary occupations gain earnings, while routine tasks and low-skill occupations face displacement and wage pressure.
Asserted in the supplied example contribution; no occupational employment or wage data are presented.
AI-driven automation accelerates occupational task reallocation, raising productivity but producing uneven wage effects.
Asserted in the supplied example contribution; no underlying paper, dataset, sample, or statistical analysis is provided.
AI-enabled recruitment is associated with a shift toward data-driven decision-making in which machine intelligence complements human expertise.
The paper's literature synthesis and qualitative examination of AI-powered recruitment practices, including chatbots, video interviews, targeted job advertisements, and predictive analytics.
The study advocates balancing automation gains with ethical safeguards and human oversight in AI-driven talent acquisition.
Qualitative exploration of AI-enabled recruitment experiences, including participants' concerns about privacy, organisational readiness, and ethical governance.
Organisational changes associated with AI adoption differ across phases of digital HR maturity.
The study interprets interview themes using Dave Ulrich's digital HR progression framework, comprising efficiency, innovation, information, and connection phases.
The strength of psychological barriers to enterprise AI adoption differs by enterprise size, industry type, and employees' prior AI experience.
Multi-group comparison analyses examined heterogeneity across enterprise and employee subgroups in a three-enterprise survey.
Ichnology is a useful testbed for formalizing inference because trace evidence is highly ambiguous: one organism can produce diverse traces, while similar traces can result from different organisms or abiotic processes.
Conceptual analysis of ambiguity in trace-based interpretation and comparison of biotic and abiotic explanations.
Realizing a required distinction may require revealing additional information, creating a tradeoff between privacy and the ability to make the required judgment.
Conceptual analysis of realization and informational repair; illustrated across institutional and automated decision contexts.
For middle managers, AI has both positive and negative effects: it supports data analysis and managerial decision-making while creating concerns about automation of some managerial responsibilities.
Cross-study synthesis of findings differentiated by organizational level.
Leadership effects in studies of AI adoption and productivity are endogenous to firm selection and internal processes, so causal studies should use instruments or quasi-experimental designs.
The paper's methodological implication concerning endogeneity and causal inference in AI adoption and productivity research.
Digital-era leadership research increasingly frames leadership as distributed and technologically mediated, with AI systems serving as decision aids, communication intermediaries, or partial substitutes for leader tasks.
Synthesis of emerging e-leadership and digital leadership literatures.
The effects of transformational leadership operate through cognitive and affective mediators and vary according to contextual moderators.
Review synthesis of meta-analytic findings concerning mediators and boundary conditions.
The interaction of the AI Act and GDPR creates synergies in governance and accountability but can also compound compliance burdens, producing trade-offs between reduced harms and trust on one hand and efficiency and innovation on the other.
Synthesis of the doctrinal analysis and the paper's discussion of compliance costs, trust, innovation, and regulatory interaction; no quantitative trade-off estimate is provided.
The combined regulatory regimes may induce firms to relocate activities, partition product lines, or maintain dual compliance tracks, thereby affecting the location of data processing and AI development.
Analytical inference from the interaction of extraterritorial obligations, compliance costs, and regulatory differences; no firm-level relocation data are reported.
EU rules may diffuse globally as de facto standards because firms adopt uniform EU-aligned practices to preserve market access, potentially reducing regulatory fragmentation while exporting EU norms.
Comparative legal analysis of extraterritoriality and market-access incentives, interpreted through the regulatory-governance and regulatory-capitalism literature.
Governance-by-design may shift innovation toward safer and more auditable systems, while potentially slowing time-to-market.
Theoretical and interpretive analysis of how embedded compliance requirements may affect product design and innovation incentives; no quantitative innovation or time-to-market estimate is provided.
Sales experts showed only partial agreement with the features identified by the model as important: some features with strong predictive power were regarded by experts as unintuitive or unimportant.
Five sales experts reviewed SHAP explanations for four correctly classified regional sales cases through a structured questionnaire, including ratings of feature importance and agreement with explanations.
Incorporating constitutive dynamics into matching theory implies that worker preferences and suitability may be endogenous and shaped by organizational experience rather than fully stable and observable in advance.
Theoretical implication for labor-market matching and market-design models; no equilibrium model or empirical estimate is presented.
Correspondence-based fit research is most compatible with surveys, alignment metrics, dyadic or market-matching measures, and causal estimation, while constitutive-fit research is more compatible with qualitative methods, longitudinal process tracing, and analysis of interpretation and affect.
Methodological implications derived from the distinct epistemological assumptions of the two ontologies.
The two ontologies imply different process explanations for organizational outcomes: correspondence models emphasize trait alignment leading to outcomes such as satisfaction and retention, whereas constitutive models emphasize interpretive work leading to meaningfulness and adjustment.
The article develops separate processual explanations for each ontology; the claim is conceptual rather than an estimate from observed data.
Person–organization fit research rests on two distinct ontologies: a correspondence ontology that treats fit as alignment between person and organization attributes, and a constitutive ontology that treats fit as an enacted and interpretive accomplishment.
Conceptual and theoretical synthesis distinguishing two underlying ontologies; no new empirical data are reported.