Evidence (16034 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).
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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 |
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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.
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.
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.
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.
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.
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.
Supervisor support was directly associated with higher occupational resilience and lower affective numbing.
Longitudinal panel; supervisor support was measured at T1, while occupational resilience and affective numbing were measured at T2.
Perceived AI autonomy was directly associated with higher occupational resilience and lower affective numbing.
Longitudinal panel; AI autonomy was measured at T1, while occupational resilience and affective numbing were measured at T2, controlling for AI work pressure and supervisor support where specified.
Country-level perceived corruption functions as a contextual moderator of the relationship between experience–expectation disconfirmation and review sentiment.
The study integrates expectancy–disconfirmation and institutional trust theories and applies statistical moderation tests to Booking.com review sentiment.
The broader directional relationship between home-country corruption, experience–expectation discrepancies, and review sentiment is reproduced in a separate Dubai sample, supporting generalizability across cities.
Robustness analysis using 242,541 Booking.com hotel reviews from Dubai and comparing the findings with the primary New York City sample.
Tourists from low-corruption countries show stronger emotional responses to discrepancies between their hotel experience and expectations, with sentiment amplified in both positive and negative directions.
Moderation tests examining whether home-country perceived corruption conditions the relationship between experience–expectation disconfirmation and textual sentiment in the New York City sample of 56,260 reviews.
Hallucination-detection approaches based on response dispersion can provide sample-level evidence of hallucination without access to model internals, but they cannot detect confident, consistent errors.
Formal review of reference-free, internal-state, and retrieval-alignment detection methods, including the worked example in the paper.
On 20 draw.io tasks, ASIL matches draw.io’s MCP content contract for GPT-5.4 but performs worse than it for sonnet4.6.
The paper reports matched native-interface comparisons on 20 draw.io tasks for two models.
In the 150-request Claude-MCP command benchmark, 73.3% of requests were executed directly, 22.7% required clarification, and 4.0% requested unavailable operations.
Command-profile analysis of the deployed interactive Claude-MCP agent.
The paper argues that age influences employment-transition outcomes indirectly through digital literacy and access to training opportunities rather than acting as a deterministic factor.
The claim is stated in the abstract and conceptual framework; the supplied text does not report age-stratified estimates or a formal moderation analysis.
The paper argues that adaptive capacity is the core mediator of employment divergence following AI-related employment shocks.
This is the study's stated interpretive conclusion, derived from questionnaire evidence and grounded-theory-informed coding involving frontline workers and managers; no formal mediation analysis is reported.
The management responses associate AI-related employment change with distributional consequences, institutional governance and organisational responsibility, not only productivity.
Coding of 159 usable Q23 management responses identified social fairness, industry regulation, data privacy, human–AI collaboration, skills training and employee welfare as recurring categories.
Frontline workers expressed both fear of unemployment and expectations that AI could create new employment opportunities.
Q19 coding found 16 responses (10.70%) expressing fear of unemployment and 16 responses (10.70%) identifying new employment opportunities.
The most frequently reported frontline-worker concerns about AI were the need for skills training, the need for policy support and labour-market reshuffling, each reported by 22 of 150 respondents (14.70%).
Frequency distribution of eight recurring categories in 150 usable responses to Q19.
AI-related workplace change simultaneously produces efficiency gains or improved work organisation and displacement-related effects for low-skilled workers.
Thematic coding of frontline-worker responses identified workflow optimisation, reduced repetitive tasks and reduced overtime alongside partial job substitution and income decline.
Among the surveyed UK low-skilled workers, AI was experienced primarily as task restructuring and transformation rather than the immediate elimination of entire jobs.
Inductive coding of 150 usable frontline-worker responses to Q14 identified workflow optimisation, reduction of repetitive tasks, task simplification and partial job substitution as recurring categories.
GPT-5.6-Luna with a manager matched GPT-5.6-Terra's single-call accuracy while using 44% of the cost: 77.8% versus 77.0% at $1.50 versus $3.41 per pass.
Five-pass accuracy and cost comparison on 100 LiveCodeBench problems; the accuracy difference was not statistically significant (two-sided p = 0.76), while the cost difference was significant.
Qwen3.8-27B with a manager achieved 86.4% pass@1 versus 87.4% for single-call Claude Fable 5 while costing $9.36 less per 100-problem pass.
Five-pass managed Qwen results and five single-call Fable results on the LiveCodeBench benchmark; the accuracy difference was not statistically resolved (p = 0.73), while the cost saving was reported as p = 0.005.
In settings where strategic and operational authority are fused, the decision to delegate tasks to AI or retain human discretion is endogenous to the GM's locus of authority.
Conceptual implication applying the locational-assumption finding to AI task allocation; it is not directly tested with AI deployment data.
AI adoption and performance in hospitality-like settings should be expected to vary across managers because blended strategic-operational roles, managerial adaptability, leadership style, and governance context can produce heterogeneous implementation choices and returns.
Conceptual implication derived from the hospitality GM framework; no direct AI adoption experiment or quantified treatment-effect estimate is reported.
The organizational context affecting GM decisions should be modeled as co-constituted by GM actions and governance structures rather than treated solely as an exogenous moderator.
Cross-domain theoretical synthesis emphasizing governance, institutional constraints, stakeholder interactions, and operational feedback loops.
Static models linking stable managerial traits to stable decisions are insufficient because the effects of GM characteristics depend on dynamic competence, situational expression of values, leadership adaptability, and recognition of gendered traits.
Thematic synthesis of studies on GM characteristics and leadership styles, used to qualify the dispositional assumption.
In hospitality, general managers often combine strategic and operational authority, so the relationship between leader characteristics and organizational outcomes is conditional on the GM's blended role.
Cross-domain synthesis of hospitality GM literature addressing the locational assumption and the convergence of strategic and operational authority.
Upper Echelons Theory does not function as a universal set of assumptions for hospitality general managers; its locational, dispositional, situational, and temporal assumptions operate as boundary conditions that require qualification in high-contact service settings.
Integrative review and cross-domain theoretical synthesis of 92 empirical and conceptual studies on hospitality general managers, covering GM characteristics, leadership styles, and succession.
The company-level version of SCAF conceptualizes vulnerability as exposure created by product deployment reach, coping as features that help users respond to and recover from failures, and adaptive capacity as features and practices that reduce future failures and improve learning.
Table 2's operational definitions of company contributions to vulnerability, coping, and adaptive capacities.
Companies that deploy AI agents increase societal vulnerability to agentic-AI risks, while the same companies can also build societal resilience through design decisions that improve responses to failure.
The paper's explicit claim that deployment increases exposure, paired with its framework for coping and adaptive contributions.
In the observed Verified comparisons, the treatment-control performance gap was largest at the 20,480-token window and closest to zero at the 262,144-token window.
Three successive Verified comparisons using the same 169 task IDs and fixed 480-second budget; the paper explicitly cautions that the cross-window ordering does not isolate window size from run-era change.
The shift-share decomposition indicates that exposed tasks lose ground mainly through changes in which occupations are posted, while the task mix within surviving occupations remains broadly flat.
Shift-share decomposition of posting-share and within-occupation task-composition changes.
The commitment effect is concentrated among models rather than universal: three of the 12 models were strongly seduced by the panels, four never committed under any panel, three committed regardless of the panel, and two responded weakly.
Disaggregated analysis of commitment behavior across the 12-model frontier-model roster.
Successful AI implementation in S&OP depends on contextual conditions and mechanisms, and implementation involves both opportunities and barriers.
Paper IV’s analysis of existing AI implementation cases and Paper V’s CIMO-based conceptualization of AI implementation in supply-chain planning.
The first study finds that integration requirements differ across S&OP subprocesses and planning situations, indicating that a one-size-fits-all approach to S&OP is insufficient.
Paper I, described as an empirical multiple-case study examining integration requirements within S&OP subprocesses.