Evidence (3308 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
9875 claims
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Productivity
8807 claims
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Governance
7870 claims
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Human-AI Collaboration
7560 claims
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Org Design
4892 claims
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Innovation
4781 claims
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Labor Markets
4004 claims
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Skills & Training
3308 claims
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Inequality
2332 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 | 870 | 233 | 116 | 1066 | 2363 |
| Governance & Regulation | 976 | 451 | 218 | 133 | 1809 |
| Organizational Efficiency | 949 | 224 | 144 | 88 | 1416 |
| Technology Adoption Rate | 764 | 287 | 141 | 122 | 1325 |
| Research Productivity | 501 | 152 | 74 | 362 | 1101 |
| Output Quality | 542 | 216 | 69 | 69 | 896 |
| Decision Quality | 387 | 198 | 94 | 54 | 740 |
| Firm Productivity | 513 | 67 | 101 | 27 | 714 |
| AI Safety & Ethics | 249 | 303 | 73 | 36 | 667 |
| Market Structure | 190 | 192 | 134 | 27 | 548 |
| Task Allocation | 243 | 77 | 91 | 36 | 452 |
| Innovation Output | 291 | 33 | 55 | 20 | 401 |
| Skill Acquisition | 206 | 72 | 65 | 21 | 364 |
| Employment Level | 133 | 63 | 115 | 22 | 335 |
| Fiscal & Macroeconomic | 153 | 79 | 52 | 32 | 323 |
| Task Completion Time | 206 | 37 | 12 | 15 | 272 |
| Firm Revenue | 179 | 52 | 29 | 5 | 266 |
| Consumer Welfare | 130 | 76 | 47 | 13 | 266 |
| Inequality Measures | 48 | 137 | 51 | 6 | 242 |
| Worker Satisfaction | 101 | 81 | 25 | 13 | 220 |
| Error Rate | 84 | 110 | 11 | 5 | 210 |
| Wages & Compensation | 98 | 47 | 30 | 10 | 185 |
| Regulatory Compliance | 88 | 73 | 17 | 7 | 185 |
| Automation Exposure | 66 | 64 | 33 | 16 | 182 |
| Team Performance | 105 | 29 | 30 | 11 | 176 |
| Training Effectiveness | 109 | 22 | 14 | 21 | 168 |
| Developer Productivity | 114 | 21 | 14 | 8 | 158 |
| Job Displacement | 12 | 90 | 24 | 1 | 127 |
| Hiring & Recruitment | 57 | 9 | 9 | 5 | 80 |
| Skill Obsolescence | 6 | 56 | 9 | 1 | 72 |
| Social Protection | 43 | 17 | 8 | 2 | 70 |
| Creative Output | 35 | 21 | 9 | 4 | 70 |
| Labor Share of Income | 18 | 21 | 17 | 1 | 57 |
| Worker Turnover | 15 | 16 | — | 4 | 35 |
| Industry | — | — | — | 1 | 1 |
Skills Training
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The research contrasts tool-shaping (AI behavior/prototype) and mind-shaping (user strategy training) pathways and reports differing effects between them.
Paper presents both a tool-shaping experiment (Study 1) and a mind-shaping experiment (Study 2) and discusses comparative findings across these pathways.
Cognitive flexibility is examined as a moderator (boundary condition) of the interventions' effects.
Paper reports including cognitive flexibility as an individual-differences moderator in analyses across the two studies (moderation analysis planned/reported).
The prominence of machine learning, Internet of Things (IoT), and cybersecurity varies depending on organisational context and role requirements within the wind sector.
Paper reports variation across data sources and organisational contexts based on interviews, surveys, and job-posting patterns; no subgroup sample sizes or statistical tests reported in summary.
The same practice input carries opposite signs depending on whether the environment screens for it.
Synthesis of empirical patterns: in unscreened CF environment AI-style practice predicts smaller rating gains (for non-affiliated users) while in screened ICPC environment it predicts higher non-AI-aided scores.
In open Codeforces contests a stronger AI-style signature predicts smaller rating gains for users with no ICPC/IOI affiliation, but not for those who qualified for the AI-prohibited contests.
Comparative empirical analysis of CF contest rating gains by users' affiliation (ICPC/IOI qualification status) and individual AI-style signature strength; methods likely regression/heterogeneity analysis—sample sizes not reported in abstract.
Advanced economies have integrated AI technologies at scale, while emerging economies such as Algeria face structural and institutional challenges that limit the potential impact of AI on productivity growth.
Asserted in the paper with supporting literature citations (Agrawal et al., 2019; Acemoglu & Restrepo, 2020) and comparative use of World Bank and Oxford Insights indices; no specific sample-size based causal estimate provided.
There is significant cross-national, cross-industry, and cross-regional heterogeneity in AI's impact.
Conclusion from the systematic literature review indicating variation across countries, industries and regions in the effects reported by prior studies.
Research has shown that artificial intelligence is primarily driven by substitution effects in the short term, but will generate complementary and creative effects in the long term.
Synthesis claim from the literature review; the paper reports this as an aggregate finding from prior studies (no single-study sample size provided).
The paper analyzes the direct impact of artificial intelligence on employment structure, occupational tasks, and skill demand, as well as its indirect effects on job mobility, cross-border and industry differences, and policy interventions.
Descriptive claim of scope drawn from the systematic literature review conducted by the authors; no single empirical sample reported.
The rapid development of artificial intelligence is profoundly reshaping the global labor market landscape.
Statement in paper based on a systematic literature review synthesizing prior studies; no single empirical sample reported.
Overall, STARA technologies can both enhance skill development, thriving and career opportunities and concurrently produce identity threats, pressures, and contextual complexities that shape long-term career trajectories—requiring integrated organisational and labour-market perspectives to design supportive approaches.
Editorial synthesis and summary of contributions in the special issue; draws on multiple cited empirical and conceptual studies included in the issue and prior literature.
In the platform economy, performance and career success are increasingly captured through alternative, often real-time metrics, diverging from traditional indicators and raising challenges for integrating conventional and non-traditional measures of career outcomes.
Synthesis of literature on platform work and algorithmic management cited in the editorial (multiple references to platform economy research and contributions to the special issue).
Algorithmic systems for productivity and performance monitoring generate efficiencies but also create new pressures in technology-mediated work environments, including the tracking of employees’ emotional and physiological responses at work and during non-work time.
Literature synthesis and citations (e.g. Giermindl et al., 2022; McCartney and Fu, 2022; Norlander et al., 2021; Downie et al., 2025).
AI usage at work can simultaneously enhance employees' thriving and induce identity threat; employees’ learning and performance goal orientations drive career growth in this context (Yuan et al., 2026, in this special issue).
Reported empirical finding from a paper in the special issue (Yuan et al., 2026) cited in the editorial.
Significant advancements in smart technology, AI, robotics and algorithms (STARA) are changing how organisations design and implement work for the current and future workforce.
Statement in the editorial supported by references to prior literature and reviews (e.g. Brougham and Haar, 2018; Raisch and Krakowski, 2021; Tang et al., 2023; Ulfert et al., 2024; Yam et al., 2023). This paper is an editorial/literature-synthesis rather than a primary empirical study.
AI functions as a conditional capability amplifier, expanding agency while producing uneven inclusion shaped by disparities in connectivity, skills, and infrastructure.
Analytical synthesis and illustrative empirical evidence from interviews showing differential effects tied to connectivity, skills, and infrastructure.
AI is changing skill requirements—some skills become obsolete and new skills are required.
Paper identifies changing skill requirements as a key area of examination (abstract). This is stated as an asserted trend based on the paper's review rather than a quantified empirical finding in the provided text.
AI has changed how work is executed (work processes and execution).
Explicit statement in the paper's abstract; presented as a qualitative/general finding from the paper's evaluation and literature synthesis (no numerical sample provided).
AI has changed who works in jobs (i.e., workforce composition).
Stated in the paper's abstract as an asserted effect of AI on employment composition; presented as part of the paper's review rather than a specific empirical estimate.
The penetrating utilization of AI-based methods to perform tasks has drastically changed how jobs are performed.
Claim asserted in the paper (abstract) as a descriptive conclusion from the paper's review/analysis; no empirical sample or quantified effect reported in the provided text.
AI is altering nearly every aspect of human interaction—such as work and society.
Statement in the paper's abstract/intro; presented as a general observation in the paper (literature review/qualitative synthesis implied). No primary sample size or empirical estimate reported in the provided text.
AI's future impact on employment will depend not only on automation capabilities but also on how responsibly enterprises manage workforce transitions.
Paper's concluding claim synthesizing arguments and proposed governance approach (normative conclusion rather than an empirically tested causal estimate in the excerpt).
AI-induced workforce disruption is not only a labor market issue but also an enterprise governance challenge.
Argument/position advanced in the paper highlighting governance responsibilities for firms implementing AI.
Artificial intelligence, especially generative AI, is transforming enterprise operations by automating tasks, enhancing decision-making, and redefining job roles.
Conceptual statement in the paper describing observed/expected effects of generative AI on enterprise operations (no specific empirical sample or experiment reported in the excerpt).
Drawing on the partial equilibrium model of Gries and Naudé (2022), existing economic frameworks may inadvertently overlook these factors.
The paper's theoretical critique referencing Gries & Naudé (2022); argument is based on model comparison and conceptual analysis rather than new empirical tests.
We identify five key moderating factors: human resource composition, baseline capability of individuals, learning curve of practitioners, incentives for fair use, and flexibility of objectives.
Explicit enumeration of proposed moderating factors in the paper (conceptual identification rather than empirical measurement).
Following the advent of high-performance generative models, AI use has been rapidly encouraged in some sectors while being restricted in others.
Descriptive claim in the paper's introduction/abstract; based on observation and literature context rather than new empirical data.
AI can raise productivity and output, but its distributional effects are uncertain and mediated by institutions and access to complementary resources.
Conceptual claim in abstract synthesizing literature; supported by secondary sources and integrative framework (OECD, ILO, UNDP, WTO, WEF). No quantified sample size reported.
The paper contributes by providing a structured synthesis that bridges efficiency-driven and labor-oriented perspectives on AI-driven manufacturing.
Authors' stated contribution in the paper: a structured thematic synthesis integrating two perspectives from the reviewed literature.
While new high-skill roles emerge from AI adoption, their limited accessibility constrains workforce transition.
Literature synthesis indicating emergence of high-skill roles alongside barriers to access (skills, education, hiring practices) reported in reviewed studies.
This study analyzes three key dimensions: labor displacement as a structural risk, the limitations of job transformation, and the emergence of human-centered AI.
Explicit methodological statement in the paper: systematic literature review and thematic synthesis focusing on three named dimensions.
AI redefines job roles.
Authors' thematic analysis of secondary sources and peer-reviewed literature (qualitative synthesis). No sample size reported.
Artificial Intelligence (AI) has changed how people work across various fields and businesses, especially in the Indian Information Technology (IT) industry.
Authors' qualitative synthesis of peer-reviewed literature and thematic evaluation of secondary data (literature review). No sample size reported.
Key mechanisms of AI's impact on employment structure were identified: automation of routine processes, formation of new professional profiles, and changes in requirements for employees' competencies.
Qualitative analysis of statistical data, industry reviews, and regulatory legal documents described in the paper (no experimental or survey sample size reported).
The effects of digital transformation on labor demand vary substantially across types of digital technologies.
Analysis across different digital technology categories reported in the paper showing heterogeneous effects on labor demand (data: Chinese A-share manufacturing firms, 2011–2024). (Sample size not stated in provided text.)
The impact of digital transformation on labor demand differs across firms with different ownership structures, factor intensity, and asset sizes.
Heterogeneity analysis reported in the paper using subsample or interaction regressions by firm ownership, factor intensity, and asset size (Chinese A-share manufacturing firms, 2011–2024). (Sample size not stated in provided text.)
AI is changing informal cultural practices like professional mentoring that are key to helping professionals settle in their positions, stay engaged with their work, and grow their careers.
Participant reports from the 24 interviews indicating changes to informal practices such as mentoring, onboarding, and informal feedback.
AI is changing formal role responsibilities and collaborations between those roles.
Qualitative interview data from 24 product-focused employees describing shifts in formal responsibilities and inter-role collaboration.
AI adoption is allowing professionals to blur and extend the boundaries of their corporate roles.
Reported by interview participants (qualitative evidence) from the 24 interviews at one large technology firm.
Resource (digital talent) agglomeration should remain at a moderate level and achieve coordinated development, because excessive concentration can reduce the growth benefits (implied by the inverted-U finding).
Policy implication drawn from the paper’s finding of an inverted-U relationship between talent agglomeration, industrial digitalization, and regional economic growth (normative recommendation based on empirical nonlinear result).
For the country as a whole and for the eastern, central, and western regions, there is a deviation from the conjugate (coordinated) state between digital talent agglomeration and industrial digitalization.
Subsample/regional analysis across China’s regions (national and by eastern/central/western regions) reported in the paper indicating lack of positive coordination between talent agglomeration and industrial digitalization in these areas. Exact methodology and sample sizes by region not provided in the excerpt.
The relation among digital talent agglomeration, industrial digitalization, and regional economic growth follows an inverted-U shape (consistent with the Williamson hypothesis).
Systematic empirical examination of China's provincial regions using regional-level empirical analysis (paper reports an econometric test of nonlinear/quadratic relationships between digital talent agglomeration, industrial digitalization, and regional economic growth). Sample size (number of provinces/observations) not stated in the excerpt.
High-information AI improves short-run (immediate) performance without reducing post-AI outcomes on average in the experiments, but effects are heterogeneous across participants.
Experimental condition with high-information AI in the controlled logical reasoning task showing improved short-run performance and no average reduction in post-AI outcomes; heterogeneity in effects reported (sample size not provided in abstract).
High-AIC participants realized outsized gains from GenAI access; low-AIC participants saw limited or even negative marginal returns.
Subgroup analysis of the randomized experiment comparing treatment effects by AIC level; authors report large positive treatment effects for high-AIC subgroup and small or negative effects for low-AIC subgroup.
The distribution of gains from GenAI access was highly uneven across users.
Experimental results showing heterogeneous effects across participants (variance/heterogeneity analyses reported in the paper).
The future of work will be shaped by decisions made at every level of society.
Normative/concluding statement in the chapter; presented as an implication of the prior analysis rather than an empirically tested claim.
AI affects the labour market through four channels: evolution of existing roles, creation of entirely new ones, redistribution across geographies and demographics, and selective displacement concentrated among older and lower-mobility workers.
Chapter synthesises labour market data, historical analogy, and emerging workplace evidence to propose these four channels; selective displacement claim references demographic concentration (older and lower-mobility workers).
Adaptation determines who benefits from technological (AI) change.
One of five lessons; argued using historical analogy and labour market patterns (qualitative claim in chapter).
Artificial intelligence (AI) is rapidly reshaping knowledge-intensive work by automating, augmenting, and reconfiguring core professional activities.
Paper asserts this as a motivating observation based on prior literature and descriptive claims; no original empirical sample or quantified data reported.
Augmented work agency is shaped by whether applications are generative or non-generative, by employees' experiences of anxiety and technostress, and by micro-politics through which teams negotiate AI use and AI ethics.
Thematic findings from semistructured interviews (28 participants) and document review identifying these factors as shaping agency in practice.