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Evidence (4857 claims)

Adoption
5586 claims
Productivity
4857 claims
Governance
4381 claims
Human-AI Collaboration
3417 claims
Labor Markets
2685 claims
Innovation
2581 claims
Org Design
2499 claims
Skills & Training
2031 claims
Inequality
1382 claims

Evidence Matrix

Claim counts by outcome category and direction of finding.

Outcome Positive Negative Mixed Null Total
Other 417 113 67 480 1091
Governance & Regulation 419 202 124 64 823
Research Productivity 261 100 34 303 703
Organizational Efficiency 406 96 71 40 616
Technology Adoption Rate 323 128 74 38 568
Firm Productivity 307 38 70 12 432
Output Quality 260 71 27 29 387
AI Safety & Ethics 118 179 45 24 368
Market Structure 107 128 85 14 339
Decision Quality 177 75 37 19 312
Fiscal & Macroeconomic 89 58 33 22 209
Employment Level 74 34 78 9 197
Skill Acquisition 98 36 40 9 183
Innovation Output 121 12 24 13 171
Firm Revenue 98 35 24 — 157
Consumer Welfare 73 31 37 7 148
Task Allocation 87 16 34 7 144
Inequality Measures 25 76 32 5 138
Regulatory Compliance 54 61 13 3 131
Task Completion Time 89 7 4 3 103
Error Rate 44 51 6 — 101
Training Effectiveness 58 12 12 16 99
Worker Satisfaction 47 33 11 7 98
Wages & Compensation 54 15 20 5 94
Team Performance 47 12 15 7 82
Automation Exposure 27 26 10 6 72
Job Displacement 6 39 13 — 58
Hiring & Recruitment 40 4 6 3 53
Developer Productivity 34 4 3 1 42
Social Protection 22 11 6 2 41
Creative Output 16 7 5 1 29
Labor Share of Income 12 6 9 — 27
Skill Obsolescence 3 20 2 — 25
Worker Turnover 10 12 — 3 25
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Policy and managerial implication suggested: investing in short, targeted onboarding/training for GenAI tools (rather than only providing access) may deliver measurable performance gains and increase voluntary adoption.
Authors derive this implication from the randomized trial results showing increased adoption and improved scores with brief training (n = 164); this is an extrapolation from the trial findings.
speculative positive Training for Technology: Adoption and Productive Use of Gene... Organizational adoption and productivity (extrapolated from student trial outcom...
Policy implication (inference from results): prioritizing digital infrastructure investment to pass critical thresholds will unlock stronger productivity and environmental gains than focusing solely on advanced digital services.
Inference drawn from panel threshold findings (infrastructure threshold) and observed complementarities; this is a policy recommendation rather than a direct empirical test.
speculative positive Digital rural development and agricultural green total facto... AGTFP (policy-oriented inference)
The positive AGTFP gains from digital rural development are geographically heterogeneous and are concentrated in eastern provinces.
Regional heterogeneity analysis / sub-sample regressions across provinces showing larger estimated digitalization effects in eastern provinces compared with other regions.
medium-high positive Digital rural development and agricultural green total facto... AGTFP (regional subsample effects)
Digital infrastructure exhibits a threshold effect: its positive impact on AGTFP becomes stronger once digital infrastructure passes a critical level.
Panel threshold model applied to the provincial panel (2012–2022) that identifies a statistically significant threshold in the infrastructure sub-index where marginal effects increase above that value.
medium-high positive Digital rural development and agricultural green total facto... AGTFP (effect conditional on digital infrastructure level)
Authors recommend promoting a shift from single-link outsourcing (PAPM) toward whole-process integrated service provision (WAPM) as a policy implication of the findings.
Discussion/policy-implication section of the paper drawing on empirical results (TWFE and robustness checks) from the CLDS 2014–2018 analysis.
speculative positive Whole-Process Agricultural Production Chain Management and L... policy recommendation (expected productivity gains)
Unchecked shifts toward K_T-dominated production can amplify political risks (rising inequality, fiscal strain) that may fuel populism, protectionism, and demands for renegotiated social contracts.
Theoretical political‑economy discussion supported by historical analogies and model scenarios linking fiscal stress and distributional change to political-instability risks; qualitative case evidence.
speculative positive The Macroeconomic Transition of Technological Capital in the... political risk indicators (populist support, policy volatility) — discussed qual...
To make AI a driver of structural change, policy interventions must link AI investment to comprehensive energy subsidy reform and accelerated development of the new and renewable energy sector.
Policy recommendation based on integrated analysis showing that subsidy burdens and import dependence limit AI's macro impact; proposed linkage is derived from the study's scenario/logic assessment.
speculative positive (conditional) AI-Based Technological Transformation as a Driver for Develo... potential for AI to drive structural change conditional on subsidy reform and re...