Direction, evidence grade, and study type are AI-generated labels (gpt-5-mini), not human-verified. Syntheses are LLM-written. "Tensions" are machine-detected candidates, not confirmed contradictions. A research-acceleration tool, not peer review.
How this is built →
1Distinct papers
3Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
70325554
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- W. Adawiyah (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Human Ai Collab: 1 paper
- Innovation: 1 paper
- Labor Markets: 1 paper
- Productivity: 1 paper
Claim outcomes
- Firm Productivity: 1 paper
- Employment: 1 paper
- Innovation Output: 1 paper
- Organizational Efficiency: 1 paper
Papers in the Semantic Scholar view
Latest stored Semantic Scholar author observations only. Citation counts below are from the same provider and are not combined with other services.
Scroll the table horizontally to see every column.
| Paper | Author evidence | Date | Provider citations |
|---|---|---|---|
| AI helps small firms but rarely does the heavy lifting alone: a review of 29 studies shows performance gains depend on data governance, complementary capabilities and financing, with productivity and employment effects varying sharply by firm type and context.openalex | W. Adawiyah provider id |
2026-08-10 | 0 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 0 cumulative citations. This is a coverage summary, not an author score or h-index.