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
0Unique collaborators
1/1Semantic Scholar citation coverage
Publication dates unavailable. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2422449841
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Oyakhire Victor Alaba (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Innovation: 1 paper
Claim outcomes
- Other: 1 paper
- Adoption Rate: 1 paper
- Innovation Output: 1 paper
- Firm Revenue: 1 paper
- Organizational Efficiency: 1 paper
- Research Productivity: 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 |
|---|---|---|---|
| UK firms that adopt generative AI report better business performance, largely because AI fuels product innovation rather than just efficiency gains; adoption is concentrated where technological competence and top-management backing exist, and is dampened by regulatory uncertainty.semantic_scholar | Oyakhire Victor Alaba provider id |
Fetched 2026-03-15 | 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.