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 →
2Distinct papers
7Unique collaborators
2/2Semantic Scholar citation coverage
Publication span: 2025–2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2276041158
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Liang Zhao (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 2 papers
- Adoption: 1 paper
- Innovation: 1 paper
Claim outcomes
- Governance And Regulation: 2 papers
- Innovation Output: 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 |
|---|---|---|---|
| Hype about AI widens corporate technology gaps at first but can trigger catch-up: modest 'AI washing' suppresses innovation, yet beyond a tipping point firms ramp R&D and narrow the gap; participatory learning eases the harm while high investor sentiment deepens it.openalex | Liang Zhao provider id |
2026-01-07 | 8 |
| Generative AI adoption is linked to more opportunistic ESG behavior: firms using generative models show stronger environmental scores but weaker social and governance performance, a pattern amplified across supply chains and by strict regulation and green investor pressure but mitigated by analyst scrutiny and better disclosures.openalex | Liang Zhao provider id |
2025-12-26 | 10 |
Citation observation summary
Semantic Scholar supplied counts for 2 of 2 papers in this view; 0 are missing. The observed paper counts sum to 18 cumulative citations. This is a coverage summary, not an author score or h-index.