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 span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2358087393
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Shamsunnahar Chadni (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Human Ai Collab: 1 paper
- Org Design: 1 paper
- Productivity: 1 paper
Claim outcomes
- Organizational Efficiency: 1 paper
- Other: 1 paper
- Decision Quality: 1 paper
- Adoption Rate: 1 paper
- Governance And Regulation: 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 |
|---|---|---|---|
| Enterprise ML forecasting and valuation tools are associated with substantial perceived gains: forecasting and valuation jointly explain 41% of variation in reported decision quality, and decision quality (plus some direct effects) explains 53% of variation in perceived capital allocation efficiency. Trust, adoption readiness and alignment matter — misaligned forecasts and valuations are linked to notably lower allocation efficiency.openalex | Shamsunnahar Chadni provider id |
2026-01-01 | 2 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 2 cumulative citations. This is a coverage summary, not an author score or h-index.