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: 2025. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2320560087
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Prajkta Waditwar (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Innovation: 1 paper
- Org Design: 1 paper
- Productivity: 1 paper
Claim outcomes
- Firm Productivity: 1 paper
- Organizational Efficiency: 1 paper
- Innovation Output: 1 paper
- Adoption Rate: 1 paper
- Consumer Welfare: 1 paper
- Decision Quality: 1 paper
- Governance And Regulation: 1 paper
- Output Quality: 1 paper
- Task Allocation: 1 paper
- Task Completion Time: 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 |
|---|---|---|---|
| Agentic AI could overhaul Big Tech’s reverse supply chains—forecasting returns, automating inspections and routing, and recovering more value—yet its promise hinges on cross‑border data access, regulatory alignment and mitigation of privacy and bias risks.openalex | Prajkta Waditwar provider id |
2025-12-26 | 4 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 4 cumulative citations. This is a coverage summary, not an author score or h-index.