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
5Unique collaborators
1/1OpenAlex citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Openalex:
A5147362289
ORCID evidence
No valid ORCID is stored.
Observed aliases (2)
- Tejas Tabiyar (openalex, provider refresh)
- Tejas Tabiyar (openalex, source metadata)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Human Ai Collab: 1 paper
- Org Design: 1 paper
Claim outcomes
- Organizational Efficiency: 1 paper
- Output Quality: 1 paper
- Task Completion Time: 1 paper
- Decision Quality: 1 paper
- Developer Productivity: 1 paper
- Governance And Regulation: 1 paper
- Task Allocation: 1 paper
Papers in the OpenAlex view
Latest stored OpenAlex 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 |
|---|---|---|---|
| A literature review finds generative AI improves business decision-making only conditionally — task-model fit, organizational capabilities, and human oversight determine value; the authors call for integrated multi-level frameworks rather than single-level theories.openalex | Tejas Tabiyar provider id |
2026-08-17 | 0 |
Citation observation summary
OpenAlex 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.