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
3Unique collaborators
1/1OpenAlex citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Openalex:
A5007311995
ORCID evidence
No valid ORCID is stored.
Observed aliases (2)
- Daniela María Terán Muñoz (openalex, provider refresh)
- Daniela María Terán Muñoz (openalex, source metadata)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Innovation: 1 paper
- Org Design: 1 paper
- Productivity: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Firm Productivity: 1 paper
- Adoption Rate: 1 paper
- Firm Revenue: 1 paper
- Innovation Output: 1 paper
- Organizational Efficiency: 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 |
|---|---|---|---|
| AI tools can boost startup performance, but capability gaps decide the winners: a critical review finds technology alone is insufficient and offers MEAINE, a five-part roadmap emphasizing diagnosis, workforce training, data quality, leadership and continuous evaluation.openalex | Daniela María Terán Muñoz provider id |
2026-09-03 | 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.