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:
2385755654
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Huawei Ding (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Productivity: 1 paper
Claim outcomes
- Firm Revenue: 1 paper
- Decision Quality: 1 paper
- Market Structure: 1 paper
- Organizational Efficiency: 1 paper
- Consumer Welfare: 1 paper
- Firm Productivity: 1 paper
- Governance And Regulation: 1 paper
- Innovation Output: 1 paper
- Research Productivity: 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 |
|---|---|---|---|
| A coordinated suite of data-driven marketing tools can sharpen demand forecasts, personalise recommendations and steer retention budgets toward high-value customers, boosting marketing return on investment. Yet the approach depends on high-quality, fresh data and heavy engineering, and it intensifies privacy risks and potential for market-power concentration unless regulated or audited.openalex | Huawei Ding provider id |
2025-12-12 | 0 |
Citation observation summary
Semantic Scholar 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.