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AI evidence extraction

Semantic Periphery Detection in Academic Keyword Space: An Embedding-Driven Framework for Automated Research Gap Identification

Paweł Karol Frankowski, Joanna Wiśniewska, Sebastian Matysik · Journal of the Association for Information Systems · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

9/10
Relevance
0/4
Quality (LMQS)
D
Evidence
0
Citations
0.00
FWCI

Methodology & findings

Study design

Case study with computational workflow: The method combines "transformer-based keyword clustering with multi-criteria peripherality scoring to detect semantic peripheries.

Primary method

design science

Main result

In a case study on gamification in marketing (14,302 records), the framework "identified 25 research gaps and showed greater semantic coherence than VOSviewer's co-occurrence map." Additionally, "validation with five LLM annotators resulted in nearly 2x increase in GAP classification and a 30.4% rise in novelty score."

Research paradigm

positivist/computational

Author conclusions

"The method can semi-automate gap identification, reducing processing time." The framework demonstrates efficacy in automating research gap detection through combination of transformer-based clustering and multi-criteria peripherality scoring.

Risk of bias

Single domain case study (gamification in marketing) may not generalize; LLM annotator bias (only 5 annotators, consistency not reported); No comparison baseline beyond VOSviewer; Validation methodology relies on subjective annotation

Data: not_statedCode: not_statedExtracted from: pdfAgreement 81%

Explore related topics

Systematic Review AutomationLiterature DiscoveryDesign Science

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