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Literature Search Query in Academic Databases: Artificial Intelligence Think Tank Guideline for Literature Reviews

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  • Shahryar Sorooshian

    (Department of Business Administration, University of Gothenburg, SE-405 30 Gothenburg, Sweden
    Faculty of Engineering and Sustainable Development, University of Gävle, SE-801 76 Gävle, Sweden)

Abstract

Literature reviews are essential for synthesizing existing knowledge, mapping research domains, identifying intellectual structures, and highlighting research gaps within a field. However, many literature reviews are incomplete because database search strategies are not adequately specified or validated. Search strategies are frequently underreported and undermotivated across the systematic review literature and bibliometrics, while query formulation remains time-consuming, error-prone, and particularly difficult in interdisciplinary or rapidly evolving topics. This article fills that void by developing a guideline for designing a professional topic query in existing academic databases and emphasizing search design as the front-end validity problem in bibliometric research. The article uses the Artificial Intelligence Think Tank framework as a methodological engine and applies it to bibliometric retrieval engineering via structured interaction with generative AI systems and human experts. The paper assists scholars performing bibliometric studies, scientometric analyses, systematic literature reviews, scoping reviews, and hybrid evidence-synthesis projects.

Suggested Citation

  • Shahryar Sorooshian, 2026. "Literature Search Query in Academic Databases: Artificial Intelligence Think Tank Guideline for Literature Reviews," Publications, MDPI, vol. 14(2), pages 1-6, June.
    • Handle: RePEc:gam:jpubli:v:14:y:2026:i:2:p:36-:d:1956855
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