Developing Cross-Cutting Article Categories for Emerging Open-Access Journals: A Mixed Qual-Computational Approach
DOI:
https://doi.org/10.63029/nxna2f46Keywords:
Open access, Open Journal Systems (OJS), journal taxonomy, article categories, discoverability, qualitative content analysis, metadata, Gioia methodology, topic modeling, non-negative matrix factorization (NMF)Abstract
Emerging open-access journals must signal scope clearly while improving discoverability in algorithmically mediated search and indexing environments. This study proposes a replicable, evidence-based protocol for deriving cross-cutting article categories from a journal’s own corpus and operationalizing them in Open Journal Systems (OJS). Using Management Research Quarterly (MRQ) as an instrumental case, we combine Gioia-style first- and second-order coding with thematic analysis to induce candidate categories and inclusion/exclusion rules. We then triangulate the interpretive structure with TF–IDF (1–3-grams) and Non-Negative Matrix Factorization (NMF), iteratively reconciling discrepancies between human-coded themes and machine-surfaced term clusters. The process yields four deployable categories—Leadership & Organizational Change, Knowledge & Learning, Innovation & Digital Transformation, and Sustainability & ESG—supporting submission fit, reviewer assignment, and metadata quality.
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