Adoption Without Optimization: Text Mining and Business Intelligence Analysis on Culinary SME Digital Literacy - Dafitri, Rahma; Daryani, Dian; Sutisna, Entis (MALCOM: Indonesian Journal of Machine Learning and Computer Science, 2026)
Digital transformation has become critical for culinary Micro, Small, and Medium Enterprises (MSME), yet most remain digitally underperforming despite platform adoption, a gap this study terms "adoption without optimization." This study employs a sequential exploratory mixed-methods design to examine underlying barriers. Phase 1 applied NVivo-assisted thematic coding and word frequency analysis to transcripts from 10 informants. Phase 2 administered a 78-item structured questionnaire measuring five TOE-derived barrier categories (financial, technical, human resource, regulatory, and market barriers) and digital platform usage frequency across six platform types to 39 respondents, analyzed using descriptive statistics, cross-tabulation, and Pearson correlation in SPSS. Phase 3 conducted VOSviewer keyword co-occurrence analysis of 87 Scopus documents. Results reveal financial barriers rank highest (M=3.46), followed by human resource (M=3.31) and market competition barriers (M=3.25). Digital literacy is predominantly low, with 48.7% scoring in the low category (mean=14.87/30). Thematic analysis identified three novel phenomena: digital financial literacy as a distinct barrier dimension, a recursive time-constraint cycle, and the adoption-without-optimization gap, confirmed by a 79.4-percentage-point difference between platform adoption (100%) and meaningful digital performance (20.6%). Digital literacy significantly correlates with sales improvement (r=0.432, p=0.006). A five-pillar digital adaptation framework is proposed for SME digitalization policy.