Adoption Without Optimization: Text Mining and Business Intelligence Analysis on Culinary SME Digital Literacy

Abstrak

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.