An Intelligent Survey-Data Analysis of TikTok Fashion-Brand Content, Consumer Brand Responses, and Purchase-Related Intentions

TikTok supports fashion discovery and creator-mediated brand communication. This study examines associations among TikTok fashion-brand content involvement, brand awareness, brand engagement, and purchase-related intentions among Bulgarian users and illustrates a human-governed analytical architecture for structured and open survey data. An online survey yielded 295 responses; 268 eligible TikTok users comprised the sample. The architecture organizes primary measurement and structural analysis, secondary prediction and moderation, exploratory response-structure and text analysis, and descriptive multi-criteria synthesis within processing stages. Content involvement was positively associated with awareness (β = 0.683), engagement (β = 0.512), and purchase-related intentions (β = 0.283). Awareness was associated with engagement (β = 0.300) but had no incremental direct association with purchase-related intentions; engagement showed the strongest direct association (β = 0.496). The model explained 52.5% of purchase-related-intention variance, and PLS-PM prediction produced lower errors than the training-mean benchmark for all four outcome indicators. No demographic interaction received Holm-adjusted support. Illustrative multi-criteria synthesis ranked engagement first, involvement second, and awareness third under sample-derived criteria. Dictionary-assisted open-response categories concerning presentation, creators, reviews, demonstrations, music, and humor were manually checked against the responses. All reported relationships are cross-sectional associations and do not establish causal effects, intervention effectiveness, or marketing priorities. Concentrated sampling, broad working constructs, and internal validation require cautious interpretation and independent external replication.