Digital transformation in MSMEs: The role of social media marketing and brand resonance value in increasing the repurchase intention of digital food platform customers
Bibliographic record
Abstract
Advancements in social media marketing have fundamentally transformed how companies engage with consumers. However, its impact on customer repurchase intention, particularly through emotional brand connections, remains insufficiently explored. This study investigates the impact of social media marketing on repurchase intention by examining the mediating role of Brand Resonance Value. A quantitative method was used by distributing surveys to 230 users of digital food platforms in Makassar. Data were analyzed using Structural Equation Modeling (SEM) to assess the relationships among social media marketing, Brand Resonance Value, and repurchase intention. The findings indicate that social media marketing significantly enhances both Brand Resonance Value and customers' intention to repurchase. Furthermore, Brand Resonance Value serves as a critical mediating factor that links social media marketing efforts to customer loyalty. These results underscore the importance of businesses designing social media strategies that not only capture consumer attention but also foster deep emotional bonds with their brands. Strengthening Brand Resonance Value through digital engagement can thus play a pivotal role in sustaining long-term customer relationships and driving repeat purchases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".