The effect of social media and electronic word of mouth on trust and loyalty: Evidence from generation Z in coffee industry
Bibliographic record
Abstract
Coffee shops have now evolved into an integral part of the modern lifestyle embraced by today's youth, especially by Generation Z. This study aims to understand the role of technology, specifically through Social Media Usage (SMU) and Electronic Word-Of Mouth (eWOM), and how trust as a mediator affects purchase intention and subsequently impacts customer loyalty. This study involved 282 respondents from diverse backgrounds. The sampling technique employed both snowballing and random sampling methods. For analysis, the Structural Equation Modeling (SEM) technique was utilized. In the study, several relationships were tested for their significance. The relationship between SMU and Trust was found to be significant. However, the relationship between eWOM and Trust was not significant. Trust significantly influenced Purchase Intention and Customer Loyalty. The direct relationship between Purchase Intention and Customer Loyalty was not significant. Moreover, the mediated relationships of SMU through Trust to Purchase Intention were significant, while the mediated relationship of eWOM through Trust to Purchase Intention was found to be non-significant. This investigation illuminates the distinct confluence of age-old values and contemporary digital interactions in sculpting consumer behavior within Indonesia. It accentuates the imperative for coffee shop enterprises to discern and synergize with these trends to guarantee enduring prosperity.
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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 | 0.002 | 0.006 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".