Social media marketing elements, purchase intentions, and cultural moderators in fast fashion: Evidence from Jordan, Morocco, and Spain
Bibliographic record
Abstract
The fast fashion business is becoming more reliant on social media marketing (SMM) as SMM enables larger data collection and communication between the brand and its consumers. This study investigates the impact of SMM aspects such as customization, entertainment, interactivity, trendiness, and eWOM on fast fashion’s online and offline purchase intentions (PI). Similarly, it investigates the moderating influence of culture on the factors mentioned above and the relationship’s utilitarian and hedonic reasons. Additionally, 360 responses were obtained from three countries, Morocco, Jordan, and Spain, using an online questionnaire. The findings revealed that customization, amusement, and trendiness influence offline and online PI favorably. Culture was also shown to have a moderating influence on the link between SMM components and PI. Motivations were also discovered to be a mediator between eWOM, trendiness, customization, and PI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".