Use of Social Media Platforms for Purchasing Fashion Items: A Comparison of US and Chinese Consumers
Bibliographic record
Abstract
This paper develops a conceptual connection between the Revised Technology Acceptance Model and Hofstede’s Cultural Dimensions in explicating the adoption by customers of a social media platform in the fashion industry in the context of the US and China. This study shows that the trustworthiness of Facebook is positively related to US customers’ intention to purchase fashion items and the trustworthiness of WeChat is positively related to Chinese customers’ intention to purchase fashion items. Also, US customers’ perceived usefulness is not positively related to the intention of using Facebook to buy fashion items. However, their Chinese counterparts had the opposite result. The findings enhance our understanding of the factors that influence customers’ adoption of social media platforms for purchasing fashion items and provides suggestions for marketing managers as to how they can utilize social media platforms to market fashion items. The paper concludes with a discussion of possible future research in this field.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".