The effects of user generated content and traditional reference groups on purchase intentions of young consumers: A comparative study on electronic products
Bibliographic record
Abstract
This paper investigates the impact of post-purchase user generated content (UGC) and traditional reference groups on the purchase intentions for electronic products (e-products) among young consumers in Jordan. To achieve this, a descriptive methodology was adapted, with a quantitative approach and survey strategy utilizing a five-point Likert scale questionnaire distributed to 450 university and college students in Jordan. 400 filtered and screened copies underwent statistical analyses. SPSS version 21 was utilized to describe and analyze the data. The results revealed a strong impact of post-purchase UGC on purchase intentions of e-products among young consumers. The results also revealed that traditional reference groups have a lower significant impact on the purchase intentions of young consumers, indicating that young consumers rely on online communities more than they rely on family, friends, colleagues, and other social organizations. The findings are discussed with a view to their implications, with recommendations for future research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".