Investigating online purchase intention of Gen Z on TikTok live stream shopping
Bibliographic record
Abstract
The aim of this research is to investigate the potential determinants of online purchase intention on livestream shopping via TikTok by application of the Information System (IS) success model and the Uses and Gratification Theory (UGT). These theories have strongly proved to be effective in predicting human behavior from a social psychology standpoint, especially in explaining the motivation of consumers to have purchase intentions in online shopping contexts. Data were collected from 203 online and offline survey participants of Gen Z in the North of Vietnam. Regression techniques through SPSS 20 software were used to test the study hypotheses. The findings reveal that system quality, information quality, streamer attractiveness, para-social interaction, and price promotion positively influence online purchase intentions. Which price promotion has the most significant and positive impact on the online purchase intention of Gen Z consumers on TikTok’s livestream shopping. These results provide a more comprehensive understanding of online purchase intentions. The findings and conclusion address notable theoretical and practical implications.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".