The Impact of Livestream Viewing Frequency on Users' Impulse Buying Behavior
Bibliographic record
Abstract
This study examines the affective dynamics of impulsive purchases through livestream e-commerce, an emerging media format that blends real-time interactivity with persuasive marketing. While emotional drivers of consumer behavior have been widely recognized, the interplay between emotional arousal and user attitudes remain insufficiently understood in livestreaming environments. Drawing on a cross-sectional survey of 258 participants, this research analyzes how livestream viewing frequency influences impulse buying behavior, with positive and negative emotional arousal as mediators and user attitude as a moderator. The results show that viewing frequency positively predicts impulse buying both directly and through positive emotional arousal, while user attitude significantly moderates this effect. Findings from this study provide a practical basis for optimizing the user experience of the live e-commerce platform. Merchants can stimulate users positive emotions by enhancing interactive design to promote transformation. These insights advance a media-centric view of emotional digital engagement and draw attention to the design implications of interactive media environments.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".