Factors Affecting Chinese Consumers’ Impulse Buying Decision of Live Streaming E-Commerce
Bibliographic record
Abstract
From the perspective of consumers, this research studied four factors including price promotion, time pressure, interpersonal interaction and visual appeal influencing the impulse buying decision of live streaming e-commerce consumers in China. This research used a quantitative design by developing questionnaires to collect data through the convenience sampling approach from 477 Chinese users who has live streaming shopping experience in social commerce platform Taobao.com. within the past 1 year in Kunming, City. Structural Equation Model (SEM) was used to analyze the data and the hypotheses accordingly. The results confirmed that price promotion, time pressure (promotional time limit, perceived opportunity cost), interpersonal interaction (consumer-streamer interaction, consumer-consumer interaction) and visual appeal these six factors have a significantly positive effect on consumer impulse buying decision. Meanwhile, perceived risk was found negatively related to consumer impulse buying decision. Besides, price promotion and visual appeal played the most important role in influencing consumer impulse buying decision of live streaming than any other factors. Additionally, the results also show that the promotion, the perception of opportunity cost, the interpersonal interaction (the interaction between consumers and streamers, the interaction between consumers), and the visual appeal all have a significant negative impact on consumers' perceived risk. However, for time limit of time pressure, our research hypothesis that promotion time limit has a significant negative effect on consumers' perceived risk has not been verified. Finally, perceived risk was found plays a mediating role in the relationship between price promotion, perceived opportunity cost, interpersonal interaction, visual appeal and impulse buying decision. However, it did not play a mediating role in the effect of promotion time limit on impulse buying decision. The findings suggest that managers and merchants of live streaming e-commerce should make a reasonable price promotion plan and provide good visual experience for consumers, at the meantime, strengthen interpersonal interaction and try to reduce the purchase risk of consumers.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".