To Gift or Not: Understanding Gifting Behavior on Live Streaming Platforms from the Perspective of Social Influence and Herding
Bibliographic record
Abstract
Live streaming has been increasingly popular worldwide, with gift-giving emerging as a pivotal revenue stream for many streamers. While prior research has delved into the influence of streamers’ emotions and viewer-streamer interaction on viewers’ gift-giving behaviors, we suggest that peer viewers also play an essential role. In line with the principles of social influence and herding theory, the behaviors of peer viewers and the size of the viewing group are integral factors shaping individual behaviors. Hence, in the context of lives streaming, we focus on examining the impact of peer viewers’ gift-giving behaviors and the audience size on the gift-giving behaviors of individual viewers, respectively. Additionally, we examine the moderating role of viewers’ identities. We collected data from a popular live streaming platform in China and employed a panel regression model based on a sample of 651,678 viewers. This study contributes to the gift-giving literature by revealing the influence of peer viewers on focal viewers’ likelihood of gifting, gifting frequency and gifting value, and the moderating effect of viewers’ identities. Overall, these results have significant implications for both the theoretical understanding of social influence and herding in online setting, as well as the practical implications for future live streaming management. Future research could delve deeper into understanding the impact of various types of live streaming content and cultural differences on individual’s gift-giving behavior.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".