An investigation into factors affecting individuals' gifting intention in live streaming: a streamer–content perspective
Bibliographic record
Abstract
Purpose Gifting is a typical monetization strategy for live streaming platforms to motivate providers’ live content contribution. However, research regarding the factors that affect individuals’ gifting intention is still at an infant stage. Therefore, this study aims to investigate the factors that affect individuals’ gifting intention during live streaming. Design/methodology/approach The authors build a model to uncover the factors that affect individuals’ gifting intention from a streamer–content perspective, and the hypotheses are largely validated by online survey data through structural equation model analysis. Findings Individuals’ perceived attractiveness of the streamers is significantly and positively associated with gifting intention for leisure-related live streaming, whereas individuals’ perceived similarity with the streamers is significantly and positively associated with gifting intention for leisure-related and non-leisure-related live streaming. For live content-related factors, the individuals’ perceived utilitarian value of content is significantly and positively associated with gifting intention for non-leisure-related live streaming, whereas the individuals’ perceived hedonic value is significantly positively associated with gifting intention for leisure-related live streaming. Perceived symbolic value is insignificantly associated with gifting intention for neither type of live streaming. Originality/value The research is an original work and significantly contributes to live streaming and PWYW literature, and the findings derived from this study can guide live streaming platforms to regulate individuals’ gifting intentions/behaviors better.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".