Bibliographic record
Abstract
Purpose Homophily, a prominent phenomenon in social networking, profoundly shapes user behaviors on social media but has not been well studied in the livestream commerce context. This study aims to investigate its moderation role in leveraging the effects of key livestream commerce factors – perceived expertise of live streamers and perceived interaction during live streaming – on audience trust, a critical determinant of purchase intentions. Design/methodology/approach A survey was conducted among livestream shoppers on Taobao. A sample of 313 responses was analyzed. SPSS (version 29) was used for general statistical analysis. The partial least squares structural equation modeling approach with SmartPLS 4.1 software was used to assess the research model and hypotheses. Findings The results reveal noteworthy differential effects of homophily: it negatively moderates the expertise–trust association but positively moderates the interaction–trust relationship. When the audience perceives strong homophily with live streamers, their trust in these live streamers becomes increasingly contingent on the level of interaction, whereas the effect of perceived expertise diminishes. Originality/value The insights on the differential effects of homophily are novel to the literature. These findings extend theoretical understanding of the homophily effect and provide valuable guidance for live streamers, marketers and platforms seeking to reinforce audience trust and drive purchase intentions in livestream commerce.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".