Exploration of Influencing Factors on Mechanism of Chinese Users' Continuous Trust in Transactional Virtual Community
Bibliographic record
Abstract
This paper studies the formation path of user trust in transactional virtual community, explores the relationship between website perception, merchant reputation and user's personal trust tendency and user's initial trust, and introduces the characteristics of internet word of mouth and sense of virtual community into the analysis framework from initial trust to continuous trust. This research used a quantitative design by developing questionnaires to collect data through the snowball sampling approach from 390 Chinese users of the well-known transactional virtual communities as research population, such as JD community, Dianping community, Tmall community, Xiaomi Forum, Dangdang - online reading community, etc. The results show that users' website perception, merchant reputation and personal trust tendency these three factors have significant positive effects on the formation of initial trust of users. And merchant reputation played the most important role in influencing initial trust of users than any two factors, followed by user's trust tendency and website perception. Besides, initial trust of users was shown to has a significant positive effect on continuous trust of users in transactional virtual community. Furthermore, the characteristics of internet word of mouth was found have significant mediation effect on initial trust and continuous trust of users. More specifically, 41.7% of the influence of the initial trust on the continuous trust was due to the internet word-of-mouth. The findings of this study provide transactional virtual community marketers and managers’ valuable insights into developing effective marketing strategies to improve their community’s service.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".