Impact of Social Support and Reciprocity on Consumer Well-Being in Virtual Medical Communities
Bibliographic record
Abstract
This study pursues a better understanding of consumer well-being in online medical cosmetics communities by investigating the antecedents of well-being and moderating influence of community norms. A total valid sample of 484 respondents was collected from 2 popular medical cosmetics discussion platforms. A partial least squares analysis was used to validate the research model. Emotional support, informational support, and sense of belonging were important predictors of well-being. Among these 3 antecedents, emotional support showed the strongest influence on consumer well-being. Sense of belonging was significantly and positively influenced by emotional support and reciprocity, and hence plays a pivotal role in mediating the effects of emotional support and reciprocity on well-being. However, informational support does not appear to significantly influence sense of belonging. Members' compliance with community norms positively moderates the influence of sense of belonging on well-being. This study contributes to the literature on realizing members' social behaviors specifically in virtual medical cosmetics communities and provides insights for the management of online communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.017 |
| 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 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".