Impact of Emotional Support, Informational Support, and Norms of Reciprocity on Trust Toward the Medical Aesthetic Community: The Moderating Effect of Core Self-Evaluations
Bibliographic record
Abstract
BACKGROUND: The consumption of medical aesthetic services has become popular in recent years. Many people have purchased medical aesthetic services and treatments in pursuit of self-beauty. When members of online medical aesthetic communities actively participate in discussions and encourage and support one another, there is an increase in community commitment, trust toward each other, and trust toward the community, ultimately promoting social sharing in an environment of positive feedback. OBJECTIVE: This study aimed to explore via the theory of social support-grounded in a deeper social capabilities framework developed by Khan following the Nobel laureate Amartya Sen's groundbreaking work-whether emotional support, informational support, and norms of reciprocity in online communities impact group members in terms of creating trust toward other members. This enhances trust toward the community and generates a sense of community commitment, ultimately impacting social buying intention and social sharing intention. METHODS: This study used IBM SPSS and AMOS to analyze data. Data were collected through online questionnaires in online medical aesthetic community forums, thereby producing samples that were both representative and accurate. To understand whether core self-evaluation (CSE) is a moderator in the relationship between social sharing intention and social buying intention, this study averaged the point of CSEs in the sample after statistical analysis, dividing the sample into 2 groups. RESULTS: The results showed that emotional support and norms of reciprocity positively impact trust toward members, and trust toward members positively impact trust toward the community. This generates trust transfer, which positively impacts social buying intention and social sharing intention. At the same time, CSE is a moderator variable between trust toward the community and social buying intention, but CSE is not a moderator variable between trust toward the community and social sharing intention. CONCLUSIONS: This study revealed that when members of online medical aesthetic communities actively participate in discussions and encourage and support one another, community commitment, trust toward each other, and trust toward the community increases, ultimately promoting social sharing and buying intentions.
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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".