Electronic word-of-mouth (eWOM) on WeChat: examining the influence of sense of belonging, need for self-enhancement, and consumer engagement on Chinese travellers’ eWOM
Bibliographic record
Abstract
‘Friends’ circles’ on WeChat have helped make eWOM more easily accessible and influential than ever. Drawing from the social identity theory, literature on consumer engagement and eWOM, this study presents the first research that examines the influence of two personality traits, sense of belonging and need for self-enhancement, on consumer engagement and in turn leads to eWOM intention. The results suggest that the need for self-enhancement positively influences Chinese travellers’ engagement with WeChat. In addition, a partial positive relationship between consumer engagement and eWOM intention was identified: only dedication towards WeChat is directly related to travellers’ intention to engage in eWOM on WeChat. Dedication was found to mediate the influence of need for self-enhancement on eWOM intentions. Sense of belonging, however, does not have a significant impact on consumer engagement. These mixed results demonstrate changing cultural values of contemporary Chinese society. Theoretical and practical implications are discussed.
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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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".