Migrants’ knowledge sharing in ethnic mobile communities: Focusing on the roles of intra-ethnic trust, immigration status and community commitment
Bibliographic record
Abstract
Ethnic online/mobile communities (EOMCs) refer to weakly tied online/mobile collectives formed by the same ethnic members in a foreign country to share knowledge via online/mobile networks. This study proposes two ethnic factors (intra-ethnic trust [IET] and immigration status [IS]) leading to knowledge-seeking and contribution (KS and KC) intentions and behaviours through the formation of affective and continuity commitment for the EOMCs. Statistical analysis is conducted with survey data from the members of WeChat communities formed by Chinese migrants in a metropolitan city in Nova Scotia, Canada. The results suggest that IET is positively associated with affective and continuity commitments, while IS, measured with a scale from study permit (1) to Canadian citizen (6), is negatively associated with affective and continuity commitments and that affective and continuity commitments have different effects on knowledge-seeking and contribution intentions in EMCs. Other cognitive (the usefulness of knowledge) and personal (reputation and enjoy-helping) factors are also tested for their impacts on knowledge-seeking and contribution intentions, which lead to knowledge-seeking and contribution behaviours. The findings of this study will provide the administrators and users of EOMCs with insightful implications for their sustainability and contribute to the literature on immigration studies, knowledge management and online/mobile communities.
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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 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".