Effects of Individualism-Collectivism on Chinese Organizational Citizenship Behavior: Focused on Mediating Effects of Trust
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
For an organization, its’ members’ individual value-orientations play an important role in affecting on their organizational behavior. As China has been known as a collectivist country, its’ cultural value-orientation impact all the Chinese people. However, a growing spirit of “Chinese-style” individualism appeared gradually. Even though some studies have display the relationships between individualism-collectivism and OCB, lacking of the empirical studies of demonstrating that relationships in China even use the Chinese OCB dimensions urged this study with considering the mediating roles of trust. Individual level data has been acquired by 382 Chinese labors. Results indicate a positive relationship between collectivism and Chinese OCB mediated by trust. This study strengthens the Chinese OCB dimension which is still a limited one. Also results provide the guidelines for HR managers when recruiting or making training programs, select collectivists or improve the individuals’ collectivism is very important. Results suggest that while individual behavior in the organizations, they should nurture their collective orientations as to exhibit a high level of OCB which will lead to work performance later. During this process, if they trust in their organizations or their supervisors, a higher level of OCB will be acquired and then switched into later job effectiveness.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".