Cross-level effects of union practices on extra-role behaviors: the mediating role of industrial relations climate, union commitment and union instrumentality
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the relationship between union practices and two types of employees’ extra-role behaviors, namely, union citizenship behavior (UCB) and employee voice (EV), and the mechanisms that mediate this relationship. Design/methodology/approach Using matched data from 46 union leaders and 279 union members of 33 workplaces in China, this study utilized multilevel structural equation modeling to test the cross-level influences of union practices on employees’ extra-role behaviors and the mediation effects. Findings The results show that (1) union practices have a positive impact on employees’ UCB and EV, and (2) union practices increase UCB and EV through the improvement of industrial relations (IR) climate at the workplace level, as well as union commitment (UC) and union instrumentality (UI) at the individual level. Research limitations/implications Although the authors collected data from multi-sources (i.e. union leaders and members), the cross-sectional data of this study limited the ability to make casual inferences. Originality/value This study contributes to the literature by providing theoretical explanation and empirical evidence to illustrate the role of union practices in increasing the extra-role behaviors of employees (i.e. UCB and EV). This is of particular importance in elaborating the effectiveness of enterprise unions under the recent reforms in China. In addition, the authors also unpacked the antecedents of extra-role behaviors in the union context by investigating how IR climate, UC and UI mediate the relationship between union practices and extra-role behaviors of employees.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".