Cultural differences and applicants' procedural fairness perceptions
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine Chinese traditionality as a predictor of applicants' procedural fairness perceptions in selection, and both its direct and indirect relationship with applicants' recommending behavior, job performance and turnover intention three to four months post hire. Traditionality, as a moderator of perceptions‐outcomes relationships, is also tested. Design/methodology/approach Survey data of 218 supervisor‐subordinate dyads were collected from Mainland Chinese organizations. Data were gathered in two waves, with demographic and traditionality measures taken at time 1, and supervisory ratings of performance, recommending behavior and intention to turnover taken at time 2. Findings One component of traditionality alone (Respect for Authority) positively predicted applicants' procedural fairness perceptions. These perceptions, in turn, predicted recommending behavior (+), job performance (+) and turnover intentions (−). There were also direct relationships between Respect for Authority and both job performance (+) and turnover intention (−). The data failed to support the moderating effect of Chinese traditionality on the relationships between procedural fairness perceptions and outcome variables. Research limitations/implications Despite the methodological strengths of this study, the study is cross‐sectional in nature which weakens causal inferences regarding the relationships in the theoretical model. Moreover, the paper does not investigate empirically the concrete mechanisms from Chinese traditionality to fairness perceptions and from fairness perceptions to outcome variables, since its foci are the predicting and moderating roles of Chinese traditionality. Originality/value The paper's findings underscore the importance of Respect for Authority as the key and only component of Chinese traditionality that predicts procedural justice perceptions and worker outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".