Leader‐member exchange and attitudinal outcomes: role of procedural justice climate
Bibliographic record
Abstract
Purpose Building upon the “fair exchange in leadership” notion (Hollander; Scandura), the purpose of this paper was to hypothesize the mediating impact of procedural justice climate on the relationship between leader‐member exchange (LMX) and two attitudinal outcomes: organizational commitment and turnover intentions. Design/methodology/approach A total of 224 managers voluntarily participated in the study. They represented nine multinational companies located in northern Malaysia. Data were collected by means of a structured questionnaire containing widely used scales to measure LMX (contribution, affect, loyalty, and professional respect), procedural justice climate, organizational commitment (affective, normative, and continuance), and turnover intentions. After establishing the goodness of measures, hypothesized relationships were examined using Structural Equation Modeling (SEM). While commitment and LMX were, respectively, conceptualized as 3‐ and 4‐dimensional constructs, procedural justice climate and turnover intentions were each treated as unidimensional constructs. Findings Whereas hypotheses for direct effects received low‐to‐moderate support, the mediation hypothesis received substantial support only in the case of professional respect dimension of LMX. Research limitations/implications The study has obvious implications for leader‐member exchange and procedural justice in organizations. Though findings are in line with those in the past research, they should be viewed with caution – given the nature of cross‐sectional data. Originality/value Management needs to pay attention to the quality of LMX, as today's employees look for mutual trust.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".