Leader‐member exchange and subordinate outcomes: test of a mediation model
Bibliographic record
Abstract
Purpose Following Hackettet al.'s treatment of the reasonably established role of leader‐member exchange (LMX) in employee outcomes, this paper seeks to examine the mechanism which operates between LMX and various work outcomes in an attempt to bridge this gap in the literature. Design/methodology/approach The hypotheses were tested using data from 306 working software professionals in India. Data were collected through a structured questionnaire that contained standardized scales of LMX (perceived contribution and affect), satisfaction, commitment, and citizenship behavior (loyalty). Findings A confirmatory factor analysis (CFA) was done to examine the dimensionality of the study variables. Results provide support to all the hypotheses. Research limitations/implications Data were collected from a single source, direction of causality is assumed (not tested) and all the data were collected through self‐reports. Some measures are taken to control them. Practical implications The findings have implications for LMX enhancement interventions. Focusing on enhancement of the LMX‐Contribution dimension is more likely to improve the organization level commitment and citizenship behavior, whereas LMX‐Affect is likely to result in more affective reactions like satisfaction with the supervisor and the job. Originality/value The study adds to the literature by testing the proposed model in the Indian context, thus providing some empirical cross‐cultural validity to LMX‐subordinate‐related work outcomes relationships.
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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.001 |
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".