Relational age and leader–member exchange: mediating role of perceived trust
Bibliographic record
Abstract
Purpose Drawing on similarity-attraction hypothesis and generational gap literatures, this study aims to examine the impact of age difference in a leader–member dyad on leader–member exchange (LMX). The study hypothesized that relational age would impact the subordinates-reported LMX. However, given that leaders have structural power over subordinates and hence have mechanisms of interaction available to them, the age difference might not determine their perception of quality of LMX. The study also hypothesized that generation gap in values and beliefs leads to lack of trust, on the part of subordinates, which in turn might be the reason for poor quality of LMX. Design/methodology/approach A total of 200 leader–member dyads from five organizations in the National Capital Region of India were used in this study. Data were collected via separate structured questionnaires for leaders and members, which comprised of standard scales of LMX and perceived trust, and demographics. Findings Hypotheses received substantial support from the data with a few exceptions. Only the loyalty dimension of perceived trust mediates the relationship between relational age and member perception of LMX. Research limitations/implications Results have implications for relational age and LMX interventions. However, the results are to be viewed in the light of members’ perspective. While this is a common practice in LMX research, it would be interesting to explore leaders’ trust and psychological reactions as well, for additional insights into leadership practice. Originality/value Limited work has been done to explore the impact of relational age on LMX, that too mediated by trust. An attempt has been made in this study to do so via leader–member dyads.
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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 | 0.012 |
| 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 | 0.002 |
| 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".