Leader-Member Exchange in Flux: Exploring Longitudinal Profiles in the Era of Hybrid Work
Bibliographic record
Abstract
Research on leader-member exchange (LMX) has been central to the leadership literature, emphasizing high-quality relationships between leaders and followers as desirable for a range of outcomes. While LMX literature traditionally views the quality of LMX relationships as stable over time, this perspective is increasingly being challenged. Accordingly, this study aims to (1) examine the evolution of LMX relationships over a 6-month period among established dyads and identify profiles of LMX trajectories; (2) assess the impact of the current hybrid work context on the LMX trajectories by focusing on teleworking intensity, frequency and amount of leader-member interactions, monitoring practices (observational or interactional), and leader response expectations (e.g., pressure to remain available and respond rapidly to solicitations outside work hours); and (3) analyze the implications of these trajectories for established LMX outcomes (i.e., emotional exhaustion, performance, and turnover intentions). Using three-wave longitudinal data from 769 workers across various industries, we identified five distinct profiles over a period of six months (i.e., High and Increasing; Moderate and Decreasing; Moderately Low and Decreasing; Moderate and Steady; Very Low and Increasing). These profiles had differing implications for the studied outcomes, further supporting their validity. The results demonstrated that teleworking intensity and frequency and amount of leader-member interactions did not impact profile membership. However, monitoring practices played a critical role: observational monitoring was linked to the least desirable profiles, while interactional monitoring was associated with the most desirable ones. Moreover, all three managerial practices examined in this study impacted LMX trajectories, regardless of profile membership. The implications for LMX theory and managerial practices in the hybrid work context are discussed.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".