Individual Differences and Leader-Subordinate Relationships: Examining the Relations between Individual Attachment, Emotion Regulation, Leader-Member Exchange, and Employee Behaviour
Bibliographic record
Abstract
There is scant research into the influence of leader or follower personality on the development of leader-member exchange quality (LMX; Dienesch & Liden, 1986; Gerstner & Day, 1997; Liden, Sparrowe, & Wayne, 1997, Harris, Harris, & Eplion, 2007). Furthermore, where such research has been undertaken, it has focused mostly on broad-trait based personality factors (such as the Big-Five; Phillips & Bedeian, 1994; Erdogan, Liden, & Wayne, 2006). There are strong theoretical grounds for expecting that more narrow and specific relationship-based personality assessments will provide superior prediction of LMX quality, and richer insights into the LMX development process. The current study draws on attachment theory (Bowlby, 1969/1982, 1973, 1980; Mikulincer & Shaver, 2007) to examine how individuals' dispositions relate to their LMX quality and two relationship-based aspects of work performance (organizational citizenship behaviour [OCB] and counterproductive work behaviour [CWB]). The moderating influence of emotion regulation and affectivity on these relationships was also explored. Data were collected from managers, front-line staff, and their co-workers at two Canadian hospitals. Emotion regulation (Gross, 1998a; Gross & John, 2003) was found to moderate the association between attachment and LMX. Additionally, in some instances leaders' trait affectivity interacted with emotion regulation to influence the impact of leader attachment on LMX quality. Theoretical and applied implications of these findings 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 | 0.002 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".