Transformational and passive avoidant leadership as determinants of absenteeism
Bibliographic record
Abstract
Purpose Does leadership style affect absenteeism in a company? The purpose of this paper is to contrast the effects of two leadership styles – transformational and passive avoidant – on absenteeism, both legitimate and illegitimate, as mediated by job satisfaction. Design/methodology/approach A self‐report questionnaire was completed by a sample of 120 employees of a national mail delivery company. Hierarchical regressions were used to analyze the data. Findings It was found that transformational leadership decreases illegitimate absenteeism, while passive avoidant leadership increases it. In regard to legitimate absenteeism, transformational leadership is shown to have no effect, while passive avoidant leadership is shown to be negatively related to it. Together, the findings regarding passive avoidant leaders suggest their subordinates tend to come to work when ill (presenteeism), but stay away from work when well (illegitimate absenteeism). Practical implications For managers trying to reduce the costs of absenteeism, this suggests that leadership style can make a difference. Managers who give subordinates very little attention, or attention only when they have done something wrong – the passive avoidant style – are likely to experience the higher costs of both absenteeism and presenteeism. Adopting the transformational style may help to reduce these costs. Originality/value The paper helps to extend the current work on leadership; it examines the passive avoidant style, which remains understudied to date; and it enriches our understanding of the relationship between leadership style and absenteeism as an outcome variable by moving beyond a uni‐dimensional conceptualization of absenteeism. Finally, it serves as a basis for future research by providing evidence for a somewhat counter‐intuitive finding that, under passive avoidant leaders, workers appear to come to work when sick, but stay away from work when well.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".