It takes two to tango: The interactive effect of authentic leadership and organizational identification on employee silence intentions
Bibliographic record
Abstract
Organizational silence is a state of affairs in which employees refrain from voicing problematic issues at work. It often results from the dilemma between considering the short-term interests of the leader, who might perceive voicing problems as disloyal, and the long-term interests of the organization, which might suffer severe costs because of silence. In this article we propose a theoretical model that bridges authentic leadership and organizational identification to test their joint effect on organizational silence responses (exit, loyalty and neglect). Based on previous work, we hypothesized that authentic leadership is positively related to employees’ loyalty (a passive yet constructive response). However, in dilemmatic situations this effect should be buffered by a high organizational identification (as a result of conflicting loyalties). Similarly, in such situations, we predicted that the influence of authentic leadership on employees’ destructive responses may be counter-productive if not matched with a high organizational identification. We tested our proposed model with an online vignette study that involved 458 employees from German-speaking countries from diverse work sectors. We used a realistic scenario comprising a dilemmatic situation, in which a decision between voice and silence had to be made. Our results partially support the hypotheses. Implications for management and future research directions 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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".