Transformational leadership and employee voice: a model of proactive motivation
Bibliographic record
Abstract
Purpose Drawing on the model of proactive motivation, the purpose of this paper is to examine how transformational leadership influences followers’ voice behavior through three proactive motivation states, namely, “reason to,” “can do” and “energized to.” It also examines the moderating role of followers’ proactive personality in the relationship between transformational leadership and employee voice. Design/methodology/approach The online survey was distributed through Qualtrics using a two-wave design. In total, 1,454 participants completed the survey at Time 1, of those 447 also completed the survey at Time 2. Findings Transformational leadership influences employee voice via followers’ promotion focus, role-breadth self-efficacy and affective commitment. Followers’ proactive personality attenuates the impact of transformational leadership on voice, supporting the substitute for leadership hypothesis. Research limitations/implications Self-reported data are the main limitation of the present study. Other limitations include treating employee voice as a unidimensional construct and oversimplifying the impact of positive affect on voice. Practical implications The present study suggests that training managers to demonstrate more transformational leadership behavior, enhancing employees’ proactive motivation and hiring proactive individuals are strategies to facilitate employee voice. Originality/value The present study contributes to a better understanding of employee voice from a proactive motivation perspective. It also demonstrates that followers’ proactive personality is important “boundary condition” to transformational leadership.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".