Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the influence of grit, which is the tendency to pursue long-term goals with perseverance and continuing passion, on leaders’ self-reported behavior in terms of role modeling and innovating, as well as inspiring, empowering and supporting followers. Design/methodology/approach Data were collected from an international sample of 3,702 leaders in work and non-work contexts. They reported their level of grit and how frequently they engaged in five leadership practices. Moderation analysis was used to test the influence of grit on leadership behaviors across contexts. Findings High grit leaders reported more frequent role modeling and innovating behaviors, but less inspiring behavior. Grit’s effect on empowering behaviors depended on the context; grit caused leaders to empower followers more in non-work contexts, but not in work-related ones. Research limitations/implications That grit is an important predictor of leadership behavior yields both practical and theoretical implications. For practice, the results suggest that grit is a desirable trait in managers, corresponding with their greater use of various leadership behaviors. For theory, the results suggest that part of the effect of traits in leadership arises from influencing the frequency with which leaders engage in particular behaviors. Originality/value This is the first study to examine grit’s role in leadership, and it has practical and theoretical implications. For practice, the results suggest that grit is a desirable trait in leaders, but one which requires unique supports from the leader’s environment. For theory, the results begin to fill an important gap. It is well-established that personality influences leadership outcomes, but it remains uncertain how and when. The current study suggests how, since traits influence the frequency with which leaders engage in particular behaviors, and begins to define when, highlighting differences between work and non-work contexts.
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 | 0.007 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".