The Role of Emotions in Leadership
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Emotions play an important role in the field of leadership. The intelligent use of emotions may be beneficial for leaders in achieving success. In fact, good use of emotions has been seen as a prerequisite for effective leadership. Leaders with high emotional competencies are perceived as more efficient by their followers. Besides, the use of emotional intelligence (EI) was found to be linked with transformational leadership. Furthermore, leaders’ EI was found to be affecting followers’ and organizational outcomes. Thus, we provide a selective review on the relationship between emotions and leadership. Our findings showed that EI is essential for successful leadership as it provides many benefits to leaders. With the use of EI, leaders could manage the team intelligently, handle difficult situations properly and reduce their stress effectively. Hence, we identified that it is necessary for researchers to further study the links between EI and leadership to gain the most benefits from it. We conclude with a discussion on the theoretical and practical implication, as well as some recommendation for future research with the promising use of EI in leadership development.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it