An Investigation of Corporate Leaders’ Experiences in using Emotional Intelligence while Leading Teams in Onsite and Virtual Environments
Bibliographic record
Abstract
The benefits of emotional intelligence (EI) in the workplace, particularly for those in leadership roles, are well documented and researched, yet there is ample evidence to suggest that EI can be challenging to develop. Furthermore, due to the Covid-19 pandemic, there has been a rise in the virtual work environment, requiring many employees to adapt. As such, the purpose of this qualitative study was to learn about the experiences of corporate leaders in understanding and using EI while they led teams in onsite and virtual environments and compared their experiences in these mediums. The experiences of these leaders provided key insights that are valuable to both EI scholars and practitioners. The five themes that emerged from the data analysis were as follows: a) the virtual environment presented greater challenges in using empathy, social skills and motivation, b) leaders believed EI was important, emphasizing empathy, c) leaders’ motivation to use EI was highly dependent on organizational culture and support from senior leaders, d) the virtual environment was an advantage for using self-regulation when compared to onsite and e) strategies used primarily focused on empathy and social skills. Detailed findings pertaining to each theme are provided along with discussions and implications. Recommendations are also provided for those interested in the advancement of EI within corporate organizations – for onsite, virtual and hybrid work environments.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".