Leadership in crisis: emotional intelligence and effectiveness at Luxe Salon and TechWerse Enterprises
Bibliographic record
Abstract
Leadership plays an important role in shaping organisational success, whether in small businesses or large enterprises. Effective leaders improve collaboration, productivity, and loyalty, but leadership development is rarely instantaneous or without challenges. This study examines two case studies that highlight the complexities of leadership. The first follows a well-known hairdresser who acquires a salon and must navigate the transition from stylist to business owner while managing existing employees and implementing new business strategies. The second case explores the leadership struggles of Sarah Matthews, the newly appointed CEO of TechWerse Enterprises, whose lack of emotional intelligence (EI) leads to declining employee morale and disengagement. The study further discusses an intervention program involving 360-degree feedback, executive coaching, and empathy training to enhance Sarah's leadership capabilities. These two cases emphasise the importance of EI in leadership development and the need for targeted strategies to improve leader effectiveness and organisational performance. The two cases study provide a teaching guide to show the differences in context and the similarity of leadership struggles. The concepts of EI, leader development and follower morale are the basis of this comparison
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".