The Importance and Application of a Coaching Leadership Style in Businesses
Bibliographic record
Abstract
In today’s volatile business environment, command and control leadership is increasingly inadequate for sustaining engagement, innovation and resilience. This review synthesises peer-reviewed evidence on coaching leadership style (CLS) published between 2000 and 2025. A systematic search across four databases yielded eleven high-quality empirical studies and three meta-analyses. The findings indicate that CLS enhances employee motivation, facilitates skill development, promotes psychological safety and strengthens organisational adaptability, while concurrently advancing leaders’ effectiveness and emotional intelligence. Notably, recent trials demonstrate that both virtual and face-to-face coaching modalities produce comparable performance gains. This review also identifies contextual constraints—such as time intensity and crisis-driven situations—where CLS may be less advantageous. Practical recommendations are offered for embedding coaching behaviours into daily management routines, including phased roll outs, leader as coach training and metrics for monitoring engagement and innovation. Future research should prioritise longitudinal, cross-cultural studies that examine CLS efficacy in digitally transformed, post-pandemic workplaces. Collectively, the evidence positions coaching leadership not as an optional enhancement but as a strategic requirement for organisations seeking sustained competitive advantage.
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 | 0.014 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".