The impact of executive coaching on self‐efficacy related to management soft‐skills
Bibliographic record
Abstract
Purpose Executive coaching has become an increasingly common method to skill development. However, few rigorous empirical studies have tested its capacity to generate outcomes. The purpose of this paper is to investigate the links between executive coaching and self‐efficacy in regard to supervisory coaching behaviors. Design/methodology/approach The paper reports on a pretest‐posttest study of a leadership development program using three training methods: classroom seminars, action learning groups, and executive coaching. Data are collected in a large international manufacturing company from 73 first‐ and second‐level managers over an eight‐month period. Findings Results indicate that, after controlling for pre‐training self‐efficacy and other training methods, the number of coaching sessions has a positive and significant relationship with post‐training self‐efficacy. Results also show that utility judgment, affective organizational commitment, and work‐environment support have each a positive and significant relationship with post‐training self‐efficacy. Practical implications The paper first suggests that an organization that wishes to improve its return on investment with regard to coaching should implement a program with multiple sessions spread over a period of several months. This paper also suggests that organizations should consider coaching from a systemic point of view, that is, taking into account not only the design but also individual and situational variables. Originality/value This paper contributes to the scientific literature by investigating, with a solid methodological design, the capacity of executive coaching to increase self‐efficacy related to management skills.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".