Developing a Performance Coaching Model for Overqualified Employees in Public Sector: Implications for Career Growth and Organizational Effectiveness
Bibliographic record
Abstract
Despite the well-documented benefits of performance coaching in employee development, its efficacy for overqualified employees—a critical yet overlooked talent segment—remains poorly understood. This study bridges this gap by proposing a novel coaching framework specifically designed for overqualified professionals in Iran’s Electrical Industry. Leveraging grounded theory methodology, we analyze data from 16 semi-structured interviews to develop a comprehensive model featuring 131 distinct elements categorized into 18 core constructs. Our results demonstrate a dynamic interplay between Casual Factors, Contextual Conditions, and Intervening Factors in shaping job and organizational competencies. These competencies subsequently inform strategic interventions in organizational development and talent management, generating multi-level impacts across individual, team, and organizational outcomes. The proposed model not only advances theoretical understanding of coaching efficacy but also provides practitioners with an evidence-based framework for optimizing the performance of overqualified employees—a crucial resource in contemporary talent management.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".