Enhancing Subordinate Job Performance Through Coaching Behaviors: A Lay Epistemic Approach
Bibliographic record
Abstract
ABSTRACT This paper explores the downstream effects of coaching behaviors, conceptualized as a behavioral intervention that helps subordinates learn in an organization. We propose that supervisors serve as “facilitators of learning” and knowledge transfer agents in an organization by coaching their subordinates. Drawing on the lay epistemic approach, we examine the effects of guidance versus facilitation coaching behaviors on psychological and job‐related outcomes. Supervisors who display guidance facilitation coaching behaviors tend to provide their subordinates with direct feedback and knowledge, whereas those who display facilitation coaching behaviors provide inspiration and exploration opportunities. The guidance style is particularly effective for subordinates with a high need for closure, an epistemic motivation that drives individuals to conduct quick searches for answers and solutions in psychologically uncertain situations, whereas a facilitation style is more effective for subordinates with a low need for closure who engage in more elaborate and prolonged searches for information. We investigate subordinates' self‐efficacy, trust in their supervisor, and job performance as outcomes of the interaction effect of coaching behaviors and subordinates' need for closure. Field data collected from supervisors and their subordinates using a two‐wave, multisource design indicate that when supervisors engaged in facilitation coaching, subordinates with a low need for closure reported higher job‐based self‐efficacy, leading to improved supervisor‐rated job performance. Conversely, guidance‐oriented supervisors gained the trust of their subordinates, which in turn positively influenced the subordinates' job performance. The implications of the epistemic properties of coaching behaviors and learning approaches through interaction with supervisors are discussed.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".