A Self-Verification Perspective on Manager-Employee Agreement on Proactive Competencies
Bibliographic record
Abstract
Research on performance appraisals in organizations has emphasized that performance appraisals should be accurate reflections of an employees’ work and developmental in tone. Very little research, however, has examined the importance of agreement between managers and employees in their perceptions of employee behaviors, competencies, and performance. We address this gap by investigating the important role of manager-employee agreement about employees’ proactive competencies – the self-starting and future-oriented behaviors that are essential to an employee’s job in a fast-changing world of work. Drawing on self-verification theory, we argue that agreement between a manager and an employee about the focal employee’s proactive competency has important implications for how employees perceive and experience their work environment. Using multilevel polynomial regression and response-surface analysis, we tested our hypotheses in a sample of managers and employees at a large financial institution. We found that employee perceptions of voice safety—and subsequent stress—was maximized (stress minimized) when managers and employees agreed about employees’ proactive competency and minimized when they disagreed. Notably, this effect was consistent even when a manager and employee both evaluated the employee’s proactive competency poorly, suggesting that an employee’s experience of work depends in part on the extent to which they and their manager are “on the same page” regarding their capabilities. We discuss the implications of these findings for several literatures.
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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 | 0.023 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".