Employee empowerment
Bibliographic record
Abstract
Purpose The purpose of this paper is to extend past research by investigating the relationships between supervisors' empowering management practices (SEMP), employees' psychological empowerment (PE), and a new measure of employees' behavioral empowerment (BE). A mediation model is hypothesized. Design/methodology/approach A questionnaire study is conducted among 359 non‐managerial employees. Because BE is both self‐reported and externally assessed, relationships are verified with single‐source and multi‐source data. Findings SEMP are quite strongly related to PE, but more weakly related to BE. Structural equation analyses tend to support a model where PE completely mediates the relationship between supervisors' managerial practices and employees' BE. Research limitations/implications This cross‐sectional study does not provide indication of causality among the variables. Practical implications First, this paper suggests that feeling empowered is a pivotal mindset that needs to be created by supervisors to generate proactive behaviors. Second, the moderate correlation found between PE and BE measures suggests that they capture different facets of empowerment. Given management concern for bottom line results, behavioral measures should not be ignored in assessing employee empowerment. Originality/value This paper used a behavioral criterion to assess employee empowerment rather than relying solely on a psychological measure.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".