Psychological and environmental empowerment: antecedents and consequences
Bibliographic record
Abstract
Purpose The aims of this paper are to clarify empowerment as a construct, assess whether environmental and psychological empowerment differentially predicts job outcomes, and investigate the effects of transformation and transactional leadership on empowerment. Design/methodology/approach University students (n=197) rated leadership and empowerment in their workplaces and a number of job outcomes using an on‐line questionnaire. Findings Results supported the proposition that empowerment should be separated into its behavioral and psychological components. The dimensions of empowerment also differentially predicted job outcomes. In particular, environmental empowerment was better at predicting outcomes than was psychological empowerment. It was also found that transformational and transactional leadership predicted environmental empowerment more strongly than psychological empowerment. Research limitations/implications Limitations include that the study was cross‐sectional, used a student sample, and a single common method for collecting the data. The primary implication for research is that empowerment should be separated into two constructs, environmental and psychological. Practical implications Practical implications include that environmental empowerment has more predictive power than does psychological empowerment on workplace outcomes and that leadership has a stronger impact on environmental than psychological empowerment. Originality/value This study is the first to call into question the way empowerment has been measured in prior studies and provides useful directions with which to pursue future research in this area.
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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 | 0.008 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".