Greening the workplace through supervisory behaviors: assessing what really matters to employees
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Research in environmental sustainability shows consistently that supervisory support plays a critical role in shaping the work context to create conditions that favor employee eco-friendly behaviors. We investigate the combined effect of supervisory support, trust in supervisor and commitment to the supervisor for predicting employees’ environmental behavior using data from a sample of 142 full-time employees in a fuzzy sets qualitative comparative analysis (fsQCA). Our findings show first, that employee environmental behaviors are motivated by supervisory support, regardless of trust in supervisor or commitment to the supervisor. Our results also show that in the absence of supervisory support, employees’ environment behavior is fostered by trust in supervisor and commitment to the supervisor. This research contributes to the literature by clarifying how supervisors can encourage subordinates to behave in an eco-friendly way.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it