The mediation effect of intention in the pro-environmental workplace (PEW) behavior of Malaysian public employees
Bibliographic record
Abstract
During the past few years, many studies associated with sustainability of the organizations have primarily analyzed the financial and performance related implications of sustainability of an organization, and perception of these practices by neglecting the role of the employees who might play a vital role through taking part in pro-environmental behavior in the workstation.Thus, this study focuses on the individual's pro-environmental workplace (PEW) intention to determine its mediating effect between the factors in the Theory of Planned Behavior (TPB) and the PEW behavior context among public workers in Malaysia.Random sampling on a list of ministries situated in Federal Territory of Putrajaya was used in the first stage followed by systematic sampling based on officer's list of the directory in those ministries.Cleaned data resulted in 460 questionnaires gathered on demographic characteristics, PEW intention, attitude towards environmental practice in the workplace, subjective norms, perceived environmental workplace control, and PEW behavior of public sector context in Malaysia.The results reveal significant influences of PEW subjective norms, and perceived PEW control on the PEW behavior.While a series of regression analyses show that the PEW intention mediated only the two relationships between PEW subjective norms, and perceived PEW control on PEW behavior.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".