Evaluating determinants of employees' pro-environmental behavioral intentions
Bibliographic record
Abstract
Purpose The aim of this study was to identify and quantitatively assess the importance of psychosocial and organizational factors that influence employees' intentions to engage in pro-environmental behaviors at the workplace. Design/methodology/approach A questionnaire based on the theory of planned behavior was completed by 318 employees. To validate three suggested hypotheses, a series of path analysis models were constructed using AMOS software. Findings The theory of planned behavior explained 79 percent and 37.7 percent of variance in predicting intentions of employees to travel to work using alternative transportation and to make eco-suggestions directed toward the workplace, respectively. While organizational barriers did not play a significant role in predicting intentions to use alternative transportation, some organizational obstacles (opinion of colleagues, required paperwork) influenced workers' intention to make eco-suggestions. Originality/value This is one of the first articles in the field of pro-environmental workplace behaviors in which the theory of planned behavior is implemented in a systematic manner (qualitative exploration of beliefs followed by their quantitative evaluation). This article contributes to the existing literature by shedding light on the disproportionate influence of organizational and psychosocial factors on pro-environmental workplace behaviors.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".