The Measurement of Green Workplace Behaviors: A Systematic Review
Bibliographic record
Abstract
The literature on the green behaviors adopted by individuals in workplace settings has grown significantly over the past decade. Many studies have examined the factors associated with individual actions. However, the comparability of the studies conducted on the subject is a common concern, mainly because of the wide range of measurement tools based on different sets of items used in such research. Therefore, the aim of this study is to determine the degree of methodological maturity of green workplace behaviors based on a systematic review of research published on the subject between 1977 and 2016. Five major trends were identified from the 53 papers reviewed as part of this research: (a) the predominance of scales for measuring “green office” behaviors, (b) the redundancy of certain items, (c) the limited efforts devoted to measuring counterproductive green behaviors, (d) the emergence of new subcategories of proenvironmental behaviors, and (e) and the abundance of scales measuring voluntary green behaviors (extra-role). Through an analysis of existing measurement tools, this article proposes a decision tree designed to help scholars choose appropriate items for their studies. This may, in turn, contribute to the literature on green workplace behaviors by reducing bias and limiting the unnecessary creation of new measurement scales.
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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 | 0.016 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.023 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".