University Students’ Subjective Knowledge of Green Computing and Pro-Environmental Behavior
Bibliographic record
Abstract
This cross-sectional survey examined the structure of university students’ subjective knowledge of green computing–hypothesized to be a multidimensional construct with three important dimensions–and its association with pro-environmental behavior (PEB). Using a previously validated green computing questionnaire, data were collected from 842 undergraduate students attending ten different public universities in Malaysia. The sample was split into two random halves (n1 = 400 and n2 = 442) to allow for Factor Analysis procedures and Structural Equation Modeling (SEM) to be conducted. Principal Component Analysis extracted a three-factor structure of subjective knowledge consisting of knowledge about green computing (GC) vocabulary, computer nature or characteristics, and e-waste, while Confirmatory Factor Analysis procedures confirmed the structure’s measurement validity. SEM fit statistics indicated a strong influence of subjective GC knowledge on PEB with its three extracted dimensions cumulatively explaining 37% of students’ reported PEB. The results confirmed the study’s hypotheses regarding the multidimensionality of subjective knowledge, the adequacy of the measurement model of subjective knowledge, and its strong positive role in influencing PEB. The article concludes with guidelines for future research in areas involving green computing, subjective knowledge and PEB with an emphasis on the conceptualization and measurement of each construct.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.003 | 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".