The effects of sustainability knowledge dimensions on sustainability intention, sustainable attitude and sustainable behavior toward water supply and consumption
Bibliographic record
Abstract
The purpose of this paper is to empirically investigate the effect of sustainability knowledge dimensions which are recycling knowledge, reuse knowledge and use efficiently knowledge on sustainability intention, attitude and behavior toward water consumption. This study is important while it is the first empirical study that has examined the links between a set of sustainable water consumption concepts within an “water-poor,” Arab developing country context. A conceptual model of the connections between the above-mentioned factors was developed and the posited hypotheses were tested using a survey data set of 512 questionnaires collected from consumers in Jordan. The findings show that use efficiently knowledge has the strongest effect on sustainable intention, followed by recycling knowledge and reuse knowledge. Moreover, use efficiently knowledge has the strongest effect on a sustainable attitude followed by reuse knowledge and recycling knowledge. Additionally, sustainable intention has a stronger effect on sustainable behavior than sustainable attitude. Such results would provide useful insights for policymakers, nonprofit organizations, and businesses to integrate themes of sustainable behavior into their policies and programs, thereby facilitating better progress toward the sustainability of water resources in developing countries.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".