Understanding Employees’ Energy Saving in the Workplace: DR and the Philippines’ Realities
Bibliographic record
Abstract
Understanding how employees act at work to save energy and the meaning for sustainability and environmental protection is essential. This research aimed to analyze the influences of Subjective Norms (SN), Descriptive Norms (DN), and Environmental Knowledge (EK) on employees’ intention to save energy (ISE) in the Philippines (PH) and the Dominican Republic (DR). The effects of SN, DN, and EK on ISE were evaluated by comparing two developing countries and the mediation effect of EK on the relationship between DN, SN, and ISE. Confirmatory factor analysis (CFA), followed by structural equation modeling and path analysis based on samples collected from employees from DR (340) and PH (339), was performed. Also, construct convergent and discriminant validity were assessed using composite reliability, maximal reliability, average variance extracted, and maximum shared variance. The findings of this study indicate that SN influences ISE positively among employees in PH (β = 0.15, p < 0.05) but not among employees in DR. Descriptive Norms positively influence ISE among employees in PH (β = 0.47, p < 0.01) and DR (β = 0.27, p < 0.01), while EK has a positive and significant influence on the ISE among employees in PH (β = 0.22, p < 0.01) and not in DR. There is a partial mediation effect between SN and EK on ISE when EK is the mediator in PH, and no mediation effects for RD. The intention to save energy is significant in economic terms because reducing energy consumption can help decrease energy costs and improve business profitability and competitiveness; in social terms, it can reduce energy consumption worldwide and improve social health, reducing gas emissions and pollution.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".