Modelling the significance of value-belief-norm theory in predicting workplace energy conservation behaviour
Bibliographic record
Abstract
A country’s energy usage can depict the development of its economy. Excessive energy consumption generates carbon emissions that degrade the climate and present challenges for sustainable global development. China is achieving economic development with excessive energy consumption and excessive carbon emissions, damaging the climate. As more energy is consumed at workplaces than in households and other buildings, energy conservation behaviors at workplaces can help mitigate environmental issues. In this study, we explore energy conservation behaviors in the workplace using the value-belief-norm (VBN) theory that has been extended and tested with survey data collected from China. Online survey-based data were collected from a total of 1,061 respondents and analyzed with partial least square regression structural equation modelling (PLS-SEM). The results of our analysis indicate that biospheric values significantly predict pro-environment beliefs, awareness of consequences, and ascription of responsibility. Moreover, pro-environment beliefs positively affect awareness of consequences, and awareness of consequences positively affects the ascription of responsibility. Findings further revealed that pro-environment beliefs, awareness of consequences, an ascription of responsibility, and social norms positively affect personal norms. Furthermore, social and personal norms lead to intentions to engage in energy conservation behavior, which influences energy conservation behavior in the workplace. The current study contributes to our knowledge and understanding about workplace energy conservation behaviors by constructing biospheric values that lead to developing the necessary beliefs and norms to activate energy conservation behaviors. Policy and managerial implications are reported, which involve inculcating the necessary values and beliefs that generate norms that lead to pro-climate behavior.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".