Determinants of Household Energy Saving Behaviour: An Application of the Goal Framing Theory
Bibliographic record
Abstract
Households are an important group that can be targeted to help reduce energy consumption and mitigate climate change. Drawing on the Goal Framing Theory (GFT), the study investigated the determinants of household energy (electricity) saving behaviour. The study examined the direct effects of the three principal constructs of the GFT (gain motivation, normative motivation and hedonic motivation) on household electricity saving behaviour. In addition, the study investigated the mediating effects of normative and hedonic motivations in the relationship been gain motivation and energy saving behaviour. The study adopted the quantitative research approach and the cross-sectional survey method was used to collect data from the respondents. The Partial Least Square Structural Equation modelling (PLS SEM) was used to test the hypotheses. The results indicated that gain, normative and hedonic motivations are significantly positively related to household energy saving behaviour. The mediating effects of normative motivation and hedonic motivation in the relationship between gain motivation and electricity saving behaviour are significant. The innovation of the study is the development and testing of a theoretical model that examined both the direct and indirect effects of the GFT constructs in the context of household energy saving behaviour. Empirically, the study contributed to the body of knowledge on the factors that affect household energy saving behaviour. Recommendations include communicating the economic and environmental impact of energy saving behaviour to households.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".