Behavioral Drivers of Solar Energy Adoption: Implications for Business Analysis and Sustainable Management
Bibliographic record
Abstract
The need to switch to renewable energy sources is growing due to environmental issues in developing countries, where green technologies can achieve sustainable development. Thus, this research focuses on the psychological and social antecedents of solar energy adoption through the integration of constructs derived from Value-Belief-Norm (VBN) theory and the Theory of Planned Behavior (TPB). The research focuses on how perceived consequences and ascription of responsibility from the VBN theory affect TPB constructs attitude, subjective norms, and perceived behavioral control which drive behavioral intention. Research results after testing 5000 bootstrap samples from 362 respondents, the findings suggest that perceived behavioral control has the strongest direct effect on intention to solar energy adoption (beta = 0.268, p-value = 0.000), while perceived behavioral control affects attitude to solar energy adoption (beta = 0.239, p-value = 0.000). The findings by the present study reveal that perceived environment partially mediates the association between the attitude with behavioral intention and subjective norms with behavioral intention and holistically mediate the behavioral control with intention to solar energy adoption but the moderating role of perceived environment is insignificant for behavioral control with intention to solar energy adoption. The practical effects include removing self-efficacy barriers and finding working-age workers. Future research should include demographic cluster analysis and longitudinal views to improve core understanding.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".