Potential Clients’ Purchase Intention of Green Building in Kathmandu Valley
Bibliographic record
Abstract
Green building practices have gained global attraction, with developed countries and emerging economies adopting eco-conscious construction. However, Nepal faces challenges in its building sector, lacking a mandatory energy-efficient rating system. This, this study aims to investigate the potential clients purchase intension of green building in Kathmandu Valley. Explanatory research design was used to explore the cause-and-effect relationship among factors influencing Potential Clients' Adoption Preference of Green Buildings in the Kathmandu Valley. Data is collected from 404 potential clients through purposive sampling and structured questionnaires. The Kobo Toolbox administers the data, which is then analyzed using partial least squares structural equation modeling (PLS-SEM) version 4.0 software. Research findings indicated significant relationship of relative advantage and compatibility show significant relationships with green building adoption, while environmental opinion leadership, innovativeness, simplicity, and trialability does not demonstrate significant relationship. The study highlights that respondents were highly aware about the green building. Similarly, respondents had heard about the green building practices through social media platforms, newspapers, publications and article, word of mouth, and radios etc. Furthermore, delving into additional factors such as individual risk preferences and socio-economic influences will provide a more comprehensive understanding of green building adoption.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".