High Involvement and Ethical Consumption: A Study of the Environmentally Certified Home Purchase Decision
Bibliographic record
Abstract
Sustainable and energy efficient (SEE) attributes in the housing market have become a focus in Canada. Similarly, understanding the consumer’s decision-making process of this high-involvement ethical product has become a burgeoning area for researchers. This study describes the development of the subject, highlighting the nature of the ethical decision-making process and how it relates to this known intention–behaviour gap. An observation, followed by two studies consisting of in-depth interviews with real estate agents and sales representatives (n = 15) and home purchasers/consumers (n = 15), were conducted. Transcriptions were analysed qualitatively with NVivo Pro 12 software (NVivo Pro 12, QSR International Pty Ltd, Melbourne, Australia). Inductive thematic analysis revealed two main driving themes: information and trust in seller/realtor. Attribute investment return uncertainty was identified as a theme that affects the strength of the relationship between purchase intention and behaviour, whereas the trust in seller/realtor speaks to how and why this effect occurs. The findings present relationships among the driving factors that were identified by realtors and consumers in the SEE housing market, as well as barriers (investment return uncertainty) that prevent consumers from purchasing high-involvement ethical products.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".