Justice motivation theory in sustainable home purchases
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the understudied topic of sustainability as a factor in the Canadian residential real estate purchase decision using the unique lens of justice motivation theory. Design/methodology/approach Using a qualitative approach, the study draws on transcripts from 14 interviews with realtors and residential buyers in three different Ontario cities. This paper adopts an exploratory perspective to investigate justice-based motivations related to sustainability in the real estate decision process. Findings The research finds that the three requirements of justice motivation are satisfied in the context of a broad understanding of sustainability that includes social, economic and environmental dimensions. The residential real estate decision offers opportunities for sellers to appeal to those motivated by justice. Practical implications Policymakers should consider ways of easing these barriers for those consumers who a financially unable to satisfy their justice motivation when purchasing a home as well as bolstering regulatory enforcement. Sellers should clearly articulate functional explanations of features as well as benefits to enhance the cognitive processing of the sustainable home as a choice alternative. Originality/value This paper makes a unique contribution by arguing that the social psychology theory of justice motivation helps explain the role of sustainability in the residential real estate purchase decision-making process.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".