Customer satisfaction in construction projects: Impact of property type and income
Bibliographic record
Abstract
In this research, which examines the factors affecting customer satisfaction in construction projects, the convenience sampling method was preferred, and data were collected from participants aged 18 and over by face-to-face survey method. Smart PLS statistical software was used to analyse the data with a structural equation model. According to the results of the analyses, cost-effectiveness and lead time stand out as factors affecting customer confidence. Customer confidence directly affects customer satisfaction. In addition, quality perception has a strong effect on customer satisfaction. According to the results of multiple group analysis, customer satisfaction varies according to different buyer segments. While quality perception, cost-effectiveness and lead time are at the forefront for those who buy a property for commercial use, customer confidence has a more decisive role for those who buy a property for personal use. In addition, groups with lower incomes prioritise cost-effectiveness and customer confidence, while groups with higher incomes attach more importance to lead time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".