Determination of Home Purchase Decisions with Technology Adoption as a Moderating Variable
Bibliographic record
Abstract
The residential property market in Indonesia has experienced significant growth, with a 1.89% increase in the Residential Property Price Index and a 31.16% rise in sales in the first quarter of 2024, though Surabaya recorded the lowest price growth in Java at 0.34%. This study aims to examine the determinants of home purchase decisions, including purchasing power, price, location, marketing advertisements, and developer brand image, with technology adoption as a moderating variable. Using a quantitative approach, data were collected from 225 respondents across four housing projects in Gresik, East Java, through questionnaires analyzed with Moderated Structural Equation Modeling. The findings reveal that all determinants significantly influence home purchase decisions, with developer brand image having the strongest effect. Technology adoption enhances these relationships by improving information access and consumer trust through digital platforms. The study concludes that developers should prioritize digital marketing strategies, such as virtual tours and social media campaigns, to boost consumer engagement and address declining sales trends. These insights offer strategic guidance for enhancing marketing effectiveness in the evolving digital landscape of the housing sector.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".