Consumer Behavior Analysis in Choosing Conventional or Sharia Mortgage Product in Indonesia
Bibliographic record
Abstract
The objectives of this study are 1) Analyze sharia mortgage market segment based on demographic and psychographic, 2) Analyze the factors that influence the type selection of sharia mortgage, 3) Formulate the managerial implications to increase market share sharia mortgage. This study used two approaches, the first approach is to use AIO (Attitude, Interest, Opinions) psychographic factors to measure consumer lifestyles before mapped into multiple segments. The second approach is to use the planned behavior theory (TPB). TPB is used to find out the factors that influence consumer in making decisions to purchase sharia mortgages. The data used is primary data that is quantitative. The primary data was obtained from questionnaires by consumers who will buy a house through mortgage facilities. The sample is determined intentionally (convenience sampling). The respondents are 150 people. The results showed that consumers are going to buy a house through mortgage facilities divided into 3 clusters. The first cluster is respondents who chose a sharia mortgage have monotonous, introvert, conservative and conformist personalities. The second cluster are respondents who chose conventional mortgages and have dynamic, extrovert, risk-taker and rationalist personalities. Meanwhile, the third cluster is respondents who chose conventional and sharia mortgages simultaneously and have dynamic, extrovert, risk-taker and universalist personalities. Based on the multiple regression analysis it shows that the output evaluation factor (X2) significantly affects the respondents’ interest in proposing a sharia mortgage, while the trust behavior factor (X1), the trust control (X3), the motivation (X4), the trust control (X5), power factor control (X6) have no significant effect. This study implies that sharia bank management should focus on 3rd clusters because it has a big potential market through generic strategies, market challenge strategies and marketing mix strategies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".