PENGARUH STORE ATMOSPHERE, PROMOSI PENJUALAN DAN KELENGKAPAN PRODUK TERHADAP IMPULSE BUYING
Bibliographic record
Abstract
This study aims to determine and analyze the direct influence of store atmosphere on impulse buying in Consumers of Kartasura Mitra Department Store, to determine and analyze the direct influence of sales promotion on impulse buying in Consumers of Kartasura Mitra Department Store, to determine and analyze the direct influence of product completeness on impulse buying in Consumers of Kartasura Mitra Department Store and to determine and analyze the simultaneous influence of store atmosphere, sales promotion, product completeness on impulse buying in Consumers of Kartasura Mitra Department Store. This study uses a quantitative method with a sample of 100 employee respondents at Consumers of Kartasura Mitra Department Store. Data were analyzed using multiple linear regression analysis using IBM SPSS Statistics. The results of the study indicate that directly store atmosphere has a significant effect on impulse buying in Consumers of Kartasura Mitra Department Store, directly product completeness has a significant effect on impulse buying in Consumers of Kartasura Mitra Department Store, directly sales promotion has a significant effect on impulse buying in Consumers of Kartasura Mitra Department Store, and directly and simultaneously store atmosphere, sales promotion, and product completeness have a significant effect on impulse buying in Consumers of Kartasura Mitra Department Store
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".