The Influence of Location, Products, Promotions, Services with Respect to Consumer Behavior (Studies in the Indo March Raya Darmo Surabaya)
Bibliographic record
Abstract
The purpose of this research is to know and test the influence of the location of businesses, products, promotions, services, consumer behavior towards Indomaret Point Raya Darmo Surabaya. This research is explanatory study, which explains the influence between variables through hypothesis testing. The research was held in Indomaret Point Raya Darmo Surabaya. The sample in this study were 80 respondents. The independent variables are: location (X 1), product (X 2), promotion (X 3) and services (X 4), the dependent Variable is the consumer’s behavior (Y), while its analysis technique using multiple linear regression analysis are used as statistical methods. The results of data analysis in this study is there is a very strong relationship between location (X 1), product (X 2), Promotion (X 3), services (X 4) to consumer behavior, the value of the coefficient of determination (R square) of 0,936. This figure shows that the variable location (X 1), product (X 2), Promotion (X 3), services (X 4) can account for the variation or able to contribute to the performance of variable 93.6%, while the rest amounted to 6.4% due to other variables that are not included in the study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".