PENGARUH HARGA, KUALITAS LAYANAN DAN TEKNOLOGI E-COMMERCE TERHADAP KEPUTUSAN UNTUK MENGGUNAKAN LAYANAN GO-JEK DI WILAYAH KECAMATAN CIBINONG
Bibliographic record
Abstract
In this era, some people use E-commerce for online transportation. Go-Jek is one of the alternatif online transportation that used by people. The aim of the research is to know the effect of factor cost, quality of services, and E-Commerce technologi to decision for using Go-Jek service in Kecamatan Cibinong. The type of research is used correlational research. The data analysis is used linear regression of multiple with helping SPSS program version 17.0. Result of the research shows the test linear regression of multiple obtained the regression equation is: Y = 3.340+ 0,631X1+ 0,180X2+ 0,678X3. The meaning is every it increases the values of free variable (cost, quality of services, and E-Commerce) so it will increase the value of customers decision using Go-Jek in Kecamatan Cibinong. The results of the coefficient of determination show that the value Adjust R Square of 0,446 (44,6%) Which means together factors cost , the quality of services and E-Commerce have contribution or affecting decision to use Go-Jek in Kecamatan Cibinong of 44,6 %, while the rest 55,4% affect by the other factors that is not discussed in the research. The results of the hypothesis whether partial and simultaneously Prove that factors price , the quality of services and e-commerce affect decision costumers using Go-Jek in Kecamatan Cibinong. The most dominant factors affecting decision to using go-jek in Kecamatan Cibinong is a factor information technology E-Commerce , this is proven by value the regression coefficient of 0,678
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".