ANALYSIS OF PURCHASE DECISION MODEL TOWARDS AIRLINES TICKET BOOKING IN TRAVELOKA (MERCU BUANA UNIVERCITY CASE STUDY)
Bibliographic record
Abstract
Traveloka is an airline ticket purchase application that has the most visitors in Southeast Asia, but starting in the first quarter of 2019 they experienced a decline in the number of visitors. The decline in the number of visitors was the most contributed by application users from Indonesia. The decline in the number of visitors is a phenomenon that needs attention because it can potentially affect ticket sales through the application. there are several factors that influence the pattern of ticket purchases through online applications, including internet knowledge, service quality, prices, trust, understanding of risk, and user perceptions. The objectives in this study is to determine the effect of price, service quality, brand image on purchase decisions on the Traveloka application. The samples of this study are a 100 post graduate students of Mercu Buana University. The results of this study show that price does not have a positive and significant effect on brand image on the Traveloka application. Price, service quality and brand image have a positive and significant influence on purchase decisions on the Traveloka application. Traveloka companies are advised to increase their responsiveness in providing quality services so that they will be able to create a better brand image. Keywords: price, service quality, brand image, purchase decision, traveloka
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".