PURCHASE DECISION ANALYSIS IN SHOPEE THROUGH MARKETPLACE AND CREDIBILITY
Bibliographic record
Abstract
Transactions with online sellers for customers will consider uncertainties and risks as compared to traditional buying and selling transactions. Buyers are given limited opportunities to see the quality of the goods and test the desired product through the media provided by the seller. When a customer makes a purchase from an unknown seller's website, they cannot see the quality of the goods and services being offered. Previous research (Doney, Cannon and Mullen (2003); Eden (1988); Kim, Silvasailam, Rao (2004) shows that trade, trust are very significant factors in explaining the online shopping process. Factors that can increase buyer's trust in online shopping, among others, buyers' knowledge of technology, good quality websites, and good company quality.Technology knowledge here is defined as the extent to which a person believes in himself that he can carry out a specific task or do something. Young and Dan (2005) ) explained that knowledge of Internet technology is very influential on the results expected by users in online shopping transactions. Indonesia is one of the trending countries with online shops or online shops, this can be seen from the many online stores such as Lazada, olx, tokopedia, JDid, Bukalapak, hijup, Zalora and many more that can be found easily according to the merchandise category will be sought or purchased. One of the online shops that is quite developing in 2017 is Shopee. Keywords: Purchasing Decision, shopee, markeplace and credibility
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".