The relationship between trends in technology use and repurchase intention
Bibliographic record
Abstract
Technology use to buy products online as a new innovation on marketing is significantly influencing the buyer’s behavior in marketing and important to understand. The way suppliers present their product is interesting, especially to encourage people repurchase from their shops. The purpose of the study is to explore the relationship between advertisement, promotion, and lifestyle towards repurchase intention of the university students doing online shopping at e-commerce Shopee platform during the pandemic of COVID-19. The study uses a quantitative method for research. The data were collected using an electronic questionnaire on Microsoft Forms from 212 university students who used the e-commerce Shopee platform during the COVID-19 pandemic to shop. The purposive sampling method was used to collect the data from all the students. SEM-AMOS was used to analyze the data. The results indicated as follows: the advertisement variable has no significant effect on repurchase intention. Promotion and Lifestyle variables have a significant effect on the repurchase intention of university students at Jabodetabek area in shopping online at Shopee during the COVID-19 pandemic. From this study we can conclude that technology to advertise products has no relationship with repurchase intention of students while promotion and lifestyle has a significant relationship for students to repurchase products in the transformation of new normal activities in Indonesia.
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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 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".