The relationship between trends in technology use and repurchase intention
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it