How to purchase an order from brick and mortar retailers during COVID-19 pandemic? A rise of crowdshipping
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
This paper aims to determine factors that affect consumers’ intention to re-purchase by combining consumers’ attitudes and satisfaction in mobile shopping via sharing economy platforms. The research sample consists of 367 valid participants in the metropolitan area of Ho Chi Minh City who experienced buying products from brick and mortar retailers by using crowdshipping service, using PLS-SEM. The results confirm that Personal Innovativeness significantly affected Perceived Ease of Use (PEOU) and trust. Consumers' attitudes toward buying products via crowdshipping services in sharing economy platforms are determined by PEOU and trust, and their satisfaction of an purchasement is impacted by Check out attributes and Delivery attributes, leading to re-purchase an order via this platform. Brick and mortar retailers need to create a corporation with crowdshipping service platforms to increase sales during COVID-19 pandemic. Besides, shipping quality should be ensured to satisfy consumers which leads to long-term usage.
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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.004 |
| 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