Bibliographic record
Abstract
The primary focus of this study was to explore the attitudes of consumers in Poland towards online grocery shopping, and the impact of the COVID-19 pandemic on the e-grocery market. A direct survey was conducted on a sample of 800 respondents from across Poland in the first quarter of 2020. A questionnaire was used as a research tool. As revealed by data analysis, in 2020 more than a half (60%) of consumers in Poland shopped for groceries in online stores run by brick-and-mortar grocery chains. The respondents usually shopped for groceries several times a month, preferably choosing products with a long shelf life, and home delivery options, each time spending around PLN 201-300. Convenience was found to be the key driver that encouraged consumers to shop for groceries online, and concerns about the quality of products purchased online was the major disincentive. Moreover, the coronavirus pandemic, and the resulting health concerns, were shown to have the least effect on the willingness of respondents to shop for groceries online, or the frequency of online grocery shopping. From a practical point of view, this research can be used to create marketing strategies for enterprises operating in the food retail industry, as well as to expand knowledge about the dynamically developing e-grocery market in Poland.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".