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Record W4206652744 · doi:10.32008/nordsci2021/b2/v4/12

POLISH CONSUMERS’ ATTITUDES TOWARDS ONLINE GROCERY SHOPPING

2021· article· en· W4206652744 on OpenAlexaboutno aff
Gabriela Hanus

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Retail Behavior Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessGrocery shoppingBrick and mortarMarketingQuarter (Canadian coin)AdvertisingPandemicQuality (philosophy)Grocery storeCoronavirus disease 2019 (COVID-19)Sample (material)The Internet

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.060
GPT teacher head0.287
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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