The role of ICT and animal welfare concerns in Estonian food purchasing channel preferences
Bibliographic record
Abstract
The modern consumer is now more attentive to animal welfare practices, and this is an important factor when making food purchasing decisions. Different levels of concern for animal welfare as well as the use of information and communication technology (ICT) affect consumer food consumption and purchasing decisions and information access. This study explored both consumer preferences for food purchasing channels by using principal component analysis and regression analysis as well as the interaction between ICT use and animal welfare concerns. To analyse consumer preferences for food purchasing channels, we utilised data from a survey on the use of various retail outlets, such as buying online, retail stores, local markets, directly from producers, and organic or specialised shops. We used the varimax rotation method in principal component analysis to find the main factors in consumer choices related to animal health and welfare. Results from five generalised linear models indicated that Estonian consumers are more willing to purchase products of animal origin directly from producers or in organic or specialised shops when they pay attention to animal health and welfare. Animal health and general welfare practices aimed at ensuring the safety of food in large-scale production and distribution are significant for retail shop users. More frequent ICT use does not translate to a preference for online food shopping over more traditional retail purchasing. Compared with other age-gender groups, younger women prefer to buy food directly from producers. Older individuals in particular favour purchasing food from traditional retail stores rather than online. Consumers who prefer to buy food online are more concerned about animal health and welfare.
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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".