Analysis of Value-Added Meat Product Choice Behaviour by Canadian Households
Bibliographic record
Abstract
The competitive landscape in retailing has changed over the past decade. Moreover, the degree of product differentiation has been increasing: households are able to choose between an increasing number of store brands and national brands of similar products. The value added meat market is no different than any other sector of the grocery market – both national brands and private label brands are being developed to appeal to the consumer‘s desire for convenience, health, production and environmental attributes. Understanding the factors that are influencing consumers‘ value added meat product preferences is important for meat manufacturers who wish to add value to their firm‘s performance and increase market share. This knowledge is required in order to predict changes in demand and develop new products and marketing strategies that respond to changing consumer needs. The objective of the paper is to provide information on value added meat consumption patterns in Canada at the household level using household purchase information from a representative sample of the Canadian population collected through Nielsen Homescan™. Specifically the focus is on how meat consumers make their decision to purchase value-added meat products – the impact of value added meat types, store choices and brands preference on meat demand. The study undertakes an empirical investigation of Canadian household value added meat demand for the period 2002 to 2007. A comparison of consumers‘ preferences is performed with respect to store-switching, brand loyalty and meat expenditure. Multivariate regression analysis is employed to explain consumer preferences for the examined stores, products and brands. We find that meat price, advertising, the number of stores visited, household socio-demographic characteristics and regional segments are strongly related to meat expenditure levels. Value added meat product preferences vary widely across meat types - for example, consumer behaviour towards pork is not a good predictor of behaviour towards poultry, in terms of national brand/store brand choice. The data developed in this analysis can highlight6 marketing opportunities that exist for meat producers and processors to increase the value of total sales for their particular products. The results of this study highlight the impact of number of stores regularly shopped at on purchases of national brand versus private label meat products, the impact of expenditure on meat by product form on national brand versus private label and the impact of demographic and regional variables on all meat purchases, by animal species.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".