Developing consumer segments in Canada for a shift towards sustainable diets
Bibliographic record
Abstract
Purpose The purpose of this study was to identify consumer segments for the Canadian population based on factors guiding their food choices, to leverage their preferences and dietary characteristics as a robust starting point for the development of interventions aimed at promoting sustainable eating behaviours. Design/methodology/approach An online nation-wide survey was developed and administered collecting socio-demographic and food attitude and behaviour related data. A total of 3,329 respondents were included in the study. Using exploratory factor analysis to identify the determinants of food choices, followed by a cluster analysis, respondents were grouped into segments to create relatively homogeneous groups. Findings Five factors were identified as determinants of food choices including: sustainability and health, food influencers, joy and pleasure, convenience and familiarity. Six consumer segments were also identified: the “concerned” consumer, the “trend and tradition-follower” consumer, the “conventional” consumer, the “eat what you love” consumer, the “sustainable and healthy” consumer and the “convenience seeker.” This study highlighted that convenience and familiarity tend to be important deciding factors of food choices across all segments, and almost one fifth of the consumers were reluctant to try new food concepts or brands, further emphasising the importance of familiarity. Finally, respondents reported health as one of the main reasons why they would make short-term or long-term dietary changes, and health and sustainability, were deciding factors for many consumers. Originality/value This study contributes to the field of consumer segmentation as it is applied to sustainable eating patterns, and to leverage points for transforming the food system. Additionally, this study used the social cognitive theory as the underlying framework to create consumer segments, and it provides a more effective understanding of the Canadian population’s eating patterns, identifying two new segments. Finally, the findings support the necessity of a transition towards sustainable eating behaviours through a systems approach rather than focusing on individual level interventions that create short-term adjustments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".