Unpacking the Intention-Behaviour Gap in Canadian Consumers’ Food Purchasing Decisions
Bibliographic record
Abstract
Canada has a deep-rooted reliance on single-use plastic in the food industry, with little evidence of changing its use of plastic as a durable, convenient, and cost-effective choice of packaging material to process, package, deliver and sell food to Canadian consumers (Schweitzer et al., 2018; Sundqvist-Andberg & Åkerman, 2021). A majority of Canadian consumers (73.4 percent) support banning single-use plastic food packaging in favour of more sustainable food packaging options, according to a consumer survey by Dalhousie University (Walker et al., 2021). However, barriers at the point-of-purchase, including the higher price tag and limited availability of food without plastic packaging, limit the purchase of plastic-free food products in Canada. This quantitative research project was undertaken to 1) segment consumers based on their consumer opinions regarding single-use food packaging, 2) determine the pre-purchasing intentions and point-of-purchase behaviour of Canadian consumers, and 3) determine if any gap exists between consumers’ intention and their purchasing decisions when food shopping. The Theory of Planned Behaviour explains the connection between consumer intention (“intention”) and purchasing decisions (“behaviour”) (Ajzen, 1991). Data was collected from Ontario food shoppers using a custom mobile app to capture both consumers' intentions before shopping for food and their purchasing decisions while food shopping. The data of 95 participants who completed the study - a competition rate of 42.04 percent - was segmented into Green (20 percent) and Grey (80 percent) consumer segments for comparative analysis between the two groups. The results show that both Green and Grey Consumers are more strongly influenced by their in-store purchasing decisions regarding packaging than their pre-shopping intentions which current intention-only surveys would not reveal. This research provides evidence of consumer support for the shift to more plastic-free food products in Canadian supermarkets. The researcher intends to develop the mobile app used for this research project into a commercially viable version to support the shift away from single-use plastic food packaging.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".