Consumers’ Emotional and Behavioural Responses to COVID-19 in Canada
Bibliographic record
Abstract
COVID-19 disrupted our lives since the very first day it was announced to be a global pandemic in early 2020. In Canada, social distancing measures and quarantine and health-protective regulations affected people’s emotional stability and changed how they undergo certain consumer behaviours to cope with those emotional effects. I surveyed 687 participants residing in Canada to understand some of the emotional and behavioural changes they went through during the past two and a half years since the pandemic began. Participants were asked questions on their emotional responses during the early stages of the COVID-19 pandemic, their current well-being, and on some of their current consumer coping behaviours. Lastly, they were asked to report some demographic characteristics. My conceptual model, therefore, tests the relationship of consumers’ initial emotional response to COVID-19 in Canada with five coping behaviours via their current well-being indicators, moderated by two demographic characteristics—gender and income level. Results of this study showed that there is a significant relationship between the initial emotional response to COVID-19 (IERC) and buying behaviour via depression and loneliness, moderated by income level. While the rest of the indirect relationships were not significant, the research revealed significant direct relationship between IERC and all coping behaviours except social media behaviour and to have directly affected feelings of depression, loneliness, and hopelessness. This research has many theoretical contributions to the consumer behaviour and healthcare literature and managerial contributions that could be used by marketers, mental health professionals, public employees, and the government.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".