Consumers’ Emotional and Behavioural Responses to COVID-19 in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it