How Do Anxiety about Contracting COVID-19 and the Perceived Risk of Financial Loss from COVID-19 Interact to Increase Consumer Impulse Buying?
Bibliographic record
Abstract
While preceding research has focused on various aspects of the pandemic, there is still a need for further exploration of the relationship between preventive behavior against the pandemic and impulse buying. This study fills a gap by exploring how the fear of contracting COVID-19 and perceived financial losses from the pandemic interplay, and how they combine to drive impulse buying behavior, while considering the mediating role of preventive behavior. To investigate our hypothesis, we collected data from 760 respondents in South Korea through in-person survey. Using the PROCESS macro in SPSS model-58, we analyzed the data and found that the mediator role of preventive behavior and moderating role of perceived financial loss risk from COVID-19 significantly influence the relationship between the fear of COVID-19 infection and impulse buying. Specifically, when individuals perceive a higher risk of infection, they are more likely to engage in preventive behaviors. However, the negative relationship between preventive behavior and impulsive purchases weakens when there is a high perceived risk of financial loss.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".