The closer I am, the safer I feel: The “distance proximity effect” of COVID‐19 pandemic on individuals' risk assessment and irrational consumption
Bibliographic record
Abstract
The unprecedented crisis of COVID-19 posed severe negative consequences for consumers, marketers, and society at large. By investigating the effect of individuals' distance from the COVID-19 epicenter (i.e., the geographical area in which COVID-19 pandemic is currently most severe) on consumers' risk perception and subsequent behaviors, this research provides novel empirical findings that can offer practical insights for marketers. While intuitively, people expect individuals closer to the COVID-19 epicenter to generate a greater risk perception of the pandemic, empirical evidence from four studies provides consistent results for the opposite effect. We find that a closer (vs. farther) distance to the epicenter associates with lower (vs. higher) perceived risk of the pandemic, leading to less (vs. more) irrational consumption behaviors. We refer to this phenomenon as the "distance proximity effect," which holds for both physical and psychological distances. We further demonstrated that this effect is mediated by consumers' perception of uncertainty and moderated by individuals' risk aversion tendency. The current research contributes to the literature of consumers' risk perception and irrational consumption by highlighting a novel factor of distance proximity. It also offers some timely insights into managing and intervening COVID-19 related issues inside and outside an epicenter.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".