Antecedents of panic buying behavior during the COVID-19 pandemic
Bibliographic record
Abstract
The new Coronavirus disease (COVID-19) pandemic triggers panic buying behavior among consumers in several countries. Previous research on panic buying behavior is less likely to look at the phenomenon from the perspective of consumers (demand-side). Therefore, this study aims to examine the influence of media credibility and social contagion on panic buying behavior and the mediating effect of consumer anxiety in the relationship between media credibility, social contagion and panic buying behavior. Data was collected from young and adult consumers in Greater Jakarta, Indonesia using convenience sampling techniques. Three hundred and fifty responses were collected through an online survey. The hypotheses were tested using structural equation models that could simultaneously analyze the effects of variables in the complex model. The results indicate that media credibility does not have a significant effect on consumer anxiety and panic buying. On the other hand, social contagion has a direct and indirect influence on consumer anxiety. Likewise, consumer anxiety mediates the relationship between social contagion and panic buying behavior. The findings from this research can be used by manufacturers and retailers to maintain goods availability during the pandemic, and the government as a basis for economic decision making. This research also contributes to development of the academic literature related to consumer behavior during a pandemic.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".