Impacts of Health and Safety Concerns on E-Commerce and Service Reconfiguration During the COVID-19 Pandemic: Insights from an Emerging Economy
Bibliographic record
Abstract
The COVID-19 pandemic has brought unprecedented growth to the e-commerce industry, triggering widespread digital service transformation across various business segments in Vietnam. A pressing concern for both businesses and policymakers is whether the sudden peak in customer interest in e-commerce can be sustained in the future. This research seeks to address this concern by considering whether and how customers’ motivations to participate in e-commerce activities have changed. We collected primary data from a self-administered survey to empirically examine how health and safety concerns influence customers’ online shopping behavior during the pandemic, alongside other known determinants for e-commerce participation, namely technology readiness and connectedness. The results confirm that health and safety concerns have a positive influence on customers’ usage of e-commerce after controlling for technology readiness and connectedness. Furthermore, customers in age groups with higher risks of severe COVID-19 symptoms and mortality are more likely to increase e-commerce usage during the social distancing and isolation period. Our results support the idea that the customer base for e-commerce and digital services have expanded beyond the typical tech-savvy and young customers in their twenties. These are promising signs for postpandemic recovery and even expansion, as firms may leverage the momentum of change in customers’ motivations and start tailoring their public relation campaigns to address a wider age range of potential consumers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".