Modeling Telecommuting and Teleshopping Preferences in the Post-Pandemic Era
Bibliographic record
Abstract
COVID-19 mitigation measures triggered a sharp increase in the adoption of teleshopping and telecommuting activities. However, there is a need to understand the extent to which past frequencies and experiences will affect post-pandemic teleactivity behavior. Moreover, teleshopping and telecommuting are interconnected, and a relationship may exist between them in the post-pandemic world. This study investigates post-pandemic preferences toward online grocery shopping, online food ordering, and working from home by using a multivariate ordered probit (MVOP) model. The data come from a web-based survey conducted for the Central Okanagan region of Canada. Model results confirm the presence of unobserved factors influencing telecommuting and teleshopping choices. Looking at endogeneity, working from home after the pandemic revealed a positive effect on online grocery shopping. However, results were not the same for post-pandemic online food ordering. Model results also confirm the significant impact of past teleactivity frequencies and experiences on post-pandemic preferences. Overall, the findings provide important insights into post-pandemic activity and travel patterns which can be used for robust policymaking.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".