Strategic responses to the pandemic: a case study of the US department store industry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The study focuses on (1) the success of three strategies employed during the pandemic – two “persevering” strategies, curbside pickup and return window extension and one innovative strategy, virtual try-on technology and (2) whether the strategies are likely to be successful in the post-pandemic world. Design/methodology/approach The authors utilize a panel dataset containing 17 department store chains in the US The panel includes weekly sales by the retailers at the city level from 2018 to 2021, encompassing both a pre-COVID-19 period and a period during the pandemic. A two-way fixed effects model, including retailer-city fixed effects and year-week fixed effects, is used to estimate department store sales. Findings The authors find that the two persevering strategies offset the negative impact of government-imposed containment and health measures on sales performance. On the other hand, the innovative strategy is more effective with a low level of containment and health measures, leading to our observation that virtual try-on may be more sustainable than the other two strategies in a post-pandemic environment. Originality/value This paper makes the following contributions: First, the authors contribute to the literature on strategies that may be used to respond to crises. Second, the authors contribute to the retail management literature, assessing the impact of the three retail strategies on department store sales. Finally, the authors compare the impact on sales of the two persevering strategies to the innovative strategy and conclude that a mix of these types of strategies may be most effective at generating short-term sales during a crisis and longer-term sales post crisis.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it