When the going gets tough, do the tough go shopping?
Bibliographic record
Abstract
Abstract This study examines the impacts of consumer confidence on stockpiling behavior and, subsequently, retail inventory management. We show how stockpiling behavior evolved during the “Great Recession” of 2008–2009 as consumer confidence waned and demonstrate the impact of this development on inventory management. Drawing on the two‐segment household inventory theory consisting of nonstockpiling and stockpiling segments, we use a panel dataset (2005–2015) to calibrate household inventory holdings. This dataset then serves as input for a retailer‐level case study. Our empirical analysis reveals significant impacts from changing stockpiling behavior. When consumer confidence is low, both stockpiling and nonstockpiling segments respond by reducing weekly consumption rates; however, the stockpiling segment also significantly lengthens the time between shopping trips, and ultimately increases the duration of inventory holdings. These changes to consumption and stockpiling add complexity to inventory planning, requiring retailers to carefully adjust inventory levels to maintain service levels.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".