The Evolution of Seasonal Shopping Events: Global Perspectives
Bibliographic record
Abstract
In this article, we define the phenomenon of seasonal shopping events (SSEs) and review the research on consumer behavior during such events. SSEs refer to specialized shopping events that frequently occur during and around national or religious holidays. They often reflect a celebration of cultural values and aim to appeal to a wide variety of experiential, hedonic, and other consumer motives. Retailers attract consumers by offering discounts, sales, and promotions related to gift-giving. SSEs often evolve into social and traditional occasions for friends and families. We describe four global examples of SSEs: Black Friday (U.S.), Fukubukuro (“lucky bag,” Japan), Singles’ Day (China), and Boxing Day (Canada, U.K., Australia, New Zealand, and South Africa). We examine these SSEs fromboth the consumer and retailer perspectives, review their histories, and indicate how they have grown beyond their cultures of origin. We then identify common patterns and elements associated with SSEs. We next provide a general framework for how successful SSEs have emerged and discuss the cultural and cross-cultural implications of the SSE phenomenon. Finally, we illustrate some of the ways that changes in the consumer and retail environments might affect the future of SSEs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| 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".