Bibliographic record
Abstract
The 2007–2009 recession has dramatically affected businesses and consumers around the world. High unemployment figures along with a downturn in other economic indicators alarmed consumers and brought about feelings of uncertainty and instability for their futures. In response, they adopted a multitude of new purchasing patterns like preferring cheaper items, trading down their regular brands for generic ones, delaying non-urgent purchases, etc. During recessions, marketers feel the impact of the recession much sooner than other departments because of new consumer behaviours, their reduced purchasing power, and consequently decreasing sales. However, this challenging new environment might be a source of new growth opportunities for those companies who can respond quickly and adapt their marketing strategies to changing consumer preferences. Therefore, it is crucial for marketers to understand how these behaviour shifts will influence their business, and use this to formulate new marketing strategies. In this article, after reviewing literature on how consumer behaviour changes during recessionary times and how companies tailor their marketing strategies, we illustrate some practical examples of how companies modified their marketing strategies during the last recessions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".