The Emergence of the Deconstructed Consumer: Exploring Shopping and Buying Behavior During the COVID-19 Pandemic
Bibliographic record
Abstract
The coronavirus (COVID-19) that swept across the US has dramatically impacted consumer behavior. The abruptness of pandemic for individuals brought forth an opportunity for understanding the influence of COVID-19 on consumer responses. While there is a growing understanding that household spending has changed in response to COVID-19, there is a lack of a deeper understanding behind changes in consumer behavior, including consumer sentiment and motivations to alter consumption during this critical time. Therefore, this study explores the nature of buying behavior during the pandemic using an in-depth qualitative approach. Through semi-structured in-depth interviews, four major themes emerged from the data that comprise what authors term the “deconstructed” consumer. The deconstructed consumer was based on the following thematic areas: (i) “Do-it-Yourself” culture or DIY, (ii) passionate pursuits, (iii) community, and (iv) self-reflection and discovery. By exploring changes in consumption attitudes and behaviors as the crisis unfolds, we can develop an understanding of the consumer and suggest potential retailer strategy shifts that can be deployed to maneuver during uncertain times of crisis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".