Navigating the Field of Contemporary Political Consumerism: Consumer Boycott and Consumer Buycott Vistas
Bibliographic record
Abstract
The aim of this paper is to understand and compare the two growing forms of contemporary political consumerism, boycott and buycott, in competitive marketplace. We used an existential-phenomenology approach and conducted 15 in-depth interviews. This resulted in 75 boycott and buycott consumption experiences and 229 pages of interview transcripts. Content analysis shows that consumers perceive boycott and buycott as two distinct actions. They differ in terms of goal orientation (avoidance vs. approach), ease of participation, as well as consumer information search and learning style. Further, we identify similar and unique motivational factors for boycott and buycott. Specifically, we show motivations within individual context, boycott activity context, and societal context. Strong evidence indicates that, within individual context, motivations relating to self-enhancement, required resources and associated costs are important for both boycott and buycott consumerism. Motivations within societal context also seems to be relevant for both types of consumerism. However, only the motivations within the boycott activity context were found in this study. Theoretical and managerial implications are also discussed.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".