Consumer subversion and its relationship to anti‐consumption, deviant and dysfunctional behaviors, and consumer revenge
Bibliographic record
Abstract
Abstract Consumer subversion refers to consumer acts that are intended to impede the ability of marketers to develop and implement a marketing strategy, including market segmentation, target marketing, and the formulation of a marketing mix. Consumer subversion has featured across many bodies of marketing literature, yet it has not been explicitly defined and studied. This paper identifies and organizes examples of consumer subversion from marketing literature. It delineates how consumer subversion is related to, yet different from, related terms, such as anti‐consumption, consumer revenge and retaliation, dysfunctional and deviant consumer behavior, and consumer movements. Consumer subversion can be proactive or reactive, and can be targeted at specific firms or towards the marketing function in general. Such acts range from individual exit or using ad blockers to extreme hostility via revenge or sabotage. This study derives a classification of consumer subverters, discusses the psychological “need to win” linked to consumer subversion, and presents a research agenda organized around consumers who subvert, firms that are subverted, and antecedents and consequences of subversion.
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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".