Compensating for Innovation: Extreme Product Incongruity Encourages Consumers to Affirm Unrelated Consumption Schemas
Bibliographic record
Abstract
New products are often extremely incongruent with expectations. The inability to make sense of these products elevates anxiety and leads to negative evaluations. Although scholars have predominantly focused on combating the negative response to extreme incongruity, we propose that extreme incongruity may have implications that extend beyond the category. We base our predictions on the concept of fluid compensation, which suggests that when people struggle to make sense of something, they will nonconsciously reinforce highly accessible schemas in unrelated domains. Four studies confirm that extreme incongruity encourages fluid compensation, such that it elevates preference for dominant brands (study 1), green consumption (studies 2 and 4), and ethnocentric products (study 3). We isolate the causal role of anxiety using moderation tasks and biometric feedback. Furthermore, we demonstrate that compensation has an immediate dampening effect on arousal intensity. Thus, if consumers can compensate before explicitly evaluating an extremely incongruent product, their evaluations tend not to be negative. Taken together, we document that extreme innovations encourage compensation, and in compensating, consumers can become more receptive to extreme innovations.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".