Telling (mis)fitting new product stories: The role of consumer orientations, product innovativeness, and message framing on new product evaluations
Bibliographic record
Abstract
Abstract Extant research proposes that consumer goal orientation‐message frame fit ( consumer–message fit ) leads to favorable product evaluations. However, there is evidence that consumer goal orientation‐message frame misfit ( consumer–message misfit ) at times also result in favorable evaluations of new products. This study develops and tests a novel prediction that shows how consumer–message misfit results in consumers having more favorable responses toward new products when the type of innovation (incremental vs. radical) is perceived as instrumental to achieving the consumer's goals with the product ( innovation–consumer fit ), creating a kind of fit–misfit effect. Results from four experiments encompassing multiple new product categories support this fit–misfit prediction. We further show that surprise mediates the effect of fit–misfit. This research builds on current theory involving goal orientations and schema‐incongruity, recommends several areas for future research, and presents practical implications for managing new product communication messages.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".