How rejected recommendations shape recommenders' future product intentions
Bibliographic record
Abstract
Abstract When a consumer (a recommender) recommends a product to another consumer (a recommendee), it is not uncommon to learn whether the recommendee chose the recommended option (i.e., accepted the recommendation) or a different option (i.e., rejected the recommendation). Our research examines how rejected recommendations affect recommenders' subsequent intentions toward the originally recommended product. We find that upon learning one's recommendation was rejected, recommenders are less likely to repurchase or choose the product in the future. This negative effect emerges because recommenders question their knowledge about the recommended product (i.e., self‐perceived expertise is reduced). Such questioning is more likely to occur when the recommendee is a close other and less likely to occur when the recommended product is perceived to primarily differ from alternatives due to subjective preferences (i.e., horizontal differentiation is salient). Importantly, this rejected recommendation effect is shown to be distinct from a social proof account. The current research contributes to WOM theory by identifying a novel outcome of recommendation interactions—rejected recommendations—and by demonstrating that this outcome can cause consumers to shift away from a product despite having felt positively enough about the product to recommend it to others.
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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 | 0.005 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".