How rumors and preannouncements foster curiosity toward products
Bibliographic record
Abstract
Purpose The purpose of this paper is to focus on the potentially positive role of rumors in generating curiosity about new products, and further shows how this prior knowledge through rumors affects consumer responses to subsequent official preannouncements about these products. Design/methodology/approach Building on the seminal work by Rogers (2003) on the innovation-adoption process, the authors examine how two factors – product newness (incremental vs radical) and rumor ambiguity (ambiguous vs unambiguous) shape consumer interest (curiosity) toward new products. Findings Study 1 experimentally tests the assumption that incremental and radical new products may benefit from different types of rumors, and shows that radical new products benefit more from ambiguous rumors as compared to incremental new products in terms of increased curiosity toward the product. Study 2 links rumors to preannouncements, and shows that rumors set expectations that become confirmed or disconfirmed by preannouncements. The results show that the curiosity evoked by the rumor has a significant impact on purchase intentions toward the new product, especially when they are confirmed by the preannouncements about the same product. Originality/value There is scant research investigating how rumors may shape consumer expectations about new products despite the prevalence of rumors in the marketplace, and this research provides a first outlook on the positive role that rumors play in the marketplace.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".