Assessing the economic worth of new product pre‐announcement signals: theory and empirical evidence
Bibliographic record
Abstract
Actual and intended new product introduction announcements constitute significant events for firms’ customers, competitors, and investors. Typically, past research has focused on the economic impact of actual new product introduction announcements. However, research relating to firms’ intentions to introduce new products is relatively uncommon. These intended introductions or “pre‐announcements” have important strategic objectives and affect a firm’s customers and competitors in significant ways. Builds upon existing theory to study the economic impact of product pre‐announcement signals. Adopts the event study methodology and explores the relationship between product pre‐announcements and stock prices. Results show that relatively irreversible product pre‐announcements, i.e., those containing “evidence” are valued positively by the stock market. In contrast, the stock market ignores bluffs or easily reversible announcements that lack such evidence. Given the significance of pre‐announcements, managers should take these signals seriously. Discusses how product managers may use these results to develop actionable strategies for communicating with investors. Outlines the contribution of this paper to product management theory.
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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 | 0.011 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".