Assessing the economic worth of new product pre‐announcement signals: theory and empirical evidence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.017 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it