Role of substantive and rhetorical signals in the market reaction to announcements on AI adoption: a configurational study
Bibliographic record
Abstract
How do shareholders respond to technologies hyped in general discourse, e.g., artificial intelligence (AI), if a common understanding is lacking and the technologies are still evolving? Do they respond primarily to substantive signals in technology announcements, such as AI capabilities, or do rhetorical signals also play a significant role? Adopting signalling theory as a theoretical lens, we conceptualise announcements of AI capabilities as substantive signals and linguistic elements in the announcements pertaining to organisational time horizon and risk-reward considerations as rhetorical signals. Departing from the typical focus on bijective relationships, we consider holistic, complex configurations of interdependent factors using the qualitative comparative analysis (QCA) methodology. Notably, announcements pertaining to AI capabilities are not necessarily associated with positive market reactions; in fact, when all three types of AI are included in announcements without explicit consideration of risks, shareholders react negatively. We find that shareholder response is based on joint evaluation of substantive and rhetorical signals, and that these signals interact in a complex way to produce positive and negative market reactions. These findings motivate several propositions for market reactions to IT announcements, providing implications for both theory and practice.
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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 | 0.016 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".