The Hype Watershed: Media Attention and Market Responses to New Venture’s Involvement with AI
Bibliographic record
Abstract
While the role of media in molding public perception and market response has long been of interest to researchers, there has been a dearth of exploration into how media representations of new ventures’ AI involvement influence market responses. This study examines the intricate interplay between media characteristics and market response to new ventures’ purported AI involvement. Through comprehensive financial market and media data analysis on all Chinese high-tech new ventures that have successfully applied for IPOs, we reveal what we term the “Hype Paradox,” an unexpected negative relationship between media sentiment regarding a new venture’s involvement with AI and its performance in the financial market, and the “Hype Watershed,” a term capturing the unforeseen curvilinear moderating effect of media affiliation based on the ratio of news from state-controlled media in the overall news coverage on sentiment-financial performance link. We further expose the “Watershed Diminisher,” where increased media coverage intensity blurs the “Hype Watershed” relationship, serving as the moderating effect on the curvilinear moderating impact of state-controlled media coverage. These insights shed light on the field’s ongoing discussions on the business impact of AI at the intersection of entrepreneurship, AI, media, and market behavior, specifically the AI hype’s multifaceted impact on business ventures’ outcomes.
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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 | 0.002 | 0.019 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".