Risk-Mitigating Technologies: The Case of Radiation Diagnostic Devices
Bibliographic record
Abstract
We study the impact of consumers’ risk perception on firm innovation. Our analysis exploits a major surge in the perceived risk of radiation diagnostic devices following extensive media coverage of a set of overradiation accidents involving computed tomography (CT) scanners in late 2009. Using data on radiation diagnostic device patents and Food and Drug Administration (FDA) product clearances, we find that the increased perception of radiation risk spurred the development of new technologies that mitigated such risk and led to a greater number of new products. Using CT scanners as a case study, we provide an in-depth characterization of two different types of risk-mitigating technologies that firms developed after the shock. Firm-level analysis shows that, although firms were similarly responsive in their patenting activities, large incumbents were significantly more responsive than smaller firms in terms of new product introductions, and, in the case of CT scanners, large incumbents were also significantly more responsive in terms of the more radical type of risk-mitigating technologies. We also provide qualitative evidence and describe patterns of equipment usage and upgrade that are consistent with increased risk perception and, consequently, a greater willingness to pay for safety. Overall, our findings suggest that changes in risk perception can be an important driver of innovation, can shape the direction of technological progress, and can impact market structure. This paper was accepted by Ashish Arora, entrepreneurship and innovation.
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.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".