Privacy Breaches and the Effect of Customer Notification
Bibliographic record
Abstract
Laws requiring firms to disclose privacy breaches to their customers have been adopted extensively worldwide. However, the manner in which these laws affect the security protection behavior of firms disclosing a data breach is poorly understood. To shed light on this issue, we leveraged institutional theory and examined how U.S. state data breach notification laws (DBNLs), under which firms must notify customers of personal information breaches, influenced firm-level incidence of security breaches and how such influence manifested heterogeneously across firms. Exploiting the staggered enactments of DBNLs in a difference-in-differences analysis, we found that firms experienced a significant reduction in data breach incidents after the implementation of DBNLs. This effect was more pronounced among firms that were more reliant on sensitive customer data, operated in stricter privacy protection environments, or held more intangible and digital assets. We document evidence that compared to firms not subject to DBNLs, firms subject to these laws are more likely to appoint IT-specialized executives and remediate IT-related internal control weaknesses, which suggests potential channels that may facilitate DBNLs’ curbing of data breaches. We also found that the reduction in breach incidences following DBNL-mandated disclosure policies relates to both endogenous breaches and exogenous cyberattacks.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".