Revisiting the (disappearing) cost of data breach disclosures
Bibliographic record
Abstract
Purpose The detrimental impact of data breaches on organizations and their customers has been well documented in the literature. These breaches expose sensitive information, raising concerns about reputational damage and substantial financial losses for affected firms. Prior research has consistently demonstrated the significant financial repercussions of data breach disclosures, with a significant decline in the market value of breached firms following the incident’s revelation. However, recent literature has documented the shift in consumer perception toward data breaches, warranting a revisit of this important and relevant issue with more recent data. This study aims to revisit the cost of data breach disclosures by empirically analyzing the impact of recent data breach incidents on the market value of affected firms. Design/methodology/approach The authors collect the data regarding data breach incidents among publicly traded companies in the USA listed in the S&P 500 index from 2013 to 2021. The empirical analysis relies on the event study approach, and the market value of each firm is estimated using the Fama-French three-factor model. Findings This study finds that the negative market reaction to data breach announcements in recent years has been significantly weaker than those reported in prior works from the past decade. This result confirms the shift in consumer perception toward data breaches in the market. Originality/value While prior research has quantified the cost of data breach disclosures, the authors posit that a renewed examination is essential within the contemporary digital environment. Consumer behavior and market sentiment have undergone significant transformations in recent years, necessitating a revisit of this important issue with updated data. This study not only documents this evolving phenomenon but also yields crucial policy recommendations. Notably, it challenges the conventional wisdom to rely on market forces as an adequate deterrent against data breaches. Consequently, updated regulations may be necessary to effectively navigate the complexities of the evolving digital landscape.
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 | 0.009 | 0.123 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".