The benefits of a cyber-resilience posture on negative public reaction following data theft
Bibliographic record
Abstract
Research shows that customers are insufficiently motivated to protect themselves from crimes that may derive from data theft within an organisation. Instead, the burden of security is placed upon the businesses that host their personal information. Companies that fail to sufficiently secure their customers’ information thus risk experiencing potentially ruinous reputational harm. There is a relative dearth of research examining why some businesses that have been breached stay resilient in the face of negative public reaction while others do not. To bridge this knowledge gap, this study tackles the concept of cyber-resilience, defined as the ability to limit, endure, and eventually bounce back from the impact of a cyber incident. A vignette-based experimental study was conducted and featured: (1) a breached business described as having a strong cyber-resilience posture; (2) a breached business described as having a weak cyber-resilience posture. Overall, a convenience sample of 605 students in Canada were randomly assigned to one of the two main experimental conditions. The results show that a strong cyber-resilience posture reduces negative customer attitudes and promotes positive customer behavioral intentions, in comparison to a weak cyber-resilience posture. Similarly, the more negative attitudes a customer holds toward a breached business, the less likely they are to behave favorably toward it. As a result of this study, cyber-resilience, which has hitherto primarily received conceptual attention, gains explanatory power. Furthermore, this research project contributes more generally to business victimology, which is an underdeveloped field of criminology.
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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.002 | 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".