Surviving Data Breaches: A Multiple Case Study Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study examines how organizations could potentially overcome the fallout of data breaches and achieve competitive advantage, by enhancing key firm capabilities through the process of sensing threats, seizing opportunities, and transforming/reconfiguring their existing resource base. We use the dynamic capabilities framework as the theoretical basis for this study. A multiple case study approach is applied to this study, using secondary data from the case studies of Target, Anthem, and Yahoo data breaches. Our findings indicate that utilizing the dynamic capability framework and its orchestration processes of sensing, seizing, and transforming/reconfiguring the resource base worked favorably in the case of Target and Anthem. However, for Yahoo, failure to utilize the aforementioned framework and orchestration processes had negative impacts on the firm. Our findings have implications for organizations regarding how they could restructure their internal practices and contain the fallout after a data breach.
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.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it