Vendor selection in the wake of data breaches: A longitudinal study
Bibliographic record
Abstract
Abstract With the increasing digitization and networking of medical data and personal health information, information security has become a critical factor in vendor selection. However, limited understanding exists regarding how information security influences vendor selection. Drawing from the attention‐based view (ABV), this study examines the potential impact of data breaches on hospitals' selection of electronic medical record system (EMRS) vendors. To test our hypotheses, we compile a unique dataset spanning 12 years of observations from US hospitals. Utilizing a coarsened exact matching (CEM) technique combined with a difference‐in‐differences (DiD) approach, our study shows that hospitals tend to replace their EMRS vendors after experiencing data breaches. Moreover, breached hospitals tend to prioritize information security in such a vendor replacement process by switching to star vendors and migrating towards a single‐sourcing configuration. Further post‐hoc analyses reveal that these impacts of data breaches are mitigated as the relationship between breached hospitals and vendors matures or when hospitals belong to large healthcare systems. Additionally, we find that the effects of data breaches are contingent on the scale of the breach and are short‐term in nature. This research underscores the significance of information security as a crucial consideration in vendor selection for both academia and practitioners.
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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 | 0.012 | 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 | 0.001 | 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".