Comparison of disclosures and legitimacy strategies employed after a cybersecurity incident: the case of Desjardins
Bibliographic record
Abstract
The magnitude of the personal data breach at Desjardins financial cooperative in 2019 raises legitimacy concerns for Desjardins and its peers. Our study examines changes in the scope of cybersecurity disclosures and the legitimation strategies used in Desjardins's annual reports and compares them to those of other Canadian companies in the financial, communications and technology sectors. Based on a content analysis grid developed by Héroux and Fortin (2020), our results for Desjardins show an increase in disclosures related to the incident to repair damages to the organisation's legitimacy, but little change otherwise. Other companies in the sample, which need to maintain their legitimacy, also had very few changes in their cybersecurity disclosures. Our results reveal legitimation strategies that differ from past reactions to comparable legitimacy threatening incidents. Our contributions highlight conformity strategies targeting the incident for Desjardins and reluctance to change cybersecurity disclosures both for Desjardins and for its peers.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".