Digital Security Risk Disclosure and Investment Process
Bibliographic record
Abstract
Growing interconnectedness and extensive access to cybersecurity systems increased related threats that could exploit organisations’ assets. To protect the assets, organisations can implement risk mitigation measures, or transfer risks to third parties. These organisations need to disclose the digital security implemented as part of the investor relations efforts. Because of this growing cybersecurity concern, this paper examines whether investors will invest in organisations that provide the digital security risk disclosure, since it is important to assess organisations’ ability to stay resilient and viable during this fast-paced technology advancement age. The researchers solicited two hundred and nineteen (219) responses from Malaysian organisations through questionnaires. Smart PLS was used to analyse the data. The results suggest that disclosure of digital security strategy, its risk mitigation, and its cyber events significantly impact the investment decision. Theoretically, this paper contributes to the literature on legitimacy theory, especially from the institutional pressure when organisations try to address the legitimacy gap during cybersecurity events. Digital security risk is growing in relevance to organisations and investors, but the current disclosure is insufficient, management should pay more attention to improving this area. Future studies may examine factors that impact digital security risks such as the role of financial implications, reputational concerns, and industry-specific regulations.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".