Voluntary cybersecurity risk disclosures and firms’ characteristics: the moderating role of the knowledge-intensive industry
Bibliographic record
Abstract
Purpose This study examines voluntary cybersecurity risk disclosures (VCRD) by listed Indian companies. It also investigates how it relates to firm-specific characteristics such as size, leverage, profitability, liquidity, beta, market growth and industry. Design/methodology/approach The extent of VCRD was measured by assessing the cumulative occurrence of cybersecurity risk keywords in the annual report of 100 listed Indian non-financial companies. Keyword extraction and occurrence counts were performed using Python software. A multiple regression analysis was applied to predict the characteristics of VCRD. Findings The results showed that the theoretical frameworks underpinned by agency and signalling theories continued to provide a valid explanation of VCRD by Indian companies. Specifically, the findings emphasized the importance of firm size, leverage, and beta as significant VCRD determinants. Additionally, the study found that knowledge-intensive industries had a favourable impact on the extent of VCRD. Research limitations/implications This study is relevant because it informs company management, regulators and investors about the nature and characteristics of companies that satisfy stakeholder demands to prevent cyber breaches. Originality/value Understanding disclosure characteristics is crucial from policy and regulatory perspectives. Studies on cybersecurity disclosures are related to developed economies such as the United States of America and Canada. This is the first study to explore this issue in a developing nation, in general, and in India, in particular, where cybersecurity risk disclosure has yet to be recognized.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".