User participation in information systems security risk management
Bibliographic record
Abstract
This paper examines user participation in information systems security risk management and its influence in the context of regulatory compliance via a multi-method study at the organizational level. First, eleven informants across five organizations were interviewed to gain an understanding of the types of activities and security controls in which users participated as part of Sarbanes-Oxley compliance, along with associated outcomes. A research model was developed based on the findings of the qualitative study and extant user participation theories in the systems development literature. Analysis of the data collected in a questionnaire survey of 228 members of ISACA, a professional association specialized in information technology governance, audit, and security, supported the research model. The findings of the two studies converged and indicated that user participation contributed to improved security control performance through greater awareness, greater alignment between IS security risk management and the business environment, and improved control development. While the IS security literature often portrays users as the weak link in security, the current study suggests that users may be an important resource to IS security by providing needed business knowledge that contributes to more effective security measures. User participation is also a means to engage users in protecting sensitive information in their business processes.
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.
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 | 0.017 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".