Strategic value alignment for information security management: a critical success factor analysis
Bibliographic record
Abstract
Purpose Effective information security management is a strategic issue for organizations to safeguard their information resources. Strategic value alignment is a proactive approach to manage value conflict in information security management. Applying a critical success factor (CSF) analysis approach, this paper aims to propose a CSF model based on a strategic alignment approach and test a model of the main factors that contributes to the success of information security management. Design/methodology/approach A theoretical model was proposed and empirically tested with data collected from a survey of managers who were involved in decision-making regarding their companies’ information security ( N = 219). The research model was validated using partial least squares structural equation modeling approach. Findings Overall, the model was successful in capturing the main antecedents of information security management performance. The results suggest that with business alignment, top management support and organizational awareness of security risks and controls, effective information security controls can be developed, resulting in successful information security management. Originality/value Findings from this study provide several important contributions to both theory and practice. The theoretical model identifies and verifies key factors that impact the success of information security management at the organizational level from a strategic management perspective. It provides practical guidelines for organizations to make more effective information security management.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".