Strategic value alignment for information security management: a critical success factor analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.010 |
| Open science | 0.001 | 0.000 |
| 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 it