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Record W2107954152

Critical Success Factors Analysis on Effective Information Security Management: A Literature Review

2014· review· en· W2107954152 on OpenAlexaff
Zhiling Tu, Yufei Yuan

Bibliographic record

Venuenot available
Typereview
Languageen
FieldComputer Science
TopicInformation and Cyber Security
Canadian institutionsMcMaster University
Fundersnot available
KeywordsInformation security managementInformation securityInformation security management systemKnowledge managementSecurity information and event managementSecurity managementStandard of Good PracticeBusinessITIL security managementCritical success factorComputer scienceRisk analysis (engineering)Process managementComputer securitySecurity serviceCloud computing securityNetwork security policy
DOInot available

Abstract

fetched live from OpenAlex

Information security has been a crucial strategic issue in organizational management. Information security management is a systematic process of effectively coping with information security threats and risks in an organization. With the pressure of high implementation and maintenance cost, organizations need to distinguish between controls they need and those that are less critical. Applying critical success factors approach, this study proposes a theoretical model to investigate main factors that contribute to successful information security management. By reviewing the information security standards and literature in IS field, six critical success factors are identified and the relationship among these factors are proposed. The results reveal that with business alignment, organizational support, IT competences, and organizational awareness of security risks and controls, information security controls can be effectively developed, resulting in success of 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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0120.015
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.009
GPT teacher head0.312
Teacher spread0.303 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations36
Published2014
Admission routes1
Has abstractyes

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