Factors affecting Cybersecurity Readiness from Dynamic Capabilities Perspective: A Thematic Review
Bibliographic record
Abstract
Understanding the factors influencing cybersecurity readiness is crucial for strategic investment in cyber capabilities. However, there is a gap in applying Dynamic Capabilities (DC) theory, which focuses on organizational agility in adapting to changing environments. This study fills this gap by using DC theory to analyze and categorize existing literature, offering a framework to classify cybersecurity readiness factors across different stages. This approach enhances our understanding of how organizations maintain a competitive edge amidst evolving cyber threats. The study addresses the following research question: What are the key factors influencing cybersecurity readiness in organizations, as discussed in literature from 2014 to 2023? Utilizing a thematic review with Atlas.ti 23, it examines literature from 2014 to 2023, providing a broad perspective on relevant factors. By applying DC theory, this research offers valuable insights for academics, organizations, and policymakers, aiding in strategy development and informed policymaking.
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How this classification was reachedexpand
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".