The impact of risk management and agile methodology on cybersecurity project success: the mediating role of team collaboration
Bibliographic record
Abstract
This study investigates the impact of risk management and agile methodology on the success of cybersecurity projects, emphasizing the mediating role of team collaboration within the financial sector. Based on a sample of 229 professionals, including IT specialists, project managers, and risk officers, data was collected using a structured survey instrument and analyzed through Structural Equation Modeling (SEM) using SmartPLS. The results confirm that both risk management and agile methodology have direct positive effects on cybersecurity project success. Additionally, both factors significantly enhance team collaboration, which in turn positively influences project outcomes, thereby confirming its mediating role in the relationship between these variables and cybersecurity project success. All proposed hypotheses were supported. The findings highlight the crucial interplay between management practices and team dynamics in ensuring project success. The findings also provide valuable theoretical insights and practical implications for enhancing cybersecurity initiatives in the financial industry.
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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 | 0.003 | 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 | 0.000 | 0.001 |
| 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".