Risk and vulnerability management, project agility and resilience: a comparative analysis
Bibliographic record
Abstract
The main objective of this paper is to present a critical analysis of the project management literature on four concepts; risk management, vulnerability management, project agility and project resilience. The goal is to understand the strengths and weaknesses of these concepts to deal with disruptive events through the development of a conceptual framework that captures their differences and convergences. Therefore, a review of recent literature from international journals, specialized mainly in project risk management, vulnerability management, project agility, and project resilience has been conducted. A systematic literature review is adopted to compare the four key concepts of this study and to draw conclusions. A case from the information technology field is used to better illustrate the comparison.Results from this study show that risk management and vulnerability management are proactive concepts focusing on the management of known events or actions. Alternatively, project agility is a reactive concept that aims to adapt to changes, but not necessarily disruptive events. Project resilience is a mix concept – proactive and reactive – focusing on recovering from known and unknown disruptive events. In addition, this comparative analysis and the conceptual framework developed can be used to exploit future areas of research and exhibit new opportunities where project management best practices can be improved to deal with disruptive events.
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 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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".