Assessing IT disaster recovery plans
Bibliographic record
Abstract
Purpose This purpose of this paper is to assess information technology (IT) disaster recovery plans (DRPs) in publicly listed companies on Abu Dhabi securities exchange (ADX) in the United Arab Emirates. The authors assessed, among other things, DRP preparedness, documentation, employees’ preparedness and awareness and the most significant physical and logical risks that pose the most threads to drive the development of the DRP, etc. Design/methodology/approach The authors surveyed publicly listed companies on the ADX using a questionnaire adapted from past research papers as well as from audit programs published by the Information Systems Audit and Control Association. The surveys were completed through interviews with middle and senior management familiar with their firm’s IT practices. Findings The majority of the respondents reported having a DRP, and a significant number of the respondents reported that their top management were extremely committed to their DRP. Employees were generally aware of their role and the existence of the DRP. The greatest risk/threat to their organization’s IT system was logical risk followed closely by power and network connectivity loss as the second highest physical risk. The most highly ranked consequence of an IT disaster was loss of confidence in the organization. Research limitations/implications Because this paper only examined publicly listed companies on ADX, the research results may lack generality. Therefore, further research is needed in this area for determining the extent of the deployment of the DRP in the region. Practical implications Results of this paper could be used for IT DRP planning bench-marking purposes. Originality/value This paper adds value to research by investigating the current IT DRP practices by public companies listed on ADX.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
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 source (direct Gemma or distilled Codex), 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".