Business continuity management: trends, structures and future issues
Bibliographic record
Abstract
Purpose This study assesses the current landscape of business continuity management (BCM) research while exploring research trends, structures and delineating potential future directions. Design/methodology/approach A comprehensive bibliometric analysis was conducted on 360 articles from the Scopus and Web of Science databases using Biblioshiny software. A meta-synthesis was employed to aggregate and synthesize findings from the bibliometric results. Findings The results demonstrate a notable increase in publication numbers since the onset of the pandemic, reaching a peak in 2022 with a total of 342 articles. A collaborative bond among scholars transcends geographical boundaries and national affiliations. The analytical results propose avenues for future research, addressing crucial areas such as the integration of business continuity management systems (BCMS), the development of BCM frameworks and a comparative analysis of business impact analysis (BIA) frameworks through pertinent theories. Research limitations/implications The study contributes theoretical and practical implications, serving as a valuable resource for academics and practitioners seeking to deepen their understanding of BCM’s role in business recovery and preserving organizational continuity in the face of disruptions. Originality/value This study pioneers a comprehensive approach by integrating bibliometric analysis and qualitative meta-synthesis, providing a consolidated overview of BCM research. Additionally, it presents future research proposals in this area.
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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 | 0.034 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.030 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".