Lesson learned from covid-19 pandemic and its impact on business continuity management BCM in the tourism sector in Jordan
Bibliographic record
Abstract
The preservation of business operations during and post the COVID-19 pandemic has emerged as a significant challenge for many enterprises. This research focuses on identifying the critical factors that influence the effective management of business continuity, based on lessons learned from the post-pandemic era. The primary objective of the investigation was to understand the impact of performance adaptability, innovation, talent preservation, and the implementation of a proactive, forward-thinking strategy on business continuity management. Furthermore, the study delves into the mediating role of strategic agility in these relationships. The study gathered data from personnel within the tourism sector of Jordan's prestigious Golden Triangle, which includes Petra, Wadi Rum, and Aqaba. The research utilized Partial Least Squares Structural Equation Modelling (PLS-SEM) to analyze the structural model and to investigate the propositions outlined. The findings indicated that performance adaptability, innovation, talent preservation, strategic agility, and a proactive strategy positively impact business continuity management. Moreover, strategic agility was found to moderate the relationship between performance adaptability, innovation, talent preservation, and a proactive strategy, thereby enhancing business continuity management. These research outcomes offer insightful implications for managerial personnel within hotels and tourism companies, acting as a comprehensive guide for improving their business continuity management in the post-pandemic world.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".