Resilience during the COVID-19 pandemic: a comparative case study on how challenges were addressed and how new working models were implemented
Bibliographic record
Abstract
The COVID-19 pandemic broke out in the first quarter of 2020, immediately raising challenges for the ways companies work. By March 2020, most companies around the world had followed governmental lockdown rules, which, for many, meant shifting to remote working overnight. This comparative case study analyzes how the companies implemented new working models and addressed the correlated challenges. The key objective is to identify the factors that made the companies resilient in terms of preparedness and adaptation. To achieve this, the cases of four companies were examined to understand how they responded to the pandemic and the changes they made, the new working models they adopted, how they coped with the challenges, which of their existing characteristics enabled them to cope effectively, and finally, if they made any permanent changes to their work models. Resilience is defined as the “preparedness to react flexibly to a crisis, awareness of risks and opportunities, and the ability to respond effectively and rapidly to changing circumstances” within the study. It revealed that open-minded and innovative company culture, employeeoriented behavior, and constant reassessments of remote work strategies and policies were key factors for the successful implementation of remote work and can be defined as factors of resilience. As governmental regulations and therefore the circumstances in which the companies operate changed constantly throughout the pandemic, Facebook, Microsoft, Shopify and Spotify had to be extremely flexible in adjusting their strategies and policies. All of the above recognized the opportunities that emerged from the situation to develop new working models, acknowledged employees’ needs as a priority in order to respond effectively and address possible challenges. Their innovative and open-minded company culture enabled flexible and rapid implementations of newly developed strategies and policies regarding remote working, but also hybrid work models.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| 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".