Discovering Strategies to Lead Teams Abruptly Forced into Virtual Environments Due to the COVID-19 Pandemic
Bibliographic record
Abstract
COVID-19 was an unprecedented time in the global economy, causing a massive shift for many organizations conducting business. The speed at which organizations needed to implement World Health Organization’s restrictions and transition their teams from face-to-face to virtual environments was unpredictable. The purpose of this qualitative single case study was to explore what leaders prepared for an organization to cope with situations like COVID-19 when abruptly moving employees from face-to-face to virtual environments. A conceptual framework based on the organizational change theory and the team adaptation theory was used to direct this study. The research question address what strategies leaders within an organization now think they could have used during COVID-19 to adapt to an abrupt transition from face-to-face teams into virtual teams. Semistructured interviews were used to collect the data from 11 mid-to-senior level managers in a retail home renovation organization in Canada. A thematic analysis and Saldaña’s two-tiered coding process were conducted. The following four themes emerged: (a) bringing humanity back into the workplace, (b) mitigating extraordinary crisis and change, (c) swiftly pivoting to providing structure to business, and (d) adapting to the unconventional workplace environment. Within these four themes, leadership strategies for coping with the abrupt changes brought on by COVID-19 were discovered. The findings can contribute to positive social change by teaching leaders and managers what skills are needed, what strategies work, and how to continue to put the employees’ needs first to foster productivity at every level of the organization.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".