Toward Sustainable Project Management Practices: Lessons from the COVID-19 Pandemic Using the Most Significant Change Method
Bibliographic record
Abstract
The COVID-19 pandemic prompted a major shift in project management practices, offering a unique opportunity to assess organizational resilience and sustainability. This study explores how project professionals in Quebec adapted to the early months of the pandemic, focusing on emergent practices in communication, decision making, stakeholder engagement, resource management, and scheduling. Using the most significant change (MSC) method, we collected and analyzed 114 stories from practitioners operating at both strategic and operational levels across multiple sectors. The findings reveal how project contributors reconfigured their practices to sustain value delivery amid disruption—adopting digital tools, modifying governance structures, and redefining engagement strategies. Operational contributors showed greater adaptability, while strategic actors experienced challenges with control and oversight. These stories illustrate not only reactive adaptations but also the foundations of more resilient and sustainable governance frameworks. By surfacing lived experiences and perceptions, this research contributes methodologically through its use of MSC and conceptually by linking crisis response with long-term sustainability in project contexts. Our study invites reflection on how temporary adaptations may evolve into embedded practices, reinforcing the interconnection between adaptability, resilience, and sustainability in the governance of project-based organizations.
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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 | 0.030 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".