Post-pandemic Resilience of Hybrid Software Teams
Bibliographic record
Abstract
Background. The COVID-19 pandemic triggered a widespread transition to hybrid work models (combinations of co-located and remote work) as software professionals' demanded more flexibility and improved work-life balance. However, hybrid work models reduce the spontaneous, informal face-to-face interactions that promote group maturation, cohesion, and resilience. Little is known about how software companies can successfully transition to a hybrid workforce or the factors that influence the resilience of hybrid software development teams. Goal. The purpose of this study is to explore the relationship between hybrid work and team resilience in the context of software development. Method. Constructivist Grounded Theory was used, based on interviews of 26 software professionals. This sample included professionals of different genders, ethnicities, sexual orientations, and levels of experience. Interviewees came from eight different companies, 22 different projects, and four different countries. Consistent with grounded theory methodology, data collection, and analysis were conducted iteratively, in waves, using theoretical sampling, constant comparison, and initial, focused, and theoretical coding. Results. Software Team Resilience is the ability of a group of software professionals to continue working together effectively under adverse conditions. Resilience depends on the group's maturity. The configuration of a hybrid team (who works where and when) can promote or hinder group maturity depending on the level of intra-group interaction it supports. Conclusion. This paper presents the first study on the resilience of hybrid software teams. Software teams need resilience to maintain their performance in the face of disruptions and crises. Software professionals strongly value hybrid work; therefore, team resilience is a key factor to be considered in the software industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| 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".