A grounded theory of coordination in remote-first and hybrid software teams
Bibliographic record
Abstract
While the long-term effects of the COVID-19 pandemic on software professionals and organizations are difficult to predict, it seems likely that working from home, remote-first teams, distributed teams, and hybrid (part-remote/part-office) teams will be more common. It is therefore important to investigate the challenges that software teams and organizations face with new remote and hybrid work. Consequently, this paper reports a year-long, participant-observation, constructivist grounded theory study investigating the impact of working from home on software development. This study resulted in a theory of software team coordination. Briefly, shifting from in-office to at-home work fundamentally altered coordination within software teams. While group cohesion and more effective communication appear protective, coordination is undermined by distrust, parenting and communication bricolage. Poor coordination leads to numerous problems including misunderstandings, help requests, lower job satisfaction among team members, and more ill-defined tasks. These problems, in turn, reduce overall project success and prompt professionals to alter their software development processes (in this case, from Scrum to Kanban). Our findings suggest that software organizations with many remote employees can improve performance by encouraging greater engagement within teams and supporting employees with family and childcare responsibilities.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| 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".