A Configural Approach to Coordinating Expertise in Software Development Teams1
Bibliographic record
Abstract
Despite the recognition of how important expertise coordination is to the performance of software development teams, understanding of how expertise is coordinated in practice is limited. We adopt a configural approach to develop a theoretical model of expertise coordination that differentiates between design collaboration and technical collaboration. We propose that neither a strictly centralized, top-down model nor a largely decentralized approach is superior. Our model is tested in a field study of 71 software development teams. We conclude that because design work addresses ill-structured problems with diverse potential solutions, decentralization of design collaboration can lead to greater coordination success and reduced team conflict. Conversely, technical work benefits from centralized collaboration. We find that task knowledge tacitness strengthens these relationships between collaboration configuration and coordination outcomes and that team conflict mediates the relationships. Our findings underline the need to differentiate between technical and design collaboration and point to the importance of certain configurations in reducing team conflict and increasing coordination success in software development teams. This paper opens up new research avenues to explore the collaborative mechanisms underlying knowledge team performance.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".