Alignment between social and technical capability in software development teams
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The purpose of this paper is to highlight the importance of alignment at the team level. Because teams are important in software development projects, the paper focuses on studying the influence of alignment between the social and technical capabilities on software development team performance. Drawing on socio‐technical theory and software product development literature, the paper aims to identify social and technical capabilities for software development teams. Design/methodology/approach Empirical data from 192 software development teams were analyzed. The profile deviation approach was used to calculate alignment. Findings The findings suggest that misalignment between capabilities negatively impacts product and process performance. Research limitations/implications The study provides an intellectually coherent view in studying software development team performance. The study contributes to the literature by assessing alignment needs at the team level. Practical implications The study provides a holistic view for studying team capabilities and guides software development team leaders and managers to consider both the social and technical aspects in assessing team performance. Originality/value Alignment or misalignment is mostly studied in the literature from a macro level/organizational perspective. There exists a gap in the literature for studying alignment at more granular levels such as between various business sub‐units or within teams. The study addresses the gap by studying alignment within teams.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it