Understanding Leadership in Agile Software Development Teams: Who and How?
Bibliographic record
Abstract
Abstract The principles in the Agile Manifesto, the Scrum Guide and most other approaches to agile software development emphasize self-organizing teams, but rarely address issues of leadership. In this paper we report on a study of the nature of different aspects of leadership in agile teams. We used an established model of leadership, distinguishing transactional and transformational styles, and asked IT professionals a set of questions about the leadership they experience, both from direct supervisors (hierarchical leadership) and from the team itself (shared leadership). We determined correlation measures of these four types of leadership with the extent of agility in the whole organization. Our results show that agility is indeed related to the transformational style, but that the transactional style also plays a part, especially as shared leadership. Furthermore, even in highly agile software development, leadership by direct supervisors still plays an important role. We propose that, as software development becomes more agile, the transactional aspects of leadership may shift away from the leadership dyad between supervisor and employee into the agile team, while transformational leadership is important for both the team and supervisors. We discuss our results in light of applications for both research and practice.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".