Issues in the Adoption of the Scaled Agile Framework
Bibliographic record
Abstract
Agile methods were originally introduced for small sized, colocated teams. Their successful products immediately brought up the issue of adapting the methods also for large and distributed organizations engaged in projects to build major, complex products. Currently the most popular multi-teams agile method is the Scaled Agile Framework (SAFe) which, however, is subject to criticism: it appears to be quite demanding and expensive in terms of human resource and project management practices. Moreover, SAFe allegedly goes against some of the principles of agility. This research attempts to gather a deeper understanding of the matter first reviewing and analysing the studies published on this topic via a multivocal literature review and then with an extended empirical investigation on the matters that appear most controversial via the direct analysis of the work of 25 respondents from 17 different companies located in eight countries. Thus, the originality of this research is in the systemic assessment of the “level of flexibility” of SAFe, highlighting the challenges of adopting this framework as it relates to decision making, structure, and the technical and managerial competencies of the company. The results show that SAFe can be an effective and adequate approach if the company is ready to invest a significant effort and resources into it both in the form of providing time for SAFe to be properly absorbed and specific training for individuals.
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 | 0.099 | 0.141 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".