Knowledge management in agile projects: a Scrum-KM extension proposal endorsed by Canadian and Brazilian project managers
Bibliographic record
Abstract
Purpose Considering the inherent tensions between the structured, long-cycle approaches of Knowledge Management (KM) and the dynamic, short-term nature of agile environments, the paper’s objective is to propose a Scrum-based extension designed to facilitate the seamless integration of KM practices within agile projects. Design/methodology/approach The research methodology employed a three-stage approach: thematic literature review, framework development, and framework validation. The thematic review encompassed works in the fields of KM, Information System and Agile Project Management literature with a focus on studies proposing KM and Agile frameworks, and systematic literature reviews. A lightweight Scrum-KM was designed to delineate appropriate KM activities, artifacts and structures throughout the Scrum cycles. In-depth semi-structured interviews were conducted with 11 senior project managers from large Brazilian and Canadian companies to endorse the proposal. Findings The results unveiled a lack of a structured KM strategy and short-term perspective, resulting in rework, knowledge loss, and limited knowledge exchange between projects. The proposed Scrum-KM extension reinforces the KM responsibilities of PMO and offers guidelines for structuring project knowledge repositories within scaling agile contexts. Practical implications The Scrum-KM extension guides practitioners in establishing inter-project forums to foster knowledge sharing, leverage project analytics, and nourish project knowledge repositories. Originality/value The Scrum-KM extension considers squad dynamics and the PMO’s role in fostering inter-project knowledge sharing, the importance of Content Management Systems for structuring knowledge bases, the challenge of leveraging Kanban charts for Project Management Analytics, and retrospective meetings as crucial opportunities for capturing lessons learned.
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 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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".