Training Teams in Managing Projects in a Matrix Structure
Bibliographic record
Abstract
Projects are performed in different kinds of organizations: functional structure, project-based structure or matrix structure. The matrix organization is a combination of the functional organization and the “pure” project organization. In a matrix organization, there are usually two chains of command. The chain dealing with issues related to the functional division and the chain dealing with issues related to the project. Due to the split authority between project managers and functional managers, management becomes much more complicated. The cooperation between the project managers is vital for the matrix organization to perform well. Therefore, training teams of project managers in the matrix structure environment is required. A new method for training teams of project managers is presented. The proposed method is based on a real-time simulation called the Project Team Builder (PTB). PTB simulates a dynamic, stochastic multi-project management environment. A project management course for graduate students in systems engineering utilized PTB. The students used the simulator in a multi-user multi-project mode. A class of undergraduate engineering students participated in the same experiment as a control group. The 132 participants were divided into teams of three students (44 teams) which performed repetitive simulation-runs. Three factors were investigated: 1. Previous experience, 2. History recording mechanism, and 3. Team debriefing process. The findings indicate that for the initial learning phase, and for the transfer to different scenario phase, these three independent factors affect the performances. Furthermore, the interactions between the experience and history factors; between the experience and debriefing factors; and between the history and debriefing factors were all significant. Based on these findings a new paradigm for simulation-based team-learning model in a matrix organization structure is presented. The new model includes integration of history mechanism and debriefing procedure in the Kolb’s Team Learning Experience model.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".