Comparing the power and influence of functional managers with that of project managers in matrix organisations: The challenge in duality of command
Bibliographic record
Abstract
Since its inception four decades ago, there has been widespread adoption of the matrix organisational design, particularly in project-based organisations. However, several challenges remain, one of which is related to the ambiguity of authority as a result of the dual command structure. This study examines the perceptions of the types of power and influence mechanisms used by the functional manager and the project manager to influence project personnel, and the effect of these mechanisms on attitudinal outcomes. The research used a two-phase design. The first qualitative phase validated the constructs of power and influence. In Phase 2, quantitative data was obtained from 22 functional managers, 28 project managers and 92 project personnel in South Africa, Italy and Canada from one large project execution technology company. There appears to be a large perceptual gap between project managers, functional managers and project personnel. Managers perceive themselves to be using aspirational and personal influence mechanisms, whereas project personnel perceive the managers to be using positional, punitive mechanisms. Relationships were observed between the perceived type of influence being used by the managers and the project personnel’s satisfaction with their manager, overall job satisfaction, their performance and level of engagement. Functional and project managers are associated with very different attitudinal outcomes among project team members.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".