The linkage between leadership style of project manager and project performance: Evidence from telecommunication industry
Bibliographic record
Abstract
This research analyses how different leadership styles affect project managers in the telecommunications sector, focusing on transactional and transformational leadership's direct effects on project performance. Ambidextrous Leadership's role as a mediator is explored alongside the influence of Project Management approaches (Waterfall, Agile, Hybrid) and Project Manager Certifications. Data from 224 Project Managers in 77 Indonesian telecom companies was examined using Structural Equation Modelling Partial Least Square (SEM PLS). The findings indicate that Transactional and Transformational Leadership alone don't directly affect Project Performance, but Ambidextrous Leadership significantly enhances it. Different Project Management Approaches (Waterfall, Hybrid, Agile) amplify the impact of leadership styles. Transactional leadership is strongly linked to the waterfall, while transformational and ambidextrous leadership aligns with the agile and hybrid approach. Project Management Certification strengthens Transactional Leadership's effect on Project Performance, with less impact on Transformational Leadership. The research emphasizes the significance of Ambidextrous Leadership in improving project performance and how project management approaches and certifications can enhance or moderate the influence of leadership styles on telecommunication project management. These findings offer industry practitioners and organizations valuable insights, contributing to leadership, project management, and telecommunications research.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".