Do the Project Manager’s Soft Skills Matter? Impacts of the Project Manager’s Emotional Intelligence, Trustworthiness, and Job Satisfaction on Project Success
Bibliographic record
Abstract
Recent warnings have been raised about the project success rate in organizations. Among many reasons of disappointing results, research on project management reveals a gap in examining project success. Traditionally, project success has been widely studied from the rational view but rarely from the behavioral view. Today’s businesses are facing multiple challenges and opportunities in a volatile market environment that require constant changes within organizations and leaders’ behavior. The role of project managers is no longer the same. This study attempts to update the discussion of project managers soft skills by examining two major behavioral factors: project manager’s emotional intelligence and trustworthiness and their impact on job satisfaction and project success. This research compiles a quantitative survey. Data were collected from 101 project team professionals. The results reveal that project managers’ emotional intelligence and their team members’ trust in them impact project success significantly. The findings provide organizations with a necessary complementary behavioral view of project management. Organizations can take project manager trustworthiness and emotional intelligence into account when recruiting and training project managers and throughout the project planning and execution life span.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".