When Project Managers Face Extra-Scope Requirements: The Case of Talent Management
Bibliographic record
Abstract
This paper examines how project managers perceive and practice talent management while navigating extra scope requirements beyond their formally defined responsibilities. Drawing on 13 semi-structured interviews with project managers from Canadian organizations, we develop an initial program theory that explains project managers’ motivations and the conditions under which they engage in talent management practices. Our findings reveal that project managers often extend their role to address talent-related challenges, even though these roles and responsibilities may not be included in the formal scope definitions of their projects. This study contributes by (1) identifying key contextual factors influencing project managers' involvement in talent management, (2) highlighting misalignments between strategic talent priorities and project-level constraints, and (3) offering insights for improving alignment between organizational talent strategies and project execution. These findings provide actionable recommendations for project organizations seeking to integrate talent management more effectively into project workflows.
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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 | 0.016 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".