Navigating Emotional Challenges During the Execution Phase: HRD Interventions for Early-Career Construction Project Managers
Bibliographic record
Abstract
The current study has two objectives: (a) to explore the emotional challenges faced by early-career construction project managers (PMs) during the execution phase, and (b) to propose Human Resource Development (HRD) interventions to enhance emotional awareness and regulation among early-career PMs. This study employs a qualitative methodology, utilizing semi-structured interviews with 24 senior construction PMs selected through purposive sampling. The data were analyzed using reflexive thematic analysis. The study identifies four key emotional challenges faced by early-career PMs during the execution phase: (a) unpredictability and shifting plans, (b) unhealthy hierarchy and lack of support, (c) lack of flexibility and stakeholder alignment, and (d) managing team cohesion. To address these challenges, the study proposes three strategies: (a) preparing for uncertainty, (b) practicing emotional detachment and stress management, and (c) offering peer support and mentorship. This research is novel due to its exclusive focus on the execution phase and its introduction of previously overlooked HRD strategies. It bridges a critical gap in project management literature by offering practical recommendations to enhance early-career PMs' emotional competence, helping organizations develop more effective and resilient project teams by embedding HRD strategies that enhance emotional intelligence, strengthen leadership capabilities, and improve adaptive decision-making, ultimately fostering a more sustainable and high-performing project environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".