Project Managers and the Journey from Good to Great: Rethinking Project Management Training and Education in the Oil and Gas industry
Bibliographic record
Abstract
This study explores how education and training institutions can prepare project managers more effectively by evaluating project management education and development from the perspective of working project managers. The main objectives of this study are to describe the perceptions of project managers in the oil and gas sector regarding the skills, knowledge, and competencies necessary for success of future project managers; and to provide a practical framework to support project management education and training for developing project managers’ competency to face the complexity of projects. This study applied grounded theory, Delphi technique, and focus groups to solicit the opinions of project managers and project engineers working in the oil and gas sector in Calgary. The results of this study have provided insight into four main areas which educational institutions should consider in developing and preparing future project managers: 1) developing skills for dealing with complexity, 2) developing softer parameters of managing projects; especially interpersonal skills and leadership as opposed to just technical skills, 3) preparing project managers to be engaged within the context of real life projects, and 4) continuous skill development for project managers. The author argues that the education and training systems must do more to prepare project managers on their journey from good to great. The findings of this study reveal the importance of content, process of training, and education within the context of projects. The proposed model represents the bridge between the real world practice of project management and what should be included in project management education and training provided by educational institutions and industries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.006 |
| 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".