Project management elements as strategic assets: preliminary findings
Bibliographic record
Abstract
Purpose To examine project management assets and to explore the link between these and the achievement of competitive advantage from the project management process through it being valuable, rare, inimitable, and having organizational support. Design/methodology/approach An online survey with North American Project Management Institute®members was conducted. Exploratory factor analysis was used to identify tangible and intangible elements of project management and the achievement of competitive characteristics of the project management process. Findings Six factors were extracted that comprised project management assets and three factors that comprised the competitive characteristics of the project management process. Research limitations/implications This was an exploratory study. It is expected to further develop the instrument, refine the model and constructs, and test it with a larger sample. Practical implications This study highlights the importance of developing intangible project management assets to achieve competitive advantage from the process. Originality/value Few papers have used the resource based view lens and applied it to project management. This paper contributes to the literature on the resource based view of the firm and to an improved understanding of project management as a source of competitive advantage.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".