Intangible project management assets as determinants of competitive advantage
Bibliographic record
Abstract
Purpose To explore the role of intangible project management assets in achievement of competitive advantage from the project management process through it being valuable, rare, inimitable, and having organizational support. Design/methodology/approach Data were collected on tangible and intangible project management process assets and competitive characteristics of the project management process using an online survey of North American Project Management Institute™ members. Three key tangible asset factors, one intangible asset factor, and three competitive characteristics were identified using exploratory factor analysis. The relationship between these project management assets and project management process characteristics are examined using multivariate analysis. Findings Intangible project management assets are found to be a source of competitive advantage, directly and through a mediating role in the relationship between tangible project management assets and the competitive characteristics of the project management process. Practical implications This study highlights the importance of developing intangible project management assets, in addition to investment in tangible project management assets, to achieve competitive advantage from the process. Research limitations/implications This was an exploratory study. The authors expect to further develop the instrument, refine the model and constructs, and test it with a larger sample. 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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".