Project management assets and project management performance outcomes
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine characteristics of project management assets and project management performance outcomes as a step towards exploring the link between assets being valuable, rare, inimitable, and having organizational support and the achievement of competitive advantage. Design/methodology/approach This paper analyzes data from responses to an online survey by 198 North American Project Management Institute®members. Exploratory factor analysis is used to identify characteristics of project management assets and project management performance outcomes. Findings In total, six factors that comprised the characteristics of project management assets, three factors that comprised organizational support for project management assets, and two factors that comprised the project management performance outcomes were extracted. Research limitations/implications Limitations of this study include sample size, response rate, and self‐report bias, calling for a larger sample in ongoing research. This study is a step towards making the link between project management assets and performance outcomes. Practical implications This study draws managerial attention to project management assets as sources of competitive advantage, applying the resource based view of the firm that assets are sources of competitive advantage if they add economic value, are rare, are difficult to imitate, and have organizational support. Originality/value Few papers have applied the resource based view of the firm to examine project management capabilities as a source of competitive advantage. This paper contributes to the literature on the resource based view of the firm and contributes to an improved understanding of project management as a source of competitive advantage.
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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".