Mediated effect of project management asset characteristics on firm performance
Bibliographic record
Abstract
Purpose The purpose of this paper is to study how project-level performance mediates the effect of project management assets on firm-level performance by examining the direct and mediated relationships between the project management process characteristics: valuable, rare, inimitable and organizationally supported on project-level and firm-level performance outcomes. Design/methodology/approach This paper analyzes data from an online survey completed by 198 North American Project Management Institute® members. Linear regression and Sobel Tests are used to examine the relationships between nine factors extracted from an exploratory factor analysis that comprise project management asset characteristics, one factor that comprises project-level performance outcomes, and one factor that comprises firm-level performance outcomes. Findings Not only does project-level performance positively and significantly affect firm-level performance, but project-level performance also significantly mediates the effect of project management asset characteristics (for all nine factors) on firm performance. Research limitations/implications Limitations of this study include sample size and self-report bias, calling for a larger sample in ongoing research. Practical implications This study contributes to the stream of literature on project management assets as sources of competitive advantage and makes the case for sustained organizational investments in the project management process. Originality/value This paper contributes to the limited, but increasing interest in applying the resource-based view of the firm to project management capabilities 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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".