Classifying project management resources by complexity and leverage
Bibliographic record
Abstract
Purpose The purpose of this paper is to present a conceptual framework to classify project management resources as sources of competitive advantage. Design/methodology/approach The paper draws on the resource‐based view of the firm and project management literature to explore the level of competitive advantage from 17 project management resources based on their degree of complexity and level of leverage in the project management process. This exploratory study drew on a small sample of practitioners in the classification. Findings The paper proposes a conceptual model to show the relationship between four categories of resources and their contribution to competitive advantage by being valuable, rare, inimitable, and organizationally supported. Research limitations/implications This paper is exploratory in nature and uses a small sample of practitioners. Practical implications The authors believe that the classification of project management resources based on complexity and leverage provides a useful framework for managers considering the impact of investment in these resources for competitive advantage. Originality/value This paper provides a classification of project management resources based on the complexity of the resource and its leverage in the project management process. It is posited that resources that are complex and can be highly leveraged to develop further resources warrant attention as sources 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".