Through the Looking Glass: Examining Theory Development in Project Management with the Resource-Based View Lens
Bibliographic record
Abstract
Project management is a young discipline and young disciplines tend to lack well-developed theories. This paper examines several topics that help with theory development – the use of a common terminology and holistic frameworks, the importance of avoiding tautologies, and the merits of analogies. To guide the process, the paper draws from a recent empirical study that used the Resource-Based View to study project management as a strategic asset. The paper discusses how these four topics that contribute to theory development were managed in the study. Applying theory construction practices enables us to be more aware of the challenges related to research and improves our understanding of variables as used in conceptual and empirical papers. By applying the Resource-Based View to project management, the paper also shows how we can improve our 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 | 0.076 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.010 | 0.055 |
| Scholarly communication | 0.017 | 0.027 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".