Standing on the shoulders of strategic management giants to advance organizational project management
Bibliographic record
Abstract
Purpose – The purpose of this paper is to examine relevant issues within the strategic management domain related to concepts and terms used within the resource-based view and dynamic capabilities (DC) theory. The paper explains how these theories from strategic management can be translated for organizational project management (OPM). The paper also shares lessons learned by the co-authors as used in project management. Design/methodology/approach – Based on a literature review and research experience of co-authors, the paper bridges two theories from the strategic management field to OPM and demonstrates conceptual challenges experienced. Findings – From a translational perspective, the paper outlines how theories from strategic management can be adopted to OPM. Since OPM is evolving, there is merit in drawing from a solid theoretical foundation such as those found in strategic management. Research limitations/implications – This paper is conceptual and makes a case for further empirical research using strategic management literature. Only recently has research in project management raised the important topic of translating knowledge from more established fields (the giants) to project management research. Practical implications – Strategic management theories offer insights that can be leveraged to make OPM environments more effective through improved research foundations. Originality/value – By critically exploring and assessing the resource-based view and DC bodies of literature, this paper's value rests in applying learnings from these fields to OPM and to develop a clearer understanding of concepts and emphasize their importance.
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 | 0.025 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".