What Do We Really Know? A 40-Year Scientific Realist Examination of Theory Testing in Project Management
Bibliographic record
Abstract
ABSTRACT The scholarly project management literature that focuses on theory includes calls for theory adaptation and cross-fertilization, greater domestic theory development, and explicit communication of the philosophical underpinnings of theories. The nature and extent of theory testing, an indicator of intellectual progress, is an unexamined area. We address the research gap via a scientific realist analysis of theories tested in 4,033 articles published in three core project management journals (1983–2023). The results reveal steady growth in empirical research; a large volume of single tests of domestic theories commensurate with results in neighbouring management and organization studies disciplines; and a reliance on foreign theories for knowledge creation. We propose a more protective stance vis-à-vis foreign theories to enhance scholarly legitimacy and innovation, and hence propose a borrowed theory assessment framework to scrutinize them prior to admittance.
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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 | 0.112 | 0.157 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".