Theory usage in empirical operations management research: a review and discussion
Bibliographic record
Abstract
Purpose The purpose of this paper is to analyze more than three decades of theory testing published in leading operations management (OM) journals. Design/methodology/approach This piece examines the amount of theory testing, the extent to which theories are tested multiple times, and the disciplinary origins of the theories that are tested. Findings The analysis revealed that empirical OM researchers have increasingly responded to demands for more theory-driven knowledge over time. OM researchers are developing and using a wide array of domestic theories to understand empirical data. The examination also revealed a substantial focus on theory borrowed from other scientific fields. Originality/value The findings here suggest that OM is clearly a maturing discipline. As the discipline matures, it is important to consider to what extent borrowed theories and frameworks can offer value to OM. A preliminary vetting model is advanced in order to critically assess foreign theory. It is hoped that future screening promotes only the most useful non-domestic theory, thereby ensuring sufficient journal space for domestic theory and resulting in effective solutions to the pressing, practical problems of the OM field.
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 | 0.041 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.018 | 0.021 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".