What Do We Know About the Science of Science in Business and Economics? Insights From the Top 50 Journals, 2008–2022
Bibliographic record
Abstract
We conduct a descriptive analysis of the performance of disciplines, journals, authors, and universities based on the number of publications in 50 business and economics journals represented in the Financial Times list (FT 50) from 2008 to 2022 using data from the Web of Science. We analyze more than 55,000 papers published by more than 54,000 unique authors during this period to document four sets of findings. First, our analysis suggests that Operations Management and Multidisciplinary areas experienced significant publication growth from 2008 to 2022, increasing their publication and citation share in FT 50 journals, even after accounting for the relative handicap in terms of number of journals in the FT 50 list. Second, the growth in publications of the Operations Management discipline is driven by significant growth in Production and Operations Management and Manufacturing and Service Operations Management . The Journal of Business Ethics is a notable outlier, it alone published nearly 10% of the papers published in the FT 50 list, more than the number of papers published by the five disciplines each: Accounting, Marketing, Multidisciplinary, Operations Management, and Information Systems. Third, we find that the most prolific authors in Management and Organizations, Operations Management, Economics, and Marketing had a higher number of publications during the 2008–2022 period than the most prolific authors of other disciplines. Finally, our analyses of the most prolific 150 worldwide universities published in the FT 50 list suggest that the list is dominated mostly by about a dozen countries that include North American countries (the USA and Canada), European countries (Netherlands, Germany, the UK, France, and Spain), China (including Hong Kong), Australia, and Singapore. We discuss the implications of our findings for ranking organizations, editors, academic associations, individual scientists, administrators, and policymakers.
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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 | 0.013 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.036 | 0.047 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".