Academic business research: Impact on academics versus impact on practice
Bibliographic record
Abstract
Business journalists and editors of academic business journals have lamented that academic research has little use for any nonacademic stakeholders, including companies, nonprofits, regulators, and governments. Although emotionally unsettling, these commentaries are bereft of evidence on how well a journal's academic impact (measured by impact factor) translates into practice impact. The authors provide this evidence. Specifically, they sample 56 journals, spanning 12 business disciplines, from 2000 to 2020. For each journal-year, they measure two- and five-year impact factor, which proxies the impact on academics. Next, for each article published in each journal-year, they collect attention score-a weighted sum of the number of times the article is cited in 19 types of practitioner outlets-from Altmetric. The authors then measure the correlation coefficient between the impact factor and attention score for each journal in periods of two-year and five-year. The coefficient indicates how well the journal's academic impact has translated into practice impact. Among the 12 disciplines, international business discipline tops the chart, while information systems, accounting, and finance occupy the bottom positions. American Economic Review leads the 56 journals, with Journal of Marketing Research and California Management Review as close followers. The findings highlight the impact of academic business research-or the lack thereof.
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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 | 0.047 | 0.294 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.025 | 0.040 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".