Journal of Business Research Publications 1973–2024: Topics, methodological approaches, data, and analyses conducted
Bibliographic record
Abstract
This study analyzes trends in research published in the Journal of Business Research ( JBR ) by examining 10,211 abstracts from 1973 to 2024. The analysis uses categorization (of articles published in 1977–1988 versus 2013–2024) and topic modeling (of articles published from 1973 to 2024) to identify key patterns. Key findings include: (1) A shift from conceptual papers to more empirical research with a substantive focus, (2) An increase in studies utilizing interviews, surveys, and secondary data, while papers with no data usage have decreased, and (3) A rise in advanced analysis techniques, including regression, structural models, machine learning, and textual analysis. The topic modeling analysis reveals an underlying topical structure of 19 JBR research themes. Emerging themes pertain to digital transformation and technology, value co-creation, customer and brand equity, green (product) development, transformational management, service management, and innovation management and performance. Stable JBR themes include family businesses, leadership and executive boards, communication channels, strategic decision making, business ethics and responsibility, knowledge management, advertising, consumer behavior, (international) entrepreneurship, and buyers and sellers. Declining research themes concern performance and uncertainty, and forecasting and foresight. Thus, the research areas have grown more diverse, with clear subfields receiving increased attention. At the same time, more studies have delved into narrower, specialized topics, allowing for deeper investigation.
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 | 0.026 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.052 | 0.100 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.031 |
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".