Mapping the Research Landscape of Sustainable Development Goals in Business, Management, and Accounting: A Bibliometric and Thematic Synthesis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Ongoing research related to the sustainable development goals (SDGs) agenda within business, management, and accounting has garnered significant scholarly attention due to its potential to address global sustainability challenges. This study presents a comprehensive bibliometric and content analysis of 1536 research articles published between 2011 and 2024. Using a bibliometric and thematic synthesis, the study unearths thematic trends, top contributors, and active research areas, including energy efficiency, circular business models, and sustainable reporting. The study unearths new empirical outlines in the field, revealing that SDGs such as No Poverty (SDG1), Zero Hunger (SDG2), Good Health and Well‐being (SDG3), and Life Below Water (SDG14) remain under‐researched in business and management scholarship, while SDGs 7, 8, 9, and 17 exhibit the broadest thematic diversity. Following an integrated approach that combines entropy analysis, bibliographic coupling, and content analysis, the study presents one of the first triangulated views of how SDG research in business disciplines has evolved from a CSR‐centric discourse toward multifaceted sustainability frameworks. These findings strengthen the theory and practice by identifying thematic blind spots, emergent multidisciplinary linkages, and implementable research directions for academia, policymakers, and industry.
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.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.026 | 0.072 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it