Assessing the Societal Impact of Academic Research With Artificial Intelligence ( <scp>AI</scp> ): A Scoping Review of Business School Scholarship as a ‘Force for Good’
Bibliographic record
Abstract
ABSTRACT This study addresses critical questions about how current evaluative frameworks for academic research can effectively translate scholarly findings into practical applications and policies to tackle societal ‘grand challenges’. This scoping review analysis was conducted using bibliometric methods and AI tools. Articles were drawn from a wide range of disciplines, with particular emphasis on the business and management fields, focusing on the burgeoning scholarship area of ‘business as a force for good’. The novel integration of generative AI research approaches underscores the transformative potential of AI‐human collaboration in academic research. Metadata from 4051 articles were examined in the scoping review, with only 370 articles (9.1%) explicitly identified as relevant to societal impact. This finding reveals a substantial and concerning gap in research addressing the urgent social and environmental issues of our time. To address this gap, the study identifies six meta‐themes related to enhancing the societal impact of research: business applications; faculty publication pressure; societal impact focus; sustainable development; university and scholarly rankings; and reference to responsible research frameworks. Key findings highlight critical misalignments between research outputs and the United Nations Sustainable Development Goals (SDGs) and a lack of practical business applications of research insights. The results emphasise the urgent need for academic institutions to expand evaluation criteria beyond traditional metrics to prioritise real‐world impacts. Recommendations include developing holistic evaluation frameworks and incentivising research that addresses pressing societal challenges—shifting academia from a ‘scholar‐to‐scholar’ to a ‘scholar‐to‐society’ paradigm. The implications of this shift are applied to business‐related scholarship and its potential to inspire meaningful societal impact through business practice.
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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 | 0.205 | 0.461 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.074 | 0.056 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.023 | 0.014 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".