Assessing the Societal Impact of Academic Research With Artificial Intelligence (AI): A Scoping Review of Business School Scholarship as a ‘Force for Good’

IF 2.2 3区 管理学 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE
David Steingard, Kathleen Rodenburg
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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.

Abstract Image

用人工智能(AI)评估学术研究的社会影响:商学院奖学金作为“善的力量”的范围审查
本研究解决了一些关键问题,即当前的学术研究评估框架如何有效地将学术发现转化为实际应用和政策,以应对社会“重大挑战”。使用文献计量学方法和人工智能工具进行范围综述分析。文章来自广泛的学科,特别强调商业和管理领域,关注新兴的“商业为善的力量”的学术领域。生成式人工智能研究方法的新整合强调了人工智能-人类合作在学术研究中的变革潜力。在范围审查中检查了4051篇文章的元数据,其中只有370篇(9.1%)明确确定与社会影响相关。这一发现揭示了在解决我们这个时代紧迫的社会和环境问题的研究方面存在重大和令人担忧的差距。为了解决这一差距,该研究确定了与增强研究的社会影响相关的六个元主题:商业应用;教师出版压力;关注社会影响;可持续发展;大学和学术排名;并参考负责任的研究框架。主要发现突出了研究成果与联合国可持续发展目标(sdg)之间的严重偏差,以及研究见解缺乏实际的商业应用。研究结果强调,学术机构迫切需要将评估标准扩展到传统指标之外,以优先考虑现实世界的影响。建议包括发展整体评估框架和鼓励研究解决紧迫的社会挑战——将学术界从“学者对学者”的模式转变为“学者对社会”的模式。这种转变的含义适用于商业相关的学术研究,以及它通过商业实践激发有意义的社会影响的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Learned Publishing
Learned Publishing INFORMATION SCIENCE & LIBRARY SCIENCE-
CiteScore
4.40
自引率
17.90%
发文量
72
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