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<p>We investigate how Artificial Intelligence (AI) enhances the productivity and quality of research in business studies. We identified 1760 journal articles from SCOPUS and Web of Science, and analyzed 62 of them by conducting a systematic literature review based on PRISMA guidelines. The findings are categorized into three main areas: the quality and quantity of research output, disciplinary impacts, and ethical issues. We show that AI helps reduce research time and improve data management. Methods like machine learning and natural language processing can effectively uncover patterns and trends that conventional research methods may overlook. We also highlight significant challenges that require attention, including data privacy, intellectual property rights, and algorithmic bias. While we acknowledge limitations related to data usage and the generalizability of our reviews, especially given the rapid evolution of AI technologies, we recommend that researchers effectively integrate AI into their research activities and establish ethical frameworks for its application. Moreover, policymakers and managers should educate themselves and their teams about AI to maximize its benefits while minimizing associated risks. Overall, this study highlights the advantages of AI integration and its potential drawbacks, providing essential perspectives for researchers and policymakers.</p>

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How does artificial intelligence shape the productivity and quality of research in business studies? A systematic literature review and future research framework

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月28日

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  • Sugandha Agarwal,
  • Qian Long Kweh,
  • Dima Jamali,
  • Walton Wider,
  • Syed Far Abid Hossain,
  • Muhammad Ashraf Fauzi

摘要

We investigate how Artificial Intelligence (AI) enhances the productivity and quality of research in business studies. We identified 1760 journal articles from SCOPUS and Web of Science, and analyzed 62 of them by conducting a systematic literature review based on PRISMA guidelines. The findings are categorized into three main areas: the quality and quantity of research output, disciplinary impacts, and ethical issues. We show that AI helps reduce research time and improve data management. Methods like machine learning and natural language processing can effectively uncover patterns and trends that conventional research methods may overlook. We also highlight significant challenges that require attention, including data privacy, intellectual property rights, and algorithmic bias. While we acknowledge limitations related to data usage and the generalizability of our reviews, especially given the rapid evolution of AI technologies, we recommend that researchers effectively integrate AI into their research activities and establish ethical frameworks for its application. Moreover, policymakers and managers should educate themselves and their teams about AI to maximize its benefits while minimizing associated risks. Overall, this study highlights the advantages of AI integration and its potential drawbacks, providing essential perspectives for researchers and policymakers.