Qin Yong, Xu Zeshui, Wang Xinxin, Skare Marinko
Business School, Sichuan University, Chengdu, China.
Juraj Dobrila University of Pula, Pula, Croatia.
J Knowl Econ. 2023 Mar 11:1-35. doi: 10.1007/s13132-023-01183-2.
In today's environment of the rapid rise of artificial intelligence (AI), debate continues about whether it has beneficial effects on economic development. However, there is only a fragmented perception of what role and place AI technology actually plays in economic development (ED). In this paper, we pioneer the research by focusing our detective work and discussion on the intersection of AI and economic development. Specifically, we adopt a two-step methodology. At the first step, we analyze 2211 documents in the AI&ED field using the bibliometric tool Bibliometrix, presenting the internal structure and external characteristics of the field through different metrics and algorithms. In the second step, a qualitative content analysis of clusters calculated from the bibliographic coupling algorithm is conducted, detailing the content directions of recently distributed topics in the AI&ED field from different perspectives. The results of the bibliometric analysis suggest that the number of publications in the field has grown exponentially in recent years, and the most relevant source is the "Sustainability" journal. In addition, deep learning and data mining-related research are the key directions for the future. On the whole, scholars dedicated to the field have developed close cooperation and communication across the board. On the other hand, the content analysis demonstrates that most of the research is centered on the five facets of intelligent decision-making, social governance, labor and capital, Industry 4.0, and innovation. The results provide a forward-looking guide for scholars to grasp the current state and potential knowledge gaps in the AI&ED field.
在当今人工智能（AI）迅速崛起的环境下，关于其对经济发展是否具有有益影响的争论仍在继续。然而，对于人工智能技术在经济发展（ED）中实际扮演的角色和所处的地位，人们只有零散的认知。在本文中，我们率先开展研究，将调查工作和讨论聚焦于人工智能与经济发展的交叉领域。具体而言，我们采用了两步法。第一步，我们使用文献计量工具Bibliometrix分析人工智能与经济发展领域的2211篇文献，通过不同的指标和算法呈现该领域的内部结构和外部特征。第二步，对通过文献耦合算法计算得出的聚类进行定性内容分析，从不同角度详细阐述人工智能与经济发展领域近期分布主题的内容方向。文献计量分析结果表明，近年来该领域的出版物数量呈指数级增长，最相关的来源是《可持续性》期刊。此外，深度学习和数据挖掘相关研究是未来的关键方向。总体而言，致力于该领域的学者已全面开展了密切的合作与交流。另一方面，内容分析表明，大多数研究集中在智能决策、社会治理、劳动力与资本、工业4.0和创新这五个方面。研究结果为学者们把握人工智能与经济发展领域的现状和潜在知识差距提供了前瞻性指导。
