Digital transformation (DT) is prevalent in businesses today. However, current studies to guide DT are mostly qualitative, resulting in a strong call for quantitative evidence of exactly what DT is and the capabilities needed to enable it successfully. With the aim of filling the gaps, this paper presents a novel bibliometric framework that unearths clues from scientific articles and patents. The framework incorporates the scientific evolutionary pathways and hierarchical topic tree to quantitatively identify the DT research topics’ evolutionary patterns and hierarchies at play in DT research. Our results include a comprehensive definition of DT from the perspective of bibliometrics and a systematic categorization of the capabilities required to enable DT, distilled from over 10,179 academic papers on DT. To further yield practical insights on technological capabilities, the paper also includes a case study of 9,454 patents focusing on one of the emerging technologies - artificial intelligence (AI). We summarized the outcomes with a four-level AI capabilities model. The paper ends with a discussion on its contributions: presenting a quantitative account of the DT research, introducing a process-based understanding of DT, offering a list of major capabilities enabling DT, and drawing the attention of managers to be aware of capabilities needed when undertaking their DT journey.

数字化转型 (DT) 在当今的企业中很普遍。然而，目前指导 DT 的研究大多是定性的，因此强烈要求定量证据证明 DT 究竟是什么以及成功实现它所需的能力。为了填补空白，本文提出了一种新的文献计量框架，从科学文章和专利中挖掘线索。该框架结合了科学进化路径和分层主题树，以定量识别 DT 研究主题在 DT 研究中的进化模式和层次结构。我们的结果包括从文献计量学的角度对 DT 的全面定义，以及对启用 DT 所需能力的系统分类，这些分类是从超过 10,179 篇关于 DT 的学术论文中提炼出来的。为了进一步对技术能力产生实用的见解，该论文还包括一项针对 9,454 项专利的案例研究，重点关注其中一项新兴技术——人工智能 (AI)。我们用四级 AI 能力模型总结了结果。论文最后讨论了其贡献：对 DT 研究进行了定量描述，介绍了对 DT 的基于过程的理解，提供了支持 DT 的主要功能列表，并提请管理人员注意在以下情况下所需的功能开始他们的 DT 之旅。