The primary goal of this article is to identify the research fronts on the application of data-driven methods in business and economics. For this purpose, the research literature of the business and economic sciences Subject Categories from the Web of Science is mapped using BibExcel and VOSviewer. Since the assignment to subject categories is done at the journal level and since a journal is often assigned to several subject categories in Web of Science, two mappings are performed: one without considering multiple assignments (broad view) and one considering only those (articles from) journals that have been assigned exclusively to the business and economic sciences subject categories and no others (narrow view). A further aim of this article is therefore to identify differences in the two mappings. Surprisingly, engineering sciences play a major role in the broad mapping, in addition to the economic sciences. In the narrow mapping, however, only the following clusters with a clear business-management focus emerge: (i) Data-driven methods in management in general and data-driven supply chain management in particular, (ii) Data-driven operations research analyses with different business administration/management focuses, (iii) Data-driven methods and processes in economics and finance, and (iv) Data-driven methods in Information Systems. One limitation of the narrow mapping is that many relevant documents are not covered since the journals in which they appear are assigned to multiple subject categories in WoS. The paper comes to the conclusion that the multiple assignments of subject categories in Web of Science may lead to massive changes in the results. Adjacent subject areas—in this specific case the application of data-driven methods in engineering and more mathematically oriented contributions in economics (econometrics) are considered in the broad mapping (not excluding subject categories from neighbouring disciplines) and are even over-represented compared to the core areas of business and economics. If a mapping should only consider the core aspects of particular research fields, it is shown in this use case that the exclusion of Web of Science-subject categories that do not belong to the core areas due to multiple assignments (narrow view), may be a valuable alternative. Finally, it depends on the reader to decide which mapping is more beneficial to them.

本文的主要目标是确定数据驱动方法在商业和经济学中的应用的研究前沿。为此，使用 BibExcel 和 VOSviewer 映射来自 Web of Science 的商业和经济科学学科类别的研究文献。由于对学科类别的分配是在期刊级别完成的，并且由于在 Web of Science 中通常将期刊分配到多个学科类别，因此执行两种映射：一种不考虑多个分配（广义），另一种只考虑那些（来自) 仅被指定为商业和经济科学学科类别而没有其他类别的期刊（狭义）。因此，本文的另一个目的是确定两个映射中的差异。出奇，除了经济科学之外，工程科学在广泛的绘图中也发挥着重要作用。然而，在狭义的映射中，只有以下具有明确业务管理重点的集群出现：(i) 一般管理中的数据驱动方法，特别是数据驱动的供应链管理，(ii) 数据驱动的运营研究分析具有不同的业务管理/管理重点，（iii）经济和金融中的数据驱动方法和流程，以及（iv）信息系统中的数据驱动方法。窄映射的一个限制是许多相关文档没有被涵盖，因为它们出现的期刊被分配到 WoS 中的多个主题类别。论文得出的结论是，Web of Science 中学科类别的多重分配可能会导致结果发生巨大变化。相邻学科领域——在这种特定情况下，数据驱动方法在工程中的应用和经济学（计量经济学）中更多以数学为导向的贡献被考虑在广泛的映射中（不排除相邻学科的学科类别），甚至与商业和经济的核心领域。如果映射应仅考虑特定研究领域的核心方面，则在此用例中显示，由于多项分配（狭义视图）而排除不属于核心领域的 Web of Science 主题类别可能是一个有价值的选择。最后，取决于读者来决定哪种映射对他们更有利。