Machine Learning in Management Accounting Research: Literature Review and Pathways for the Future
综述了机器学习在管理会计研究中的应用现状，指出其仍处于早期阶段，并提出了四个最有前景的应用方向，包括利用文本数据、量化非结构化数据、改进预测以及使用可解释人工智能。
This paper explores the possibilities of employing machine learning (ML) methods and new data sources in management accounting (MA) research. A review of current accounting and related research reveals that ML methods in MA are still in their infancy. However, a review of recently published ML research from related fields reveals several new opportunities to utilize ML in MA research. We suggest that the most promising areas to employ ML methods in MA research lie in (1) the exploitation of the rich potential of various textual data sources; (2) the quantification of qualitative and unstructured data to create new measures; (3) the creation of better estimates and predictions; and (4) the use of explainable AI to interpret ML models in detail. ML methods can play a crucial role in MA research by creating, developing, and refining theories through induction and abduction, as well as by providing tools for interventionist studies.