AIS research opportunities utilizing Machine Learning: From a Meta-Theory of accounting literature

Abstract

We use Accounting Information Systems (AIS) meta-theory to develop a framework for analyzing and using machine learning in accounting research, emphasizing 1) specific accounting research tasks, 2) supervised and unsupervised models, and 3) inductive vs. deductive research designs. We apply our framework to organize AIS and accounting research and highlight opportunities for future AIS research using machine learning. We discuss the changes in technology that have made machine learning more feasible in practice and research and how these changes might motivate and influence future research projects. We conclude by providing directions for future work in machine learning in AIS research and discussing the potential application to practice.