A Hybrid Method to Predict Human Action Actors in Accounting Information System

Abstract

Recent literature shows that adopting an accounting information system (AIS) can lead to better decision-making, planning, efficiency and on-time management control, and organisational functionality. However, the impact of AIS implementation on role creation in the organisation is unclear. With the digital transformation of AIS and daily advances in machine learning and other innovative technologies, it is also unclear how these changes interact with human roles in organisations and which AIS components are considered essential. This paper addresses the above issues by applying the actor-network theory to examine the impact of deep machine learning modules in predicting the human actor roles in accounting information systems in organisations. We targeted 120 human actors and examined the influence of deep machine learning modules in predicting 11 personnel and professional features of human actors, based on multivariate statistical analysis. Our findings show that two human factors (familiarity with accounting information and time spent on becoming familiar with it) are the most influential elements that can predict the human actor roles in accounting information systems in organisations. So, human and non-human actors are both essential parts of an integrated AIS that must be considered. The current literature has focused on the AIS structure with less on the interaction between human and non-human actors. One of the main contributions of this study is providing evidence that AIS heavily relies on its human and non-human actors to form a coherent and united AIS network to promote AIS management strategies. The practical implication of the results is that investing in either technology or human resources alone is not enough to achieve the best productivity and performance in organisations. Instead, there must be a balance between human and non-human actors.