An Experimental Investigation of the Effects of Artificial Intelligence Systems on the Training of Novice Auditors

The primary objective of this research is to investigate the impact of task-technology fit on users' performance when using artificial intelligence systems for auditing tasks. Four artificial intelligence auditing systems, two problem-solving programs, and four questionnaires were developed. A laboratory experiment was performed with two hundred and ninety-two undergraduate auditing students. The results suggested that the effect of task-technology fit on accuracy in solving problems was marginal for case-based reasoning with unstructured tasks. No significant effect was found on problem-solving accuracy for rule-based reasoning with structured tasks. Task-technology fit did not affect length of time in solving problems. The task-technology fit, however, marginally increased users' certainty of the correctness of their solutions. The task-technology fit had an effect on confidence in conclusions provided by the artificial intelligence systems and perceptions of ease of use of the artificial intelligence systems only for rule-based reasoning with structured task. For case-based reasoning with unstructured task, the opposite direction to that expected was found on confidence in conclusions provided by the systems and perceptions of ease of use of the systems. Perceptions of usefulness of the artificial intelligence systems were not affected by task-technology fit