Evaluating the impact of CAATs adoption on fraud detection: an integrated approach using DeLone & McLean and task-technology fit models

The rapid advancement of technology necessitates that the audit profession adopt innovative tools such as Computer-Assisted Audit Tools (CAATs). This study integrated the DeLone and McLean (D&M) model and the Task-Technology Fit (TTF) model to evaluate the alignment between auditors' tasks and the capabilities of CAATs, as well as to assess the impact of CAATs' quality on fraud detection. A quantitative approach was employed, utilizing primary data from 135 auditors at public accounting firms in Greater Jakarta. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that CAATs' information quality significantly impacts usage and satisfaction, whereas system quality does not. Meanwhile, CAATs' service quality enhances satisfaction but does not affect usage. Furthermore, the TTF model strongly supports auditors' intention to use CAATs, which is primarily driven by technological characteristics rather than task characteristics. Thus, although auditors' satisfaction with CAATs influences fraud detection, the intention to use CAATs does not. These findings offer valuable insights for audit firms and policymakers to focus on optimizing the quality of CAATs and their compatibility with audit tasks to maximize their effectiveness in detecting fraud, rather than solely promoting adoption.