Exploring Factors Influencing the Adoption of AI Tools in Auditing: A Mixed-Methods Study

Abstract

Artificial Intelligence's (AI) rise has created value for organizations and society, prompting scholars to study its spread across many areas. However, the impact of AI adoption on governmental organizations still needs to be explored. Governmental entities face unique challenges distinct from private organizations, and existing research often focuses on the perspectives of AI experts or senior management, neglecting the insights of lower-level employees who will use the system daily.

This study investigates the multifaceted factors influencing the intention to adopt AI tools within a governmental auditing bureau in Saudi Arabia. To the best of our knowledge, no previous study has specifically delved into AI adoption within the context of governmental auditing in the literature. This study employs an exploratory mixed-method approach based on IS guidelines by Venkatesh et al. (2013, 2016). This research combines qualitative and quantitative methods to comprehensively investigate the factors influencing the intention to adopt AI tools in auditing.

Initially, the study identifies key factors and develops a conceptual model grounded in qualitative data and theoretical background. The model is then validated and tested through a survey using a larger sample within the governmental bureau. The findings support many hypotheses, emphasizing the significance of technological factors such as AI complexity, perceived scalability, relative advantage, and security in the intention to adopt AI tools in auditing. The study also highlights the need to align governmental auditing tasks and AI tools, and the importance of Task Technology fit. Organizational factors, such as leadership support and strategic AI implementation, are crucial for successfully adopting AI. Additionally, environmental factors underscore the pivotal role of higher authorities in facilitating and supporting AI adoption in governmental organizations.

This study offers several contributions. It extends organizational AI adoption literature by broadening the understanding of AI adoption factors, emphasizing the value of studying government organizations due to their unique nature, and providing insights into the factors affecting AI adoption from the end-user's viewpoint. It offers practical benefits for the governmental auditing agency and similar governmental organizations. Educationally, this dissertation functions as a rich case study within the Information Systems (IS) field, providing a valuable educational resource.

Possible limitations include sample selection constraints, sample size in Phase I, and the limited contextual scope of the study. Directions for future research include examining the dynamics of AI implementation over time through longitudinal studies, testing the conceptual model across different governmental sectors and similar cultural and socio-political contexts, and investigating how AI tools affect auditors' compensation and job satisfaction.