Designing a Model for Developing Auditors’ Data Literacy to Address Data-Driven Challenges: A Grounded Theory Approach

Abstract

This study aimed to design a systematic and context-specific model for developing auditors’ data literacy to facilitate effective responses to digital transformation and emerging data-driven auditing challenges. This applied qualitative study employed the systematic grounded theory approach developed by Strauss and Corbin. The participants comprised 15 academic and professional auditing experts in Iran who were selected during the second half of 2024 through purposive and snowball sampling until theoretical saturation was achieved. Data were collected through in-depth, semi-structured interviews lasting approximately 45–70 minutes. The interview data were analyzed through open, axial, and selective coding and the constant comparative method. The trustworthiness of the findings was established through participant validation, recording and repeated review of the interviews, parallel coding by researchers, and intercoder agreement analysis. The intercoder reliability coefficient was 0.91, indicating satisfactory consistency in the coding process. The analysis generated 6 major dimensions, 16 axial categories, and 48 concepts. Regulatory expectations and legal challenges, the data-driven economy, and the digital transformation of auditing emerged as causal conditions. Digital-era auditing standards, restrictions on access to financial data, the relationship between academia and the auditing profession, and digital audit-processing tools constituted the contextual conditions. Big data, blockchain and data security, and audit automation were identified as intervening conditions. The central phenomenon involved developing auditors’ data literacy through financial and business data analysis and the application of artificial intelligence and machine learning. Data-driven risk management and the development of auditors’ analytical skills emerged as the principal strategies. The sustainability of audit firms in the digital era was identified as the ultimate consequence of implementing these strategies. Developing auditors’ data literacy requires an integrated approach encompassing the revision of auditing standards, investment in technological infrastructure, stronger university–profession collaboration, continuous professional education, and the implementation of data-driven risk-management mechanisms. The proposed model can improve audit accuracy, efficiency, transparency, and reliability while strengthening the long-term sustainability and adaptability of audit firms in increasingly complex digital environments.