Exploring how accounting firms build dynamic capabilities in Artificial Intelligence-driven Analytics (AIDA) as they pursue digital transformation

Lee, Benjamin Huan Zhou (2025) Exploring how accounting firms build dynamic capabilities in Artificial Intelligence-driven Analytics (AIDA) as they pursue digital transformation. PhD thesis, University of Glasgow.

Abstract

This thesis investigates how accounting firms develop dynamic capabilities (DCs) in Artificial Intelligence-driven Analytics (AIDA) whilst pursuing digital transformation. Through qualitative multiple case studies involving 24 participants from 11 accounting firms across different size categories in Singapore—including Big 4s, Mid-tiers, and Boutiques—the research examines the processes through which accounting firms sense opportunities for AIDA adoption, seize these opportunities through strategic investments, and transform to effectively integrate these technologies.

By using the DCs framework as the main theoretical lens, supplemented by strategy-as practice (SAP) and technologies-in-practice (TIP) perspectives, the study identifies distinct patterns in how accounting firms of different sizes build capabilities for AIDA adoption. The findings reveal that while accounting firms share common objectives of enhancing client service delivery, their approaches to developing DCs vary based on firm type, market position, and strategic priorities. Big 4s prioritise global integration with local flexibility, Mid-tiers focus on operational efficiency within resource constraints going with pragmatic and workflow-specific implementations, and Boutiques stay agile and emphasise client-specific customisation to specialise in niche areas.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Colleges/Schools: College of Social Sciences > Adam Smith Business School > Management
• Supervisor's Name: Warner, Dr. Karl and Dudau, Professor Adina
• Date of Award: 2025
• Depositing User: Theses Team
• Unique ID: glathesis:2025-85406
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 26 Aug 2025 06:50
• Last Modified: 26 Aug 2025 06:52
• Thesis DOI: 10.5525/gla.thesis.85406
• URI: https://theses.gla.ac.uk/id/eprint/85406