Data underlying the PhD thesis: A Principle-based Framework for Audit Analytics Implementation

This research aims to develop a principle-based framework for audit analytics implementation, which addresses the challenges of AA implementation and acknowledges its socio-technical complexities and interdependencies among challenges. This research relies on mixed methods to capture the phenomena from the research’s participants through various approaches, i.e., MICMAC-ISM, case study, and interview with practitioners, with literature exploration as the starting point. The raw data collected consists of multimedia data (audio and video recordings of interviews and focused group discussion), which is then transformed into a text file (transcript), complemented with a softcopy of the documents from the case study object.<br>The published data in this dataset, consists of the summarized or analyzed data, as the raw data (including transcript) is not allowed to be published according to the decision by the Human Research Ethics Committee pertinent to this research (Approval #1979, 14 February 2022). This dataset's published data are text files representing the summarized/analyzed raw data as an online appendices to the thesis.本研究旨在构建一套基于原则的审计分析（Audit Analytics，以下简称AA）实施框架，以应对审计分析实施过程中的各类挑战，并充分考量其社会技术复杂性及各项挑战间的相互依存关系。本研究采用混合研究方法，以文献调研为起点，通过MICMAC-ISM分析法、案例研究法以及从业者访谈法等多种路径，从研究参与者处捕捉相关研究现象。所采集的原始数据包含多媒体资料（访谈与焦点小组讨论的音视频录像），经处理后转换为文本文件（即访谈转录稿），并辅以案例研究对象的相关文档电子版。本数据集的公开数据均为经过汇总或分析后的数据集，原因在于根据本研究相关的人类研究伦理委员会2022年2月14日第1979号审批决议，原始数据（含转录稿）不得公开。本数据集的公开数据为代表汇总/分析后原始数据的文本文件，作为学位论文的在线附录发布。