Quality information is critical to organisations’ success in today’s highly competitive environment. Accounting information systems (AIS) as a discipline within information systems require high quality data. However, empirical evidence suggests that data quality is problematic in AIS. Therefore, knowledge of critical factors that are important in ensuring data quality in accounting information systems is desirable. A literature review evaluates previous research work in quality management, data quality, and accounting information systems. It was found that there was a gap in the literature about critical success factors for data quality in accounting information systems. Based on this gap in the literature and the findings of the exploratory stage of the research, a preliminary research model for factors influence data quality in AIS was developed. A framework for understanding relationships between stakeholder groups and data quality in accounting information systems was also developed. The major stakeholders are information producers, information custodians, information managers, information users, and internal auditors. Case study and survey methodology were adopted for this research. Case studies in seven Australian organisations were carried out, where four of them were large organisations and the other three are small to medium organisations (SMEs). Each case was examined as a whole to obtain an understanding of the opinions and perspectives of the respondents from each individual organisation as to what are considered to be the important factors in the case. Then, cross-case analysis was used to analyze the similarities and differences of the seven cases, which also include the variations between large organisations and small to medium organisations (SMEs). Furthermore, the variations between five different stakeholder groups were also examined. The results of the seven main case studies suggested 26 factors that may have impact on data quality in AIS. Survey instrument was developed based on the findings from case studies. Two large-scale surveys were sent to selected members of Australian CPA, and Australian Computer Society to further develop and test the research framework. The major findings from the survey are: 1. respondents rated the importance of the factors consistent higher than the actual performance of those factors. 2. There was only one factor, ‘audit and reviews’, that was found to be different between different sized organisations. 3. Four factors were found to be significantly different between different stakeholder groups: user focus, measurement and reporting, data supplier quality management and audit and reviews. 4. The top three critical factors for ensuring data quality in AIS were: top management commitment, education and training, and the nature of the accounting information systems. The key contribution of this thesis is the theoretical framework developed from the analysis of the findings of this research, which is the first such framework built upon empirical study that explored factors influencing data quality in AIS and their interrelationships with stakeholder groups and data quality outcomes. That is, it is now clear which factors impact on data quality in AIS, and which of those factors are critical success factors for ensuring high quality information outcomes. In addition, the performance level of factors was also incorporated into the research framework. Since the actual performance of factors has not been highlighted in other studies, this research adds new theoretical insights to the extant literature. In turn, this research confirms some of the factors mentioned in the literature and adds a few new factors. Moreover, stakeholder groups of data quality in AIS are important considerations and need more attention. The research framework of this research shows the relationship between stakeholder groups, important factors and data quality outcomes by highlighting stakeholder groups’ influence on identifying the important factors, as well as the evaluation of the importance and p erformance of the factors.