Abstract

Master data quality issues negatively affect the implementation of Enterprise Systems (ES), yet master data quality is not given enough attention during implementation planning. At the same time, there is increasing organisational pressure for accurate rep orting to aid timely decision making, especially as organisations look to Artificial Intelligence (AI) for decision support, that too is reliant on quality master data. Critical Realism (CR) was used as the paradigm and Affordance Theory as the lens to ide ntify six events in the case study of a Research Information System (RIS) implementation with the aim of understanding precursors, as mechanisms, of poor master data quality. By addressing these events, organisations can enhance report functionality and effectively plan for future implementations. In addition, t he identification and d escription of events is the first step for further research using Affordance Theory to identify mechanisms of poor master data quality

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