ERP system fit – an explorative task and data quality perspective

Abstract

Purpose – The purpose of this paper is to facilitate understanding of enterprise resource planning (ERP) system and data quality interdependency by presenting ERP systems’ use within data quality management. Design/methodology/approach – The authors apply task technology fit (TTF) in an explorative study, conducting semi-structured expert interviews with participants in information technology strategic decision making. The authors analyzed the interviews with iterative descriptive and subsequent interpretive coding. Findings – Although considered sustainable, continuously increasing regulations challenge ERP systems. However, compliance with regulations may serve as a bridge for organizations to engage in data analysis. Organizations are embedded into evolving task environments with the need to continuously adapt their systems or the organization and the need for contextual understanding of data quality. Research limitations/implications – With ERP systems being used for administrative functions, future research might draw on extant ERP systems research from the manufacturing sector. However, for insurance-specific tasks, ERP systems and their data need to be considered in a sector-specific context with the need for further research. Practical implications – ERP systems are considered sustainable. High initial fit is desirable, but the sector's relevance for ERP system vendors might be more important for sustainability. Ensuring TTF will be an increasing challenge with increasing task non-routineness. Originality/value – Applying TTF provides guidance for fit research, while the qualitative approach accounts for a deeper understanding, especially when exploring data quality issues since deficiencies might have several root causes. The authors show that ERP systems have an impact on data quality beyond its typically examined functionality.