Classification systems, their digitization and consequences for data-driven decision making: Understanding representational quality

Classification systems are foundational in many standardized software tools. This digitization of classification systems gives them a new 'materiality' that, jointly with the social practices of information producers/consumers, has significant consequences on the representational quality of such information systems. Based on a multi-site field study, we suggest that representational quality is achieved through four types of negotiations that human actors engage in when confronted with the materiality of a new IS. These negotiations are associated with three broad practices (instantiation, renarration and meta-narration), and three different information production / consumption situations. We contribute to the relational theorization of representational quality and extend classification systems research by drawing explicit attention to the importance of 'materialization' of classification systems and the foundational role of representational quality in understanding the success and consequences of data-driven decision-making. © (2013) by the AIS/ICIS Administrative Office All rights reserved