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Researchers have studied the diffusion of information technology (IT) mainly as a process of adoption. However, the practices of supply-side agents of diffusion are just as critical to guaranteeing the adaptation of technology to the changing needs of the adopters. The execution of these practices over time and across multiple supply-side agents is both complex and understudied. Therefore, this study investigates IT diffusion practices from the perspective of supply-side agents. Drawing from a longitudinal case of development, sale, and institutionalisation of a healthcare information system, this article proposes a model of the supply side of technology diffusion. The model shows that diffusion depends on supply-side agents engaging in practices that are both demanding and possibly misaligned with traditional agent practices. Accordingly, the study provides an additional explanation for the complexity of technological diffusion and extends the theoretical framework of technology diffusion by adding supply-side dynamics. [ABSTRACT FROM AUTHOR]

Data are often vividly depicted as strategic assets that organisations can (re)use to create value for myriad purposes. However, the same qualities that make data so appreciated – that is, their volume, their value for a plurality of stakeholders and their indefinite reuse capacity – also have a dark side: data reuse can lead to deviant data use that undermines the legitimacy of data analytics initiatives. To investigate this dynamic, we build on the notion of data ecosystems and provide empirical evidence from a longitudinal, 15-year case study of the emergence, expansion and eventual collapse of a large-scale data analytics project – called DAMD – in the Danish health care sector. We demonstrate that data reuse in the evolving data ecosystem elicited a data reuse dark side that was so dominant that it eventually resulted in the project's demise. We conceptualise the reuse dark side's three major mechanisms as function creep, stakeholder creep and data creep. Based on these insights, we develop an empirically grounded Data Analytics Ecosystem Model that extends the current understanding of data ecosystems and provides a view of these ecosystems as having both a bright and a dark side. [ABSTRACT FROM AUTHOR]