There is a substantial literature on e-government that discusses information and communication technology (ICT) as an instrument for reducing the role of bureaucracy in government organizations. The purpose of this paper is to offer a critical discussion of this literature and to\ud provide a complementary argument, which favors the use of ICT in the public sector to support the operations of bureaucratic organizations. Based on the findings of a case study – of the Venice municipality in Italy – the paper discusses how ICT can be used to support rather than\ud eliminate bureaucracy. Using the concepts of e-bureaucracy and functional simplification and closure, the paper proposes evidence and support for the argument that bureaucracy should be preserved and enhanced where e-government policies are concerned. Functional simplification\ud and closure are very valuable concepts for explaining why this should be a viable approach

This paper investigates a web-based, medical research network that relies on patient self-reporting to collect and analyze data on the health status of patients, mostly suffering from severe conditions. The network organizes patient participation in ways that break with the strong expert culture of medical research. Patient data entry is largely unsupervised. It relies on a data architecture that encodes medical knowledge and medical categories, yet remains open to capturing details of patient life that have as a rule remained outside the purview of medical research. The network thus casts the pursuit of medical knowledge in a web-based context, marked by the pivotal importance of patient experience captured in the form of patient data. The originality of the network owes much to the innovative amalgamation of networking and computational functionalities built into a potent social media platform. The arrangements the network epitomizes could be seen as a harbinger of new models of organizing medical knowledge creation and medical work in the digital age, and a complement or alternative to established models of medical research.

Contemporary digital ecosystems produce vast amounts of data every day. The data are often no more than microscopic log entries generated by the elements of an information infrastructure or system. Although such records may represent a variety of things outside the system, their powers go beyond the capacity to carry semantic content. In this article, we harness critical realism to explain how such data come to matter in specific business operations. We analyse the production of an advertising audience from data tokens extracted from a telecommunications network. The research is based on an intensive case study of a mobile network operator that tries to turn its subscribers into an advertising audience. We identify three mechanisms that shape data-based production and three properties that characterize the underlying pool of data. The findings advance the understanding of many organizational settings that are centred on data processing.

Many organizations develop social media networks with the aim of engaging a wide range of social groups in the production of information that fuels their processes. This effort appears to crucially depend on complex data structures that allow the organization to connect and collect data from a myriad of local contexts and actors. One such organization, PatientsLikeMe, is developing a platform with the aim of connecting patients with one another while collecting self-reported medical data, which it uses for scientific and commercial medical research. Here the question of how technology and the underlying data structures shape the kind of information and medical evidence that can be produced through social media-based arrangements comes powerfully to the fore. In this observational case study, I introduce the concepts of information cultivation and social denomination to explicate how the development of such a data collection architecture requires a continuous exercise of balancing between the conflicting demands of patient engagement, necessary for collecting data in scale, and data semantic context, necessary for effective capture of health phenomena in informative and specific data. The study extends the understanding of the context-embeddedness of information phenomena and discusses some of the social consequences of social media models for knowledge making