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Full-Text Articles in Science and Technology Studies
Editorial, New Trends In Information Systems Development, Karlheinz Kautz, Linda Dawson, Peter Nielsen, Nancy Russo
Editorial, New Trends In Information Systems Development, Karlheinz Kautz, Linda Dawson, Peter Nielsen, Nancy Russo
Associate Professor Linda Dawson
Information systems development (ISD), at the core of the information systems discipline, is an evolving field, faced with persistent challenges due to rapidly changing social and business environments as well as emerging technologies and technical infrastructures. Many of these issues have been discussed in the Information Systems Journal (see, for example, Kautz et al., 2007).
Towards A Framework For Mobile Information Environments: A Hospital-Based Example, Linda Dawson, Sea Ling, Maria Indrawan, Stephen Weeding, Juanita Femando
Towards A Framework For Mobile Information Environments: A Hospital-Based Example, Linda Dawson, Sea Ling, Maria Indrawan, Stephen Weeding, Juanita Femando
Associate Professor Linda Dawson
We propose a conceptual framework to describe and understand mobile information environments. In our proposal, such an environment can be categorised into different abstraction levels or communities: user level, workflow level, device level and architecture level. A hospital-based example is then used as an illustration for the proposed framework.
The Information In Aggregate Data, David Steel, Eric Beh, Raymond Chambers
The Information In Aggregate Data, David Steel, Eric Beh, Raymond Chambers
Professor David Steel
Ecological inference attempts to draw conclusions concerning individual-level relationships using data in the form of aggregates for groups in the population. The groups are often geographically defined. A fundamental statistical issue is how much information aggregate data contain concerning the relationships and parameters that we are trying to estimate. The information affects the standard errors of estimates as well as the power of any tests of hypothesis. It also affects the ability to tell, from the aggregate data, which different models under consideration are supported by the data. In this chapter likelihood-based methods are considered. We show in general how …