Borderline Fields of Information Architecture: Information Overload, the Literacies, and Personal Information Management

Borderline Fields of Information Architecture: Information Overload, the Literacies, and Personal Information Management

Tibor Koltay (Szent István University, Hungary)
DOI: 10.4018/978-1-5225-3417-4.ch099
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This chapter addresses the complex relationships between information architecture and information overload from the viewpoint of the information professional. It is discussed in the light of information literacy, which cannot be considered without a discussion of other related literacies. Special attention and extended length will be given to data literacy, which is relatively new, but is on the way of gaining particular weight with the growing importance of data-related issues. As borderline fields of information architecture, information overload, the different literacies and personal information management play dissimilar roles. Information overload is the symptom, while well-known approaches and tools of information architecture, information literacy and other literacies, as well as personal information management offer different tools to alleviate these symptoms. Notwithstanding, there is undeniable connection between them, which should be made subject to further research.
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This chapter will approach information architecture from the viewpoint of the information professional with emphasis placed on its relationship with a number of various phenomena, some of which pertain to the same family. The focus of the argument will be on literacies, mentioned above, which are often pooled under the name of new literacies.

The broader context of the writing is identical with one of the functions of IA, described by Davis (2010), i.e. to explore ways to organize and create semantic and contextual informational relationships that accommodate user goals and behaviour.

IA consists of at least two main elements. It comprises an emerging community of practice that appears for example in the definition of the Information Architecture Institute (2007). The other component is “structuring and organizing information environments to help people effectively fulfil their information needs” (Toub, 2000). While approaching IA, we will concentrate mostly on the latter, not forgetting that without communities, IA would be incomplete.

Well-known approaches and tools of IA play decisive rule in enhancing our information environment. Coupling these tools with the advantages that are offered by new literacies can make IA more efficient and produce a favourable information ecology that corresponds to the complexity of IA issues (Resmini, 2012). This can be achieved by creating an information space and a network of relationships that can be built between the content, its contexts, the tools used the users, who access that content (Hagedorn, 2000) In other words, bringing new literacies closer to information architecture will cover a part of the gap that exist between information that is available in abundance and information that is findable and usable.

The topic of this chapter is small information architecture, i.e. its issues can be managed relatively easily, in contrast to big IA that has an ambitious agenda of designing information spaces and user experience of life on multiple levels, beyond the World Wide Web (Dillon, 2002). Thus, giving attention to the relationship between IO, the literacies and PIM encourages research in small IA.

Information architecture in its traditional sense (if there is one) should play an important role in helping people and organizations to combat information overload (Davis, 2012). As a starting point we must state the following. We cannot assume that people will want our information; even if we know that they need that information (Morville, 2005). One aspect of combating information overload is the proper design of information systems, based on a deep understanding of our users and their social contexts. Wrong models of users and their information-seeking behaviour result in failed design. When modelling users, we have to know that different people have differing motivations and mental models for handling information. This is an issue that has to be acknowledged by IA (Brown, 2010).

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