A Semiotic Analysis of a Model for Understanding User Behaviours in Ubiquitously Monitored Environments

A Semiotic Analysis of a Model for Understanding User Behaviours in Ubiquitously Monitored Environments

Keiichi Nakata (University of Reading, UK) and Stuart Moran (University of Reading, UK)
Copyright: © 2011 |Pages: 12
DOI: 10.4018/jissc.2011070103
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Abstract

Improvements in electronics and computing have increased the potential of monitoring and surveillance technologies. Although now widely used, these technologies have been known to cause unintended effects, such as increases in stress in those being observed. Further advancements in technology lead people towards the ‘pervasive era’ of computing, where a new means of monitoring ubiquitously becomes possible. This monitoring differs from existing methods in its distinct lack of physical boundaries. To address the effects of this kind of monitoring, this paper proposes a model consisting of a series of factors identified in the monitoring and pervasive literature believed to influence behaviour. The model aims to understand and predict behaviour, thereby preventing any potential undesirable effects, but also to provide a means to analyse the problem. Various socio-technical frameworks have been proposed to guide research within ubiquitous computing; this paper uses the semiotic framework to analyse the model in order to better understand and explain the behavioural impact of ubiquitous monitoring.
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Background Monitoring

The current application of information technology for monitoring has been known to result in unintended effects on users (Vorvoreanu & Botan, 2000). These undesirable effects are often found to outweigh the benefits of such systems, creating an overall negative impact (Botan & Vorvoreanu, 2005). Simply having awareness of the monitoring changes behaviour, and a well known, but often contested example of this, is the Hawthorne effect (Roethlisberger & Dickson, 1939). Employees in the Hawthorne works were monitored as part of an experiment to establish a relationship between their productivity and lighting levels. The employee’s productivity was unexpectedly found to increase regardless of the light intensity; this was attributed to the fact that they were aware that they were being observed. Awareness of monitoring has even been shown to cause changes in a person’s writing style and internet browsing habits (Botan & Vorvoreanu, 2005). While previous studies provide an insight into the likely effects of UM (Hayes et al., 2007; Zweig, 2005), its pervasiveness remains an area that has had little attention. This may result in an overall increase in the negative effects found in existing monitoring systems, or even generate previously unseen effects.

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