Gifting personalised trajectories in museums and galleries
Publication Date
December 14, 2016
Creator
Abstract
The designers of digital technologies for museums and galleries are increasingly interested in facilitating rich interpretations of a collection’s exhibits that can be personalised to meet the needs of a diverse range of individual visitors. However, it is commonplace to visit these settings in small groups, with friends or family. This sociality of a visit can significantly affect how visitors experience museums and their objects, but current guides can inhibit group interaction, especially when the focus is on personalisation towards individuals.
This thesis develops an approach to tackling the combined challenge of fostering rich interpretation, delivering personalised content and supporting a social visit. Three studies were undertaken in three different museum and gallery settings. A visiting experience was developed for pairs of visitors to a sculpture garden, drawing upon concepts from the trajectories framework (Benford et al., 2009). Next, a study at a contemporary art gallery investigated how gift-giving could be used as a mechanism for personalisation between visitors who know each other well. Finally, the third study, at an arts and history museum, explored how gift-giving could be applied to small groups of friends and family.
The thesis reports on how the approach enabled visitors to design highly personal experiences for one another and analyses how groups of visitors negotiated these experiences together in the museum visit, to reveal how this type of self-design framework for engaging audiences in a socially coherent way leads to rich, stimulating visits for the whole group and each individual member. The thesis concludes by recommending the design and gifting of museum and gallery interpretation experiences as a method for providing deeply personalised experiences, increasing visitor participation, and delivering meaningful group experiences.
Item Type
ethesis
Thesis Type
PhD
Keywords
Subjects (LC)
Associated Schools / Departments
School of Computer Science (UK)
eprints ID
38013
UoN Repository URI
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Thesis-with-corrections.pdf
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Full-text
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Examined
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120.73 MB
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