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Store buildings as tourist attractions: Mining retail meaning of store building pictures through a machine learning approach

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)304-310
Number of pages7
JournalJournal of Retailing and Consumer Services
Volume51
Early online date3 Jul 2019
DOIs
DateAccepted/In press - 27 Jun 2019
DateE-pub ahead of print (current) - 3 Jul 2019
DatePublished - 1 Nov 2019

Abstract

The aim of this paper is to understand the extent to which a store building can function as a tourism attraction, using a large luxury department store as case research. The study draws upon the idea that people complete a hermeneutic circle to create an extraordinary tourism experience to share with others. The data gathering is based on the collection of pictures posted online on Flickr and analysed using a machine learning approach. A sample of 1,557 pictures related to a specific area in London (UK) were collected and analysed by means of a cluster analysis in order to determine which objects are most photographed. Findings reveal that the store building of a luxury department store is the central object in the majority of pictures within a 1km radius of the store main entrance, which demonstrates the role of store building attractiveness in tourism experience. The theoretical contribution is that this is the first paper adding the exterior of the building as attribute of the department store, and demonstrating the role of department stores in place attractiveness.

    Research areas

  • Big data analytics, Consumer behaviour, Place attractiveness, Retail store buildings, Tourism behaviour

Documents

Documents

  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the final published version of the article (version of record). It first appeared online via Elsevier at https://doi.org/10.1016/j.jretconser.2019.06.018 . Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 764 KB, PDF document

    Embargo ends: 3/01/21

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    Licence: CC BY-NC-ND

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