Store buildings as tourist attractions: mining retail meaning of store building pictures through a machine learning approach
Pantano, Eleonora and Dennis, Charles ORCID: https://orcid.org/0000-0001-8793-4823
(2019)
Store buildings as tourist attractions: mining retail meaning of store building pictures through a machine learning approach.
Journal of Retailing and Consumer Services, 51
.
pp. 304-310.
ISSN 0969-6989
[Article]
(doi:10.1016/j.jretconser.2019.06.018)
|
PDF
- Final accepted version (with author's formatting)
Available under License Creative Commons Attribution-NonCommercial-NoDerivatives 4.0. Download (738kB) | Preview |
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 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.
Item Type: | Article |
---|---|
Research Areas: | A. > Business School > Marketing, Branding and Tourism |
Item ID: | 30574 |
Notes on copyright: | © 2019. This author's accepted manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ |
Useful Links: | |
Depositing User: | Charles Dennis |
Date Deposited: | 26 Jun 2020 11:03 |
Last Modified: | 29 Nov 2022 18:43 |
URI: | https://eprints.mdx.ac.uk/id/eprint/30574 |
Actions (login required)
![]() |
View Item |
Statistics
Additional statistics are available via IRStats2.