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Personality differences and hotel web design study using targeted positive and negative association rule mining

Leung, Rosanna, Rong, Jia, Li, Gang and Law, Rob 2013, Personality differences and hotel web design study using targeted positive and negative association rule mining, Journal of hospitality marketing and management, vol. 22, no. 7, pp. 701-727, doi: 10.1080/19368623.2013.723995.

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Title Personality differences and hotel web design study using targeted positive and negative association rule mining
Author(s) Leung, Rosanna
Rong, Jia
Li, GangORCID iD for Li, Gang orcid.org/0000-0003-1583-641X
Law, Rob
Journal name Journal of hospitality marketing and management
Volume number 22
Issue number 7
Start page 701
End page 727
Total pages 27
Publisher Taylor & Francis
Place of publication London, Eng.
Publication date 2013
ISSN 1936-8623
1936-8631
Keyword(s) association rule mining
Big Five personality
web design
user preference
Summary As people have unique tastes, the way to satisfy a small group of targeted customers or to be generic to meet most people's preference has been a traditional question to many fashion designers and website developers. This study examined the relationship between individuals' personality differences and their web design preferences. Each individual's personality is represented by a combination of five traits, and 15 website design-related features are considered to test the users' preference. We introduced a data mining technique called targeted positive and negative association rule mining to analyze a dataset containing the survey results collected from undergraduate students. The results of this study not only suggest the importance of providing specific designs to attract individual customers, but also provide valuable input on the Big Five personality traits in their entirety.
Language eng
DOI 10.1080/19368623.2013.723995
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2013, Taylor & Francis
Persistent URL http://hdl.handle.net/10536/DRO/DU:30076112

Document type: Journal Article
Collection: School of Information Technology
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