Using data mining to analyse fashion consumers preferences from a cross-national perspective

Osmud Rahman, Benjamin C.M. Fung, Wing Sun Liu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)

Abstract

The purpose of this study is twofold: (1) to cluster the respondents into three consumer groups-fashion innovator, fashion follower and laggard and (2) to extract association rules from the data set in order to understand consumers preferences. A data-mining method was employed to analyse considerable amount of data collected from four cities as well as to understand the complexity of the diffusion process of multiple apparel products. According to the results of the present study, style was not an important factor for the fashion leaders to purchase socks in Toronto, Hangzhou and Johor Bahru. In terms of t-shirts and evening dresses/suits, 53% and 51% of fashion laggards in China had shown their strong preferences for fit and comfort, respectively. Additionally, 60% of the fashion leaders in Canada had shown a strong preference for fit and style of t-shirts. Although this study is exploratory in nature, we believe that data mining has great potential for investigating fashion diffusion of innovativeness, and more replication of this type of research will be worthwhile and meaningful.
Original languageEnglish
Pages (from-to)42-49
Number of pages8
JournalInternational Journal of Fashion Design, Technology and Education
Volume7
Issue number1
DOIs
Publication statusPublished - 2 Jan 2014

Keywords

  • consumer preferences
  • cross-national study
  • data mining
  • diffusion of fashion innovation
  • product attributes

ASJC Scopus subject areas

  • Education
  • Visual Arts and Performing Arts
  • Industrial and Manufacturing Engineering

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