Fashion论文模板 – Critiquing the article ‘Using data mining to analyze fashion consumers’ preferences from cross-national perspective’

The article ‘Using data mining to analyze fashion consumers’ preferences from a cross-national perspective,’  by Rahman, Fung and Liu (2014) seeks to address how data mining can be used as a method to critically analyze different fashion preferences for consumers from a cross-national standpoint. The main purpose of this research study is to bring together different respondents into customer groups which include fashion follower, laggard and fashion leader. The other purpose of this study is to remove association rules from the research data set for the enhancement of the understanding of consumers’ fashion preferences.

This journal article explains and helps readers understand how fashion followers are influenced by fashion leaders. This explanation has previously been echoed by Kim, Fiore & Kim (2011). Moreover, the journal article provides credible information to the decision makers in the fashion industry on the marketing strategies and product launch.  This study found that consumers perceive the attributes of some fashion products differently whereas there are other attributes that they perceive the same. For instance, various consumer groups perceive the colour of a wedding dress to be the same (Rahman, Fung and Liu, 2014).

This journal article exhibits some major strength that helps in explaining what it addresses. For example, the data used in the research study is analyzed using an open-source tool known as Rapid Miner. This tool is important because it supports every process of operation procedures and data-mining (Aspers, 2010). Additionally, this open-source tool maintains multi-layered data view concepts which help in ensuring transparent and efficient data handling (Jackson and Shaw, 2008). Furthermore, the study employed the technique of data pre-processing to ensure that the data set analyzed in the study is reliable. Also, the researchers employed a data-transformation method to get a satisfactory data format to perform correlation and association analysis (Rahman, Fung and Liu, 2014). Besides, some questions in the questionnaire used in the study were designed to have choices that were listed in a reversed way. This helped in compensating for any random and careless answers.

Despite having some strong areas, this journal article exhibits various weaknesses.  For example, after the clustering analysis was conducted in four locations, the results obtained were not consistent. Moreover, the size of some samples used in the research study is small. For example, the size sample in Toronto which was comprised of male participants was quite small. The research study would have used a large sample to strengthen the reliability and validity of the data set used in the study. Besides, this journal article is not straightforward in giving the results of the research study. Further, the introduction part of the journal article is not clear to the reader. This is because it does not give a clear sense of what is done in the research study. Also, there is not a clear introduction to the research problem/question. Additionally, the summary of dataset statistics used in the journal article are complex and thus not easily understood by the reader. The journal article has some weaknesses in the literature review part. This is because none of the research studies used in the literature review examines fashion innovativeness with numerous products from a cross-national perspective.

Overall, the article is well-written and has an important message for the fashion designers and manufacturers. Nevertheless, to make it more attractive and informative to many readers, it needs to explain the results of the research study more concisely and increase the number of samples used to make the data set reliable and valid.


Aspers, P. (2010). Orderly fashion: a sociology of markets. Princeton, N.J., Princeton University Press.

Jackson, T., & Shaw, D. (2008). Mastering fashion marketing.

Kim, E., Fiore, A. M., & Kim, H. (2011). Fashion trends: analysis and forecasting. Oxford, Berg.

Rahman, O., Fung, B.C. and Liu, W.S., (2014). Using data mining to analyse fashion consumers’ preferences from a cross-national perspective. International Journal of Fashion Design, Technology and Education7(1), pp.42-49.

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