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http://dx.doi.org/10.5351/KJAS.2018.31.3.315

Study on analysis with partial least square path modeling using multiple factor analysis  

Park, Ri-Ra (Department of Statistics, Ewha Womans University)
Lee, Eun-Kyung (Department of Statistics, Ewha Womans University)
Publication Information
The Korean Journal of Applied Statistics / v.31, no.3, 2018 , pp. 315-328 More about this Journal
Abstract
In this paper, we examine the methodology to predict consumer preferences using several groups of attributes of products and application to real data. In the food industry, studies are in progress to investigate the relationship between product attributes and consumer preferences; consequently, various methodologies are proposed. Among these methodologies, we consider multiple factor analysis (MFA). The result of the MFA enable the division of consumers into four clusters with similar liking and the defining of preference characteristics for each cluster. Also, using the results of multiple factor analysis, we find the partial least squares path model to predict consumer preferences through the characteristics of the product and the characteristics evaluated by consumers. We can understand the relationship between the cluster of consumers and the preferred/undesirable characteristics of products through the partial least squares path model applied to two clusters with different liking. When multiple factor analysis is used in the partial least squares path model, it is possible to investigate relationships between products and consumers by analyzing product characteristics and consumer preferences simultaneously. The results can be applied to product developments and sales which makes this methodology important and useful.
Keywords
multiple factor analysis; partial least square; path analysis; sensory evaluation;
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