Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/67819
Title: Conjoint analysis and clustering techniques in ready-to-cook dried pork product development
Authors: Rungchat Chompu-Inwai
Pelapon Suwanacheep
Trasapong Thaiupathump
Keywords: Engineering
Social Sciences
Issue Date: 1-Sep-2019
Abstract: © 2019 IEEE. The case study company produces and sells fresh products such as pork, chicken and eggs, and processed meat products. It is currently encountering the problem that the newly released ready-to-cook dried pork product has not been popular among consumers in Thailand. This research, therefore, aims at analyzing attributes of ready-to-cook dried pork products affecting the consumer purchasing preferences using Conjoint Analysis and Clustering Techniques together. With the use of Conjoint Analysis, consumers were asked to consider many attributes jointly rather than considering each attribute separately. Five attributes were studied; taste, standards guaranteeing tastiness, price, sodium levels, and the meat type used. The k-means clustering technique was used to cluster groups of consumers into three groups. In terms of attribute importance, it was found that the results of the entire group were consistent with the results of each cluster, that the top two most important attributes are taste and price. However, in terms of preferred attribute levels, although the preferred taste of the three clusters are the same as the overall group preference (equally salty and sweet), the other attribute levels are different among three clusters, as well as different from the overall preference. As a result, different types of products were recommended for each group of consumers. The results can be used to develop products to better respond to each market segment which will eventually help increase the case study company's sales.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85076362520&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/67819
Appears in Collections:CMUL: Journal Articles

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