Identifying Sustainable Style Consumers with Decision Tree Predictive Model

dc.contributor.author Kwon, Theresa
dc.contributor.author Cho, Erin
dc.contributor.author Kim, Youn-Kyung
dc.date 2018-10-19T02:37:26.000
dc.date.accessioned 2020-06-30T05:38:15Z
dc.date.available 2020-06-30T05:38:15Z
dc.date.issued 2016-11-08
dc.description.abstract <p>Sustainable Style consumption (SSC) refers to “a distinctive mode of pursuing an individual style whose design one perceives classic and at the same time speaks about oneself.†Current study identifies pivotal factors that describe sustainable style consumers by creating a decision tree with RPART (recursive partitioning) method. A decision tree of SSC was built based on six pertinent lifestyle traits. Results indicated that the largest segment (19%) of high SSC group was explained by high level of fashion consciousness and high frugality. Although high fashion consciousness was regarded to be negatively associated with sustainable apparel consumption, our results show that high fashion consciousness, jointly with high frugality and high social responsibility, characterizes the high SSC segment. As high SSC consumers are more likely to purchase more environmental apparel and engage in sustainable apparel divestment, our decision tree model can be used to efficiently target and develop marketing strategies.</p>
dc.identifier archive/lib.dr.iastate.edu/itaa_proceedings/2016/presentations/76/
dc.identifier.articleid 1559
dc.identifier.contextkey 9919080
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath itaa_proceedings/2016/presentations/76
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/51473
dc.relation.ispartofseries International Textile and Apparel Association (ITAA) Annual Conference Proceedings
dc.source.bitstream archive/lib.dr.iastate.edu/itaa_proceedings/2016/presentations/76/SSR_Kwon_Sustainable_20Style_20Consumption_RES.pdf|||Sat Jan 15 01:50:59 UTC 2022
dc.title Identifying Sustainable Style Consumers with Decision Tree Predictive Model
dc.type event
dc.type.genre event
dspace.entity.type Publication
relation.isSeriesOfPublication 5d0f3f8c-2190-47b2-bb58-b59e2d1740d5
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