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Profiling Green Consumers with Data Mining

Authors: Alireza Ziaei-Bideh; Mahsa Namakshenas-Jahromi;

Profiling Green Consumers with Data Mining

Abstract

Concern about the environment has led to a new segment of consumers called green consumers. Because not all the consumers are equally green, using target marketing for persuading them to buy green product is essential. The first step in target marketing strategy is to segment the market and then develop profiles of the resulting market segments. This study aims to identify distinct green market segments based on demographic, psychographic, and behavioral variables and also investigate the relationship between each variable and green consumer behavior. This study uses self-organizing maps (SOM) to segment and then develop profiles of Iranian green consumers. Based on the results, four market segments have been identified and were named intense greens, potential greens, egoist browns, and intense browns based on profiles of consumers in each segment. The results of this study also indicate that the level of education and income together with egoistic value and environmental unfriendly habits correlate negatively with the greenness (intent and intense of green behaviors) of Iranian consumers and the age of consumers together with environmental attitude and knowledge, biospheric and altruistic values, and religiosity correlate positively.

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    Top 10%
    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
4
Top 10%
Average
Average
Green
hybrid
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