Journal
APPETITE
Volume 108, Issue -, Pages 191-202Publisher
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.appet.2016.09.019
Keywords
Consumer perception; Concept maps; Network analysis; Cluster analysis; Qualitative research; China; Organic food
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Funding
- European Commission [TH/ASIA-LINK/006, 91-652]
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Despite some similarities with developed countries, the growth of organic market in China seems to follow a different path. Thus, important questions are how Chinese urban consumers perceive organic food, and what are the main concepts associated to the organic attribute. We aimed at representing in graphic form the network of mental associations with the organic concept. We used an adapted version of the Brand concept mapping method to acquire, process, and draw individual concept networks perceived by 50 organic food consumers in Shanghai. We then analyzed the data using network and cluster analysis to create aggregated maps for two distinct groups of consumers. Similarly to their peers in developed countries, Chinese consumers perceive organic food as healthy, safe and expensive. However, organic is not necessarily synonymous with natural produce in China, also due to a translation of the term that conveys the idea of a technology advanced product. Organic overlaps with the green food label in terms of image and positioning in the market, since they are easily associated and often confused. The two groups we identified show clear differences in the way the organic concept is associated to other concepts and features. The study provides useful information for practitioners: marketers of organic products in China should invest in communication to emphasize the differences with Green Food products and they should consider the possibility of segmenting organic consumers; Chinese policy makers should consider implementing information campaigns aimed at achieving a better understanding of the features of these quality labels among consumers. For researchers, the study confirms that the BCM method is effective and its integration with network and cluster analysis improves the interpretation of individual and aggregated maps. (C) 2016 Elsevier Ltd. All rights reserved.
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