Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7867
Title: The Role of Mixed-emotion in Shaping Consumers’ Behavior: A Study of Online and Brick-and-mortar Fashion Retailing in Hong Kong
Authors: Lam, S. T. 
Prof. LAU Kung Wong, Nick 
Lau, H. F. 
Issue Date: 2014
Source: Applied Economics and Business Review, 2014, 1(2), 31-39
Journal: Applied economics and business review 
Abstract: This article explains the relationship between consumers’ mixed emotions and the impact of their purchasing behavior in Hong Kong online and brick-and-mortar fashion retail channels. Approach-avoidance conflict and consumer experiences are applied to explain consumer choice of the approach and avoidance shopping in particular fashion retail channels. Fashion retail elements, such as retailing environment, social influence and product risks, are found out that have impact in consumers’ emotional states and result in purchasing responses. Mixed emotions are from different stimulations in retail experiences and all of them would affect the final purchase decision. Hong Kong fashion retailers should focus their elements that stimulate negative emotion states and try to improve it. Series of correlation, t-test and multiple mediated regression analyses are applied to test the relationship between consumers’ experience, mixed emotions and behavior in Hong Kong brick-and-mortar and online fashion retail channels. Our research team find that there are different between brick-and-mortar and online fashion retail channels. Store elements, products and social influence have different extend in affecting consumers’ emotional states and purchasing in Hong Kong. Several suggestions are recommended to Hong Kong brick-and-mortar and online fashion retail channels. They need to improve their retail elements, such as store environment, products and social influence, in order to increase their sales revenues in Hong Kong.
Type: Peer Reviewed Journal Article
URI: http://hdl.handle.net/20.500.11861/7867
ISSN: 2333-8717
DOI: 10.12966/aebr.04.01.2014
Appears in Collections:Applied Data Science - Publication

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