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Decision-making Behaviours toward Online Shopping

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  • Liying Wei

Abstract

The development of online shopping services is stimulated by both retailers and consumers, and understanding the decision-making behaviours of consumers becomes one of the crucial issues for retailers. Decision-making process, which refers to brand choice and price sensitivity, is unique in online purchase. Several motivation factors, such as situational factors, characteristics of products as well as the experience of previous e-shopping can influence consumers¡¯ attitudes to shop online. Moreover, available decision support systems can help people to make wise decisions among overwhelming information. A successful online retailer¡ªASOS is chosen as an example of how consumers¡¯ decision making can be supported through the online arena. As a suggestion, trust building and maintaining, brand loyalty building as well as recommendation agent are key points of online retailers¡¯ development in future. Furthermore, introduction of customer design system is the key contribution of this paper and detailed illustration of that is stated in suggestions and conclusion.

Suggested Citation

  • Liying Wei, 2016. "Decision-making Behaviours toward Online Shopping," International Journal of Marketing Studies, Canadian Center of Science and Education, vol. 8(3), pages 111-121, June.
  • Handle: RePEc:ibn:ijmsjn:v:8:y:2016:i:3:p:111-121
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    References listed on IDEAS

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    1. Bhatnagar, Amit & Papatla, Purushottam, 2016. "Increasing online sales by facilitating spillover shopping," Journal of Retailing and Consumer Services, Elsevier, vol. 29(C), pages 58-69.
    2. Brian T. Ratchford, 1982. "Cost-Benefit Models for Explaining Consumer Choice and Information Seeking Behavior," Management Science, INFORMS, vol. 28(2), pages 197-212, February.
    3. George M. Kasper, 1996. "A Theory of Decision Support System Design for User Calibration," Information Systems Research, INFORMS, vol. 7(2), pages 215-232, June.
    4. Chang, Chingching, 2011. "The Effect of the Number of Product Subcategories on Perceived Variety and Shopping Experience in an Online Store," Journal of Interactive Marketing, Elsevier, vol. 25(3), pages 159-168.
    5. Schultz, Don E. & Block, Martin P., 2015. "U.S. online shopping: Facts, fiction, hopes and dreams," Journal of Retailing and Consumer Services, Elsevier, vol. 23(C), pages 99-106.
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    Cited by:

    1. Tracy Wangechi Maina & Jackson Ndolo, 2019. "To Buy Online or Not? An Analysis of Online Consumer Decision Making," International Journal of Science and Business, IJSAB International, vol. 3(3), pages 135-140.
    2. Christina K. Dimitriou & Emad AbouElgheit, 2019. "Understanding generation Z’s travel social decision-making," Tourism and Hospitality Management, University of Rijeka, Faculty of Tourism and Hospitality Management, vol. 25(2), pages 311-334, December.

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    More about this item

    Keywords

    consumer behaviour; online shopping; decision-making process; decision support system; interactive decision aids; customer design system;
    All these keywords.

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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