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Factors affecting female user information adoption: an empirical investigation on fashion shopping guide websites

Author

Listed:
  • Lifang Peng

    (Xiamen University)

  • Qinyu Liao

    (University of Texas Rio Grande Valley, Brownsville)

  • Xiaorong Wang

    (Xiamen University)

  • Xuanfang He

    (Xiamen University)

Abstract

With the prosperity of online shopping platforms, similar or even the same products tend to have a large variety of sources to be purchased from. More and more consumers seek the product information from online review websites before making a purchase, as they are willing to provide reviews or share their purchase experience. These behaviors turn the online review websites into vertical and community-based sales channels. Based on the Information Adoption Model, this study conducted an empirical investigation to analyze female users’ information adoption process when using fashion shopping guide website. The results show that information quality and source credibility have significant impact on information usefulness, which in turn contributes to information adoption. In addition, users with different levels of purchasing motivation demonstrate different dependence on information quality and source credibility.

Suggested Citation

  • Lifang Peng & Qinyu Liao & Xiaorong Wang & Xuanfang He, 2016. "Factors affecting female user information adoption: an empirical investigation on fashion shopping guide websites," Electronic Commerce Research, Springer, vol. 16(2), pages 145-169, June.
    • Handle: RePEc:spr:elcore:v:16:y:2016:i:2:d:10.1007_s10660-016-9213-z
    DOI: 10.1007/s10660-016-9213-z
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    References listed on IDEAS

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    1. Stephanie Watts Sussman & Wendy Schneier Siegal, 2003. "Informational Influence in Organizations: An Integrated Approach to Knowledge Adoption," Information Systems Research, INFORMS, vol. 14(1), pages 47-65, March.
    2. Chrysanthos Dellarocas, 2003. "The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms," Management Science, INFORMS, vol. 49(10), pages 1407-1424, October.
    3. Liu, Zhiwei & Park, Sangwon, 2015. "What makes a useful online review? Implication for travel product websites," Tourism Management, Elsevier, vol. 47(C), pages 140-151.
    4. Fred D. Davis & Richard P. Bagozzi & Paul R. Warshaw, 1989. "User Acceptance of Computer Technology: A Comparison of Two Theoretical Models," Management Science, INFORMS, vol. 35(8), pages 982-1003, August.
    5. Chris Forman & Anindya Ghose & Batia Wiesenfeld, 2008. "Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets," Information Systems Research, INFORMS, vol. 19(3), pages 291-313, September.
    6. Floyd, Kristopher & Freling, Ryan & Alhoqail, Saad & Cho, Hyun Young & Freling, Traci, 2014. "How Online Product Reviews Affect Retail Sales: A Meta-analysis," Journal of Retailing, Elsevier, vol. 90(2), pages 217-232.
    7. Nelson, Phillip, 1970. "Information and Consumer Behavior," Journal of Political Economy, University of Chicago Press, vol. 78(2), pages 311-329, March-Apr.
    8. Alba, Joseph W & Hutchinson, J Wesley, 1987. "Dimensions of Consumer Expertise," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 13(4), pages 411-454, March.
    9. Garbarino, Ellen & Strahilevitz, Michal, 2004. "Gender differences in the perceived risk of buying online and the effects of receiving a site recommendation," Journal of Business Research, Elsevier, vol. 57(7), pages 768-775, July.
    10. James E. Bailey & Sammy W. Pearson, 1983. "Development of a Tool for Measuring and Analyzing Computer User Satisfaction," Management Science, INFORMS, vol. 29(5), pages 530-545, May.
    11. Rodgers, Shelly & Harris, Mary Ann, 2003. "Gender and E-Commerce: An Exploratory Study," Journal of Advertising Research, Cambridge University Press, vol. 43(3), pages 322-329, September.
    12. Dellarocas, Chrysanthos, 2003. "The Digitization of Word-of-mouth: Promise and Challenges of Online Feedback Mechanisms," Working papers 4296-03, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    13. C. Nadine Wathen & Jacquelyn Burkell, 2002. "Believe it or not: Factors influencing credibility on the Web," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 53(2), pages 134-144.
    14. Bin Gu & Jaehong Park & Prabhudev Konana, 2012. "Research Note ---The Impact of External Word-of-Mouth Sources on Retailer Sales of High-Involvement Products," Information Systems Research, INFORMS, vol. 23(1), pages 182-196, March.
    15. Ratneshwar, S & Chaiken, Shelly, 1991. "Comprehension's Role in Persuasion: The Case of Its Moderating Effect on the Persuasive Impact of," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 18(1), pages 52-62, June.
    16. Janiszewski, Chris, 1998. "The Influence of Display Characteristics on Visual Exploratory Search Behavior," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 25(3), pages 290-301, December.
    17. Doran, KathleenBrewer, 2002. "Lessons learned in cross-cultural research of Chinese and North American consumers," Journal of Business Research, Elsevier, vol. 55(10), pages 823-829, October.
    18. Pradeep K. Chintagunta & Shyam Gopinath & Sriram Venkataraman, 2010. "The Effects of Online User Reviews on Movie Box Office Performance: Accounting for Sequential Rollout and Aggregation Across Local Markets," Marketing Science, INFORMS, vol. 29(5), pages 944-957, 09-10.
    Full references (including those not matched with items on IDEAS)

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