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Product classifications, consumer characteristics, and patronage preference for online auction

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  • Korgaonkar, Pradeep
  • Becerra, Enrique
  • O’Leary, Bay
  • Goldring, Deborah

Abstract

An important unanswered question in the area of Internet auctions is what dynamics influences consumer preferences for shopping on these online auctions. Two key aspects of studying what sells on the Internet auctions are (a) the characteristics of products to be purchased and (b) consumer characteristics. This research focuses on the relationship between product purchase preference, prior experience, and socioeconomic characteristics for the online auction participants. It uses product classification theory rooted in information economics and marketing. The study results are based on a US national sample of online auction participants. The findings suggest that product class affects online auction patronage, yet socioeconomic characteristics may not be as influential as once perceived. The online merchants can benefit from the inquiry to improve auctions for search, experience, and credence goods. The five key contributions of the findings add value to the theory and practice of consumer behavior.

Suggested Citation

  • Korgaonkar, Pradeep & Becerra, Enrique & O’Leary, Bay & Goldring, Deborah, 2010. "Product classifications, consumer characteristics, and patronage preference for online auction," Journal of Retailing and Consumer Services, Elsevier, vol. 17(4), pages 270-277.
    • Handle: RePEc:eee:joreco:v:17:y:2010:i:4:p:270-277
    DOI: 10.1016/j.jretconser.2010.02.004
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    2. Audrain-Pontevia, Anne-Françoise & N’Goala, Gilles & Poncin, Ingrid, 2013. "A good deal online: The Impacts of acquisition and transaction value on E-satisfaction and E-loyalty," Journal of Retailing and Consumer Services, Elsevier, vol. 20(5), pages 445-452.
    3. Hamelin, Nicolas & Thaichon, Park, 2016. "Consumer motives and impact of western media on the Moroccan luxury buyer," Journal of Retailing and Consumer Services, Elsevier, vol. 32(C), pages 164-170.
    4. Behera, Rajat Kumar & Gunasekaran, Angappa & Gupta, Shivam & Kamboj, Shampy & Bala, Pradip Kumar, 2020. "Personalized digital marketing recommender engine," Journal of Retailing and Consumer Services, Elsevier, vol. 53(C).

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