Paper ID
2471
Description
Pricing-oriented web technologies are known as the most important tools for e-marketplace sellers to stimulate customers to buy and increase their sales performance in the purchase stage; however, little is known about their impacts on product returns in the post-purchase stage. Drawing on cognitive dissonance theory and the e-commerce literature on reputation, we developed a research model that incorporates four types of pricing-oriented web technologies, seller reputation and product returns. Specifically, we posit that four common and popular types of pricing-oriented web technologies (i.e., limited time discount, price bundling, shop VIP, and quantity discount) impact product returns differentially. These relationships vary depending on seller reputation, manifested as an overall rating. To validate the research model, we collected a unique longitudinal dataset of 40,000 seller-month observations of 4,000 Taobao sellers in different industries over a period of 10 months.
Recommended Citation
Li, Mengyi; Li, Huifang; Fang, Yulin; Wang, Youwei; and Ming, Qingfei, "Pricing-oriented Web Technologies and Product Returns in the E-marketplace: The Moderating Role of Seller Reputation" (2019). ICIS 2019 Proceedings. 18.
https://aisel.aisnet.org/icis2019/economics_is/economics_is/18
Pricing-oriented Web Technologies and Product Returns in the E-marketplace: The Moderating Role of Seller Reputation
Pricing-oriented web technologies are known as the most important tools for e-marketplace sellers to stimulate customers to buy and increase their sales performance in the purchase stage; however, little is known about their impacts on product returns in the post-purchase stage. Drawing on cognitive dissonance theory and the e-commerce literature on reputation, we developed a research model that incorporates four types of pricing-oriented web technologies, seller reputation and product returns. Specifically, we posit that four common and popular types of pricing-oriented web technologies (i.e., limited time discount, price bundling, shop VIP, and quantity discount) impact product returns differentially. These relationships vary depending on seller reputation, manifested as an overall rating. To validate the research model, we collected a unique longitudinal dataset of 40,000 seller-month observations of 4,000 Taobao sellers in different industries over a period of 10 months.