A Study of Online Auction Sellers’ Intention to Switch Platform: The Case of Yahoo!Kimo Versus Ruten_eBay*

Abstract

The study of consumers' switching from one service provider to another has a long tradition in economics, information systems, and marketing. The emergence of electronic commerce presents new challenges in understanding consumers' switching intentions in the context of e-commerce in general and online auctions in particular. With the abundance of literature on online auctions, there is a surprising lack of research on auction sellers' intentions to switch from one online auction platform to another. Using the competition between Yahoo!Kimo and Ruten eBay, two leading auction platforms in Taiwan, as the backdrop, we developed a research model and collected empirical data based on this real case to study what factors influence auction sellers to switch to a competing service provider. We find that the higher the procedural switching costs, financial switching costs, relational switching costs, site design quality, or interaction quality, the lower the intention of an auction seller to switch to a competing auction service provider. A higher perceived transaction fee, however, leads to a higher switching intention.

Antecedents for user intention to adopt another auction site

“…First, the topics are different. Lin et al (2012) discusses auction sellers' intention to switch completely from one incumbent auction site to another. The focus is on how a leading auction site (Yahoo!Kimo Auction) may keep its customers from switching to competitors.…”

“…Yen and Lu (2008) applied expectancy disconfirmation theory to explore the determinants of repurchase intention in online auctions and found bidders' disconfirmation of online auctions is positively associated with their satisfaction, which is positively associated with their repurchase intentions. Recently, Lin et al (2012) examined the factors influencing online auction sellers' intention to switch platform. In practice, however, buyers and sellers do not switch completely from one platform to another.…”

Factors for User Intention to Switch Browsers: A Cross-National Survey

“…Considering that adoption is not synonymous with continuous use 25,26 , researchers have constructed their research models based on theories of technology adoption. 14,19,20,21,22 Literature indicates that the technology acceptance model (TAM), theory of planned behavior (TPB) and expectation-confirmation theory (ECT) were extended to investigate continued adoption.…”

“…12,13,15,28,20 Conceptually, mooring factors are the situational or contextual factors that influence migration intention. 24 Literature review indicates that sunk cost, 15,23 switching cost 9,10,14,17 or switching barrier 29 are most used to assess mooring factors. However, in a focus group and panel discussion of six Internet users and four scholars to assess the mooring factor, everyone agreed that web browsers are free, they have little to no switching cost or lock-in effect, and people can use switching in fingertips.…”

Design of Auction Parameters to Reduce the Effect of Collusion

“…Several auction theorists have attempted to design or modify auctions in order to generate higher revenue (Estache & Iimi, 2011;Janssen, 2004;Rosen & Madlener, 2013). However, in government organizations, it is very difficult to change the auction mechanism or platform (Lin, Cheng, Wang, & Chang, 2012). In these organizations, factors like tendering procedure, bid period, splitting of work content, and allowable profit margin influence the auction outcomes.…”