Ratings of e-auction sellers have been an important index for e-auction buyers to form their trust. Many researches show that trust impacts the success of e-auction. Reputation or ratings have significant impacts on trust in e-auction. In order to investigate the phenomena of ratings and trust, this research adopts the diffusion of innovation model to perform an empirical data fitting analysis for the trends of seller¡¦s ratings. The results show that the rating grows slowly in the beginning, and it accelerates when the ratings are accumulated. The maximum growth rate change usually is in the range of 240 to 320 for the numbers of ratings. It implies the sellers with ratings of the amounts sell more and grow quicker. It implies they have gained more trust from the e-auction buyers. The other possibility is that those sellers become more familiar and skillful to perform e-auction.

With the rapid Internet development, E-auction is also popular in recent years. In E-auction, the rating is the most effective indicator that can provide a referral for buyer and seller. In addition, buyers can use rating mechanism as feedback to respond their satisfaction after they bought goods. In the other hand, the rating of seller could reflect his transaction history before. When the positive rating is more, which means satisfied and successful transaction is more also, and represents that seller¡¦s credit accumulation. This study uses innovation diffusion model to analyze the seller¡¦s growth trend of rating, critical mass of rating by real data, classify sellers that equal to cluster analysis and discuss further. The samples are the sellers who sell t-shirt in eBay from December 1 in 2010 to January 31 in 2011. We get 8,304 sellers¡¦ data, and pick 116 of them randomly as samples, which are fit in with our research requests. This research is to answer three research questions. The first research question is to verify that growth trend of rating could fit in with diffusion of innovation, then, to analyze and discuss the growing trend and the rating accumulation. That result does verify that rating accumulation fit in with diffusion of innovation, and growing trend fit in with S-shaped curve. Furthermore, rating raise at first if it is affected by external influence, like key searching, website payment advertisement. On the contrary, the rating increases quickly for some time that the seller has good reputation if the rating is affected by internal influence, like word of mouth. The second research question is to calculate the critical mass of rating by Bass model. The result shows that the rating accelerates when it reaches critical mass between 1129 and 1402, it represents the seller accumulates considerable sale amount and customer satisfaction, and also let potential buyers more confident and promote their willingness to purchase. In addition, it can represent the sellers have enough experience and can provide the better marketing strategies when the sellers¡¦ rating reaches critical mass of rating. The third research question is to divide the sellers by cluster analysis and investigate. The result shows the diverseness between the growth trend of rating, the critical mass of rating, product price, and buyer repeated purchase. This study can provide a referral for the novice sellers, and they can develop their marketing strategy base on their characteristics of product.