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研究生:陳則文
研究生(外文):Tse-Wen Chen
論文名稱:網路拍賣中賣家評價的成長趨勢–創新擴散模型的應用
論文名稱(外文):The Growth Trend of E-Auction Seller''s Rating – An Application of the Diffusion of Innovation Model
指導教授:林芬慧林芬慧引用關係
指導教授(外文):Fen-Hui Lin
學位類別:碩士
校院名稱:國立中山大學
系所名稱:資訊管理學系研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2010
畢業學年度:98
語文別:英文
論文頁數:54
中文關鍵詞:網路拍賣創新擴散賣家評價
外文關鍵詞:Diffusion of InnovationE-AuctionSeller Rating
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網拍賣家的評價一直都是網路拍賣中很好的一個指標,買家因為賣家的評價高低而影響對賣家的信任感。已經很多研究指出,信任會影響網路拍賣的成功,以及評價對信任有顯著的影響,為了更深一步研究評價與信任的現象,本研究採用創新擴散模型,利用實際的數據,分析賣家的評價趨勢。研究結果顯示,評價在一開始的時候成長緩慢,並隨著評價的累積而漸漸加速,當評價達到240至320的時候,成長速率的變化達到最高峰,這個現象代表了賣家會因為評價累積而賣出更多的產品,也賣得更快,也代表了賣家建立了買家的信任,除此之外,也有可能是因為這些賣家越來越熟悉操作網路拍賣的技巧,所以評價累積的速度才會增快。
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.
Chapter 1. Introduction 1
Chapter 2. Literature Review 4
2.1. E-Auction 4
2.2. Seller’s Factors 5
2.3. Sellers’s Reputation 6
2.4 Reputation and Trust 8
2.5 Diffusion Models of Innovation 9
Chapter 3. Research Methodology 12
3.1 Data Collection 12
3.1.1 Process of Data Collection 12
3.1.2 Descriptive Statistics of the Sample 14
3.2 Fitting Bass Model 16
3.3 Finding the Maximum Slopes Changes 20
3.3.1 Method 1: Second-Order Differential Equation 20
3.3.2 Method 2: Center Moving Averages 21
Chapter 4. Analysis and Results 23
4.1 Bass Curve: Coefficient of Imitation 23
4.2 Average Maximum Slope Changes 27
4.2.1 Method 1: Second-Order Differential Equation 27
4.2.2 Method 2: Center Moving Averages 27
Chapter 5. Conclusions 29
5.1 Empirical Findings and Applications 29
5.2 Limitation 30
5.3 Future Research 30
References 32
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