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研究生:曾若慈
研究生(外文):Juo Tzu Tseng
論文名稱:應用智慧型代理人於C2B電子商務之評估
論文名稱(外文):The Evaluation of Intelligent Agent into the Consumer-to-Business E-Commerce
指導教授:梁文耀梁文耀引用關係
指導教授(外文):Wen Yau Liang
學位類別:碩士
校院名稱:國立彰化師範大學
系所名稱:資訊管理學系所
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:2011
畢業學年度:99
語文別:英文
論文頁數:77
中文關鍵詞:C2B電子商務議價智慧型代理人
外文關鍵詞:C2B E-CommerceNegotiationIntelligent Agent
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近幾年電子商務不僅改變了傳統的商業交易模式,消費者更可以透過網路使用電子商務平台。而網路提供不只多樣性的產品和服務,更提供消費者更多的選擇。近幾年集體購買受到許多消費者的歡迎,並已成為目前新的購物選擇方式之一。因此,越來越多消費者會選擇上網參加團購活動。然而,消費者購買的產品或服務的選擇價格不相同,因此,本研究希望提出一個集體議價代理人的模型和流程,蒐集購買者的需求資訊,透過議價方式與賣方進行溝通協商。因此,本研究的主要目的為提出且評估應用智慧型代理人於C2B電子商務平台,首先提出一個C2B模型,進而建立系統平台,進行實驗設計和收集數據資料,最後對於問卷調查的結果,透過資料分析的方式來評估及佐證系統效益。研究結果顯示,應用智慧型代理人於C2B電子商務平台,可以有效地提升使用者的滿意度、降低績效風險,並能提升使用者的知覺公平。
In recent years, electronic commerce has changed the outlook of traditional business trading behavior, then consumers can easily through the Internet e-commerce platform to consumer. However, the Internet provides the diversity products and service, that consumers have more choices and considerations. Recently, the group-buying is very popular and it’s become one of the new choices. Thus, there have more and more consumer to partake in the online group buying. Then, consumers regard to the different with the price of buying product or demand of the service. Thus, the research present applies intelligent agent to buyer collective negotiation, and it can effectively to decrease the research of buyer demand information, trade cost and seller negotiation. This research propose an apply agent into C2B E-commerce model and process, and the research focus evaluation the system that through the experiment design collection the experiment data and then a questionnaire is conducted to investigate the benefits of intelligent agent systems. Results show that intelligent system to conform to the user satisfaction, reduce the performance risk and raise perceived fairness.
中文摘要 i
Abstract ii
誌 謝 iii
Index iv
Figure Index v
Table Index vi
1 INTRODUCTION 1
2 LITERATURE REVIEW 4
2.1 C2B E-Commerce 4
2.2 Intelligent Agent 8
2.3 Agent of Negotiation 10
2.4 Internet shopping experience 13
3 THE C2B E-COMMERCE MODEL 16
3.1 C2B E-Commerce Model Process 16
3.2 System Screen 24
3.3 Product Utility and Negotiation Strategy 38
4 RESEARCH METHOD 43
4.1 Customer Satisfaction 44
4.2 Performance Risk 45
4.3 Perceived Fairness 46
4.4 Perceived Value 47
4.5 Question Items 49
4.6 Laboratory Experiment 53
4.7 Experimental System Design 54
4.8 Experimental Participation 55
4.9 Experimental Situation 56
5 DATA ANALYSIS 58
5.1 Data Analysis Method 58
5.2 Reliability analysis 59
5.3 Validity analysis 61
5.4 T-tests 64
5.5 Discussion 66
6 CONCLUSION 68
REFERENCES 70

Figure Index
Fig. 1 Experimental process 16
Fig. 2 The C2B E-Commerce Model 17
Fig. 3 C2B E-Commerce process 23
Fig. 4 Login screen 25
Fig. 5 Option of user 25
Fig. 6 Join the Group-Buying Activity 26
Fig. 7 Product attributes 27
Fig. 8 Price of attribute setting 28
Fig. 9 CPU of attribute setting 29
Fig. 10 Hardware of attribute setting 30
Fig. 11 Memory of attribute setting 31
Fig. 12 AHP setting 32
Fig. 13 Result of user's option 33
Fig. 14 User and buyer agent setting value 35
Fig. 15 Negotiation result 37
Fig. 16 Research model 43

Table Index
Table 1 Agent of Negotiation into different domain 11
Table 2 Customer satisfaction50
Table 3 Performance Risk 50
Table 4 Perceived Fairness 51
Table 5 Perceived Value 52
Table 6 Experimental participations statistics data 58
Table 7 Reliability Statistics 59
Table 8 Item Statistics 60
Table 9 KMO value and Bartlett ball-type test 61
Table 10 Total Variance Explained 62
Table 11 Component Matrixa 63
Table 12 T-test - Paired Sample T test64
Table 13 Result of hypotheses test 66
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