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系統識別號 U0026-1206201815451700
論文名稱(中文) 以眼動儀實驗探討P2P網路借貸平台投資意圖之影響因素
論文名稱(英文) An Eye-Tracking Study of Exploring Factors affecting Intention of Investment on Online P2P Lending Platform
校院名稱 成功大學
系所名稱(中) 資訊管理研究所
系所名稱(英) Institute of Information Management
學年度 106
學期 2
出版年 107
研究生(中文) 林葳
研究生(英文) Wei, Lin
學號 R76051082
學位類別 碩士
語文別 英文
論文頁數 90頁
口試委員 指導教授-謝佩璇
口試委員-龔俊嘉
口試委員-楊政達
口試委員-呂執中
口試委員-王凱
中文關鍵字 共享經濟  P2P 網路借貸平台  投資意圖  眼動追蹤法 
英文關鍵字 sharing economy  online P2P lending platform  investment intention  eye tracking method 
學科別分類
中文摘要 共享經濟隨著網路科技的進步與行動裝置的普及而正迅速地發展中,除了物品、服務、房屋和汽車能透過各種網路平台共享,甚至連金錢也可以共享,P2P網路借貸市場也因此搭上這股熱潮而崛起。由於傳統銀行的貸款審核過程繁瑣,標準也較嚴格,中小企業主、沒有充足的財力證明或是踏入社會工作的申貸人等因此難以取得銀行的貸款,而P2P網路借貸平台成為了他們進行小額借貸更佳的管道。因此,這些申貸人發佈到平台上的貸款資訊要如何吸引投資者的目光並影響他們的投資意圖,使眾多投資者放款並完成募資,成為了值得探討的議題。本研究目的為探討P2P網路借貸平台上影響投資者的投資意圖之因素,採用推敲可能性模型(Elaboration Likelihood Model, ELM)分析貸款資訊、個人資訊與自願資訊此三類貸款成功的決定因素,並進行眼動追蹤實驗(Eye-tracking)收集投資者觀看呈現在畫面中投資案的眼動資料,利用凝視次數、凝視時間來分析這些因素對投資意圖的影響,眼動實驗結束後進行半結構訪談,以深入了解投資者的投資意圖。研究結果發現信用等級是最顯著影響投資意圖的因素,申貸目的與詳細說明雖然相比其它因素較不受到投資者注意,但是對於商業貸款的申貸案,詳細的申貸說明能有效說服投資者並使他們選擇此申貸案投資。期待此研究能提供申貸人上市申貸案到平台時有可供參考的具體建議,使借貸雙方的媒合更容易,也期待未來研究可考慮透過不同的研究方法來探討影響投資意圖的因素。
英文摘要 Sharing economy is rapidly developing as network and mobile technology advances. Apart from the sharing of goods, services, spaces, and vehicles through various online platforms, even money can be shared on platforms. Due to the complicated process of loan approval and the more stringent standards in traditional banks, small and medium-sized enterprise (SME) owners, borrowers who don’t have enough financial resources, and people who are just starting a career have difficulty obtaining bank loans. However, online Peer-2-Peer (P2P) lending platforms have become a better way for them to borrow small loans. Therefore, it is worth discussing how loan information released on the platforms can attract investors' attention and influence their investment intention. The purpose of this study is to explore the factors affecting intention of investment on online P2P lending platforms and analyze loan information, personal information, and voluntary information by using the Elaboration Likelihood Model (ELM). We collected eye movement data on the screen of loan information through eye-tracking experiment, using fixation time and fixation count. We also conducted semi-structured interviews after the experiment to analyze the impact of these factors on the investment intentions. The study found that the credit rating is the most significant factor affecting the investment intention. Although the loan purpose and detailed description are less noticeable by investors than other factors, it can effectively persuade them to invest through commercial loans. We expect this study can provide specific suggestions for borrowers on online P2P lending platform, making it easier to match investors and borrowers.
論文目次 Chapter 1 Introduction 1
1.1 Research Background and Motivation 1
1.2 Research Purposes 7
1.3 Research Scope and Limitations 8
1.4 Research Procedures 9
Chapter 2 Literature Review 10
2.1 The State of Online Sharing Economy 10
2.1.1 Characteristic of Online Sharing Economy 10
2.1.2 Classification of Sharing Economy and Related Research 12
2.2 Online P2P Lending Platform 13
2.2.1 Development of Online P2P Lending Platform 14
2.2.2 Research on Online P2P Lending 16
2.2.3 Elaboration Likelihood Model (ELM) 18
2.3 Factors Affecting Investment Intention 19
2.3.1 Credit Level 19
2.3.2 Loan Interest Rate and Loan Period 21
2.3.3 Loan Amount 23
2.3.4 Loan Purpose 24
2.3.5 Borrower Information 25
2.4 Eye Movement and Eye Tracking 27
2.4.1 Eye Movement and Eye Tracking and Related Research 27
2.4.2 Eye Movement Indicators and Investment Intentions 29
Chapter 3 Research Methodology 33
3.1 Research Model and Process 33
3.2 Research Participants and Scenario 36
3.3 Eye-Tracking Method 36
3.3.1 Experimental Environment 36
3.3.2 Experimental Scenario 40
3.3.3 Experimental Process 42
3.4 Data Collection and Analysis 45
3.4.1 Data Collection 45
3.4.2 Data Analysis 45
3.5 Semi-Structured Interview 47
Chapter 4 Data Analysis 49
4.1 Analysis of Eye Movement Data 50
4.1.1 Total Fixation Time (TFT) 50
4.1.2 Total Fixation Count (TFC) 55
4.2 Analysis of Behavior Data 61
4.2.1 Selection of Loan Requests 61
4.2.2 Cross-Analysis of Eye Movement Data and Behavioral Data 66
4.3 Analysis of Interview 68
4.3.1 Primary and Secondary Factors Affecting Investment Intention 69
4.3.2 Key Points of Affecting Investment Intention 70
Chapter 5 Conclusions and Discussions 75
5.1 Discussion 75
5.2 Research Implications 77
5.2.1 Academic Contribution 77
5.2.2 Practical Contribution 79
5.3 Limitations and Recommendations 81
Reference 83
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