近年來，隨著科技進步，幾乎人人擁有智慧型手機以及網路。由於社群軟體的興起，幾乎每個人都會有一個社群媒體帳號。而在眾多的社群平台中，又以Facebook的使用人數為最多。在2016年時臉書開啟了直播功能，許多業者看準此商機，紛紛透過直播的方式來販賣商品。直播拍賣就好比傳統的電視購物頻道，但是與傳統購物頻道不同之處是直播銷售能夠讓觀眾即時與業者進行問答，透過更直接且方便的互動讓顧客願意進行消費。但本研究觀察到，消費者的棄標行為常常會造成直播主的額外成本。
本研究以廣告訴求為理論基礎，透過內容分析法將直播拍賣的影片內容轉化為結構化資料，探討那些因素會影響觀眾的棄標行為。研究結果顯示廣告訴求中的獎勵效果、觀看人數以及下標數對於棄標數量為正向顯著影響，而直播主人數、產品拍賣順序對於棄標數量為負向顯著影響。

In recent years, with the advancement of technology, everyone has a smartphone and the Internet. Due to the rise of social media, everyone has a social media account. Among many social media platforms, Facebook has the largest number of users. Facebook launched Facebook live in 2016. Many sellers have spotted this business opportunity and sell products through Facebook live. Live streaming auctions are like traditional TV shopping channels. However, it is different from the traditional shopping channels. The live streaming auction enables the audience to ask question to the seller in live. Due to the convenient communication, consumers are more willing to purchase products. From observation made in this study, consumers’ breach of contract tends to become the live streaming host’s additional cost.
This study based on advertisement appeal strategy is to explore the factors which might influence consumers' breach of contract. This study uses content analysis to transform live streaming auction videos to structural data. The results show that reward appeal, number of viewers, and number of bids have a positive impact on breach of contract, on the other hand, number of main live streaming host and product appearance sequence have a negative impact.

論文審定書 i
摘要 ii
Abstract iii
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation 4
1.3 Research Question 5
1.4 Research Purpose 6
1.5 Research process 7
Chapter 2 Literature Review 8
2.1 Social Commerce 8
2.2 Live Streaming 12
2.3 Advertisement Appeal Strategy in Persuasive Communication 16
2.3.1 Persuasive Communication 16
2.3.2 Advertisement Appeal Strategy 18
Chapter 3 Research Method 21
3.1 Research Model 21
3.2 Hypothesis 22
3.2.1 Relevance of Live Streaming Host 22
3.2.2 Relevance of Live Streaming 24
3.2.3 Live Streaming Auction Process 25
3.2.4 Relevance of Product 26
3.3 Content Analysis 28
3.3.1 Data Collection 28
3.3.2 Variable Conceptualization and Operationalization 30
3.3.3 Training and Coding 35
3.3.4 Reliability Check 37
Chapter 4 Result 39
4.1 Data Description and Processing 39
4.2 Results of Regression Analysis 47
4.3 Model Verification 51
4.4 Summary of Analysis 52
4.4.1 Relevance of Live Streaming Host 54
4.4.2 Relevance of Live Streaming 54
4.4.3 Live Streaming Auction Process 55
4.4.4 Relevance of Product 55
4.5 Robustness Check 57
Chapter 5 Conclusion and Suggestion 61
5.1 Conclusion 61
5.2 Contributions for Academia and Industry 62
5.3 Limitations and Future Suggestion 63
Reference 64
Appendix: Multigroup PLS Analysis 69

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