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系統識別號 U0002-0102201801325100
中文論文名稱 如何將使用者生成內容轉為廠商的製作者生成內容—從社群網路分析中發展衡量指標
英文論文名稱 The Study of How to Take User Generated Content as Firm’s Producer Generated Content—Developing the Diagnostic Metrics through Social Network Analysis
校院名稱 淡江大學
系所名稱(中) 國際企業學系碩士班
系所名稱(英) Master's Program, Department Of International Business
學年度 106
學期 1
出版年 107
研究生中文姓名 楊丹桂
研究生英文姓名 Nattakan Iusakul
學號 604555028
學位類別 碩士
語文別 英文
口試日期 2017-01-22
論文頁數 83頁
口試委員 指導教授-黃哲盛
委員-鄭明顯
委員-李月華
中文關鍵字 使用者生成內容  製作者生成內容  社群網路分析  社交網站  社交網路  電子口碑  口碑行銷  衡量指標  推特 
英文關鍵字 Consumer-generated content(UGC)  producer-generated content(PGC)  social network analysis(SNA)  NodeXL  social media sites  social media network  word-of-mouth(WOM)  electronic word-of-mouth(eWOM)  diagnostic metrics  Twitter 
學科別分類
中文摘要 凡是與產品有關的資訊,無論來自廠商或消費者,皆能在網際網路中自由流通。網路資訊對外公開、歷久不衰且能透過各種不同管道取得。

對於購買產品來說,消費者高度依賴意見領袖及其他同輩的看法及建議。這些以品牌資訊及使用經驗作為參考標準的現象,對消費者考慮購買產品 及最終購買選擇有著強大的影響力。

根據使用者生成內容(UGC),消費者對資訊的採用受兩大主要因素影響,分別是資訊的品質及來源的可信度,因此企業應善加利用這些由消費端所提供的免費資訊,作為企業生產端的參考內容。

以Twitter來說,每一則貼文或信息都是直接傳達給觀眾,且允許其他使用者與貼文互動。因此有著大量追隨者的用戶,擁有將資訊散播給大量用戶的能力,且與用戶們有著相當程度的互動。

本研究因此建立由實務研究所衍生出的,以使用者生成內容(UGC)轉換到製作者生成內容(PGC)之過程的概念模型。而NodeXL與社群網絡的分析方法便用於檢視使用者生成內容(UGC)轉換過程的診斷指標之效果。

根據本研究得到證據,不只回答了本研究問題,也展示實務上的行銷方法,因此從業人員與企業能用來提升其網路行銷的表現。
英文摘要 Product-related information generated from both marketers and consumers flow freely on the internet, it is persistent and accessible through various channels, conjunction with consumers rely highly on opinion leader and other peer consumers’ opinion and recommendation, these phenomena formed the powerful impact of brand-related user-generated content on product consideration and consumers’ purchase decision.

According to UGC contains two major elements that affect information adoption, which are information quality and source credibility, therefore firms should be savvy to exploit those free marketing contents generated by their consumer as the firms’ producer-generated content effectively.

In Twitter, the message or tweet is conveyed to the audience instantly and its basic features allow other users to interact with that tweet, therefore the participant with a large number of followers possesses the ability of information diffusion to massive recipients and tends to receive a high level of engagement. The conceptual model of UGC transformation to PGC process retrieved from the empirical study is developed, then NodeXL and social network analysis method are applied to investigate the effect of diagnostic metrics on the UGC transformation process.

The valuable evidences obtained from this study, not only answered the research questions but also demonstrate the pattern of practical marketing practice that any practitioners or firm can adopt in order to improve their online marketing performance
論文目次 Table of Contents

Chapter 1 Introduction 1
1.1 Research Background and Motivation 1
1.2 Research Purpose 3

Chapter 2 Literature Review 6
2.1 Word-of-Mouth (WOM) and Electronic Word-of-Mouth (eWOM) 6
2.1.1 Impact of eWOM communication 8
2.1.2 Positive and Negative Online Review 10
2.2 User-generated content (UGC) vs Producer-generated content (PGC) 12
2.3 Opinion Leader 14
2.4 Microblogging (Twitter) 16
2.5 Social Media Engagement 18
2.5.1 Number of Follower 19
2.5.2 Retweet 19
2.5.3 Favorite 20
2.5.4 Hashtag(#) 21
2.5.5 Mention(@) 21

Chapter 3 Research Design and Methodology 23
3.1 Research Design 23
3.1.1 Social Network Analysis 24
3.1.2 Introduction to NodeXL 25
3.2 Research Method 29
3.3 Research Process 40
3.4 Case Selection 43
3.4.1 Thailand and Its World Ranking in Using Social Media 43
3.4.2 Beauty and Personal Care Chain Store in Thailand 44
3.4.3 EVEANDBOY and Its Marketing Strategies on Social Media Sites 45

Chapter 4 Results and Findings 49
4.1 Numerical Data Evaluation 49
4.2 Empirical Analysis of Relevance Tweet 52
4.2.1 Centrality Measurement 52
4.2.2 Relevance Tweet Properties 54

Chapter 5 Conclusion and Implication 62
5.1 Conclusion 62
5.2 Implication 64
5.2.1 Academic Contributions 64
5.2.2 Managerial Implication 65
5.3 Research Limitation and Direction for Future Work 66

References 68



List of Figures

Chapter 2
Figure 2.3.1 Two-step flow model of communication 15

Chapter 3
Chapter 3.1
Figure 3.1.2.1 The Blank Workbook of NodeXL Template 25
Figure 3.1.2.2 The Direct Relationship Between Vertex 1
and Vertex 2 26
Figure 3.1.2.3 The Type of Interaction Between Vertex 1
and Vertex 2 26
Figure 3.1.2.4 The Tweet Content Between Vertex 1 and Vertex 2
in Details 27
Figure 3.1.2.5 The Hashtags Appear in Each Tweet 27
Figure 3.1.2.6 Total Number of Participants in The Particular
Network of Study 28
Figure 3.1.2.7 Display of the Detail of Each Individual 28
Chapter 3.2
Figure 3.2.1 Fetching the Data from Twitter Search Network 29
Figure 3.2.2 Getting the Data of the Particular Firm from Twitter Search Network with Expand URLs 30
Figure 3.2.3 Display the Weight of Each Edge in the workbook 31
Figure 3.2.4 Selection of Graph Metrics for Calculation 32
Figure 3.2.5 Adjusting the Ties Color to Identify Reciprocal
Relationships 33
Figure 3.2.6 Assign the Reciprocity into Numerical Value 34
Figure 3.2.7 Changing the Color of Reciprocate Edges 34
Figure 3.2.8 Adjust the Vertex Opacity According to Level of
Out-Degree 35
Figure 3.2.9 Adjust the Vertex Size According to Level of
In-Degree 36
Figure 3.2.10 The Network Displayed the Properties of Edges,
Vertex and Relationships 37
Figure 3.2.11 Generate Sub-Group Within the Particular
Community 37
Figure 3.2.12 The Overall Network Graph Grouped in Cluster 38
Figure 3.2.13 Layout the Graph into Treemap 39
Figure 3.2.14 Change the Vertex Shape from Disk to Twitter Profile Image 39
Figure 3.2.15 Final Network Graph 40
Chapter 3.3
Figure 3.3.1 The Conceptual Model of UGC transformed to PGC 42
Chapter 3.4
Figure 3.4.3.1 Number of Instagram Followers of EVEANDBOY’s
Brand Endorser 45
Figure 3.4.3.2 The Proper Use of Hashtag in Instagram Conducted
by EVEANDBOY 46
Figure 3.4.3.3 The Example of Some Beauty Influencers that the
Firm is Following 47

Chapter 4
Figure 4.2.1.1 The Graph of EVEANDBOY Retweet UGC from
Other Users 52
Figure 4.2.1.2 The In-Degree of Influencers that the Firm Retweeted their UGC 53
Figure 4.2.2.1 The Firm Respond to Customer’s Question via
the Mention Feature 55
Figure 4.2.2.2 The Firm Exploit the Mention Feature to Involve in UGC of Opinion Leader 56
Figure 4.2.2.3 UGC with the Top Ranked Hashtag from User with
Small Number of Followers 57
Figure 4.2.2.4 UGC with the Top Ranked Hashtag from User with
High Level of Social Media Engagement 58
Figure 4.2.2.5 Typical Pattern of Relevance UGC Mentions to
The Firm 59
Figure 4.2.2.6 Typical Pattern of UGC from Opinion Leader 60



List of Tables

Table 3-1 Summary of criteria used to describe UGC selection
process 41
Table 4-1 Most frequently used hashtag found in the network 50
Table 4-2 Top Hashtag Classification 51
Table 4-3 Top UGC creator shared (retweeted) by the firm, calculated from the summation of out-degree and edge weight between firm and each user 51
Table 4-4 Summary of Diagnostic Metrics and Criteria Affecting
UGC Transformation to PGC 61
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