||The Study of How to Take User Generated Content as Firm’s Producer Generated Content—Developing the Diagnostic Metrics through Social Network Analysis
||Master's Program, Department Of International Business
social network analysis(SNA)
social media sites
social media network
||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
||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
List of Figures
Figure 2.3.1 Two-step flow model of communication 15
Figure 220.127.116.11 The Blank Workbook of NodeXL Template 25
Figure 18.104.22.168 The Direct Relationship Between Vertex 1
and Vertex 2 26
Figure 22.214.171.124 The Type of Interaction Between Vertex 1
and Vertex 2 26
Figure 126.96.36.199 The Tweet Content Between Vertex 1 and Vertex 2
in Details 27
Figure 188.8.131.52 The Hashtags Appear in Each Tweet 27
Figure 184.108.40.206 Total Number of Participants in The Particular
Network of Study 28
Figure 220.127.116.11 Display of the Detail of Each Individual 28
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
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
Figure 3.2.9 Adjust the Vertex Size According to Level of
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
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
Figure 3.3.1 The Conceptual Model of UGC transformed to PGC 42
Figure 18.104.22.168 Number of Instagram Followers of EVEANDBOY’s
Brand Endorser 45
Figure 22.214.171.124 The Proper Use of Hashtag in Instagram Conducted
by EVEANDBOY 46
Figure 126.96.36.199 The Example of Some Beauty Influencers that the
Firm is Following 47
Figure 188.8.131.52 The Graph of EVEANDBOY Retweet UGC from
Other Users 52
Figure 184.108.40.206 The In-Degree of Influencers that the Firm Retweeted their UGC 53
Figure 220.127.116.11 The Firm Respond to Customer’s Question via
the Mention Feature 55
Figure 18.104.22.168 The Firm Exploit the Mention Feature to Involve in UGC of Opinion Leader 56
Figure 22.214.171.124 UGC with the Top Ranked Hashtag from User with
Small Number of Followers 57
Figure 126.96.36.199 UGC with the Top Ranked Hashtag from User with
High Level of Social Media Engagement 58
Figure 188.8.131.52 Typical Pattern of Relevance UGC Mentions to
The Firm 59
Figure 184.108.40.206 Typical Pattern of UGC from Opinion Leader 60
List of Tables
Table 3-1 Summary of criteria used to describe UGC selection
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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