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。

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

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

References

Alan, S. D. and Kunal, B. (1994), “Customer loyalty: Toward an integrated conceptual framework,” Journal of the Academy of Marketing Science, 22(2), 99–113.
Alex, L., Evan B., David F. and Sam G. (2009), “New approaches for analyzing influence on twitter,” A publication of the Web Ecology project, 1–18.
Amir, H. Z., Ramesh, S. (2014), “Modeling brand post popularity dynamics in online social networks,” Decision Support Systems, 65, 59–68.
Anderson, E. and Linda S. (2003), “The Formation of Market-level Expectations and its Covariates,” Journal of Consumer Research, 30, 115–24.
Andrew, N. S., Eileen, F. and Chen, Y. (2012), “How Does Brand-related User-generated Content Differ across YouTube, Facebook, and Twitter?,” Journal of Interactive Marketing, 26, 102–113.
Angella, J. K. and Kim, K. P. J. (2016), “Power of consumers using social media: Examining the influences of brand-related user-generated content on Facebook,” Computers in Human Behavior, 58, 98–108.
Arnaud, D. B. and Gary, L. L. (2008), “A Multi-Stage Model of Word-of-Mouth Influence Through Viral Marketing,” International Journal of Research in Marketing, 25, 151–163. 
Ashley, C. and Tuten, T. (2015), “Creative strategies in social media marketing: An exploratory study of branded social content and consumer engagement,” Psychology & Marketing, 32, 15–27.
Aviv, S. and Ayalla, R. (2008), “Opinion Leaders and Followers: A Replication and Extension,” Psychology & Marketing, 25(3), 280–297.
Azlin, Z. B. and Mazzini, M. (2016), “The Impact of User – Generated Content (UGC) on Product Reviews towards Online Purchasing – A Conceptual Framework,” Procedia Economics and Finance, 37, 337 – 342.
Bailey, A. A. (2005), “Consumer Awareness and Use of Product Review Websites,” Journal of Interactive Advertising, 6(1), 90–108.
Bansal, H. S. and Peter, A. V. (2000), “Word-of-Mouth Processes Within a Services Purchase Decision Context,” Journal of Service Research,3(2), 166–77.
Barbara, A. L. and Ronald, E. G. (1999), “Corporate Credibility’s Role in Consumers’ Attitudes and Purchase Intentions When a High versus a Low Credibility Endorser Is Used in the Ad,” Journal of Business Research, 44, 109–116.
Bernard, J. J., Mimi, Z., Kate, S. and Abdur, C. (2009), “Twitter Power: Tweets as Electronic Word of Mouth,” Journal of The American Society for Information Science and Technology (ASIS&T), 60(11), 2169–2188.
Bone, P. F. (1995), “Word-of-Mouth Effects on Short-Term and Long-Term Product Judgments,” Journal of Business Research, 32(3), 213–23.
Bongwon, S., Lichang, H., Peter, P. and Ed, H. C. (2010), “Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network,” International Conference on Social Computing / International Conference on Privacy, Security, Risk and Trust, IEEE, 177–184.
Bowman, D., and Narayandas, D. (2001), “Managing customer-initiated contacts with manufactures: The impact on share of category requirements and word-of-mouth behavior,” Journal of Marketing Research, 38(3), 281–297.
Danah, B., Scott, G. and Gilad L. (2010), “Tweet, Tweet, retweet: Conversational aspects of retweeting on Twitter,” in Proceedings of the 43rd Hawaii International Conference on System Sciences (HICSS), IEE, 1–10.
Carlos, C., Marcelo, M. and Barbara, P. (2011), “Information Credibility on Twitter,” International World Wide Web Conference Committee (IW3C2), ACM, Hyderabad, India, 675–684.
Cheol, P. and Thae, M. L. (2009), “Antecedents of Online Reviews' Usage and Purchase Influence: An Empirical Comparison of U.S. and Korean Consumers,” Journal of Interactive Marketing, 23, 332–340.
Christodoulides, G., Jevons, C. and Bonhomme, J. (2012). “Memo to marketers: Quantitative evidence for change,” Journal of advertising research, 52(1), 53–64.
Christopher, A., Paul, R. and Richard, Z. (1999), “The Market for Evaluations, The American Economic Review, 89(3), 564-584.
Christy, M. K. C., Bo, X. and Ivy, L. B. L. (2012), “The Impact of Observational Learning and Electronic Word of Mouth on Consumer Purchase Decisions: The Moderating Role of Consumer Expertise and Consumer Involvement,” 45th Hawaii International Conference on System Sciences, IEEE, Hawaii, USA, 3228–3237.
Christy, M. K. C., Bo, S. X. and Ivy, L. B. L. (2014), “Do actions speak louder than voices? The Signaling Role of Social Information Cues in Influencing Consumer Purchase Decisions,” Decision Support Systems, 65, 50–58.
Christy, M. K. C. and Dimple, R. T. (2012), “The Impact of Electronic Word-of-Mouth Communication: A Literature Analysis and Integrative Model,” Decision Support Systems, 54, 461–470.
Christy, M. K. C. and Dimple, R. T. (2010), “The Effectiveness of Electronic Word-of-Mouth Communication: A Literature Analysis,” 3rd Bled eConference eTrust: Implications for the Individual, Enterprises and Society, Bled, Slovenia, 329–345.
Christy, M. K. C., Matthew, K. O. L. and Neil R. (2008), "The impact of electronic word‐of‐mouth: The adoption of online opinions in online customer communities", Internet Research, 18(3), 229–247.
Chu, S. C., and Choi, S. M. (2011), “Electronic word-of-mouth in social networking sites: a cross-cultural study of the United States and China,” Journal of Global Marketing, 24(3), 263–281.
Claudia, K. and Arvind, M. (2016), “How CEOs Can Leverage Twitter,” MIT Sloan Management Review, 57(2), 73–79. 
Clemons, E. K., Guo, D. G. and Lorin M. H. (2006), “When Online Reviews Meet Hyperdifferentiation: A Study of the Craft Beer Industry,” Journal of Management Information Systems, 23(2), 149–71.
David, C., Dave, E., Susie, M. and Ole, J. V. (2009), “The Consumer Decision Journey,” McKinsey Quarterly, 3, 96–107.
Dellarocas, C., X.Q. Zhang, and N.F. Awad (2007), "Exploring the value of online product reviews in forecasting sales: The case of motion pictures", Journal of Interactive Marketing, 21(4), 23–45.
DI Marketing (2016, September 30). Influencer people in Thailand 2016. Retrieved from http://www.di-onlinesurvey.com/en/2016/09/30/influences-of-key-opinion-leaders-in-thailand/.
Donna, L. H. and Thomas, P. N. (2012), “Toward a Deeper Understanding of Social Media,” Journal of Interactive Marketing, 26, 69–70.
Eleanora, P. and Vincenzo, C. (2012), “The Impact of Experience on Companies’ Reactions to Negative Comments on Social Networks,” Journal of Direct, Data and Digital Marketing Practice, 14(3), Macmillan Publisher, 214–223.
Eric, W. K. See-To and Kevin, K.W. Ho (2014), “Value co-creation and purchase intention in social network sites: The role of electronic Word-of-Mouth and trust – A theoretical analysis,” Computers in Human Behavior, 31, 182–189.
Ernest, D. (1966), “How Word-of-Mouth Advertising Works,” Harvard Business Review, 44(6), 147–66.
Eun, J. L. and Soo, Y. S. (2014), “When do consumers buy online product reviews? Effects of review quality, product type, and reviewer’s photo,” Computers in Human Behavior, 31, 356–366.
Evelien, O. and Ronald, R. (2002), “	Social network analysis: a powerful strategy, also for the information sciences,” Journal of Information Science, 28(6), 441–453.
Feng, Z. and Xiao, Q. Z. (2010), "Impact of Online Consumer Reviews on Sales: The Moderating Role of Product and Consumer Characteristics", Journal of Marketing, 74(2), 133–148.
Fernando, R. J., and Norma, A. M. (2013), “Too Popular to Ignore: The Influence of Online Reviews on Purchase Intentions of Search and Experience Products,” Journal of Interactive Marketing, 27, 226–235.
Florian, M., David, E. and Max, L. W. (2014), “More than Liking and Bookmarking? Towards Understanding Twitter Favoriting Behavior,” Proceedings of the Eighth International Association for the Advancement of Artificial Intelligence (AAAI) Conference on Weblogs and Social Media, 346–355.
Francis, A. B. (1998), “Word-of-Mouth: understanding and managing referral marketing,” Journal of Strategic Marketing, 6, 241–254.
Gal, O. S. and Lior, Z. (2009), “Paying for Content or Paying for Community? The Effect of Social Involvement on Subscribing to Media Web Sites,” International Conference on Information Systems Proceedings, 9, 1–18.
Gillian, S. N. (2016), “Complaining Complementing and Word-of-Mouth in The Digital Age: Typology and Terms,” Journal of Consumer Satisfaction, Dissatisfaction and Complaining Behavior, 29, 131–142.
Godes, D. and Mayzlin, D. (2004), “Using online conversations to study word-of-mouth communication,” Marketing Science, 23(4), 545–560.
Goldsmith, R. E. and David, H. (2006), “Measuring Motivations for Online Opinion Seeking,” Journal of Interactive Advertising, 6(2), 1–16.
Guopeng, Y., Christer, C. and Deqiang, Z. (2014), “Exploring the Influence of User-Generated Content Factors on the Behavioral Intentions of Travel Consumers,” 25th Australasian Conference on Information Systems, ACIS, Auckland, New Zealand. 
Haewoon, K., Changhyun, L. Hosung, P. and Sue, M. (2010), “What Is Twitter, A Social Network or A News Media?,” International World Wide Web Conference Committee (IW3C2), ACM, Raleigh, North Carolina, USA, 591–600.
Haven, B. (2007). Marketing’s new key metric: engagement. Marketing.
Helene, B. Z. (2015), “A Comparative Analyses of eWOM’s Flow and Influence in a Social Media Setting: The Consumption-based Community and the Personal Online Social Network,” Copenhagen Business School, 1–198.
Hyerim, A. and Ji, H. P. (2015), “The Structural Effects of Sharing Function on Twitter Networks: Focusing on The Retweet Function,” Journal of Information Science, 41(3), 354–365.
Hyuk, J. C. and Margaret, A. M. (2008), “Consumers’ Reliance on Product Information and Recommendations Found in UGC,” Journal of Interactive Advertising, 8(2), 38–49.
James, R. O. J. (2010), “Source Credibility of Company-Produced and User-Generated Content on the Internet: An Exploratory Study on the Filipino Youth,” Philippine Management Review, 17, 121–132.
Jasmine, A. L. Y., Joshua, I. and T. Ramayah (2014), “Determining Consumers’ Most Preferred eWOM Platform for Movie Reviews: A Fuzzy Analytic Hierarchy Process Approach,” Computers in Human Behavior, 250–258.
Jin, K. (2012), “The institutionalization of YouTube: From user-generated content to professionally generated content,” Media, Culture & Society, 34(1), 53–67.
Jing, L., Zhong, Y., Fu, T. Z. and Hao, L. (2016), “Search Product and Experience Product Online Reviews: An Eyetracking Study on Consumers' Review Search Behavior,” Computers in Human Behavior, 65,420–430.
Jing, Z., Jie, T. Juan, Z. L., Yang, L. and Chun, X. X. (2015), “Who Influenced You? Predicting Retweet via Social Influence Locality,” on Knowledge Discovery from Data, 9(3), AMC, Article 25, 1–25.
Julian, K. A., Norman, A. and Rob, L. (2013), “Do We Believe in TripAdvisor? Examining Credibility Perceptions and Online Travelers’ Attitude toward Using User-Generated Content,” Journal of Travel Research, 52(4), 437 –452.
Jumin, L., Do-Hyung, P. and Ingoo, H. (2008), “The effect of negative online consumer reviews on product attitude: An information processing view,” Electronic Commerce Research and Applications, 7, 341–352.
Katz, E., and Lazarsfeld, P. F. (1955), “Personal influence: The part played by people in the flow of mass communication,” Glencoe, IL: Free Press.
Kenneth, A. L., Patric, R. S., Autumn, E., Katie, M. R. and Chad, E. (2014), “If You Are Quick Enough, I Will Think About It: Information Speed and Trust in Public Health Organizations,” Computers in Human Behavior, 33,377–380.
Khim, Y. G., Cheng, S. H. and Zhi, J. L. (2013), “Social Media Brand Community and Consumer Behavior: Quantifying the Relative Impact of User- and Marketer Generated Content,” Information Systems Research, 24(1), 88–107.
Kofi, O. F and Graeme McLean (2017), “Examining the Moderating Effects of Firm Generated Content on Online Social Brand Engagement,” 27th Annual RESER Conference: The Crucial Role of Services in Business and Cities Competitiveness. Milan, Italy, 1–17.
Kyung, T. L. and Dong, M. K. (2012), “Effects of attribute and valence of e-WOM on message adoption: Moderating roles of subjective knowledge and regulatory focus,” Computers in Human Behavior, 28, 1974–1984.
Lawrence, G. C. (1971), “People Who Claim to Be Opinion Leaders: Identifying Their Characteristics by Self-Report,” Journal of Marketing, 35(4), 48–53.
Lee, S., Hwang, T., Lee, H.-H., (2006), “Corporate blogging strategies of the Fortune 500
Companies,” Management Decision, 44, 316–334.
Lee, Y. P. and Jyh, S. C. (2011), “How Much Can You Trust Online Information? Cues for Perceived Trustworthiness of Consumer-generated Online Information,” Journal of Interactive Marketing, 25, 67–74.
Lei, Y., Tao, S., Ming, Z. and Qiaozhu, M. (2012), We Know What @You #Tag: Does the Dual Role Affect Hashtag Adoption?,” International World Wide Web Conference Committee–Session: Behavioral Analysis and Content Characterization in Social Media, ACM, Lyon, France, 261–270.
Leisa, R. F., Ronald, E. G. and Jacqueline, K. E. (1996), “Opinion leaders and Opinion Seekers: Two New Measurement Scales,” Journal of the Academy of Marketing Science, 24(2), 137–147. 
Li, X. & Hitt, L. (2008). Self-Selection and Information Role of Online Product Reviews. Information Systems Research, 19(4), 456.
Libo, L., Christy, M. K. C. and Matthew, K. O. L. (2016), “An empirical investigation of information sharing behavior on social commerce sites,” International Journal of Information Management, 36, 686–699.
Matthias, H. and Viviane, A. (2013), “Perceived bridging and bonding social capital on Twitter: Differentiating between followers and followees,” Computers in Human Behavior, 29, 2134–2142.
Marsden, P. (2009), Top social commerce survey findings. Retrieved 15 January, 2015, from. http://socialcommercetoday.com/top-socialcommerce-surveyfindings-ripple6/
Man, Y. C., Chuan, L., Choon, L. S. and Huaping, C. (2009), “Credibility of electronic word-of-mouth: informational and normative determinants of on-line consumer recommendations,” International Journal of Electronic Commerce, 13(4), 9–38.
Maxham, J. G., III, and Netemeyer, R. G. (2002), “A longitudinal study of complaining customers' evaluations of multiple service failures and recovery efforts,” Journal of Marketing, 66(4), 57−71.
Meeyoung, C., Hamed, H., Fabricio, B and Krishna, P. G. (2010), “Measuring User Influence in Twitter: The Million Follower Fallacy,” Proceedings of the 4th International Association for the Advancement of Artificial Intelligence (AAAI) on Weblogs and Social Media, 10–17.
Michael, T., Randolph, E. B. and Koen, P. (2009), “Effects of Word-of-Mouth Versus Traditional Marketing: Findings from an Internet Social Networking Site,” Journal of Marketing, 73 (September), 90–102.
Miles, E. (2010), “Hashtag Retrieval in a Microblogging Environment,” SIGIR’10, ACM, Geneva, Switzerland, 787–788.
Mira, L. and Seounmi, Y. (2009), "Electronic word of mouth (eWOM) How eWOM platforms influence consumer product judgement", International Journal of Advertising, 28(3), 473–499.
Moe, Wendy W. and David A. Schweidel (2012), “Online Product Opinions: Incidence, Evaluation and Evolution,” Marketing Science, 31(3), 372–86.
Mohammad, R. J., Sharif, S.E. and Neda, S. (2011), “Electronic Word-of-Mouth: Challenges and Opportunity,” Procedia Computer Science, 3, 42–46.
Nagaraju, B., Eric, D. M., and Shrisha, R. (2006), “Method and system for analyzing user-generated content,” US7930302 B2.
Nyilasy and Greg (2005), “Word of Mouth–What We Really Know–And What We Don't,” in Connected Marketing, Justin Kirby, Paul Marsden, editors. London, UK: Butterworth-Heinemann. 161–85.
Nida, S. K., Maria, A. and Quratulain, R. (2015),  “Identification of Opinion Leaders in Social Network,” International Conference on Information and Communication Technologies (ICICT), IEEE, Karachi, Pakistan.
Pierre, R. B., Leyland, F. P., Kirk, P. and Daniel, S. (2012), “Marketing meets Web 2.0, social media, and creative consumers: Implications for international marketing strategy,” Business Horizons, 55, 261—271.
Pranjal, G. and Judy, H. (2010), “How e-WOM recommendations influence product consideration and quality of choice: A motivation to process information perspective,” Journal of Business Research, 63, 1041–1049.
Qian, X. (2014), “Should I trust him? The effects of reviewer profile characteristics on eWOM credibility,” Computers in Human Behavior, 33, 136–144.
Qiuju, L. and Dixi, Z. (2015), “Using social network analysis to explain communication characteristics of travel-related electronic word-of-mouth on social networking sites,” Tourism Management, 46, 274–282.
R. van der Merwe and G. van Heerden (2009), “Finding and Utilizing Opinion Leaders: Social Networks and The Power of Relationships,” South African Journal of Business,40(3), 65–76.
Raffaele, F. (2015), “What makes online reviews helpful? A diagnosticity-adoption framework to explain informational and normative influences in e-WOM,” Journal of Business Research, 68, 1261–1270.
Richins, M.L. (1983), “Negative word-of-mouth by dissatisfied consumers: a pilot study”, Journal of Marketing, 47(1), 68–78.
Robert, A. K., Pradeep, R. and Victoria, D. B. (2014), “What We Know and Don't Know About Online Word-of-Mouth: A Review and Synthesis of the Literature,” Journal of Interactive Marketing, 167–183.
Ruby, R. D. and Brian, S. (1977), “Highly Credible Sources: Persuasive Facilitators or Persuasive Liabilities?,” Journal of Consumer Research, 3(4), 223–232.
Sandeep, K. and Wenyu, D. (2008), “Note from Special Issue Editors: Advertising with Use-Generated Content: A Framework and Research Agenda,” Journal of Interactive Advertising, 8(2), 1–4.
Sasa, P., Miles, O. and Victor, L. (2011), “RT to Win! Predicting Message Propagation in Twitter,” Proceedings of the Fifth International Association for the Advancement of Artificial Intelligence (AAAI) Conference on Weblogs and Social Media, 586–589.
Senecal, S. and Nantel, J. (2004), “The influence of online product recommendations on consumers’ online choices”, Journal of Retailing, 80(2), 159–69.
Seoyoen, H. and Hee, S. P. (2012), “Computer-mediated persuasion in online reviews: Statistical versus narrative evidence,” Computers in Human Behavior, 28, 906–919.
Shahana, S. and Dawn, L. (2007), “Why are you telling me this? An examination into negative consumer reviews on the Web,” Journal of Interactive Marketing, 21(4), 76–94.
Shihari, S. and Raji, S. (2012), “Social Influence Effect in Online Product Rating,” Journal of Marketing, AMA, 76, 70–88.
Shilad, S., Shyong, K. L., Al, M. R., Dan C., Dan F., Jeremy, O. F. M. Harper, and John R. (2006), “Tagging, Communities, Vocabulary, Evolution,” In 20th anniversary conference on Computer Supported Cooperative Work (CSCW), 181–190.
Sofus, A. M. and Matthew, M. (2011), “,” Proceedings of the Fifth International Association for the Advancement of Artificial Intelligence (AAAI) Conference on Weblogs and Social Media, 209–216.
Steffes, E. M. and Burgee, L. E. (2009), “Social ties and online word-of-mouth,” Internet Research, 19(1), 42.
Suchit, L. N. and Terry, F. (2017, May 24). Thailand in social media world's top 10. Retrieved from https://www.bangkokpost.com/learning/advanced/1255403/thailand-in-social-media-worlds-top-10
Sue, V. H. (2016), “User-Generated Content About Brands: Understanding Its Creators and Consumers,” Journal of Business Research, 69,137–144.
Surendra, S. and Axin, S. (2014), “Hashtag Recommendation for Hyperlinked Tweets,” SIGIR’14, ACM, Gold Coast, Queensland, Australia, 831–834.
Syeda, N. F., Chen, D. and Alireza, S.  (2016), “Retweet Prediction Considering User's Difference as an Author and Retweeter,” International Conference on Advances in Social Networks Analysis and Mining (ASONAM), IEE/ACM, San Francisco, CA, USA, 852–859.
Terry, D., Matthew, S. E. and Laura, B. (2008), “Exploring Consumer Motivations for Creating User-Generated Content,” Journal of Interactive Advertising, 8(2), 16–25.
Thomas, J. J. and Barbara, K. K. (2004), “Wag the Blog: How Reliance on Traditional Media and The Internet Influence Credibility Perceptions of Weblogs Among Blog Users,” Journalism and Mass Communication Quarterly, 81(3), 622–642.
Thorsten, H. T, Caroline, W. and Fabian, F. (2012), “Exploring the “Twitter Effect:” An Investigation of the Impact of Microblogging Word of Mouth on Consumers’ Early Adoption of New Products,” working paper, 1–48.
Thorsten, H. T., Kevin, P. G., Gianfranco, W. and Dwayne, D. G. (2004), "Electronic word of mouth via consumer opinion platforms: What motivates consumers to articulate themselves on the Internet?", Journal of Interactive Marketing, 18(1), 38–52.
U.S. Embassies abroad. (2017, July 7). Thailand - Personal Care and Beauty Products. Retrieved from https://www.export.gov/article?id=Thailand-personal-care-and-beauty-products
Von Wangenheim, F. and Tomàs B. (2007), “Behavioral Consequences of Overbooking Service Capacity,” Journal of Marketing, 71(4), 36–47.
Wei, L. W. and Yi, C. L. (2012), “The Effect of Blog Trustworthiness, Product Attitude, And Blog Involvement on Purchase Intention,” International Journal of Management & Information Systems, 16(3), 265–275.
Yan, S. (2016), “How credible are online product reviews? The effects of self-generated and system-generated cues on source credibility evaluation,” Computers in Human Behavior, 55, 633–641.
Ya, Y., Gautham, G. V. and Amit, M. J. (2015), “A Meta-Analysis of Electronic Word-of-Mouth Elasticity,” Journal of Marketing, 79, 19–39.
Ye, Q., Fang, B., He, W. and Hsieh, J. J. P.-A. (2012), “Can social capital be transferred cross the boundary of the real and virtual worlds? An empirical investigation of Twitter,” Journal of Electronic Commerce Research, 13, 145–156.
Yubo, C., Yong, L. and Jurui, Z. (2011), “When Do Third-Party Product Reviews Affect Firm Value and What Can Firms Do? The Case of Media Critics and Professional Movie Reviews,” Journal of Marketing, 75, 116–134.
Yu, G. and Zou, D. (2015), "Which User-generated Content Should Be Appreciated More? - A Study on UGC Features, Consumers' Behavioral Intentions and Social Media Engagement," Proceedings at AIS Electronic Library (AISeL), 23th European Conference on Information System (ECIS), Completed Research Papers, Paper 211.
Zhiheng, X. and Qing, Y. (2012), “Analyzing User Retweet Behavior on Twitter,” International Conference on Advances in Social Networks Analysis and Mining, IEEE/ ACM, 46–50.