scholarly journals Seeded word-of-mouth marketing strategy: mapping and analysis of a network of political supporters

2019 ◽  
Vol 18 (4) ◽  
pp. 177-195
Author(s):  
Fagner Oliveira Dias ◽  
Edgar Reyes Jr ◽  
Flávio Saab
Keyword(s):  
Social Network ◽  
Word Of Mouth ◽  
Political Actor ◽  
Network Marketing ◽  
Marketing Efficiency ◽  
Seeding Strategy ◽  
The Difference ◽  
Proportional Elections ◽  
Qualitative Phase ◽  
Total Network

Objective:   To analyze how a structure-based seeding strategy can improve word-of-mouth marketing efficiency in proportional elections.Methodology:  This study is quantitative and qualitative. In the quantitative phase, an egocentric social network analysis (SNA) of a candidate was conducted. Using Bonacich's degree, intermediation and power centralities, the study identified, then interviewed nine seed actors, which through the snowball technique, led to the identification of 31 other interviewees and the formation of a total network of 232 actors. In the qualitative phase, the reasons for supporting ego and alters as well as their preceding characteristics were analyzed using report content analysis.Originality / Relevance: This is a study of the characteristics of the actors along with their centrality in the strategy of seeding, spreading and maintaining the reputation of the political actor, especially candidates for proportional elections.Results: The characteristics of the most central supporters within the network reflect the bonds of trust built and deposited in the candidate, with ideology and friendship being the most prone to a more effective word-of-mouth marketing. The precedents for commitment and perceived value of word-of-mouth propensity were also verified.Theoretical / Methodological Contributions: This study includes the discussion of proportional elections in electoral marketing, highlighting the power of supporters through analysis of social network marketing.  It also contributes to the analysis of the characteristics of actors in the effectiveness of seeding strategy in word-of-mouth marketing.Contributions to Management: The candidate may adopt a seeding strategy in his campaign, thereby promoting effectiveness in resource allocation, while allowing the supporter to understand the difference of his role as a supporter.

scholarly journals A study on audience perception of fact and fiction in documentary

2017 ◽  
Author(s):  
◽  
Tiffany Crouse
Keyword(s):  
Focus Groups ◽  
Media Literacy ◽  
Qualitative Data ◽  
Word Of Mouth ◽  
Study Data ◽  
Structured Interviews ◽  
The Difference ◽  
Depth Interviews ◽  
Qualitative Phase

This study addresses the disconnect between how a documentary consumer goes into a film thinking they are skeptical of the information; when they tend to have a crewed understanding of filmmaking and media literacy. An explanatory, experimental qualitative design was used. This involved collecting qualitative data through the use of focus groups and then expanding upon those data with in-depth interviews. In the first qualitative phase of the study, data was collected from volunteer participants from three different cities in Missouri. Three focus groups where conducted to recognize the volunteers' understanding of the distinctions between fact and fiction in documentary and to assess whether that relates to further word-of-mouth misinformation. The second qualitative phase was conducted as follow up to the focus groups. In this study, the researcher looked at how members of the first data study consume documentaries. She did this through one-on-one in-depth semi-structured interviews with two participants from each of the focus groups. The researcher then conducted a textual analysis of the transcribed material that came from the qualitative data collected in both the focus groups and the interviews. Ultimately addressing the question: how do audience members understand the difference between fact and fiction in documentary?

scholarly journals Pengaruh Social Network Marketing (Snm) Dan Electronic Word Of Mouth (Ewom) Terhadap Minat Beli Pelanggan

2019 ◽  
Vol 2 (6) ◽  
Author(s):  
Wenny Kartika Susanto Dan Keni
Keyword(s):  
Social Media ◽  
Social Network ◽  
Purchase Intention ◽  
Structural Equation ◽  
Quantitative Research ◽  
Word Of Mouth ◽  
Easy Access ◽  
Young Generation ◽  
Electronic Word Of Mouth ◽  
Network Marketing

Competition that is getting more competitive in smartphone industry has made customers feel quite difficult in deciding which product the customers should buy. This is because companies are doing marketing through mass media, both offline and online. Companies are trying to change customers’ way of thinking through emotion, need, want, and demand. Nowadays, in this globalization the traditional retailers are facing big challenge because young generation is starting to switch to online shopping. This fact poses as threat for traditional retailers, but can also be used as opportunity due to easy access to product and brand via social media. Therefore, this research objective was to find what the influences of social network marketing and electronic word of mouth (independent variables) toward customer purchase intention (dependent variable). Quantitative research was chosen as the method of this research. The population was smartphone users from 26 – 50 years old in Jakarta. Non – sampling method, specifically convenience sampling was used because this method allowed researched to approach random respondent easily. The researcher used 166 questionnaires as sample size. 166 valid data were analysed with Structural Equation Modelling to test hypothesises in the research. There are two hypothesises tested on this research. Based on analysis, all hypothesises are supported. In conclusion, the most significant value is Social Network Marketing (SNM) which contributed 35.3% toward purchase intention. Based on findings, it is suggested for the company to pay more attention in marketing activities at social media by giving positive experience to customers so that the customers can give positive feedback in social media as reference for future customers.

The Impact of Social Network Marketing on University Students' Purchase Intention and How It Is Affected of Consumer Engagement

2020 ◽  
Vol 6 (2) ◽  
pp. 87-91
Author(s):  
Hartiwi Prabowo ◽  
Rini Kurnia Sari ◽  
Stephanie Bangapadang
Keyword(s):  
Social Network ◽  
Data Collection ◽  
University Students ◽  
Purchase Intention ◽  
Research Method ◽  
Quantitative Research ◽  
Consumer Engagement ◽  
Network Marketing ◽  
Consumer Purchase Intention ◽  
The Impact

The research conducted is to know the impact of social network marketing on consumer purchase intention and consumers who become research are active students at private universities in Jakarta, and how social network marketing also affect consumer engagement (as moderate variable). The research method used in this research is quantitative research method. A method of data collection used in this research is a questionnaire distributed to 119 university students. The results of this study showed that social network marketing has a strong and significant impact oncustomer engagement, customer engagementhas a strong and significant impact on consumer purchase intention, social network marketing has a strong and significant impact consumer purchase intention, and also there is a significant impact from social network marketing on consumer purchase intention through consumer engagement.

E-retailer cognitive and emotional relationship quality: their experiential antecedents and differential impact on brand evangelism

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ibrahim Al Nawas ◽  
Shadi Altarifi ◽  
Nabil Ghantous
Keyword(s):  
Relationship Quality ◽  
Social Presence ◽  
Structural Equation ◽  
Online Survey ◽  
Word Of Mouth ◽  
Equation Modeling ◽  
Growth Strategies ◽  
Content Type ◽  
The Difference ◽  
Brand Evangelism

PurposeLimited knowledge exists on the difference in the antecedents and outcomes of relationship quality's cognitive and emotional aspects for e-retailers. This research tests how utilitarian and hedonic shopping values differentially affect “cognitive and emotional” relationship quality components and how the latter differentially affects word-of-mouth and brand evangelism.Design/methodology/approachOnline survey data were collected from 450 Jordanian online shoppers. Structural equation modeling (AMOS 24.0) was employed to analyze the data.FindingsFirst, e-retailer's informativeness and transaction convenience (i.e. utilitarian values), drive more strongly cognitive than emotional relationship quality, whereas e-retailer's escapism and social presence (i.e. hedonic values) drive more strongly emotional than cognitive relationship quality. Second, emotional relationship quality has a strong significant effect on brand evangelism, whereas cognitive relationship quality's effect is insignificant. Third, there are no statistically significant differences concerning the effect of cognitive and emotional relationship quality on word-of-mouth.Originality/valueThe findings of our research are expected to enhance our understanding of e-retailer relationship quality, its emergence and consequences. They would also provide e-retailers with guidance on how to execute growth strategies by focusing on specific types of brand relationship quality, on the other hand.

ANALISA INTERAKSI USER DI MEDIA SOSIAL MENGENAI INDUSTRI FINTECH MENGGUNAKAN SOCIAL NETWORK ANALYSIS (STUDI KASUS: GOPAY, OVO DAN LINKAJA)

2020 ◽  
Vol 4 (3) ◽  
pp. 341-351
Author(s):  
Alisya Putri Rabbani ◽  
Andry Alamsyah ◽  
Sri Widiyanesti
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Customer Relationship Management ◽  
Word Of Mouth ◽  
Relationship Management ◽  
Customer Relationship ◽  
Customer Engagement ◽  
Electronic Word Of Mouth ◽  
Financial Technology

Financial technology (Fintech) mengalami pertumbuhan yang cukup pesat sejak awal kehadirannya di Indonesia. Fintech merupakan industri jasa finansial yang memanfaatkan teknologi sehingga memungkinkan penggunanya melakukan berbagai transaksi keuangan secara digital. Saat ini banyak fintech baru yang bermunculan di Indonesia, sehingga dibutuhkan strategi yg tepat untuk bisa bersaing dgn kompetitor. Analisis interaksi pengguna media sosial, biasa disebut dengan Electronic Word of Mouth (EWOM) dapat memberikan informasi yang dapat mendukung berbagai keputusan bisnis, salah satunya adalah terkait customer engagement. Tujuan dari penelitian ini adalah mengidentifikasi customer engagement yang terbentuk dari hasil implementasi Social Customer Relationship Management (SCRM) yang dilakukan oleh perusahaan. Data yang digunakan dalam penelitian ini adalah data sekunder yang merupakan data tweets berisi interaksi pengguna twitter mengenai 3 fintech di Indonesia yaitu GoPay, OVO, dan LinkAja. Analisis data dilakukan peneliti mengunakan metode social network analysis dengan menghitung properti jaringan dari ketiga objek penelitian. Hasil menunjukkan bahwa LinkAja mebentuk customer engagement lebih optimal lewat implementasi SCRM yang dilakukan perusahaan.  

scholarly journals Retweet Prediction Based on User Behavior

2021 ◽  
Author(s):  
Syeda Nadia Firdaus
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Matrix Factorization ◽  
Information Diffusion ◽  
User Behavior ◽  
Past Research ◽  
The Difference ◽  
The Impact

Social network is a hot topic of interest for researchers in the field of computer science in recent years. These social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. Social network data are created by its users. Users’ online activities and behavior have been studied in various past research efforts in order to get a better understanding on how information is diffused on social networks. In this study, we focus on Twitter and we explore the impact of user behavior on their retweet activity. To represent a user’s behavior for predicting their retweet decision, we introduce 10-dimentional emotion and 35-dimensional personality related features. We consider the difference of a user being an author and a retweeter in terms of their behaviors, and propose a machine learning based retweet prediction model considering this difference. We also propose two approaches for matrix factorization retweet prediction model which learns the latent relation between users and tweets to predict the user’s retweet decision. In the experiment, we have tested our proposed models. We find that models based on user behavior related features provide good improvement (3% - 6% in terms of F1- score) over baseline models. By only considering user’s behavior as a retweeter, the data processing time is reduced while the prediction accuracy is comparable to the case when both retweeting and posting behaviors are considered. In the proposed matrix factorization models, we include tweet features into the basic factorization model through newly defined regularization terms and improve the performance by 3% - 4% in terms of F1-score. Finally, we compare the performance of machine learning and matrix factorization models for retweet prediction and find that none of the models is superior to the other in all occasions. Therefore, different models should be used depending on how prediction results will be used. Machine learning model is preferable when a model’s performance quality is important such as for tweet re-ranking and tweet recommendation. Matrix factorization is a preferred option when model’s positive retweet prediction capability is more important such as for marketing campaign and finding potential retweeters.

scholarly journals Retweet Prediction Based on User Behavior

2021 ◽  
Author(s):  
Syeda Nadia Firdaus
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Matrix Factorization ◽  
Information Diffusion ◽  
User Behavior ◽  
Past Research ◽  
The Difference ◽  
The Impact

Social network is a hot topic of interest for researchers in the field of computer science in recent years. These social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. Social network data are created by its users. Users’ online activities and behavior have been studied in various past research efforts in order to get a better understanding on how information is diffused on social networks. In this study, we focus on Twitter and we explore the impact of user behavior on their retweet activity. To represent a user’s behavior for predicting their retweet decision, we introduce 10-dimentional emotion and 35-dimensional personality related features. We consider the difference of a user being an author and a retweeter in terms of their behaviors, and propose a machine learning based retweet prediction model considering this difference. We also propose two approaches for matrix factorization retweet prediction model which learns the latent relation between users and tweets to predict the user’s retweet decision. In the experiment, we have tested our proposed models. We find that models based on user behavior related features provide good improvement (3% - 6% in terms of F1- score) over baseline models. By only considering user’s behavior as a retweeter, the data processing time is reduced while the prediction accuracy is comparable to the case when both retweeting and posting behaviors are considered. In the proposed matrix factorization models, we include tweet features into the basic factorization model through newly defined regularization terms and improve the performance by 3% - 4% in terms of F1-score. Finally, we compare the performance of machine learning and matrix factorization models for retweet prediction and find that none of the models is superior to the other in all occasions. Therefore, different models should be used depending on how prediction results will be used. Machine learning model is preferable when a model’s performance quality is important such as for tweet re-ranking and tweet recommendation. Matrix factorization is a preferred option when model’s positive retweet prediction capability is more important such as for marketing campaign and finding potential retweeters.

scholarly journals Dior and digital storytelling: on the marketing of luxury brand narratives

2021 ◽  
Author(s):  
Emily Raymond
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Marketing Strategy ◽  
Digital Storytelling ◽  
Word Of Mouth ◽  
Postmodern Theory ◽  
Luxury Brand ◽  
Brand Communities

A postmodern theory and contemporary marketing strategy, digital storytelling is the virtual means by which a story can be organized. Less traditional to the beginning, middle and end of conventional narratives, this framework suggests that individuals connect the dots of a story by comparing their reading with others. To conceptualize this model within fashion, this paper follows Christian Dior’s Secret Garden campaign as it is broadcasted and diffused through Instagram and YouTube. Carried out by consumers’ interpretations as the story unfolds, this study aims to measure the interaction of media and audience within the parameters of social network analysis following Rihanna’s casting as Dior’s newest protagonist. Characterized by its hyperrealistic nature and speeded-up cultural tropes, this case underlines the epistemic shift for luxury brand communities today. As a result, this paper indicates the success of e-word-of-mouth marketing, and denotes the strength of fashion film as an illustrative medium of communication.

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