scholarly journals Analysis of the Impact of Viral Marketing in Social Networks on the Purchase Intention of Consumers: A Case Study of Telegram Social Network

2018 ◽  
Vol 9 (18) ◽  
pp. 243-267
Author(s):  
Abolfazl Danaei ◽  
Elham Momen
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Purchase Intention ◽  
Viral Marketing ◽  
The Impact

scholarly journals Coworking spaces and virtual learning communities in Social Networks: Case Study of #ElClaustroDeIG on Instagram

Aula Abierta ◽  
2021 ◽  
Vol 50 (1) ◽  
pp. 453-464
Author(s):  
Raquel Gómez-Martínez ◽  
Luis M. Romero-Rodríguez
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Learning Communities ◽  
Preschool Education ◽  
Virtual Learning ◽  
Ideal Space ◽  
Good Practices ◽  
Patterns Of Use ◽  
The Impact

Social networks are consolidated as spaces for the exchange of valuable content. It is not surprising to find spaces for the exchange of good teaching practices in virtual learning communities, so the objective of this study is to analyze, through the case study of #ElClaustrodeIG on Instagram, the educational content focused on teaching that is shared for the Ibero-American community in Spanish, as well as the analysis of the users who share their experiences and good practices in this social network. In order to do this, content analysis is first carried out, through an analysis sheet to 300 posts and a questionnaire is applied to 130 users of this hashtag on Instagram in order to analyze their patterns of use, their interests and motivations, their training in ICT and social networks, as well as the impact that the use of this social network has on their professional development and the gratification they expect. The results show that most of the publications are about good practices for primary and preschool education while most of the users think that Instagram is an ideal space for non-formal learning, applying in the classrooms many of the good practices shared in this coworking space. 

The use of social networks in marketing: the Italian co-operative experience

2015 ◽  
Vol 7 (1) ◽  
pp. 31-57 ◽  
Author(s):  
Patrizia Battilani ◽  
Giuliana Bertagnoni
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Marketing Strategy ◽  
Local Community ◽  
National Level ◽  
Viral Marketing ◽  
Original Research ◽  
Content Type ◽  
The Social

Purpose – The main aim of our study is to demonstrate that the Italian way to marketing included not only the “advertising artists” but also what can be labelled as the social network approach, which was mainly used by cooperative enterprises. Focussing on the case study of the Granarolo co-operative, the paper discusses the social network method of marketing as it emerged during the 1950s and 1960s in Italy. Design/methodology/approach – The research draws on different types of primary sources, including co-operative business records, interviews, publications, newspaper articles and advertisements. Findings – In the age of mass consumption, the Granarolo co-operative developed an original marketing strategy based on social networks. This strategy can be considered a kind of community brand based on shared values pre-existing to the brand itself and a kind of viral marketing put in place before the electronic revolution. Research limitations/implications – The research focusses on the Granarolo case study. It can be extended to other co-operative enterprises. However, it is unknown whether the anticipation of viral marketing has also been used by private enterprises. Practical implications – The marketing strategies analyzed in the paper could be a interesting solution for undertakings strictly connected and rooted in their local community or in their Web community. Social implications – In today’s world of the Web, this physical constraint no longer exists, and the social method of marketing exceeds the regional and even the national level. In conclusion, this was an innovative method of marketing and advertising that came into being, ahead of its time, about a half a century before modern Web-based social networks were conceived, yet uses the same concepts, hence its extraordinary originality. Originality/value – This study is the result of an original research which tries to highlight what we could label the Italian way to marketing. Taking into consideration the first two decades of the Granarolo history and focussing on the marketing strategy, our contribution seeks to examine how the social networks approach worked and in what it differs from today brand community and viral marketing.

MapTap and Cornelia Slow Digital Art History and Formal Art Historical Social Network Research

2016 ◽  
Vol 79 (3) ◽  
pp. 315-330 ◽  
Author(s):  
Koenraad Brosens ◽  
Klara Alen ◽  
Astrid Slegten ◽  
Fred Truyen
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Early Modern ◽  
Data Model ◽  
Art History ◽  
Digital Art ◽  
Unique Data ◽  
Network Research ◽  
Custom Made

Abstract The essay introduces MapTap, a research project that zooms in on the ever-changing social networks underpinning Flemish tapestry (1620 – 1720). MapTap develops the young and still slightly amorphous field of Formal Art Historical Social Network Research (FAHSNR) and is fueled by Cornelia, a custom-made database. Cornelia’s unique data model allows researchers to organize attribution and relational data from a wide array of sources in such a way that the complex multiplex and multimode networks emerging from the data can be transformed into partial unimode networks that enable proper FAHSNR. A case study revealing the key roles played by women in the tapestry landscape shows how this kind of slow digital art history can further our understanding of early modern creative communities and industries.

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.

Prevent/Control Identity Theft

2012 ◽  
Vol 25 (3) ◽  
pp. 30-60 ◽  
Author(s):  
Mahmud A. Shareef ◽  
Vinod Kumar
Keyword(s):  
Literature Review ◽  
Purchase Intention ◽  
Banking Sector ◽  
Identity Theft ◽  
Operational Performance ◽  
Application Framework ◽  
Management Tools ◽  
And Control ◽  
The Impact

This study provides an application framework toward measures to prevent/control identity theft in conjunction with sources. It also identifies the impact of overall protection of identity theft on consumer trust, the cost of products/services, and operational performance, all of which in turn contribute to a purchase intention using E-commerce (EC). For the first objective, this study proposes a matrix of sources and measures to prevent and control identity theft. From this matrix, using knowledge from a literature review and judgment based on plausibility, the authors identify global laws, controls placed on organizations, publications to develop awareness, technical management, managerial policy, risk management tools, data management, and control over employees are the potential measuring items to prevent identity theft related to EC. A case study in banking sector through a qualitative approach was conducted to verify the proposed relations, constructs, and measuring items. For the second objective, this research paper conceptualizes a model based on literature review and validates that based on the case study in the financial sector. The model reflects the effects of preventing and controlling identity theft on the costs of products/services, operational performance, and customers’ perception of trust, which would lead to purchase intention in EC.

scholarly journals I Know Where You Are Coming From: On the Impact of Social Media Sources on AI Model Performance (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13971-13972
Author(s):  
Yang Qi ◽  
Farseev Aleksandr ◽  
Filchenkov Andrey
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Model Performance ◽  
User Profiling ◽  
Personalized Recommendation ◽  
Modal Data ◽  
Social Media Networks ◽  
The Impact

Nowadays, social networks play a crucial role in human everyday life and no longer purely associated with spare time spending. In fact, instant communication with friends and colleagues has become an essential component of our daily interaction giving a raise of multiple new social network types emergence. By participating in such networks, individuals generate a multitude of data points that describe their activities from different perspectives and, for example, can be further used for applications such as personalized recommendation or user profiling. However, the impact of the different social media networks on machine learning model performance has not been studied comprehensively yet. Particularly, the literature on modeling multi-modal data from multiple social networks is relatively sparse, which had inspired us to take a deeper dive into the topic in this preliminary study. Specifically, in this work, we will study the performance of different machine learning models when being learned on multi-modal data from different social networks. Our initial experimental results reveal that social network choice impacts the performance and the proper selection of data source is crucial.

scholarly journals Personality and the Impact of Celebrity Endorsements on Instagram

2021 ◽  
Vol 18 (3) ◽  
pp. 468-474
Author(s):  
Yair Amichai-Hamburger ◽  
Shir Etgar ◽  
Hadar Gil-Ad ◽  
Michal Levitan-Giat ◽  
Gaya Raz
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Locus Of Control ◽  
Strong Influence ◽  
Online Social Network ◽  
Strong Impact ◽  
Celebrity Endorsements ◽  
Internal Locus Of Control ◽  
The Impact ◽  
The Relationship

Celebrities are famous people who often belong to entertainment industry. They are known to have a strong influence on people’s behavior. In the digital age this impact has expanded to include the online arena. Celebrities increasingly utilize Instagram, an online social network, to promote commercial products. It is important to learn to what extent people are influenced by this type of promotion and what sort of people are likely to be swayed by it. Research has demonstrated that people’s personalities have a strong impact on their behaviors online. However, until now, these investigations have not included the relationship between personality and the degree of celebrity influence through social networks. This study examines how much the personality of a user is related to the degree to which he or she is influenced by these Celebrity Instagram messages. Participants comprised 121 students (34 males, 87 females). They answered questionnaires which focused on their personality and were asked about the degree of influence celebrities exerted upon them through Instagram. Results showed that people who are characterized as being open and having an internal locus of control are more resistant to such celebrity influences. This paper demonstrates that the personality of a recipient is likely to influence the degree of impact that a celebrity endorsement is likely to produce. The implications of these results are discussed.

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.

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