Alliance of Commerce and Financial Intermediation with Social Networks: Problems and Prospects

2020 ◽  
pp. 1-17
Author(s):  
Igor K. Klioutchnikov ◽  
Oleg. I. Kliuchnikov ◽  
Olga A. Molchanova
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Financial Intermediation ◽  
Social Contacts ◽  
Business To Consumer ◽  
Business To Business

The embedding of trading and banking functions in a social network offers promising prospects for trade, financial intermediation, and social networks, as well as for network users. Social sites are becoming more sophisticated due to the variety of applications and interaction channels that provide not only social contacts (P2P), but also business-to-business (B2B), business-to-consumer (B2C), and consumer-to-consumer systems (C2C).

Predictive Analytics of Social Networks

2018 ◽  
pp. 823-862
Author(s):  
Ming Yang ◽  
William H. Hsu ◽  
Surya Teja Kallumadi
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Predictive Analytics ◽  
Concept Drift ◽  
Customer Relationship ◽  
Community Formation ◽  
Heterogeneous Information ◽  
Network Information ◽  
Partial Data ◽  
Business To Consumer

In this chapter, the authors survey the general problem of analyzing a social network in order to make predictions about its behavior, content, or the systems and phenomena that generated it. They begin by defining five basic tasks that can be performed using social networks: (1) link prediction; (2) pathway and community formation; (3) recommendation and decision support; (4) risk analysis; and (5) planning, especially causal interventional planning. Next, they discuss frameworks for using predictive analytics, availability of annotation, text associated with (or produced within) a social network, information propagation history (e.g., upvotes and shares), trust, and reputation data. They also review challenges such as imbalanced and partial data, concept drift especially as it manifests within social media, and the need for active learning, online learning, and transfer learning. They then discuss general methodologies for predictive analytics involving network topology and dynamics, heterogeneous information network analysis, stochastic simulation, and topic modeling using the abovementioned text corpora. They continue by describing applications such as predicting “who will follow whom?” in a social network, making entity-to-entity recommendations (person-to-person, business-to-business [B2B], consumer-to-business [C2B], or business-to-consumer [B2C]), and analyzing big data (especially transactional data) for Customer Relationship Management (CRM) applications. Finally, the authors examine a few specific recommender systems and systems for interaction discovery, as part of brief case studies.

scholarly journals Impact of Social Networks on the Labor Market Inequalities and School-to-Work Transitions

2021 ◽  
Vol 11 (1) ◽  
pp. 38-50 ◽  
Author(s):  
Mahmut Özer ◽  
Matjaz Perc
Keyword(s):  
Higher Education ◽  
Social Networks ◽  
Labor Market ◽  
Social Network ◽  
Human Resources ◽  
Social Contact ◽  
School To Work ◽  
Social Contacts ◽  
Work Transitions

Countries invest in education systems in order to increase the quality of their human capital. In this context, it is seen that especially after the expansion of the higher education systems, countries try to increase higher education graduation rates in order to improve the quality of human resources in the labor market. The ultimate goal of these efforts is to facilitate the transitions from school-to-work, and to increase social welfare by meeting the human resources needs of the labor market. The facilitation of school-to-work transitions has a direct impact on youth unemployment. School-to-work transitions are influenced not only by the quality of education from primary to higher education but also by the dynamics of the labor market. Social network analysis can provide important insights into this dynamics, and in doing so reveal that there are indeed many factors that play a key role in determining who gets a job and why, including, first and foremost, social contacts. An analysis of job search channels reveals that partners, friends, and relatives are those social contacts that are most decisive for employment outcomes. Research reveals that employers use social-contact-based reference channels much more frequently than formal channels for recruitment. Thus, employers frequently use such reference channels in recruitment. It has also been shown that the use of social-contact channels reduces employers' costs of finding suitable employees and increases productivity since employees hired through these channels also stay longer in their firms. We here explore the full potential of social network analysis to better our understanding of school-to-work transitions, to reveal in no uncertain terms the importance of social contacts, and to show how these insights can be leveraged to level the labor market for all involved. An important take-home message is that the labor market dynamics is strongly affected by the Matthew effect, such that the inequalities and the gaps between opportunities only grow and widen as the underlying social networks evolve. It is therefore important to mitigate these effects well before school-to-work transitions come into play, namely during the education. In particular, we assert that minimizing the inequalities during education should effectively mitigate the uneven impact of social networks on school-to-work transitions.

Predictive Analytics of Social Networks

2014 ◽  
pp. 297-333
Author(s):  
Ming Yang ◽  
William H. Hsu ◽  
Surya Teja Kallumadi
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Predictive Analytics ◽  
Concept Drift ◽  
Customer Relationship ◽  
Community Formation ◽  
Heterogeneous Information ◽  
Network Information ◽  
Partial Data ◽  
Business To Consumer

In this chapter, the authors survey the general problem of analyzing a social network in order to make predictions about its behavior, content, or the systems and phenomena that generated it. They begin by defining five basic tasks that can be performed using social networks: (1) link prediction; (2) pathway and community formation; (3) recommendation and decision support; (4) risk analysis; and (5) planning, especially causal interventional planning. Next, they discuss frameworks for using predictive analytics, availability of annotation, text associated with (or produced within) a social network, information propagation history (e.g., upvotes and shares), trust, and reputation data. They also review challenges such as imbalanced and partial data, concept drift especially as it manifests within social media, and the need for active learning, online learning, and transfer learning. They then discuss general methodologies for predictive analytics involving network topology and dynamics, heterogeneous information network analysis, stochastic simulation, and topic modeling using the abovementioned text corpora. They continue by describing applications such as predicting “who will follow whom?” in a social network, making entity-to-entity recommendations (person-to-person, business-to-business [B2B], consumer-to-business [C2B], or business-to-consumer [B2C]), and analyzing big data (especially transactional data) for Customer Relationship Management (CRM) applications. Finally, the authors examine a few specific recommender systems and systems for interaction discovery, as part of brief case studies.

scholarly journals Social network activity of students in conditions of an epidemiological threat

2021 ◽  
Vol 13 (4) ◽  
pp. 749-757
Author(s):  
Natalia V. Kalinina ◽  
Muliat M. Tkhugo ◽  
Lyudmila P. Shipovskaya ◽  
Svetlana I. Matafonova ◽  
Tatyana L. Khudyakova ◽  
...  
Keyword(s):  
Social Sciences ◽  
Social Networks ◽  
Content Analysis ◽  
Social Network ◽  
Network Activity ◽  
The Internet ◽  
Social Contacts ◽  
Statistical Program ◽  
Observation Method

The period of restrictions on social contacts introduced by the governments of different countries increased the time spent by users in social networks. The paper aims to analyze the Internet activity of students in social networks as a way of overcoming interaction difficulties in the context of an epidemiological threat. The research used the observation method and questionnaires to collect data for this research. The sample for the research consisted of 300 students between ages of 18 and 22, who were purposively sampled. Using content analysis and the statistical program for social sciences, the collected data were analyzed. As a result of the study, it was established that the Internet activity of students in social networks in the context of an epidemiological threat and globalization in general, determines the level of development and the content framework of a person's self-attitude.   Keywords: activity; epidemiological threat; social network; manifestations of activity; self-attitude

Predictive Analytics of Social Networks

2016 ◽  
pp. 1080-1116
Author(s):  
Ming Yang ◽  
William H. Hsu ◽  
Surya Teja Kallumadi
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Predictive Analytics ◽  
Concept Drift ◽  
Customer Relationship ◽  
Community Formation ◽  
Heterogeneous Information ◽  
Network Information ◽  
Partial Data ◽  
Business To Consumer

In this chapter, the authors survey the general problem of analyzing a social network in order to make predictions about its behavior, content, or the systems and phenomena that generated it. They begin by defining five basic tasks that can be performed using social networks: (1) link prediction; (2) pathway and community formation; (3) recommendation and decision support; (4) risk analysis; and (5) planning, especially causal interventional planning. Next, they discuss frameworks for using predictive analytics, availability of annotation, text associated with (or produced within) a social network, information propagation history (e.g., upvotes and shares), trust, and reputation data. They also review challenges such as imbalanced and partial data, concept drift especially as it manifests within social media, and the need for active learning, online learning, and transfer learning. They then discuss general methodologies for predictive analytics involving network topology and dynamics, heterogeneous information network analysis, stochastic simulation, and topic modeling using the abovementioned text corpora. They continue by describing applications such as predicting “who will follow whom?” in a social network, making entity-to-entity recommendations (person-to-person, business-to-business [B2B], consumer-to-business [C2B], or business-to-consumer [B2C]), and analyzing big data (especially transactional data) for Customer Relationship Management (CRM) applications. Finally, the authors examine a few specific recommender systems and systems for interaction discovery, as part of brief case studies.

scholarly journals THE EFFECTIVENESS IN THE USE OF INSTAGRAM IN THE PROMOTION OF B2B COMPANIES

2019 ◽  
Vol 8 (6) ◽  
Author(s):  
Aleksey A. Nikitin ◽  
Tatyana I. Nikitina ◽  
Irina M. Kravchenko
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Participant Observation ◽  
Marketing Research ◽  
Quantitative Content ◽  
The Social ◽  
Statistical Studies ◽  
Marketing Tool ◽  
Promotion Process ◽  
Business To Business

This paper contains data from statistical studies of foreign agencies on the use of social networks in promoting companies of the B2B segment (Business to business) and their effectiveness. The emphasis was placed on the social network Instagram, as it is one of the fastest-growing platforms for marketing and it has fundamental differences from other social networks. As the main methods for conducting the study, non-participant observation, monitoring of articles and results of marketing research, a comparative analysis of Instagram accounts of B2B companies and quantitative content analysis were used. It is assumed that the chosen methodology most adequately reflects the real specifics of the promotion process in the segment selected for the study. In the process of studying articles of promotion specialists on social networks, the goals of promoting B2B companies, their target audience, the main types of content and other features of maintaining an account for using the Instagram platform as an effective marketing tool were identified and described. As a result of the analysis of statistical data obtained within the framework of various large studies and successful cases on the natural promotion of Instagram accounts of world B2B companies, conclusions were drawn about the effectiveness of using the social network Instagram for the B2B segment

Valuing e-inclusion: Social media and the social networks of adolescents with intellectual disability

2019 ◽  
Vol 24 (3) ◽  
pp. 381-397 ◽  
Author(s):  
Pippa White ◽  
Rachel Forrester-Jones
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Intellectual Disability ◽  
Interpersonal Relationships ◽  
Communication Technology ◽  
Typically Developing ◽  
Social Contacts ◽  
Media Usage ◽  
The Social

Background: Social media is a growing phenomenon, yet people with intellectual disability (ID) may not experience comparable access to this communication technology. Adolescents with ID may benefit from e-inclusion, especially as individuals with ID are at risk of having smaller social networks. Materials and Methods: The Social Network Guide was adapted to measure social media usage and used to examine the interpersonal relationships of adolescents with and without ID. Results: Adolescents with ID held smaller social networks with less developed informal relationships. However, friendship quality was comparable or superior to typically developing peers. Adolescents with ID interacted with a smaller percentage of contacts using social media. Social media use was predictive of the number of reported friendships and did not significantly predict critical comments. Conclusions: Findings suggest that adolescents with ID have comparable access to social media but use these sites to interact with a smaller number of social contacts.

scholarly journals Exploiting the business opportunities in biotech connections: The power of social networks

1969 ◽  
Vol 13 (4) ◽  
Author(s):  
Ian P McCarthy ◽  
Leyland Pitt ◽  
Colin Campbell ◽  
Rian Van der Merwe ◽  
Esmail Salehi-Sangeri
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Theory ◽  
Social Network Theory ◽  
Entrepreneurial Opportunities ◽  
Business Environments ◽  
Business Opportunities ◽  
Additional Value ◽  
Internet Networks ◽  
Business To Business

Networks have a well-established importance in business. Here network analysis, grounded in social network theory, is used to analyse two international biotech business-to-business environments. Of additional value, the methodology employed is described for the benefit of academics and practitioners alike. Swedish and Australian biotech firms are analysed through the examination of internet networks. Once gathered and analysed following the described methodology, several features of the networks can be determined. Most critically, identification of important actors and structural holes within networks allows valuable entrepreneurial opportunities to be unearthed. Biotech firms and suppliers, particularly those with a global reach, are best positioned to take advantage of such information.

Gospodarka elektroniczna - formy elektronicznego biznesu

2001 ◽  
pp. 31-33
Author(s):  
Arkadiusz Januszewski
Keyword(s):  
Business To Consumer ◽  
Business To Business

Obecnie Internet, obok zastosowań edukacyjnych i domowych, stał się narzędziem do prowadzenia biznesu. Coraz większą popularność zdobywają takie pojęcia, jak gospodarka informacyjna i elektroniczna, elektroniczne rynki, elektroniczny biznes i elektroniczny handel. W artykule omówiono te pojęcia. Przedstawiono formy e-biznesu: B2C 9 (business to consumer), B2B (business to business), C2B (consumer to business), C2C (consumer to consumer).

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