Intermediaries, Trust and Efficiency of Communication: A Social Network Perspective

Context: The importance of the involved stakeholders and their networks in vocational education and training (VET) focussing on international transfer and cooperation is highlighted in various empirical studies. A systematic empirical survey of these by means of social network analysis, however, has hardly been applied to date. This article is concerned with the development of social capital in the course of network formation and its sustainability. The object of investigation is the funded European VET innovation project AI Pioneers within the Erasmus+ program of the European Union. The main objective of the project is to establish and expand an international network in the context of VET in order to support the exchange of expertise on the use of artificial intelligence (AI) in education. Approach: To answer the research questions, the first step was to combine theoretical approaches from a social network perspective from psychology in relation to the analysis of interpersonal trust, sociology regarding the social capital approach and business administration by addressing the roles of actors in innovation processes. Among others, the social network perspective in this study is based on the work of Granovetter as well as Marsden and Campbell. For the data collection, a fully structured interview questionnaire and a semi-structured interview guideline were developed based on the theoretical framework of the study. In the second step, a multi-perspective egocentric network analysis was carried out: Data on a total of N = 10 egocentric networks were collected from the funded partners in the AI Pioneers project to gain an overall picture of the combined social capital and network structures. For the visualisation of the network data, the type of structured and standardised network maps was used. Findings: Regarding the establishment of social capital in the analysed innovation project AI Pioneers, it can be emphasised that a total of 74 relationships have been recorded in the 10 egocentric networks combined. In line with the project objectives, the education sector is addressed by the majority of the analysed relationships (n = 54), with (technical) vocational schools making up a substantial part of these. Focussing on the sustainability of the surveyed network structures: Most of the analysed relationships already existed before the project start and were consolidated during it (n = 57), while new ones were also established (n = 17). In addition, the continuous development of mutual trust and the need for equal cooperation is emphasised: A relatively high level of mutual trust can be recorded overall in the analysed egocentric networks (n = 55), while a low mutual trust is present in 19 relationships which is described due to e.g. asymmetrical power relations or a lack of commitment. The results show that the relationships analysed primarily contribute their resources in the form of expertise and their networking knowledge to the egocentric networks. Furthermore, a high level of interest and willingness to support the AI Pioneers project can be captured, particularly due to the novelty of the topic and the application of AI in VET. Conclusions: The study makes a significant contribution to VET research and its methodological set by using social network analysis with a combination of qualitative approaches for analysing egocentric networks from multiple perspectives. The importance of allocating resources to the creation of social capital regarding cooperation, network building and the sustainable maintenance of established structures can be emphasised. In this respect the benefits of a network-based approach can be highlighted in the context of the Erasmus+ program and the partnerships for innovation on forward-looking topics. In addition, the development of the two structured survey instruments in this study can be emphasised, which can be further developed on the basis of future research. Further quantitative network analyses would be valuable for VET research, especially against the background of innovation drivers and network formation, such as market and trend-related drivers due to demands and developments in the field of AI in education.

Purpose The purpose of this paper is to investigate when and how to best use social network analysis (SNA) in the supply chain management (SCM) discipline. In doing so, the study identifies SCM phenomena that have been examined from a social network perspective (SNA approach) in the SCM literature and highlights additional SCM phenomena that would be worth investigating using social network research. Then, the study critically investigates the application of SNA as a methodology (SNA method), with the goal of assessing and mitigating methodological risks in future studies. Design/methodology/approach This study carries out a systematic literature review of articles published in 11 top-tier SCM journals over a 20-year period. Findings First, while social network research has gained momentum especially since 2010, scholars are not yet entirely aware of the many possibilities the SNA approach offers to the SCM field. Second, expanded possibilities also hold for the development of SNA as a method. Originality/value The paper guides future SCM research by investigating when SNA is the right approach to use and how SNA as a method should be performed. Theoretically richer and practically more relevant research should result.

Social network analysis (SNA) is a multidisciplinary field dedicated to the analysis and modeling of relations and diffusion processes among various objects in nature and society, and other information/knowledge processing entities with an aim of understanding how the behavior of individuals and their interactions translates into large‐scale social phenomenon. Because of exploding popularity of online social networks and availability of huge amount of user‐generated content, there is a great opportunity to analyze social networks and their dynamics at resolutions and levels not seen before. This has resulted in a significant increase in research literature at the intersection of the computing and social sciences leading to several techniques for social network modeling and analysis in the area of machine learning and data mining. Some of the current challenges in the analysis of large‐scale social network data include social network modeling and representation, link mining, sentiment analysis, semanticSNA, information diffusion, viral marketing, and influential node mining.WIREs Data Mining Knowl Discov2013, 3:408–444. doi: 10.1002/widm.1105This article is categorized under:Algorithmic Development > Web MiningCommercial, Legal, and Ethical Issues > Social ConsiderationsTechnologies > Structure Discovery and Clustering

Over the last 20 years, there has been growing interest in social network analysis across a variety of social science disciplines, particularly in sociology, antropology, communication science, health and medicine, managment and political science. The roots of a social network perspective can be found in Simmel and Moreno discovery that the nature of ties themselves rather than the social group per se that lay at the center of many human behaviors. Despite these early insights, social network analysis gain its prominence only with revived interest in social networks as essence of social structure in 1980s (Burt, Bourdieu, Coleman, Putnam, Castells, et al.). This article aims to explore current metodological social network analysis approaches and how they can be succesfully employed in migration studies. It is argued that previuos dominant quantitative social network analysis based on full network and egocentric network analysis nowadays is renewed by social capital theory and social support approach. This new social network perspective allows to investigate how social networks function, how they can be constructed, maintained and reproduced, how are network dynamics across time and space.

There is promising evidence that public food procurement from family farming (PFPF) can serve as a powerful policy instrument in transitions towards more sustainable food systems. Despite the evidence around PFPF, there is lack of systemic and actor-oriented approaches analysing the relational and interactional dynamics among the multiple and diverse sets of actors in PFPF programs. In this paper, we address this gap by presenting an integrative framework that brings together food systems research, innovation studies and social network analysis, to assess the role of actor networks in PFPF. To illustrate the usefulness of the framework, we present the case of public procurement from family farming in Uruguay. We show how the framework has potential to: highlight the composition and diversity of networks of actors in PFPF; unravel individual and network barriers faced by actors in food systems; and, identify how interactions and (intermediary and brokerage) roles of network actors stimulate innovation or block the changes that are needed for PFPF to catalyse the transition towards sustainable food systems.

Conflict situations create changes. On the one hand, at the individual level, the subjects involved in the conflict are emotionally affected by it. On the other hand, at a social level, conflict management and its resolution transform in many cases, the context and the relationship of the people involved. How the conflict is managed will determine, to a large extent, the future of the relationship between the parties involved as well as the context. This chapter explores conflict management from a social network perspective in organizations. Scholars have used social network analysis (SNA) to understand the effects of a range of events in organizations. Conflict can spread through the fabric of the organization and disrupt business activity. Based on psychological and managerial literature, we first describe the concept of conflict management and SNA. Later, we analyze the interaction between those concepts. Finally, we explore how SNA becomes a powerful tool to diagnose and manage organizational conflict, as well as the change involved. Areas of future research are discussed.

With hundreds of millions of users worldwide, social networks provide incredible opportunities for social connection, learning, political and social change, and individual entertainment and enhancement in a wide variety of forms. In light of these notable outcomes, understanding information diffusion over online social networks is a critical research goal. Because many social interactions currently take place in online networks, we now have have access to unprecedented amounts of information about social interaction. Prior to the advent of such online networks, investigations about social behavior required resource-intensive activities such as random trials, surveys, and manual data collection to gather even small data sets. Now, massive amounts of information about social networks and social interactions are recorded. This wealth of data can allow us to study social interactions on a scale and at a level of detail that has never before been possible. We present an integrated approach to information diffusion in online social networks focusing on three key problems: (1) Querying and analysis of online social network datasets; (2) Modeling and analysis of social networks; and (3) Analysis of social media and social interactions in the contemporary media environment. The overarching goals are to generate a greater understanding of social interactions in online networks through data analysis, to develop reliable and scalable models that can predict outcomes of these social processes, and ultimately to create applications that can shape the outcome of these processes. We start by developing and refining models of information diffusion based on realworld data sets. We next address the problem of finding influential users in this data-driven framework. It is equally important to identify techniques that can slow or prevent the spread of misinformation, and hence algorithms are explored to address this question. A third interest is the process by which a social group forms opinions about an idea or product, and we therefore describe preliminary approaches to create models that accurately capture the opinion formation process in online social networks. While questions relating to the propagation of a single news item or idea are important, these information campaigns do not exist in isolation. Therefore, our proposed approach also addresses the interplay of the many information diffusion processes that take place simultaneously in a network and the relative importance of different topics or trends over multiple spatial and temporal resolutions.

The aim of the study is a practical analysis of social network services as a place to store multimedia resources that allow you to store and share digital photos, audio and video recordings, text files, presentations, and organize resource discussions for free. They can be used as educational materials (educational videos, photos on various topics, audio and video recordings of scientists' speeches, eyewitnesses of events, etc.), as well as means to store student video, photo, audio files and creative works. Method. Theoretical materials of the research are the best practices of foreign and domestic scientists in the field of network communications and their impact on the future specialists preparing in geography and tourism, namely articles, monographs, abstracts, dissertations. The study uses the method of theoretical materials analysis, as well as methods of observation, abstraction in identifying the qualification characteristics of social Internet networks. Content analysis (analysis of the social networks community messages content); statistical and comparative information analysis was carried out. Empirical research on the social Internet networks lies in creating and filling a community on the social Internet network, as well as engaging students in that community. Results. In the research it was found out that social networks can be used to solve the following tasks: to organize the collective work of students in the classroom (and outside it), which will be favourable to the communication, to gain the team experience; to ensure the development of the students personalized learning environment; to promote students independent learning, as everyone works in his own rhythm; to promote informal communication between the tutor and the students; to change the tutor's role, who will become an assistant, friend, mentor; to create digital educational content of the disciplines. The scientific novelty of the obtained results lies in the fact that on the comprehensive analysis of social internet networks, internet blogs, tourist internet resources basis, their place and role in the information and educational environment of higher education institutions are determined, in particular: the specifics of the content, prospects and mechanisms of the development of the social Internet networks from the point of view of training future tourists are determined; the dependence of the functions of the Social Internet Network, Internet blogs on their structural elements is established. Practical significance. The obtained scientific results are used in the educational process of future specialists in the field of training "Tourism", with the educational qualification "tourism expert, organizer of excursion and animation activities" when teaching the disciplines "study of local lore and tourism", "tourist work", "recreational geography", "geography of Ukraine", "professional competencies in tourism business", "organization of tourist activities". The research practices can be used to conduct tourist, museum, local history, excursion and other types of student's practice.

IntroductionSocial networks can affect health beliefs, behaviours and outcomes through various mechanisms, including social support, social influence and information diffusion. Social network analysis (SNA), an approach which emerged from the...

Information diffusion is a classical problem in social network analysis, which has been largely investigated with reference to single social networks. However, the current scenario is multi-social-network. Here, many social networks coexist and are strictly connected to each other, thanks to those users who join more social networks, acting as bridges among them. But, what happens to information diffusion when passing from a single-social-network context to a multi-social-network scenario? In this paper, we answer this question. In particular, thanks to the definition of a framework for handling these issues and to a large set of experiments, we show that, in this context, new actors and new features play the key roles. We also identify two possible improvements of our framework, namely the management of some "activation nucleuses" (i.e., some starting-node configurations that are likely to improve information diffusion) and the management of topics concerning the information to spread. In these activities, answer set programming provided us with a powerful and flexible tool for an easy setup and implementation of our investigation.

Modeling, analysis, control, and management of complex social networks represent an important area of interdisciplinary research in an advanced digitalized world. In the last decade social networks have produced significant online applications which are running on top of a modern Internet infrastructure and have been identified as major driver of the fast growing Internet traffic. The "Second International Workshop on Modeling, Analysis and Management of Social Networks and Their Applications" (SOCNET 2018) held at Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany, on February 28, 2018, has covered related research issues of social networks in modern information society. The Proceedings of SOCNET 2018 highlight the topics of a tutorial on "Network Analysis in Python" complementing the workshop program, present an invited talk "From the Age of Emperors to the Age of Empathy", and summarize the contributions of eight reviewed papers. The covered topics ranged from theoretical oriented studies focusing on the structural inference of topic networks, the modeling of group dynamics, and the analysis of emergency response networks to the application areas of social networks such as social media used in organizations or social network applications and their impact on modern information society. The Proceedings of SOCNET 2018 may stimulate the readers' future research on monitoring, modeling, and analysis of social networks and encourage their development efforts regarding social network applications of the next generation.

An emerging trend in educational research is the use of social network theory and methodology to understand how teacher collaboration can support or constrain teaching, learning, and educational change. This article provides a critical synthesis of educational literature on school social networks among educators to advance our understanding of the embeddedness of teachers in the social context of schools. It focuses on how researchers currently use a social network perspective as a theoretical lens and methodology to frame studies on teacher collaboration in schools. Building on exemplary findings of recent studies, it shows that the pattern of social relationships among teachers may significantly enhance our understanding of the ways in which teacher collaboration takes place and contributes to student learning, teachers’ instructional practice, and the implementation of reform. Finally, limitations and future directions in the conceptualization, measurement, and analysis of school social networks are indicated.

This study focuses exclusively on migrant students in German higher education and examines the relationship between their social networks, social capital, and dropout intention. We identify two different forms of social capital – resources and norms – that students derive from their networks, and are related to their educational outcomes. These two forms of social capital also correspond with the two well-established arguments for explaining immigrants’ educational outcomes: (1) immigration optimism and (2) social background disadvantages. Using data from the German National Education Panel Study, we use Lin (2001) concepts of ‘capital deficit’ and ‘return deficit’ to examine the differences in access and utilization of social capital between native and migrant students, further differentiated by whether one or both parents were born abroad. Using linear and logistic regressions we find that the networks of migrant students are characterized by high norms and aspirations, which have a shielding role for dropout. Further, students with both parents born abroad are at a higher risk of dropout than mixed background students. We specifically focus on factors that are unique to migrant students going beyond the well-established social background explanations for migration-related disadvantages.

In this study, the structure of a service supply chain is analysed using social network analysis approach. Service supply chains consist of peoples and organisations connected in form of a network. The network is formed by the combination of ties between different players in the chain/network. These players are dependent upon each other and there exists flow of material or flow of information between players. Despite the longevity of the 'social networks' perspective, the product supply chains as well as the supply chain in services are less explored from the social network perspective. A case study on supply chain of property purchase has been discussed in this paper that is analysed with the help of social network analysis approach.

Social networks were first investigated in social, educational and business areas. Academic interest in this field though has been growing since the mid twentieth century, given the increasing interaction among people, data dissemination and exchange of information. As such, the development and evaluation of new techniques for social network analysis and mining (SNAM) is a current key research area for Internet services and applications. Key topics include contextualized analysis of social and information networks, crowdsourcing and crowdfunding, economics in networks, extraction and treatment of social data, mining techniques, modeling of user behavior and social networks, and software ecosystems. These topics have important areas of application in a wide range of fields, such as academia, politics, security, business, marketing, and science.