The Role of Social Capital in the Internationalization of Startups

ObjectivesSocial network analysis focuses on the relationships between people and structures that form through their interactions. Research in the field has shown that people can be influenced by their social...

ObjectivesSocial network analysis focuses on the relationships between people and structures that form through their interactions. Research in the field has shown that people can be influenced by their social...

Chapter 3 - Privacy-Preserving Social Network Integration, Analysis, and Mining

The seventh SNA-KDD workshop is proposed as the seventh in a successful series of workshops on social network mining and analysis co-held with KDD, soliciting experimental and theoretical work on social network mining and analysis in both online and offline social network systems. In recent years, social network research has advanced significantly, thanks to the prevalence of the online social websites and instant messaging systems as well as the availability of a variety of large-scale offline social network systems. These social network systems are usually characterized by the complex network structures and rich accompanying contextual information. Researchers are increasingly interested in addressing a wide range of challenges residing in these disparate social network systems, including identifying common static topological properties and dynamic properties during the formation and evolution of these social networks, and how contextual information can help in analyzing the pertaining social networks. These issues have important implications on community discovery, anomaly detection, trend prediction and can enhance applications in multiple domains such as information retrieval, recommendation systems, security and so on. The past SNA-KDD workshops have achieved significant attentions from the world-wide researchers working in different aspects of social network analysis, including knowledge discovery and data mining in social network, social network modeling, multi-agent based social network simulation, complex generic network analysis and other related studies that can bring inspirations or be directly applied to social network analysis. Each year we received more than 30 submissions. The average acceptance rate is around 1/3.

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.

The social network paradigm and applications in pharmacy

Purpose This study aim to propose a conceptual model to explain and enhance knowledge sharing in small and medium-sized enterprises (SMEs), with a particular focus on the role of social capital, network structure and social networking technologies (SNTs) in facilitating tacit knowledge flows across supply chains. Design/methodology/approach The study integrates theoretical foundations from social capital theory, social network analysis (SNA) and the knowledge-based view (KBV) of the firm to build a multidimensional conceptual framework. The model is illustrated through a case-based application in a culturally embedded SME ecosystem in Alcoi (Spain), using qualitative methods, including document analysis, semi-structured interviews and participant observation. Findings The model identifies how structural and relational social capital, network configuration and SNTs jointly enable knowledge sharing processes, especially tacit knowledge. The case demonstrates that SMEs embedded in dense, trust-based networks can leverage simple digital tools and educational partnerships to enhance collaboration, innovation and supply chain agility. Research limitations/implications This is a conceptual and exploratory contribution. Future research should empirically validate the model across multiple SME settings and develop instruments to measure knowledge-sharing readiness and digital maturity. Practical implications The model provides actionable insights for SME managers and policymakers. It supports the design of strategies to reinforce trust-based knowledge flows, strengthen network ties and adopt digital tools aligned with informal collaboration practices. Social implications Knowledge sharing is shown to contribute to community resilience, intergenerational learning and the preservation of cultural identity in regional production ecosystems. The model also supports inclusive innovation policies for SMEs. Originality/value This paper offers a novel integration of KM and supply chain perspectives by combining social capital and SNA with digital enablers of tacit knowledge exchange. It contributes both a conceptual framework and a practical roadmap for enhancing knowledge flows in SME-dominated environments.

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.

Co-management or collaborative management is considered tobe a relational institution, since it places emphasis not only on the administrationof resources as such, but also on the social relations between multipleactors with diverse interests in the territory. For this reason, growing interestexists in studying comanagement using social network analysis (sna) and therelated concept of social capital. In this article, we present two applicationsfor the use of sna to understand co-management, using the Chilean systemof Management and Exploitation Areas for Benthic Resources (amerb) as acase study. sna tools are applied speci-fically to: 1) describe and analyse thegovernance network underlying the amerb system, and 2) explore the roleof fisher organizations’ social capital in co-management functioning andperformance. Based on the applications described in the article, we proposethe social network approach as a promising and complementary standpointfor the development and reformulation of more effective public policy, withgrowing participation of local communities.

Through an exhaustive review of the literature, this article looks at the applicability of social network analysis (SNA) in the field of humanresource development. The literature review revealed that a number of disciplines have adopted this unique methodology, which has assisted in the development of theory. SNA is a methodology for examining the structure among actors, groups, and organizations and aides in explaining variations in beliefs, behaviors, and outcomes. The article is divided into three main sections: social network theory and analysis, the social network approach and application to HRD. First, the article provides an overview of social network theory and SNA. Second, the process for conducting an SNA is described and third, the application of SNA to the field of HRD is presented. It is proposed that SNA can improve the empirical rigor of HRD theory building in such areas as organizational development, organizational learning, leadership development, organizational change, and training and development.

This paper explores the efficacy of a development model designed to enhance social capital where social capital is seen as a catalyst to increased economic opportunities. The study covers smallholder livestock producers in Malawi and the Philippines. The key question examined in this paper is whether communities that received this specific development intervention significantly increased their access to social capital over time and whether this social capital is positively correlated with net farm income and improved livelihoods. We draw data from two case studies: Heifer International’s Malawi Smallholder Dairy Development Project (MSDD1) and the Philippine Raising Income of Families through Sustainable Agri-Business Project (RICSA). The examination of these two cases increases our understanding of how implementation and contextual variables may influence changes in social capital and livelihoods. The study applies social network analysis (SNA) to understand the connection between asset-based/human capacity interventions and social capital. SNA provides a quantitative measure of networks and households’ position within those networks as indicators of social capital. Real net income which includes the value of home consumption is used as a proxy for ‘improved livelihoods.’ A quasi-experimental design is used to determine whether social capital and income variables change significantly over time and whether social capital is correlated with income. The results revealed significant differences across the two cases. In the Malawi case, the intervention significantly contributed to social capital formation. In the Philippine case study, the social capital did not demonstrate a significant difference. The dissimilarity between the two cases shows that it takes time to deepen bonds of trust and reciprocity and to extend networks as conduits of knowledge, information and economic opportunity.

Social network analysis dates back to the early 20th century, with initial studies focusing on small group behavior from a sociological perspective. The emergence of the Internet and subsequent increase in the use of online social networking applications has caused a shift in the approach to this field. Faced with complex, large datasets, researchers need new methods and tools for collecting, processing, and mining social network data.Social Network Mining, Analysis and Research Trends: Techniques and Applications covers current research trends in the area of social networks analysis and mining. Containing research from experts in the social network analysis and mining communities, as well as practitioners from social science, business, and computer science, this book proposes new measures, methods, and techniques in social networks analysis and also presents applications and case studies in this changing field.

The rapid growth in popularity of online social networks provides new opportunities in computer science, sociology, math, information studies, biology, business, and more. Social network analysis (SNA) is a paramount technique supporting understanding social relationships and networks. Accordingly, certain studies and reviews have been presented focusing on information dissemination, influence analysis, link prediction, and more. However, the ultimate aim is for social network background knowledge and analysis to solve real-world social network problems. SNA still has several research challenges in this context, including users’ privacy in online social networks. Inspired by these facts, we have presented a survey on social network analysis techniques, visualization, structure, privacy, and applications. This detailed study has started with the basics of network representation, structure, and measures. Our primary focus is on SNA applications with state-of-the-art techniques. We further provide a comparative analysis of recent developments on SNA problems in the sequel. The privacy preservation with SNA is also surveyed. In the end, research challenges and future directions are discussed to suggest to researchers a starting point for their research.

Understand the relational influences on help-seeking for mental health and substance use problems among people experiencing social marginalisation, with a focus on research applying social capital theory and social network analysis methods. Scoping review. EMBASE, Web of Science, Criminal Justice Abstracts and SocINDEX were searched up to June 2023, and Web of Science email alerts were used to capture any further publications up to June 2024. English-language, peer-reviewed publications that (1) focused on/discussed help-seeking for mental health or substance use problems; (2) included adults experiencing social marginalisation beyond sociodemographic factors; and (3) applied social capital theory or social network analysis methods. We extracted and charted data pertinent to review objectives and narratively synthesised results. Twenty-seven papers were included. Most (n=19) focused on the experiences of people who used drugs. Five specifically focused on help-seeking, four of which applied quantitative social network analysis, one was framed by network theories of social capital and one referred to social capital in interpreting findings. The remaining 22 papers discussed help-seeking while focused on different phenomena. Seven of these framed their approach with social capital, but none explicitly applied social capital to help-seeking. Eight papers used social network analysis, with four focused on help-seeking and seven using personal networks. Social/relational influences identified included: fear of losing social capital, the risks of high bonding capital, service providers as social capital, selective help-seeking, trust and network culture. Social capital, interconnected with the tight-knit bonds within marginalised groups, could deter help-seeking. Knowledge and attitudes towards help and help-seeking, shaped by past experiences and network cultures, influenced help-seeking and contributed to a cautious and selective approach. Theoretical elaboration and empirical research are required to better appreciate the relational influences on help-seeking for mental health and substance use problems among people experiencing social marginalisation. Social capital may provide a useful theoretical approach. While social network analysis methods have been applied, they are under-utilised.

We present the software tool visone which combines graph-theoretic methods for the analysis of social networks with tailored means of visualization. Our main contribution is the design of novel graph-layout algorithms which accurately reflect computed analyses results in well-arranged drawings of the networks under consideration. Besides this, we give a detailed description of the design of the software tool and the provided analysis methods.