Trust and Social Capital

Regardless of any approach taken for examining social capital, researchers continuously converge on some key issues such as trust and yet diverge on several others about concrete and consistent indicators for measuring social capital. Many researchers believe that presence or absences of social capital can be solely linked to trusting relationships people build with each other as well as social institutions of civil engagement. It is not clearly known however, whether trust itself is a precondition for generating social capital or whether there are other intermediary variables that can influence the role of trust in creating social capital. In addition, similar to social capital, the definition of trust is problematic and it remains a nebulous concept and equally, with many dimensions. Interests in the analysis of trust are wide spread among many disciplines, notably policy analysis, economic development, reliability and security of distributed computational systems and many others. The variety of approaches currently employed to investigate trust and different interpretations of its role in fostering social capital has resulted into a diverse array of knowledge about the concept and its relationship to social capital. This Chapter provides a broader overview of work on trust. It discusses how researchers have used trust as a proxy for measuring social capital.

Conceptual and Theoretical Foundations of Social Capital

Social capital is a complex multifaceted and litigious theory, discussed in the Social Sciences and the Humanities. It is a theory increasingly researchers questioned its scientific legitimacy and yet paradoxically many other researchers continuously use it as a conceptual and theoretical framework to explain the structural and functional operations of communities. This Chapter discusses work done on the theory. It covers some of the theoretical controversy with a goal of aligning its conceptualization and distinguishing it from other types of capitals. The Chapter is organized first the basic theoretical and conceptual foundations of social capital are described. The aim is to present the reader with a basic understanding of what constitutes social capital, by opening discussion about various forms of capital(s)—as discussed in the disciplines of Economics and Sociology. Second, the Chapter discusses the origin of the theory as well as the work of key scholars who have contributed to the development of the theory. Furthermore, in order to identify the strengths and the weaknesses of the theory, the Chapter provides the reader with analysis of benefits and shortcomings of social capital both as a theoretical and analytical tool for studying communities.

A Computational Model of Social Capital

This Chapter presents the Bayesian Belief computational model of social capital developed within the context of virtual communities discussed in Chapter 7. The development of the model was based on insights drawn from research. The Chapter presents the key variables constituting social capital in virtual communities and shows how the model was created and updated. The scenarios described in the Chapter were authentic cases drawn from several virtual communities. The key issues predicted by the model as well as challenges encountered in building, verifying and updating the model are discussed. The ultimate goal of the Chapter is to share experiences in developing a model of social capital and to encourage the reader to think about how such experiences can be extended to model similar constructs or build more scenarios to update the model. The model presented in the Chapter is a proof-of-a concept and a demonstration of a procedure. Notwithstanding that some of the model’s predictions are accurate while other require more substantial empirical corroboration.

Overview of Bayesian Belief Network

Statistical and probability inferences are basically dependent on two major methods of reasoning, conventional (frequentist) and Bayesian probability. Frequentists’ methods are mainly based on numerous events, where Bayesian probability applies prior knowledge and subjective belief. Frequentist models of probability do not permit the introduction of prior knowledge into the calculations. This is traditionally to maintain the rigour of a scientific method and as way to prevent the introduction of extraneous data that might skew the experimental results. However, there are times when the use of prior knowledge would be a useful contribution to evaluation a situation. The Bayesian approach was proposed to help us reason in situation where prior knowledge is need, and especially under highly uncertain circumstances. This Chapter provides an overview of the main principles underlying the Bayesian method and Bayesian belief networks. The ultimate goal is to provide the reader with the basic knowledge necessary for understanding the Bayesian Belief Network approach to building computational model. The Chapter does not go into more technical details of probability theory and Bayesian statistics. But to make it more accessible to a wide range of readers, some technical details are simplified.

Virtual Communities

The growth of virtual communities and their continuous impact on social, economic and technological structures of societies has attracted a great deal of interest among researchers, systems designers and policy makers to examine the formation, development, sustainability and utility of these communities. Over the last two decades, the growth in research into virtual communities, though fairly diverse, can be broadly categorized into two dominant perspectives—technological determinism and social constructivism. The basic tenet of the technology determinism research is that technology shapes cultural values, social structure, and knowledge. This Chapter provides a general overview of research on virtual communities. It describes two particular types of virtual communities relevant to the analysis of social capital described in the book; virtual learning communities and distributed communities of practice. The goal of the Chapter is to provide an overall context in which social capital is reported in the book. The Chapter also describes other areas in which virtual communities are currently used. These include education, health care, business, socialization and mediating interaction among people in Diaspora.

Social Computing and Social Software

The World Wide Web is one of the most profound technological inventions of our time and is the core to the development of social computing. The initial purpose of the Web was to use networked hypertext system to facilitate communication among its scientists and researchers, who were located in several countries. With the invention of the Web came three important goals. The first was aimed at ensuring the availability of different technologies to improve communication and engagement. The second goal was to make the Web an interactive medium that can engage individuals as well as enrich communities’ activities. The third goal was for the Web to create a more intelligent Web, in addition to being a space browseable by humans. The Web was developed to be rich in data, promoting community engagement, and encouraging mass participation and information sharing. This Chapter describes general trends linked to the development of the World Wide Web and discusses its related technologies within the milieu of virtual communities. The goal is to provide the reader with a quick, concise and easy way to understand the development of the Web and its related terminologies. The Chapter does not account for a more comprehensive analysis of historical trends associated with the development of the Web; neither does it go into a more detailed technical discussion of Web technologies. Nonetheless, it is anticipated that the materials presented in the Chapter are sufficient to provide the reader with a better understanding of the past, present and future accounts of the Web and its core related technologies.

Social Capital Use-Case Application Areas

Despite lack of meticulousness, social capital continues to occupy a central position in many discussions about community, trust and social networks. The multidimensionality and multivariate nature of social capital provides a foundation for explaining, although sometimes vaguely so, various social issues in communities and social networks. In most of the discussions in scientific work, social capital is continuously treated as either an output or an input. Researchers write about communities performing better due to higher levels of social capital, others attribute superior performance of social amenities such as national economy to the prevalence of higher social capital. Some writers mentioned the construct as a circumventing term to mean one or more of its core variables (trust, shared understanding, social protocols etc.) and their application to specific areas of interests, while others take a holistic view to describe all of its variables and their utility to addressing social problems anchored in communities. This Chapter discusses the application of social capital in a variety of contexts to solve community problems. This is by no means a complete comprehensive coverage of cases in which social capital is currently utilized, rather the cases presented here are considered sufficient to illustrate the growing relevance of social capital to many application areas. The goal of the Chapter is to expose the reader to key application areas and to think about the practical and theoretical relevance of social capital to research and practice in other emergent cases.

Model Validation

Though computational models take a lot of effort to build, a model is generally not useful unless it can help people to understand the world being modelled, or the problem the model is intended to solve. A useful model allows people to make useful predictions about how the world will behave now and possibly tomorrow. Validation is the last step required in developing a useful Bayesian model. The goal of validation is to gain confidence in a model and to demonstrate and prove that a model produces reliable results that are closely related to the problems or issues in which the model is intended to address. The goal of the Chapter is to provide the reader with a basic understanding of the validation process and to share with them key lessons learned from the model of social capital presented in the book. While sensitivity analysis is intended to ensure that a Bayesian model is theoretically consistent with goals and assumptions of the modeller (how the modeller views the world) or the accuracy of sources of data used for building the model, the goal of validation is to demonstrate the practical application of the model in real world settings. This Chapter presents the main steps involved in the process of validating a Bayesian model. It illustrates this process by using examples drawn from the Bayesian model of social capital.

Foundations of Social Modeling

A model is an abstract representation of reality. It can be an object, a system or an idea. In general terms, one could say that a model is a simplification of reality. Modeling is a fundamental and quantitative way to understand complex phenomena and systems. Modelling make up a scientific approach that can be applied to analyse a wide range of physical and social problems. Modelling of complex systems is becoming increasingly a common practice in virtually different disciplines, giving rise to active fields of studies such as mathematical modelling, econometrics, social modelling, computational physics, chemistry, mechanics, and biology, to name just a few. Through modeling one can readily cross over from one discipline to another, the basic concepts and techniques are relatively the same. Computational models are useful tools for representing abstractions and concrete realities. Computational models are intended to provide knowledge about social and technical aspect of systems and their users. They are capable of providing computer systems designers and research analysts with rich insights to build processes, procedures and tools to support systems operations in order to adapt these operations to peoples’ technology needs. This Chapter presents an overview of computational modelling. It provides examples of computational models types and how they are currently used to inform our understanding of issues connected to users and computer systems. The goal of the Chapter is to present the reader with the background knowledge necessary for understanding the Bayesian computational approach presented in this book and to draw their attention to think about ways in which modelling can be used to analyse and understand problems in other social systems.

Knowledge and Knowledge Sharing in Virtual Communities

Knowledge management and knowledge sharing are topics best addressed in a different book. This Chapter is intentionally introduced in the book to introduce the reader to knowledge management and knowledge sharing and to think about these growing areas where there are potential opportunities to apply social capital to solve real world practical problems. Though the use of social capital and knowledge management was briefly introduced in Chapter 3, by reiterating these two issues here it will broaden the reader’s understanding of the critical role social capital plays in enhancing knowledge management through knowledge sharing in virtual communities. In addition, this Chapter discusses some of the most important challenges to knowledge sharing in virtual communities. Furthermore, this Chapter describes the basic concepts often associated with knowledge management and social capital.