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Building a Network Theory of Social Capital

by Nan Lin
Connections ()
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Abstract

In the past two decades, social capital in its various forms and contexts has emerged as one of the most salient concepts in social sciences . While much excitement has been generated, divergent views, perspectives, and expectations have also raised the serious question : is it a fad or does it have enduring qualities that will herald a new intellectual enterprise? This presentation's purpose is to review social capital as discussed in the literature, identify controversies and debates, consider some critical issues, and propose conceptual and research strategies in building a theory. I will argue that such a theory and the research enterprise must be based on the fundamental understanding that social capital is captured from embedded resources in social networks . Deviations from this understanding in conceptualization and measurement lead to confusion in analyzing causal mechanisms in the macro- and microprocesses. It is precisely these mechanisms and processes, essential for an interactive theory about structure and action, to which social capital promises to make contributions . The paper will begin by exploring the nature of capital and various theories of capital, so that social capital can be properly perceived and located . It will then identify certain controversies which, unless clarified or resolved, will hinder the development of a theory and the research enterprise . By considering social capital as assets in networks, the paper will discuss some issues in conceptualizations, measurements, and causal mechanisms (the factors leading to inequality of social capital and the returns following investments in social capital) . A proposed model will follow . The paper will conclude by calling attention to the rise of a new form of social capital, cybernetworks, and briefly suggesting how research on this topic promises to make important contributions to the research enterprise .

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Readership Statistics

8562 Readers on Mendeley
by Discipline
 
93% Social Sciences
 
3% Business, Management and Accounting
 
1% Computer Science
by Academic Status
 
25% Student > Master
 
19% Student > Ph. D. Student
 
18% Student > Bachelor
by Country
 
1% United States
 
1% United Kingdom
 
0% Spain

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