Effects of user behaviors on accumulation of social capital in an online social network

Drawing upon Jasperson, Carter, and Zmud’s feature-centric view of technology (Jasperson, Carter, & Zmud, 2005) and Nahapiet and Ghoshal’s three dimensions of social capital factors (Nahapiet & Ghoshal, 1998), this chapter develops a conceptual model to elaborate the dynamic interactions between Social Network Services (SNS) features, social capital factors, and motivational antecedents on continuous participation in knowledge sharing activities among Online Social Network (OSN) community members. A number of SNS features, social capital factors, and motivational antecedents are set forth in this chapter. And the mechanism that links these factors is reviewed. It is proposed that, with embedded social mechanism, SNS features can strengthen motivations to continued participation through social capital facilitators.

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Building Social Capital in Higher Education With Online Opportunistic Social Matching

International Journal of Social Media and Online Communities ◽

10.4018/ijsmoc.2021010101 ◽

2021 ◽

Vol 13 (1) ◽

pp. 1-18

Author(s):

Daniel Raymond Trí Đặng Firpo ◽

Sonya Zhang ◽

Lorne Olfman ◽

Kittisak Sirisaengtaksin ◽

Joe Tawan Roberts

Keyword(s):

Higher Education ◽

Social Capital ◽

Social Network ◽

Distance Learning ◽

Sense Of Community ◽

Information Overload ◽

Online Social Network ◽

Context Aware ◽

Personal Context ◽

Cognitive Social Capital

Recently, colleges and universities have been forced to utilize distance learning. With students spending less time on campus, their sense of community may decrease because they would be less likely to participate in the community. This puts higher education commuter institutions at a disadvantage in terms of generating and maintaining social capital. The authors investigate the possibility to counter this disadvantage by actively promoting participation in a mobile online social network (OSN) supported by a context-aware notification and recommender system (NARS) to achieve opportunistic social matching, which mitigates information overload by considering each user's relational, social, and personal context as predictors of match opportunities. The results suggest that introducing a purposefully designed OSN has the potential to facilitate the creation of structural and relational social capital, but that it might not have an effect on cognitive social capital.

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Factors affecting bridging social capital in on-line professional networks : an exploratory study of social capital formation on LinkedIn

10.32920/ryerson.14653137.v1 ◽

2021 ◽

Author(s):

Natalia Gilewicz

Keyword(s):

Social Capital ◽

Social Network ◽

Structural Equation ◽

Online Survey ◽

Online Social Network ◽

Equation Modeling ◽

Bonding Social Capital ◽

Factors Affecting ◽

Bridging Social Capital ◽

On Line

This thesis investigates factors contributing to bridging social capital on LinkedIn. An online social network is one that allows users to make and share contacts by way of displaying their network (Boyd & Ellison, 2007). The intention of a network such as LinkedIn, is to create professional opportunities for its participants. Here, social capital is seen as the resource embedded within the social network, and as such is conceptualized as the benefit associated with online social network participation. Bridging social capital typically exists between weakly tied colleagues, It has been said to a superior type of social capital for 'getting ahead' (Putnam, 2000). Understanding how to create opportunities to increase bridging social capital in an online environment is useful to potentially overcoming barriers that exist offline. Using the partial least squares approach to structural equation modeling, the thesis analyzes data collected from an online survey (n:167) of LinkedIn members. Driven by theory, three constructs are conceptualized as contributing to the variance in bridging social capital. Ease of sue, browsing behaviours, and bonding social capital all have a positive relationship with bridging social capital, and together explain 53.8% of this variance. These findings are then extended to explore the broader design implications they have an online social network.

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