Is social capital always “Capital”?: Measuring and leveraging social capital in online user communities for in-group diffusion

Pyo, Tae-Hyung; Tamrakar, Chanchal; Lee, Jae Young; Choi, Yun Seob

doi:10.1016/j.jbusres.2023.113690

Cited by 4 publications

(3 citation statements)

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“…Recent research has employed social network analysis to characterize online interactions from various aspects such as number, direction, strength, and redundancy, among others, to investigate their diverse impacts on user behavior (Li et al, 2021;Qi et al, 2021). Other studies calculated indicators that describe certain network attributes to measure social capital in online user communities from three dimensions (i.e., structural, relational, and cognitive capital) (Pyo et al, 2023;Wang et al, 2022). Chen et al (2015) consider online interactions in one crowdsourcing community from the direction, size, and frequency of interactions, find positive effects of interactive relations on individuals' idea generation.…”

Section: Social Network Analysis In Online Communitiesmentioning

confidence: 99%

“…Recent studies have used social network analysis to characterize online relations from multiple dimensions and explore their impacts on user innovation (Pyo et al, 2023;Qi et al, 2021;Rishika & Ramaprasad, 2019). As a quantitative method for studying relational data, social network analysis provides methodological support for depicting the whole picture of user online interaction and deconstructing the characteristics of user online interaction.…”

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confidence: 99%

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User Interaction Within Online Innovation Communities

Chen,

Li,

Feng

et al. 2023

International Journal of Web Services Research

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In the digital era, enterprises have established online innovation communities to attract customers to participate. Presented in this study is user interactions within these communities using social network analysis. By identifying distinct subgroups within the network and comparing the user interactions among these subgroups, this research aims to identify the group diversity of online interactions. The findings indicate that dialogists are more willing to interact and hold a favorable network position, followed by questioners, while answerers have the lowest level of interaction. User subgroups are identified using k-core analysis. The higher the value of the core k, the more interactions between users in the k-core subgroup and the better the network position. By combining both ego-centered and group dimensions of online interaction characteristics, this paper also outlines an investigation into an empirical study on the influence of user interactions on community recognition. The results confirm heterogeneous effects among different subgroups.

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Section: Social Network Analysis In Online Communitiesmentioning

confidence: 99%

mentioning

confidence: 99%

User Interaction Within Online Innovation Communities

Chen,

Li,

Feng

et al. 2023

International Journal of Web Services Research

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“…To the best of our knowledge, this serial mediation study pioneers in addressing the aforementioned research gaps among Chinese manufacturing enterprises. The findings contribute by revealing the interrelationships among various dimensions of social capital [32], dissecting the underlying paths through which they influence DC and PGI, and exploring the mediating role of DC in this complex framework [33,34].…”

Section: Introductionmentioning

confidence: 96%

Does structural social capital lead to proactive green innovation? a three-part serial mediation model

Gao

2024

PLoS ONE

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Enhancing green innovation for business sustainability represents a pressing global challenge. In the context of the manufacturing industry, the relationship between proactive green innovation (PGI) and structural social capital (SSC) remains a profoundly under-researched area. Drawing upon the theories of social capital and dynamic capability (DC), this study investigated the relationship between SSC and PGI within manufacturing enterprises via three individual and sequential mediating factors, namely cognitive social capital (CSC), relational social capital (RSC), and DC. Adopting a cross-sectional quantitative design, this study collected survey data from 485 manufacturing sector employees in China using purposive sampling. Structural equation modeling analysis of the data revealed no significant direct impact of SSC on PGI, but a strong indirect impact through the sequential mediating influences of CSC, RSC, and DC. The findings suggests that PGI within manufacturing enterprises is not wholly shaped by SSC; rather, firm-level dynamic capabilities, characterized by a sequential mechanism, plays a crucial role in achieving PGI within these enterprises. This paper offers both theoretical and practical contributions and provides recommendations for future research based on its limitations.

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