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To better understand the success of an open source software (OSS) project, this study aims to examine the role of social dependency networks (i.e. social and technical dependencies) in online communities.

This study focuses on dependencies using three network metrics – degree centrality, betweenness centrality and closeness centrality – in developer and module networks. A longitudinal analysis from the projects hosted at Sourceforge.net is conducted to examine the effects of social and technical networks on the success of OSS projects. To address our research questions, we have constructed research models to investigate the social network effects in developer networks, the technical network effects in module networks, and the social-technical network effects in both types of networks.

The results reveal nonlinear relationships between degree centrality in both social and technical networks and OSS success, highlighting the importance of a moderate level of degree centrality in team structure and software architecture. Meanwhile, a moderate level of betweenness centrality and a lower level of closeness centrality between developers lead to a higher chance of OSS project success.

This study is the first attempt to consider the network metrics in both module networks of the technical sub-system and developer networks of the social sub-system to better understand their influences on project success.

The importance of supply chain network centrality has been widely recognized in the manufacturing literature. This study extends this literature by investigating the relationship between network centrality and innovation value. “Innovation value” refers to an innovation's impact, quality and potential future contribution to technological development in a business community. This study also aims to examine whether the relationship between supplier centrality and innovation value is contingent upon supplier acquisition.

The study hypotheses are tested with archival data collected from US high-tech manufacturing industries over the 17-year period. Negative binomial regression analysis was used to test the hypotheses.

The authors found that supplier centrality has a significant positive relationship with innovation value. Moreover, an increase in supplier acquisition activities makes the relationship between supplier centrality and innovation value more positive.

This study contributes to the manufacturing and technology management research by exploring the impact of supplier centrality on innovation value. The results of this study shed light on the importance of a firm's central network position in enhancing the impact and quality of innovation. This study uncovers contingencies that help realize the potential benefits of holding a central network position.

Previous studies have addressed the importance of knowledge base and its effect on innovation outputs. However, few studies have focused on the antecedents of dynamic changes of the organizational knowledge base. This study aims to shed light on the antecedents of dynamic change of the organizational knowledge base by examining how network centrality in an organization's collaboration network impacts this change and the moderating role of knowledge network cohesion.

The empirical setting of this study is the smartphone collaboration network. The authors selected patent data from the Derwent Innovation Database. A negative binomial model was used to test the hypotheses.

The results verified that network centrality has a positive effect on the change in coupling among existing knowledge domains and has an inverted U-shaped relationship with the coupling between new and existing knowledge domains. Furthermore, when local cohesion is high, network centrality has a stronger positive effect on the change in coupling among existing knowledge domains. Global cohesion moderates this process in such a way that when it is at a high level, the coupling between new and existing knowledge domains can benefit more from a moderate level of network centrality.

This study sheds light on the antecedents of dynamic change of the organizational knowledge base and links the literature on collaboration and knowledge networks by providing novel insights to match collaboration network centrality with knowledge network cohesion for successful improvement of the organizational knowledge base.

This study aims to empirically investigate how susceptibility to social influence in new product adoption varies with one’s structural location in a social network.

The social network data were collected based on a sociometric network survey with 589 undergraduate students. Social network analysis and ordinary least squares regression analyses were used to test the hypotheses.

This study finds that consumers with high degree centrality (i.e. hubs) who have a large number of connections to others and consumers with high betweenness centrality (i.e. bridges) who connect otherwise distant groups in social networks are both less sensitive to informational influence from others. More importantly, the authors find evidence that consumers with moderate levels of degree/betweenness centrality are more susceptible to normative influence and status competition than those with low or high degree/betweenness centrality. The inverse-U patterns in the above relations are consistent with middle-status conformity and anxiety.

This research complements social influence and new product diffusion research by documenting important contingencies (i.e. network locations) in consumer susceptibility to different types of social influence from a social network perspective.

The findings will assist marketers to leverage social influence by activating relevant social ties with effective messages in their network marketing strategies.

This research provides a better understanding of the mechanisms driving susceptibility to social influence in new product diffusion.

The purpose of this paper is to adapt the job demands–resources perspective to extend social network literature by examining the effectiveness of psychological (work engagement and emotional exhaustion) and instrumental (access to benefits) mechanisms as mediators of the relationship between employees’ centrality in positive and negative ties networks and job performance.

The survey data were collected from 103 employees working at a public bank in three stages.

The study results supported the hypotheses that an individual’s centrality in a negative network increases his/her experience of emotional exhaustion, while individual centrality in a positive network increases his/her work engagement. In addition, the findings showed that centrality in positive networks will be more predictive of work engagement when negative ties centrality increases, and the relationship between centrality in the negative network and emotional exhaustion is weaker when centrality in the positive network is higher.

The study extends out to the social ledger model (Labianca and Brass, 2006) by examining the dual pathways of effects of positive and negative ties in predicting employee outcomes. In addition, the authors’ model enriches the understanding of the nature of social network ties more broadly by suggesting that its effects in the workplace can extend beyond psychological effects to include instrumental impact.

In recent years, centrality measures have been extensively used to analyze real-world complex networks. Water distribution networks (WDNs), as a good example of complex networks, exhibit properties not shared by other networks. This raises concerns about the effectiveness of applying the classical centrality measures to these networks. The purpose of this paper is to generate a new centrality measure in order to stick more closely to WDNs features.

This work refines the traditional betweenness centrality by adding a hydraulic-based weighting factor in order to improve its fit with the WDNs features. Rather than an exclusive focus on the network topology, as does the betweenness centrality, the new centrality measure reflects the importance of each node by taking into account its topological location, its demand value and the demand distribution of other nodes in the network.

Comparative analysis proves that the new centrality measure yields information that cannot be captured by closeness, betweenness and eigenvector centrality and is more accurate at ranking the importance of the nodes in WDNs.

The following practical implications emerge from the centrality analysis proposed in this work. First, the maintenance strategy driven by the new centrality analysis enables practitioners to prioritize the components in the network based on the priority ranking attributed to each node. This allows for least cost decisions to be made for implementing the preventive maintenance strategies. Second, the output of the centrality analysis proposed herein assists water utilities in identifying the effects of components failure on the network performance, which in turn can support the design and deployment of an effective risk management strategy.

The new centrality measure, proposed herein, is distinct from the conventional centrality measures. In contrast to the classical centrality metrics in which the importance of components is assessed based on a pure topological viewpoint, the proposed centrality measure integrates both topological and hydraulic attributes of WDNs and therefore is more accurate at ranking the importance of the nodes.

The complicated social relationship in Chinese culture requires human resource management (HRM) practitioners to be aware of the implications of social network. This paper aims to disclose the relevance between social network structure and employees’ performance.

An empirical research is exploited in this paper. Based on cluster sampling method, the authors collected 336 employees’ socio-demographic characteristic data and social network data by means of face-to-face interviews using the structured questionnaire. In addition, employees’ performance data were obtained from the financial department, to support subsequent correlation analysis, grey relational analysis and OLS regression analysis.

The informal network, rather than the formal network, was observed to contribute more toward employees’ individual performance. We further found that structure centrality has a distinct advantage in delineating an individual’s power and status in the network, success over degree centrality, thus predicting individual performance.

For the management practice of an organization, the present study’s empirical results demonstrate that informal relations have a more decisive influence on individual performance than formal relations. This research also found that the structure centrality, from the perspective of networks’ structure heterogeneity, is valuable in discovering crucial staff in social networks, especially those in informal network associations.

First, the relations between network structure and individual performance have been summarized. Second, the different influences between formal and informal networks on individual performance have been discussed. Third, a new index – structure centrality to recapitulate network structure for developing social network theory – introduced. Finally, this paper is an attempt to explore the associations between social network structure and employees’ performance from the perspective of the whole network.

This study investigates the extent to which a firm’s centrality and autonomy in its supply network are associated with the intensity and complexity of its competitive actions.

Utilizing social network analysis and dynamic panel data models, this study analyzes a comprehensive panel dataset with 10,802 firm-year observations across various industries between 2011 and 2018 to test the hypotheses.

Our findings show that a firm’s level of centrality in its supply network has an inverted U-shaped relationship with both competitive intensity and competitive complexity. In addition, the turning points of these two inverted U-shaped relationships differ in that firms with a lower level of centrality tend to compete aggressively by launching more actions within fewer categories, while firms with a higher level of centrality tend to compete aggressively by launching fewer actions that cover a larger range of categories. Finally, we find that a firm’s structural autonomy has a positive relationship with competitive complexity.

This study bridges the gap between the supply chain management literature and strategic management literature and investigates how supply networks shape competitive aggressiveness. In particular, this research investigates how a firm’s structural position in its supply network affects its competitive actions, an important intermediate mechanism for competitive advantage that has been overlooked in the supply chain management literature.

Networks linking tourist attractions or organizations are a major focus of tourism research. Despite extensive research on tourism networks, academic research on the spatial structure and formation of wine tourism networks is limited. This study aims to investigate the spatial structure and factors influencing the development of a network among Ningxia wineries, an emerging wine tourism destination in China.

This study uses social network analysis to uncover “what” the spatial structure of wine tourism networks looks like. Sixteen in-depth interviews were conducted among key stakeholders to explain the “why” of such structural characteristics.

The results show that in an emerging wine tourism destination, popular tourist attractions enjoy high centrality and hold key positions in the wine tourism network. Small wineries exhibit high closeness centrality, and only one winery serves as a network broker. According to the stakeholders, the importance of network actors will increase as their economic and political importance increase, while small wineries that lack differentiation in the network may perish.

Local governments can implement the suggested measures for improving network connections, and wineries are advised to find suitable positions to improve the experiences of tourists.

This study pioneers the identification of the distinct structure and factors influencing the network of an emerging wine tourism destination, thus enriching the understanding of the interplay and roles of different actors.

Social loafing in knowledge contribution (namely, knowledge contribution loafing [KCL]) usually happens in group context, especially in the mobile collaboration tasks. KCL shows dynamic features over time. However, most previous studies are based on static assumption, that is, KCL will not change over time. This paper aims to reveal the dynamics of KCL in mobile collaboration and analyze how network centrality influences KCL states considering the current loafing state.

This study is based on empirical study design. Real mobile collaboration behavioral data related to knowledge contribution were collected to investigate the dynamic relationship between network centrality and KCL. In total, 4,127 chat contents were collected through Slack (a mobile collaboration APP). The text data were first analyzed using the text analysis method and then analyzed by a machine learning method called hidden Markov model.

First, the results reveal the inner structure of KCL, showing that it has three states (low, medium and high). Second, it is found that network centrality positively influences individuals involved in medium and high loafing state, while it has a negative influence on individuals with low loafing state.

The limitations are related to the single machine learning method and no subdivision of social network. First, this paper only uses one kind of text classification model (TF-IDF) to divide chat contents, which may not be superior to other classification models. This paper considers the eigenvector centrality, and not further divides the social network into advice network and expressive network.

This study helps companies infer tendency of different KCL and dynamically re-organize a mobile collaborative team for better knowledge contribution.

First, previous studies based on static assumptions regarding KCL as static and the relationship between loafing reducing mechanisms and team members KCL does not change over time. This study relaxes static assumptions and allows KCL to change during the process of collaboration. Second, this study allows the impact of network centrality to be different when members are in different KCL states.