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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.

In this study, the authors sought to investigate how the implicit social ties of both project owners and potential backers are associated with crowdfunding project success.

Drawing on social ties theory and factors that affect crowdfunding success, in this research, the authors developed a model to study how project owners' and potential backers' implicit social ties are associated with crowdfunding projects' degrees of success. The proposed model was empirically tested with crowdfunding data collected from Kickstarter and social media data collected from Twitter. The authors performed the test using an ordinary least squares (OLS) regression model with fixed effects.

The authors found that project owners' implicit social ties (specifically, their social media activities, degree centrality and betweenness centrality) are significantly and positively associated with crowdfunding projects' degrees of success. Meanwhile, potential project backers' implicit social ties (their social media activities and degree centrality) are negatively associated with crowdfunding projects' degrees of success. The authors also found that project size moderates the effects of project owners' social media activities on projects' degrees of success.

This work contributes to the literature on crowdfunding by investigating how the implicit social ties of both potential backers and project owners on social media are associated with crowdfunding project success. This study extends the previous research on social ties' roles in explaining crowdfunding project success by including implicit social ties, while the literature explored only explicit social ties.

Effective knowledge integration is vital for decision making in interdisciplinary research (IDR) teams. However, there is a lack of knowledge about the antecedents of knowledge integration. This study aims to examine how social capital at different levels and their interaction influences knowledge integration in IDR teams. Specifically, this study explores the moderating role of team social capital in the relationship between individual social capital and knowledge integration.

A survey on 346 individuals from 46 IDR teams in a research university in China is conducted. A multilevel analysis of the hierarchical linear model is used to process the sociometric data.

Results reveal that team social capital interacts with individual social capital by influencing knowledge integration. At the individual level, tie strength and structural equivalence have a positive influence on knowledge integration. There is an inverted U-shaped relationship between betweenness centrality and knowledge integration. Furthermore, team cohesion negatively moderates the positive effect of tie strength and structural equivalence on knowledge integration. No support is found for the moderating role of team cohesion on the effect of betweenness centrality.

First, different from previous research on social capital at single levels, this study links the individual-level and the team-level views to explore the effects of social capital on knowledge integration. Second, this study enriches research on inducing factors of knowledge integration. Third, this study extends social capital research and knowledge integration research to the IDR team context.

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.

This study explores the characteristics of high-speed rail (HSR) and air transportation networks in China based on the weighted complex network approach. Previous related studies have largely implemented unweighted (binary) network analysis, or have constructed a weighted network, limited by unweighted centrality measures. This study applies weighted centrality measures (mean association [MA], triangle betweenness centrality [TBC], and weighted harmonic centrality [WHC]) to represent traffic dynamics in HSR and air transportation weighted networks, where nodes represent cities and links represent passenger traffic. The spatial distribution of centrality results is visualized by using ArcGIS 10.2. Moreover, we analyze the network robustness of HSR, air transportation, and multimodal networks by measuring weighted efficiency (WE) subjected to the highest weighted centrality node attacks. In the HSR network, centrality results show that cities with a higher MA are concentrated in the Yangtze River Delta and the Pearl River Delta; cities with a higher TBC are mostly provincial capitals or regional centers; and cities with a higher WHC are grouped in eastern and central regions. Furthermore, spatial differentiation of centrality results is found between HSR and air transportation networks. There is a little bit of difference in eastern cities; cities in the central region have complementary roles in HSR and air transportation networks, but air transport is still dominant in western cities. The robustness analysis results show that the multimodal network, which includes both airports and high-speed rail stations, has the best connectivity and shows robustness.

Because of the multiple design elements and complicated relationship among design elements of complex products design, it is tough for designers to systematically and dynamically express and manage the complex products design process.

To solve these problems, a supernetwork model of complex products design is constructed and analyzed in this paper. First, the design elements (customer demands, design agents, product structures, design tasks and design resources) are identified and analyzed, then the sub-network of design elements are built. Based on this, a supernetwork model of complex products design is constructed with the analysis of the relationship among sub-networks. Second, some typical and physical characteristics (robustness, vulnerability, degree and betweenness) of the supernetwork were calculated to analyze the performance of supernetwork and the features of complex product design process.

The design process of a wind turbine is studied as a case to illustrate the approach in this paper. The supernetwork can provide more information about collaborative design process of wind turbine than traditional models. Moreover, it can help managers and designers to manage the collaborative design process and improve collaborative design efficiency of wind turbine.

The authors find a new method (complex network or supernetwork) to describe and analyze complex mechanical product design.

This study investigated the impact of the coronavirus disease 2019 (COVID-19) pandemic on the Asian-Australasian cruise shipping network. The analysis was carried out using complex network analysis and data collected for two periods, before and after the pandemic outbreak. The analysis revealed that the network structure and properties have changed after the outbreak of the COVID-19 pandemic. Interestingly, the network’s density and the number of links have increased, but its scale-free property remains with the degree distribution follows the power law. The network has a higher connectivity efficiency with a smaller average path length and a higher clustering coefficient. Its hub ports still maintain an extensive connection. The network’s flow efficiency becomes higher and connectivity stronger after the pandemic. The role of cruise ports has changed as indicated by the degree, betweenness, closeness and eigenvector centralities. The study’s findings indicate that the cruise shipping sector could further enhance efficiency and identify strategies to assist the management in similar circumstances.

The purpose of this paper is to examine the leadership role of authors, institutions, and countries based on research co-authorship networks in the field of operations management (OM) and ranks European authors, institutions, and countries using network centrality measures. It also identifies the primary research areas of each of the leading European authors and maps the areas that European research in general has focussed most on.

Based on co-authorships in publications appearing in a representative set of three leading OM journals over the 15-year period of 1998-2012, network measures of total degree centrality and betweenness centrality are used to identify influential European agents serving as leaders and bridge builders in OM research. Keyword analysis is used to identify the dominant areas of OM research in Europe as well as the primary areas of research of the leading authors.

With UK, Spain, The Netherlands, and Italy accounting for the dominant share of authorship of papers in the journal set, many authors and institutions from these countries are also found to rank high on network centrality measures. While certain authors, institutions, and countries are found ranking high on total degree centrality based on number of direct connections in the network, others are found to play uniquely important roles as gatekeepers and bridge builders in network relationships. The body of research is found to be focussed most on the area of supply chain management. It is also found to be focussed more on manufacturing than service.

The examination of networks in this study based on co-authorships in publications in the set of three leading journals: Journal of Operations Management, International Journal of Operations & Production Management, and Production and Operations Management, is not all encompassing as there are likely other co-authorship relationships of OM researchers that go beyond publications in this set of journals.

Co-authorship of papers in the leading academic journals in a discipline provides a window on patterns of collaboration among key researchers within that academic community. The findings of this study inform the community of stakeholders on who the leading European agents in OM research are, what the primary areas of research of the leading European authors are, and areas that European research has focussed most on.

This is the first study of its kind that identifies and maps key European authors, institutions, and countries based on the analysis of co-authorship networks of researchers who have published in a set of leading OM journals that are considered to be among the most relevant outlets in the field of OM. It also maps the primary areas of research.

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.