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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.

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.

Taking a network approach, the purpose of this paper is to investigate the social antecedents of work-related engagement and exhaustion in a sample of Dutch healthcare workers, examining how employees’ structural position in the team (i.e. their centrality in the network) is associated with their engagement and exhaustion. Distinctions are made between instrumental networks (i.e. structural dependencies) and expressive networks (i.e. emotional connectedness through friendships).

Associations between job characteristics (quantitative demands, emotional demands and influence) and employees’ centrality in the instrumental and expressive networks at work with their self-reported engagement and exhaustion were examined. Network centrality was assessed though a sociometric survey by the total number of nominations each employee received (in-degree centrality) or gave (out-degree centrality) and for both networks separately.

The results show that whereas job characteristics but not network centrality were associated with exhaustion, network centrality in the expressive network but not job characteristics was associated with engagement. In-degree centrality (being nominated by many co-workers as a friend) was positively associated with engagement, whereas out-degree centrality (nominating many co-workers as a friend) was negatively associated with engagement.

The results support recent findings concerning more multifaceted antecedents of engagement and exhaustion, and underline the importance considering social network characteristics in investigations of work engagement. On a methodological level, the differing results for in-degree and out-degree centrality underline the importance of not only relying on self-reported social relationships but to also capture other-reported data.

The purpose of this paper is to explore the nature of the relationship between a scholar’s research performance (using weighted journal-impact factor average) and their degree centrality; the impact of author-homophily (in terms of gender, institutional sector, academic age, academic ranks, province and city) on this relationship is investigated as well.

Using scientific publishing data and journal-impact factors from Thomson Reuters’ ISI Web of Science (SCI) and Journal Citation Reports, respectively, the domestic co-authorship network of chemistry researchers in Pakistan during 2002-2009 was constructed then modeled via ordinary least squares regression.

Results show that the personal characteristics of a researcher do not necessarily lead to high degree centrality, i.e. attributes may not be causal to co-author relationships. Instead, high degree centrality is more so a function of the forerunning research performance of the researcher: those whom publish more in terms of impact factor, attract more co-authors (high degree centrality). Moreover, the relationship between research performance and degree centrality is positively moderated by age and province homophily and negatively moderated by city homophily.

Data are sourced wholly from the Pakistani chemistry research community; results many not be generalizable to other sub-populations or the wider research community.

The findings provide insights to performance-seeking authors: knowing that their research performance enhances their centrality, which in-turn may lead to increased research performance and various other desirable professional outcomes. In addition, researchers can look toward establishing similar (homophilous) or dissimilar (heterophilous) ties knowing that the relationship between research performance and centrality will likely be stronger when similarity or dissimilarity exists.

This study supports the idea that high research performance attracts more potential co-authors, which in-turn may lead to ever greater research performance, which suggests that the research community will be fragmented between high- and low-performing researchers. Also researcher will have similar or dissimilar ties in terms of various characteristics which in turn moderate the research performance centrality relationship.

This paper counteracts the empirical belief that researchers are attractive as potential co-authors according to their personal and professional characteristics. It is actually their research performance and homophily or heterophily of their ties which matters.

The purpose of this paper is to explore the dynamics of an online community by examining its participants’ centrality measures: degree, closeness, and the betweenness centrality. Each centrality measure shows the different roles and positions of an individual participant within an online community. To be specific, this research examines how an individual participant’s role and position affects her/his information sharing activities within an online community over time. Additionally, it investigates the differences between two different online communities (a personal interest focussed community and a social interest focussed community), in terms of the interaction patterns of participants.

For this research, the authors collected log files from Korean online discussion communities (café.naver.com) using a crawler program. A social network analysis was used to explore the interaction patterns of participants and calculate the centrality measures of individual participants. Time series cross-sectional analysis was used to analyze the effects of the roles and the positions on their information sharing activities in a longitudinal setting.

The results of this research showed that all three centrality measures of an individual participant in previous time periods positively influenced his/her information sharing activity in the current periods. In addition, this research found that, depending on the nature of the discussion issues, the participants showed different interaction patterns. Throughout this research, the authors explored the interaction patterns of individual participants by using a network variable, the centrality, within a large online community, and found that the interaction patterns provided strong impact on their information sharing activities in the following months.

To investigate the changes of participant’s behaviors, this study simply relies on the numbers of comments received and posted without considering the contents of the comments. Future studies might need to analyze the contents of the comments exchanged between participants, as well as the social network among participants.

Online communities have developed to take a more active role in inviting public opinions and promoting discussion about various socio-economic issues. Governments and companies need to understand the dynamics which are created by the interactions among many participants. This study offers them a framework for analyzing the dynamics of large online communities. Furthermore, it helps them to respond to online communities in the right way and in the right time.

Online communities do not merely function as a platform for the free exchange and sharing of personal information and knowledge, but also as a social network that exerts massive influence in various parts of society including politics, economy, and culture. Now online communities become playing an important role in our society. By examining communication or interaction behaviors of individual participants, this study tries to understand how the online communities are evolved over time.

In the area of online communities, many previous studies have relied on the subjective data, like participant’s perception data, in a particular time by using survey or interview. However, this study explores the dynamics of online communities by analyzing the vast amount of data accumulated in online communities.

The purpose of this paper is to investigate whether potentially disadvantaged groups of researchers derive more research performance benefits from co-authorship network centrality (degree, closeness and betweenness).

The paper builds on Badar et al. (2013), which found positive associations of network centrality on research performance with a moderating relationship of gender for female authors. Using data from ISI Web of Science (SCI), the authors study the same domestic co-authorship network of Chemistry researcher in Pakistan publishing from years 2002-2009 and investigate the moderating role of academic age and institutional sector on the relationship between co-authorship network centrality (degree, closeness, and betweenness) and the academic research performance (aggregate impact factor) of chemistry university/institute faculty members in Pakistan.

Ordinary least squares (OLS)-regression findings indicated a positive relationship between degree centrality and research performance with a positive moderating relationship for both academic age and institutional sector on the relationship between degree centrality and research performance for junior faculty members and faculty members employed in private sector universities/research institutes.

The findings can be heartening and motivating for junior faculty and private institute faculty in Pakistan in suggesting opportunities to surpass barriers of domination and poor resource access through co-authorship ties and structural social capital.

This paper adds to the limited research by strengthening the argument that potentially disadvantaged faculty with certain individual (academic age) and work-related characteristics (institutional sector) may benefit differentially from their co-authorship network centrality.

This study aims to present a methodological framework to evaluate the relationship between social network centrality, individual competitiveness and network competitiveness.

Tilapia fish farmers in the Canoas I hydroelectric dam (states of São Paulo and Paraná, Brazil) were provided with a roster of all actors in their network and interviewed to obtain information on relational and competitiveness variables. UCINET was used to calculate the degree centrality of each farmer. Seven competitiveness drivers were combined into a single indicator to determine the level of competitiveness. A four-quadrant matrix was constructed to investigate the relationship between degree centrality and level of competitiveness.

A positive relationship was found between degree centrality and level of competitiveness.

Agents upstream or downstream of fish farming were not interviewed, precluding an in-depth analysis of competitiveness in terms of market structure and market relations. The authors suggest that future studies should investigate the influence of upstream and downstream agents on the social network and competitiveness of fish farmers. It is also important to monitor changes in the level of competitiveness of fish farmers in the event of a national economic crisis.

Development of a novel methodological framework on the basis of two methodologies, social network analysis and competitiveness analysis.

In contrast to the vertical supply chain structure, firms are increasingly engaging in horizontal inter-firm collaborations to develop new technologies, products and services, yet, little is known about factors that influence the governance mechanism and performance of such collaborative projects. The purpose of this paper is to investigate how different factors affect the role or the centrality of the governing mechanism (the broker) in inter-firm collaborative networks.

Using a case study method approach, this paper studies three dynamic networks comprising firms that are not in a traditional supply chain relationship. The networks comprised firms engaged in the development of different aspects of an innovative automotive technology.

The study finds that broker’s centrality varies over time and is directly related to project performance; network structure and perceived broker power are related to broker centrality in dynamic networks. The more loosely connected a network is (open network), the more the degree of broker centrality. The higher the degree of expert power that a broker is perceived to possess, the higher is the degree of broker centrality.

Investigating governance mechanism and determinants of network outcomes in inter-firm collaboration for new product development represents a departure from the traditional studies on similar phenomena in vertically structured supply chain arrangements; thus, contributes to the literature on innovation in inter-firm arrangements. Understanding how the salient factors contribute to performance at the network level builds on firm level and dyadic level of analysis or focus of previous studies.

Little attention has been paid to restructuring existing massive amounts of literature data such that evidence-based meaningful inferences and networks be drawn therefrom. This paper aims to structure extant literature data into a network and demonstrate by graph visualization and manipulation tool “Gephi” how to obtain an evidence-based literature review.

The main objective of this paper is to propose a methodology to structure existing literature data into a network. This network is examined through certain graph theory metrics to uncover evidence-based research insights arising from existing huge amounts of literature data. From the list metrics, this study considers degree centrality, closeness centrality and betweenness centrality to comprehend the information available in the literature pool.

There is a significant amount of literature on any given research problem. Approaching this massive volume of literature data to find an appropriate research problem is a complicated process. The proposed methodology and metrics enable the extraction of appropriate and relevant information from huge quantities of literature data. The methodology is validated by three different scenarios of review questions, and results are reported.

The proposed methodology comprises of more manual hours to structure literature data.

This paper enables researchers in any domain to systematically extract and visualize meaningful and evidence-based insights from existing literature.

The procedure for converting literature data into a network representation is not documented in the existing literature. The paper lays down the procedure to structure literature data into a network.

This research aims to utilize a social network analysis approach to examine the effect of organizational position within a network of strategic partnerships on innovation as measured by perceptions of industry analysts. Specifically, the purpose of the paper is to examine how network characteristics such as degree centrality (being centrally located in a network), between centrality (being positioned as an intermediary), and closeness centrality (having a short average distance to all other firms in the network) affect the innovation ranking of the focal firm.

Data for 563 firms are generated from three distinct data sources (SDC Platinum: Alliances and Joint Ventures, COMPUSTAT, and Fortune's America's Most Admired Companies) and analyzed via social network analysis and linear regression.

The network characteristics of degree centrality and between centrality positively relate to industry perceptions or innovativeness whereas closeness centrality had no significant effect. Additionally, there were no discernable differences in innovativeness when comparing manufacturing firms to service organizations.

Insignificant findings related to closeness centrality and the good/service differential may be attributable to the data sources, in that, the information is limited to firms within the respective sources. This data limitation may limit the potential of examining the effect of all network characteristics. Additionally, some included companies participate in multiple industries (i.e., have multiple SIC codes), which may serve as the blurring of any differences between good and service firms.

The results highlight the importance of considering strategic partnerships that establish configurations of partnership webs when pursuing innovation activities. Specifically, the findings suggest that firms should seek numerous strategic partnerships (high degree centrality) and attempt to broker information or control the extent to which partners collaborate (high between centrality). These results provide insights for firms seeking to establish new supply‐chain relationships in order to enhance their level of innovation.

This research provides a unique empirical examination of the impact of network positional characteristics on the innovativeness of a focal firm.