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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.

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.

Search engine users are faced with long lists of search results, each entry being of a varying degree of relevance. Often users' expectations based on the short text of a search result hold false expectations about the linked web page. This leads users to skip relevant information, missing valuable insights, and click on irrelevant web pages wasting time. The purpose of this paper is to propose a new summary generation technique, ReClose, which combines query‐independent and query‐biased summary techniques to improve the accuracy of users' expectations.

The authors tested the effectiveness of ReClose summaries against Google summaries by surveying 34 participants. Participants were randomly assigned to use one type of summary approach. Summary effectiveness was judged based on the accuracy of each user's expectations.

It was found that individuals using ReClose summaries showed a 10 per cent increase in the expectation accuracy over individuals using Google summaries, and therefore better user satisfaction.

The survey demonstrates the effectiveness of using ReClose summaries to improve the accuracy of user expectations.

This paper presents a novel summary generation technique called ReClose, a new approach to summary evaluation and improvements upon previously proposed summary generation techniques.

The increased complexity of water distribution networks (WDNs) emphasizes the importance of studying the relationship between topology and vulnerability of these networks. However, the few existing studies on this subject measure the vulnerability at a specific location and ignore to quantify the vulnerability as a whole. The purpose of this paper is to fill this gap by extending the topological vulnerability analysis further to the global level.

This paper introduces a two-step procedure. In the first step, this work evaluates the degree of influence of a node by employing graph theory quantities. In the second step, information entropy is used as a tool to quantify the global vulnerability of WDNs.

The vulnerability analysis results showed that a network with uniformly distributed centrality values exhibits a lower drop in performance in the case of partial failure of its components and therefore is less vulnerable. In other words, the failure of a highly central node leads to a significant loss of performance in the network.

The vulnerability analysis method, developed in this work, provides a decision support tool to implement a cost-effective maintenance strategy, which relies on identifying and prioritizing the vulnerabilities, thereby reducing expenditures on maintenance activities.

By situating the research in the entropy theory context, for the first time, this paper demonstrates how heterogeneity and homogeneity of centrality values measured by the information entropy can be interpreted in terms of the network vulnerability.

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.

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.

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.

This study aims to examine the impact of a chief executive officer (CEO) social network centrality on corporate social responsibility (CSR) performance.

This study carries out a multivariate linear regression analysis on a panel data sample of 11,507 firm-year observations (representing 1,386 unique US firms) from 2004 to 2014.

This paper finds a significant negative relation between CEO network centrality and irresponsible CSR performance (measured as CSR concerns). The findings suggest that better-connected CEOs can better mitigate CSR concerns or weaknesses, leading to improved overall CSR performance of a firm.

This is the first study that directly examines the empirical link between CEO centrality and CSR performance.

The purpose of this paper is to examine the evolution of collaboration among researchers in Library Hi Tech based on the co-authorship network analysis.

The Library Hi Tech publications were retrieved from Web of Science database between 2006 and 2017. Social network analysis based on co-authorship was analyzed by using BibExcel software and a visual knowledge map was generated by Pajek. Three important social capital indicators: degree centrality, closeness centrality and betweenness centrality were calculated to indicate the co-authorship. Cohesive subgroup analysis which includes components and k-core was then applied to show the connectivity of co-authorship network of Library Hi Tech.

The results indicated that around 42 percent of the articles were written by single author, while an increasing trend of multi-authored articles suggesting the collaboration among researchers in librarian research field becomes popular. Furthermore, the social network analysis identified authorship network with three core authors – Markey, K., Fourie, I. and Li, X. Finally, six core subgroups each included six or seven tightly connected researchers were also identified.

This study contributed to the existing literature by revealing the co-authorship network in librarian research field. Key researchers in the major subgroup were identified. This is one of the limited studies that describe the collaboration network among authors from different perspectives showing a more comprehensive co-authorship network.

While in everyday practice, school leaders are often involved in social relationships with a variety of stakeholders both within and outside their own schools, studies on school leaders’ networks often focus either on networks within or outside schools. The purpose of this paper is to investigate the extent to which principals occupy similar positions in their school’s network and the larger district network. In addition, the authors examined whether principals’ centrality in both networks can be attributed to demographic characteristics and transformational leadership (TL).

Using social network analysis, correlational and regression analysis, and an advanced social network technique, namely p2 modeling, the authors analyzed data collected among 708 educators in 46 Dutch elementary schools. The authors also offer a visualization of the district social network to explore principals’ relationships with other principals in the district.

Results suggest that principals who occupy a central position in their school’s advice network are also more likely to occupy a central position in their district’s collaborative leadership network. Moreover, TL was found to affect the extent to which principals are central in both networks.

The study is unique as it simultaneously explores principals’ social relationships in schools and the larger district. Moreover, the authors advance the knowledge of TL as a possible mechanism that may shape the pattern of these relationships, thereby connecting two streams of literature that were until now largely disconnected. Limitations to the study warrant further qualitative and longitudinal research on principals’ social relationships in schools, districts, and the larger community.