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Participants in leadership development programs take part in multiple developmental experiences that can influence the composition of their social network and enhance social capital. However, further investment in such practices may be limited because little is known about the relationship between leadership development approaches, networking ability, and social capital. This study explores how common developmental approaches may contribute to social capital, taking into consideration the role networking ability plays in this relationship. This descriptive, correlational study included a sample of graduates (N= 231) from 15 statewide agricultural-based leadership development programs. Our findings reveal that: 1) Networking is an antecedent to social capital, 2) skill building and personal growth approaches to leadership development are significant predictors of networking ability, and 3) networking ability is a significant predictor of social capital.

This monograph seeks to summarise the key influences of a role‐based perspective on leadership when making decisions as to how organisational resources can best be deployed.

Application of new frameworks provides insight into the leadership roles executives can adopt when part of formal, informal and temporary groups within the organisation's senior management team and those parts of the organisation for which they are responsible. The methodology adopted is qualitative, focusing on application of previously developed frameworks.

Adoption of an appropriate leadership role, and the timely switch from one role to another as circumstances change, are found to facilitate improvement in the ability of executives to mobilise organisational resources, and in so doing effectively address those challenges with which the organisation is faced.

A one‐organisation intensive case study of a multinational engineering company engaged in the design, development and manufacture of rotating turbomachinery provides the platform for the research. The research intent is to validate two frameworks in a different organisation of a similar demographic profile to those in which the frameworks were developed. The frameworks will require validating in organisations of different demographic profiles.

The concepts advanced, and implications discussed, provide an insight into the role‐based nature of leadership. The practical steps individual executives can take to develop their ability to adopt different leadership roles are highlighted.

This monograph is an investigation into, and study of the contribution of theory that provides insight into, the process by which executives effectively mobilise organisational resources. This differs from the original contributions to theory, which focused on methodology, data gathering and validation in contrast with the current study that is focused on practical application.

Network analysis is a well consolidated research area in several disciplines. Within management and organizational studies, network scholars consolidated a set of research practices that allowed ease of data collection, high inter case comparability, establishment of nomological laws and commitment to social capital motivation. This paper aims to elicit the criticism it has received and highlight the unsettled lacunae.

This paper sheds light on Network Analysis’s breakthroughs, while showing how its scholars innovated by responding to critics, and identifying outstanding debates.

The paper identifies and discusses three streams of criticism that are still outstanding: the role of human agency, the meaning of social ties and the treatment of temporality.

This paper brings to fore current debates within the Network Analysis community, highlighting areas where future studies might contribute.

The intensive blooming of social media, specifically social networks, pushed users to be integrated into more than one social network and therefore many new “cross-network” scenarios have emerged, including cross-social networks content posting and recommendation systems. For this reason, it is mightily a necessity to identify implicit bridge users across social networks, known as social network reconciliation problem, to deal with such scenarios.

We propose the BUNet (Bridge Users for cross-social Networks analysis) dataset built on the basis of a feature-based approach for identifying implicit bridge users across two popular social networks: Facebook and Twitter. The proposed approach leverages various similarity measures for identity matching. The Jaccard index is selected as the similarity measure outperforming all the tested measures for computing the degree of similarity between friends’ sets of two accounts of the same real person on two different social networks. Using “cross-site” linking functionality, the dataset is enriched by explicit me-edges from other social media websites.

Using the proposed approach, 399,407 users are extracted from different social platforms including an important number of bridge users shared across those platforms. Experimental results demonstrate that the proposed approach achieves good performance on implicit bridge users’ detection.

This paper contributes to the current scarcity of literature regarding cross-social networks analysis by providing researchers with a huge dataset of bridge users shared between different types of social media platforms.

The purpose of this paper is to present a novel approach to coordination of multi‐agent teams, and in particular multi‐robot teams. The new approach is based on models of organisational sociology, namely the concept of social networks. The social relationships used in the model that is presented in this paper are trust and kinship relationships, but modified for use in heterogeneous multi‐robot teams.

The coordination of a robot team is achieved through task allocation. The proposed task allocation mechanism was tested in the multi‐robot team task allocation simulation.

The social networks‐based task allocation algorithm has performed according to expectations and the obtained results are very promising. Some intriguing similarities with higher mammalian societies were observed and they are discussed in this paper. The social networks‐based approach also exhibited the ability to learn and store information using social networks.

The research focused on simulated environments and further research is envisaged in the physical environments to confirm the applicability of the presented approach.

In this paper, the proposed coordination was applied to simulated multi‐robot teams. It is important to note that the proposed coordination model is not robot specific, but can also be applied to almost any multi‐agent system without major modifications.

The paper emphasizes applicability of considering multi‐robot teams as socially embodied agents. It also presents a novel and efficient approach to task allocation.

Expert profiling plays an important role in expert finding for collaborative innovation in research social networking platforms. Dynamic changes in scientific knowledge have posed significant challenges on expert profiling. Current approaches mostly rely on knowledge of other experts, contents of static web pages or their behavior and thus overlook the insight of big social data generated through crowdsourcing in research social networks and scientific data sources. In light of this deficiency, this research proposes a big data-based approach that harnesses collective intelligence of crowd in (research) social networking platforms and scientific databases for expert profiling.

A big data analytics approach which uses crowdsourcing is designed and developed for expert profiling. The proposed approach interconnects big data sources covering publication data, project data and data from social networks (i.e. posts, updates and endorsements collected through the crowdsourcing). Large volume of structured data representing scientific knowledge is available in Web of Science, Scopus, CNKI and ACM digital library; they are considered as publication data in this research context. Project data are located at the databases hosted by funding agencies. The authors follow the Map-Reduce strategy to extract real-time data from all these sources. Two main steps, features mining and profile consolidation (the details of which are outlined in the manuscript), are followed to generate comprehensive user profiles. The major tasks included in features mining are processing of big data sources to extract representational features of profiles, entity-profile generation and social-profile generation through crowd-opinion mining. At the profile consolidation, two profiles, namely, entity-profile and social-profile, are conflated.

(1) The integration of crowdsourcing techniques with big research data analytics has improved high graded relevance of the constructed profiles. (2) A system to construct experts’ profiles based on proposed methods has been incorporated into an operational system called ScholarMate (www.scholarmate.com).

One shortcoming is currently we have conducted experiments using sampling strategy. In the future we will perform controlled experiments of large scale and field tests to validate and comprehensively evaluate our design artifacts.

The business implication of this research work is that the developed methods and the system can be applied to streamline human capital management in organizations.

The proposed approach interconnects opinions of crowds on one’s expertise with corresponding expertise demonstrated in scientific knowledge bases to construct comprehensive profiles. This is a novel approach which alleviates problems associated with existing methods. The authors’ team has developed an expert profiling system operational in ScholarMate research social network (www.scholarmate.com), which is a professional research social network that connects people to research with the aim of “innovating smarter” and was launched in 2007.

The purpose of this paper is to propose an approach to generate recommendations for groups on the basis of social factors extracted from a social network. Group recommendation techniques traditionally assumed users were independent individuals, ignoring the effects of social interaction and relationships among users. In this work the authors analyse the social factors available in social networks in the light of sociological theories which endorse individuals’ susceptibility to influence within a group.

The approach proposed is based on the creation of a group model in two stages: identifying the items that are representative of the majority's preferences, and analysing members’ similarity; and extracting potential influence from members’ interactions in a social network to predict a group's opinion on each item.

The promising results obtained when evaluating the approach in the movie domain suggest that individual opinions tend to be accommodated to group satisfaction, as demonstrated by the incidence of the aforementioned factors in collective behaviour, as endorsed by sociological research. Moreover the findings suggest that these factors have dissimilar impacts on group satisfaction.

The results obtained provide clues about how social influence exerted within groups could alter individuals’ opinions when a group has a common goal. There is limited research in this area exploring social influence in group recommendations; thus the originality of this perspective lies in the use of sociological theory to explain social influence in groups of users, and the flexibility of the approach to be applied in any domain. The findings could be helpful for group recommender systems developers both at research and commercial levels.

Leadership is a social phenomenon. Therefore, it has to be examined according to its social context. The point to be underlined by the social context is the social network in which the leader emerges. Considering the studies, the social network side of leadership is relatively ignored comparing with sociometric studies. In that sense, the aim of this study is to reveal whether there is a relationship between the positions of the military personnel, who are defined as one of the gray-collar working groups in the literature, within the social network mechanisms of which they are members, and their self-leadership perceptions. To answer the question, a self-leadership scale was applied to 69 gray-collar employees working in a military unit and network analyses were performed. According to results, there is a strong, positive and significant relationship between the network mechanism centrality criteria indegree, reach centrality and closeness and the self-leadership perceptions of individuals. In addition, there was no significant relationship between eigenvector centrality and honest brokerage, which are among the network mechanism criteria, and the actors’ self-leadership perceptions. The study has aimed at accenting and adding different perspectives to the leadership studies and gray-collar literature.