scholarly journals Who takes the lead? Social network analysis as pioneering tool to investigate shared leadership within sports teams.

2017 ◽  
Author(s):  
Katrien Fransen ◽  
Stef Van Puyenbroeck ◽  
Todd M. Loughead ◽  
Norbert Vanbeselaere ◽  
Bert De Cuyper ◽  
...  
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Interpersonal Relationships ◽  
Shared Leadership ◽  
Leadership Roles ◽  
Sports Teams ◽  
Leadership Quality ◽  
Athlete Leadership ◽  
Leadership Structure

Leaders do not operate in social vacuums, but are imbedded in a web of interpersonal relationships with their teammates and coach. The present manuscript is the first to use social network analysis to provide more insight in the leadership structure within sports teams. Two studies were conducted, including respectively 25 teams (N = 308; Mage = 24.9 years old) and 21 teams (N = 267; Mage = 24.3 years old). The reliability of a fourfold athlete leadership categorization (task, motivational, social, external leader) was established by analyzing leadership networks, which mapped the complete leadership structure within a team. The study findings highlight the existence of shared leadership in sports teams. More specifically, regarding the task and external leadership roles, no significant differences were observed between the leadership quality of coaches and athlete leaders. However, athlete leaders were perceived as better motivational and social leaders than their coaches. Furthermore, both the team captain and informal athlete leaders shared the lead on the different leadership roles. Social network analysis was found to be a pioneering but valuable tool for obtaining a deeper insight in the leadership structure within sports teams.

scholarly journals The Art of Athlete Leadership: Identifying High-Quality Athlete Leadership at the Individual and Team Level Through Social Network Analysis

2015 ◽  
Vol 37 (3) ◽  
pp. 274-290 ◽  
Author(s):  
Katrien Fransen ◽  
Stef Van Puyenbroeck ◽  
Todd M. Loughead ◽  
Norbert Vanbeselaere ◽  
Bert De Cuyper ◽  
...  
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Leadership Role ◽  
High Quality ◽  
Sports Teams ◽  
Leadership Quality ◽  
Athlete Leadership ◽  
Strongly Connected ◽  
The Individual

This research aimed to introduce social network analysis as a novel technique in sports teams to identify the attributes of high-quality athlete leadership, both at the individual and at the team level. Study 1 included 25 sports teams (N = 308 athletes) and focused on athletes’ general leadership quality. Study 2 comprised 21 sports teams (N = 267 athletes) and focused on athletes’ specific leadership quality as a task, motivational, social, and external leader. The extent to which athletes felt connected with their leader proved to be most predictive for athletes’ perceptions of that leader’s quality on each leadership role. Also at the team level, teams with higher athlete leadership quality were more strongly connected. We conclude that social network analysis constitutes a valuable tool to provide more insight in the attributes of high-quality leadership both at the individual and at the team level.

scholarly journals Who takes the lead? Social network analysis as a pioneering tool to investigate shared leadership within sports teams

2015 ◽  
Vol 43 ◽  
pp. 28-38 ◽  
Author(s):  
Katrien Fransen ◽  
Stef Van Puyenbroeck ◽  
Todd M. Loughead ◽  
Norbert Vanbeselaere ◽  
Bert De Cuyper ◽  
...  
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Shared Leadership ◽  
Sports Teams

Who is the boss? Identifying key roles in telecom fraud network via centrality-guided deep random walk

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yi-Chun Chang ◽  
Kuan-Ting Lai ◽  
Seng-Cho T. Chou ◽  
Wei-Chuan Chiang ◽  
Yuan-Chen Lin
Keyword(s):  
Social Network ◽  
Random Walk ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Random Walks ◽  
Interpersonal Relationships ◽  
Interaction Network ◽  
Data Set ◽  
Content Type ◽  
Structural Equivalence

PurposeTelecommunication (telecom) fraud is one of the most common crimes and causes the greatest financial losses. To effectively eradicate fraud groups, the key fraudsters must be identified and captured. One strategy is to analyze the fraud interaction network using social network analysis. However, the underlying structures of fraud networks are different from those of common social networks, which makes traditional indicators such as centrality not directly applicable. Recently, a new line of research called deep random walk has emerged. These methods utilize random walks to explore local information and then apply deep learning algorithms to learn the representative feature vectors. Although effective for many types of networks, random walk is used for discovering local structural equivalence and does not consider the global properties of nodes.Design/methodology/approachThe authors proposed a new method to combine the merits of deep random walk and social network analysis, which is called centrality-guided deep random walk. By using the centrality of nodes as edge weights, the authors’ biased random walks implicitly consider the global importance of nodes and can thus find key fraudster roles more accurately. To evaluate the authors’ algorithm, a real telecom fraud data set with around 562 fraudsters was built, which is the largest telecom fraud network to date.FindingsThe authors’ proposed method achieved better results than traditional centrality indices and various deep random walk algorithms and successfully identified key roles in a fraud network.Research limitations/implicationsThe study used co-offending and flight record to construct a criminal network, more interpersonal relationships of fraudsters, such as friendships and relatives, can be included in the future.Originality/valueThis paper proposed a novel algorithm, centrality-guided deep random walk, and applied it to a new telecom fraud data set. Experimental results show that the authors’ method can successfully identify the key roles in a fraud group and outperform other baseline methods. To the best of the authors’ knowledge, it is the largest analysis of telecom fraud network to date.

scholarly journals An examination of the relationship between athlete leadership and cohesion using social network analysis

2016 ◽  
Vol 34 (21) ◽  
pp. 2063-2073 ◽  
Author(s):  
Todd M. Loughead ◽  
Katrien Fransen ◽  
Stef Van Puyenbroeck ◽  
Matt D. Hoffmann ◽  
Bert De Cuyper ◽  
...  
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Athlete Leadership ◽  
The Relationship

scholarly journals Social Organization in Prison: A Social Network Analysis of Interpersonal Relationships among Dutch prisoners

2019 ◽  
pp. 1-23
Author(s):  
Miranda Sentse ◽  
Derek A. Kreager ◽  
Anouk Q. Bosma ◽  
Paul Nieuwbeerta ◽  
Hanneke Palmen
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Organization ◽  
Interpersonal Relationships

SOCIAL NETWORK ANALYSIS AS A NEW METHODOLOGICAL TOOL TO UNDERSTAND UNIVERSITY–INDUSTRY COOPERATION

2015 ◽  
Vol 19 (01) ◽  
pp. 1550013 ◽  
Author(s):  
MIGUEL LINHARES PINHEIRO ◽  
CÂNDIDA LUCAS ◽  
JOSÉ CARLOS PINHO
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Interpersonal Relationships ◽  
Methodological Approach ◽  
Work Relationships ◽  
Methodological Tool ◽  
High Volatility ◽  
Downstream Processes ◽  
University Industry

Purpose: This work tests the use of social network analysis (SNA) as a new methodological approach to better understand university–industry (U–I) relationships in the context of research and development (R&D) cooperation networks for innovation. Methodology: Following a thorough review of the literature on U–I links from the last two decades, focusing on methodologies, SNA was applied to data on work relationships, obtained through a survey of the participants from University and Industry, engaged on a FP7 project. Findings: SNA is suggested as a useful and relevant tool to understand and examine U–I R&D cooperation at both personal and organizational levels. In support of this statement, several examples and an empirical illustration are provided. The assessment of the processes underlying the establishment and maintenance of U–I relationships within R&D cooperation with SNA suggested that interpersonal relationships are crucial for the establishment of successful cooperative activities. Unlike other tools, SNA allows the recognition of preferential relationships between institutions, and reveals asymmetries from within the U–I R&D network. Originality/value: This paper addresses the interactional dynamics embedded in U–I links. Most studies regarding U–I links focus on describing the downstream processes associated with technology transfer and commercialization. This study applies SNA to understand the ex ante establishment and maintenance of U–I relationships within R&D cooperation. The high volatility of these relationships, in view of the importance of the expected outcomes, justifies the need to understand the fundamentals of successful cooperation.

Sign in / Sign up

Export Citation Format

Share Document