Of Leaders and Leadership Through Emotions

2019 ◽  
pp. 88-108
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
The Self ◽  
Leadership Traits ◽  
Emotional Labour ◽  
Work Related ◽  
Social Settings ◽  
Huge Impact ◽  
Research Findings

In this chapter, the authors explore leadership and its relation to emotion. While looking at who is a leader, they present the basics around the concept of emotional intelligence, and its huge impact in the last decades. Research findings will be presented to highlight fundamental characteristics of leaders, such as mindfulness, the ability to manage emotions of the self and the others, empathy, as well as social skills, intended as the ability to handle relationships, in group and social settings. Furthermore, they introduce the concept of emotional labour, which consists of a range of work-related emotions, and the four Cs theory that suggests we should appreciate emotions according to context, challenges, communication, and community. Lastly, the authors present models and processes to measure leadership traits, such as performing a social network analysis or a personality test.

Finding Automated (Bot, Sensor) or Semi-Automated (Cyborg) Social Media Accounts Using Network Analysis and NodeXL Basic

2017 ◽  
pp. 383-424 ◽  
Author(s):  
Shalin Hai-Jew
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Research Question ◽  
Other Information ◽  
The Face ◽  
Research Findings ◽  
Levels Of Automation ◽  
Information Streams

Various research findings suggest that humans often mistake social robot (‘bot) accounts for human in a microblogging context. The core research question here asks whether the use of social network analysis may help identify whether a social media account is fully automated, semi-automated, or fully human (embodied personhood)—in the contexts of Twitter and Wikipedia. Three hypotheses are considered: that automated social media account networks will have less diversity and less heterophily; that automated social media accounts will tend to have a botnet social structure, and that cyborg accounts will have select features of human- and robot- social media accounts. The findings suggest limited ability to differentiate the levels of automation in a social media account based solely on social network analysis alone in the face of a determined and semi-sophisticated adversary given the ease of network account sock-puppetry but does suggest some effective detection approaches in combination with other information streams.

Scholarly Influence Research (SIR)

2012 ◽  
Vol 2 (1) ◽  
pp. 17-31 ◽  
Author(s):  
Hirotoshi Takeda ◽  
Duane P. Truex ◽  
Michael J. Cuellar ◽  
Richard Vidgen
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Influence ◽  
The Social ◽  
Scholarly Influence ◽  
Research Findings ◽  
Publication Counts ◽  
Definition Of ◽  
Ideational Influence

Following previous research findings, this paper argues that the currently predominant method of evaluating scholar performance - publication counts in “quality” journals - is flawed due to the subjectivity inherent in the generation of the list of approved journals and absence of a definition of quality. Truex, Cuellar, and Takeda (2009) improved on this method by substituting a measurement of “influence” using the Hirsch statistics to measure ideational influence. Since the h-family statistics are a measure of productivity and the uptake of a scholar’s ideas expressed in publications, this methodology privileges the uptake of a scholar’s ideas over the venue of publication. Influence is built through other means than by having one’s papers read and cited. The interaction between scholars resulting in co-authored papers is another way to build scholarly influence. This aspect of scholarly influence, which the authors term social influence, can be assessed by Social Network Analysis (SNA) metrics that examine the nature and strength of coauthoring networks among IS Scholars. The paper demonstrates the method of assessing social influence by analysis of the social network of AMCIS scholars and compares the results of this analysis with other co-authorship networks from the ECIS and ICIS communities.

Understanding the Greek Family in Australia

1978 ◽  
Vol 3 (1) ◽  
pp. 24-37
Author(s):  
Loula Rodopoulos
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Family Functioning ◽  
Relevant Literature ◽  
Immigrant Families ◽  
Host Community ◽  
Helping Professions ◽  
Research Findings

AbstractThis paper reviews the relevant literature and attempts to highlight the implications of the research findings of Social Network Analysis for understanding the Greek family in Australia, with particular emphasis on its relevance for practitioners in the “helping” professions.The author is concerned with the paucity of information available to practitioners in the field in the variety of settings where immigrant families seek personal help, and believes that the lack of such information lends itself to reinforcing, rather than eliminating, stereotypes and to inappropriate intervention in the lives of Greek immigrant families. The paper also highlights the need to understand Greek immigrant families within the context of the host community and to consider factors with regard to family functioning, that are common to all families.

Finding Automated (Bot, Sensor) or Semi-Automated (Cyborg) Social Media Accounts Using Network Analysis and NodeXL Basic

2020 ◽  
pp. 1250-1289
Author(s):  
Shalin Hai-Jew
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Research Question ◽  
Other Information ◽  
The Face ◽  
Research Findings ◽  
Levels Of Automation ◽  
Information Streams

Various research findings suggest that humans often mistake social robot (‘bot) accounts for human in a microblogging context. The core research question here asks whether the use of social network analysis may help identify whether a social media account is fully automated, semi-automated, or fully human (embodied personhood)—in the contexts of Twitter and Wikipedia. Three hypotheses are considered: that automated social media account networks will have less diversity and less heterophily; that automated social media accounts will tend to have a botnet social structure, and that cyborg accounts will have select features of human- and robot- social media accounts. The findings suggest limited ability to differentiate the levels of automation in a social media account based solely on social network analysis alone in the face of a determined and semi-sophisticated adversary given the ease of network account sock-puppetry but does suggest some effective detection approaches in combination with other information streams.

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