Social Network Analysis techniques: implications for information and knowledge sharing in virtual learning communities

Author(s):  
Ben K. Daniel ◽  
Gordon I. McCalla ◽  
Richard A. Schwier
2019 ◽  
Vol 8 (1) ◽  
pp. 009
Author(s):  
Carlos G. Figuerola ◽  
Tamar Groves ◽  
Francisco J. Rodríguez

The practice of historical research in recent years has been substantially affected by the emergence of the so-called digital humanities. New computer tools have been appearing, software systems capable of processing vast quantities of information in ways that until recently were inconceivable. Text mining and social network analysis techniques are sophisticated instruments that can help render a more enriching reading of the available data and draw useful conclusions. We reflect on this in the first part of this article, and then apply these tools to a practical case: quantifying and identifying the women who appear in university-related articles in the newspaper El País from its founding until 2011.


2020 ◽  
Vol 185 ◽  
pp. 02024
Author(s):  
Yuqing Liao ◽  
Jingliang Chen

Based on the green finance policies in China from 2017 to 2019, this paper extracts feature and high-frequency words from policy documents, uses word cloud diagram, co-occurrence matrix and social network analysis techniques to quantitatively analyse the information contained in the green finance policies over the past three years and highlights the hot issues in question, thus providing a multi-layered and wideranging pathway for facilitating the orderly development of green finance industries across China.


Author(s):  
PUSHPA PUSHPA ◽  
Dr. Shobha G

Social Network Analysis (SNA) is a set of research procedures for identifying group of people who share common structures in systems based on the relations among actors. Grounded in graph and system theories, this approach has proven to be powerful measures for studying networks in various industries like Telecommunication, banking, physics and social world, including on the web. Since Telecommunication industries deals with huge amount of data, manual analysis of data is very difficult. In this paper we explore the Social Network Analysis techniques for Churn Prediction in Telecom data. Typical work on social network analysis includes the construction of multi-relational telecom social network and centrality measures for prediction of churners in telecom social network.


Author(s):  
Eve D. Rosenzweig ◽  
Elliot Bendoly

Our study demonstrates the value of taking a more encompassing and explicit view of competition in manufacturing strategy research. In doing so, we go beyond a dyadic-based approach and investigate the ways in which the degree of competition among firms in a network influences performance. Using social network analysis techniques, we develop a novel measure—which we refer to as competitor infighting—that captures the extent to which a firm's rivals compete amongst themselves. Our results suggest that a firm has a greater, unimpeded opportunity to demonstrate market gains as the degree of competition among its rivals increases, all else equal. In fact, competitor infighting is a better predictor of market performance in our sample than is a simpler, though perhaps more traditional, count of competitors. It serves an important moderating role in the relationship between a firm's operational weaknesses and market performance. As predicted, we find that as competitor infighting increases, the relationship between operational weaknesses and market performance is diminished.


Author(s):  
Eve D. Rosenzweig ◽  
Elliot Bendoly

Our study demonstrates the value of taking a more encompassing and explicit view of competition in manufacturing strategy research. In doing so, we go beyond a dyadic-based approach and investigate the ways in which the degree of competition among firms in a network influences performance. Using social network analysis techniques, we develop a novel measure—which we refer to as competitor infighting—that captures the extent to which a firm's rivals compete amongst themselves. Our results suggest that a firm has a greater, unimpeded opportunity to demonstrate market gains as the degree of competition among its rivals increases, all else equal. In fact, competitor infighting is a better predictor of market performance in our sample than is a simpler, though perhaps more traditional, count of competitors. It serves an important moderating role in the relationship between a firm's operational weaknesses and market performance. As predicted, we find that as competitor infighting increases, the relationship between operational weaknesses and market performance is diminished.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jiayuan Liu ◽  
Jianzhou Yan

PurposeThis study examines the relationships between structural holes, guanxi and knowledge sharing among groups of stakeholders within a Chinese destination network.Design/methodology/approachThis study conducted surveys, social network analysis and semi-structured interviews to gather data from the stakeholders of a popular Chinese tourist destination to test its hypotheses.FindingsKnowledge sharing within the destination network was impeded by structural holes but facilitated by guanxi. Furthermore, the impeding effect of structural holes on knowledge sharing is alleviated by guanxi.Originality/valueThis study illustrates the ways that stakeholders exploit structural holes and guanxi to promote knowledge sharing, and thus offers novel insights into how destination network structures affect the efficacy of stakeholders when it comes to sharing knowledge and promoting their destination.


Sign in / Sign up

Export Citation Format

Share Document