Network Analysis for Economics and Management Studies

2016 ◽  
pp. 1-60 ◽  
Author(s):  
Lucio Biggiero
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Network Formation ◽  
Competitive Equilibrium ◽  
Group Structure ◽  
Network Modeling ◽  
Descriptive Analysis ◽  
Economic Networks ◽  
Main Research

Sociology and other social sciences have employed network analysis earlier than management and organization sciences, and much earlier than economics, which has been the last one to systematically adopt it. Nevertheless, the development of network economics during last 15 years has been massive, alongside three main research streams: strategic formation network modeling, (mostly descriptive) analysis of real economic networks, and optimization methods of economic networks. The main reason why this enthusiastic and rapidly diffused interest of economists came so late is that the most essential network properties, like externalities, endogenous change processes, and nonlinear propagation processes, definitely prevent the possibility to build a general – and indeed even partial – competitive equilibrium theory. For this paradigm has dominated economics in the last century, this incompatibility operated as a hard brake, and presented network analysis as an inappropriate epistemology. Further, being intrinsically (and often, until recent times, also radically) structuralist, social network analysis was also antithetic to radical methodological individualism, which was – and still is – economics dominant methodology. Though culturally and scientifically influenced by economists in some fields, like finance, banking and industry studies, scholars in management and organization sciences were free from “neoclassical economics chains”, and therefore more ready and open to adopt the methodology and epistemology of social network analysis. The main and early field through which its methods were channeled was the sociology of organizations, and in particular group structure and communication, because this is a research area largely overlapped between sociology and management studies. Currently, network analysis is becoming more and more diffused within management and organization sciences. Mostly descriptive until 15 years ago, all the fields of social network analysis have a great opportunity of enriching and developing its methods of investigation through statistical network modeling, which offers the possibility to develop, respectively, network formation and network dynamics models. They are a good compromise between the much more powerful agent-based simulation models and the usually descriptive (or poorly analytical) methods.

Network Analysis for Economics and Management Studies

2020 ◽  
pp. 269-328
Author(s):  
Lucio Biggiero
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Network Formation ◽  
Competitive Equilibrium ◽  
Group Structure ◽  
Network Modeling ◽  
Descriptive Analysis ◽  
Economic Networks ◽  
Main Research

Sociology and other social sciences have employed network analysis earlier than management and organization sciences, and much earlier than economics, which has been the last one to systematically adopt it. Nevertheless, the development of network economics during last 15 years has been massive, alongside three main research streams: strategic formation network modeling, (mostly descriptive) analysis of real economic networks, and optimization methods of economic networks. The main reason why this enthusiastic and rapidly diffused interest of economists came so late is that the most essential network properties, like externalities, endogenous change processes, and nonlinear propagation processes, definitely prevent the possibility to build a general – and indeed even partial – competitive equilibrium theory. For this paradigm has dominated economics in the last century, this incompatibility operated as a hard brake, and presented network analysis as an inappropriate epistemology. Further, being intrinsically (and often, until recent times, also radically) structuralist, social network analysis was also antithetic to radical methodological individualism, which was – and still is – economics dominant methodology. Though culturally and scientifically influenced by economists in some fields, like finance, banking and industry studies, scholars in management and organization sciences were free from “neoclassical economics chains”, and therefore more ready and open to adopt the methodology and epistemology of social network analysis. The main and early field through which its methods were channeled was the sociology of organizations, and in particular group structure and communication, because this is a research area largely overlapped between sociology and management studies. Currently, network analysis is becoming more and more diffused within management and organization sciences. Mostly descriptive until 15 years ago, all the fields of social network analysis have a great opportunity of enriching and developing its methods of investigation through statistical network modeling, which offers the possibility to develop, respectively, network formation and network dynamics models. They are a good compromise between the much more powerful agent-based simulation models and the usually descriptive (or poorly analytical) methods.

scholarly journals Group Measures and Modeling for Social Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Vincent Levorato
Keyword(s):  
Complex System ◽  
Social Networks ◽  
Graph Theory ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Network Modeling ◽  
Complex Interactions ◽  
Social Network Modeling

Social network modeling is generally based on graph theory, which allows for study of dynamics and emerging phenomena. However, in terms of neighborhood, the graphs are not necessarily adapted to represent complex interactions, and the neighborhood of a group of vertices can be inferred from the neighborhoods of each vertex composing that group. In our study, we consider that a group has to be considered as a complex system where emerging phenomena can appear. In this paper, a formalism is proposed to resolve this problematic by modeling groups in social networks using pretopology as a generalization of the graph theory. After giving some definitions and examples of modeling, we show how some measures used in social network analysis (degree, betweenness, and closeness) can be also generalized to consider a group as a whole entity.

scholarly journals Agonistic Interactions in Pigs–Comparison of Dominance Indices with Parameters Derived from Social Network Analysis in Three Age Groups

Animals ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. 929 ◽  
Author(s):  
Kathrin Büttner ◽  
Irena Czycholl ◽  
Katharina Mees ◽  
Joachim Krieter
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Structural Equation ◽  
Dominance Hierarchy ◽  
Rank Order ◽  
Group Structure ◽  
Age Groups ◽  
Agonistic Interactions ◽  
Dominance Indices

Dominance indices are often calculated using the number of won and lost fights of each animal focusing on dyadic interactions. Social network analysis provides new insights into the establishment of stable group structures going beyond the dyadic approach. Thus, it was investigated whether centrality parameters describing the importance of each animal for the network are able to capture the rank order calculated by dominance indices. Therefore, two dominance indices and five centrality parameters based on two network types (initiator-receiver and winner-loser networks) were calculated regarding agonistic interactions observed in three mixing events (weaned piglets, fattening pigs, gilts). Comparing the two network types, the winner-loser networks demonstrated highly positive correlation coefficients between out-degree and outgoing closeness and the dominance indices. These results were confirmed by partial least squares structural equation modelling (PLS-SEM), i.e., about 60% of the variance of the dominance could be explained by the centrality parameters, whereby the winner-loser networks could better illustrate the dominance hierarchy with path coefficients of about 1.1 for all age groups. Thus, centrality parameters can portray the dominance hierarchy providing more detailed insights into group structure which goes beyond the dyadic approach.

scholarly journals Social Network Analysis of a Chimpanzee (Pan troglodytes) Group in Captivity Following the Integration of a New Adult Member

2020 ◽  
Vol 41 (5) ◽  
pp. 683-700
Author(s):  
Sergio Díaz ◽  
Lindsay Murray ◽  
Sam G. B. Roberts ◽  
Paul Rodway
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Pan Troglodytes ◽  
Group Structure ◽  
First Year ◽  
Primate Behavior ◽  
Significant Difference ◽  
In Captivity ◽  
The Stability

AbstractManagement of primates in captivity often presents the challenge of introducing new individuals into a group, and research investigating the stability of the social network in the medium term after the introduction can help inform management decisions. We investigated the behavior of a group of chimpanzees (Pan troglodytes) housed at Chester Zoo, UK over 12 months (divided into three periods of 4 months) following the introduction of a new adult female. We recorded grooming, proximity, other affiliative behaviors, and agonistic behaviors and used social network analysis to investigate the stability, reciprocity, and structure of the group, to examine the effect of rearing history on grooming network position and the role of sex in agonistic behavior. Both the grooming and agonistic networks correlated across all three periods, while affiliative networks correlated only between periods 2 and 3. Males had significantly higher out-degree centrality in agonistic behaviors than females, indicating that they carried out agonistic behaviors more often than females. There was no significant difference in centrality between hand-reared and mother-reared chimpanzees. Overall, the group structure was stable and cohesive during the first year after the introduction of the new female, suggesting that this change did not destabilize the group. Our findings highlight the utility of social network analysis in the study of primate sociality in captivity, and how it can be used to better understand primate behavior following the integration of new individuals.

Social Network Analysis in Knowledge Management Research

2011 ◽  
Vol 474-476 ◽  
pp. 1007-1011
Author(s):  
Bing Wu ◽  
Jun Ge ◽  
Wen Xia Xu
Keyword(s):  
Knowledge Management ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
The United States ◽  
Library Science ◽  
Knowledge Network ◽  
Future Research ◽  
Management Research ◽  
Main Research

This study is a productivity review on the literature gleaned from SSCI, SCIE databases concerning social network analysis in knowledge management research. The result indicates that the number of related literature is still growing especially in recent two years. The main research development country is the United States, then England and German, and from the analysis of the subject area, Information Science & Library Science is the most popular subject. Concerning source title, Knowledge Management Research & Practice is in the priority. Moreover the research focuses on this topic are mainly in close relationship with knowledge network. Typical references were analyzed in detail, including limitations and future research.

scholarly journals Descriptive analysis of a tuberculosis outbreak from a northern Saskatchewan First Nations community—December 2018 to May 2019

2021 ◽  
Vol 47 (11) ◽  
pp. 479-484
Author(s):  
Nnamdi Ndubuka ◽  
Braeden Klaver ◽  
Sabyasachi Gupta ◽  
Shree Lamichhane ◽  
Leslie Brooks ◽  
...  
Keyword(s):  
Risk Factors ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
First Nations ◽  
Descriptive Analysis ◽  
Outbreak Investigation ◽  
Active Case ◽  
Contact Investigation ◽  
Northern Saskatchewan

Background: The tuberculosis (TB) incidence rate for northern Saskatchewan First Nations on-reserve is 1.5 higher than the national average. In December 2018 a member of one of these communities was diagnosed with 4+ smear-positive TB, spurring an outbreak investigation. Objectives: To describe the public health response to TB outbreak investigation and highlight the risk factors associated with TB transmission in northern Saskatchewan; and to highlight the relevance of social network contact investigation tool in outbreak management. Methods: Descriptive analysis included active TB cases and latent TB infection (LTBI) cases linked by contact investigation to the index case. Data were collected from active TB case files. Statistical analyses were performed and social network analysis conducted using household locations as points of contact between cases. Results: A total of eight active TB cases and 41 LTBI cases were identified as part of the outbreak between December 2018 and May 2019. Half of the cases (4/8) were 25 to 34 years old, and five were smear negative. One-third of the people with LTBI were 15 to 24 years old, and about a half tested positive to the new tuberculin skin test (TST). The commonly reported risk factors for TB and LTBI cases were alcohol use, cigarette use, marijuana use, previous TB infection and homelessness. Social network analysis indicated a relationship between increased node centrality and becoming an active case. Conclusion: Real-time social network contact investigation used in active-case finding was very successful in identifying cases, and enhanced nursing support, mobile clinics and mobile X-ray worked well as a means of confirming cases and offering treatment. TB outbreaks in northern Saskatchewan First Nations on-reserve communities are facilitated by population-specific factors. Efforts to implement context-specific interventions are paramount in managing TB outbreaks and preventing future transmission.

scholarly journals Social Network Analysis and the Study of University Industry Relations

2019 ◽  
pp. 550-562
Author(s):  
Fernando Cabrita Romero
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Methodological Approach ◽  
Future Research ◽  
Main Research ◽  
Differential Outcomes ◽  
Research Questions ◽  
Industry Relations ◽  
University Industry

The aim of this chapter is to give an overview of the use of social network analysis in the study of university industry relations. The structure of networks can be analyzed through the lens of social network analysis. This methodological approach is briefly described, and its fundamental concepts are presented. The chapter reviews the applications of this approach on the study of university industry relations. Different structures in the relations may result in different innovation outcomes, and the use of SNA may be particularly useful to understand differential outcomes. This chapter is based on a review of available literature on the topics. The chapter aims at systematizing the information and knowledge related to the application of SNA on university industry networks, highlighting the main research pathways, the main conclusions, and pointing to possible future research questions.

Influence of Opinion Leaders on Purchase Intention Using Social Network Analysis

2018 ◽  
Vol 10 (1) ◽  
pp. 1-16
Author(s):  
Yi-Fen Chen ◽  
Chia-Wen Tsai ◽  
Yu-Fu Ann
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Purchase Intention ◽  
Mobile Apps ◽  
Peer Group ◽  
Group Structure ◽  
Perceived Value ◽  
Opinion Leader ◽  
Network Centrality

This article examines social network centralities to identify peer group's opinion leader with the aim of determining whether an opinion leader and perceived value influence purchase intention in the field of paid mobile apps. Social network analysis (SNA) and regression analysis are applied to examine the hypotheses within the theoretical framework. The experiment involved a peer group of college students with total of 46 subjects. Using SPSS to analyze the influences of perceived value and the group's opinion leader on purchase intention, the results showed that consumer purchase intention is positively influenced by both the perceived value of paid mobile apps and positive advices given by opinion leader. In addition, an analysis using Ucinet 6 to examine consulting network centrality, friendship network centrality, and information centrality of every member of the group revealed that based on group structure, the group member having the highest centrality has the group's potential to be the opinion leader.

Sign in / Sign up

Export Citation Format

Share Document