Social network analysis of the relationship between risks of overseas high-speed rail projects

2019 ◽  
pp. 642-649
Author(s):  
Jianbin Zhang ◽  
Pengcheng Xiang ◽  
Ting Yun ◽  
Mingming Hu
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
High Speed ◽  
High Speed Rail ◽  
The Relationship

scholarly journals FACTORS AFFECTING DISPOSITION EFFECT IN EQUITY INVESTMENT: A SOCIAL NETWORK ANALYSIS APPROACH

2020 ◽  
Vol 5 (3) ◽  
pp. 64-86
Author(s):  
K. Kajol ◽  
Prasita Biswas ◽  
Ranjit Singh ◽  
Sana Moid ◽  
Amit Kumar Das
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Stock Market ◽  
Influencing Factors ◽  
Social Trust ◽  
Complete Analysis ◽  
Disposition Effect ◽  
Factors Affecting ◽  
The Relationship

The study aims at identifying the factors influencing the disposition effect acting on equity investors and further identifying the relationship between the influencing factors. The study aims at conducting a complete analysis of the influencing factors along with measuring their impact on disposition effect using Social Network Analysis (SNA).The factors affecting disposition effect on investors were identified through the literature review. Experts’ opinions were sought for determining the relationship among the factors and finally, the importance of those factors was analyzed using Social Network Analysis (SNA). It was found that social trust, investor emotion are the two most important factors affecting the other factors of disposition effect and consequently disposition effect finally. Besides, mental accounting; regret aversion, trading intensity, trading volume, and portfolio performance strongly influence the effect of disposition on investors because of their higher in-degree and out-degree. Therefore, the policymakers need to impart training to the investors to understand the mechanism of the stock market so that they can evaluate their standing in the stock market which, in the long run, will be reflected in their investment behavior. 

scholarly journals Social Networks and Supply Chain Management in Fish Trade

SAGE Open ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 215824402093181
Author(s):  
Carmen Pedroza-Gutiérrez ◽  
Juan M. Hernández
Keyword(s):  
Supply Chain ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Real Life ◽  
Primary Data ◽  
Structured Interviews ◽  
Knowledge Based ◽  
The Relationship

This study aims to construct a theoretical framework to analyze the elements of the network structure and the relationship system within the seafood supply chain. The scope of the investigation is to evaluate how these elements influence the flow of products and the efficiency of the seafood supply chain and why these social interactions can create value and enhance competitive advantage. The model combines the resource- and knowledge-based view and the social network analysis applied to seafood supply chains. To demonstrate the application of the model, two theoretical examples and a real case study of the Mercado del Mar in Guadalajara, Mexico, are used. Primary data are obtained from semi-structured interviews, social network analysis metrics, and qualitative analysis. Findings are based on the analysis of theoretical examples and must be considered with caution. Nevertheless, the observations in the examples and case study provide new arguments to the relationship between the pattern of interrelationship and the efficiency of a supply chain. This study emphasizes the necessity of combining quantitative and qualitative analyses to understand and explain real-life supply networks.

Application of Computer System in the Study of Sociology

2014 ◽  
Vol 926-930 ◽  
pp. 1680-1683
Author(s):  
Ying Ming Xu ◽  
Shu Juan Jin
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Computer Algorithms ◽  
Useful Knowledge ◽  
Social Network Data ◽  
Analysis Task ◽  
Original Analysis ◽  
The Social ◽  
The Relationship

With the development of information technology, more and more data about social to be collected. If we can analyze them effectively, it will help people to understand sociological understanding, promoting the development of social science. But the increasing amount of data and analysis to put forward a huge challenge. Now the social networks have already surpassed the processing ability of the original analysis means, must use a more effective tool to complete the analysis task. The computer as a way of helping people from massive data to find the potential useful knowledge tools, play an important role in many fields. Social network analysis, also known as link mining, refers to the handling of the relationship between social network data in the computer method. In this paper, the methods of computer and the social network analysis was introduced in this paper and the computer algorithms are summarized in the application of social network analysis.

scholarly journals Using Social Network Analysis to Understand the Effects of a Social Media-based Weight Loss Intervention Targeting Low-SES Adults (Preprint)

2020 ◽  
Author(s):  
Ran Xu ◽  
David Cavallo
Keyword(s):  
Social Support ◽  
Social Media ◽  
Weight Loss ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Self Efficacy ◽  
Network Characteristics ◽  
Using Data ◽  
The Relationship

BACKGROUND Obesity is a known risk factor for cardiovascular disease (CVD) risk factors including hypertension and type II diabetes. Although numerous weight-loss interventions have demonstrated efficacy, there is considerably less evidence about the theoretical mechanisms through which they work. Delivering lifestyle behavior change interventions via social media provides unique opportunities for understanding mechanisms of intervention effects. Server data collected directly from online platforms can provide detailed, real-time behavioral information over the course of intervention programs that can be used to understand how interventions work. OBJECTIVE The objective of this study was to demonstrate how social network analysis can facilitate our understanding of the mechanisms underlying a social-media based weight loss intervention. METHODS This study performed secondary analysis using data from a pilot study that delivered a dietary and physical activity intervention to a group of low-SES participants via Facebook. We mapped out participants’ interaction networks over the 12-week intervention period, and linked participants’ network characteristics (e.g. in-degree, out-degree and network constraint) to participants’ changes in theoretical mediators (i.e. dietary knowledge, perceived social support, self-efficacy) and weight loss using regression analysis. This study also performed mediation analyses to explore how the effects of social network measures on weight loss could be mediated by the aforementioned theoretical mediators. RESULTS 47 participants from two waves completed the study and were included in the analysis. We found that participants creating posts, comments and reactions predicted weight-loss (β=-.94, P=.042); receiving comments positively predicted changes in self-efficacy (β=7.81, P=.009); the degree to which one’s network neighbors are tightly connected with each other weakly predicted changes in perceived social support (β=7.70, P=.08). In addition, change in self-efficacy mediated the relationship between receiving comments and weight-loss (Indirect effect=-.89, P=.017). CONCLUSIONS Our analyses using data from this pilot study have linked participants’ network characteristics with changes in several important study outcomes of interest, such as self-efficacy, social support and weight. Our results point to the potential of using social network analysis to understand the social processes and mechanisms through which online behavioral interventions affects participants’ psychological and behavioral outcomes. Future studies are warranted to validate our results and further explore the relationship between network dynamics and study outcomes in similar and larger trials.

scholarly journals Exploratory Experiment on Co-Authorship Network using Social Network Analysis Metrics and Measures

2018 ◽  
Vol 7 (4.35) ◽  
pp. 782
Author(s):  
Pritheega Magalingam ◽  
Ganthan Narayana Samy ◽  
Nurazean Maarop ◽  
Wan Nazirul Hafeez Wan Safie ◽  
Muhammad Khairul Rijal ◽  
...  
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Single Measure ◽  
Research Issues ◽  
Structure Topology ◽  
Relationship Analysis ◽  
Exploratory Experiment ◽  
Centrality Analysis ◽  
The Relationship

This paper contributes in understanding and gaining meaningful insight about the relationship among the scientist in the co-authorship network using social network analysis. We argue that the relationship analysis is not always a straightforward process. In the past one single measure, for example, the egocentric or centrality measure was used to describe the scientific collaboration patterns separately. In this paper, various analysis such as centrality analysis, ego network, community detection, largest clique and word frequency have been used to examine and interpret the collaboration among the authors. This research is not dominated by known researchers but involves an overall exploration of the network. Our research is mainly guided by the creation of research issues, assessing the type of dataset and the objectives for presenting the co-authorship relationships. It is important to identify the motive of the selected measures in order to achieve the predefined objective. Specific methodology and procedures are designed to solve each research issue respectively. This study reveals that the network interpretation should not be solely based on one network measure, but an explorative analysis results need to be considered because it allows exploring the hidden information through the changes in the network structure, topology patterns and nodes’ position.

An Investigation of Competitor Networks in Manufacturing Strategy and Implications for Performance

2018 ◽  
pp. 850-879
Author(s):  
Eve D. Rosenzweig ◽  
Elliot Bendoly
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Market Performance ◽  
Manufacturing Strategy ◽  
Strategy Research ◽  
Analysis Techniques ◽  
The Relationship ◽  
Moderating Role

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.

An Investigation of Competitor Networks in Manufacturing Strategy and Implications for Performance

2017 ◽  
pp. 43-82
Author(s):  
Eve D. Rosenzweig ◽  
Elliot Bendoly
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Market Performance ◽  
Manufacturing Strategy ◽  
Strategy Research ◽  
Analysis Techniques ◽  
The Relationship ◽  
Moderating Role

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.

Cross-docking Centre Location in a Supply Chain Network: A Social Network Analysis Approach

2018 ◽  
Vol 19 (3_suppl) ◽  
pp. S218-S234 ◽  
Author(s):  
Prashant Barsing ◽  
Yash Daultani ◽  
Omkarprasad S. Vaidya ◽  
Sushil Kumar
Keyword(s):  
Supply Chain ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Supply Chains ◽  
Food Supply ◽  
Humanitarian Relief ◽  
Cross Docking ◽  
Relief Operations ◽  
The Relationship

The level of uncertainty, unpredictability and complexity is magnified in a food supply chain as compared to the conventional supply chains such as automobile and FMCG. This is mainly because of the short product shelf life and the need of high variety. This necessitates the food industry to adopt various quick response systems to achieve effective supply chain management. The situation becomes even more critical when dealing with humanitarian relief operations where time window is very short (usually 24 hours). One of the solutions which are adopted in modern food supply chains is to locate cross-docking centre (CDC). Cross-docking is used to reduce the turnaround time of the food products. The practical situation is complex as it caters to multiple customers. The number of suppliers, in such cases, plays a significant role. Selection of a right CDC is, therefore, a crucial task. It is a strategic decision and needs to be taken by considering the relationships between each stakeholder present in the supply chain. In this article, we present an approach to select one (or few) CDC/s facilities among n CDCs. The method is based on the relationship between each actor (actors are the stakeholders in the supply chain). The relationship is in terms of the physical flow of materials or information flow or another kind of flows or relationships that connects them to form a network. These network characteristics are required to find out key stakeholders. The present article proposes the application of social network analysis (SNA) to analyse the characteristics of the network, thus helping supply chain managers to locate strategic CDCs considering both qualitative and quantitative aspects. The proposed methodology can be easily extended to locate temporary warehouse site in the context of humanitarian relief operations.

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