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.

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.

scholarly journals Actors Determination Strategy in Registered Social Network Patents in the Framework of Rewards towards Inventors A Case Study: LIPI Registered Patents 2016

2017 ◽  
Vol 20 (2) ◽  
pp. 64
Author(s):  
Aris Yaman
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
The Social ◽  
Central Actor ◽  
Relationship Of ◽  
The Relationship

Patent is one of the lever factors in improving the nation's competitiveness. A reward to an inventor is one way that can be taken to increase the productivity and patent development in Indonesia. The social network analysis on the relationship of co-invention and registered patents can identify inventing actors who deserves the award. Joko Waluyo obtained an actor who is considered central to the co-invention social network relationships of registered patents in LIPI. It is based on the high value of betweeness and closeness to the node Joko Waluyo. In addition, it was found that the prolific inventor does not always mean a central actor in the social network.

scholarly journals Evaluation of Coordination of Emergency Response Team through the Social Network Analysis. Case Study: Oil and Gas Refinery

2015 ◽  
Vol 6 (1) ◽  
pp. 30-34 ◽  
Author(s):  
Iraj Mohammadfam ◽  
Susan Bastani ◽  
Mahbobeh Esaghi ◽  
Rostam Golmohamadi ◽  
Ali Saee
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Emergency Response ◽  
Oil And Gas ◽  
The Social ◽  
Response Team ◽  
Gas Refinery

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 Identifying Integration and Differentiation in a Hospital’s Logistical System: A social network analysis of a case study

2020 ◽  
Author(s):  
Annelies van der Ham ◽  
Frits Van Merode ◽  
Dirk Ruwaard ◽  
Arno Van Raak
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Network Structure ◽  
Organization Theory ◽  
Exploratory Study ◽  
Case Study Research ◽  
Study Research ◽  
And Performance

Abstract Background Integration, the coordination and alignment of tasks, has been promoted widely in order to improve the performance of hospitals. Both organization theory and social network analysis offer perspectives on integration. This exploratory study research aims to understand how a hospital’s logistical system works, and in particular to what extent there is integration and differentiation. More specifically, it first describes how a hospital organizes logistical processes; second, it identifies the agents and the interactions for organizing logistical processes, and, third, it establishes the extent to which tasks are segmented into subsystems, which is referred to as differentiation, and whether these tasks are coordinated and aligned, thus achieving integration.Methods The study is based on case study research carried out in a hospital in the Netherlands. All logistical tasks that are executed for surgery patients were studied. Using a mixed method, data were collected from the Hospital Information System (HIS), documentation, observations and interviews. These data were used to perform a social network analysis and calculate the network metrics of the hospital network.Results This paper shows that 23 tasks are executed by 635 different agents who interact through 31,499 interaction links. The social network of the hospital demonstrates both integration and differentiation. The network appears to function differently from what is assumed in literature, as the network does not reflect the formal organizational structure of the hospital, and tasks are mainly executed across functional silos. Nurses and physicians perform integrative tasks and two agents who mainly coordinate the tasks in the network, have no hierarchical position towards other agents. The HIS does not seem to fulfill the interactional needs of agents. Conclusions This exploratory study reveals the network structure of a hospital. The cross-functional collaboration, the integration found, and position of managers, coordinators, nurses and doctors suggests a possible gap between organizational perspectives on hospitals and reality. This research sets a basis for further research that should focus on the relation between network structure and performance, on how integration is achieved and in what way organization theory concepts and social network analysis could be used in conjunction with one another.

scholarly journals Examining the Diffusion of Research-Based Instructional Strategies Using Social Network Analysis: A Case Study of SCALE-UP

2020 ◽  
Author(s):  
Xaver Neumeyer ◽  
Kathleen Foote ◽  
Robert Beichner ◽  
Melissa Dancy ◽  
Charles Henderson
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Instructional Strategies ◽  
Scale Up

Measuring the Impact of Social Media

2011 ◽  
pp. 24-36 ◽  
Author(s):  
Kimiz Dalkir
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Evaluation Method ◽  
Real Life ◽  
Work Processes ◽  
The Social ◽  
Post Test ◽  
The Impact

This chapter focuses on a method, social network analysis (SNA) that can be used to assess the quantity and quality of connection, communication and collaboration mediated by social tools in an organization. An organization, in the Canadian public sector, is used as a real-life case study to illustrate how SNA can be used in a pre-test/post-test evaluation design to conduct a comparative assessment of methods that can be used before, during and after the implementation of organizational change in work processes. The same evaluation method can be used to assess the impact of introducing new social media such as wikis, expertise locator systems, blogs, Twitter and so on. In other words, while traditional pre-test/post-test designs can be easily applied to social media, the social media tools themselves can be added to the assessment toolkit. Social network analysis in particular is a good candidate to analyze the connections between people and content as well as people with other people.

Case Study

2021 ◽  
pp. 345-366
Author(s):  
Magy Seif El-Nasr ◽  
Truong Huy Nguyen Dinh ◽  
Alessandro Canossa ◽  
Anders Drachen
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
User Research ◽  
Social Games ◽  
Work Done

This chapter discusses Social Network Analysis, a technique used to analyze social networks within social games as a method to enhance retention in games. We will show how one can use this method by applying it to the problem of retention within the game Tom Clancy’s The Division (TCTD). Using the game and the analysis will help you understand how to use SNA to understand types of players and influential players, and, as a result, understand how to engage different players, especially influencers, to increase retention. While the chapter will focus on the use of SNA for TCTD as a case study, the methods discussed under SNA can be applied to other types of games. Please note that this chapter is an extension of the work done by several collaborators to the authors, including Casper Harteveld (professor, Northeastern University), Sebastian Deterding (professor, York University), and Ahmad Azadvar (User Research Lead at Ubisoft Massive), and the work was accomplished with the support of Ubisoft, the Games Lab, and the Live Ops team at Massive Entertainment.

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