scholarly journals A Correlative Scrutiny for Improving the Career Guidance Links in Social Network

2019 ◽  
Vol 9 (1) ◽  
pp. 1466-1470
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Career Development ◽  
Network Analysis ◽  
Clustering Algorithm ◽  
Career Guidance ◽  
Effective Interaction ◽  
Professional Behaviors ◽  
Influence Propagation ◽  
Development Framework

Social Network analysis techniques have shown the ability to meet educators and influence career development guidance. Career education mentoring plays an important role in the career supporting which are the interest, the strength, and the aspirations of the students. In this paper, we proposed a career development framework enhances the node influence propagation and effective interaction between nodes is taken as a strong link, from the node influence propagation we divide it into two categories such as: (1) career predictions to persuade prospective graduates to pursue the desired career path, with career prospects considered by them as a learning opportunity. (2) Social network analysis and persuasive techniques are used to motivate within a social networking framework where there is a tendency to adopt desired professional behaviors. The process begins with the discovery of behavioral features to create a cognitive profile and to identify individual disabilities. We compare a clustering algorithm that predicts the accuracy values and pattern of creations to a social network for achieving collaborative cognition.

Social network analysis: organizational implications in tourism management

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Marco Valeri ◽  
Rodolfo Baggio
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Single Case ◽  
Effective Interaction ◽  
Organizational Structures ◽  
Review Literature ◽  
Future Research ◽  
Economic Sectors ◽  
Content Type

Purpose The purpose of this paper is to provide an overview of how quantitative analysis methods have been and can be used to improve the competitiveness of tourism destination. The focus of the study is social network analysis (SNA). Design/methodology/approach The research methodology is qualitative and consists of the review literature relevant to this thesis. This methodology is necessary to give an account of the methods and the techniques adopted for the data collection used in other economic sectors. Findings SNA is needed to analyze the creation and configuration of communities of practice within destination and to identify possible barriers to effective interaction. Essentially, it is a complex adaptive socio-economic system. It shares many (if not all) of the characteristics usually associated with such entities, namely, non-linear relationships among the components, self-organization and emergence of organizational structures, robustness to external shocks. Research limitations/implications The most important limit of this paper is that all the results presented here do not concern a single case study. Future research studies will provide a larger number of cases and examples to give the necessary validation to the findings presented here. Practical implications This paper provides a view into the network of relationships that may give tourism organization managers a strong leverage to improve the flow of information and to target opportunities where this flow may have the most impact on regulatory or business activities. Originality/value SNA can help to detect actual expertise and consequently project the potential losses deriving from an inefficient flow of knowledge. In addition, the authors will be able to define roles in the organizational networks and make an evaluation of informal organizational structures over the formal ones. Traditional organizational theories lack a concrete correspondence with mathematical studies and in this respect the authors sought to identify a correspondence.

scholarly journals Assessing Accessibility of Dockless Sharing-Bike Networks by the Social Network Analysis Method

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Pei Liu ◽  
Junlan Chen ◽  
Heyang Sun ◽  
Xiucheng Guo ◽  
Yan Wang ◽  
...  
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Clustering Algorithm ◽  
Spatial Interaction ◽  
Interaction Model ◽  
Deep Understanding ◽  
Sustainable Transportation ◽  
The Social ◽  
In Transit

Dockless sharing bikes play an increasingly significant role in transit transfer, especially for the first/last mile. However, it is not always accessible for users to find sharing bicycles. The objective of this paper is to assess the accessibility of dockless sharing bikes from a network perspective, which would provide a decision-making basis not only for potential bike users but also for urban planners, policymakers, and bicycle suppliers to optimize sharing-bike systems. Considering bicycle travel characteristics, a hierarchical clustering algorithm was applied to construct the dockless sharing-bike network. The social network analysis (SNA) method was adopted to assess the accessibility of the bike network. Then, a spatial interaction model was chosen to conduct a correlation analysis to compare the accessibility obtained from the SNA approach. The case study of Shanghai indicates a strong connection between the accessibility and the SNA indicators with the correlation coefficient of 0.779, which demonstrates the feasibility of the proposed method. This paper contributes to a deep understanding of dockless sharing-bike network accessibility since the SNA approach considers both the interaction barriers and the network structure of a bicycle network. The developed methodology requires fewer data and is easy to operate. Thus, it can serve as a tool to facilitate the smart management of sharing bikes for improving a sustainable transportation system.

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