Social network analysis of an emerging innovation: direct-to-garment printing technology

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yanan Yu ◽  
Marguerite Moore ◽  
Lisa P. Chapman
Keyword(s):  
Data Mining ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Computational Technique ◽  
Fashion Industry ◽  
Printing Technology ◽  
Content Type ◽  
Context Specific ◽  
Insight Into

PurposeThe study primarily aims to examine an emerging fashion technology, direct-to-garment (DTG) printing, using data mining-driven social network analysis (SNA). Simultaneously, the study also demonstrates application of a group novel computational technique to capture, analyze and visually depict data for strategic insight into the fashion industry.Design/methodology/approachA total of 5,060 tweets related to DTG were captured using Crimson Hexagon. Python and Gephi were applied to convert, calculate and visualize the yearly networks for 2016–2019. Based on graph theory, degree centrality and betweenness centrality indices guide interpretation of the outcome networks.FindingsThe findings reveal insights into DTG printing technology networks through identification of interrelated indicators (i.e. nodes, edges and communities) over time. Deeper interpretation of the dominant indicators and the unique changes within each of the DTG communities were investigated and discussed.Practical implicationsThree SNA models suggest directions including the dominant apparel categories for DTG application, competing alternatives for apparel decorating approaches to DTG and growing market niches for DTG. Interpretation of the yearly networks suggests evolution of this domain over the investigation period.Originality/valueThe social media based, data mining-driven SNA method provides a novel path and a powerful technique for scholars and practitioners to investigate information among complex, abstract or novel topics such as DTG. Context specific findings provide initial insight into the evolving competitive structures driving DTG in the fashion market.

The Rise of Fashion Informatics: A Case of Data-Mining-Based Social Network Analysis in Fashion

2018 ◽  
Vol 37 (2) ◽  
pp. 87-102 ◽  
Author(s):  
Li Zhao ◽  
Chao Min
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Large Scale ◽  
Dynamic Network ◽  
Fashion Industry ◽  
Cognitive Computing ◽  
Data Set

With the advent of modern cognitive computing technologies, fashion informatics researchers contribute to the academic and professional discussion about how a large-scale data set is able to reshape the fashion industry. Data-mining-based social network analysis is a promising area of fashion informatics to investigate relations and information flow among fashion units. By adopting this pragmatic approach, we provide dynamic network visualizations of the case of Paris Fashion Week. Three time periods were researched to monitor the formulation and mobilization of social media users’ discussions of the event. Initial textual data on social media were crawled, converted, calculated, and visualized by Python and Gephi. The most influential nodes (hashtags) that function as junctions and the distinct hashtag communities were identified and represented visually as graphs. The relations between the contextual clusters and the role of junctions in linking these clusters were investigated and interpreted.

Dynamic Social Network Mining

2013 ◽  
pp. 122-144
Author(s):  
Luca Cagliero ◽  
Alessandro Fiori
Keyword(s):  
Data Mining ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Knowledge Discovery ◽  
Network Mining ◽  
Social Network Mining ◽  
Dynamic Social Network ◽  
Main Work ◽  
Insight Into

This Chapter overviews most recent data mining approaches proposed in the context of social network analysis. In particular, it aims at classifying the proposed approaches based on both the adopted mining strategies and their suitability for supporting knowledge discovery in a dynamic context. To provide a thorough insight into the proposed approaches, main work issues and prospects in dynamic social network analysis are also outlined.

scholarly journals Data Mining in Social Networks and its Application in Counterterrorism

2019 ◽  
Vol 8 (3) ◽  
pp. 1278-1284
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Graph Theory ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Theory Analysis ◽  
Graph Theory Analysis ◽  
Insight Into ◽  
Novel Algorithm

Social Networks are best represented as complex interconnected graphs. Graph theory analysis can hence be used for insight into various aspects of these complex social networks. Privacy of such networks lately has been challenged and a detailed analysis of such networks is required. This paper applies key graph theory concepts to analyze such social networks. Moreover, it also discusses applications and proposal of a novel algorithm to analyze and gather key information from terrorist social networks. Investigative Data Mining is used for this which is defined as when Social Network Analysis (SNA) is applied to Terrorist Networks to gather useful insights about the network..

Social network analysis applications in sustainable construction and built environment management: a review

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nandun Madhusanka Hewa Welege ◽  
Wei Pan ◽  
Mohan Kumaraswamy
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Built Environment ◽  
Methodological Approach ◽  
Relevant Literature ◽  
Sustainable Construction ◽  
Centrality Measures ◽  
Environment Management ◽  
Content Type

PurposeApplications of social network analysis (SNA) are evidently popular amongst scholars for mapping stakeholder and other relational networks in improving the sustainability of construction activities and the resulting built environment. Nevertheless, the literature reveals a lack of thorough understanding of optimal SNA applications in this field. Therefore, this paper aims to convey a comprehensive critical review of past applications of SNA in this field.Design/methodology/approach95 relevant journal papers were initially identified from the “Web of Science” database and a bibliometric analysis was carried out using the “VOS Viewer” software. The subsequent in-depth review of the SNA methods, focussed on 24 specifically relevant papers selected from these aforesaid 95 papers.FindingsA significant growth of publications in this field was identified after 2014, especially related to topics on stakeholder management. “Journal of Cleaner Production”, “International Journal of Project Management” and “Sustainability” were identified as the most productive sources in this field, with the majority of publications from China. Interviews and questionnaires were the popular data collection methods while SNA “Centrality” measures were utilised in over 70% of the studies. Furthermore, potential areas were noted, to improve the mapping and thereby provide useful information to managers who could influence relevant networks and consequentially better sustainability outcomes, including those enhanced by collaborative networks.Originality/valueCloser collaboration has been found to help enhance sustainability in construction and built environment, hence attracting research interest amongst scholars on how best to enable this. SNA is established as a significant methodological approach to analysing interrelationships and collaborative potential in general. In a pioneering application here, this paper initiates the drawing together of findings from relevant literature to provide useful insights for future researchers to comprehensively identify, compare and contrast the applications of SNA techniques in construction and built environment management from a sustainability viewpoint.

The evolution of the intellectual structure of strategic management between 1980 and 2019

2020 ◽  
Vol 13 (4) ◽  
pp. 503-534
Author(s):  
Mehmet Ali Köseoğlu ◽  
John Parnell
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Strategic Management ◽  
Citation Analysis ◽  
Intellectual Structure ◽  
Future Directions ◽  
Content Type ◽  
The Social ◽  
Economic Foundation

PurposeThe authors evaluate the evolution of the intellectual structure of strategic management (SM) by employing a document co-citation analysis through a network analysis for academic citations in articles published in the Strategic Management Journal (SMJ).Design/methodology/approachThe authors employed the co-citation analysis through the social network analysis.FindingsThe authors outlined the evolution of the academic foundations of the structure and emphasized several domains. The economic foundation of SM research with macro and micro perspectives has generated a solid knowledge stock in the literature. Industrial organization (IO) psychology has also been another dominant foundation. Its robust development and extension in the literature have focused on cognitive issues in actors' behaviors as a behavioral foundation of SM. Methodological issues in SM research have become dominant between 2004 and 2011, but their influence has been inconsistent. The authors concluded by recommending future directions to increase maturity in the SM research domain.Originality/valueThis is the first paper to elucidate the intellectual structure of SM by adopting the co-citation analysis through the social network analysis.

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