Network analysis of inter-organizational success factor relationships

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Author listKongburan W., Padungweang P., Krathu W., Chan J.

PublisherHindawi

Publication year2017

Volume numberPart F130152

Start page83

End page84

Number of pages2

ISBN9781450352925

ISSN0146-9428

eISSN1745-4557

URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85030751316&doi=10.1145%2f3105971.3108449&partnerID=40&md5=f400d0c3a8bad73ad388cc32538b8ff0

LanguagesEnglish-Great Britain (EN-GB)


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Abstract

One of the best ways to deal with the problem of knowledge distillation in unstructured text is applying text mining. This machine learning-based approach can provide extracted useful information from large body of texts, in a reasonable time. However, the results are usually in complicated forms, which meant it is a non-trivial task in term of interpretation. In this paper, we present appropriate visualizations and analyses in order to tackle the tangled network representing relationship between entities (i.e. terms extracted from raw text). Conducted on a case study of information extraction in the business management domain, our results reveal the hidden relationships of inter-organizational success factors in a simple structured way. These results can assist companies to understand and generate business strategies, in terms of the collaboration aspect. ฉ 2017 ACM.


Keywords

Circle graphInter-organizational relationshipsNetwork analysisOrganic graphSuccess factor


Last updated on 2023-25-09 at 07:35