DocumentCode
249385
Title
Enterprise Relationship Network: Build Foundation for Social Business
Author
Liqiang Wang ; Shijun Liu ; Li Pan ; Lei Wu ; Xiangxu Meng
Author_Institution
Sch. of Comput. Sci. & Technol., Shandong Univ., Jinan, China
fYear
2014
fDate
June 27 2014-July 2 2014
Firstpage
347
Lastpage
354
Abstract
Social business moves beyond linear, process-driven organizations to create new, dynamic, networked businesses that focus on customer value. Enterprise relationship network (ERN) can be used to support social business by maximizing current and future opportunities and facilitate network-enabled processes, which can lead to value co-creation. In this paper we give the specification of ERN, which links the main entities in social business together, such as enterprises, business activities, employees and products. ERN provides a set of methods for analyzing the structure of whole entities as well as a group of algorithms for exploring the patterns in these structures. We present the technique architecture of ERN and describe how the ERN supports social business. We can get a lot of valuable information from ERN, which can be used in enterprise management, employee collaborations and networked businesses. At last, through a case study on the platform of SDCMSP, we evaluate how our proposed approach supports social business and show some relationship visualization results.
Keywords
business communication; graph theory; personnel; social sciences computing; ERN architecture; SDCMSP platform; customer value; dynamic networked businesses; employee collaborations; enterprise management; enterprise relationship network; entity structure analysis; linear-process-driven organizations; network-enabled processes; networked business activities; relationship network; relationship visualization; social business; valuable information; value co-creation; Big data; Collaboration; Complexity theory; Data visualization; Organizations; Social network services; enterprise relationship network; socail network; social business;
fLanguage
English
Publisher
ieee
Conference_Titel
Big Data (BigData Congress), 2014 IEEE International Congress on
Conference_Location
Anchorage, AK
Print_ISBN
978-1-4799-5056-0
Type
conf
DOI
10.1109/BigData.Congress.2014.57
Filename
6906800
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