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
fDate :
June 27 2014-July 2 2014
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;
Conference_Titel :
Big Data (BigData Congress), 2014 IEEE International Congress on
Conference_Location :
Anchorage, AK
Print_ISBN :
978-1-4799-5056-0
DOI :
10.1109/BigData.Congress.2014.57