DocumentCode :
1666266
Title :
Crowdsourcing Service Design for Social Enterprise Insight Innovation
Author :
Wei-Feng Tung ; Jordann, Guillaume
Author_Institution :
Fu Jen Catholic Univ., New Taipei City, Taiwan
fYear :
2015
Firstpage :
367
Lastpage :
373
Abstract :
Social enterprises (SE) have started up not a few innovative business models or cause-related marketing in modern society, which can discover more and more critical social issues need to solve urgently. In recently years, SEs often use social media of Web 2.0 (e.g., Facebook, Twitter) to seek various opportunities, resources, and supports. Thus, this research is to develop an integrative ´crowd sourcing´ as an altruism social media that can connect and leverage external resources to help people undertake some cause-related projects (e.g., Volunteer activity) or start-ups of SE. It can answer what would new SE look like? How could SE work? Is this possible sustainable? A proposed a crowd sourcing platform called ´HIVE´ means a kind of corporation concepts. As we know, all bees work in cooperation with each other and settled in a hive. Thus, the various social issues and resources information can be integrated and emerged for innovative business insights to support the developments of social enterprises.
Keywords :
business data processing; innovation management; social networking (online); HIVE; cause-related marketing; cause-related projects; corporation concepts; crowd sourcing platform; crowdsourcing service design; innovation; innovative business insights; innovative business models; resources information; social enterprise; social issues; social media; volunteer activity; Biological system modeling; Blogs; Business; Collaboration; Crowdsourcing; Media; Technological innovation; Collective Intelligence; Crowdsourcing; Hive; SE; Social Enterprises; Start-up;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Big Data (BigData Congress), 2015 IEEE International Congress on
Conference_Location :
New York, NY
Print_ISBN :
978-1-4673-7277-0
Type :
conf
DOI :
10.1109/BigDataCongress.2015.61
Filename :
7207245
Link To Document :
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