Fostering path of ecological sustainable entrepreneurship within big data network system

  • Juying ZengEmail author


Traditional growth modes has encountered with frustrating social and environmental problems, while the intervention and governance of controlling or alleviating the negative externalities of enterprises do not bring about satisfying results since the traditional entrepreneurs do not take social, environmental and sustainable responsibilities as their imperative responsibility. Furthermore, the previous literatures concerning entrepreneurship does not consider entrepreneurship with the advantage of big data internet system. In this sense, the research establishes a theoretically framework to understand the fostering path of ecological sustainable entrepreneurship within big data internet system, especially with the comprehension of the situation in developing context. The obtained conclusions are listed as follows. First, the theoretical framework of endogenous and exogenous motivations for traditional entrepreneurship takes economic achievement as the primary motivation for traditional growth. Second, the ecological sustainable entrepreneurship within big data internet system focuses on dealing with negative issues and realizing better off in both social and ecological aspects from the very beginning to the end. Finally and most importantly, the research provides the identification of fostering path of ecological sustainable entrepreneurship within big data internet system, and indicates the fostering path lies in the dimensions of (1) positive ecological or environmental, social, and sustainable concerns and actions during the whole period; (2) collective spirit, challenge and learning; (3) crowd funding in financial start-up capital; (4) shorten growth period; and (5) even greater economic profit than traditional enterprises.


Fostering path Ecological sustainable entrepreneurship Big data internet system Traditional entrepreneurship 



This work is supported by grant from Youth Project of National Social Sciences Foundation in China (No.16CTJ010), The author also gratefully acknowledges the helpful comments and suggestions of the reviewers, which have improved the paper.


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© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.School of Data Science, China Institute of Regulation ResearchZhejiang University of Finance and EconomicsHangzhouChina

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