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Nurturing business ecosystem with modular architecture

Nurturing business ecosystem with modular architecture

Lin, Yong ORCID: 0000-0001-7118-2946, Luo, Jing, Huang, Lin, Zhou, Li ORCID: 0000-0001-7132-5935 and Ieromonachou, Petros ORCID: 0000-0002-5842-9585 (2015) Nurturing business ecosystem with modular architecture. In: Proceedings of the 2015 European Academy of Management Conference. EURAM, Warsaw, Poland.

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This paper aims to identify the structural elements of a business ecosystem from a view of modularity. The paper proposed that the architecture of the business ecosystem is consisted of three structural layers, including organization, product/service, and technology. Moreover, the structural elements in the business ecosystem can be divided into three categories, which are evolutional module, developmental module, and fundamental module. This paper extends the modularity research into the context of business ecosystem, and links the modularity in biology with the business studies. The three-layer modular architecture of the business ecosystem provides guidance to practitioners to nurture and evolve their business ecosystem. The identified modules clarify the roles of each actor and position themselves better in the business ecosystem. This paper proposes a modular logic to analyse the business ecosystem, which integrates the modularity theory both from ecology and technology into business/management studies.

Item Type: Conference Proceedings
Title of Proceedings: Proceedings of the 2015 European Academy of Management Conference
Additional Information: EURAM 2015 Conference, 17-19 June 2015, Warsaw, Poland.
Uncontrolled Keywords: Business ecosystem, Modularity, Architecture
Faculty / Department / Research Group: Faculty of Business
Faculty of Business > Department of Systems Management & Strategy
Faculty of Business > Networks and Urban Systems Centre (NUSC) > Supply Chain Management Research Group
Last Modified: 14 Oct 2016 09:37
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None

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