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The way to the ‘comedy of commons’ of a new business model-finding from Naples in Italy, and Jeju Island in South Korea

Title
The way to the ‘comedy of commons’ of a new business model-finding from Naples in Italy, and Jeju Island in South Korea
Author(s)
Yun, Jinhyo JosephZhao, XiaofeiPark, KyungBaeDella Corte, ValentinaDel Gaudio, Giovanna
Issued Date
2023-05
Citation
European Planning Studies, v.31, no.5, pp.947 - 973
Type
Article
Author Keywords
Common goodsopen innovationbusiness modelregional innovation systemgrounded theory
Keywords
COLLECTIVE ACTIONPROPERTY-RIGHTSTRAGEDYEVOLUTIONCAPITALISMMANAGEMENTKNOWLEDGERESOURCEPLATFORMFUTURE
ISSN
0965-4313
Abstract
In this study, we seek to examine the success factors of the classical common goods of various capitalist economies and to apply them to diverse platforms that appear as new common goods. Our research question is as follows: Are there any common success factors that can be applied to produce and use common goods across economic conditions? With the goal of identifying the grounded theory of common goods, the study employed the interview method using a semi-structured questionnaire. Furthermore, it conducted participatory observation and a literature review of case studies as well as a comparative study of 20 Korean commons and 20 Italy commons. According to cases of common goods in Jeju, South Korea, the coupling effects of several public policies could motivate the privatization of common goods. Second, an empty area in common goods consists of a high level of participation with democratization and a low level of common condition. High participation with democratization can avoid privatization and increase new open business models of common goods. Third, active open innovation with a high participation in democratization and expansion of new business models based on tacit knowledge of common goods could be the activating engine of regional innovation systems. © 2022 Informa UK Limited, trading as Taylor & Francis Group.
URI
http://hdl.handle.net/20.500.11750/17349
DOI
10.1080/09654313.2022.2132780
Publisher
Routledge
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Division of AI, Big data and Block chain 1. Journal Articles

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