RETRACTED ARTICLE: Startups and the innovation ecosystem in Industry 4.0

Rocha, Clarissa Figueredo; Mamédio, Diórgenes Falcão; Quandt, Carlos Olavo

doi:10.1080/09537325.2019.1628938

Cited by 66 publications

(43 citation statements)

References 15 publications

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“…In terms of the technological scope, this is claimed to differentiate innovation ecosystems based on either the classification of the underlying industry using OECD recommendations, i.e. high-tech, mediumtech and low-tech innovation ecosystems (Ritala et al 2013;Rocha et al 2019), or the number of underlying technology platforms, i.e. mono-versus multi-platform innovation ecosystems (Gomes et al 2018;Su et al 2018;Vasconcelos Gomes et al 2018).…”

Section: Innovation Ecosystem Typology Criteriamentioning

confidence: 99%

“…Finally, regarding the physical scope, innovation ecosystems can be divided into those operating only in cyberspace (i.e. digital innovation ecosystems) and those operating in both the virtual and the real world, commonly known as bricks & clicks (Gomes et al 2018;Rocha et al 2019). In both cases, innovation ecosystems are shown as leveraging the dynamics of digital entrepreneurship (Beliaeva et al 2019).…”

Section: Innovation Ecosystem Typology Criteriamentioning

confidence: 99%

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Species in the wild: a typology of innovation ecosystems

Klimas

Czakon

2021

Rev Manag Sci

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The purpose of this paper is to offer a comprehensive and useful typology of innovation ecosystems. While recent conceptual efforts have been allocated to delineating innovation ecosystems from other phenomena, much less systematic attention has been given to the diversity found within the innovation ecosystem realm. We run a thematic analysis of systematic literature reviews and collect 34 specific types of innovation ecosystems. We expand this list with criteria-derived complementary types and propose a set of 50 distinct innovation ecosystem varieties. Next, we identify the 14 typology criteria used so far in the literature, thematically analyse them and aggregate them into a set useful for further rigorous scrutiny and for the incremental collection of empirical findings. Innovation ecosystems can thus be categorized into (1) life cycle, (2) structure, (3) innovation focus, (4) scope of activities, and (5) performance.

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Section: Innovation Ecosystem Typology Criteriamentioning

confidence: 99%

Section: Innovation Ecosystem Typology Criteriamentioning

confidence: 99%

Species in the wild: a typology of innovation ecosystems

Klimas

Czakon

2021

Rev Manag Sci

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“…This study builds on the conclusion that collaborating with external partners offers the benefit of risk diversification wherein uncertainties are shared. Adopting an OI model eliminates a company's dependence on its own R&D for innovation [3,24]. As a result, interorganizational networking must be used extensively to capture, incorporate, and expand on both knowledge and ideas [2].…”

Section: Literature Review and Research Frameworkmentioning

confidence: 99%

“…To maintain and leverage capability to collaborate, however, it is necessary to manage open innovation models, involving different actors in the collaboration process, with different motivations and purposes [2]. The concept of open innovation (OI) holds that innovation capabilities are spread among many actors of an innovation ecosystem [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Trouble in Paradise? Barriers to Open Innovation in Regional Clusters in the Era of the 4th Industrial Revolution

McPhillips

2020

Journal of Open Innovation: Technology, Market, and Complexity

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The purpose of this paper is to provide an insight into the barriers faced by clusters as open innovation intermediaries. Literature review and an empirical study were performed, involving a nation-wide survey, case studies, and in-depth interviews with cluster actors involved in open innovation activities. This article conceptually links open innovation and clusters in the context of the fourth industrial revolution, empirically identifies barriers hindering open innovation in clusters, and indicates factors that might affect the open innovation processes in networked ecosystems. The findings confirm that the perception of barriers hindering open innovation in clusters differs between clusters already implementing open innovation and those which are still not active in this area. The findings contribute to a comprehensive understanding of the potential roles of clusters as open innovation intermediaries in the context of transitioning economies. With clusters playing a role in open innovation intermediary, public support at cluster level could increase openness to cooperation not only for member companies, but all participants in the regional innovation ecosystem.

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“…There are also multiple prospects related to sustainable innovation and industrial processes. One of them consists in providing support to innovation startups and SMEs 6 with promising innovation initiatives for the digital transformation of economy and Industry 4.0 [1,78]. Smart contracts, on the other hand, could enforce policies aligned with contaminant reduction, specially if this line of action is aligned with the advantages of AI to automate and make penalty Fig.…”

Section: Analysis For the Economic Dimension: Economic And Technological Developmentmentioning

confidence: 99%

A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: progress and prospects

et al. 2021

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The 17 Sustainable Development Goals (SDGs) established by the United Nations Agenda 2030 constitute a global blueprint agenda and instrument for peace and prosperity worldwide. Artificial intelligence and other digital technologies that have emerged in the last years, are being currently applied in virtually every area of society, economy and the environment. Hence, it is unsurprising that their current role in the pursuance or hampering of the SDGs has become critical. This study aims at providing a snapshot and comprehensive view of the progress made and prospects in the relationship between artificial intelligence technologies and the SDGs. A comprehensive review of existing literature has been firstly conducted, after which a series SWOT (Strengths, Weaknesses, Opportunities and Threats) analyses have been undertaken to identify the strengths, weaknesses, opportunities and threats inherent to artificial intelligence-driven technologies as facilitators or barriers to each of the SDGs. Based on the results of these analyses, a subsequent broader analysis is provided, from a position vantage, to (i) identify the efforts made in applying AI technologies in SDGs, (ii) pinpoint opportunities for further progress along the current decade, and (iii) distill ongoing challenges and target areas for important advances. The analysis is organized into six categories or perspectives of human needs: life, economic and technological development, social development, equality, resources and natural environment. Finally, a closing discussion is provided about the prospects, key guidelines and lessons learnt that should be adopted for guaranteeing a positive shift of artificial intelligence developments and applications towards fully supporting the SDGs attainment by 2030.

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RETRACTED ARTICLE: Startups and the innovation ecosystem in Industry 4.0

Cited by 66 publications

References 15 publications

Species in the wild: a typology of innovation ecosystems

Species in the wild: a typology of innovation ecosystems

Trouble in Paradise? Barriers to Open Innovation in Regional Clusters in the Era of the 4th Industrial Revolution

A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: progress and prospects

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