Open Innovation in Regional Innovation Systems: Assessment of Critical Success Factors for Implementation in SMEs

Oliveira, Lindomar Subtil de; Echeveste, Márcia Elisa Soares; Cortimiglia, Marcelo Nogueira; Gularte, Aline Cafruni

doi:10.1007/s13132-019-00619-y

Cited by 14 publications

(8 citation statements)

References 98 publications

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“…The cluster approach has gained extensive acceptance among academics and policymakers as an effective development strategy both for an industry and society [75], as evidenced by a number of studies focusing on innovation in SMEs [76][77][78][79] or technology SMEs [80] or their performance [81][82][83]. In this paper, according to statistical models, SMEs were divided into four clusters.…”

Section: General Discussion Of Resultsmentioning

confidence: 99%

Classification of Small- and Medium-Sized Enterprises Based on the Level of Industry 4.0 Implementation

Pech

Vrchota

2020

Applied Sciences

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Due to Industry 4.0 technologies, small- and medium-sized enterprises have a great opportunity to increase their competitiveness. However, the question remains as to whether they are truly able to implement such modern technologies faster and carry out digital transformation. The main aim of the paper is to classify small- and medium-sized enterprises into various groups, according to the level of implementation of Industry 4.0, using the Index of Industry 4.0. Based on the results of the cluster analysis, the small and medium enterprises are categorized into four different groups, according to the level of implementation of Industry 4.0. There are top Industry 4.0 technological enterprises, I4 start enterprises, noobs enterprises, and I4 advances enterprises. So far, the largest group consists of the small- and medium-sized enterprises that are just starting out with the introduction of Industry 4.0 technologies, such as IT infrastructure, digitalization (data, cloud, data analysis, and information systems), and sensors. On the other hand, the top I4 technological enterprises group is the least numerous. The analysis carried out comparing the small- and medium-sized enterprises with the large enterprises shows that the SMEs still have a lower level of Industry 4.0 implementation. This confirms the assumption that the large enterprises have greater opportunities to use new technologies and transform them into smart factories. However, this situation may change in the future if new technologies become more accessible, and SMEs are worth investing in Industry 4.0 in terms of the return on investment.

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Section: General Discussion Of Resultsmentioning

confidence: 99%

Classification of Small- and Medium-Sized Enterprises Based on the Level of Industry 4.0 Implementation

Pech

Vrchota

2020

Applied Sciences

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“…As Lejpras (2019) states, the supply of external knowledge impacts on the development of the internal knowledge base. We may therefore state that the utilisation of internal and external knowledge focused on developing innovation processes that extend beyond the creation of knowledge boosts the efficiency and effectiveness of organisations (de Oliveira et al , 2019). The relationship between companies and the external environments provides a source of learning that nurtures the creation of new knowledge (Ben Arfi et al , 2018).…”

Section: Discussionmentioning

confidence: 99%

“…According to this perspective, strategy seeks to exploit the deployment of internal and external knowledge to accelerate the innovation processes and enable companies to improve their performance and levels of effectiveness in innovation, creating and capturing value through means of advancing knowledge and cooperation with actors in external knowledge networks (de Oliveira et al , 2019).…”

Section: Theoretical Background and Hypothesesmentioning

confidence: 99%

“…According to Santoro et al (2018), SMEs primarily take closed approaches to innovation and depend on internal sources to develop new products and services. Furthermore, de Oliveira et al (2019) analysed the critical factors to the success of implementing open innovation in companies participating in regional innovation systems before reporting that people and culture are the factors generating the greatest impact.…”

Section: Introductionmentioning

confidence: 99%

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The spinner innovation model: understanding the knowledge creation, knowledge transfer and innovation process in SMEs

Veiga

Figueiredo

Ferreira

et al. 2021

BPMJ

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PurposeThe objective of this article is to empirically study the influence of the characteristics of small- and medium-sized enterprises (SMEs) in the processes of knowledge creation, knowledge transfer and innovation in conjunction with the utilisation of private and public knowledge (KM) in accordance with the “spinner innovation model” (SIM).Design/methodology/approachThe article deploys a sample of primary data generated by a questionnaire applied to the managers of hotel SMEs in Portugal. This involved the application of the covariance and multiple regression analytical methods.FindingsThe results demonstrate that some of the SME characteristics return significant impacts on private and public KM: the processes of knowledge creation, transfers of knowledge and innovation. The results also identify how private KM statistically predicts the processes of knowledge creation and transfer and innovation while public KM shapes and influences the creation of knowledge.Research limitations/implicationsAs with any other such study, the key limitation stems from the sample made up of 82 hotel directors, which represents only a low rate of response even though the project deployed all of the procedures available to avoid such an outcome.Practical implicationsThe SIM approach to the innovation process may assist strategic decision-makers to improve their tools and relations, avoid repeated working overlaps in existing processes as well as enabling more competitive approaches in terms of innovation.Social implicationsFurthermore, the responses ascertained reflect only the universe of study, conditioned by the context that produced them; hence, any generalisation of the results requires due caution.Originality/valueThis is the first study to empirically analyse the influence of the characteristics of SMEs over the processes of creating and transferring knowledge and innovation based upon applying the SIM and observing the extent of public and private knowledge in the hotel sector of Europe, more specifically, Portugal.

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“…Therefore, companies started to look beyond their boundaries and engage with other organisations to absorb knowledge as input to satisfy their scarce resources or capabilities, know-how, and reduce the risk of new product/service development. This strategical approach is known as open innovation, and opening their R&D boundaries for collaboration is the core of this paradigm (Arrigo, 2018;Chesbrough et al, 2006;Subtil de Oliveira et al, 2019). There are different levels of "openness" based on the firms' operating environment and the organisations with which the industries established new collaborations such as universities, intermediate institutions, and government bodies.…”

Section: Introductionmentioning

confidence: 99%

An Investigation of the Translational Asset: A Proposed Classification

2021

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Translational assets (TAs) are considered one of the actors that play a critical role within the national innovation system (NIS) of every country that embraces university, government, and industry collaboration. Moreover, these organisations have been established to support industries, companies, and particularly SMEs, filling the university-industry gap. Although the establishment of translational assets creates many benefits, this organisational ecosystem has been a controversial topic. It is hampered by a lack of consensus on how to define and classify translational assets. The problem arises because of their heterogeneity. This study identifies the critical factors for presenting a general classification from the analysis of academic papers and technical reports. The proposed classification is built, showing the factors of organisational structure, resources, and motivation for collaboration on a bidirectional continuum. Therefore, this paper’s findings provide a proposed classification of three main types of TAs, which are as follows: exploratory, plug, and developer/solver. This is a heuristic classification that provides enrichment to the literature and a better understanding for practitioners of these organisations' behaviour.

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Open Innovation in Regional Innovation Systems: Assessment of Critical Success Factors for Implementation in SMEs

Cited by 14 publications

References 98 publications

Classification of Small- and Medium-Sized Enterprises Based on the Level of Industry 4.0 Implementation

Classification of Small- and Medium-Sized Enterprises Based on the Level of Industry 4.0 Implementation

The spinner innovation model: understanding the knowledge creation, knowledge transfer and innovation process in SMEs

An Investigation of the Translational Asset: A Proposed Classification

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