Identification of Challenges to be Overcome in the Process of Enhancing Innovativeness Based on Implementation of Central European Projects Funded from Interreg Programme

Cholewa, Mariusz; Helman, Joanna; Molasy, M.; Rosienkiewicz, Maria

doi:10.1007/978-3-319-97490-3_18

Cited by 6 publications

(4 citation statements)

References 8 publications

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“…A micro-meso approach was proposed by Cholewa et al (2019), who state that the qualitative analysis of projects implementation can reveal the main barriers in reaching their objectives. By analysing two Interreg projects they concluded that these barriers are: (1) semantic, which refers the varying levels of partners; (2) language, which is about having to collaborate in a language there than your mother tongue; (3) organisational, which refers to the complicated bureaucratic processes and the lack of coordination among partners; (4) resource, which is mainly focused on the access to necessary data; and (5) project-specific that has to do with some certain issues dealt in projects that cannot be generalised.…”

Section: Discussionmentioning

confidence: 99%

“…By analysing two Interreg projects they concluded that these barriers are: (1) semantic, which refers the varying levels of partners; (2) language, which is about having to collaborate in a language there than your mother tongue; (3) organisational, which refers to the complicated bureaucratic processes and the lack of coordination among partners; (4) resource, which is mainly focused on the access to necessary data; and (5) project-specific that has to do with some certain issues dealt in projects that cannot be generalised. Cholewa et al (2019) proceeded with some suggestions to face some of these problems, A first recommendation is about the preparation of the application for funding. It has to do with the composition of the consortium and the skills needed to implement the project.…”

Section: Discussionmentioning

confidence: 99%

“…The data were collected from 1/9/2019 till 15/10/2019 2. We should remark that the term "bioeconomy" provided no results…”

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confidence: 99%

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Fostering ‘Bioeconomy Strategy’ through synergies: managerial implications from Interreg Europe

Mouzakitis¹,

Tsoulfas²

2019

Proceedings of the 2nd International Conference on Management, Economics and Finance

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Back on 2012, the European Commission launched and adopted the Bioeconomy Strategy in order to reduce dependency on fossil fuels and meet its energy and climate change policy targets. In this vein, the concept of bioeconomy, namely the production of renewable biological resources and the conversion of these resources and waste streams into value added products, is multi-faceted in terms of ambitions, initiators, and barriers, involving several types of stakeholders, in different (local, regional and national) settings. This paper sheds light on the regional level of bioeconomic development, focusing on initiatives which are financially supported by EU. More specifically, we conduct secondary research on 20 regional schemes funded by the Interreg Europe Initiative, and we analyse /compare the above case-studies with respect to 26 variables. Based on this evidence, we discuss the effectiveness, the greenness and the innovativeness of the selected projects, and we evaluate the contribution and the potential of (funded) cross-regional synergies as a policy tool in the transition towards a bio-based model of sustainable development.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Fostering ‘Bioeconomy Strategy’ through synergies: managerial implications from Interreg Europe

Mouzakitis¹,

Tsoulfas²

2019

Proceedings of the 2nd International Conference on Management, Economics and Finance

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show abstract

“…To that end, a time series should be determined, i.e. an ordered string of values of the time-variable [11,12,13]. The time series can be in form of a vector [𝑦(𝑡 1 ), 𝑦(𝑡 2 ), … , 𝑦(𝑡 𝑁 )].…”

Section: Model Of Artifical Neural Networkmentioning

confidence: 99%

Efficiency forecasting of electric bundle assembly with use of ANN model

Burduk

Dybała

Machado

2022

J. Phys.: Conf. Ser.

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Management of production systems requires making immediate decisions based on the data generated in bulk by IT systems. In this case, it can be helpful to use models of artificial neural networks (ANN) that, on the grounds of accessible data, will determine results of the made decision. One of the key problems in production companies is determination of execution time and cost of a production order. The problem is especially important in a company manufacturing a variable product line with a big part of manual operations. In the article, the way of building an ANN model for efficiency forecasting of the assembly process of electric bundles is presented. With regard to the very wide and variable product line, the products with different complexity degree are manufactured on three types of assembly lines. The assembly processes are performed on the assembly lines manually by groups of workers, so efficiency of the process is influenced mostly by skills and experience of these workers. Therefore, numbers of new assembly workers assigned to individual assembly lines and quantities of new products in the production schedule are selected as explanatory variables in the ANN model. The explained variable in the ANN model is production volume of the manufactured electric bundles.

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Risk Assessment of a Production System with Series-Parallel Reliability Structure

Burduk

2022

Advances in Manufacturing Processes, Intelligent Methods and Systems in Production Engineering

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Identification of Challenges to be Overcome in the Process of Enhancing Innovativeness Based on Implementation of Central European Projects Funded from Interreg Programme

Cited by 6 publications

References 8 publications

Fostering ‘Bioeconomy Strategy’ through synergies: managerial implications from Interreg Europe

Fostering ‘Bioeconomy Strategy’ through synergies: managerial implications from Interreg Europe

Efficiency forecasting of electric bundle assembly with use of ANN model

Risk Assessment of a Production System with Series-Parallel Reliability Structure

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