Implementing smart specialisation roadmaps in Lithuania: Lost in translation?

Paliokaitė, Agnė; Martinaitis, Žilvinas; Sarpong, David

doi:10.1016/j.techfore.2016.01.005

Cited by 27 publications

(20 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Several valuable proposals have been concluded while analyzing an innovative policy that takes into account smart specializations for emerging regions in Poland (Kamrowska-Zaluska and Soltys, 2016). Similar studies, in Europe (Thalassinos et al, 2015) and in other countries, were presented the process of identifying smart specializations for Romania (Sandu, 2012;Dodescu and Chirilă, 2012), for Lithuania (Paliokaitė, Martinaitis and Sarpong, 2016) and for Slovakia (Borseková, Vaňová and Vitálišová, 2017). It should also be noted the interesting studies conducted on the role of smart specialization in less-developed countries (Krammer, 2017), as well as the research on smart specialization policies as a support for the sustainable development policy in the face of new global challenges (Rusu, 2013).…”

mentioning

confidence: 83%

Specialization Integrated Strategy of Innovations: Effective Model for Emerging Regional Economy Development?

Malik¹,

Mach²,

Szewczuk-Stępień³

et al. 2020

ERSJ

View full text Add to dashboard Cite

Purpose: The purpose of our work is to propose a strategic model of smart specialization identification around the concept of regional knowledge and cognitive transfer (Double Helix Matrix, DHM). Design/Methodology/Approach: While operationalization of smart specialization-oriented policy is still rather limited because of the lack of agreed development policy tools, we proposed the DHM model developed in order to provide the implementation of regional smart specialization oriented strategy. The model has been statistically verified by multidimensional comparative analysis on the example of a chosen weak innovative emerging region in Poland (Opolskie). Findings: The proposed methodology let us to find the need of redefining the smart specialization concept as a part of regional innovation strategy. Practical Implications: The new approach to the regional innovation policy has been proposed.. The findings have been used to construct the regional policy directives for potential smart specializations development within 2027 perspective. Originality/Value: The new model to conduct regional innovation strategy for smart specialization has been proposed and verified on the example of the weak innovation region.

show abstract

mentioning

confidence: 83%

Specialization Integrated Strategy of Innovations: Effective Model for Emerging Regional Economy Development?

Malik¹,

Mach²,

Szewczuk-Stępień³

et al. 2020

ERSJ

View full text Add to dashboard Cite

show abstract

“…The smart specialization approach is considered as a key instrument to promote smart, sustainable and inclusive economic growth in Europe (Foray et al, 2011;Paliokaitė et al, 2016). Facing increased global competition, the European Union (EU) strives to encourage its regions to define their unique capabilities and to identify areas of specialization in which they can have competitive advantages.…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies suggest measuring Smart Specialization Strategy based on patent and industry analysis (Paliokaitė et al, 2016;Santoalha, 2016;Kaivo-oja et al, 2017;Asheim et al, 2017;Balland et al, 2018), but both these approaches have drawbacks in defining indices of smart specialization. First of all, not all inventions are patentable and, for this reason, patent analysis lacks indices to measure knowledge capabilities and competencies in a region.…”

Section: Introductionmentioning

confidence: 99%

“…Smart Specialization Strategy (S3) is a novel approach to regional economic development policy (Kaivo-oja et al, 2017). The major idea that differentiates Smart Specialization Strategy (S3) from traditional industrial and innovation policy is its emphasis on a place-based and bottom-up approach in priority-setting (Foray at al., 2011;Boschma, 2016;Paliokaitė et al, 2016). In traditional industrial and innovation policy, a decision making process in mapping promising sectors for investment and specialization was mainly centralized and top-down (Barca at al., 2012).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Data-based Startup Profile Analysis in the European Smart Specialization Strategy: A Text Mining Approach

Bzhalava¹,

Kaivo-oja²,

Hassan³

2018

EurInsStud

View full text Add to dashboard Cite

The aim of the paper is to develop novel scientific metrics approach to the European Smart Specialization Strategy. The European Union (EU) has introduced Smart Specialization Strategy (S3) to increase the innovation and competitive potential of its member states by identifying promising economic areas for investment and specialization. While the evaluation of Smart Specialization Strategy requires measurable criteria for the comparison of rate and level of development of countries and regions, policy makers lack efficient and viable tools for mapping promising sectors for smart specialization. To cope with this issue, we used a text mining approach to analyze the business description of startups from Nordic and Baltic countries in order to identify sectors in which entrepreneurs from these regions see new business opportunities. In particular, a topic modeling, Latent Dirichlet Allocation approach is employed to classify business descriptions and to identify sectors, in which start-up entrepreneurs identify possibilities of smart specialization. The results of the analysis show country-specific differences in national startup profiles as well as variations among entrepreneurs coming from developed and less developed EU regions in terms of detecting business opportunities. Finally, we present policy implications for the European Smart Specialization Strategy.

show abstract

“…It can also support relevant background information, which enables realistic discoveries about potential new business models that originate in regions' strengths (see e.g. Johnson 2015, Gheorghiu et al 2016, Jucevicius & Galbuogiene 2014, Paliokaite, Martinaitis & Sarpong 2016. This provides the foundation for knowledge-based management and strategy building, supporting bold openings for experiments and the regional strengthening of the Entrepreneurial Discovery Processes.…”

mentioning

confidence: 98%

Identification of Smart Regions with Resilience, Specialisation and Labour Intensity of globally Competitive Sector – The Examination of the LAU-1 Regions in Finland

Haukioja¹,

Kaivo-oja²,

Karppinen³

et al. 2018

EurInsStud

View full text Add to dashboard Cite

The purpose of the study was to construct smart specialisation indicators for LAU-1 regions in Finland. Established indices are based on indicators of the regions' revealed comparative advantage and the degree of diversification in the sub-regional industrial structure. Furthermore, we introduce an indicator that can be used to assess the socio-economic importance (employment) of diversification and specialisation for a region. The indices data is based on Statistics Finland (2015) data for the 70 Local Administrative Unit level 1 (LAU1) sub-regions in Finland. The potential S3 indices measured here reveal the position of each sub-region's smart specialisation among the 70 sub-regions in 2015.It is common economic knowledge that manufacturing industries are the most export-oriented, highly productive and thus can approximate a region's success in international trade and competitive advantages. The study is based on three smart specialisation indices: the Herfindahl-Hirschman Index for regional resilience (HHI), the regional relative specialisation index (RRSI) based on the Balassa-Hoover Index (B-H), and the relative employment volume index in the manufacturing sector (LIMI). Through index examination, we can obtain knowledge about a region's smart specialisation status and potential. The results reveal that each sub-region has its own smart specialisation characteristics with a different risk profile. Sub-regions like Helsinki and Tampere have a similar industrial structure to Finland as a whole and are resilient: they will benefit from nationwide economic and industrial policy, and they have a good capability of resisting economic shocks. Our study reveals that there are some other similar smaller (LAU-1) sub-regions in Finland -for example Rauma. As such, it is critical that this kind of research-based basic information be taken into account when constructing sustainable strategies for regional development. Similar calculations could be performed for all regions in Europe.European Integration Studies 2018/12

show abstract

Implementing smart specialisation roadmaps in Lithuania: Lost in translation?

Cited by 27 publications

References 24 publications

Specialization Integrated Strategy of Innovations: Effective Model for Emerging Regional Economy Development?

Specialization Integrated Strategy of Innovations: Effective Model for Emerging Regional Economy Development?

Data-based Startup Profile Analysis in the European Smart Specialization Strategy: A Text Mining Approach

Identification of Smart Regions with Resilience, Specialisation and Labour Intensity of globally Competitive Sector – The Examination of the LAU-1 Regions in Finland

Contact Info

Product

Resources

About