Regional Innovation Systems in Poland: How to classify them?

IF 0.5 Q3 AREA STUDIES
D. Ciołek, Anna Golejewska, Adriana Zabłocka-Abi Yaghi
{"title":"Regional Innovation Systems in Poland: How to classify them?","authors":"D. Ciołek, Anna Golejewska, Adriana Zabłocka-Abi Yaghi","doi":"10.17059/ekon.reg.2021-3-19","DOIUrl":null,"url":null,"abstract":"The literature emphasises the role of regional and local innovation environment. Regional Innovation Systems show differences in innovation outputs determined by different inputs. Understanding these relationships can have important implications for regional and innovation policy. The research aims to classify Regional Innovation Systems in Poland according to their innovation capacity and performance. The analysis covers 72 subregions (classified as NUTS 3 in the Nomenclature of Territorial Units for Statistics) in 2004–2016. Classes of Regional Innovation Systems in Poland were identified based on a combination of linear and functional approaches and data from published and unpublished sources. It was assumed that innovation systems in Poland differ due to their location in metropolitan and non-metropolitan regions, thus, the Eurostat NUTS 3 metro/non-metro typology was applied for this purpose. Panel data regressions as models with individual random effects were estimated separately for metropolitan and non-metropolitan groups of subregions. The study identified common determinants of innovation outputs in both NUTS 3 types: share of innovative industrial enterprises, industry share, unemployment rate, and employment in research and development. Next, NUTS 3 were classified within each of two analysed types in line with output- and input-indices, the latter being calculated as non-weighted average of significant inputs. Last, the subregions were clustered based on individual inputs to enable a more detailed assessment of their innovation potential. The cluster analysis using k-means method with maximum cluster distance was applied. The results showed that the composition of the classes identified within metropolitan and non-metropolitan systems in 2004– 2016 remains unstable, similarly to the composition of clusters identified by inputs. The latter confirms the changes in components of the capacity within both Regional Innovation System types. The observed situation allows us to assume that Regional Innovation Systems in Poland are evolving. In further research, the efficiency of Regional Innovation Systems should be assessed, taking into account the differences between metropolitan and non-metropolitan regions as well as other environmental factors that may determine the efficiency of innovative processes.","PeriodicalId":51978,"journal":{"name":"Ekonomika Regiona-Economy of Region","volume":null,"pages":null},"PeriodicalIF":0.5000,"publicationDate":"2021-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ekonomika Regiona-Economy of Region","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.17059/ekon.reg.2021-3-19","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"AREA STUDIES","Score":null,"Total":0}
引用次数: 5

Abstract

The literature emphasises the role of regional and local innovation environment. Regional Innovation Systems show differences in innovation outputs determined by different inputs. Understanding these relationships can have important implications for regional and innovation policy. The research aims to classify Regional Innovation Systems in Poland according to their innovation capacity and performance. The analysis covers 72 subregions (classified as NUTS 3 in the Nomenclature of Territorial Units for Statistics) in 2004–2016. Classes of Regional Innovation Systems in Poland were identified based on a combination of linear and functional approaches and data from published and unpublished sources. It was assumed that innovation systems in Poland differ due to their location in metropolitan and non-metropolitan regions, thus, the Eurostat NUTS 3 metro/non-metro typology was applied for this purpose. Panel data regressions as models with individual random effects were estimated separately for metropolitan and non-metropolitan groups of subregions. The study identified common determinants of innovation outputs in both NUTS 3 types: share of innovative industrial enterprises, industry share, unemployment rate, and employment in research and development. Next, NUTS 3 were classified within each of two analysed types in line with output- and input-indices, the latter being calculated as non-weighted average of significant inputs. Last, the subregions were clustered based on individual inputs to enable a more detailed assessment of their innovation potential. The cluster analysis using k-means method with maximum cluster distance was applied. The results showed that the composition of the classes identified within metropolitan and non-metropolitan systems in 2004– 2016 remains unstable, similarly to the composition of clusters identified by inputs. The latter confirms the changes in components of the capacity within both Regional Innovation System types. The observed situation allows us to assume that Regional Innovation Systems in Poland are evolving. In further research, the efficiency of Regional Innovation Systems should be assessed, taking into account the differences between metropolitan and non-metropolitan regions as well as other environmental factors that may determine the efficiency of innovative processes.
波兰的区域创新系统:如何分类?
文献强调了区域和地方创新环境的作用。区域创新系统在不同投入决定的创新产出上存在差异。了解这些关系可以对区域和创新政策产生重要影响。本研究旨在根据波兰区域创新系统的创新能力和绩效对其进行分类。该分析涵盖了2004-2016年的72个分区域(在领土统计单位命名法中被分类为NUTS 3)。波兰区域创新系统的类别是基于线性和功能方法的结合以及来自已发表和未发表来源的数据确定的。假设波兰的创新系统因其在大都市和非大都市地区的位置而有所不同,因此,欧盟统计局NUTS 3地铁/非地铁类型适用于此目的。面板数据回归作为具有个体随机效应的模型,分别对分区域的大都市和非大都市组进行了估计。研究发现了两种类型创新产出的共同决定因素:创新工业企业份额、行业份额、失业率和研发就业。接下来,根据产出和投入指数,将NUTS 3分类为两种分析类型中的每一种,后者被计算为重要投入的非加权平均值。最后,根据个别投入对分区域进行聚类,以便更详细地评估其创新潜力。采用聚类距离最大的k-means方法进行聚类分析。结果表明,2004 - 2016年,大都市和非大都市系统中识别的类别组成保持不稳定,类似于输入识别的集群组成。后者证实了两种区域创新系统类型中能力组成部分的变化。观察到的情况使我们能够假设波兰的区域创新系统正在发展。在进一步的研究中,应该评估区域创新系统的效率,考虑到大都市和非大都市地区之间的差异以及其他可能决定创新过程效率的环境因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
1.80
自引率
20.00%
发文量
23
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信