Territorial innovation models: to be or not to be, that's the question

被引:13
|
作者
Doloreux, David [1 ]
Gaviria de la Puerta, Jose [2 ]
Pastor-Lopez, Iker [2 ]
Porto Gomez, Igone [3 ]
Sanz, Borja [2 ]
Mikel Zabala-Iturriagagoitia, Jon [4 ]
机构
[1] HEC Montreal, Dept Int Business, Montreal, PQ, Canada
[2] Univ Deusto, Fac Engn, Ave Universidades 24, Bilbao 48007, Spain
[3] Univ Deusto, Deusto Business Sch, Ave Universidades 24, Bilbao 48007, Spain
[4] Univ Deusto, Deusto Business Sch, Camino Mundaiz 50, Donostia San Sebastian 20012, Spain
基金
欧盟地平线“2020”;
关键词
Territorial innovation models; Bibliometric analysis; Natural language processing; Regional development; RESEARCH-AND-DEVELOPMENT; INDUSTRIAL DISTRICTS; KEYWORD COOCCURRENCE; LEARNING REGION; CLUSTERS; SCIENCE; SYSTEMS; POLICY; DYNAMICS; KNOWLEDGE;
D O I
10.1007/s11192-019-03181-1
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Industrial agglomerations are key in explaining the development paths followed by territories, particularly at sub-national levels. This field of research has received increasing attention in the last decades, what has led to the emergence of a variety of models intended to characterize innovation at the regional level. Moulaert and Sekia (Reg Stud 37:289-302, 2003) introduced the concept of 'Territorial Innovation Models' (TIMs) as a generic name that embraced these conceptual models of regional innovation in the literature. However, the literature does not help to assess the extent to which convergence or divergence is found across TIMs. In this paper we aim to clarify if there are clear boundaries across TIMs, so each TIM has particular characteristics that make it conceptually different from others, and hence, justify its introduction in the literature. Based on natural language processing methodologies, we extract the key terms of a large volume of academic papers published in peer review journals indexed in the Web of Science for the following TIMS: industrial districts, innovative milieu, learning regions, clusters, regional innovation systems, local production systems and new industrial spaces. We resort to Rapid Automatic Keyword Extraction to identify the associations between the topics extracted from the previous corpus. Finally, a configuration to visualise the results of the methodology followed is also proposed. Our results evidence that the previous models do not have a unique flavour but are rather similar in their taste. We evidence that there is quite little that is truly new in the different TIMs in terms of theory-building and the concepts being used in each model.
引用
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页码:1163 / 1191
页数:29
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