Measuring Knowledge Bases in Swedish Regions

Martin, Roman

doi:10.1080/09654313.2012.708022

Cited by 46 publications

(48 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Hitherto, Asheim and colleagues' model has mainly been applied empirically through in-depth case studies (e.g., Strambach and Dieterich, 2011;Liu et al, 2013), or with regions or industries as the level of analysis (e.g. Asheim & Coenen, 2005;Moodysson et al, 2008;Coenen & Moodysson, 2009); very few quantitative, firm-level studies have sought to measure knowledge bases empirically; two exceptions being Martin (2012) and Tether et al (2012), both of which use occupations, and occupational data, to infer the knowledge base(s) being utilised.…”

Section: Applying the Sas Knowledge Bases To Kibs 'Industries'mentioning

confidence: 99%

Towards understanding variety in knowledge intensive business services by distinguishing their knowledge bases

2016

View full text Add to dashboard Cite

Knowledge intensive business services (KIBS) are known to play a significant role in innovation systems. Past research has however mostly treated KIBS as a homogenous group; it is now time to understand better the variety that exists among KIBS. In this study, we apply a mix of qualitative and quantitative methods to examine a dataset of 362 UK-based KIBS firms active in three 'sectors': architecture and engineering consulting; specialist design; and software and IT consulting. By applying content analysis techniques to information drawn from firms' websites, we identify each firm's primary 'knowledge base', be that analytical, synthetic or symbolic knowledge. We then relate the firms' primary knowledge base to their engagement in R&D, design and innovation, and examine how the 'drivers' of innovation vary between firms with different primary knowledge bases. The paper thereby contributes to the literature, first by identifying empirically 'knowledge bases', then relating these to the variety that exists among KIBS. The paper concludes by highlighting issues for further conceptual, methodological and empirical research.

show abstract

Section: Applying the Sas Knowledge Bases To Kibs 'Industries'mentioning

confidence: 99%

Towards understanding variety in knowledge intensive business services by distinguishing their knowledge bases

2016

View full text Add to dashboard Cite

show abstract

“…One key characteristic of the food sector is that it that its diversity means that it uses analytical, synthetic, and symbolic knowledge in the course of innovation (Moodysson, Coenen, and Asheim 2008;Zukauskaite and Moodysson 2016). Thus, the knowledge required for innovation may be scientific in nature (analytical knowledge), focus on design and problem solving (synthetic knowledge), or be aesthetic in nature (symbolic knowledge) (Asheim et al 2007;Martin 2012;Tether, Li, and Mina 2012;Zukauskaite and Moodysson 2016).…”

Section: Introductionmentioning

confidence: 99%

Open innovation and the formation of university–industry links in the food manufacturing and technology sector

Johnston

2020

EJIM

View full text Add to dashboard Cite

Purpose Despite typically being regarded as “low tech”, the food manufacturing and technology sector is increasingly turning to open innovation practices involving collaboration with universities in order to innovate. Given the broad range of activities undertaken by this sector and the fact that it utilises analytical, synthetic and symbolic knowledge for innovation, it makes an interesting case study on the factors that influence the formation of university–industry (U–I) links. The paper aims to discuss this issue. Design/methodology/approach Using data from 249 collaborative projects that occurred between UK universities and food manufacturing and technology firms, the analysis utilises a logistic regression model based on a “synthetic counterfactual approach” to modelling the probability a collaborative link will be established with one university and not others. Findings The results suggest that organisational proximity, conceptualised through the presence of prior ties between actors, has the largest influence on the formation of U–I links. In addition, spatial and technological proximities between actors also have a positive influence on link formation. This result suggests that the specificity of knowledge of the food sector is important in the formation of these U–I links. Research limitations/implications The results suggest that the open innovation practices of food manufacturing and technology firms are like other sectors, even though their innovation practices are considered to be different. However, the limitations of the paper mean that these findings may be specific to firms in the food manufacturing and technology sector in the UK. Originality/value The food sector is under-represented in empirical studies on university collaboration; this paper addresses this and provides new insights into the formation of these links.

show abstract

“…In this context, a location quotient (LQ) is operationalised to define the local specialisation in textile and clothing industry. The adoption of LQ as a geographical concentration measure is a classical technique used in economic geography to compare the presence of certain industries in a local economy with respect to a reference economy (Klosterman, 1990;Martin, 2012). It is measured as the ratio of the share of self-employers in textile and clothing industry at local and national level.…”

Section: Local Specialisation In Textile and Clothing Industry (Loc_smentioning

confidence: 99%

Factors driving the share and growth of Chinese entrepreneurship in Italy

Noni

Ganzaroli

Apa

2020

IJESB

View full text Add to dashboard Cite

show abstract

Measuring Knowledge Bases in Swedish Regions

Cited by 46 publications

References 33 publications

Towards understanding variety in knowledge intensive business services by distinguishing their knowledge bases

Towards understanding variety in knowledge intensive business services by distinguishing their knowledge bases

Open innovation and the formation of university–industry links in the food manufacturing and technology sector

Factors driving the share and growth of Chinese entrepreneurship in Italy

Contact Info

Product

Resources

About