Nikoletta Nagy (p)

The appreciation of the role of tacit knowledge in business organizations has led to a repositioning and reassessment of the potential of local, geographically concentrated networks of companies. Knowledge creation, the flow of knowledge and the ability to share knowledge are essential elements of a region's potential for development and innovation alternatives. Geographical proximity has a positive impact on the quality and scale of cooperation within the region, and promotes innovation and the dissemination of knowledge. However, the type and extent of influence and its outcome can only be mapped by examining relational proximity.
In Capello and Faggian's (2005) model, cognitive proximity, organizational proximity, social proximity, and institutional proximity form dynamically interacting relationships that build a relational space separate from physical space, which has a positive effect on the strengthening of relationship capital, formal and informal relationships, cooperation and the spread of knowledge (Vas 2009). The combination of geographical and relational proximity can help companies create competitive advantages such as reduced uncertainty (e.g. collection, selection and sharing of information), reduced coordination costs (e.g. transaction or control costs), and the continued maintenance of collective learning (e.g. through the possibility of sharing hidden knowledge).
We examine the sources of competitiveness and development of economic networks created by leather industry companies in the Sibiu agglomeration of Romania, in the light of geographical and relational proximity, relational capital, and finally, as an essential element of regional development, the sharing of local tacit knowledge.

Keywords: tacit knowledge, knowledge sharing, regional industrial district

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Petra Kinga Kezai (p)

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Sara Maioli (p), Pattanapong Tiwasing, Matthew Gorton, Jeremy Phillipson, Robert Newbery

A thin upper tail of high-productivity firms, a fat lower tail of low-productivity firms and significant spatial variations in productivity characterise the UK economy. This paper presents an analysis of the determinants of Small and Medium Sized Enterprise (SME) labour productivity, with a particular focus on how place and productivity interact. The analysis draws on data from the UK Government’s Longitudinal Small Business Survey (LSBS) for the years 2015 to 2017. It employs a multilevel regression analysis to understand determinants in enterprise labour productivity in different localities and regions and effectively account for the contextual environment. We applied multilevel analysis to capture the nested structure of our data, modelling a fixed-effects part (at firm level or level one) and a random-effects part at Local Enterprise Partnership (LEP) level (or level two). This allows for the separation of the role of firms’ determinants from LEP (sub-regional) effects.
Regarding firm-level factors, the results show that microbusinesses and sole traders tend to have lower productivity. In contrast, business capabilities to develop and implement business plans, and obtain external finance, as well as receiving external advice in the previous year, positively contribute to productivity. The sector in which a business operates also matters with health and social work generally associated with lower productivity. Digital capabilities, internal to the SME, as well as some types of network membership contribute to higher productivity. Regarding ownership, after controlling for other factors, the results reveal that family businesses are not more or less productive than non-family ones, but, women-led businesses record significantly lower productivity. At the LEP level, the findings reveal that firms located in LEPs with a more skilled and educated population tend to have higher labour productivity. Improved broadband speeds, in some models, are also associated with higher productivity. Taken together the results give credence, in terms of explaining variations in SME productivity, to industrial organisation theory, the Resource-Based View relating to business capabilities and institutional and network effects.
Not surprisingly, our analysis confirms previous findings from the ONS about the regional disparities in the UK, as we find that firms located in London and the South East demonstrate higher labour productivity. However, we find a lack of supporting evidence for agglomeration theories which stress the benefits of urban areas per se in stimulating higher SME productivity, since our analysis shows that firms located in rural areas perform as well as urban firms.