Alexandra Wicht, Per Kropp, Barbara Schwengler (p)

This study tries to evaluate whether functional regions (i.e. regions with strong commuting and economic activity within and few connections to outside regions) are more homogeneous than administrative regions and, therefore, better able to capture spatial clustering of economic characteristics at the level of municipalities resulting from spatial interrelations. This would be a point in favour of functional regions because it could enable political actors to apply a rather simple set of policies within functional regions.
In order to test this assumption, we draw on core economic and labour market indicators (unemployment, employment and income per capita) from the year 2014 at the level of 4,453 basic-units and compare two-level hierarchical linear models with different regionalizations at a higher level of aggregation and three-level hierarchical linear models that additionally take into account spatial clustering at a lower aggregation level. We calculate the intraclass correlation coefficient (ICC) which measures the degree of resemblance between basic units belonging to the same areal unit at a higher aggregation level. Comparing the models based on ICCs determines the importance of spillover effects at a lower aggregation level and the importance of administrative or functional regions at a higher aggregation level.
We find that functional regions do not always reveal intraregional homogeneity better than administrative regions. This holds especially for functional regions that capture mobility patterns very well. They exhibit lower ICCs than administrative regions. In general, our analyses reveal a complex relationship between (functional) delineations and the aim to delineate homogeneous regions. For Germany, the spatial distribution of employment and income is not necessarily shaped by commuting patterns. In some cases, strong commuting flows connect very heterogeneous regions, which include cities and their commuter belts. Therefore, methods that capture commuting patterns well are not suitable for delineating homogeneous regions, at least for Germany. This is an argument in favour of not relying solely on measures related to self-containment. Homogeneity-related measures, or measures of interaction density may be appropriate indices for delineating well differentiated functional areas, which are especially capable of capturing spatial clustering at a lower aggregation level.

Konstantina Ragazou (p)

Food industry is a dominant sector of the Greekeconomy, as it concerns 25% of the firms of the manufacturing industry. Also, it ranks first among the manufacturing sectors followed by metal products (14%) and clothing (7%). Food industry is obviously of fundamental importance in purely economic terms, with both production value and gross value-added reaching almost 24%,while occupying the first position in financial turnover too(25%). At the same time, food industry employs almost one-third of the manufacturing industry’s employees, making it one of the largest employers in Greek labor market.
In the context of the economic crisis that erupted in the early 2010s,it appeared that the Food industry’s firms have designed and developed strategies with major objective, the preservation of their human resources.Consequently, the aim of thepresent paper is firstly, to highlight the strategies that were developed by the food industryduring the crisis,especially as regards the preservation ofhuman resource from possible discharges.The second objective of this paper is to examine to which extent the strategies for human resource contribute to the resilience of the food sector in the context of crisis.
The study is focused on the food sector of the Central Greece Region, in which was conducted field research in eleven firms of different size and sub-sector of food industry. The field research was conducted through questionnaires and interviews with different staff members of these firms.Two questionnaires have been fulfilled: the first one using closed-ended questions with Likert format , allows to assess the strategies developed by the firms during the crisis while the second one, using open-ended questions, gave the opportunity to the staff members to implement the “The Goals Grid”, a tool which contributes to better defining and prioritizing their goals as regards human resource. The data processing was carried out usingthe NVivo12 Plus, a software that enables in-depth content analysis (qualitative approach) as well as efficient visualization of the proper conclusions.

Francisco García-lillo (p), Enrique Claver-Cortés, Bartolomé Marco-Lajara, Pedro Seva-Larrosa, Javier Martínez-Falcó

In recent years, author and document citation and co-citation analyses have often been applied to map the intellectual structure or knowledge base of different scientific fields, including the literature on clusters, industrial districts, local systems, and territorial innovation models (TIMs) and the field of entrepreneurship. However, the technique of bibliographic coupling between scientific documents (Kessler, 1963), which seeks to identify active research fronts in a scientific field or discipline –and, particularly suitable for detecting current trends and future research priorities– has been less commonly used. This study utilized this last technique to identify and visualize active research fronts in the context of peer-reviewed journal articles dealing with the phenomenon of cluster entrepreneurship published in a wide variety of journals from 2005 to 2020* (database consultation data: January 26, 2020), seeking to shed light on the current state-of-the art on this topic –high quality, thematic review papers, bibliographic, and meta-analyses can provide this state-of-the-art understanding to scholars and thus play an important role in a discipline’s progress–. The aim is not only to complement and expand the results obtained in prior studies that have used other types of systematization, such as qualitative content analysis methodology, but also to propose new avenues for future research.