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To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jrurstud.2009.09.001 Byline: Gianluca Nardone (a), Roberta Sisto (b), Antonio Lopolito (a) Abstract: This paper focuses on the introduction of a suitable method for the measurement of social capital in the context of rural development policies. We present an empirical application of the method to four case studies from the south of Italy. In order to overcome some limits affecting previous empirical research, we have grounded the measurement framework upon a clear decomposition of the concept of social capital characterizing three main dimensions: structural, relational and cognitive. This has allowed us to build five direct indicators for the core components of social capital created within the EU local agencies for rural development (the Local Action Groups). Moreover we have set a synthetic measure capturing the specific configuration of the internal social capital of the groups as a whole. Author Affiliation: (a) Department of Production Science, Engineering, Economics in Agricultural and Livestock Systems (PRIME), University of Foggia, Via Napoli 25, 71100 Foggia, Italy (b) Department of Economics, Mathematics and Statistics, University of Foggia, Largo Papa Giovanni Paolo II, 71100 Foggia, Italy

The experiment presented is a concrete example of how to gather and mobilize relevant knowledge from local stakeholders to support local agencies in designing effective local strategies. The hypothetical introduction of a bio-based industry in some rural areas from south Italy is used as empirical case. The application of Fuzzy Cognitive Maps (FCMs) enabled small groups of participants to depict a clear structure of their knowledge. A fuzzy inference technique allowed to simulate the effects of several policy measures. The results consist of the identification of the preferred policy options according to the features of the local context. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we investigate socio-technical transition pathways concentrating our attention on innovation niches formations. Specifically, we present first a methodological conceptualization of innovation niches and suggest an empirical methodology, based on a Social Network Analysis (SNA), aimed at capturing and measuring the internal development status of innovation niches. Further, we propose a taxonomy of various niche development statuses. The proposed empirical methodology is subsequently tested on a specific case study where we assess the development status of the emerging bio-refinery niche situated in the province of Foggia (Apulia Region, Italy). Although not exhaustive, we believe this study represents an important step in a much-needed direction, which is the development of a commonly shared empirical methodology for innovation studies dealing with socio-technical transitions. [ABSTRACT FROM AUTHOR]

The present paper illustrates a methodological strategy to address the first phases of the policy cycle – namely agenda setting, impact analysis, and policy formulation – with the aim of fostering the development of an innovation niche. Through a combination of fuzzy cognitive maps and social network analysis, our methodology allows firstly assessing the needs of the niche and then, the identification of the most effective instrument mix and policy brokers to overcome the niche's criticalities. We investigate the case of the Italian liquid biofuel niche, which offers a near-ideal laboratory for testing our methodological approach due to its recent slump. Our simulations show that the persistence of negative context conditions in the niche under investigation would be detrimental for all three niche development mechanisms – i.e., networking, learning, and convergence of expectations – and especially for the last one, thus providing a clear priority in setting the policy agenda. Additionally, the impact analysis highlights that the combination of different policy instruments better supports the niche mechanisms than the implementation of single instruments. Finally, our methodology enables identification of the most effective instrument mix by considering interactions and feedback effects among instruments. • The liquid biofuel niche in Italy is examined. • Fuzzy Cognitive Maps enabled the identification of the most effective instrument mix. • Social Network Analysis is employed to find policy brokers. • Policy makers should consider interactions and feedback effects among instruments. [ABSTRACT FROM AUTHOR]

Support for technology clusters is an important element of innovation policy, with an increasing number of national and regional plans promoting Research and Development projects in collaborative networks. This promotes the need to evaluate the effectiveness and efficiency of these policies. Several methods and techniques have been proposed, but a standardised approach has not yet emerged. It is important to assess the role of non-economic effects and, in this paper, we propose a methodology, which uses a social network approach, to assess the social capital outcomes of a technology district policy. Building on social capital theory, we construct a set of indicators to account for structural, relational and cognitive components, as well as bonding, bridging and linking kinds. This approach is not costly, in terms of data and information collection and can be combined with other methods that measure policy performance via physical, as well as economic, outputs and impacts. [ABSTRACT FROM AUTHOR]

Abstract The amount of food wastage produced at the global level generates high environmental, economic and social costs, such as greenhouse gas emissions, soil degradation, waste generation, consumption of natural resources, as well as economic losses, inequality and poverty. Taking stock of these problems, this paper conducts an empirical investigation in order to identify and recommend the most effective policy actions and private initiatives that might modify the current unsustainable food consumption model, characterizing high income countries, in order to achieve a significant reduction in the amount of food wastage. Specifically, it performs a fuzzy inference simulation by means of a three-step methodology: analyzing the use of language within the food waste reduction/valorization debate to identify system variables; deducing the map of causal-effect relationships among the identified system variables through interviews to a pool of experts; and finally, performing a fuzzy inference simulation, to identify drivers potentially able to discourage current unsustainable consumer behaviors. Among other things, the fuzzy analysis shows how some policy drivers, as "Public food waste rules", "Investments and infrastructure" and "Small-scale farming" are particularly effective in supporting a new and sustainable food consumption model. At the same time, the experts' knowledge allows highlighting the crucial role of "biorefinery" in fostering a transition towards a sustainable consumption model. This is an interesting result, as it points at the ability of biorefineries to enhance a general positive attitude of consumers which, in turn, would translate into more sustainable consumption practices. Highlights • Effective policy drivers and private initiatives to reduce the amount of food waste are identified. • Food waste system variables are elicited by means of discourse analysis. • Fuzzy inference is employed to investigate the causal-effect map derived from experts' knowledge. • Side-effects should be considered in designing policy interventions. [ABSTRACT FROM AUTHOR]