Purpose: Nowadays, the agri-food sector is facing several challenges due to a rapid technological change which calls for knowledge sharing (KS) practices to enhance businesses’ performance. This has spurred a collaborative approach and the creation of networks. Since there still is a paucity of research on the quality degree of KS, the purpose of this study is to offer an empirical research on the quality degree of KS by exploring outcome expectations and social exchange dimensions. Theoretically, it is examined by a double lens of social capital and social cognitive theory.Design/methodology/approach: This study offers an empirical analysis of 313 directors of 11 “consortia” in the agri-food sector in Italy by using the fuzzy expert system. The model allows to aggregate multi criteria dimensions of KS and rates its quality.Findings: As resulted, the quality degree of KS is influenced by outcome expectations – personal and community expectations – and three forms of dimensions of social exchange: structural, relational and cognitive. The paper ends with a discussion of research findings, its limitations and implications.Originality/value: As there is still a paucity of research on the determinants of quality degree of KS, the research adopts a double lens of social capital and social cognitive theories to explore what are these determinants.

The main aim of this study is to find a method to measure the intellectual capital (IC) of an organization which is able to combine management and measurement views, to reflect the newest concepts regarding IC, and to take into consideration the ‘vague’ interactions between IC categories. We posit the idea that a fuzzy expert system model can address these issues, since it takes account of the qualitative nature of most IC indicators and the different IC subcategories. The main advantage of an IC score developed through a FES model is to provide a reliable IC index. The model presented in this article applied to data derived from the Austrian universities’ IC reports is a pilot model, sufficiently flexible for individual adaptations and adjustments. The main limitation of the study is that further tests can be carried out only in the presence of available and comparable IC data which are currently not available.