Giovanni Mastroleo

This paper introduces a model for rating a firm’s default risk based on fuzzy logic and expert system and an associated model of sensitivity analysis (SA) for managerial purposes. The rating model automatically replicates the evaluation process of default risk performed by human experts. It makes use of a modular approach based on rules blocks and conditional implications. The SA model investigates the change in the firm’s default risk under changes in the model inputs and employs recent results in the engineering literature of Sensitivity Analysis. In particular, it (i) allows the decomposition of the historical variation of default risk, (ii) identifies the most relevant parameters for the risk variation, and (iii) suggests managerial actions to be undertaken for improving the firm’s rating.

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.

Purpose: – The purpose of this paper is to propose a method to measure intellectual capital (IC) in firms involved in strategic alliances, an area that has received scant attention in the literature, as existing research is focused mainly on organizational level mainly and increasingly on macro-level unit such as regions or nations. There are very few works at the meso-level (i.e. alliances, clusters), and the paper aims to fill this void, by providing researchers and practitioners with a tool capable of combining measurement and management aims, developed at organizational level with the active participation of the researchers.Design/methodology/approach: – The method of analysis is based on a model formalized through a fuzzy expert system (FES). The FES are able to merge the capabilities of an expert system to simulate the decision-making process with the vagueness typical of human reasoning, maintaining the ability to still have a numeric value as a response. Its construction requires the participation of experts, whose knowledge of the problem is accumulated in the form of blocks of rules. These features make it possible to formalize the decision-making process related to the IC valuation, handling qualitative and quantitative variables, and exploring the cognitive mechanisms underlying this process.Findings: – The outcome of the application is a system designed to measure the intangible performance deriving from participation in a strategic alliance using FES. This study contributes to the broadening of the research community’s understanding regarding the alternative measurement of IC created within strategic alliances.Research limitations/implications: – To the best of the authors’ knowledge, IC literature lacks methods expressly designed to measure the incremental value of IC originating from collaboration among firms. From a measurement perspective, the results may be regarded as valuable proof that IC performance within strategic alliances can be measured quantitatively.Practical implications: – On the management side, the possibility of retracing the determinants of different IC intermediate indicators composing the final IC index allows strategic alliances managers to use this information for decision-making purposes.Originality/value: – To the best of the authors’ knowledge this is the first study applying FES to measure IC in a firm belonging to a strategic alliance. In the authors’ opinion, fuzzy logic methodology, recently applied in empirical work designed to evaluate IC, represents a reliable methodology because of the “fuzzy” nature of IC.