The success of marine protected areas (MPAs) in achieving conservation and sustainable development goals hinges on, among other things, their social acceptability by local communities. Small-scale fishing communities represent a key stakeholder category within and around MPAs. Although many authors have examined the social acceptability of MPAs, relatively few studies have addressed this issue by considering how MPA acceptability is built and can be preserved. This study assessed the latent structure of MPA social acceptability and identified the individual and institutional variables driving stakeholders’ acceptability. Using questionnaire surveys, 124 small-scale fishers’ perceptions of MPAs and their social acceptability were explored in six Mediterranean MPAs (three were implemented, and three were designated). The results show that MPA acceptability is positively related to fishers’ age. The findings also highlight that the formal establishment of MPAs is not a sufficient condition for increasing MPA acceptability among fishers. Considerations about the possibility that MPA acceptability can be increased by building support and compliance emerged. MPA managers should implement successful long-term stakeholder engagement initiatives to increase commitment around conservation measures and to improve overall MPA effectiveness.

It is possible to exploit potentials of Big Data in the shipbuilding industry in order to increase efficiency and company performance. Big Data analysis will probably have a great impact on strengthening the competitiveness in the whole sector, providing various types of benefits and effective support to the decision-making system. Academics maintain that analysis methods and algorithms can offer spe-cific guidelines to managers and practitioners in order to satisfy their information needs. Even though it is recognized that the techniques for Big Data analysis are relevant, only a few studies provide practical guidelines on how to apply these techniques in specific industries like shipbuilding. This preliminary study aims to develop a conceptual framework of Big Data anal-ysis based on the value chain approach. By using a deductive methodology, the framework is built taking into consideration four phases of the value chain in the shipbuilding industry - i.e. pre-production, design, production, and post-production. For its relevance, the study considers the pre-production phase, trying to classify data sources, analysis methods, and algorithms for the main activities of this node and also providing various suggestions to shipbuilding managers and practitioners. The researchers develop the framework by considering secondary data collected from the literature analysis. Our results can successfully support decision making in shipbuilding companies, making processes and operations more cost-effective and helping companies be more competitive. Specifically, in the pre-production node this will lead to real-time demand forecasting and a more reliable estimation of initial production costs.

Published under the terms of the Creative Commons CC BY-NC-ND 4.0 License Kataložni zapis o publikaciji (CIP) pripravili v Narodni in univerzitetni knjižnici v Ljubljani COBISS.

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The relationship between ownership structure (private vs State-owned) and company performance has been deeply analyzed by scholars and practitioners. Prior studies found mixed results about this topic; some scholars demonstrated that private firms perform better than State-Owned Enterprises (SOEs) and others came to opposite or undefined results. Further, during the global financial crisis, this topic gained relevance. To our best knowledge, Italian framework suffers of a lack of these studies and, in particular, no ones focused on the level of reputation risk in both SOEs and private firms. Aim of this paper is to analyse the difference in the performance and in the reputation risk between Italian SOEs and private firms. To do so we performed a t-test analysis on a sample of 18 State-owned listed firms and 212 private listed firms. Our empirical results found that SOEs have higher ROE and higher Cash flow/sales, but a lower Tobins’ Q than private firms. Further, no statistically significant differences in the reputation risk have been found; therefore financial analysts do not perceive any difference in the reputation risk between private and SOEs. Our results can help practitioners and policy-makers in making investment decisions and choices about the privatization process.