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Social dimension is a fundamental element in the evaluation of initiatives and policies that are demanded and promoted by public and private organizations as well as society as a whole. Thus, there is a thriving interest in social impact research, especially from the point of view of its measurement and valuation. In this work, we explored the rising attention on the concept of social impact to identify salient agents in the field and categorize the conceptual structure of research. To achieve this, we used evaluative and relational techniques combining traditional bibliometric analysis using VOSviewer and a text mining analysis based on natural processing language (NLP) to search for documents with the term “social impact” in the title. The documents were extracted from the database Web of Science (WoS) for the period of 1938–2020. As a result, we mapped the concept of social impact from up to 1677 documents, providing an overview of the topics in which the concept was used (e.g., health, finance, environment and development, etc.) and the trends of research. This work seeks to serve as a roadmap that reflects not only the evolution of social impact but also future lines of research that require attention.

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Purpose-The main purpose of this paper is to analyse whether Labour Social Responsibility (LSR) practices influence on Corporate Reputation (CR) and on Labour Reputation (LR). Design/methodology/approach-LSR is defined as all those labour practices made by a company for the benefit of employees voluntarily and not imposed by labour legislation. An index developed by content analysis was created to measure LRS. CR and LR scores were obtained from the Business Monitor of Corporate Reputation (MERCO) for the period of 2006-2010. Furthermore, based on the previous literature, the study considers other generic variables that influence the process of creating reputation, such as visibility and environmental impact, as well as intrinsic characteristics of each company (size, financial performance and debt). The model was estimated by the Generalized Method of Moments (GMM) on a data panel for the 100 most reputable firms in Spain in each year during the period 2006-2010. Findings-The results obtained show that LSR carried out by the company has a direct and positive relationship with the reputation. Thus, corporate and labour reputation and their evolution depend on ability of the LSR strategy of the company to satisfy to future expectations of stakeholders. Originality/value-Previous literature considered the impact of different dimensions of Corporate Social Responsibility on CR, e.g., environmental, communication, quality of products, but did not consider labour practices.

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Since the 1990s, fishing production has stagnated and aquaculture has experienced an exponential growth thanks to the production on an industrial scale. One of the major challenges facing aquaculture companies is the management of breeding activity affected by biological, technical, environmental and economic factors. In recent years, decision-making has also become increasingly complex due to the need for managers to consider aspects other than economic ones, such as product quality or environmental sustainability. In this context, there is an increasing need for expert systems applied to decision-making processes that maximize economic efficiency of the operational process. One of the production planning decisions more affected by these changes is the feeding strategy. The selection of the feed determines the growth of the fish, but also generates the greatest impact of the activity on the environment and determines the quality of the product. In addition, feed is the main production cost in finfish aquaculture. In order to address all these problems, the present work integrates a multiple-criteria methodology with a genetic algorithm that allows determining the best sequence of feeds to be used throughout the fattening period, depending on multiple optimization objectives. Results show its utility to generate and evaluate different alternatives and fulfill the initial hypothesis, demonstrating that the combination of several feeds at precise times may improve the results obtained by one feed strategies.

The purpose of this study is the economic optimisation of seabream farming through the determination of the production strategies that maximise the present operating profits of the cultivation process. The methodology applied is a particle swarm optimisation algorithm based on a bioeconomic model that simulates the process of seabream fattening. The biological submodel consists of three interrelated processes, stocking, growth, and mortality, and the economic submodel considers costs and revenues related to the production process. Application of the algorithm to seabream farming in Spain reveals that the activity is profitable and shows competitive differences associated with location. Additionally, the applications of the particle swarm optimisation algorithm could be of interest for the management of other important species, such as salmon (Salmo salar), catfish (Ictalurus punctatus) or tilapia (Oreochromis niloticus).