The information contained in corporate social responsibility (CSR) reports is a controversial issue, and it has generated an important debate among academics regarding company disclosure strategies. Environmental matters are especially relevant given their impact on sustainable development. The present study has two objectives. The first is to determine which Global Reporting Initiative (GRI) environmental indicators are reported less frequently. The second is to predict the evolution of these indicators in light of the institutional pressures that companies try to resist. Specifically, the study of the environmental dimension of the GRI focusses on an analysis of the following: materials, energy, water, biodiversity, emissions, effluents and waste, products and services, compliance, transport, environmental assessment, and environmental grievance mechanisms. A content analysis of CSR reports from some of the world's largest companies reveals that the indicators least disclosed by companies relate to the environmental aspects of biodiversity. The dissemination of environmental indicators is influenced by normative, mimetic, and (to a lesser extent) coercive pressures. In addition, we observe that mimetic institutional pressures under a national and industrial vision influence the dissemination of environmental information. In terms of cultural dimensions, companies located in long-term, feminine, and collectivist countries tend to disseminate environmental information accordingly.

The complexity of the business world and current business models has motivated an increasing number of companies to disclose corporate information through sustainability reports. This reporting and stakeholders engagement may bring shared value to business and society in general although working towards sustainable development goals. This work adopts a new analytical approach by determining the global reporting initiative indicators related to labour practices and decent work, human rights, society, and product responsibility that are reported less frequently by companies. The final

Currently, the debate on corporate social responsibility (CSR) and the strategies implemented by organizations to disseminate their business actions, fuels the discussion on aspects that point to sustainable development. To show their CSR strategies, one of the mechanisms used by companies is the presentation of sustainability reports. In this work, we have modified the analysis approach traditionally used to demonstrate the characteristic factors of transparency in the field of CSR. Specifically, we focus on analyzing which indicators are the least disclosed by companies. Within this framework, the objective of this work is to analyse the practices of dissemination of environmental information based on the sustainability reports of the global reporting initiative produced by large companies, in order to establish differences and similarities in corporate social responsibility. The results obtained for a sample of 80 large companies and 67 multinational enterprises (MNEs) indicate a slight difference in the disclosure of environmental indicators. Concretely, the results reveal that 56.12% of environmental indicators are not disclosed by large companies; while, in MNEs, 51.23% of these indicators are not reported. In large companies, the greatest deficiencies in the disclosure of environmental information correspond to the categories of biodiversity, environmental grievance mechanisms, and effluents and waste. In the case of MNEs, the least disclosed categories are biodiversity, environmental grievance mechanisms, and environmental protection expenditures and investments (overall).

The HJ biplot is a multivariate analysis technique that allows us to represent both individuals and variables in a space of reduced dimensions. To adapt this approach to massive datasets, it is necessary to implement new techniques that are capable of reducing the dimensionality of the data and improving interpretation. Because of this, we propose a modern approach to obtaining the HJ biplot called the elastic net HJ biplot, which applies the elastic net penalty to improve the interpretation of the results. It is a novel algorithm in the sense that it is the first attempt within the biplot family in which regularisation methods are used to obtain modified loadings to optimise the results. As a complement to the proposed method, and to give practical support to it, a package has been developed in the R language called SparseBiplots. This package fills a gap that exists in the context of the HJ biplot through penalized techniques since in addition to the elastic net, it also includes the ridge and lasso to obtain the HJ biplot. To complete the study, a practical comparison is made with the standard HJ biplot and the disjoint biplot, and some results common to these methods are analysed.