A Comparative Picture of Corporate Social Responsibility Approaches by Leading Companies in the United Kingdom and Brazil

Purpose - This paper compares CSR strategy, stakeholder engagement and overseas approaches of six leading companies which have large potential environmental and social impacts, influential stakeholders and notable CSR actions. Design/methodology/approach - It is an exploratory survey based on interviews of senior executives from British and Brazilian companies operating in the steel, petroleum and retail sectors and makes comparisons between and within them. Findings - British companies interviewed are more rule-based, adopt an implicit CSR approach; react to stakeholder’s demands based on moral motives and focus on environmental issues. The Brazilian companies, reviewed in this study, adopt an explicit CSR approach, have relational motives to engage with stakeholders and are more concerned with building a responsible image and narrowing social gaps. Research limitations/implications - The survey is based on perceptions of senior executives interviewed which may or may not correspond to actual practices. The sample size restricts generalization of results and specific firms interviewed may not represent the prevailing CSR business strategy in their respective countries. Practical implications - British companies can learn from the Brazilian experience how to become more innovative in a broader approach to CSR. Brazil should reinforce its legal framework to provide a more systematic and rule-based approach to CSR close to the UK experience. Originality/value - The way CSR is conceived and implemented depends on the ethical, socioeconomic, legal and institutional environment of the country in which the firm operates

Kernelized log linear models for continuous speech recognition

Large margin criteria and discriminative models are two effective improvements for HMM-based speech recognition. This paper proposed a large margin trained log linear model with kernels for CSR. To avoid explicitly computing in the high dimensional feature space and to achieve the nonlinear decision boundaries, a kernel based training and decoding framework is proposed in this work. To make the system robust to noise a kernel adaptation scheme is also presented. Previous work in this area is extended in two directions. First, most kernels for CSR focus on measuring the similarity between two observation sequences. The proposed joint kernels defined a similarity between two observation-label sequence pairs on the sentence level. Second, this paper addresses how to efficiently employ kernels in large margin training and decoding with lattices. To the best of our knowledge, this is the first attempt at using large margin kernel-based log linear models for CSR. The model is evaluated on a noise corrupted continuous digit task: AURORA 2.0. © 2013 IEEE.