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

Semantic transform: Weakly supervised semantic inference for relating visual attributes

Relative (comparative) attributes are promising for thematic ranking of visual entities, which also aids in recognition tasks. However, attribute rank learning often requires a substantial amount of relational supervision, which is highly tedious, and apparently impractical for real-world applications. In this paper, we introduce the Semantic Transform, which under minimal supervision, adaptively finds a semantic feature space along with a class ordering that is related in the best possible way. Such a semantic space is found for every attribute category. To relate the classes under weak supervision, the class ordering needs to be refined according to a cost function in an iterative procedure. This problem is ideally NP-hard, and we thus propose a constrained search tree formulation for the same. Driven by the adaptive semantic feature space representation, our model achieves the best results to date for all of the tasks of relative, absolute and zero-shot classification on two popular datasets. © 2013 IEEE.