Addressing the fact that there are few studies exploring the relationship between board characteristics and corporate social responsibility (CSR) in non-Western contexts, this study examines the relationship in South Korean corporate contexts. We concentrate on foreign directors as a board attribute, which is reported as a remarkable change in Korean corporate boards, and propose that foreign directors have different impacts on CSR investment depending on their nationality (Anglo-Americans vs. non-Anglo-Americans) and director types (insiders vs. outsiders). In detail, the presence of directors from Anglo-American countries (e.g., the United States, the United Kingdom) decreases firms’ CSR involvement, whereas the presence of directors from non-Anglo-American countries (e.g., France, Germany) increases firms’ CSR involvement. Moreover, the effects of Anglo-Americans on CSR are strengthened when they are inside (rather than outside) directors. Empirical analyses using a sample of 1828 Korean firms from 2002 to 2015 provide evidence to support the predictions. This study theoretically contributes to CSR and corporate governance literature in that it sheds light on the CSR in non-Western companies and reveals varied effects of foreign directors contingent upon their individual attributes. It also has practical implications for policymakers and corporate managers by providing insights of the changes generated by foreign members in a boardroom.

Our objective in this study is to understand how adolescents respond to the food industry's corporate social responsibility (CSR) activities, especially the effects of such activities on consumers' emotional responses, perceived authenticity, and attitudes toward the company. Understanding which types of CSR actions most influence adolescents is important for managers. This study examines adolescents' responses to three types of CSR actions (career-related, environment-related, and wellbeing-related) across two types of products (unhealthy and healthy foods). We find that CSR actions related to career issues have the greatest effects on adolescents' emotional responses, perceived authenticity, and attitudes toward a company under the condition of healthy food products. In other words, when a healthy food company offers a career-related CSR program, adolescents have better responses than when an unhealthy food company offers the same CSR program.

Mutual information, a general measure of the relatedness between two random variables, has been actively used in the analysis of biomedical data. The mutual information between two discrete variables is conventionally calculated by their joint probabilities estimated from the frequency of observed samples in each combination of variable categories. However, this conventional approach is no longer efficient for discrete variables with many categories, which can be easily found in large-scale biomedical data such as diagnosis codes, drug compounds, and genotypes. Here, we propose a method to provide stable estimations for the mutual information between discrete variables with many categories. Simulation studies showed that the proposed method reduced the estimation errors by 45 folds and improved the correlation coefficients with true values by 99 folds, compared with the conventional calculation of mutual information. The proposed method was also demonstrated through a case study for diagnostic data in electronic health records. This method is expected to be useful in the analysis of various biomedical data with discrete variables.

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Supplementary data are available at Bioinformatics online.

In the context of ethical consumption, we examine the effects of farmers’ facial expression in print advertising on consumers’ responses to local food. Furthermore, we try to verify the moderating role of emotional intelligence (EI) on consumers’ responses to the advertising message strategy. The advertising message strategy that connects farmers and consumers is expected to create more favorable responses among consumers toward local food and its retailers. This study examines consumers’ responses (perceived product quality, trust, and a positive attitude toward the local food retailer) to three conditions of farmers’ facial expression in the advertisement (neutral facial expression, positive facial expression, and product only, with no portrait) across two levels of EI (low and high). We find that farmers’ positive facial expressions in the advertisements have the greatest positive effects on consumers’ perceived product quality, trust, and attitude toward the local food retailer under a high level of EI. Therefore, individuals with a high level of EI were more influenced by facial expressions in print advertising, whereas those with a low level of EI were less influenced by facial expressions in print advertising, and their responses were indifferent to whether the local food farmer had a neutral or a positive facial expression in print advertising. Our findings suggest that marketing practitioners consider personal characteristics such as EI in persuading local food consumers in target markets to implement strategies to promote local food purchase and consumption.

With recent developments of data technology in biomedicine, factor data such as diagnosis codes and genomic features, which can have tens to hundreds of discrete and unorderable categorical values, have emerged. While considered as a fundamental problem in statistical analyses, the estimation of probability distribution for such factor variables has not studied much because the previous studies have mainly focused on continuous variables and discrete factor variables with a few categories such as sex and race. In this work, we propose a nonparametric Bayesian procedure to estimate the probability distribution of factors with many categories. The proposed method was demonstrated through simulation studies under various conditions and showed significant improvements on the estimation errors from the previous conventional methods. In addition, the method was applied to the analysis of diagnosis data of intensive care unit patients, and generated interesting medical hypotheses. The overall results indicate that the proposed method will be useful in the analysis of biomedical factor data.