The Hong Kong Umbrella Movement as a non-profit organization: An empirical study on the use of visual branding practices for social change

In this article, we draw our attention to the growth of a new social movement, as a non-profit organization which aims to effectively communicate its collective identity and messages to larger audiences. Initially, we provide a critical discussion on the interrelationships between marketing theory/practice and protest groups' promotional tactics. Afterwards, we focus on the interface between visual branding practices and new social movement's strategies to create a visual branding identity around their protest symbols and aims. In order to do so, we adopted a moderate participant observation approach to explore how the 2014 Hong Kong Umbrella Movement employed forms of visual branding to engage local and global audiences and induce social change. Drawing on a close examination of field notes and a visual analysis of digital archives and images from the protest sites, we identify and discuss the presence of several visual branding techniques for the imaginative promotion of the movement's demands and causes. Our findings suggest that the 2014 Umbrella Movement protesters coordinated and acted as non-profit organization which employed innovative and creative visual branding methods to enhance the movement's unity and trigger emotional responses from diverse audiences. We conclude the article with suggestions for future research around the interrelationships between social movements' protest symbols, transnational visual branding practices and non-profit organizational practices.     

Competition and Fan Substitution Between Professional Sports Leagues

Review of Industrial Organization - A peculiarity in professional sports is the fact that leagues regularly hold monopoly power within their sports. However, whether and to what extent these...     

Modelling failure rates with machine-learning models: Evidence from a panel of UK firms

In this study, we investigate the ability of machine-learning techniques to predict firm failures and we compare them against alternatives. Using data on business and financial risks of UK firms over 1994–2019, we document that machine-learning models are systematically more accurate than a discrete hazard benchmark. We conclude that the random forest model outperforms other models in failure prediction. In addition, we show that the improved predictive power of the random forest model relative to its counterparts persists when we consider extreme economic events as well as firm and industry heterogeneity. Finally, we find that financial factors affect failure probabilities.     

Government ideology and international migration*

We provide the first empirical evidence that government ideology affects the choice of migration destinations. As ruling political parties differ in their discourse, policies, and positions on migration, the ideology differential between the host and home country governments can shape the relative generosity of the welfare system, the degree of tolerance towards out-groups, and the restrictiveness of migration policies, all acting as important drivers of international migration. Using data on bilateral migration and government ideology for OECD countries between 1990 and 2016, we show that migration flows increase when the government at the destination becomes more left-wing relative to the government at the origin, particularly when both countries are members of the European Economic Area.