Model-Based Method for Social Network Clustering

We propose a simple mixed membership model for social network clustering in this note. A flexible function is adopted to measure affinities among a set of entities in a social network. The model not only allows each entity in the network to possess more than one membership, but also provides accurate statistical inference about network structure. We estimate the membership parameters by using an MCMC algorithm. We evaluate the performance of the proposed algorithm by applying our model to two empirical social network data, the Zachary club data and the bottlenose dolphin network data. We also conduct some numerical studies for different types of simulated networks for assessing the effectiveness of our algorithm. In the end, some concluding remarks and future work are addressed briefly

“What Do I Feel? Who Am I?”: Exploring Secondary School EFL Student Teachers’ Emotion Labor in Identity Construction from a Post-Structuralist Perspective

 This study examined the emotion labor of student teachers in Chinese secondary schools and explored how they negotiated and constructed their teacher identities, with Zembylas’ model of three levels of teacher emotions (intrapersonal, interpersonal, and intergroup levels) as the analytical framework. The primary data was collected through semi-structured in-depth interviews with three student teachers who served as interns for fifteen weeks. To ensure data triangulation, emotional diaries, student focus group interviews, classroom observations and relevant documents were also gathered as supplementary data. Findings revealed the inextricably interwoven relationship between teacher emotion labor and identity. Firstly, student teachers’ confusion about their identity emerged partly due to their student-teacher equal educational philosophy. Secondly, the prerequisite for natural expressions of emotions is the mutual trust among both parties. Lastly, self-reflections evoked by emotion labor could facilitate student teachers’ identity formation and transformation. The implications derived are significant for stakeholders in the field of teacher education

Equity incentive system in Alibaba, Tencent and Baidu

JEL Classification: J33, M52More and more Chinese companies are applying equity incentive plans nowadays. However, the effectiveness of these equity incentive systems is unclear as some research shows a positive relationship between the equity incentive system and firm performance whereas others show a negative relationship and still others found that there is no relationship at all. In this dissertation, we analyzed the equity incentive system in three Chinese high tech companies: Baidu, Alibaba and Tencent, the so called BAT. The research used secondary data from the latest 5 to 10 year Annual Financial Statements of these three companies and applied Spearman’s correlation coefficient to calculate the relationship between their equity incentive system and performance. Results showed a higher and positive relationship in the case of Baidu and Tencent rather than in Alibaba. Finally possible factors that may influence the effectiveness of the equity incentive system were discussed. This study and the results obtained may provide some guidance and suggestions not only for the subject companies but also to others and assist them in selecting a suitable type of equity incentive plan.Cada vez mais empresas chinesas aplicam hoje em dia programas de incentivos baseados em ações. No entanto, a eficácia destes sistemas continua pouco clara. Algumas investigações evidenciam uma correlação positiva entre o sistema de incentivos baseado em ações e o desempenho da empresa, enquanto que outras mostram uma correlação negativa e, outras ainda não evidenciam nenhuma correlação. Nesta investigação, foi analisado o sistema de incentivos baseado em ações de três empresas chinesas de alta tecnologia, as designadas BAT: Baidu, Alibaba e Tencent. A investigação baseou-se em dados secundários extraídos dos Relatórios Financeiros Anuais destas três empresas e aplicou o coeficiente de correlação de Spearman para calcular o tipo de correlação existente entre os seus planos de incentivos e o desempenho. Os resultados revelaram que a correlação é melhor nas empresas Baidu e Tencent e não tão positiva na Alibaba. Por fim foram discutidos os fatores que podem influenciar a eficácia do sistema. Os resultados poderão fornecer alguma orientação e sugestões não só para as empresas estudadas mas para outras que pretendam adoptar planos de incentivos por ações