This article analyzes the relationships among nationalism (N), individualism (I), ethnocentrism (E), and authoritarianism (A) in continuous time (CT), estimated as a structural equation model. The analysis is based on the General Election Study for Flanders, Belgium, for 1991, 1995, and 1999. We find reciprocal effects between A and E and between E and I as well as a unidirectional effect from A on I. We furthermore find relatively small, but significant, effects from both I and E on N but no effect from A on N or from N on any of the other variables. Because of its central role in the N-I-E-A complex, mitigation of authoritarianism has the largest potential to reduce the spread of nationalism, ethnocentrism, and racism in Flanders.

No abstract

World Health Organization (WHO) stated Covid-19 as a global pandemic in March, 2020. This pandemic has influenced people’s life in many sectors such as the economy, health, tourism, and many more. One way to end this pandemic is to make herd immunity obtained through the vaccination program. This program still raises pros and cons at the beginning of its implementation in Indonesia. Many people doubt the safety and side effects of the vaccine. There are also pros and cons to vaccination programs in social media such as Twitter. This platform generates a huge amount of text data containing people's perceptions about vaccines. This research aims to predict sentiment using supervised learning such as support vector machine (SVM) and random forest and capture sentiment about vaccines in Indonesia in the first two weeks of the program. The result shows SVM was a better model than random forest based on the precision and F1-score metrics. The SVM approach produces a precision value of 0.50, a recall of 0.64, and an F1-score of 0.52. In the study, it was also found that tweets with neutral sentiment dominated the twitter user sentiment in the study period. Tweets with negative sentiment decreased after the first week of the COVID-19 vaccination program.

Purpose This paper aims to test the effect of customer relationship management (CRM) strategy on customer loyalty of bank customers. Design/methodology/approach The questionnaire derived from previous studies along with relevant literature was completed by 100 customers of conventional banks and 100 customers of Islamic banks. Structural equation modeling assessed the impact on customer loyalty on three key constructs of CRM programs (continuity marketing, one to one marketing and partnering). Findings Two out of three variables, which is continuity marketing and partnering, have significant effects on both banks. Continuity marketing is the dominant variable at conventional banks. Partnering is the dominant variable at Islamic banks. Research limitations/implications The effects of CRM programs on customer loyalty observed in this study required further research. The data used in this study were only gathered from the banking industry in Indonesia, and so more research studies are needed to support the conclusion. Practical implications It is reasonable to conclude that customer loyalty can be built, strengthened and retained by CRM programs, aimed at increasing security and building trust in each transaction, improving partnership, optimize another bank’s service product like internet banking and SMS banking and communicating with customers in a timely manner. Originality/value Advanced and specific knowledge relevant to CRM in banking service industries.

This paper deals with symmetrical data that can be modelled based on Gaussian distribution, such as linear mixed models for longitudinal data. The latent factor linear mixed model (LFLMM) is a method generally used for analysing changes in high-dimensional longitudinal data. It is usual that the model estimates are based on the expectation-maximization (EM) algorithm, but unfortunately, the algorithm does not produce the standard errors of the regression coefficients, which then hampers testing procedures. To fill in the gap, the Supplemented EM (SEM) algorithm for the case of fixed variables is proposed in this paper. The computational aspects of the SEM algorithm have been investigated by means of simulation. We also calculate the variance matrix of beta using the second moment as a benchmark to compare with the asymptotic variance matrix of beta of SEM. Both the second moment and SEM produce symmetrical results, the variance estimates of beta are getting smaller when number of subjects in the simulation increases. In addition, the practical usefulness of this work was illustrated using real data on political attitudes and behaviour in Flanders-Belgium.