Extending Social Learning Theories to Collectivist Cultures: The Effect of Behavior Modeling Training, Service Orientation and Language Skills on Service Skills and Behaviors

Although previous research has suggested that training approaches using behavior modeling yield better results than lecture-based approaches, these assumptions have not been tested in collectivist cultures. This study examined the effects of these alternative training methods for service knowledge and service behavior with a field experiment involving 117 Russian hotel employees. Despite no previous exposure to behavior modeling and no cultural context for service, the behavioral modeling training approach relative to the lecture-based approach yielded higher levels of both service knowledge and behavior. Since the setting was an English speaking hotel, difference in language ability were also considered and behavioral modeling was found to be a more effective training approach regardless of English ability. It also appears that service orientation is positively associated with both knowledge and behavior. The results indicate behavior modeling may be most helpful to those employees least predisposed to service or with lower language abilities

Overview of mathematical approaches used to model bacterial chemotaxis II: bacterial populations

We review the application of mathematical modeling to understanding the behavior of populations of chemotactic bacteria. The application of continuum mathematical models, in particular generalized Keller–Segel models, is discussed along with attempts to incorporate the microscale (individual) behavior on the macroscale, modeling the interaction between different species of bacteria, the interaction of bacteria with their environment, and methods used to obtain experimentally verified parameter values. We allude briefly to the role of modeling pattern formation in understanding collective behavior within bacterial populations. Various aspects of each model are discussed and areas for possible future research are postulated

Modeling the Searching Behavior of Social Monkeys

We discuss various features of the trajectories of spider monkeys looking for food in a tropical forest, as observed recently in an extensive {\it in situ} study. Some of the features observed can be interpreted as the result of social interactions. In addition, a simple model of deterministic walk in a random environment reproduces the observed angular correlations between successive steps, and in some cases, the emergence of L\'evy distributions for the length of the steps.Comment: 7 pages, 3 figure

Modeling Paying Behavior in Game Social Networks

Online gaming is one of the largest industries on the Internet, generating tens of billions of dollars in revenues annually. One core problem in online game is to find and convert free users into paying customers, which is of great importance for the sustainable development of almost all online games. Although much research has been conducted, there are still several challenges that remain largely unsolved: What are the fundamental factors that trigger the users to pay? How does users? paying behavior influence each other in the game social network? How to design a prediction model to recognize those potential users who are likely to pay? In this paper, employing two large online games as the basis, we study how a user becomes a new paying user in the games. In particular, we examine how users' paying behavior influences each other in the game social network. We study this problem from various sociological perspectives including strong/weak ties, social structural diversity and social influence. Based on the discovered patterns, we propose a learning framework to predict potential new payers. The framework can learn a model using features associated with users and then use the social relationships between users to refine the learned model. We test the proposed framework using nearly 50 billion user activities from two real games. Our experiments show that the proposed framework significantly improves the prediction accuracy by up to 3-11% compared to several alternative methods. The study also unveils several intriguing social phenomena from the data. For example, influence indeed exists among users for the paying behavior. The likelihood of a user becoming a new paying user is 5 times higher than chance when he has 5 paying neighbors of strong tie. We have deployed the proposed algorithm into the game, and the Lift_Ratio has been improved up to 196% compared to the prior strategy