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Searching for a job is an important process that influences short- and long-term career outcomes as well as well-being and psychological health. As such, job search research has grown tremendously over the last two decades. In this chapter, the authors provide an overview of prior research, discuss important trends in current research, and suggest areas for future research. The authors conceptualize the job search as an unfolding process (i.e., a process through which job seekers navigate through stages to achieve their goal of finding and accepting a job) in which job seekers engage in self-regulation behaviors. The authors contrast research that has taken a between-person, static approach with research that has taken a within-person, dynamic approach and highlight the importance of combining between- and within-person designs in order to have a more holistic understanding of the job search process. Finally, authors provide some recommendations for future research. Much remains to be learned about what influences job search self-regulation, and how job self-regulation influences job search and employment outcomes depending on individual, contextual, and environmental factors.

Drawing on the social learning theory, the purpose of this paper is to examine the antecedents and consequences of users’ excessive online social gaming. Specifically, the authors develop a model to propose that observational learning and reinforcement learning mechanisms together determine excessive online social gaming, which further foster adverse consequences.

The model is empirically validated by a longitudinal survey among users of a popular online social game: Arena of Valor. The empirical data are analyzed using component-based structural equation modeling approach.

The empirical results offer two key findings. First, excessive online social gaming is determined by observational learning factors, i.e. social frequency and social norm, and reinforcement learning factors, i.e. perceived enjoyment and perceived escapism. Second, excessive online social gaming leads to three categories of adverse consequences: technology-family conflict, technology-work conflict and technology-person conflict. Meanwhile, technology-family conflict and technology-work conflict further foster technology-person conflict.

This study contributes to the literature by developing a nomological framework of excessive online social gaming and by extending the social learning theory to excessive technology use.

A web site usually contains a large number of concept entities, each consisting of one or more web pages connected by hyperlinks. In order to discover these concept entities for more expressive web site queries and other applications, the web unit mining problem has been proposed. Web unit mining aims to determine web pages that constitute a concept entity and classify concept entities into categories. Nevertheless, the performance of an existing web unit mining algorithm, iWUM, suffers as it may create more than one web unit (incomplete web units) from a single concept entity. This paper presents two methods to solve this problem. The first method introduces a more effective web fragment construction method so as reduce later classification errors. The second method incorporates site‐specific knowledge to discover and handle incomplete web units. Experiments show that incomplete web units can be removed and overall accuracy has been significantly improved, especially on the precision and F1 measures.