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Achievement is considered to be an important value for students. The purpose of this paper is to explore what achievement values were derived from playing massively multiplayer online role-playing games (MMORPGs), especially for high-engaged MMORPGs players.

The research employed two approaches to collect data: focus group and a web survey of online game players were conducted. Moreover, an addiction-engagement scale was used to ensure all participants were genuinely highly engaged MMORPGs players in data collection process. There are 12 highly engaged players were recruited as focus group members in Phase 1. The online survey yielded 315 responses, of which 267 were considered valid, and 177 of those were considered to be genuine highly engaged players in Phase 2. In the study, exploratory factor analysis was performed to reveal underlying structure of achievement values.

The result focussing on popular MMORPGs reveal that highly engaged players derived six achievement values from playing MMORPGs, including fantasy satisfaction, adventure, victory, socialization ability, self-actualization, and advancement of wealth and status.

In view of the prevalence of MMORPGs, it is imperative to draw attention to understand students’ achievement values derived from playing MMORPGs. By doing that, educators know how to bridge students’ achievement values to their academic performance.

With the liberalization and internalization of financial markets, in terms of the entrance of the World Trade Organization, banks in Taiwan face pressures in service quality and administrative efficiency. Predicting customers’ intention to adopt Internet banking is an important issue. Attempts to understand how an individual's belief, embracing attitude, subjective norm and perceived behavioral control, can influence intention. Two versions of the model of the theory of planned behavior (TPB) – pure and decomposed – are examined and compared to the theory of reasoned action (TRA). Data are collected from approximately 425 respondents and structural equation modeling is used to analyze the responses. Results generally support TRA and TPB and provide a good fit to the data.

The cost of retaining a customer is lower than that of obtaining a new one, so potential customer defection is an important issue in the fiercely competitive environment of electronic commerce. Accordingly, this paper aims to present a new way for gauging customer loyalty and predicting their possibility of defection reference to a set of quality attributes satisfaction and three types of belief in the theory of planned behavior (TPB).

The performance of the classification utilization artificial neural networks (ANNs) was compared to that of traditional analytic tools, such as multiple discriminant analysis (MDA) and classificatory data mining technique – decision tree.

The analytical result represented that the predicted accuracy of ANNs is better then MDA and decision tree in both training and testing phases. Degree of repurchase intention has been classified correctly with a success rate of 83 percent using neural networks.

Like all research, this study has its limitations. One such limitation is that the predictive model was designed for application to online bookstores. A further limitation of this survey is that it reflects intentions instead of actual behavior. Finally, despite of ANNs has been applied to numerous areas and have demonstrated a degree of classification success, it is difficult to extract rules for explanation. Therefore, enhancing ability of model explanation would be a valuable work in the future.

The contribution of this paper is to predict how marketing practitioners can tactically market to customers with weak repurchase intentions to prevent defections.

The purpose of this paper is threefold. First, this study seeks to provide a framework for, and endeavor to gain, an in‐depth understanding of the decision‐making process in relation to playing or not playing massively multiplayer online role‐playing games (MMORPGs) by using qualitative data from interviewing both players and non‐players. Second, it seeks to establish a better measure and model using an ethnographic decision tree model that describes the decision‐making process of internet users. Finally, for cross‐validity purposes, it aims to verify the model using quantitative data from face‐to‐face surveys of players/non‐players in an online gaming context.

A two‐phase mixed method is adopted here. During the first phase, the decision tree development phase, to capture the concerns of internet users who are facing the choice of playing MMORPGs or not, qualitative‐based procedures are used to identify decision factors and organize them into a decision tree for better understanding a research problem. In the second phase, the decision tree verification phase, to test the predictive capability of the composite model, a quantitative‐based survey instrument, which represents the decision criteria identified in the first phase, is adopted for data collection and analysis.

The results revealed that 14 criteria, formulated as a decision tree, were identified in the model‐building phase. In the model‐testing phase, 137 decision processes were successfully predicted and 17 predictive pathways led to a decision against the factual, giving a predictive rate of 89 per cent.

The study bridges the gap between the research in online games and in decision making. At the theoretical level, the decision tree model can provide a more comprehensive and in‐depth explanation of the online games‐playing phenomenon. In practice, marketers can easily become aware of the user considerations and identify exactly what needs to be done to fulfil users' criteria for playing MMORPGs. And, from a methodological standpoint, a mixed method can help researchers improve techniques used in study for building more predictive models.