These results may contribute to a better understanding of the Final Fantasy XIV player groups and provide a basis for future work, as well as provide a case study on clustering techniques applied over a limited set of game data. Regarding the identified player profiles by the clustering process, the results suggested the presence of five different categories: Beginner, Casual, Dedicated, Hardcore and Intermittent, characterized according to their behavior within the game. The following seven clustering algorithms were used: The k-means (partitional clustering), WARD (hierarchical), DBSCAN (density-based), spectral-based, BANG (grid-based), SOM (model-based), and Fuzzy C-means (Fuzzy Clustering). Different clustering methods are compared in order to find out the best results in the context of this game, which presents a narrow range of data for analysis. Therefore, this work studies the various clustering techniques as a game data mining tool. In this context, the object of study of this research is Final Fantasy XIV, a very popular MMOG that provides a limited amount of open data and also has been little researched in the literature.

One of the challenges in the Game Analytics field is to determine the type of information that can be obtained from a specific MMOG, as well as which data mining technique to use or develop depending on the peculiarities of its database. Based on the categories established after the mapping and the review findings, we also discuss the limitations of game analytics and propose potential research points for future research. Finally, we summarize the research areas that have already been covered in literature but need further development. Fourth, we also list different algorithms that have been used in game analytics for prediction. Third, we present the problems or challenges in the game area, which can be addressed by using game analytics. Second, we find out the main purposes of using analytics in the game industry. First, we explore game analytics aspects in the available literature based on the traditional game value chain.

This research mainly studies and discusses five aspects. This paper provides a comprehensive literature review of BI used in the game industry, focusing primarily on game analytics. However, related research lacks systematic examination and precise classification. From a business perspective, development in the game industry has led to the application of Business Intelligence (BI) becoming more and more extensive. They can also accept problems associated with them when it comes to their intention to use them.Īs a business model, the essence of games is to provide a service to satisfy the player experience. Consumers trust them enough but notice the risks they carry. These findings reveal that virtual currencies are seen as useful and easy to use. Perceived trust predicts consumers’ perceived usefulness and perceived ease of use. The results showed that the best predictor of consumers’ intention to use virtual currencies is perceived usefulness, followed by perceived ease of use. The data set was analysed using SmartPLS 3 software. The study utilises an online survey to obtain data from 205 virtual currency consumers. The partial least square method of structural equation modelling is employed to test the proposed research model. The study tested and used an adapted TAM model in the context of virtual currencies using structural equation modelling. This article aims to examine how perceived usefulness, perceived ease of use, perceived risks, and perceived trust significantly impact consumers’ virtual currencies’ behaviour. Virtual worlds have created unique economies whose outcome is e-currencies, which link the electronic universe and reality.

Browserquest pvp area