Game engagement, as one of the most fundamental objec- tives for game designers to achieve, has become an attractive industrial and academic topic. An important direction in this area is to construct a model to predict how long a player could be engaged with a game. This paper introduces a pure data driven method to foresee whether a player will quit the game given their previous activity within the game, by constructing decision trees from historical gameplay data of previous players. The method will be assessed on two popular commercial online games: I Am Playr and Lyroke. The former is a football game while the latter is a music game. The results indicate that the decision tree built by our method is valuable to predict the players’ disengagement and that its human-readable form allow us to search out further reasons about what in-game events made them quit.
Game Intelligence is knowledge gained by the player or by analysing the data players generate by playing digital games. Serious games for education, raising public awarenesss or changing the players' behaviour are well established and have provided Game Intelligence for decades. However, more recently a trend has begun, inspired by the success of FoldIt, of developing games for scientific discovery. These games lower the barrier of entry to complex scientific topics, allowing garners to contribute to cutting edge research. We argue that this approach is currently underutilized and explore a vision where these games have wider impact. Furthermore, we will discuss the potential of extracting Game Intelligence from games designed originally for entertainment, potentially making all games into scientific discovery games.
Diffusion of multi-generational high-technology products
Previous multi-generational product diffusion (MGPD) models were developed based on the diffusion patterns at that time, but may not be adopted in today's cases. By incorporating the effect of customers' forward-looking behaviour, this paper offers a parsimonious and original model that captures the dynamics of MGPD in current high-technology markets. We empirically examine the feasibility of using previous MGPD models and our suggested model to explain the market growth of new products from high-technology industries. The results show that the new model exhibits better curve fitting and forecasting performance than the prior MGPD models in the cases of this study. For marketing researchers, our model and its results suggest customers' forward looking behaviour is perhaps one of the key sales affecting factors that are missing in previous MGPD models in explaining nowadays' cases. For marketing practitioners, this study offers a valuable tool for marketing strategies in high-tech industries.