Motivations: The Bigger Picture
The best predictors of problematic usage were explored using a multiple regression with gender, age, hours per week and the 10 subcomponents as factors. A survey scale for problematic usage was developed using the following items (on a fully-labeled 5-point scale using construct-specific response options). These items are based on Ian Danforth's work that teased apart the Engagement factor from the Addiction factor.
• Do you spend more time than you think you should playing the game?
A principal components analysis revealed a single factor with an eigenvalue greater than 1 that accounted for 47% of the overall variance. All items loaded onto this factor with a factor loading between .52 and .79.
The multiple regression was significant at p < .001 with an adjusted R2 of .33 (a good model with strong predictors). The best predictor of problematic usage was the escapism subcomponent (Beta = .31, p < .001), followed by hours played per week (Beta = .27, p < .001) and then the advancement subcomponent (Beta = .18, p < .001).
The results of this multiple regression are interesting in that it shows the escapism subcomponent to be the best predictor of problematic usage. In other words, it is the players who use the online environment as an escape from RL problems that are most likely to develop problematic usage patterns. This is in contrast with claims that it is something inherent in online games that cause addiction and problematic usage. Now, the advancement subcomponent is also a good predictor but not as strong as the escapism subcomponent. The data show that the primary cause of problematic usage are pre-existing RL problems rather than something inherent to online games, and that the game mechanics (often claimed to be strongly addictive) are in fact weaker predictors of problematic usage than the state-of-mind of the player.
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