Predictors Of Problematic Internet Use Research Articles

Loneliness, Depression, and Computer Self-Efficacy as Predictors of Problematic Internet Use

This study investigated whether university students' levels of loneliness, depression, and computer self-efficacy were significant predictors of their problematic Internet use levels. The study was carried out with 559 Turkish university students. The research data were analyzed by multiple regression analysis. The findings indicated that loneliness, depression, and computer self-efficacy were significant predictors of problematic Internet use. Loneliness was found as the most important predictive variable. Depression predicted problematic Internet use on the second rank, and computer self-efficacy on the third rank.

Problematic Internet Use among Information Technology Workers in South Africa

This exploratory study looks at the prevalence and correlates of problematic Internet use (PIU) in South African technology workers (N = 630) and other workers (N = 769). The results indicated that the prevalence of PIU in this sample was higher in the IT group (3.81%) than the non-IT group (1.91%), both considerably lower than in other countries. In both groups, respondents were more likely to have higher PIU scores if they were younger and male, if they spent a large amount of time online, and if they used the interactive functions of the Internet. The best predictors of PIU in both groups were online procrastination, online flow experiences, spending a long period of time online in a single session, and chatting online.

Predictors of Problematic Internet Use on Turkish University Students

The current study set out to examine the relationships between the levels of problematic Internet use and gender, having any addiction problems, the level of psychological symptoms, and some online experiences related to the Internet use of university students. Data was gathered from 437 university students. The results indicated that factors such as gender, level of psychological symptoms, connecting to the Internet most often at night, and using the Internet primarily to communicate with their relatives and friends and to have a good time predicted significantly the level of the problematic Internet use of the students. Moreover, the predictors differentiated in terms of the domains of the problematic Internet use.