The internet use has grown exponentially during the past two decades. The development of the internet has brought convenience but also problems such as internet addiction (IA). Young (1998) defined IA as losing control over the time spent on the internet . IA could potentially impair academic and occupational function as well as family life (Gao et al., 2010; Young, 1999). Although there have been few worldwide studies on the prevalence rate of IA, studies conducted in different geographic regions suggest that it is a severe problem shared by many countries (the China Youth Internet Association, 2005; Goel et al, 2013; Greenfield, 1999). Furthermore, IA is associated with an increased rate of mental health problems. People with heavy IA, such as pathological heavy online game consumers, are likely to develop psychiatric disorders such as depression, social anxiety, and somatic symptoms (Wei et al., 2012).
The use of internet-based entertainment differs by gender and geographic region. In the United States, male internet users spend more time on pornography, and females usually spend time on online shopping (Young, 2013). In Europe, male internet users usually play single-player online games, while females spend most of their time on social media (Durkee et al., 2012). In Korea, 56.3% of males play online multiplayer games (Ha and Hwang, 2014). In Hong Kong, 30% of participants play online games (Wang et al., 2015). Similar to other addictive behaviours, people engage in online entertainment for different reasons. Shiffman and Rathbun (2011) found that people use smoking as a way to cope with negative affect . IA has a clinical presentation similar to that of substance abuse and behavioural abuse, such as withdrawal symptoms, tolerance, and craving . It is worth noting the difference between males and females in addictive behaviours. For example, women were more likely than men to use smoking to cope with negative affect, and a more recent study suggested that men are more likely to report somatic symptoms such as “ill, in pain, or uncomfortable” as triggers for using drugs (Kennedy et al, 2012).
Previous studies suggested that the severity of IA is positively correlated with the amount of time individuals spend on the internet (Rooij et al; Schoenmakers et al, 2010). However, currently, as technology use skyrockets, internet-based entertainment has become more accessible through smartphones, tablets, and other portable gaming devices (e.g., Nintendo Switch). Consequently, it is more difficult to assess the amount of time and the frequency of use in the traditional manner [5, 6]. In a more recent study, Li and colleagues (2015) found that sustained time spent on online entertainment tends to increase as IA severity increases . The duration of sustained online entertainment for each episode of internet use might be a better indicator to measure the amount of time of internet use.
Existing studies on risk factors for IA were conducted under the biopsychosocial model. Under this model, the family environment was considered one of the most important social factors. Single-parent, frequent migration, left-behind by parents during childhood, and deceased parents were the most common risk factors (Guo et al; Chen et al, 2012). Boys who experience negative life events are more susceptible to IA . In addition to the family environment, interpersonal interaction with peers has also been studied. As a platform for sharing similar activities, the internet is a place where adolescents socialize with each other. As a result, the influence of friends could exacerbate adolescents’ dependence on the internet [9, 10].
Previous studies have suggested that biological factors, social environment factors (e.g. family, friends, etc.), and other psychological conditions (e.g., depression and social anxiety) together impact individuals’ internet use behaviour . Positive emotions such as happiness, relaxation, confidence, and the sense of achievement that people receive from online activities could serve as moderators that exacerbate IA . The findings are mixed on the effect of socioeconomic status (SES) and geographic location on IA. Family SES was found to be a predictor of IA. Specifically, children from higher SES families were more vulnerable to IA [13–15]. Parents’ unemployment status and low education levels were also found to be associated with a higher risk of IA (Petry et al, 2014; American Society of Addiction Medicine, 2015). However, findings are inconsistent, and other studies suggested that parents’ education level and family socioeconomic status had no impact on children’s IA (Ghamari et al, 2011; Tang et al, 2014).
The current study aims to explore social risk factors for IA in college undergraduate students and to provide evidence for interventions and early prevention that target this specific population.