Predicting adolescent Internet addiction: The roles of demographics, technology accessibility, unwillingness to communicate and sought Internet gratifications
Introduction
Due to the continuous development of the Internet infrastructure, the Internet has already penetrated deep into our lives. The Internet has various positive effects on human life, such as the expansion of social networks (Hampton and Wellman, 2003, Katz and Aspden, 1997), the promotion of psychological wellbeing (Chen et al., 2002, Kang, 2007), and the betterment of living conditions (Bauer, Gai, Kim, Muth, & Wildman, 2002). However, excessive use of the Internet also has negative implications for human wellbeing. Excessive Internet use may lead to reduced social interaction with friends and family, loneliness, alienation, disputes in social relationships, degradation of academic performance, and even mental illness (Chen and Peng, 2008, Chou, 2001, Leung, 2004, Nalwa and Anand, 2003, Shapira et al., 2000, Suhail and Bargees, 2006, Young, 2004). Excessive Internet use might also result in Internet addiction (Kim & Haridakis, 2009).
The research examining Internet addiction is as old as the earliest forms of the Internet itself; however, as yet there is no agreement on appropriate terminology to describe the condition (Kim & Haridakis, 2009). It has therefore become difficult to predict if any psychopathological state is associated with this phenomenon (Shaffer, 2004). Various terminologies have been utilized to date, including Internet addiction (IA) (Ghassemzadeh et al., 2008, Young, 1998), Internet dependence (Lu, 2008), problematic Internet use (Caplan, 2002), pathological Internet use (Davis, 2001), and compulsive Internet use (Greenfield, 1999, Meerkerk et al., 2009). However, clarification of the exact boundaries between these interrelated concepts is currently lacking (Kim & Haridakis, 2009). For consistency reasons, we utilize the term “IA” throughout this study to represent a pathological state that occurs due to Internet abuse and overuse.
Prior IA literature has shed some light on the possible reasons behind IA conditioning among Internet users. These include an internal human need to communicate (Ball-Rokeach, 1985, Rubin and Windahl, 1986), information sharing, or escape from real-life problems (Kim & Haridakis, 2009). However, questions remain unanswered such as “Why isn’t every Internet user an addict?” “What kinds of Internet users are more vulnerable to IA?” and “Why do some Internet users learn to moderate their own Internet use, while others fail to do so?” According to Kim and Haridakis (2009), an extreme affinity for the Internet does not mean that a particular individual is addicted to it. Similarly, Kubey, Lavin, and Barrows (2001) argued that spending a great deal of personal time on the Internet, or heavily using the Internet does not necessarily result in IA symptoms. But, at the same time, prior research has also concluded that Internet addicts are much more excessively engaged in Internet use than those who are only dependent or excessive Internet users (Kubey et al., 2001). This has led to the examination of various differences between Internet addicts and non-addicts, e.g. significant differences in their sought Internet gratifications and background characteristics (Chou and Hsiao, 2000, Leung, 2004, Yang and Tung, 2007). However, these examinations do not provide a complete picture of the relationships shared among IA, Internet gratifications and Internet users’ background characteristics simply due to the limited focus of the prior work (Kim & Haridakis, 2009).
To bridge these gaps in the existing IA literature, the present study examines the relationships among IA, demographic profile, technology accessibility status, personality attributes and Internet gratifications. In addition to this, the predictive powers of these study variables were assessed with respect to IA and the dichotomization of Internet addict and non-addict cohorts. A cross-sectional survey was administered to 1914 adolescent Internet users from India. There were a number of reasons behind choosing India and specifically adolescent Internet users, namely (1) India has a rapidly developing economy, and currently hosts the world’s second largest Internet user base (India Internet usage, 2013). The consumer base of over 400 million people makes India a lucrative market for Internet based companies (Ranchhod & Gurau, 2014); (2) Despite the fact that the Indian Internet market has witnessed a 566.4% increase in Internet adoption and use (Asia Internet Usage, 2013), the Indian population has largely remained understudied with respect to the gratifications underlying their Internet use (Roy, 2009); (3) Almost all of the available literature concerning IA and Internet U&G (except for Leung (2014)) consists of either college students (Kim & Haridakis, 2009) or a wide age range of Internet users (Roy, 2009). Therefore, the present study specifically focuses on the adolescent user group.
Section snippets
Background literature
The Uses and Gratifications (U&G) theoretical framework has been extensively used to understand the role of Internet U&Gs in predicting IA in the prior literature (Kim and Haridakis, 2009, Leung, 2004, Leung, 2014, Song et al., 2004). According to the U&G theory, individuals have different social and psychological needs that are satisfied through media use (Dimmick, Sikand, & Patterson, 1994; Lin, 1999; Rubin, 1983). These needs actually drive their motivation to utilize a given media platform (
Research questions
RQ1 How is Internet addiction among adolescents related to their background characteristics (demographic profile, technology accessibility status, and unwillingness to communicate attitudes)? How do Internet addicts and non-addicts differ in terms of their background characteristics? RQ2 How is Internet addiction among adolescents related to the gratifications they seek from the Internet? How do Internet addicts and non-addicts differ in their Internet U&Gs? RQ3 How can adolescents’ background
Participants and sampling procedure
A total of 25 schools were randomly drawn from an online directory and were contacted by an email and/or phone call. The schools were typical private schools in India where English is used as the medium of instruction and communication. All contacted schools cater to students from low to middle income groups. The school principal or the management representatives were informed of the study objectives, research questions and related process. Afterwards a face-to-face meeting was organized with
Relationship between IAT and demographics
The Pearson correlation analysis results reveal that the IAT scores shared a very weak correlation with age (r = .06, p < .01), a weak positive correlation with parents’ attitude towards Internet use (r = .17, p < .01), a weak negative correlation with academic performance (r = −.22, p < .01), and a very weak correlation with CAP (r = −.08, p < .01). No significant relationship was found between IAT scores and family monthly income. An independent t-test revealed significant gender differences (t = 13.724, p <
Discussion
The main aim of the present study is to examine the missing relationships among IA, adolescents’ background characteristics, and Internet U&Gs. The main contributions of the present study are: First, the prior IA and Internet U&G literature was reviewed, and the gaps in the existing literature were outlined, e.g. utilization of heterogeneous Internet gratification constructs, missing conceptual and theoretical links depicting the relationships among IA, Internet U&Gs, and Internet users’
Study implications
The present study has several practical and theoretical implications for Internet U&G and IA research. These include: (1) the present study bridges some existing gaps in the IA literature by providing the missing conceptual links between IA and Internet U&Gs, and IA and adolescents’ background characteristics. Furthermore, the present study outlines the differences between addicts and non-addicts in terms of background characteristics and sought Internet U&Gs; (2) The present study points out
Study limitations and future work
In this section, a number of limitations of the present study are presented since these also open up avenues for future research on Internet U&Gs. First, the present study utilizes an arbitrary cut-off score of 70 or above, as provided by previous IA research, to discriminate IA addicts from non-addicts. This arbitrary score might not be able to successfully dichotomize addict and non-addict cohorts, even though the present study results show clear differences between the two cohorts in terms
Acknowledgement
We acknowledge the support received from the Academy of Finland, Mind the Gap (Project Number 1265528), Researcher’s mobility grant to Taiwan (Decision No. 265969) and South Africa (Decision No. 277571), Teknillisen korkeakoulun tukisäätiö, TEKES funded research projects Data to Intelligence (D2I) (Project No 21143201) and Mobile Financial Services (MoFS) (Project No 211440). Additionally, we would like to acknowledge the support received from the Ministry of Science and Technology, Taiwan,
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