Computers in Human Behavior

2. Prior research

Over the years, researchers have devised different kinds of measurements to operationalize the concept of Internet addiction and related concepts. Some of these measurements were built upon the ideas of mental disorder, with items adapted from the diagnostic criteria of DSM-IV (American Psychiatric Association, 1994), whereas others were based on certain theoretical perspectives like the cognitive-behavioral model (Davis, 2001). Besides, there are others which were developed from case studies, experts’ opinions or published literature on the symptoms of Internet addiction.

Most assessment tools developed in the early stages were based on the behavioral criteria for substance abuse or substance dependence in the DSM-IV. They were usually designed as a set of diagnostic criteria or checklists to describe the phenomenon of Internet addiction and distinguish people having Internet-related addictive behaviors (Chou, Condron, & Belland, 2005). Some of these examples include Goldberg’s (1995) Internet addiction disorder diagnostic criteria (IADDC), Brenner’s (1997) Internet-Related Addictive Behavior Inventory (IRABI), and Scherer’s (1997) Clinical Symptoms of Internet Dependency (CSID).

The notion of impulse-control disorder and pathological gambling is also a foundation upon which the measurements were built. For example, borrowing from the DSM-IV criteria for pathological gambling, Young (1998b) developed the Diagnostic Questionnaire (YDQ) to survey the world-wide prevalence of Internet addiction and distinguish Internet addicts from other Internet users. In a later study, Young (1998a) expanded her YDQ and constructed a Likert scale assessment called the Internet Addiction Test (IAT). The IAT scale comprises 20 items which assess the severity of any negative consequences arising from excessive Internet use. These items cover an individual’s Internet use habits, his/her thoughts about the Internet as well as the related problems of Internet use. For each item, a graded response (1 = “not at all” to 5 = “always”) can be selected and the higher summed item scores represent higher levels of Internet addiction.

Problematic Internet Use Questionnaire (PIUQ) was developed on the basis similar to that of the IAT. According to Thatcher and Goolam (2005b), the measurement items of PIUQ were derived from the pathological gambling questionnaire (Lesieur & Blume, 1987), Young’s (1996) criteria for Internet addiction as well as published literature on the symptoms of Internet addiction. Validating the PIUQ on a sample of online respondents, Thatcher and Goolam (2005b) suggested that this instrument could measure Internet addiction from three dimensions, namely, online preoccupation, adverse effects, and social interactions.

Some researchers attempted to design the measurements rooted in theoretical perspectives other than the DSM-IV criteria. For example, based on the cognitive-behavioral model (Davis, 2001) derived from psychopathology, Davis, Flett, and Besser (2002) constructed the Online Cognitive Scale (OCS) to measure pathological Internet use. They performed confirmatory factor analysis on a sample of university students and identified four dimensions (diminished impulse control; loneliness/depression; social comfort; distraction) for the OCS. Also based on the cognitive-behavioral approach, Caplan (2002) developed the Generalized Problematic Internet Use Scale (GPIUS) and discovered seven dimensions (mood alteration; social benefits; negative outcomes; compulsive use; excessive time online; withdrawal; social control) for this instrument.

Moreover, there are some measurements that were developed from case studies, experts’ opinions or published literature on Internet addiction, such as Griffiths’s (1998) Addiction Components Criteria, and the Pathological Use Scale (PUS) devised by Morahan-Martin and Schumacher (2000). Instruments along this vein also include those developed from a factor-analytic approach. For example, using the published literature on the common diagnostic criteria for Internet addiction, Lin and Tsai (2002) designed the Internet addiction scale for Taiwanese high school students (IAST) and have found four dimensions (tolerance; compulsive use and withdrawal; family, school and health problems; interpersonal and financial problems) for this instrument. Besides, based on the items derived from experts’ opinions, Ceyhan, Ceyhan, and Gu^rcan (2007) generated the Problematic Internet Usage Scale (PIUS) to assess problematic Internet use for university students. From their results of exploratory factor analysis, they identified three dimensions (negative consequences; social benefit/social comfort; excessive use) for the PIUS.

Although the above-mentioned instruments were developed from different theoretical perspectives and operationalization procedures, our analysis of the literature has revealed certain similarities among the identified dimensions of Internet addiction. Referring to the instruments that measure Internet addiction as a multi-faceted construct – OCS (Davis et al., 2002); GPIUS (Caplan, 2002); IAST (Lin & Tsai, 2002); PIUQ (Thatcher & Goolam, 2005b); PIUS (Ceyhan et al., 2007), the following dimensions are found similar across these instruments:

• Compulsive Internet use and excessive time spent online: extent of compulsive Internet use and failure to control the amount of time spent on the Internet.

• Withdrawal symptoms: feelings of difficulties, depression or moodiness when being restricted from Internet use.

• Using the Internet for social comfort: using the Internet to seek social comfort and disposition toward using online social interaction to replace real-life interpersonal activities.

• Negative consequences related to Internet use: the negative outcomes such as social, academic, or work-related problems resulting from Internet use.

The foregoing discussion indicates that recent instrument developments have explored the multi-dimensional nature of Internet addiction and related constructs. Although the dimensionality of IAT has not been assessed when it was developed, Widyanto and McMurran’s (2004) study has extracted six factors – salience, excess use, neglecting work, anticipation, lack of self-control, and neglecting social life – from the 20-item IAT and found that these factors had moderate to good internal consistency (Cronbach’s alphas ranged from .54 to.82). While this study has provided some insights into the structure of IAT, one limitation of the study was the small sample size it utilized, as only 86 participants were recruited through the Internet to fill out the Web-based questionnaire. As mentioned, knowing the dimensions of a measurement instrument is important; the current study, thus, attempted to further investigate the factorial structure of IAT and examine how the dimensions may correlate with a number of criterion variables.

Some criterion variables that have been widely studied include the amount of time people spend online, their experience of using the Internet, the negative impacts of Internet addiction on their performances, and the differences in the severity of addictive behavior between genders, or among people involved in different types of Internet activities.

Internet usage has been regarded as an important indicator of Internet addiction, with many studies demonstrating a correlation between the amount of time spent online and the risk of having addictive behaviors ([Brenner, 1997], [Chou and Hsiao, 2000], [LaRose et al., 2003], [Leung, 2004], [Liang, 2003], [Lin and Tsai, 2002], [Morahan-Martin and Schumacher, 2000], [Suhail and Bargees, 2006], [Thatcher and Goolam, 2005a], [Wang, 2001] and [Young, 1998b]). Yet, researchers have argued that the amount of time spent online may not be a sufficient condition to determine Internet addiction as people can use the Internet for different purposes (Hansen, 2002). For example, the extent of addictive behavior could be quite different for people using the Internet for work purposes and for those using it for personal entertainment (Widyanto & McMurran, 2004).

Prior studies have also investigated whether individuals’ addictive behaviors will fade out when they become more experienced in using the Internet. However, the findings are still inconclusive. While some researchers have found that beginners are more likely to get addicted to the Internet than experienced users ([Kraut et al., 1998], [Widyanto and McMurran, 2004] and [Young, 1998b]), others have shown that there is no difference between these two groups in terms of the severity of addictive behaviors ([Leung, 2004] and [Thatcher and Goolam, 2005a]).

Moreover, Internet addiction can interfere with one’s academic performance and daily life routines ([Chou and Hsiao, 2000], [Scherer, 1997] and [Yoo et al., 2004]). Prior studies have shown that students were vulnerable to Internet addiction and they might use the Internet excessively and ignore their schoolwork ([Chou, 2001], [Nalwa and Anand, 2003] and [Tsai and Lin, 2003]). In Young’s (1998b) study, she found that the student respondents encountered work or school-related problems because they had spent too much time on the Internet.

Gender difference is another area that has interested the researchers. Although many studies have investigated this issue, researchers cannot reach an agreement on which gender represents a high-risk group of having Internet-related addictive behaviors. While some studies have shown that Internet addicts tended to comprise females ([Leung, 2004] and [Young, 1998b]), other findings have indicated that males were more inclined to develop Internet addiction than females ([Chou and Hsiao, 2000], [Liang, 2003] and [Scherer, 1997]).

In addition, previous studies have demonstrated that people might not be addicted to the Internet itself but to particular Internet activities. Research findings showed that interactive functions of the Internet were related to the negative impacts of excessive Internet use and people involved in online interactive applications tended to exhibit addictive behaviors ([Davis et al., 2002], [Leung, 2004] and [Li and Chung, 2006]). For instance, Young (1998b) noticed that Internet addicts were attracted to the social support functions of the Internet. Moreover, Thatcher and Goolam (2005a) found that people belonging to the high-risk group of Internet addiction were inclined to play online interactive games and use Internet communication tools. They also found that online gambling was one of the favorite activities for the high-risk group.

In the next section, the methods employed in the current study to investigate the factorial structure of the IAT, and the correlations between the identified IAT dimensions and the above-discussed criterion variables are described.

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