5. Discussion and conclusions

In order to understand Internet addiction from a multi-dimensional perspective and to explore the factor structure of its measurement instrument, the current study sought to explore the factor structure of Young’s Internet Addiction Test (IAT) using a confirmatory approach. The results from factor analyses show that the IAT can be characterized as exploring three dimensions of Internet behavior: Withdrawal and Social Problems, Time Management and Performance Effects, and Reality Substitution.

Widyanto and McMurran (2004) have previously proposed a six-factor structure for the IAT. Their six factors were salience, excess use, neglecting work, anticipation, lack of self-control, and neglecting social life. The results of this study suggest that a three-factor structure is a satisfactory representation of the IAT instrument. Comparing the two factor structures, the “Withdrawal and Social Problems” dimension proposed here contains items that loaded on Widyanto and McMurran’s (2004) “salience” and “neglecting social life” factors; the “Time Management and Performance” dimension resembles their “lack of self-control” and “neglecting work” factors; while the “Reality Substitute” factor does not have any correspondence. This difference in factor structure may be caused by sampling different groups of people in the respective studies. Widyanto and McMurran (2004) recruited a sample of 86 participants with diverse backgrounds and unknown nationalities through the Internet, whereas our sample consisted of 410 Chinese university students. Thus, these two samples have different demographic and cultural backgrounds. But this is just a speculation as we have got only two samples to compare. For any assessment instruments, it is important to test whether the underlying dimensions are invariant across different samples because this allows the comparison among groups (Byrne, 1989). In order to test for the stability of IAT dimensions, more studies should be conducted on people from different groups and cultures so that comparisons can be made.

Compared with the six factors identified by Widyanto and McMurran (2004), our results suggest that the symptoms for Internet addiction tend to cluster together more strongly in our sample. This information can be useful in understanding the interplay amongst various problem areas when dealing with Internet addiction. Our first factor, “Withdrawal and Social Problems”, actually comprises two blocks of items. The “Withdrawal” block is related to the DSM-IV’s set of substance dependence criteria, while the “Social Problems” block is related to its substance abuse set (American Psychiatric Association, 1994). These two building blocks have usually been developed as separate dimensions in other measurements of Internet addiction ([Cheng et al., 2003] and [Griffiths, 1998]). However, the results of the current study suggest that the two blocks of items load on a single factor, demonstrating the strong interplay between withdrawal symptoms and an individual’s interpersonal problems. This might be explained by Davis’s (2001) cognitive-behavioral model of pathological internet use. He found that people suffering from Internet addiction exhibit certain withdrawal symptoms (e.g., defensiveness, diminished impulse control) which can distress their interpersonal relationships. Although people notice that their Internet use behaviors are socially undesirable, they fail to control them, and the frustrations encountered in their offline social life can in turn lead to further withdrawal symptoms. As a result, “Withdrawal” and “Social Problems” can reinforce each other and maintain a vicious cycle. Despite the fact that these two blocks are related to different criteria sets in the DSM-IV, some studies have shown that the abuse and dependence criteria measure similar latent constructs (e.g., [Fulkerson et al., 1999], [Harrison et al., 1998] and [Lewinsohn et al., 1996]). This further supports the contention that “Withdrawal” and “Social Problems” are not easily separable due to their duality, and that it is reasonable to treat them as a single factor.

“Reality Substitute” is a particularly interesting dimension. This dimension reflects the phenomenon of some people using the Internet environment as a substitute and becoming addicted. This dimension is especially specific to Internet addiction because of the unique nature of the Internet. Many significant activities conducted in the real world – shopping, gambling, studying, social interaction, etc. – can be accomplished through the Internet where some less desirable aspects of offline interaction, such as the awkwardness of meeting new people, can be avoided. This might explain how people can become addicted to the Internet in a way that encourages them to, in some respects, live in a virtual world. Although “Reality Substitute” provides another direction for defining the diagnostic criteria for Internet addiction, its construct reliability is relatively low. To enhance the validity and diagnostic utility of this dimension, future research needs to identify a set of representative items for this dimension and validate them empirically.

In addition to measurement validation, this study has investigated how the dimensions of the modified IAT instrument relate to several other variables. It was found that Internet experience was not related to any Internet addiction dimension, while Internet usage was related only with the “Reality Substitute” dimension, and then only weakly. This result is consistent with the suggestion that it is not appropriate to use solely the amount of time spent online as the criterion for identifying Internet addicts, because people may use the Internet for different purposes (Hansen, 2002). As the functions of the Internet and Web are enhanced continuously, people are spending more and more time online to perform productive tasks. Thus, in the long run, it may be necessary to review the criteria used to judge the extent of Internet addiction.

Prior studies have shown that the Internet can distract students from their work ([Chou, 2001], [Chou and Hsiao, 2000], [Hur, 2006], [Scherer, 1997], [Tsai and Lin, 2003] and [Young, 1998b]). In keeping with these findings, the results of the present study also show a significant negative relationship between academic performance and the three dimensions of Internet addiction. This suggests that disrupted academic performance is one of the obvious problems related to Internet addictive behavior.

The results show that people who frequently participate in cyberrelationships and online gambling have relatively higher Internet addiction scores in general. They display more withdrawal symptoms and experience greater social problems, as compared with those who prefer other kinds of Internet activity. These results seem to provide some support for the view that Internet addicts are actually dependent on rewards associated with the Internet use that could also exist offline (Yellowlees & Marks, 2007).

Addictive behaviors are especially serious for people involved in cyberrelationships. This group tends to substitute the real world with the online environment and thinks that life without the Internet is empty and joyless. Consistent with the results of previous studies ([Amichai-Hamburger and Ben-Artzi, 2003], [Caplan, 2002], [Li and Chung, 2006] and [Lin and Tsai, 2002]), these results indicate that the social support offered by a cyberrelationship can lead to more severe addictive behavior. The anonymity of online communication helps ensure that people who seek social contact from the Internet are not necessarily subject to any social consequences in real-life: if an individual offends someone on the Internet, he/she can simply change online identities and start another relationship. Although this clearly might help people fulfill interpersonal needs, heavy reliance on it can make them fail in offline social encounters. For example, people who get used to the virtual context may find it difficult to get along with others without the anonymity of online social interactions, because they can no longer change their identities when they face an unsatisfactory relationship. Feeling frustrated in real-life social contacts, they might prefer to turn back to cyberrelationships and treat them as a substitute.

Gambling is another activity which plays an important role in the development of Internet addiction. This raises the possibility of escalated addiction due to the interaction between pathological gambling and Internet addiction. Gambling in itself is a challenging yet rewarding activity, as it provides people a sense of mastery by requiring them to master a changing bet outcome. The Internet offers gambling opportunities without time or geographical restrictions, and this may result in higher levels of both gambling and Internet addiction. With the growing number of online casinos, people are now facing ample gambling opportunities. Thus, future research should pay attention to the interplay between pathological gambling and the Internet as this may help to inform the proper and early treatment.

Results of this study should be interpreted in the context of its limitations. First, the data in our study were collected from Chinese students and thus the results may not be generalized to the Internet users from other groups and cultures. However, since students represent one of the groups that are vulnerable to both substance and non-substance addictions (Pallanti et al., 2006), they may be at high risk for developing problematic Internet use ([Pallanti et al., 2006] and [Yellowlees and Marks, 2007]). Along with the fact that the IAT has been used in the research on Internet addiction targeting students (e.g., [Pallanti et al., 2006] and [Yang et al., 2005]), it is practical to understand the psychometric properties of the IAT for this group. Second, we recognize that the data used in this study were cross-sectional, with the level of Internet addiction being measured at one point rather than as it was emerging. The development of addictive behavior is an ongoing process whose proper delineation requires a time dimension. Third, this study focused on the relationship between Internet addiction and the criterion variables without addressing the possibility that these variables might influence one another as the addictive behavior develops. Finally, collecting the questionnaires directly from the students might affect the participants’ disclosure of some sensitive information. Thus, a social desirability scale may be added to the questionnaire in the future studies to check this aspect of the response.

Despite these limitations, the implications drawn from the results extend the understanding of Internet-related addictive behavior and provide a good basis for future research. More studies on the structure of Internet addiction can enhance our understanding of the phenomenon and the characteristics of the measurement instruments. Future studies of a similar nature can be conducted using different groups of people so that the validity and the reliability of IAT can be evaluated. Moreover, the reality-substitute effect of the Internet is an area worth of further investigation.

References

_{American Psychiatric Association, 1994 American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (DSM-IV). (4th ed.) Washington, DC: American Psychiatric Association.}

Amichai-Hamburger and Ben-Artzi, 2003 Y. Amichai-Hamburger and E. Ben-Artzi, Loneliness and Internet use, Computers in Human Behavior 19 (1) (2003), pp. 71–80.

Anderson and Gerbing, 1988 J.C. Anderson and D.W. Gerbing, Structural equation modeling in practice: A review and recommended two-step approach, Psychological Bulletin 103 (3) (1988), pp. 411–423.

Bollen, 1989 K.A. Bollen, Structural equations with latent variables, Wiley, New York (1989).

Brenner, 1997 V. Brenner, Psychology of computer use 47: Parameters of Internet use, abuse and addiction: The first 90 days of the Internet usage survey, Psychological Reports 80 (3) (1997), pp. 879–882.

Byrne, 1989 B.M. Byrne, Multigroup comparisons and the assumption of equivalent construct validity across group: Methodological and substantive issues, Multivariate Behavioral Research 24 (1989), pp. 503–523.

Caplan, 2002 S.E. Caplan, Problematic Internet use and psychosocial well-being: Development of a theory-based cognitive-behavioral measurement instrument, Computers in Human Behavior 18 (5) (2002), pp. 553–575.

Ceyhan et al., 2007 E. Ceyhan, A.A. Ceyhan and A. Gu^rcan, The validity and reliability of the Problematic Internet Usage Scale, Educational Science: Theory and Practice 7 (1) (2007), pp. 411–416.

Chang et al., 2004 C.J. Chang, G. Torkzadeh and G. Dhillon, Re-examining the measurement models of success for Internet commerce, Information and Management 41 (5) (2004), pp. 577–584.

Chen et al., 2004 K. Chen, M. Tarn and B.T. Han, Internet dependency: Its impact on online behavioral patterns in e-commerce, Human Systems Management 23 (2004), pp. 49–58.

Cheng et al., 2003 S.H. Cheng, L.J. Weng, Y.J. Su, H.M. Wu and P.F. Yang, Development of a Chinese Internet addiction scale and it’s psychometric study, Chinese Journal of Psychology 45 (3) (2003), pp. 279–294.

Chou, 2001 C. Chou, Internet heavy use and addiction among Taiwanese college students: An online interview study, Cyberpsychology and Behavior 4 (5) (2001), pp. 573–585.

Chou and Hsiao, 2000 C. Chou and M.C. Hsiao, Internet addiction, usage, gratification, and pleasure experience: The Taiwan college students’ case, Computers and Education 35 (1) (2000), pp. 65–80.

Chou et al., 2005 C. Chou, L. Condron and J.C. Belland, A review of the research on Internet addiction, Educational Psychology Review 17 (4) (2005), pp. 363–388.

Davis, 2001 R.A. Davis, A cognitive-behavioral model of pathological Internet use, Computers in Human Behavior 17 (2) (2001), pp. 187–195.

Davis et al., 2002 R.A. Davis, G.L. Flett and A. Besser, Validation of a new scale for measuring problematic Internet use: Implications for pre-employment screening, Cyberpsychology and Behavior 5 (4) (2002), pp. 331–345.

Ferraro et al., 2007 G. Ferraro, B. Caci, A. D’Amico and M. Di Blasi, Internet addiction disorder: An Italian study, Cyberpsychology and Behavior 10 (2) (2007), pp. 170–175.

Floyd and Widaman, 1995 F.J. Floyd and K.F. Widaman, Factor analysis in the development and refinement of clinical assessment instruments, Psychological Assessment 7 (3) (1995), pp. 286–299.

Fulkerson et al., 1999 J.A. Fulkerson, P.A. Harrison and T.J. Beebe, DSM-IV substance abuse and dependence: Are there really two dimensions of substance use disorders in adolescents?, Addiction 94 (4) (1999), pp. 495–506.

Goldberg, 1995 Goldberg, I. (1995). Internet Addiction Disorder (IAD) – Diagnostic criteria. Retrieved March 30, 2007 from

Griffiths, 1998 M. Griffiths, Internet addiction: Does it really exist?. In: J. Gackenbach, Editor, Psychology and the Internet: Intrapersonal, interpersonal, and transpersonal implications, Academic Press, San Diego, CA (1998), pp. 61–75.

Griffiths, 2000 M. Griffiths, Does Internet and computer “Addiction” exist? Some case study evidence, Cyberpsychology and Behavior 3 (2) (2000), pp. 211–218.

Ha et al., 2006 J.H. Ha, H.J. Yoo, I.H. Cho, B. Chin, D. Shin and J.H. Kim, Psychiatric comorbidity assessed in Korean children and adolescents who screen positive for Internet addiction, Journal of Clinical Psychiatry 67 (5) (2006), pp. 821–826.

Hair et al., 2006 J.R.J. Hair, R. Anderson, R. Tatham, W. Black and B. Babin, Multivariate data analysis (6th ed.), Prentice Hall, New Jersey (2006).

Hansen, 2002 S. Hansen, Excessive Internet usage or “Internet addiction? The implications of diagnostic categories for student users, Journal of Computer Assisted Learning 18 (2002), pp. 235–236.

Harrison et al., 1998 P.A. Harrison, J.A. Fulkerson and T.J. Beebe, DSM-IV substance use disorder criteria for adolescents: A critical examination based on a statewide school survey, American Journal of Psychiatry 155 (4) (1998), pp. 486–492.

Man Kit Chang and Sally Pui Man Law

Department of Finance and Decision Sciences, School of Business Hong Kong Baptist University, Kowloon Tong, Kowloon, Hong Kong

doi:10.1016/j.chb.2008.03.001
Factor structure for Young’s Internet Addiction
Available online 14 April 2008