CYBERPSYCHOLOGY & BEHAVIOR Volume 11, Number 6, 2008 © Mary Ann Liebert, Inc. DOI: 10.1089/cpb.2007.0223
Rapid Communication
Problematic Internet Use among Information Technology Workers in South Africa Andrew Thatcher, Ph.D., Gisela Wretschko, M.A., and James Fisher, Ph.D.
Abstract
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.
Introduction
M
OST STUDIES that have identified groups at risk of problematic Internet use (PIU) have focused on students1 or scholars.2,3 Students and scholars have been considered to be at high risk for PIU for these reasons: (a) Studies found that problematic Internet use was negatively correlated with age4 and positively with the recency of exposure to the Internet, although some studies suggest the opposite.3 (b) Schools and universities provide good access to the Internet, which is almost a prerequisite for PIU. (c) For many students, this is the first time they have moved out of their parents’ home and away from the parental control that might be exerted over Internet use. (d) Students and scholars are characterized as having more free leisure time to spend online and fewer formal commitments and pressures than older adults to keep them offline. (e) Student and scholar samples tend to be convenience samples. However, most general surveys of Internet users5 do not specifically identify students or scholars as being at greater risk. The best predictors of PIU are Internet usage behaviors such as time spent online, Internet functionalities (online gaming or online chatting),1 and personal factors such as personality or mood.5 Research in general populations suggests that the qualities of the Internet rather than the personal qualities of the individual are the greatest risk factors. In South Africa, access to the Inter-
net has grown by approximately 113% in the last 6 years, but the Internet penetration rate is still rather modest (12%) compared to industrially developed countries (70%).4 However, the Internet penetration rate is much higher among employed, skilled, and urban people. This study specifically targets employees in the information technology (IT) sector because they are highly skilled, largely urban, and likely to have access to the Internet at their place of work (and may even require the use of the Internet in executing their work duties). The most obvious correlate of PIU is the length of time a user spends online.1,3 The length of time spent online is particularly important to investigate with IT workers because some, if not all, of these workers’ jobs might entail being online at least some of the time. One must be careful not to equate PIU simply with length of time online in a sample of IT workers because they might spend a large proportion of their working day on the Internet in the productive pursuit of work duties. It is interesting, therefore, that one study using a sample from the general population has found that the length of time spent online is unrelated to PIU.5 Additionally, interactive functions such as online chatting, shopping, and gaming tend to cause more problems than noninteractive functions such as information seeking and e-mail.3 Finally, some individuals use the Internet to avoid certain stressful or demanding situations.6 In students and scholars, online procrastination was found to be strongly related to
Department of Psychology, University of the Witwatersrand, Johannesburg, South Africa.
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PIU,2,6 but no research has investigated whether the same is true of IT workers. Methods A survey posted on the Web site of a South African online information technology magazine for a period of 12 days yielded 1,399 responses. Once respondents completed the survey, they were directed to a Web page that provided information on PIU symptoms and contact details for a free counseling service for followup consultations. Responses consisted of an IT group (N 630) who identified themselves as information technology workers and a non-IT group (N 682) who identified themselves as from other occupations. The non-IT group contained more females (31% vs. 16%) and were likely to be slightly older. The non-IT group usually spent 5 days a week online, and the IT group, 7 days a week online. Both groups were dominated by males with at least a postsecondary school diploma, were likely to have been using the Internet for at least 5 years, would access the Internet from two locations (home and work), and would usually spend 5 hours or less online at a time. The survey consisted of a biographical section (9 items), an Internet usage section (6 items), the 20-item Problematic Internet Use Questionnaire (PIUQ),7 the 7-item distraction subscale from the Online Cognition Scale,6 and an adaptation of Webster et al.’s8 12-item Flow scale. The scales demonstrated good internal reliability ( 0.92, 0.89, and 0.74 respectively) and appropriate factorial validity with the full sample of 1,399 respondents. Results and Discussion Using preestablished cutoffs on the PIUQ,8 24 respondents (3.81%) in the IT group were at high risk of PIU and 231 respondents (36.67%) were at moderate risk. In the non-IT group, 13 respondents (1.91%) were at high risk of PIU and 255 respondents (37.39%) were at moderate risk. While these were not statistically different, the prevalence rate in South African IT workers is still substantially lower than elsewhere in the world,1 although double that of 1.67% obtained previously in South Africa.7 This suggests that PIU is not highly prevalent among the working population in South Africa. The groups were not significantly different on the PIUQ (t 1.98, p 0.01) or the distraction subscale (t 0.75, p 0.01), but the IT group reported significantly greater online TABLE 1.
THE
PIUQ SCORE
R2
R2
F
Distraction online Online flow Length of time online per session Online chatting
0.512 0.196 0.134 0.111
0.464 0.514 0.542 0.557
0.049 0.028 0.015
506.43* 59.15* 36.07* 19.20*
Non-IT group
R2
R2
F
Distraction online Online flow Days per week online Length of time online per session Online chatting
0.535 0.149 0.124 0.128 0.111
0.425 0.467 0.495 0.515 0.527
0.042 0.028 0.020 0.012
452.03* 48.22* 34.05* 25.12* 14.86*
Var. 1 2 3 4 5
STEPWISE LINEAR REGRESSION MODELS PREDICTING
IT group
Var. 1 2 3 4
flow experiences (t 2.80, p 0.01). This would imply that some of the online flow experiences of IT workers are work related and therefore not perceived as problematic. Correlations with the PIUQ were statistically significant at p 0.01 and positive in both groups for the length of time online per session, the number of different Internet access points, and the number of days per week online, but were not significantly correlated with the duration since starting to use the Internet. The vast majority of research on PIU supports these findings.1,3 In the non-IT group, the PIUQ was also significantly positively correlated with the number of different uses of the Internet. This is consistent with the notion of specific pathological Internet use.7 The analyses of the biographical descriptors revealed that the PIUQ was significantly higher in males for both the IT (t 2.93, p 0.01) and the non-IT groups (t 3.00, p 0.01). Most other studies3 support this finding. As with a number of other studies,3,5 PIU correlated negatively (but weakly) with age for both the IT (r 0.15, p 0.01) and the non-IT groups (r 0.11, p 0.01). In both the IT and the non-IT groups, respondents who used the Internet for online chatting, instant messaging, online telephony, blogging, peer-to-peer file transfers, and online gaming were significantly higher on the PIUQ, but not for using e-mail or Web browsing. The PIUQ scores for the IT group were significantly higher for using FTP (t 2.66, p 0.01). These results are consistent with the research, where activities that involve a high degree of interactivity are more likely to lead to PIU.3 This would suggest that within the IT group, file transfers might be perceived as a more socially interactive function (and therefore problematic). A stepwise multiple linear regression resulted in 4 variables explaining 55.7% of the variance for the IT group and 5 variables explaining 52.7% of the variance for the non-IT group (see Table 1). Procrastination explained the greatest proportion of the variance,2,6 followed by online flow for both groups. On the basis of these results, one might interpret PIU in IT workers as a phenomenon where they use the Internet to avoid other work commitments6 and then become immersed in these activities, resulting in spending more time than intended online. One of the online activities most likely to be immersive is online chatting (although other interactive functions might also be culprits). In the non-IT group, the number of days of the week online was also included as a significant predictor of PIU. This supports the contention that it is the avoidance of work-related tasks that proves most
*Significant at p 0.01.
PIU AMONG IT WORKERS IN SOUTH AFRICA problematic rather than only the amount of time spent online or the type of activities that a person engages in while online. This study reports exploratory research, and a number of limitations should be noted. Because the sample was obtained from an IT magazine Web site, it is highly likely that most (if not all) respondents had an interest in IT issues even if they were not formally employed as IT workers. Future studies attempting to understand the risks of PIU in IT workers should make use of more distinct contrast groups. Direct comparisons between different studies are difficult to make given the inconsistency in diagnostic criteria, the instruments used to assess PIU, and the samples that have been studied. Disclosure Statement
787 4. Internet World Stats. Internet usage statistics for Africa. www.internetworldstats.com (accessed Mar. 14, 2008). 5. Widyanto L, McMurran M. The psychometric properties of the Internet Addiction Test. CyberPsychology & Behavior 2004; 7:449–56. 6. Davis RA, Flett GL, Besser A. Validation of a new scale for measuring problematic Internet use: implications for pre-employment screening. CyberPsychology & Behavior 2002; 5:331–45. 7. Thatcher A, Goolam S. Development and psychometric properties of the Problematic Internet Use Questionnaire. South African Journal of Psychology 2005; 35:793–809. 8. Webster J, Trevino LK, Ryan L. The dimensionality and correlates of flow in human-computer interactions. Computers in Human Behavior 1993; 9:411–29.
The authors have no conflict of interest. References 1. Yuen CN, Lavin MJ. Internet dependence in the collegiate population: the role of shyness. CyberPsychology & Behavior 2004; 7:379–83. 2. Nalwa, K, Anand AP. Internet addiction in students: a cause of concern. CyberPsychology & Behavior 2003; 6:653–6. 3. Lin SSJ, Tsai C-C. Sensation seeking and Internet dependence of Taiwanese high school adolescents. Computers in Human Behavior 2002; 18:411–26.
Address reprint requests to: Dr. Andrew Thatcher Department of Psychology University of the Witwatersrand WITS, 2050 South Africa E-mail:
[email protected]
