Introduction:
Internet has become an inexorable part of our daily lives. Besides providing astounding accessibility to information, Internet-mediated communication has also turned out to be an avenue for a plethora of online activities. Given the user-friendliness and accessibility of Internet, anytime or anywhere, it may come as no surprise that an alarming number of people, display ostensible signs of addiction to the digital world.
Internet Addiction (IA), otherwise called pathological or problematic Internet use is a recent and increasingly recognized disorder (1, 2) which has received growing attention worldwide over the past two decades.(3) First introduced by Goldberg (1995) (4) and made popular in Young’s (1996) (5) seminal study, the term Internet Addiction has been defined by Shapira et al., (2003) (6) as an individual’s inability to control Internet use, which in turn leads to feelings of distress and functional impairment of daily activities. Despite being the subject of numerous debates, particularly concerning its terminology, definition and theoretical basis, (7-10) IA has gradually appeared as a new mental health concern. (11)
The consequences of IA are insidious, becoming apparent, only after months of problematic Internet use and eventually engulfing all aspects of the individual’s life. (12) Negative effects of over-engagement with the Internet are multiple, and include developing problems in any of the five areas: scholastic, occupational, interpersonal, financial, or physical. (13) University students are a group that may be particularly vulnerable to IA. Burgeoning literature indicates that many university students suffer from a variety of health and psychosocial problems due to IA.(14) The accessibility of the Internet on university campuses, the personal freedom and a significant amount of unstructured time, and the academic/social challenges many students experience as they leave home for the first time, all contribute to increased rates of IA. (15, 16)
Over the years, there has been growing concerns about IA and its detrimental consequences, especially among the younger population. Hence, the main objective of the current study was to examine and identify the sociodemographic characteristics, gender differences and determinants of Internet addiction among university students. It is anticipated that findings from this study would facilitate in developing effective intervention strategies, so as to reduce the negative ramifications of IA.
Methods
Participants
A cross-sectional study was conducted among a sample comprising of 307 undergraduate students of a public university in Malaysia. A convenience sampling technique was implemented for selecting the participants who belonged to the Faculty of Cognitive Sciences and Human Development. Prior to assessment, students were briefed about the purpose of the study and assured about the anonymity of their responses. Participation was voluntary and signed consent was obtained from the students. The self-administered questionnaire was distributed during the last 10 minutes of a 2-hour class lecture and applied only to students who were present in class, on the day of assessment.
Materials and Procedure
The self-report questionnaire consisted of two parts. The first section included information about the sociodemographic characteristics of participants, such as: gender; age (categorized into age groups of 20-22, 23-25 and 26+ years); race/ethnicity; monthly family income (demarcated as < RM 5000, or > RM 5000); family type (classified as nuclear, joint, or single parent); and academic achievement calculated as cumulative grade point average (CGPA). Data was also collected about the Internet usage patterns of participants, such as: average time spent online per day; location (inside/outside lecture hall) and purpose (academic/ non-academic) for accessing Internet; and approximate monthly expenditure for Internet connectivity.
The second section comprised of the Internet Addiction Test (IAT).(17) This instrument has 20 items associated with online Internet use, including psychological dependence, compulsive use, and withdrawal, as well as the related problems of school or work, sleep, family, and time management. Initial investigation into the validity of the IAT has shown strong internal consistency (α = .90-.93) and good test-retest reliability (r = 0.85). (18-23) For the current sample, a Cronbach's alpha of 0.89 was obtained across gender, showing excellent internal consistency.
Participants were required to answer the 20 IAT items on a 6-point Likert scale ranging from 0 (not applicable) to 5 (always), which presented an overall maximum score of 100. According to IAT recommendations, participants who recorded scores less than 30 were representative of normal level of Internet usage, scores of 31 to 49 indicated mild level of Internet addiction, 50 to 79 reflected moderate Internet addictions, and scores of 80 to 100 pointed towards severe dependence.
Before commencing the survey, the IAT questionnaire was translated from English into Malay language independently by two bilingual language experts. Subsequent reviews for appropriateness of language and back-translation to English for verification ensured that it was conceptually and semantically equivalent to the original. The ensuing pilot test which was conducted amongst a group of 20 students indicated that the Malay version was comprehensible. Given that most of the participating students were proficient in English and/or Malay language, both versions were provided during the survey.
Analysis of data was conducted by Statistical Program Social Sciences (SPSS) version 21. Independent t-test and one-way analysis of variance (ANOVA) was utilized to measure the mean differences between dependent variables (IAT scores) and independent variables (potential sociodemographic risk factors). Correlation analysis was employed to test the relationship between variables. Multiple regression analysis was used to predict significant variables. Prior to conducting primary analyses, the data was examined for univariate outliers and all were found to be within range values. Data was normally distributed; hence no variable transformations were deemed necessary.
Results
Table 1 depicts the distribution of the sample across gender, portraying the IAT scores, sociodemographic factors and Internet usage patterns. Almost 41% of the participants were males, out of which 34% were classified as moderate Internet users and 3% were severely addicted. In comparison, of the 59% female students, 29% were moderately addicted and 5% were severely addicted to the Internet. However, though female students showed higher addiction score (M = 47.48, SD = 14.20) compared to male students (M = 45.87, SD = 17), t-test was not statistically significant; t (305) = 0.95, p = 0.34.
Table 1: Sociodemographic characteristics of sample, across gender |
| Variable |
Gender |
Male (n = 124) |
Female (n = 183) |
IAT scores |
|
|
None |
14 |
12 |
Mild |
49 |
54 |
Moderate |
34 |
29 |
Severe |
3 |
5 |
Age groups |
|
|
20 - 22 |
73 |
86 |
23 - 25 |
25 |
12 |
26+ |
2 |
2 |
Monthly family income |
|
|
< RM 5000 |
82 |
86 |
> RM 5000 |
18 |
14 |
Family type |
|
|
Nuclear |
71 |
77 |
Joint |
29 |
17 |
Single Parent |
7 |
5 |
Race/ Ethnicity |
|
|
Malay |
41 |
34 |
Chinese |
23 |
33 |
Indian |
16 |
8 |
Indigenous |
20 |
25 |
Average time spent online per day |
|
|
< 1 hour |
6 |
5 |
1 - 2 hours |
25 |
18 |
3 - 4 hours |
36 |
37 |
> 5 hours |
33 |
40 |
Purpose |
|
|
Academic |
32 |
20 |
Non-academic |
68 |
80 |
Location |
|
|
Inside lecture hall |
12 |
16 |
Outside lecture hall |
88 |
84 |
Monthly expenditure on internet usage |
|
|
< RM 100 |
69 |
77 |
RM 101 - RM 200 |
29 |
22 |
RM 201 - RM 300 |
2 |
1 |
CGPA |
|
|
Low |
22 |
14 |
Average |
77 |
86 |
High |
2 |
0 |
| Note: All values have been tabulated as percentage. |
A large proportion of students were in the age group of 20 to 22 years. To compare the effect of IA on age, one-way ANOVA was conducted for male and female students, independently. It was noted that the three categories of age groups did not reveal any significant effect (p < .05) of IA among male students; F (2, 121) = 0.59, p = 0.554. In contrast, there was a statistically significant effect (p < .05) of IA across the three age groups among female students; F (2, 180) = 6.0, p = 0.003. Employing the Bonferroni post hoc test, significant differences were found between the 20 to 22 years and 26+ age groups (p = 0.003), as well as amidst the 23 to 25 years and 26+ age group (p = 0.005). No significant differences were demonstrated amongst the age groups of 20 to 22 years and 23 to 25 years (p = 0.997), for the female students.
Majority of the students disclosed that their monthly family income was not more than RM 5000 (which was categorized as low-income group in the current study). Male students in higher income group (> RM 5000) showed greater level of IA (M = 49.45, SD = 16.25), compared to the low-income group (M = 45.09, SD = 14.90); t (122) = 1.22, p = 0.22. Likewise, female students in higher income group also showed greater level of IA (M = 49.04, SD = 15.14), compared to the low-income group (M = 47.23, SD = 14.08); t (181) = 0.589, p = 0.55. On the other hand, no significant associations across gender were noted amongst the students who belonged to the three categories of family types, in the present study.
Race/ethnicity profile revealed that maximum numbers of students were Malays, sequentially followed by the Chinese, indigenous group students and the Indians. Significant differences of IA were demonstrated for male students belonging to different races; F (3,120) = 8.95, p = 0.000. The Bonferroni post hoc test showed that Indian students had lesser addiction to the Internet compared to the Malay (p = 0.000) and Chinese (p = 0.026) students. Correspondingly, differences of IA were also indicated for female students belonging to different races, although it was not statistically significant; F (3,179) = 2.15, p = 0.096.
With regard to the Internet usage patterns, majority of male and female students reported that the average time spent online, frequently exceeded one hour daily. Analysis also revealed, low significant correlations between IA and time spent on the internet daily (r = 0.21, p = 0.01). Further assessments across gender exhibited that, amongst the students who expended more than five hours online every day, females were markedly more (M = 53.55, SD = 17.43) than male respondents (M = 49.87, SD = 19.30); t(95) = 1.18, p = 0.23. Furthermore, there were no significant differences observed in the usage of Internet for non-academic (M = 47.28, SD = 14.32) and academic purposes (M = 45.46, SD = 15.43); t (305) = .94, p = 0.34. Also, significant differences were not detected across gender, between location of surfing internet and IA; t (26) = .88, p = > 0.05. However, female students who reportedly browsed the Internet within the lecture hall (M = 47.12, SD = 16.81) had higher mean score for IA, in comparison to the males (M = 42.66, SD = 9.89). It was also noted that more female students (M = 52.48, SD = 16.79) spent on monthly payments for Internet usage (RM101 - RM200 range) when compared to their counterparts (M = 49.22, SD = 14.77). However, t-test was not significant; t (75) = .99, p = 0.37.
Academic achievement was found to be associated with IA, in the present study. Low inverse correlation was observed between CGPA and IA scores, for male (r = -0.15, p = 0.04; one-tailed) and female students (r = -0.16, p = 0.017; one-tailed). Subsequent analysis was executed, in order to examine if the three categories of CGPA was associated with IA, across gender.
Students in the low CGPA grouping portrayed statistically significant differences for female (M = 46.37, SD = 16.55) and male respondents (M = 39.65, SD = 10.17); t (51) = 1.77, p = 0.04.
Implementation of multiple regression analysis using the enter method, for predicting IA amongst the undergraduates, revealed a significant model for male students; F (8,115) = 3.27, p = 0.002, with 10% of explained variance (adjusted r2 = .106). Race/ethnicity profile (ß = .314, p = 0.001) and monthly expenditure on Internet usage (ß = .217, p = 0.023) were found to be significant predictors of IA amongst male students. Likewise, a significant model also emerged for female students; F(8,174) = 3.00, p = 0.003), with 8.10% of explained variance (adjusted r2 = .081). Results indicated that, average time spent online per day (ß = .207, p = 0.009) and monthly expenditure on Internet usage (ß = .180, p = 0.018) were significant predictors of IA amongst the female students. In contrast, the remaining sociodemographic variables were not found to be significant predictors of IA across gender.
Discussion
The pervasive and influential presence of the Internet in today’s society has raised concern over the existence of an Internet addiction disorder.(24) For the most part, Internet addiction (IA) is considered an impulse control disorder, (10) or a non-substance-related behavioral addiction, (2) or a combination of both.(25, 26) The overarching concern is that, increasing numbers of report have highlighted the probable adverse consequences associated with IA. Particularly worrying is that young people, especially college-aged youth, appear to be at greater risk for IA. Given that the Internet is woven into the fabric of the lives of this generation, concerns about the potential for addiction seem warranted and require a systematic estimation of the scope of the problem in a defined population of interest. (27) Therefore, the present study was conducted amongst a sample comprised of university students, with the aim of investigating the role of sociodemographic factors and internet use patterns, as variables that might be associated with IA.
Findings from this study revealed that the proportion of IA between male and female students differed, though not statistically significant. That being said, female participants portrayed marginally higher prevalence of IA than males. This outcome was unexpected, as several studies had hitherto indicated that the most common sociodemographic variable associated with Internet addiction was male gender, both in adolescents as well as in adults.(28) However, it should be reiterated that there was only a slight difference in IA scores between male and female students in the current study, and is consistent with recent reports which have shown this previously-noted gender difference to be diminishing.(29-31) Thus, it is reasonable to assume that the populations most at risk for IA (such as male gender) may have changed from what was evident in the past. Given that the study sample essentially comprised of university students who had near equality in Internet access, it is quite possible that female students’ familiarity with Internet-related technology is similar to that of male students. Conceivably, gender inequalities in Internet use are smaller among younger people (32) and perhaps moving towards gender parity.
Most of the younger generation, especially college-aged students, have grown up in the Internet era and have never lived in a world without the Internet. So, when compared to the older population, younger individuals are more technology-savvy and likely to make heavy use of the Internet. In keeping with this viewpoint, it was noted that Internet usage in this study, was highest amongst both male and female students in the lowermost age group (20-22 years) when compared to older age groups. Furthermore, Bonferroni post-hoc test showed that the younger age group (20-22 years) exhibited the highest degree of addiction. A possible explanation for this outcome could be that the younger cohorts of students were yet to acclimatize themselves to living away from home for the first time, and have achieved a greater level of independence and freedom from parental supervision. Therefore, they may be particularly susceptible to IA, largely due to the fact that they have unrestrained, unsupervised access to Internet and autonomous control of time, within the university setting.
In relation to socioeconomic indicators, high family income has often been associated with Internet addiction. (28) Likewise, in the current study as well, both male and female students who were grouped within the high-income category also demonstrated greater IA, though not statistically significant. In general, students from high socioeconomic backgrounds have considerably greater access to different types of technology, and more frequent use of computers and the Internet, prior to entering university. Hence, when compared to students from low socioeconomic status, they are likely to have more experience with, and are relatively comfortable using Internet-related technologies in college. Thus, the possibility of IA may be greater among undergraduates from high socioeconomic levels. Correspondingly, this was consistent with a recent study wherein an elevated dependency to the Internet was noted amongst students from higher socioeconomic level. (33)
As the Internet becomes progressively amalgamated into everyday life, the effect of family factors on IA may also be of importance. However, no significant association of IA with family type was noted in the current study. Among the various factors potentially involved in the development of IA, the role of race/ ethnicity profile has yet to be comprehended. This is probably because IA, similar to many other forms of addictive disorders, takes its place in most diverse cultural backgrounds. Nonetheless, significant differences of IA were demonstrated for male students belonging to different races in this study, with Indian students exhibiting lesser addiction to the Internet compared to the Malay and Chinese students. Given the lack of data in extant literature, on this aspect, the direction of the effect of race/ethnicity profile on IA is somewhat ambiguous to interpret at present.
In terms of Internet use habits, higher time spent online is considered to be a fundamental indicator of Internet addiction. (34, 35) In keeping with this implication, a significant correlation was found between time spent online and IA, in the current study. This was consistent with several previous studies which have regularly identified a link between IA and time spent online. (28) Particularly concerning is the fact that, with virtually ubiquitous use of Internet noted among the younger demographic, more and more students are likely to spend increased amount of time online. Correspondingly, it was also observed in this study, that amongst students who spent more than five hours online every day, females were considerably more than males. In retrospect, this might create difficulties with time management, and as a result negatively impact academic outcomes and conventional social interactions, which in turn could manifest as IA.
In a conceptual context, it could be tacitly assumed that students utilize the Internet for both academic and non-academic purposes, with the most intense users spending the most time in non-academic pursuits. (36) Majority of the students in this study demonstrated a predilection to access the Internet for non-academic reasons. A recent study had indicated that access to the Internet has lowered students’ thresholds for boredom such that individuals become bored more quickly and have increased difficulty concentrating on imperative, school/work-related tasks. (14) Presumably, this could lead to academic problems such as decline in study habits, lower grades, missed classes and bigger risk of being placed on academic probation. Given that Internet use has become an integrated part of students’ educational and social environment, the risk of developing IA may possibly be higher among the more vulnerable individuals, especially amongst students misusing the Internet for non-academic pursuits.
Whether students are using the Internet to enhance interpersonal connections and social networking, school work, or recreation, the Internet offers a variety of readily available options encouraging constant use. (14) Indeed, female students in the present study who reportedly browsed the Internet within the lecture hall presented higher score for IA, in comparison to the males. Students are connected to the Internet virtually all the time nowadays, either through Wi-Fi or their mobile phone contracts, and this allows them to multi-task wherever they are, including (sadly) whilst attending class. (37) And so, instead of paying attention in classes, students are distracted by the availability of diverse online activities which they find more appealing. Apparently, some students may show increased tendency to prioritize online activities over academics, which in turn may impede educational achievements and make them prone to IA. Thus, although benefits are derived from Internet use in educational settings, maladaptive use may lead to deleterious consequences, such as increased susceptibility to IA.
Student’s academic relationship with the Internet is dynamic and varied. (14) While Internet use may have positive effects on scholastic performance, for some students, excessive Internet use could detract from educational achievement. Unsurprisingly, this was observed in the current study as well, wherein inverse correlations were identified between CGPA and IA scores across gender, suggesting that students with lower CGPA may exhibit higher IA. This outcome was consistent with several studies which have likewise found excessive Internet usage or IA to be strongly associated with impaired academic performance. (38-42) Especially worrying is the fact that scholastic and academic pressures (particularly in Asian countries) appear to have a negative influence on the students’ adjustment and life satisfaction which again may lead them to seek refuge in online worlds by applying a dysfunctional coping strategy. (28) Thus, poor academic achievement itself could be considered as a risk factor for IA.
Furthermore, from the regression analysis it was found that, among the associated factors, only race/ ethnicity profile (for male students) and time spent online (for female students) were significant predictors on IA. Besides this, results also indicated that monetary expenditure on internet usage was a significant predictor of IA across gender. As mentioned earlier, time spent online has been frequently linked with IA; however the role of race/ ethnicity has seldom been regarded as a predictor, despite burgeoning literature on IA. This disparity probably reflects the perception that racial/ ethnic affiliation may not be of equal importance than the other contributing determinants. In a similar vein, observances of whether monetary expenditure (paid for internet usage) could act as a predictor of IA were admittedly, somewhat scarce in extant literature. A possible explanation for the uncommon finding in the current study could be that the students were likely to be prone to IA, not because of the monetary costs, but due to the aftermath of maintaining constant connectivity, such as misuse, maladaptive use or excessive use of the Internet.
This study also presented certain limitations. Utilization of a cross-sectional design restricted the establishment of causality. Self-reported questionnaire generally has potential for response bias. In addition, the study focused on undergraduate students from a single university which may limit the generalizability of findings. Future studies are suggested, such as implementing other techniques like long-term longitudinal design, so as to have a thorough understanding of IA among the younger demographic.
Conclusion
With the rapid growth of Internet users worldwide, addiction to the Internet has become a cause for concern. Given that IA could be antithetical to the future outcome of younger individuals, especially students, the current study endeavored to ascertain the sociodemographic determinants of IA among this at-risk population. Findings indicated no significant gender difference in the IA scores, which alternatively could hint towards gender parity in Internet usage within this sample. The study results further demonstrated that IA is significantly associated with time spent online, a finding which is largely in agreement with prior studies. Besides depicting that academic achievement was associated with IA across gender, it was also noted that students with lower CGPA exhibited higher IA. Moreover, the regression analysis indicated that only race/ ethnicity profile (for male students), time spent online (for female students) and monetary expenditure on internet usage (across gender) were independent predictors of IA. Considering that gender differences in IA were not highlighted by the present study, prevention and intervention strategies to decrease this addiction should target the more vulnerable students across gender.
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