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The relationship between optimal parenting, Internet addiction and motives for social networking in adolescence
Psychiatry Research 209 (2013) 529–534
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The relationship between optimal parenting, Internet addiction and motives for social networking in adolescence Georgios Floros a,b,n, Konstantinos Siomos b a b
Student Counseling Unit for Internet and PC addiction, 2nd Department of Psychiatry, Aristotle University of Thessaloniki, Greece Hellenic Association for the Study of Internet Addiction Disorder, Larissa, Greece
a r t i c l e i n f o
abstract
Article history: Received 22 August 2012 Received in revised form 15 January 2013 Accepted 21 January 2013
This paper presents a cross-sectional study of a large, high-school Greek student sample (N¼ 1971) with the aim to examine adolescent motives for participating in social networking (SN) for a possible link with parenting style and cognitions related to Internet addiction disorder (IAD). Exploratory statistics demonstrate a shift from the prominence of online gaming to social networking for this age group. A regression model provides with the best linear combination of independent variables useful in predicting participation in SN. Results also include a validated model of negative correlation between optimal parenting on the one hand and motives for SN participation and IAD on the other. Examining cognitions linked to SN may assist in a better understanding of underlying adolescent wishes and problems. Future research may focus in the patterns unveiled among those adolescents turning to SN for the gratification of basic unmet psychological needs. The debate on the exact nature of IAD would benefit from the inclusion of SN as a possible online activity where addictive phenomena may occur. & 2013 Elsevier Ireland Ltd. All rights reserved.
Keywords: Social networking Adolescents Internet addiction
1. Introduction Adolescent Internet use in the US has peaked at 93% and remained steady ever since 2006, with an incremental increase in the use of social networking sites from 55% in 2006, to 65% in 2008, 73% in 2009 and 80% in 2011 (Lenhart, 2010; Lenhart et al., 2011). Social media participation in Greece was reported as high as 79% for the 13–17 age group and 72% for the 18–24 age group (Observatory, 2011). Recent studies suggest that social networking is becoming integrated with typical social connectedness (Bennett, 2008) with the initial notion of ‘overlap’ slowly becoming inadequate in grasping the full extent of this integration (Subrahmanyam et al., 2008). This integration is driven in part by psychological factors: participation in social networking (SN) has been shown to have an impact in the perceived well-being of the adolescents, mediated by self-esteem (Valkenburg et al., 2006; Steinfield et al., 2008; Gonzales and Hancock, 2011). There has been a debate as to whether Internet use for socialization actually helps or hinders. A study on 286 undergraduate students (Ellison et al., 2007) reported that Facebook use supported a Social Compensation, ‘‘poor get richer’’ hypothesis,
n Corresponding author at: Student Counseling Unit for Internet and PC addiction, 2nd Department of Psychiatry, Aristotle University of Thessaloniki, 196 Langada street, 564 29 Thessaloniki, Greece. Tel.: þ 30 2310280781; fax: þ30 2312203122. E-mail addresses: georgefl[email protected], fl[email protected] (G. Floros).
0165-1781/$ - see front matter & 2013 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.psychres.2013.01.010
with those students who had low self-esteem and overall satisfaction with their college experience being helped to overcome barriers by an increase in ‘bridging social capital’, a term used to describe resources drawn from extended, weak-ties social networks. A recent study (Zywica and Danowski, 2008) showed that for a subset of users, those more extroverted and with higher selfesteem, a Social Enhancement hypothesis may be warranted with them being more popular both offline and on Facebook. Another subset of users, those less popular offline, provided results that supported a Social Compensation, ‘poor gain more’ hypotheses, because they are more introverted, had lower self-esteem and strived more to look popular on Facebook. A related study (Tong et al., 2008) measured social attractiveness attributed to individual Facebook users by observers judging accordingly to the number of their Facebook connections. The number of friends that profile owners are purported to have and others’ perceptions of their social attractiveness tended to fluctuate from least attractive, for those with the fewest friends, to most attractive for those with a high number of friends, and dropping again for those users with an excessive amount of Facebook friends. Having an exceedingly large number of friends leads to judgments that profile owners are not sociable and outgoing, but are relatively more introverted. These results indicate an empirical hypothesis shared by SN users themselves, namely that those who tend to over-reach in social networks may be compensating for lack of resources in face-to-face communication and socializing. This hypothesis was also upheld in a recent survey with a small (N¼183) college students’ sample (Kujath, 2011).
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1.1. Correlates of interactions within the family and with online acquaintances Family is the first environment for a child and a prototype for future relations and interactions. It is also the prime source for basic need gratification (including not only material but also psychological needs). Those same psychological needs will be satisfied in due course by societal relationships (Hazan and Shaver, 1994). Parental responses to the child lead to the development of patterns of attachment, leading to internal working models which will guide the individual’s perceptions, emotions, thoughts and expectations in later relationships (Bretherton and Munholland, 1999). A recent study followed longitudinally a random sample of 138 youths over a 7-year period and reported that problematic mother–teen relationships were predictive of youths’ later preference for online communication and greater likelihood of forming a friendship with someone met online (Szwedo et al., 2011). The authors stressed the importance of considering youths’ family interactions during early adolescence as predictors of future online socializing behavior and online interactions with peers. Their findings supported a notion that negative interactions with the mother are associated with a greater propensity to later seek friendship online rather than in close, face-to-face encounters. Lack of autonomy within the family has been linked to decreased attachment to offline peers even after accounting for social skills abilities (Engels et al., 2002) although there are no data on a relationship between current perception of parental practices and online socialization; an important issue since adolescents are reared in a world where SN participation starts from the elementary school (Davis, 2010).
1.2. Should excessive SN participation be included in a definition of Internet addiction? Being an avid SN user was significantly linked to Internet addiction disorder (IAD) in recent studies (Kuss and Griffiths, 2011; Leung and Lee, 2012). An official definition of IAD has not been established yet, but experts agree that there are certain online activities, like gaming, seeking pornography and gambling, that are linked to increased chances of using the Internet to the detriment of offline activities and general well-being (Young, 1996; Block, 2008). Social networking, with its ubiquitous nature, pushes the limits of a definition for addictive phenomena; how can one ascribe addictive elements to an activity which is by its very nature promotes constant involvement from every possible technological means (personal computer, laptop, tablet, cell phone, Internet-enabled TV-set)? This discussion has wider repercussions with IAD being set to be included in the forthcoming fifth version of the DSM (Diagnostics and Statistics Manual of the American Psychiatric Association) as a disorder which requires further research before possible full inclusion in the psychiatric taxonomy (O’Brien, 2010). Defining IAD will require a clear idea of whether SN should be included in a definition or not and this necessitates research on whether SN correlates with established measures of IAD and known risk factors. Davis (2001) proposed a definition of IAD based on a cognitive-behavioral paradigm, where IAD results from ‘problematic’ cognitions coupled with behaviors that either intensify or maintain this particular maladaptive activity. He posited that cognitions precede the onset of specific behaviors and may modify, intensify and eventually perpetuate the behavior in question. Based on this model, the Online Cognitions Scale (OCS) was devised and shown to provide with an accurate estimate of Internet addiction. Examining the underlying cognitions linked to SN participation may help us reach a better understanding of where excessive SN participation meets IAD.
1.3. Study goals Our study attempts to examine if and how adolescent motivations for participation in SN relate to addictive Internet use, while taking into account parental representations and practices at home. Our research hypotheses are formulated as follows:
(a) Prediction of SN participation using intrinsic motives and Internet-related cognitions is possible and the results will be meaningful (medium or larger effect sizes) (b) Internal motives for SNS participation are related to a significant degree with the various aspects of Internet addiction while taking parenting style into account. 2. Method 2.1. Study design and population The study is part of a larger research project, ‘Hippocrates 2010’, focusing on online and offline behaviors of the youth in Kos Island (total population 30,000). It was designed by the Hellenic Association for the Study of Internet Addiction Disorder in collaboration with the Drug abuse prevention center ‘Hippocrates’ of the Greek Organization against Illicit Drugs (OKANA). The official governing body of the educational system validated the research project after a review for ethics and legality. Previous research in Kos has indicated high percentages of Internet addiction symptomatology that correlated with off-line antisocial behaviors (Fisoun et al., 2012a) and chemical drug use experience (Fisoun et al., 2012b). This study was of a cross-sectional design. The research material was distributed in all of the 13 schools of the island (seven Gymnasiums and six Lyceums, the former being the junior grade and the latter the senior grade of High school education in Greece). Participation was voluntary and confidential. An estimated 3% of the students were absent and were not polled for participation at the day the research material was handed out in their classes. Our research sample consisted of 2017 adolescent students between 12 and 19 years of age. The students were informed on the purpose of the study and consented except for five students who declined to participate. Forty-one students did not use the Internet and were not included in the study.
2.2. Measures Students were handed material that included a demographics questionnaire with questions on Internet use, a 14-item questionnaire on motives for participating in SN, the Parental Bonding Index (PBI) and the Online Cognitions Scale (OCS). The demographics questionnaires included questions on sex, age, family background, school performance and related goals. The motives for SN participation questionnaire contains 14 items rated on a six-point Likert scale, ranging from zero points, for total lack of interest for the item, to five points, for considering the item as essential to the SN experience. It was formed after a pilot study where high-school students of the island who attended oral presentations on Internet safety were asked to anonymously identify reasons for using SN in free text. Their feedback was scrutinized, reformatted and organized into 14 items, conceptually organized into four factors; real-life friendship (three items corresponding to keeping in touch with real-life friends), virtual friendship (five items corresponding to seeking out and communicating with online friends), narcissistic involvement (three items corresponding to a tendency to avoid meaningful contact yet attract attention), and escapism (three items corresponding to a tendency to participate in SN to avoid real-life difficulties and obligations). Their reliabilities were assessed with the computation of the Cronbach’s alpha index that were equal to 0.792, 0.775, 0.709 and 0.714. The Greek version of the Parental Bonding Instrument (PBI) consists of 25 items rated on a four-item Likert scale with separate questionnaires for father and mother (Avagianou and Zafiropoulou, 2008). Two factors are extracted; care and overprotection. Care is measured by 12 items on a dimension with one pole defined by empathy, closeness, emotional warmth, affection and on the other by neglect, indifference and emotional coldness. Overprotection is measured by 13 items, ranging from overprotection, intrusion, excessive contact, control and prevention of independent behavior to autonomy and allowance of independence. Cronbach’s alpha values for our survey sample were 0.88 and 0.87 for fathers’ and mothers’ care factor and 0.76 and 0.71 for fathers’ and mothers’ overprotection factor respectively, similar to those of the normative Greek sample (Avagianou and Zafiropoulou, 2008). The OCS is a theory-driven, multidimensional measure of Internet addiction (Davis et al., 2002). It achieves this goal by questioning the respondent on the possible existence of maladaptive cognitions based on a cognitive-behavioral
G. Floros, K. Siomos / Psychiatry Research 209 (2013) 529–534 paradigm (Davis, 2001). It contains 36 items on a seven-point Likert scale with results grouped in four factors: social comfort (with a Cronbach’s alpha of 0.88 in our sample), loneliness/depression (0.79), diminished impulse control (0.83), and distraction (0.83). Its adaptation for use in Greek-speaking populations is described in detail elsewhere (Floros and Siomos 2012). The EQS statistical package (Bentler, 2003) was used for SEM analysis and the ‘‘IBM SPSS Statistics 20’’ package was used for all data analysis other than SEM (Nie et al., 2011).
3. Results There were a total of 1971 students who reported using the Internet and participated in the survey, aged 12–19. One thousand and nineteen of them were boys (51.7% of the sample, mean age 15.06 years, S.E. ¼0.053) and 952 girls (48.3% of the sample, mean age 15.09, S.E. ¼0.055). Age distribution was similar between the sexes. Sample demographics, along with frequency of social networking, mean frequency for all online activities and mean values for the OCS and PBI scales are presented in Table 1. Table 1 Demographics of adolescent participants. Variable
Frequency
Percentage
Sex Male Female
1019 952
51.7 48.3
70 338 394 377 365 274 121 32
3.6 17.1 20 19.1 18.5 13.9 6.1 1.8
Having used the Internet for social networking Almost never 384 Occasionally during the year 87 Couple of times on a month 98 At least once a week 230 Almost daily 394 A lot of times during a day 663 Did not reply 115
19.5 4.4 5 11.7 20 33.6 5.8
Age 12 13 14 15 16 17 18 19
OCS mean scores (S.D.) Total score Social comfort PIU Lonely/depressed PIU Impulsive PIU Distraction PIU OCS mean scores (S.D.) Maternal care Maternal overprotection Paternal care Paternal overprotection
Boys 105.726 (43.12) 36.665 (16.8) 16.346 (8.188) 30.489 (12.259) 22.785 (9.77) Boys 24.368 15.921 22.445 14.863
Girls 87.9 29.277 13.239 25.444 20.563
Social networking was the most frequent adolescent online activity, surpassing online gaming which was the most frequent activity 2 years earlier (Fisoun et al., 2012a). 3.1. Regression model for frequency of SN participation A stepwise regression analysis was performed in order to determine the extent to which motivations to participate in SN and Internet related cognitions can predict the frequency of SN participation, a dependent variable measured in a simple six point Likert scale ranging from ‘I almost never participate in SN’ to ‘I participate multiple times per day in SN’. The independent variables entered included sociodemographic factors (age, gender), time spent online, the motives to participate in SN and the types of pathological Internet use of the OCS scale. Results are presented in Table 2. The adolescent who visits SN sites more often is more likely to be older, have started using the Internet sooner than his peers, seek friendship which could extent offline or keep in contact with friends who he meets offline too, try to escape from everyday life and use the Internet impulsively. This combination of variables significantly predicted SN participation, F(5,1234) ¼133.613, po 0.001. The adjusted R squared value was 0.349. This indicates that 34.9% of the variance in SN participation was explained by the model. This is a large effect size denoting considerable importance for our results (Cohen, 1988). This analysis and the results offer proof for the first research hypotheses. 3.2. SEM model for motives related to SN participation
(40.44) (14.99) (7.47) (12.49) (10.29)
Girls 26.455 15.617 24.978 15.082
(6.59) (5.23) (6.63) (5.796)
531
(6.6) (5.35) (6.88) (5.81)
Fig. 1 presents a SEM model including factors from the SN motivation, OCS and PBI scales which was assessed in order to provide a response for our second research hypotheses. All factors are assumed to load onto a higher hypothetical construct (latent variable) for each scale and each latent variable to covariate with the rest. Each factor as an observed variable has its own error parameter which includes random measurement error and error uniqueness (error variance arising from a specific characteristic of the particular variable). This model hypothesizes that Internet addiction, parental bonding and the motives for SN participation correlate with specific patterns, to be analyzed in Section 4. The initial model assumed that errors of measurement between each observed factor were uncorrelated. The final model includes two co-variances between the error measurements for paternal and maternal care and overprotection factors of the PBI and they denote that those factors share common error variance due to measurement. Inclusion has led to a significant improvement in model fit while an inclusion of other co-variances did not and was deemed unnecessary. Goodness-of-fit indexes for both models are presented in Table 3, along with the rule-of-thumb values as proposed elsewhere (Schermelleh-Engel et al., 2003). Item factor loadings were all statistically significant with r2 values, representing the proportion of variance accounted for by their related
Table 2 Stepwise linear regression model for SNS participation frequency. Parameter
(Intercept) ‘Real-life Friendship’ motive factor Age of Internet use initiation Age ‘Escapism’ motive factor Impulsive PIU
Unstandardized estimates B
SE
1.311 0.204 0.043 0.168 0.045 0.016
0.421 0.012 0.020 0.030 0.015 0.004
Standardized beta
0.472 0.057 0.148 0.091 0.108
Unadjusted r2 ¼ 0.351; Adjusted r2 ¼ 0.349; PIU ¼ Pathological Internet Use.
95% CIs for B
Hypothesis test
Collinearity statistics
Lower bound
Upper bound
t
Sig.
Tolerance
VIF
2.137 0.180 0.083 0.109 0.016 0.008
0.486 0.227 0.004 0.226 0.074 0.024
3.117 17.065 2.172 5.624 3.008 4.070
0.002 o 0.001 0.030 o 0.001 0.003 o 0.001
0.688 0.755 0.757 0.569 0.750
1.453 1.325 1.321 1.757 1.333
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Fig. 1. Graphical depiction of the SEM model for the relationship between motives for SN, parental bonding and Internet addiction. Asterisks denote unconstrained parameters. All estimates are standardized and statistically significant at the 0.05 level. Table 3 Goodness-of-fit indexes for the motivation scale and the full model. Index
Good fit
Motivation scale
Initial model
Final model
N S–Bw2 (d.f.) NFI NNFI CFI RMSEA (90% C.I.)
(Suggested having at least 10 cases/d.f.) Ratio lower than 3 0.9 rNFI o 0.95 0.95 r NNFIo 0.97 0.95 r CFIo0.97 0.05 oRMSEA r 0.08
1649 197.608 (71) 0.939 0.941 0.946 0.067 (0.062–0.072)
1364 173.22 (51) 0.945 0.936 0.951 0.07 (0.066–0.074)
1364 122.47 (49) 0.963 0.957 0.968 0.06 (0.055–0.063)
S–Bw2 ¼ Satorra–Bentler chi-squared; d.f. ¼ degrees of freedom; NFI ¼normed fit index; NNFI¼ non-normed fit index; CFI ¼ comparative fit index; RMSEA ¼ rooted mean square error of approximation; C.I.¼ confidence intervals.
factors, ranging from 35% to 90% (Table 4). Fig. 1 presents the hypothesized four-factor model with the added co-variances.
4. Discussion SN participation in our adolescent sample was the most frequent online activity, denoting its importance for adolescents. Our regression model showed that keeping in touch with friends, be it close or far away, was the stronger reason for frequent SN participation. Interestingly, gender was not included in our stepwise analysis as an important independent variable, denoting that both sexes have a high interest for SN. SN has become a way to alleviate typical adolescent insecurities and keep in constant contact with others like oneself who face the same problems, more or less successfully. This generation is interconnected with digital ties that mean a great deal for their self-regulation; it remains to be seen whether this non-stop connection and demands for constant contact can be a source of distress by itself. Losing a substitute for real-life connectedness could have serious
implications for being able to manage the stress associated with maturity. Conversely, whenever an adolescent is forced to avoid online social connectedness, due to cyber-exclusion or cyberbullying, he/she is essentially forced to cut-down on the digital ties with his/her peers and lose the feeling of belonging to that group. This is a negative aspect of cyber-exclusion and cyberbullying which may go unnoticed by those who did not grow up with, or grow accustomed to, online social connectedness. 4.1. The importance of good parenting in the era of digital connectedness Our first research question is addressed with the SEM model; there is a clear inter-correlation of the presumed constructs, with specific directions: with regards to parental bonding factors, care loaded positively while overprotection loaded negatively on the hypothesized higher-order latent variable. This corresponds to an ideal parenting style (‘optimal bonding’) where the parents care and protect their children yet respect their autonomy (Parker, 1990). Optimal bonding correlated negatively both with pathological
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Table 4 Factor loadings, error loadings and r2 values for the final SEM model. All estimates are standardized and statistically significant at the 0.05 level. Variable
Factor
‘Paternal Overprotection’ PBI factor ‘Paternal Care’ PBI factor ‘Maternal Overprotection’ PBI factor ‘Maternal Care’ PBI factor
Parental bonding
Social comfort PIU Lonely/depressed PIU Impulsive PIU Distraction PIU ‘Real-life Friendship’ motive factor ‘Virtual Friendship’ motive factor ‘Narcissistic involvement’ motive factor ‘Escapism’ motive factor
Factor loading
Error loading
R2 value
0.559 0.530 0.567 0.662
0.829 0.848 0.824 0.749
0.312 0.281 0.321 0.439
Internet addiction
0.899 0.897 0.889 0.813
0.439 0.443 0.458 0.583
0.807 0.804 0.790 0.660
SNS usage motives
0.685 0.899 0.856 0.815
0.729 0.438 0.517 0.579
0.469 0.808 0.733 0.664
Covariances
Estimate
S.E.
Z test
Bonding—SNS usage motives Bonding—Internet addiction Internet addiction—SNS usage motives Paternal–maternal care Paternal–maternal overprotection
7.386 32.719 50.691 7.576 6.484
0.934 20.803 3.078 10.701 1.025
70.905 110.67 160.468 40.453 60.329
PIU – Pathological Internet Use.
Internet use and with the SN motives. This is the first research finding to suggest that optimal parental bonding reduces adolescent’ motivation to become involved with SN; this finding is strengthened from the parallel finding that pathology in Internet use correlates negatively with optimal bonding as well but positively with motives to participate in SN. Internet addiction in our model, conceptualized as being attributed to seeking social comfort, evading feelings of loneliness and/or depression, having diminished impulse control or seeking distraction from other problems, is a valid construct when seeking to examine inter-relationships with motives to SNS participation and parenting attributes. Internet addiction correlates negatively with a positive parenting style, and this relationship is noted in related literature (Yen et al., 2007; Park et al., 2008). The inclusion of those three inter-related constructs in a single model is however a novel finding. It signifies that good parenting can alleviate not only a need to seek solace in addictive behaviors expressed online, but can also decrease intrinsic motivation to participate in SNS. While the former finding is common with most addictive behaviors, the latter may indicate either that SNS participation has an addictive component, or that it may be a form of ‘self-cure’ that substitutes for parental neglect in a more benign fashion. We found that the motivation for SNS participation has a positive correlation with Internet addiction, denoting an underlying connection between those two constructs, a similarity of sorts.
4.2. Is social networking somehow ‘wrong’ or ‘addictive’? One needs to keep in mind that our findings need to be interpreted within our social context; adolescent online connectedness has become an extension of offline communication and socialization. If we regard the adolescent tendency to turn to peers in order to find solutions and answers as age-appropriate, then using SN in order to facilitate this process should come as no surprise. Our study suggests that optimal parenting may relieve the same tensions, or fulfill unmet needs, that could otherwise find a pathway to expression or deflation via the Internet. Escapist use however was one of the most useful parameters in estimating the level of involvement with SN and this pattern of motives does not lead to solutions but rather perpetuates any underlying problems.
4.3. Should excessive preoccupation with social networking be included in a definition of Internet addiction? Our SEM model does not attempt to ascribe etiological causations to the data at hand; cross-sectional data can only be analyzed if we set our minds as to those directions beforehand, since this type of models is typically non-recursive, i.e. there are reciprocal influences between variables and the magnitude of those influences is hard to estimate based upon only a snapshot in time. The main point is that participating in SN is increased in the presence of IAD-related cognitions and decreased in families considered to provide with optimal parenting, a known preventive factor for IAD. Thus the wider discussion on IAD should include the possibility that SN participation may be one of the online activities associated with the emergence of the new disorder. Future research should take this possibility into consideration and design appropriate components set to examine any addictive phenomena in SN participation. 4.4. Limitations of this study and directions for future research This present study is of a cross-sectional design and all of the employed measures are self-report questionnaires including the measures of SNS involvement. Caution is advised against overgeneralization of those results since they relate to exploratory analyses, rather than etiological ones. They are however a useful alternative to interview-style research which would either cover a smaller number of individuals (without an indication of relative importance in a population) or entail a much higher logistical cost. Although complex, the statistical analysis still analyzes data at a given point in time and cannot ascribe causes to the effects. A qualitative analysis can follow up on these patterns in depth, while research on marginalized groups could provide with more accurate etiological relationships.
Acknowledgments The authors wish to acknowledge the valuable assistance of the entire staff of the Hippokrates drug prevention center in Kos for the duration of the research project.
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Psychiatry Research journal homepage: www.elsevier.com/locate/psychres
The relationship between optimal parenting, Internet addiction and motives for social networking in adolescence Georgios Floros a,b,n, Konstantinos Siomos b a b
Student Counseling Unit for Internet and PC addiction, 2nd Department of Psychiatry, Aristotle University of Thessaloniki, Greece Hellenic Association for the Study of Internet Addiction Disorder, Larissa, Greece
a r t i c l e i n f o
abstract
Article history: Received 22 August 2012 Received in revised form 15 January 2013 Accepted 21 January 2013
This paper presents a cross-sectional study of a large, high-school Greek student sample (N¼ 1971) with the aim to examine adolescent motives for participating in social networking (SN) for a possible link with parenting style and cognitions related to Internet addiction disorder (IAD). Exploratory statistics demonstrate a shift from the prominence of online gaming to social networking for this age group. A regression model provides with the best linear combination of independent variables useful in predicting participation in SN. Results also include a validated model of negative correlation between optimal parenting on the one hand and motives for SN participation and IAD on the other. Examining cognitions linked to SN may assist in a better understanding of underlying adolescent wishes and problems. Future research may focus in the patterns unveiled among those adolescents turning to SN for the gratification of basic unmet psychological needs. The debate on the exact nature of IAD would benefit from the inclusion of SN as a possible online activity where addictive phenomena may occur. & 2013 Elsevier Ireland Ltd. All rights reserved.
Keywords: Social networking Adolescents Internet addiction
1. Introduction Adolescent Internet use in the US has peaked at 93% and remained steady ever since 2006, with an incremental increase in the use of social networking sites from 55% in 2006, to 65% in 2008, 73% in 2009 and 80% in 2011 (Lenhart, 2010; Lenhart et al., 2011). Social media participation in Greece was reported as high as 79% for the 13–17 age group and 72% for the 18–24 age group (Observatory, 2011). Recent studies suggest that social networking is becoming integrated with typical social connectedness (Bennett, 2008) with the initial notion of ‘overlap’ slowly becoming inadequate in grasping the full extent of this integration (Subrahmanyam et al., 2008). This integration is driven in part by psychological factors: participation in social networking (SN) has been shown to have an impact in the perceived well-being of the adolescents, mediated by self-esteem (Valkenburg et al., 2006; Steinfield et al., 2008; Gonzales and Hancock, 2011). There has been a debate as to whether Internet use for socialization actually helps or hinders. A study on 286 undergraduate students (Ellison et al., 2007) reported that Facebook use supported a Social Compensation, ‘‘poor get richer’’ hypothesis,
n Corresponding author at: Student Counseling Unit for Internet and PC addiction, 2nd Department of Psychiatry, Aristotle University of Thessaloniki, 196 Langada street, 564 29 Thessaloniki, Greece. Tel.: þ 30 2310280781; fax: þ30 2312203122. E-mail addresses: georgefl[email protected], fl[email protected] (G. Floros).
0165-1781/$ - see front matter & 2013 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.psychres.2013.01.010
with those students who had low self-esteem and overall satisfaction with their college experience being helped to overcome barriers by an increase in ‘bridging social capital’, a term used to describe resources drawn from extended, weak-ties social networks. A recent study (Zywica and Danowski, 2008) showed that for a subset of users, those more extroverted and with higher selfesteem, a Social Enhancement hypothesis may be warranted with them being more popular both offline and on Facebook. Another subset of users, those less popular offline, provided results that supported a Social Compensation, ‘poor gain more’ hypotheses, because they are more introverted, had lower self-esteem and strived more to look popular on Facebook. A related study (Tong et al., 2008) measured social attractiveness attributed to individual Facebook users by observers judging accordingly to the number of their Facebook connections. The number of friends that profile owners are purported to have and others’ perceptions of their social attractiveness tended to fluctuate from least attractive, for those with the fewest friends, to most attractive for those with a high number of friends, and dropping again for those users with an excessive amount of Facebook friends. Having an exceedingly large number of friends leads to judgments that profile owners are not sociable and outgoing, but are relatively more introverted. These results indicate an empirical hypothesis shared by SN users themselves, namely that those who tend to over-reach in social networks may be compensating for lack of resources in face-to-face communication and socializing. This hypothesis was also upheld in a recent survey with a small (N¼183) college students’ sample (Kujath, 2011).
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1.1. Correlates of interactions within the family and with online acquaintances Family is the first environment for a child and a prototype for future relations and interactions. It is also the prime source for basic need gratification (including not only material but also psychological needs). Those same psychological needs will be satisfied in due course by societal relationships (Hazan and Shaver, 1994). Parental responses to the child lead to the development of patterns of attachment, leading to internal working models which will guide the individual’s perceptions, emotions, thoughts and expectations in later relationships (Bretherton and Munholland, 1999). A recent study followed longitudinally a random sample of 138 youths over a 7-year period and reported that problematic mother–teen relationships were predictive of youths’ later preference for online communication and greater likelihood of forming a friendship with someone met online (Szwedo et al., 2011). The authors stressed the importance of considering youths’ family interactions during early adolescence as predictors of future online socializing behavior and online interactions with peers. Their findings supported a notion that negative interactions with the mother are associated with a greater propensity to later seek friendship online rather than in close, face-to-face encounters. Lack of autonomy within the family has been linked to decreased attachment to offline peers even after accounting for social skills abilities (Engels et al., 2002) although there are no data on a relationship between current perception of parental practices and online socialization; an important issue since adolescents are reared in a world where SN participation starts from the elementary school (Davis, 2010).
1.2. Should excessive SN participation be included in a definition of Internet addiction? Being an avid SN user was significantly linked to Internet addiction disorder (IAD) in recent studies (Kuss and Griffiths, 2011; Leung and Lee, 2012). An official definition of IAD has not been established yet, but experts agree that there are certain online activities, like gaming, seeking pornography and gambling, that are linked to increased chances of using the Internet to the detriment of offline activities and general well-being (Young, 1996; Block, 2008). Social networking, with its ubiquitous nature, pushes the limits of a definition for addictive phenomena; how can one ascribe addictive elements to an activity which is by its very nature promotes constant involvement from every possible technological means (personal computer, laptop, tablet, cell phone, Internet-enabled TV-set)? This discussion has wider repercussions with IAD being set to be included in the forthcoming fifth version of the DSM (Diagnostics and Statistics Manual of the American Psychiatric Association) as a disorder which requires further research before possible full inclusion in the psychiatric taxonomy (O’Brien, 2010). Defining IAD will require a clear idea of whether SN should be included in a definition or not and this necessitates research on whether SN correlates with established measures of IAD and known risk factors. Davis (2001) proposed a definition of IAD based on a cognitive-behavioral paradigm, where IAD results from ‘problematic’ cognitions coupled with behaviors that either intensify or maintain this particular maladaptive activity. He posited that cognitions precede the onset of specific behaviors and may modify, intensify and eventually perpetuate the behavior in question. Based on this model, the Online Cognitions Scale (OCS) was devised and shown to provide with an accurate estimate of Internet addiction. Examining the underlying cognitions linked to SN participation may help us reach a better understanding of where excessive SN participation meets IAD.
1.3. Study goals Our study attempts to examine if and how adolescent motivations for participation in SN relate to addictive Internet use, while taking into account parental representations and practices at home. Our research hypotheses are formulated as follows:
(a) Prediction of SN participation using intrinsic motives and Internet-related cognitions is possible and the results will be meaningful (medium or larger effect sizes) (b) Internal motives for SNS participation are related to a significant degree with the various aspects of Internet addiction while taking parenting style into account. 2. Method 2.1. Study design and population The study is part of a larger research project, ‘Hippocrates 2010’, focusing on online and offline behaviors of the youth in Kos Island (total population 30,000). It was designed by the Hellenic Association for the Study of Internet Addiction Disorder in collaboration with the Drug abuse prevention center ‘Hippocrates’ of the Greek Organization against Illicit Drugs (OKANA). The official governing body of the educational system validated the research project after a review for ethics and legality. Previous research in Kos has indicated high percentages of Internet addiction symptomatology that correlated with off-line antisocial behaviors (Fisoun et al., 2012a) and chemical drug use experience (Fisoun et al., 2012b). This study was of a cross-sectional design. The research material was distributed in all of the 13 schools of the island (seven Gymnasiums and six Lyceums, the former being the junior grade and the latter the senior grade of High school education in Greece). Participation was voluntary and confidential. An estimated 3% of the students were absent and were not polled for participation at the day the research material was handed out in their classes. Our research sample consisted of 2017 adolescent students between 12 and 19 years of age. The students were informed on the purpose of the study and consented except for five students who declined to participate. Forty-one students did not use the Internet and were not included in the study.
2.2. Measures Students were handed material that included a demographics questionnaire with questions on Internet use, a 14-item questionnaire on motives for participating in SN, the Parental Bonding Index (PBI) and the Online Cognitions Scale (OCS). The demographics questionnaires included questions on sex, age, family background, school performance and related goals. The motives for SN participation questionnaire contains 14 items rated on a six-point Likert scale, ranging from zero points, for total lack of interest for the item, to five points, for considering the item as essential to the SN experience. It was formed after a pilot study where high-school students of the island who attended oral presentations on Internet safety were asked to anonymously identify reasons for using SN in free text. Their feedback was scrutinized, reformatted and organized into 14 items, conceptually organized into four factors; real-life friendship (three items corresponding to keeping in touch with real-life friends), virtual friendship (five items corresponding to seeking out and communicating with online friends), narcissistic involvement (three items corresponding to a tendency to avoid meaningful contact yet attract attention), and escapism (three items corresponding to a tendency to participate in SN to avoid real-life difficulties and obligations). Their reliabilities were assessed with the computation of the Cronbach’s alpha index that were equal to 0.792, 0.775, 0.709 and 0.714. The Greek version of the Parental Bonding Instrument (PBI) consists of 25 items rated on a four-item Likert scale with separate questionnaires for father and mother (Avagianou and Zafiropoulou, 2008). Two factors are extracted; care and overprotection. Care is measured by 12 items on a dimension with one pole defined by empathy, closeness, emotional warmth, affection and on the other by neglect, indifference and emotional coldness. Overprotection is measured by 13 items, ranging from overprotection, intrusion, excessive contact, control and prevention of independent behavior to autonomy and allowance of independence. Cronbach’s alpha values for our survey sample were 0.88 and 0.87 for fathers’ and mothers’ care factor and 0.76 and 0.71 for fathers’ and mothers’ overprotection factor respectively, similar to those of the normative Greek sample (Avagianou and Zafiropoulou, 2008). The OCS is a theory-driven, multidimensional measure of Internet addiction (Davis et al., 2002). It achieves this goal by questioning the respondent on the possible existence of maladaptive cognitions based on a cognitive-behavioral
G. Floros, K. Siomos / Psychiatry Research 209 (2013) 529–534 paradigm (Davis, 2001). It contains 36 items on a seven-point Likert scale with results grouped in four factors: social comfort (with a Cronbach’s alpha of 0.88 in our sample), loneliness/depression (0.79), diminished impulse control (0.83), and distraction (0.83). Its adaptation for use in Greek-speaking populations is described in detail elsewhere (Floros and Siomos 2012). The EQS statistical package (Bentler, 2003) was used for SEM analysis and the ‘‘IBM SPSS Statistics 20’’ package was used for all data analysis other than SEM (Nie et al., 2011).
3. Results There were a total of 1971 students who reported using the Internet and participated in the survey, aged 12–19. One thousand and nineteen of them were boys (51.7% of the sample, mean age 15.06 years, S.E. ¼0.053) and 952 girls (48.3% of the sample, mean age 15.09, S.E. ¼0.055). Age distribution was similar between the sexes. Sample demographics, along with frequency of social networking, mean frequency for all online activities and mean values for the OCS and PBI scales are presented in Table 1. Table 1 Demographics of adolescent participants. Variable
Frequency
Percentage
Sex Male Female
1019 952
51.7 48.3
70 338 394 377 365 274 121 32
3.6 17.1 20 19.1 18.5 13.9 6.1 1.8
Having used the Internet for social networking Almost never 384 Occasionally during the year 87 Couple of times on a month 98 At least once a week 230 Almost daily 394 A lot of times during a day 663 Did not reply 115
19.5 4.4 5 11.7 20 33.6 5.8
Age 12 13 14 15 16 17 18 19
OCS mean scores (S.D.) Total score Social comfort PIU Lonely/depressed PIU Impulsive PIU Distraction PIU OCS mean scores (S.D.) Maternal care Maternal overprotection Paternal care Paternal overprotection
Boys 105.726 (43.12) 36.665 (16.8) 16.346 (8.188) 30.489 (12.259) 22.785 (9.77) Boys 24.368 15.921 22.445 14.863
Girls 87.9 29.277 13.239 25.444 20.563
Social networking was the most frequent adolescent online activity, surpassing online gaming which was the most frequent activity 2 years earlier (Fisoun et al., 2012a). 3.1. Regression model for frequency of SN participation A stepwise regression analysis was performed in order to determine the extent to which motivations to participate in SN and Internet related cognitions can predict the frequency of SN participation, a dependent variable measured in a simple six point Likert scale ranging from ‘I almost never participate in SN’ to ‘I participate multiple times per day in SN’. The independent variables entered included sociodemographic factors (age, gender), time spent online, the motives to participate in SN and the types of pathological Internet use of the OCS scale. Results are presented in Table 2. The adolescent who visits SN sites more often is more likely to be older, have started using the Internet sooner than his peers, seek friendship which could extent offline or keep in contact with friends who he meets offline too, try to escape from everyday life and use the Internet impulsively. This combination of variables significantly predicted SN participation, F(5,1234) ¼133.613, po 0.001. The adjusted R squared value was 0.349. This indicates that 34.9% of the variance in SN participation was explained by the model. This is a large effect size denoting considerable importance for our results (Cohen, 1988). This analysis and the results offer proof for the first research hypotheses. 3.2. SEM model for motives related to SN participation
(40.44) (14.99) (7.47) (12.49) (10.29)
Girls 26.455 15.617 24.978 15.082
(6.59) (5.23) (6.63) (5.796)
531
(6.6) (5.35) (6.88) (5.81)
Fig. 1 presents a SEM model including factors from the SN motivation, OCS and PBI scales which was assessed in order to provide a response for our second research hypotheses. All factors are assumed to load onto a higher hypothetical construct (latent variable) for each scale and each latent variable to covariate with the rest. Each factor as an observed variable has its own error parameter which includes random measurement error and error uniqueness (error variance arising from a specific characteristic of the particular variable). This model hypothesizes that Internet addiction, parental bonding and the motives for SN participation correlate with specific patterns, to be analyzed in Section 4. The initial model assumed that errors of measurement between each observed factor were uncorrelated. The final model includes two co-variances between the error measurements for paternal and maternal care and overprotection factors of the PBI and they denote that those factors share common error variance due to measurement. Inclusion has led to a significant improvement in model fit while an inclusion of other co-variances did not and was deemed unnecessary. Goodness-of-fit indexes for both models are presented in Table 3, along with the rule-of-thumb values as proposed elsewhere (Schermelleh-Engel et al., 2003). Item factor loadings were all statistically significant with r2 values, representing the proportion of variance accounted for by their related
Table 2 Stepwise linear regression model for SNS participation frequency. Parameter
(Intercept) ‘Real-life Friendship’ motive factor Age of Internet use initiation Age ‘Escapism’ motive factor Impulsive PIU
Unstandardized estimates B
SE
1.311 0.204 0.043 0.168 0.045 0.016
0.421 0.012 0.020 0.030 0.015 0.004
Standardized beta
0.472 0.057 0.148 0.091 0.108
Unadjusted r2 ¼ 0.351; Adjusted r2 ¼ 0.349; PIU ¼ Pathological Internet Use.
95% CIs for B
Hypothesis test
Collinearity statistics
Lower bound
Upper bound
t
Sig.
Tolerance
VIF
2.137 0.180 0.083 0.109 0.016 0.008
0.486 0.227 0.004 0.226 0.074 0.024
3.117 17.065 2.172 5.624 3.008 4.070
0.002 o 0.001 0.030 o 0.001 0.003 o 0.001
0.688 0.755 0.757 0.569 0.750
1.453 1.325 1.321 1.757 1.333
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Fig. 1. Graphical depiction of the SEM model for the relationship between motives for SN, parental bonding and Internet addiction. Asterisks denote unconstrained parameters. All estimates are standardized and statistically significant at the 0.05 level. Table 3 Goodness-of-fit indexes for the motivation scale and the full model. Index
Good fit
Motivation scale
Initial model
Final model
N S–Bw2 (d.f.) NFI NNFI CFI RMSEA (90% C.I.)
(Suggested having at least 10 cases/d.f.) Ratio lower than 3 0.9 rNFI o 0.95 0.95 r NNFIo 0.97 0.95 r CFIo0.97 0.05 oRMSEA r 0.08
1649 197.608 (71) 0.939 0.941 0.946 0.067 (0.062–0.072)
1364 173.22 (51) 0.945 0.936 0.951 0.07 (0.066–0.074)
1364 122.47 (49) 0.963 0.957 0.968 0.06 (0.055–0.063)
S–Bw2 ¼ Satorra–Bentler chi-squared; d.f. ¼ degrees of freedom; NFI ¼normed fit index; NNFI¼ non-normed fit index; CFI ¼ comparative fit index; RMSEA ¼ rooted mean square error of approximation; C.I.¼ confidence intervals.
factors, ranging from 35% to 90% (Table 4). Fig. 1 presents the hypothesized four-factor model with the added co-variances.
4. Discussion SN participation in our adolescent sample was the most frequent online activity, denoting its importance for adolescents. Our regression model showed that keeping in touch with friends, be it close or far away, was the stronger reason for frequent SN participation. Interestingly, gender was not included in our stepwise analysis as an important independent variable, denoting that both sexes have a high interest for SN. SN has become a way to alleviate typical adolescent insecurities and keep in constant contact with others like oneself who face the same problems, more or less successfully. This generation is interconnected with digital ties that mean a great deal for their self-regulation; it remains to be seen whether this non-stop connection and demands for constant contact can be a source of distress by itself. Losing a substitute for real-life connectedness could have serious
implications for being able to manage the stress associated with maturity. Conversely, whenever an adolescent is forced to avoid online social connectedness, due to cyber-exclusion or cyberbullying, he/she is essentially forced to cut-down on the digital ties with his/her peers and lose the feeling of belonging to that group. This is a negative aspect of cyber-exclusion and cyberbullying which may go unnoticed by those who did not grow up with, or grow accustomed to, online social connectedness. 4.1. The importance of good parenting in the era of digital connectedness Our first research question is addressed with the SEM model; there is a clear inter-correlation of the presumed constructs, with specific directions: with regards to parental bonding factors, care loaded positively while overprotection loaded negatively on the hypothesized higher-order latent variable. This corresponds to an ideal parenting style (‘optimal bonding’) where the parents care and protect their children yet respect their autonomy (Parker, 1990). Optimal bonding correlated negatively both with pathological
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Table 4 Factor loadings, error loadings and r2 values for the final SEM model. All estimates are standardized and statistically significant at the 0.05 level. Variable
Factor
‘Paternal Overprotection’ PBI factor ‘Paternal Care’ PBI factor ‘Maternal Overprotection’ PBI factor ‘Maternal Care’ PBI factor
Parental bonding
Social comfort PIU Lonely/depressed PIU Impulsive PIU Distraction PIU ‘Real-life Friendship’ motive factor ‘Virtual Friendship’ motive factor ‘Narcissistic involvement’ motive factor ‘Escapism’ motive factor
Factor loading
Error loading
R2 value
0.559 0.530 0.567 0.662
0.829 0.848 0.824 0.749
0.312 0.281 0.321 0.439
Internet addiction
0.899 0.897 0.889 0.813
0.439 0.443 0.458 0.583
0.807 0.804 0.790 0.660
SNS usage motives
0.685 0.899 0.856 0.815
0.729 0.438 0.517 0.579
0.469 0.808 0.733 0.664
Covariances
Estimate
S.E.
Z test
Bonding—SNS usage motives Bonding—Internet addiction Internet addiction—SNS usage motives Paternal–maternal care Paternal–maternal overprotection
7.386 32.719 50.691 7.576 6.484
0.934 20.803 3.078 10.701 1.025
70.905 110.67 160.468 40.453 60.329
PIU – Pathological Internet Use.
Internet use and with the SN motives. This is the first research finding to suggest that optimal parental bonding reduces adolescent’ motivation to become involved with SN; this finding is strengthened from the parallel finding that pathology in Internet use correlates negatively with optimal bonding as well but positively with motives to participate in SN. Internet addiction in our model, conceptualized as being attributed to seeking social comfort, evading feelings of loneliness and/or depression, having diminished impulse control or seeking distraction from other problems, is a valid construct when seeking to examine inter-relationships with motives to SNS participation and parenting attributes. Internet addiction correlates negatively with a positive parenting style, and this relationship is noted in related literature (Yen et al., 2007; Park et al., 2008). The inclusion of those three inter-related constructs in a single model is however a novel finding. It signifies that good parenting can alleviate not only a need to seek solace in addictive behaviors expressed online, but can also decrease intrinsic motivation to participate in SNS. While the former finding is common with most addictive behaviors, the latter may indicate either that SNS participation has an addictive component, or that it may be a form of ‘self-cure’ that substitutes for parental neglect in a more benign fashion. We found that the motivation for SNS participation has a positive correlation with Internet addiction, denoting an underlying connection between those two constructs, a similarity of sorts.
4.2. Is social networking somehow ‘wrong’ or ‘addictive’? One needs to keep in mind that our findings need to be interpreted within our social context; adolescent online connectedness has become an extension of offline communication and socialization. If we regard the adolescent tendency to turn to peers in order to find solutions and answers as age-appropriate, then using SN in order to facilitate this process should come as no surprise. Our study suggests that optimal parenting may relieve the same tensions, or fulfill unmet needs, that could otherwise find a pathway to expression or deflation via the Internet. Escapist use however was one of the most useful parameters in estimating the level of involvement with SN and this pattern of motives does not lead to solutions but rather perpetuates any underlying problems.
4.3. Should excessive preoccupation with social networking be included in a definition of Internet addiction? Our SEM model does not attempt to ascribe etiological causations to the data at hand; cross-sectional data can only be analyzed if we set our minds as to those directions beforehand, since this type of models is typically non-recursive, i.e. there are reciprocal influences between variables and the magnitude of those influences is hard to estimate based upon only a snapshot in time. The main point is that participating in SN is increased in the presence of IAD-related cognitions and decreased in families considered to provide with optimal parenting, a known preventive factor for IAD. Thus the wider discussion on IAD should include the possibility that SN participation may be one of the online activities associated with the emergence of the new disorder. Future research should take this possibility into consideration and design appropriate components set to examine any addictive phenomena in SN participation. 4.4. Limitations of this study and directions for future research This present study is of a cross-sectional design and all of the employed measures are self-report questionnaires including the measures of SNS involvement. Caution is advised against overgeneralization of those results since they relate to exploratory analyses, rather than etiological ones. They are however a useful alternative to interview-style research which would either cover a smaller number of individuals (without an indication of relative importance in a population) or entail a much higher logistical cost. Although complex, the statistical analysis still analyzes data at a given point in time and cannot ascribe causes to the effects. A qualitative analysis can follow up on these patterns in depth, while research on marginalized groups could provide with more accurate etiological relationships.
Acknowledgments The authors wish to acknowledge the valuable assistance of the entire staff of the Hippokrates drug prevention center in Kos for the duration of the research project.
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