Psychiatry Research 209 (2013) 529–534

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The relationship between optimal parenting, Internet addiction andmotives for social networking in adolescence

Georgios Floros a,b,n, Konstantinos Siomos b

a Student Counseling Unit for Internet and PC addiction, 2nd Department of Psychiatry, Aristotle University of Thessaloniki, Greeceb Hellenic Association for the Study of Internet Addiction Disorder, Larissa, Greece

a r t i c l e i n f o

Article history:

Received 22 August 2012

Received in revised form

15 January 2013

Accepted 21 January 2013

Keywords:

Social networking

Adolescents

Internet addiction

81/$ - see front matter & 2013 Elsevier Irelan

x.doi.org/10.1016/j.psychres.2013.01.010

esponding author at: Student Counseling Un

n, 2nd Department of Psychiatry, Aristotle Un

street, 564 29 Thessaloniki, Greece.

0 2310280781; fax: þ30 2312203122.

ail addresses: georgefloros@gmail.com, floros7

a b s t r a c t

This paper presents a cross-sectional study of a large, high-school Greek student sample (N¼1971)

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.

1. Introduction

Adolescent Internet use in the US has peaked at 93% andremained steady ever since 2006, with an incremental increase inthe use of social networking sites from 55% in 2006, to 65% in2008, 73% in 2009 and 80% in 2011 (Lenhart, 2010; Lenhart et al.,2011). Social media participation in Greece was reported as highas 79% for the 13–17 age group and 72% for the 18–24 age group(Observatory, 2011). Recent studies suggest that social network-ing is becoming integrated with typical social connectedness(Bennett, 2008) with the initial notion of ‘overlap’ slowly becom-ing inadequate in grasping the full extent of this integration(Subrahmanyam et al., 2008). This integration is driven in part bypsychological factors: participation in social networking (SN) hasbeen shown to have an impact in the perceived well-being of theadolescents, 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 forsocialization actually helps or hinders. A study on 286 under-graduate students (Ellison et al., 2007) reported that Facebook usesupported a Social Compensation, ‘‘poor get richer’’ hypothesis,

d Ltd. All rights reserved.

it for Internet and PC

iversity of Thessaloniki, 196

4gr@yahoo.gr (G. Floros).

with those students who had low self-esteem and overall satis-faction with their college experience being helped to overcomebarriers by an increase in ‘bridging social capital’, a term used todescribe resources drawn from extended, weak-ties social net-works. A recent study (Zywica and Danowski, 2008) showed thatfor a subset of users, those more extroverted and with higher self-esteem, a Social Enhancement hypothesis may be warranted withthem being more popular both offline and on Facebook. Anothersubset of users, those less popular offline, provided results thatsupported a Social Compensation, ‘poor gain more’ hypotheses,because they are more introverted, had lower self-esteem andstrived more to look popular on Facebook. A related study (Tong et al.,2008) measured social attractiveness attributed to individual Face-book users by observers judging accordingly to the number of theirFacebook connections. The number of friends that profile owners arepurported to have and others’ perceptions of their social attractive-ness tended to fluctuate from least attractive, for those with thefewest friends, to most attractive for those with a high number offriends, and dropping again for those users with an excessive amountof Facebook friends. Having an exceedingly large number of friendsleads to judgments that profile owners are not sociable and outgoing,but are relatively more introverted. These results indicate an empiri-cal hypothesis shared by SN users themselves, namely that thosewho tend to over-reach in social networks may be compensatingfor 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).

G. Floros, K. Siomos / Psychiatry Research 209 (2013) 529–534530

1.1. Correlates of interactions within the family and with online

acquaintances

Family is the first environment for a child and a prototype forfuture relations and interactions. It is also the prime source for basicneed gratification (including not only material but also psychologicalneeds). Those same psychological needs will be satisfied in duecourse by societal relationships (Hazan and Shaver, 1994). Parentalresponses to the child lead to the development of patterns ofattachment, leading to internal working models which will guidethe individual’s perceptions, emotions, thoughts and expectations inlater relationships (Bretherton and Munholland, 1999).

A recent study followed longitudinally a random sample of138 youths over a 7-year period and reported that problematicmother–teen relationships were predictive of youths’ later preferencefor online communication and greater likelihood of forming a friend-ship with someone met online (Szwedo et al., 2011). The authorsstressed the importance of considering youths’ family interactionsduring early adolescence as predictors of future online socializingbehavior and online interactions with peers. Their findings supporteda notion that negative interactions with the mother are associatedwith a greater propensity to later seek friendship online rather than inclose, face-to-face encounters. Lack of autonomy within the familyhas been linked to decreased attachment to offline peers even afteraccounting for social skills abilities (Engels et al., 2002) although thereare no data on a relationship between current perception of parentalpractices and online socialization; an important issue since adoles-cents are reared in a world where SN participation starts from theelementary 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 Internetaddiction disorder (IAD) in recent studies (Kuss and Griffiths,2011; Leung and Lee, 2012). An official definition of IAD has notbeen established yet, but experts agree that there are certainonline activities, like gaming, seeking pornography and gambling,that are linked to increased chances of using the Internet to thedetriment 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; howcan one ascribe addictive elements to an activity which is by itsvery nature promotes constant involvement from every possibletechnological means (personal computer, laptop, tablet, cellphone, Internet-enabled TV-set)? This discussion has widerrepercussions with IAD being set to be included in the forth-coming fifth version of the DSM (Diagnostics and StatisticsManual of the American Psychiatric Association) as a disorderwhich requires further research before possible full inclusion inthe psychiatric taxonomy (O’Brien, 2010). Defining IAD willrequire a clear idea of whether SN should be included in adefinition or not and this necessitates research on whether SNcorrelates with established measures of IAD and known riskfactors. Davis (2001) proposed a definition of IAD based on acognitive-behavioral paradigm, where IAD results from ‘proble-matic’ cognitions coupled with behaviors that either intensify ormaintain this particular maladaptive activity. He posited thatcognitions precede the onset of specific behaviors and maymodify, intensify and eventually perpetuate the behavior inquestion. Based on this model, the Online Cognitions Scale(OCS) was devised and shown to provide with an accurateestimate of Internet addiction. Examining the underlying cogni-tions linked to SN participation may help us reach a betterunderstanding of where excessive SN participation meets IAD.

1.3. Study goals

Our study attempts to examine if and how adolescent motiva-tions for participation in SN relate to addictive Internet use, whiletaking into account parental representations and practices athome. Our research hypotheses are formulated as follows:

(a)

Prediction of SN participation using intrinsic motives andInternet-related cognitions is possible and the results will bemeaningful (medium or larger effect sizes)

(b)

Internal motives for SNS participation are related to a sig-nificant degree with the various aspects of Internet addictionwhile 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 participat-

ing in SN, the Parental Bonding Index (PBI) and the Online Cognitions Scale (OCS).

The demographics questionnaires included questions on sex, age, family back-

ground, 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 correspond-

ing to seeking out and communicating with online friends), narcissistic involve-

ment (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) 529–534 531

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 theInternet and participated in the survey, aged 12–19. One thou-sand and nineteen of them were boys (51.7% of the sample, meanage 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 similarbetween the sexes. Sample demographics, along with frequencyof social networking, mean frequency for all online activities andmean values for the OCS and PBI scales are presented in Table 1.

Table 1Demographics of adolescent participants.

Variable Frequency Percentage

Sex

Male 1019 51.7

Female 952 48.3

Age

12 70 3.6

13 338 17.1

14 394 20

15 377 19.1

16 365 18.5

17 274 13.9

18 121 6.1

19 32 1.8

Having used the Internet for social networking

Almost never 384 19.5

Occasionally during the year 87 4.4

Couple of times on a month 98 5

At least once a week 230 11.7

Almost daily 394 20

A lot of times during a day 663 33.6

Did not reply 115 5.8

OCS mean scores (S.D.) Boys Girls

Total score 105.726 (43.12) 87.9 (40.44)

Social comfort PIU 36.665 (16.8) 29.277 (14.99)

Lonely/depressed PIU 16.346 (8.188) 13.239 (7.47)

Impulsive PIU 30.489 (12.259) 25.444 (12.49)

Distraction PIU 22.785 (9.77) 20.563 (10.29)

OCS mean scores (S.D.) Boys Girls

Maternal care 24.368 (6.59) 26.455 (6.6)

Maternal overprotection 15.921 (5.23) 15.617 (5.35)

Paternal care 22.445 (6.63) 24.978 (6.88)

Paternal overprotection 14.863 (5.796) 15.082 (5.81)

Table 2Stepwise linear regression model for SNS participation frequency.

Parameter Unstandardized estimates Standardized bet

B SE

(Intercept) �1.311 0.421

‘Real-life Friendship’ motive factor 0.204 0.012 0.472

Age of Internet use initiation �0.043 0.020 �0.057

Age 0.168 0.030 0.148

‘Escapism’ motive factor 0.045 0.015 0.091

Impulsive PIU 0.016 0.004 0.108

Unadjusted r2¼ 0.351; Adjusted r2

¼ 0.349; PIU ¼ Pathological Internet Use.

Social networking was the most frequent adolescent onlineactivity, surpassing online gaming which was the most frequentactivity 2 years earlier (Fisoun et al., 2012a).

3.1. Regression model for frequency of SN participation

A stepwise regression analysis was performed in order todetermine the extent to which motivations to participate in SNand Internet related cognitions can predict the frequency of SNparticipation, a dependent variable measured in a simple six pointLikert scale ranging from ‘I almost never participate in SN’ to‘I participate multiple times per day in SN’. The independentvariables entered included sociodemographic factors (age, gen-der), time spent online, the motives to participate in SN and thetypes of pathological Internet use of the OCS scale. Results arepresented in Table 2. The adolescent who visits SN sites moreoften is more likely to be older, have started using the Internetsooner than his peers, seek friendship which could extent offlineor keep in contact with friends who he meets offline too, try toescape from everyday life and use the Internet impulsively. Thiscombination of variables significantly predicted SN participation,F(5,1234)¼133.613, po0.001. The adjusted R squared value was0.349. This indicates that 34.9% of the variance in SN participationwas explained by the model. This is a large effect size denotingconsiderable importance for our results (Cohen, 1988). This analysisand the results offer proof for the first research hypotheses.

3.2. SEM model for motives related to SN participation

Fig. 1 presents a SEM model including factors from the SNmotivation, OCS and PBI scales which was assessed in order toprovide a response for our second research hypotheses. All factorsare assumed to load onto a higher hypothetical construct (latentvariable) for each scale and each latent variable to covariate withthe rest. Each factor as an observed variable has its own errorparameter which includes random measurement error and erroruniqueness (error variance arising from a specific characteristic ofthe particular variable). This model hypothesizes that Internetaddiction, parental bonding and the motives for SN participationcorrelate with specific patterns, to be analyzed in Section 4. Theinitial model assumed that errors of measurement between eachobserved factor were uncorrelated. The final model includes twoco-variances between the error measurements for paternal andmaternal care and overprotection factors of the PBI and theydenote that those factors share common error variance due tomeasurement. Inclusion has led to a significant improvement inmodel fit while an inclusion of other co-variances did not and wasdeemed unnecessary. Goodness-of-fit indexes for both models arepresented in Table 3, along with the rule-of-thumb values asproposed elsewhere (Schermelleh-Engel et al., 2003). Item factorloadings were all statistically significant with r2 values, repre-senting the proportion of variance accounted for by their related

a 95% CIs for B Hypothesis test Collinearity statistics

Lower bound Upper bound t Sig. Tolerance VIF

�2.137 �0.486 �3.117 0.002

0.180 0.227 17.065 o0.001 0.688 1.453

�0.083 �0.004 �2.172 0.030 0.755 1.325

0.109 0.226 5.624 o0.001 0.757 1.321

0.016 0.074 3.008 0.003 0.569 1.757

0.008 0.024 4.070 o0.001 0.750 1.333

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 3Goodness-of-fit indexes for the motivation scale and the full model.

Index Good fit Motivation scale Initial model Final model

N (Suggested having at least 10 cases/d.f.) 1649 1364 1364

S–Bw2 (d.f.) Ratio lower than 3 197.608 (71) 173.22 (51) 122.47 (49)

NFI 0.9rNFIo0.95 0.939 0.945 0.963

NNFI 0.95rNNFIo0.97 0.941 0.936 0.957

CFI 0.95rCFIo0.97 0.946 0.951 0.968

RMSEA (90% C.I.) 0.05oRMSEAr0.08 0.067 (0.062–0.072) 0.07 (0.066–0.074) 0.06 (0.055–0.063)

S–Bw2¼Satorra–Bentler chi-squared; d.f.¼degrees of freedom; NFI¼normed fit index; NNFI¼non-normed fit index; CFI¼comparative fit index; RMSEA¼rooted mean

square error of approximation; C.I.¼confidence intervals.

G. Floros, K. Siomos / Psychiatry Research 209 (2013) 529–534532

factors, ranging from 35% to 90% (Table 4). Fig. 1 presents thehypothesized four-factor model with the added co-variances.

4. Discussion

SN participation in our adolescent sample was the mostfrequent 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 frequentSN participation. Interestingly, gender was not included in ourstepwise analysis as an important independent variable, denotingthat both sexes have a high interest for SN. SN has become a wayto alleviate typical adolescent insecurities and keep in constantcontact with others like oneself who face the same problems,more or less successfully. This generation is interconnected withdigital ties that mean a great deal for their self-regulation; itremains to be seen whether this non-stop connection anddemands 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 withmaturity. Conversely, whenever an adolescent is forced to avoidonline social connectedness, due to cyber-exclusion or cyber-bullying, he/she is essentially forced to cut-down on the digitalties with his/her peers and lose the feeling of belonging to thatgroup. This is a negative aspect of cyber-exclusion and cyber-bullying which may go unnoticed by those who did not grow upwith, 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, withspecific directions: with regards to parental bonding factors, careloaded positively while overprotection loaded negatively on thehypothesized higher-order latent variable. This corresponds to anideal parenting style (‘optimal bonding’) where the parents care andprotect their children yet respect their autonomy (Parker, 1990).Optimal bonding correlated negatively both with pathological

Table 4Factor 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 Factor loading Error loading R2 value

‘Paternal Overprotection’ PBI factor Parental bonding �0.559 0.829 0.312

‘Paternal Care’ PBI factor 0.530 0.848 0.281

‘Maternal Overprotection’ PBI factor �0.567 0.824 0.321

‘Maternal Care’ PBI factor 0.662 0.749 0.439

Social comfort PIU Internet addiction 0.899 0.439 0.807

Lonely/depressed PIU 0.897 0.443 0.804

Impulsive PIU 0.889 0.458 0.790

Distraction PIU 0.813 0.583 0.660

‘Real-life Friendship’ motive factor SNS usage motives 0.685 0.729 0.469

‘Virtual Friendship’ motive factor 0.899 0.438 0.808

‘Narcissistic involvement’ motive factor 0.856 0.517 0.733

‘Escapism’ motive factor 0.815 0.579 0.664

Covariances Estimate S.E. Z test

Bonding—SNS usage motives �7.386 0.934 �70.905

Bonding—Internet addiction �32.719 20.803 �110.67

Internet addiction—SNS usage motives 50.691 3.078 160.468

Paternal–maternal care 7.576 10.701 40.453

Paternal–maternal overprotection 6.484 1.025 60.329

PIU – Pathological Internet Use.

G. Floros, K. Siomos / Psychiatry Research 209 (2013) 529–534 533

Internet use and with the SN motives. This is the first researchfinding to suggest that optimal parental bonding reduces adolescent’motivation to become involved with SN; this finding is strengthenedfrom the parallel finding that pathology in Internet use correlatesnegatively with optimal bonding as well but positively with motivesto participate in SN.

Internet addiction in our model, conceptualized as being attrib-uted to seeking social comfort, evading feelings of loneliness and/ordepression, having diminished impulse control or seeking distrac-tion from other problems, is a valid construct when seeking toexamine inter-relationships with motives to SNS participation andparenting attributes. Internet addiction correlates negatively with apositive parenting style, and this relationship is noted in relatedliterature (Yen et al., 2007; Park et al., 2008). The inclusion of thosethree inter-related constructs in a single model is however a novelfinding. It signifies that good parenting can alleviate not only a needto seek solace in addictive behaviors expressed online, but can alsodecrease intrinsic motivation to participate in SNS. While theformer finding is common with most addictive behaviors, the lattermay indicate either that SNS participation has an addictive compo-nent, or that it may be a form of ‘self-cure’ that substitutes forparental neglect in a more benign fashion. We found that themotivation for SNS participation has a positive correlation withInternet addiction, denoting an underlying connection betweenthose 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 beinterpreted within our social context; adolescent online connected-ness has become an extension of offline communication andsocialization. If we regard the adolescent tendency to turn to peersin order to find solutions and answers as age-appropriate, thenusing SN in order to facilitate this process should come as nosurprise. Our study suggests that optimal parenting may relieve thesame tensions, or fulfill unmet needs, that could otherwise find apathway to expression or deflation via the Internet. Escapist usehowever was one of the most useful parameters in estimating thelevel of involvement with SN and this pattern of motives does notlead 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 causa-tions to the data at hand; cross-sectional data can only beanalyzed if we set our minds as to those directions beforehand,since this type of models is typically non-recursive, i.e. there arereciprocal influences between variables and the magnitude ofthose influences is hard to estimate based upon only a snapshot intime. The main point is that participating in SN is increased in thepresence of IAD-related cognitions and decreased in familiesconsidered to provide with optimal parenting, a known preven-tive factor for IAD. Thus the wider discussion on IAD shouldinclude the possibility that SN participation may be one of theonline activities associated with the emergence of the newdisorder. Future research should take this possibility into con-sideration and design appropriate components set to examine anyaddictive 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 theemployed measures are self-report questionnaires including themeasures of SNS involvement. Caution is advised against over-generalization of those results since they relate to exploratoryanalyses, rather than etiological ones. They are however a usefulalternative to interview-style research which would either cover asmaller number of individuals (without an indication of relativeimportance in a population) or entail a much higher logisticalcost. Although complex, the statistical analysis still analyzes dataat 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 moreaccurate etiological relationships.

Acknowledgments

The authors wish to acknowledge the valuable assistance ofthe entire staff of the Hippokrates drug prevention center in Kosfor the duration of the research project.

G. Floros, K. Siomos / Psychiatry Research 209 (2013) 529–534534

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