1. PSYCHOLOGICAL PREDICTORS OF ADDICTIVE SOCIAL NETWORKING -21
PSYCHOLOGICAL PREDICTORS OF ADDICTIVE SOCIAL NETWORKING DANI RAMIRA I ZORANA BUJASA-
Jasna S. Milošević-Đorđević 1 & Iris L. Žeželj 2
1 Faculty for media and communications, Psychology Department, Singidunum University, Belgrade
2 Faculty of philosophy, Psychology Department, Belgrade University

2. Introduction
Internet as source of information, education, entertainment, marketing and irreplaceable tool for communication
Online social networks have gained significant popularity within general Internet space
Facebook, Tweeter, Instagram, LinkedIn and other social networks have reached hundreds of millions of users (Wilson, 2010).
Internet is mostly organized around content, online social networks are organized around users

3. Addictive social networking
Evidence of SNS abuses
People can easily develop addiction to Web, and SNS that could sometimes lead to psychological and social problems
Excessive internet use can interfere with vital life activities, such as sleep, nutrition habits, work or school
Reality: some software blockade internet connection

4. Most investigated demographic predictors of addictive SN use
Socio demographic of typical computer addict (Shotton, 1991; Niemz, 2005):
Male
Technologically sophisticated
Highly educated

5. Most investigated psychological predictors of addictive SN use
Psychological predictors of Internet use: introversion (Shotton, 1991; Amichai-Hamburger, 2002), lower self-esteem (Kim, 2009; Davis, 2001, Niemz, 2005; Armstrong, 2000;), socially disinhibition (Niemz, 2005), unrealistic optimism, anxiety (Kim, 2009), neuroticism (Ehrenberg, 2008).
Personality traits associated to (SNS) Facebook use: low self esteem and low life satisfaction (Kuss, 2011; Wilson, 2010; Tazghini, 2013; Amichai-Hamburger, 2010), neuroticism and conscientiousness (Amichai-Hamburger, 2010), openness to experience (Ross, 2009; Skues, 2012).
Profile of SNS user: less sociable, lower self-esteem, more socially disinhibited

6. Addictive social sites networking, definition
Indicators of addictive social sites networking (Kuss, 2011):
Interference with vital life activities, such as sleep, nutrition habits, work or school
Domination of virtual life over real life (real life social relationship problems)
Althought we measure addictive behavior, it is not a diagnostical tool. We aim to capture individual differences in this behavior pattern and investigate its psychological and demographic background.

7. Method Sample: 2014 respondents
Type of sample: nationally representative
Place: Serbia
Age: 12 +
Technique: telephone interview
Data collected: September 2012
*Research received funding from the donation of USAID/IREX, USAID/Institute for Sustainable Communities, Open Society Institute.
Data collection: Ipsos Strategic Marketing, Serbia

8. Elementary school and less
Sample %
Gender
Male 48
Female 52
Type of settlement
Urban 58
Rural 42
Age
12-29 25
30-44 23
45-65 36
66+ 16
Education
Elementary school and less 20
Secondary school 64
Colleague and Faculty

9. Instruments
Self-esteem: Rosenberg ten item scale (Cronbach’s Alpha = .74 ) Extraversion: subscale from BFI, eight items (John, Donahue & Kentle, 1991) (Cronbach’s Alpha = .77) Life efficacy: mini scale proposed by Bandura (2001) Addictive social networking: scale developed for the purpose of this research (tested in a pilot study on representative sample of 2034 respondents) Traditional media exposure: summing up the frequency of all traditional media consumption (scale from every day to less then once in a month). Demographic predictors: gender, age, type of settlement

10. Model Traditional media exposure Life self efficacy ADDICTIVE SOCIAL
NETWORKING
Self esteem
Demographic
(gender, age,
urban/rural)
Extraversion

11. Model Traditional media exposure Life self efficacy ADDICTIVE SOCIAL
NETWORKING
Self esteem
Demographic
(gender, age,
urban/rural)
Extraversion

12. Results

13. Addictive SNS, Serbia Items on the social networking scale M Loadings
Since I've been on social network my grades/success on work have deteriorated / my performance at work is worse
1.95
.653
It happens to me that I sleep much less than usually because of I stay longer with SN
2.09
.690
Sometimes I have an impression that I live two lives: one private and another virtual
1.76
.719
I would rather spend an afternoon and/or evening on SN than devote that time to any other activities
1.50
.656
I have better time with people that I have met over the Internet than with those that I know in person
1.34
.627
I fear that I might meat some of my virtual friends in real life
1.51
.545

14. Addictive tendencies for SNS
Average total score (theoretical span 6 to 30)
Only small group of people in Serbia reported addictive tendencies for SNS

15. Path analysis: fit indices for exshaustive and restrictive models
HF/DF
CFI
RMSEA
NFI
AIC
MODEL A
(exhaustive model: psychological predictors, demographic and traditional media exposure)
20,89
,683
,099
,677
446,969
MODEL B
(restrictive model: psychological predictors, demographic, without traditional media exposure)
26,41
,711
,112
,707
387,282
MODEL C
(restrictive model: psychological predictors, traditional media exposure, without demographic)
15,5
,934
,084
,931
92,24
MODEL D
(restrictive model: psychological predictors)
44,2
,949
,147
,948
70,20

16. Excepted model Traditional media Life self efficacy exposure ** **
ADDICTIVE SOCIAL
NETWORKING
Self esteem ** ** **
Extraversion

17. Conclusion
Given the popularity of Internet and SNS, especially among young generation, we should continue to identify the predictors of both use and overuse of all Internet applications.

18. Thank you