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Presentation at Social Media & Society 2014 conference, Toronto

The primary objective of the paper is to help better understand the role of SNS use in explanations of the effects of Internet use on three dimensions of quality of life (life satisfaction, knowledge, and sociability). The second objective is to test the possible substitutive role of the basic parameters of a respondent's social network (size, heterogeneity, and network capital), other online information and communication activities, innovativeness, digital skills and sociodemographic variables.

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Presentation at Social Media & Society 2014 conference, Toronto

  1. 1. Petr Lupač, Ph.D. Charles University in Prague World Internet Project The Czech Republic Financed due to Grant Agency of CZR (GA13-21024S) „World Internet Project – The Czech Republic II“
  2. 2. System of social inequality Unequal Internet access Unequal gains/losses The empirical evidence?
  3. 3. Source: Van Dijk, J. A. G. M. (2005) Deepening digital divide: Inequality in the information society. Thousand Oaks. CA: Sage, p. 22; simplified for purposes of presentation
  4. 4. Using the Internet can either improve or worsen people's lifes. When you think about your personal experience in the last years, how much influences your Internet use following areas of your life? Please, answer with the help of a scale, where -5 means significant worsening and +5 means significant improvement. [Scale:] -5 -4 -3 -2 -1 0 1 2 3 4 5 (-5=Significant worsening 5 = Significant improvement)
  5. 5.  My knowledge of what's going on in the Czech republic  My knowledge of what's going on in other countries  My knowledge of what's going on in your locality  My involvement in public life in your local community  Dealing with state authorities {getting subsidy, welfare, submitting documents, etc.}  Contact with my family and my family life  Contact with my friends and acquiantences  My overall financial situation (i.e., your incomes and expenses)  Building up my career and my success on labor market  Pursuing and developing my hobbies  Overall satisfaction with my life
  6. 6. Rotated Component Matrixa 1 2 3 4 5 6 7 Change in knowledge, CZR ,239 ,858 ,139 ,090 ,042 ,085 ,124 Change in knowledge, world ,095 ,873 ,160 ,108 ,115 ,089 ,105 Change in knowledge, locality ,152 ,379 ,747 -,022 ,136 ,086 ,053 Change in involvement, local public life ,092 ,035 ,880 ,191 ,043 ,062 ,103 Change in dealing with state auth. ,121 ,139 ,110 ,116 ,117 ,954 ,061 Change in family life/contact ,840 ,062 ,115 ,189 -,005 ,112 ,074 Change in contact with friends ,797 ,252 ,070 -,115 ,167 ,076 ,163 Change in financial situation ,069 ,115 ,120 ,837 ,286 ,161 ,072 Change in career / labor market succ. ,098 ,130 ,131 ,243 ,918 ,122 ,065 Change in pursuing hobbies ,225 ,195 ,133 ,108 ,068 ,066 ,932 Change in overall satisfaction with life ,608 ,181 ,166 ,554 -,007 -,055 ,144 (4) (5) . Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 6 iterations 74 % of variance (5 factors), 83 % of variance (7 factors)
  7. 7.  The supposed role of variety/number of online activities and time spent online (van Dijk 2005) ◦ -> hours online weekly, nr. of online non SNS activities performed weekly  The important role of digital skills, age and education in gaining benefits from Internet use (van Deursen, van Dijk and Peters 2011) ◦ -> operational skills index, informational skills index, age, education  Previous social skills predict well sociability gains from Internet use (rich-get-richer hypothesis findings; c.f., Lee 2009) ◦ -> ntw size index (via resource generator, sum of strong and weak ties)  The role of bridging social capital in acquiring resources ◦ -> bridging = bonding * nr. of structural holes  The role of network capital in explaining individual state (Wellman and Frank 1999) ◦ -> share of Internet users in respondent’s social environment  The ability to benefit from technological development (Rogers 2003) ◦ -> innovativeness index
  8. 8.  Donath & Boyd (2004) ◦ SNS use linked to higher amount of weak ties  Ellison, Steinfiels & Lampe (2007) ◦ The role of FB intensity of use, self-esteem and life satisfaction in predicting higher bridging social capital ◦ Compensation effect ◦ The role of FB intensity use confirmed by Steinfeld & Ellison (2008)  Valenzuela, Park & Kee (2009) ◦ Intensity of FB use linked to higher life satisfaction and civic participation, indirect link to social trust  Burke, Kraut & Marlow (2011) ◦ The effect of FB use on bridging cpt. varies according to passive x active use ◦ No relation between FB use and bonding cpt.  Lee, Noh & Koo (2013) ◦ Indirect effect of SNS use for lonely people via establishing strong ties online
  9. 9. 0. SNS use and total gains from the Internet use are sig. correlated 0.1. SNS use and sociability gains from the Internet use are sig. correlated 0.2. SNS use and knowledge gains from the Internet use are sig. correlated I. The intensity of SNS use predicts total gains from Internet use after controlling for other variables II. The intensity of SNS use predicts sociability gains from Internet use after controlling for other variables III. The intensity of SNS use predicts knowledge gains from Internet use after controlling for other variables
  10. 10.  Pilot study in May  Data collected in May and June 2014 by a specialized agency  Method of data collection ◦ CAPI F2F interviews ◦ Stratified random sampling combined with quota sampling ◦ Measures taken to include parts of the population with lower probability of being interviewed  Respondents declaring no or very low interest in being interviewed pre-recruited from CAWI panel (cca 8 % of the sample)  Trained experienced interviewers instructed to deal with soft-rejection  Financial incentives (computed or estimated from wage)  100 % of the interviews were recorded, controlled and problematic respondents were excluded  1316 respondents in the final sample, 79 % Internet users  Weighted sample representative for the population of the Czech Republic, age 15+  A good fit of results with other data sources (WIP I, CZSO, Facebook)
  11. 11. Problem: We do not know the share of non-questioned busy people in a general population -> four steps to balance the sample I. Weighting 92% of the sample (that was not pre-recruited) to fit the sociodemographic structure of the Czech population II. Finding the relations between sociodemographics and Internet use/nonuse as well as the “pure” percentage of Internet users III. Resulting Internet use added as a fixing variable to the weighting procedure IV. Weighting the whole sample by the following auxiliary variables  Region (14 categories – NUTS3), Size of municipality (5 categories), Gender (2 categories), Age (6 categories), Education (4 categories), Age x education (30 categories), Employment status (6 categories), Attended 2013 elections (2 categories) ◦ 5 iterations, weighting range: 0,5 – 2,0
  12. 12.  Core questions ◦ Frequencies of 35 online activities ◦ Places of use, connection devices ◦ Self-disclosure and privacy behavior and attitudes ◦ Importance of the Internet as a source of entertainment and information  Digital skills ◦ Operational, information, [strategic]  Social capital ◦ proxy use, binding, bridging, network, structural holes, self-reported sociability  Cultural capital ◦ Emerging, high-brow, reading books, active foreign language use  Political capital and behavior  Innovativeness  Internet indispensability  Time online weekly  Preference of online communication  Experience with the Internet use/nonuse affecting quality of life in 7 dimensions
  13. 13.  How often do you… A. Visit social networking sites (i.e., Facebook, Google+, Twitter, LinkedIn, Instagram) B. Post messages or comments on social networking sites, for example on Facebook or Twitter (if asked: also G+, Instagram, Linkedin, Lide.cz)  Possible answers:  Several times a day, daily, weekly, monthly, less than monthly, never  Transformations: ◦ SNSuse index = A+B (Min=1, Max=12; Cronbach α= 0,84; mean=6,3; SD=3,7) ◦ Logical types:  Daily users: visiting at least daily AND posting at least daily  Nonusers: Visiting never OR visiting less than monthly OR Posting never  Weak users: the rest
  14. 14. INDEX  Types of SNS users (% of CZR population 15+ ~ 8,9 mil.) Inet nonusers (21 %) SNS nonusers (40 %) SNS avg users (22 %) SNS daily users (17 %) 1,9 mil 3,5 mil. 2,0 mil. 1,5 mil. 0 20 40 1 2 3 4 5 6 7 8 9 10 11 12 35,8 27,2 4,8 3,6 7,9 4,2 11,2 9,7 10,3 14,2 8,7 19,9 POSTING VISITING Never Less than monthly Monthly Weekly Daily Several times a day
  15. 15. SNS NONUSERS (A) SNS AVG USERS (B) SNS DAILY USERS (C) Age 50BC (14) 35C (14) 30 (10) Nr. of close friends 5 (4) 6 (4) 6 (4) Hours online /week 15 (16) 22A (20) 31AB(22) Nr. of non-SNS online activities least weakly 7 (4) 12B (5) 16AB (5) Share of soc. environment using the Internet 70 % (20) 76 % (20) 84AB % (20) Life satisfaction 7/10 (2) 8/10 (2) 8/10 (2) Mean (SD) ABC - stat. sig. differences at α=.01 (Tukey post-hoc test)
  16. 16. Dependent ⇢ Independent ⇣ Total change Social contact change Knowledge change Gender 0,04 -,60 ,08* Age -,96** -,09* -,03 Education ,11*** -,03 ,09** SNS use intensity ,18*** ,21*** ,10** SNSuse (users only) ,10* ,10* ,04 Skills operational ,21*** ,17*** ,10** Skills informational ,30*** ,22*** ,24*** Innovativeness ,20*** ,18*** ,15*** OA variety – no SNS ,29*** ,24*** ,23*** Time on Internet /weak ,20*** ,15*** ,16*** Bridging soc. cpt. ,16*** ,08* ,10*** Ntw. size ,21*** ,16*** ,14*** Ntw. cpt ,26*** ,15*** ,21*** Life satisfaction ,19*** ,13*** ,09**
  17. 17. Model 1 Model 2 Model 3 Model 4 Age -,025 ,025 ,029 ,056 Education ,106** ,038 ,028 ,007 SNS use intensity ,145*** -,072 -,071 -,052 Time spent online ,057 ,055 ,039 OA variety - no SNS ,244*** ,251*** ,22*** Operational skills -,003 ,001 ,011 Informational skills ,253*** ,239*** ,197*** Innovativeness -,039 -,075 Life satisfaction ,126*** ,108** Bridging cpt. ,079* Ntw. Size ,06 Ntw. capital ,148*** Adj. R2 ,03 ,13 ,15 ,17
  18. 18. Model 1 Model 2 Model 3 Model 4 Age ,030 ,068 ,071 ,088* Education -,019 -,059* -,078* -,092** SNS use intensity ,209*** ,092 ,071 ,084 Time spent online ,041 ,025 ,012 OA variety – no SNS ,120** ,042 ,020 Operational skills ,000 ,029 ,022 Informational skills ,175*** ,162*** ,136*** Innovativeness ,029 ,003 Life satisfaction ,090** ,075* OA comm no SNS 0,14** ,141** Bridging cpt. ,023 Ntw. size ,084* Ntw. capital ,085* Adj. R2 ,04 ,07 ,09 ,10
  19. 19. Model 1 Model 2 Model 3 Model 4 Age -,020 -,012 -,018 -,001 Education ,106** -,012 ,010 ,001 SNS use intensity ,081* -,026 -,037 -,030 Time spent online ,037 ,013 ,004 OA variety -,082 -,099 -,104 Operational skills ,134*** ,128** ,124** Informational skills -,246*** -,206* -,187*** OA info ,385*** ,366*** ,353*** Innovativeness -,009 -,032 Netinfo ,202*** ,193*** Persinfo ,065* ,072* Bridging cpt. ,063* Ntw. Size ,022 Ntw. capital ,085** Adj. R2 ,02 ,16 ,20 ,21
  20. 20.  Daily SNS users and SNS nonusers differ with respect to declared Internet-induced changes in their ◦ Knowledge ◦ Contact with friend ◦ Life satisfaction  Intensity of SNS use does not appear to have a direct, independent effect on gains from Internet use  The strongest predictors in all three cases seem to be ◦ informational digital skills ◦ variety or intensity of other online activities ◦ network capital  Better predictor than online activities variety seem to be intensity of relevant types of online activity
  21. 21. Petr Lupač, Ph.D. PETR.LUPAC@FF.CUNI.CZ @PetrLupac Department of Sociology Charles University in Prague, Faculty of Arts Celetna 13, Prague The Czech Republic

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The primary objective of the paper is to help better understand the role of SNS use in explanations of the effects of Internet use on three dimensions of quality of life (life satisfaction, knowledge, and sociability). The second objective is to test the possible substitutive role of the basic parameters of a respondent's social network (size, heterogeneity, and network capital), other online information and communication activities, innovativeness, digital skills and sociodemographic variables.

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