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Social networking sites and employment status: an investigation based on Understanding Society data

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Presented at the IDIMC Conference, Loughborough University, on 13/01/2016. By John Mowbray, Professor Robert Raeside, Professor Hazel Hall, and Dr Peter Robertson.

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Social networking sites and employment status: an investigation based on Understanding Society data

  1. 1. Social networking sites and employment status: an investigation based on Understanding Society data By John Mowbray Co authors: Professor Robert Raeside Professor Hazel Hall Dr Peter Robertson 2nd International Data and Information Management Conference 12th & 13th January 2016 Twitter: @jmowb_napier
  2. 2. Structure of presentation • Understanding society: The UK household longitudinal study • Background themes from the literature – The significance of social networks to job search – Social networking sites and job search • Hypotheses • Sample and method • Results • Discussion • Future research directions 1
  3. 3. • Innovative study about 21st century life in the UK • Longitudinal perspective on how UK life is changing • Derives information about peoples’: – Social and economic circumstances – Attitude – Behaviour – Health Source: University of Essex. Institute for Social and Economic Research and National Centre for Social Research/TNS BMRB, Understanding Society: Innovation Panel, Waves 1-7, 2008-2014 [computer file]. Colchester, Essex: UK Data Archive [distributor], July 2015. SN: 6849 2
  4. 4. Variables analysed for paper: • Membership of SNS • Frequency of SNS use • Number of close friends • Employment status • Age • Sex Source: University of Essex. Institute for Social and Economic Research and National Centre for Social Research/TNS BMRB, Understanding Society: Innovation Panel, Waves 1-7, 2008-2014 [computer file]. Colchester, Essex: UK Data Archive [distributor], July 2015. SN: 6849 3
  5. 5. Background themes from the literature 5
  6. 6. The significance of social networks to job search • Crucial job information can be attained from network contacts • Network structure is key – E.g. strong & weak ties • Access to social capital is also important • “Networking” a concept in job search theory 3D Social Networking © Photo by: Potter, C. (2012) Web: www.stockmonkeys.com Licence: https://creativecommons.org/licenses/by-sa/2.0/legalcode 5
  7. 7. Social networking sites and job search Social Media apps © Photo by: Howie, J. (2013) Web: https://goo.gl/kynW66 Licence: https://creativecommons.org/licenses/by-sa/2.0/legalcode • The use of digital technologies during job search is an area which is under researched • Social media afford unprecedented information gathering capacities • SNS associated with higher levels of social capital • SNSs facilitate online networks which proffer – sharing and networking – Channels for strong/weak ties 6
  8. 8. Hypotheses H1o: Employment status is not associated with membership of SNSs. H2o: Employment status is not associated with frequency of SNS use. H3o: Employment status is not associated with number of close friends. H4o: Age is not associated with membership of SNSs. H5o: Sex is not associated with the use of SNSs. 7
  9. 9. Sample and method • Sample of 3,616 16-21 year olds – 24% employed, 11% unemployed, 65% students • Hypotheses tested using Chi square analysis and independent t-tests • Binary logistic regression model fitted to understand multivariate effects – Controlling for sex and age – To determine the relationship between SNS membership, close friends, and employment status – n=1,266 (students removed) 8
  10. 10. Results 9
  11. 11. Hypotheses a / r Analysis H1o: Employment status is not associated with membership of SNSs r 92% of employed were members. 83.2% unemployed were members (p<0.001) H2o: Employment status is not associated with frequency of SNS use. r Evidence of association, although not a linear one. H3o: Employment status is not associated with number of close friends. a 6.05 mean friends amongst employed, 5.88 amongst unemployed (p=0.674) H4o: Age is not associated with membership of SNSs. r 18.34 mean age of members, 18.68 mean age of non-members (p=0.001). H5o: Sex is not associated with the use of SNSs. r Females higher users of SNSs (90.1% to 88.1%) (p=0.001). Also, females more frequent users (33% > 3 hours per day to 28%) (p<0.001). 10
  12. 12. Hypotheses a / r Analysis H1o: Employment status is not associated with membership of SNSs r 92% employed, and 83.2% unemployed were members (p<0.001). H2o: Employment status is not associated with frequency of SNS use. r Evidence of association, although not a linear one. H3o: Employment status is not associated with number of close friends. a 6.05 mean friends amongst employed, 5.88 amongst unemployed (p=0.001) H4o: Age is not associated with membership of SNSs. r 18.34 mean age of members, 18.68 mean age of non-members (p=001). H5o: Sex is not associated with the use of SNSs. r Females higher users of SNSs (90.1% to 88.1%) (p=0.001). Also, females more frequent users (33% > 3 hours per day to 28%) (p<0.001). 11
  13. 13. Frequency of SNS use (n=3616) Hours per day spent interacting with friends through SNSs Economic Status none under an hour 1-3 hours 4-6 hours 7 or more hours Employed 4.5% 30.3% 36.9% 16.1% 12.3% Unemployed 6.3% 21.2% 33.9% 20.6% 18.0% Student 3.4% 26.5% 40.1% 17.0% 12.9% All respondents 4.0% 26.9% 38.7% 17.2% 13.3% 12
  14. 14. Hypotheses a / r Analysis H1o: Employment status is not associated with membership of SNSs r 92% employed, and 83.2% unemployed were members (p<0.001). H2o: Employment status is not associated with frequency of SNS use. r Evidence of association, although not a linear one. H3o: Employment status is not associated with number of close friends. a 6.05 mean friends amongst employed, 5.88 amongst unemployed (p=0.674) H4o: Age is not associated with membership of SNSs. r 18.34 mean age of members, 18.68 mean age of non-members (p=0.001). H5o: Sex is not associated with the use of SNSs. r Females higher users of SNSs (90.1% to 88.1%) (p=0.001). Also, females more frequent users (33% > 3 hours per day to 28%) (p<0.001). 13
  15. 15. Hypotheses a / r Analysis H1o: Employment status is not associated with membership of SNSs r 92% employed, and 83.2% unemployed were members (p<0.001). H2o: Employment status is not associated with frequency of SNS use. r Evidence of association, although not a linear one. H3o: Employment status is not associated with number of close friends. a 6.05 mean friends amongst employed, 5.88 amongst unemployed (p=0.674) H4o: Age is not associated with membership of SNSs. r 18.34 mean age of members, 18.68 mean age of non-members (p=0.001). H5o: Sex is not associated with the use of SNSs. r Females higher users of SNSs (90.1% to 88.1%) (p=0.001). Also, females more frequent users (33% > 3 hours per day to 28%) (p<0.001). 14
  16. 16. Hypotheses a / r Analysis H1o Employment status is not associated with membership of SNSs r 92% employed, and 83.2% unemployed were members (p<001). H2o: Employment status is not associated with frequency of SNS use. r Evidence of association, although not a linear one. H3o: Employment status is not associated with number of close friends. a 6.05 mean friends amongst employed, 5.88 amongst unemployed (p=0.674) H4o: Age is not associated with membership of SNSs. r 18.34 mean age of members, 18.68 mean age of non-members (p=0.001). H5o: Sex is not associated with the use of SNSs. r Females higher users of SNSs (90.1% to 88.1%) (p=0.001). Also, females more frequent users (33% > 3 hours per day to 28%) (p<0.001). 15
  17. 17. Hypotheses a / r Analysis H1o: Employment status is not associated with membership of SNSs r Logistic regression model confirmed the association, and predicted 68.8% of respondents correctly H2o: Employment status is not associated with frequency of SNS use. r Evidence of association, although not a linear one. H3o: Employment status is not associated with number of close friends. a 6.05 mean friends amongst employed, 5.88 amongst unemployed (p=0.001) H4o: Age is not associated with membership of SNSs. r 18.34 mean age of members, 18.68 mean age of non-members (p=001). H5o: Sex is not associated with the use of SNSs. r Females higher users of SNSs (90.1% to 88.1%) (p=0.001). Also, females more frequent users (33% > 3 hours per day to 28%) (p<0.001). 16
  18. 18. Discussion • Direction of causality? • What is the nature of SNS use? • Functional and/or social? • What is the role of “weak ties” in information sharing? Society Gates © Photo by: Lleberwirth, R. (2014) Web: https://goo.gl/jhzbWR Licence: https://creativecommons.org/licenses/by-sa/2.0/legalcode 17
  19. 19. Future research directions • To determine the information needs of young jobseekers • To determine how young jobseekers engage in networking behaviours during job search – Who are they asking (i.e. people and or/organisations)? – What social media tools are they using? – What is the online/offline divide? • To determine the barriers and enablers young jobseekers face to networking 19
  20. 20. References • Bell, D., & Blanchflower, D. G. (2010). Young people and recession: A lost generation?. In Fifty-Second Panel Meeting on Economic Policy, Einaudi Institute for Economics and Finance, October, 22-23. • Beaudoin, C. E., & Tao, C. C. (2007). Benefiting from social capital in online support groups: An empirical study of cancer patients. CyberPsychology & Behavior, 10(4), 587-590. • Ellison, N. B., Steinfield, C., & Lampe, C. (2007). The benefits of Facebook “friends”: social capital and college students’ use of online social network sites. Journal of Computer‐Mediated Communication, 12(4), 1143-1168. • Finlay, I., Sheridan, M., McKay, J., & Nudzor, H. (2010). Young people on the margins: in need of more choices and more chances in twenty‐first century Scotland. British Educational Research Journal, 36(5), 851–867. • Gibson, C., H. Hardy III, J., & Ronald Buckley, M. (2014). Understanding the role of networking in organizations. Career Development International, 19(2), 146-161. • Granovetter, M. S. (1973). The strength of weak ties. American journal of sociology, 1360-1380. • Granovetter, M. (1974). Getting a job. Cambridge, MA: Harvard University Press. 20
  21. 21. References (2) • Kietzmann, J. H., Hermkens, K., McCarthy, I. P., & Silvestre, B. S. (2011). Social media? Get serious! Understanding the functional building blocks of social media. Business horizons, 54(3), 241-251. • Ofcom (2014). Adults’ Media Use and Attitudes Report. [Online]. Available at: http://stakeholders.ofcom.org.uk/binaries/research/media-literacy/adults-2014/2014_Adults_report.pdf [Accessed 20th February 2015]. • Smith, S. S. (2005). Don’t put my name on it: social capital activation and job‐finding assistance among the black urban poor. American Journal of Sociology, 111(1), 1-57. • Valenzuela, S., Park, N., & Kee, K. F. (2009). Is there social capital in a social network site?: Facebook use and college students' life satisfaction, trust, and participation. Journal of Computer‐Mediated Communication, 14(4), 875- 901. • Verhaeghe, P.-P., Van der Bracht, K., & Van de Putte, B. (2015). Inequalities in social capital and their longitudinal effects on the labour market entry. Social Networks, 40, 174–184. • Wanberg, C. R., Kanfer, R., & Banas, J. T. (2000). Predictors and outcomes of networking intensity among unemployed job seekers. Journal of Applied Psychology, 85(4), 491. • Wolff, H. G., & Kim, S. (2012). The relationship between networking behaviors and the Big Five personality dimensions. Career Development International, 17(1), 43-66. 21
  22. 22. Any Questions? Blog site: www.johnmowbray.org 22

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