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

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. 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
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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
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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. Background themes from the
literature
5

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
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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
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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.
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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)
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10. Results
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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).
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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).
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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%
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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. 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. 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. 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).
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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
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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
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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.
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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.
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22. Any Questions?
Blog site:
www.johnmowbray.org
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