Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
Julie M. Birkholz1,*, Marco Seeber1 & Kim Holmberg2
*Julie.Birkholz@UGent.be
1Centre for Higher Education Governance Ghent...
Higher education institutions
are increasingly using social
media platforms as tools to
communicate to prospective
and cur...
Why online visibility matters?
Core organizational attributes
matter in explaining online
communication; where
status, reputation and size
are important ...
Is online visibility affected
by social media use or by
other organizational
characteristics?
What is the relative
contrib...
We investigate to what
extent the number of twitter
followers is predicted by the
use of Twitter and by the
organizational...
Social media visibility can be explained by:
• Hypothesis 1: the social media use of the
organization
• Hypothesis 2: a HE...
Data about
137 UK HEIs
*European Micro Data dataset
(Eumida) - a database containing
the structural characteristics of
2,4...
Collected from Twitter profiles
Dependent: Social media visibility (Twitter)
• Number of followers
• Total number of tweet...
Independent: Organizational characteristics (Owen-
Smith & Powell 2008):
• size of the university (number of staff units
a...
Control variables
• the discipline profile, as some disciplines
may attract more attention than others
because of the soci...
Mean Median Maximum Minimum
Standard
Deviation
size - units of staff 2.001 1.665 9.498 68 1.675
size - undergraduate stude...
Method: Negative binomial regression
We find that HEIs’ visibility on Twitter are only
partly explained by social media us...
Table 3 - Pearson correlation between the selected variables
1 2 3 4 5 6 7 8 9 10 11 12 13 14
1 size - units of staff 1 ,6...
Table 4 - Negative Binomial regressions models
Estimate S.E. Pr(>|z|) Estimate S.E. Pr(>|z|) Estimate S.E. Pr(>|z|) Estima...
delta: standard
deviation
proportion in number of
followers
1 size - undergraduate students 8 462 1.16
2 research intensit...
Results (5/6)
The overall predicting
capability of the full
model is fairly good.
Values below and above
the median of 15,...
Results (6/6)
Some outliers (based on number of followers) were
excluded from the initial test, but later tested:
• Open U...
Discussion (1/3)
The results emphasize the importance of considering a
range of organizational and social media use variab...
Discussion (2/3)
In addition…
The results advances our
understanding of metrics
validity and sheds light on
the practical ...
Discussion (3/3)
Future research could be
interested to investigate how
the content of tweets may
influence online visibil...
İlginiz için teşekkür ederiz
Kim Holmberg
kim.j.holmberg@utu.fi
http://kimholmberg.fi
@kholmber
Natural
Sciences
Engineering &
Technology
Medical
Sciences
Agricultural
Sciences
Social
Sciences
Humanities
Rotated Compon...
Drivers of higher education institutions’ visibility: a study of UK HEIs social media use vs. organizational characteristics
Upcoming SlideShare
Loading in …5
×

Drivers of higher education institutions’ visibility: a study of UK HEIs social media use vs. organizational characteristics

872 views

Published on

Social media is increasingly used in higher education settings by researchers, students and institutions. Whether it is researchers conversing with other researchers, or universities seeking to communicate to a wider audience, social media platforms serve as a tools for users to communicate and increase visibility. Scholarly communication in social media and investigations about social media metrics is of increasing interest for scientometric researchers, and to the emergence of altmetrics. Less understood is the role of organizational characteristics in garnering social media visibility, through for instance liking and following mechanisms. In this study we aim to contribute to the understanding of the effect of specific social media use by investigating higher education institutions’ presence on Twitter. We investigate the possible connections between followers on Twitter and the use of Twitter and the organizational characteristics of the HEIs. We find that HEIs’ social media visibility on Twitter are only partly explained by social media use and that organizational characteristics also play a role in garnering these followers. Although, there is an advantage in garnering followers for those first adopters of Twitter. These findings emphasize the importance of considering a range of factors to understand impact online for organizations and HEIs in particular.

Published in: Science
  • Be the first to comment

  • Be the first to like this

Drivers of higher education institutions’ visibility: a study of UK HEIs social media use vs. organizational characteristics

  1. 1. Julie M. Birkholz1,*, Marco Seeber1 & Kim Holmberg2 *Julie.Birkholz@UGent.be 1Centre for Higher Education Governance Ghent & Research Unit for the Sociology of Education, Ghent University, Belgium 2Research Unit for the Sociology of Education, University of Turku, Finland Drivers of higher education institutions’ visibility: a study of UK HEIs social media use vs. organizational characteristics
  2. 2. Higher education institutions are increasingly using social media platforms as tools to communicate to prospective and current students, alumni and society at large
  3. 3. Why online visibility matters?
  4. 4. Core organizational attributes matter in explaining online communication; where status, reputation and size are important predictors of hyperlink connections and centrality (Seeber et al. 2012, Lepori et al. 2013).
  5. 5. Is online visibility affected by social media use or by other organizational characteristics? What is the relative contribution of organizational characteristics and social media use in explaining social media visibility (n of followers on twitter)?
  6. 6. We investigate to what extent the number of twitter followers is predicted by the use of Twitter and by the organizational characteristics of the Higher Education Institutions (HEIs) in the UK.
  7. 7. Social media visibility can be explained by: • Hypothesis 1: the social media use of the organization • Hypothesis 2: a HEIs organizational characteristics related to organizational size, status and reputation • Hypothesis 3: both the HEI’s social media use and organizational characteristics Hypotheses
  8. 8. Data about 137 UK HEIs *European Micro Data dataset (Eumida) - a database containing the structural characteristics of 2,457 Higher Education institutions in twenty-eight European countries (Bonaccorsi et al. 2010; Eumida 2009). * 1 2
  9. 9. Collected from Twitter profiles Dependent: Social media visibility (Twitter) • Number of followers • Total number of tweets sent • The number of users that the HEIs are following as a measure of their activity • Date of first tweet • and also whether the HEIs use Twitter to share general news or to reach out to students specifically Measures (1/3)
  10. 10. Independent: Organizational characteristics (Owen- Smith & Powell 2008): • size of the university (number of staff units and undergraduate students) • reputation in the core activities of research, measured through the scientific productivity and the research intensity, and teaching, measured through the teaching burden • status, as measured through the relational centrality of the university in the system Measures (2/3)
  11. 11. Control variables • the discipline profile, as some disciplines may attract more attention than others because of the societal salience of the topics addressed • the geographical context, in terms of the urban centrality of the city where the university is located. Measures (3/3)
  12. 12. Mean Median Maximum Minimum Standard Deviation size - units of staff 2.001 1.665 9.498 68 1.675 size - undergraduate students 13.826 13.356 33.640 351 8.462 reputation - scientific productivity 274,66 72,50 1.828,00 0,00 389,03 reputation - research intensity 0,04 0,02 0,27 0,00 0,05 reputation - teaching burden 8,14 7,89 28,03 1,78 3,80 status - coreness 68 66 173 0 45 urban centrality 2,2 0,0 9,0 0,0 3,5 number of followers 17.189 15.900 46.200 1.233 10.085 number of tweets 6.792 5.598 19.000 300 4.220 days on twitter 1.918 2.019 2.644 305 342 number of following 1.312 832 12.700 107 1.506 Table 2. Variables’ descriptive statistics
  13. 13. Method: Negative binomial regression We find that HEIs’ visibility on Twitter are only partly explained by social media use and that organizational characteristics also play a role in explaining the social media visibility of HEIs. There is also an early-adopter (of social media) advantage. Results (1/6)
  14. 14. Table 3 - Pearson correlation between the selected variables 1 2 3 4 5 6 7 8 9 10 11 12 13 14 1 size - units of staff 1 ,683** ,575** ,427** -,291** ,804** -,006 ,513** -,098 -,182* ,642** ,112 -,159 ,183* 2 size - undergraduate students ,683** 1 ,187* -,065 ,176* ,564** -,152 ,459** ,057 -,208* ,477** ,264** ,046 ,106 3 reputation - scientific productivity ,575** ,187* 1 ,495** -,370** ,596** ,065 ,465** -,175* -,100 ,452** -,035 -,107 ,188* 4 reputation - research intensity ,427** -,065 ,495** 1 -,411** ,444** ,238** ,246** -,038 -,019 ,347** -,185* -,147 ,029 5 reputation - teaching burden -,291** ,176* -,370** -,411** 1 -,298** -,107 -,173* ,095 -,056 -,230** ,090 ,091 -,092 6 status - coreness ,804** ,564** ,596** ,444** -,298** 1 -,046 ,566** ,132 -,219* ,693** ,159 -,052 ,145 7 urban centrality -,006 -,152 ,065 ,238** -,107 -,046 1 -,147 -,162 ,044 -,052 -,290** -,142 ,017 8 discipline profile - factor 1 ,513** ,459** ,465** ,246** -,173* ,566** -,147 1 ,000 ,000 ,336** ,107 -,076 ,085 9 discipline profile - factor 2 -,098 ,057 -,175* -,038 ,095 ,132 -,162 ,000 1 ,000 ,066 ,089 ,060 -,069 10 discipline profile - factor 3 -,182* -,208* -,100 -,019 -,056 -,219* ,044 ,000 ,000 1 -,252** -,121 -,114 -,058 11 number of followers ,642** ,477** ,452** ,347** -,230** ,693** -,052 ,336** ,066 -,252** 1 ,323** ,294** ,326** 12 number of tweets ,112 ,264** -,035 -,185* ,090 ,159 -,290** ,107 ,089 -,121 ,323** 1 ,120 ,158 13 days on twitter -,159 ,046 -,107 -,147 ,091 -,052 -,142 -,076 ,060 -,114 ,294** ,120 1 ,033 14 number of following ,183* ,106 ,188* ,029 -,092 ,145 ,017 ,085 -,069 -,058 ,326** ,158 ,033 1 **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed). Results (2/6)
  15. 15. Table 4 - Negative Binomial regressions models Estimate S.E. Pr(>|z|) Estimate S.E. Pr(>|z|) Estimate S.E. Pr(>|z|) Estimate S.E. Pr(>|z|) Intercept 9,752 0,054 <2e-16 *** 8,862 0,089 <2e-16 *** 8,371 289,300 <2e-16 *** 7,671 0,230 <2e-16 *** size - undergraduate students 0,000023 0,000007 0,0007*** 0,000018 0,000006 0,0035** research intensity 2,774 1,088 0,01* 3,416 1,013 0,0007*** coreness 0,005 0,001 0,0002*** 0,005 0,001 0,0004*** Tweets 0,00004 0,00001 0,0003*** 0,00003 0,00001 0,0004*** days twitter 0,001 0,000 0,0003*** 0,00053 0,00011 0,000002*** orientation: news and events 0,296 0,113 0,009** orientation:students -0,312 0,183 0,09 . Null deviance 145,96 252,25 183,82 304,75 Residual 142,49 142,25 144,15 141,37 AIC: 2898,6 2822,1 2871,3 2798,4 log-likelihood: -2894,6 -2812,1 -2859,3 -2784,4 Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Empty Model Organizational variables Model Social media use Model Full Model on 136 df on 136 df on 131 df on 136 df on 132 df on 136 df on 133 df on 136 df Results (3/6)
  16. 16. delta: standard deviation proportion in number of followers 1 size - undergraduate students 8 462 1.16 2 research intensity 0.049 1.18 3 Status - coreness 45 1.24 4 Tweets 4 220 1.15 5 days twitter 342 1.20 Table 5 - Negative binomial regression model: comparing the impact of the variables. Results (4/6)
  17. 17. Results (5/6) The overall predicting capability of the full model is fairly good. Values below and above the median of 15,900 Twitter followers (Full model) were predicted correctly for 79% (positive predictive value) and 92% (sensitivity) of the cases respectively. Figure 1. Results from Full model
  18. 18. Results (6/6) Some outliers (based on number of followers) were excluded from the initial test, but later tested: • Open University (129,825 predicted vs. about 100,000 actual followers) • University of Oxford (60,180 vs. about 175,000) • University of Cambridge (85,692 vs. about 151,000) • London Business School (12,624 vs. about 69,800) While Open University is well predicted by the model, the other three are not. All four attract a larger number of followers than predicted by the model.
  19. 19. Discussion (1/3) The results emphasize the importance of considering a range of organizational and social media use variables in order to fully understand online visibility. This topic is of interest for scientometrics, as it is an additional avenue from bibliometrics to evaluate potential impact of a HEIs.
  20. 20. Discussion (2/3) In addition… The results advances our understanding of metrics validity and sheds light on the practical questions of how organizations can garner visibility online, but also of the limitations to do that, as organizational characteristics strongly affect a HEIs visibility potential online.
  21. 21. Discussion (3/3) Future research could be interested to investigate how the content of tweets may influence online visibility The existence of a few outliers suggest that few actors attract a disproportionally high attention from the public. Future research may investigate why this occurs.
  22. 22. İlginiz için teşekkür ederiz Kim Holmberg kim.j.holmberg@utu.fi http://kimholmberg.fi @kholmber
  23. 23. Natural Sciences Engineering & Technology Medical Sciences Agricultural Sciences Social Sciences Humanities Rotated Component Matrixa Component 1 2 3 sci ,657 -,095 -,131 eng ,554 ,309 ,239 med ,456 -,688 -,271 agr ,021 -,053 ,942 sosc ,228 ,806 -,221 hum -,975 -,020 -,047 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 6 iterations. We run a factor analysis on the UK sample discipline compositions and find 3 factors. In the EUMIDA dataset we have data on the share of academic staff in six disciplinary areas: The discipline profile

×