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 HEIs 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