Connected audiences in digital media marketsthe dynamics of university online video impact

  1. Germán López-Buenache 1
  2. Ángel Meseguer-Martínez 2
  3. Alejandro Ros-Gálvez 1
  4. Alfonso Rosa-García 3
  1. 1 Universidad Católica San Antonio
    info

    Universidad Católica San Antonio

    Murcia, España

    ROR https://ror.org/05b1rsv17

  2. 2 Universidad de Castilla-La Mancha
    info

    Universidad de Castilla-La Mancha

    Ciudad Real, España

    ROR https://ror.org/05r78ng12

  3. 3 Universidad de Murcia
    info

    Universidad de Murcia

    Murcia, España

    ROR https://ror.org/03p3aeb86

Zugehörigkeit anzeigen +
Zeitschrift:
European Research on Management and Business Economics

ISSN: 2444-8834

Datum der Publikation: 2022

Ausgabe: 28

Nummer: 1

Seiten: 11-20

Art: Artikel

DOI: 10.1016/J.IEDEEN.2021.100176 DIALNET GOOGLE SCHOLAR

Andere Publikationen in: European Research on Management and Business Economics

Zusammenfassung

This paper analyses whether the audience dynamics of one content provider can explain the audience dynamics of a different content provider, and the resulting network of connections among providers. The type of connections in this network determines whether the audience of one creator influences or is suscepti- ble to other creators’ audience. Granger causality networks are applied to prestigious universities that pro- vide online videos on YouTube and the structure of the Audience Dynamics Network is described. This network presents an unbalanced degree distribution and a core-periphery structure. The centrality of the universities in the network is discussed and universities with influential and susceptible roles are identified. We find that audience connection is determined by the differences in the online video impact between each pair of universities. Centrality in the network is associated with university prestige, but this relation is medi- ated by online video impact

Bibliographische Referenzen

  • Academic Ranking of World Universities, ARWU (2018). Available at: http://www. shanghairanking.com/ARWU2018.html
  • Al-Rawi, A. (2019). Viral news on social media. Digital Journalism, 7(1), 63–79. Anderson, C. (2004). The long tail. Wired. https://www.wired.com/2004/10/tail/.
  • Aral, S., & Walker, D. (2012). Identifying influential and susceptible members of social networks. Science (New York, N.Y.), 337(6092), 337–341.
  • Arroyo-Barrig€uete, J. L., Lopez-Sanchez, J. I., Minguela-Rata, B., & Rodriguez-Duarte, A. (2019). Use patterns of educational videos: A quantitative study among university students. WPOM-Working Papers on Operations Manage- ment, 10(2), 1–19.
  • Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1192.
  • Battaggion, M. R., & Drufuca, S. M. (2019). Quality competition and entry: A media mar- ket case. Journal of Economics, 1–36.
  • Billio, M., Getmansky, M., Lo, A. W., & Pelizzon, L. (2012). Econometric measures of con- nectedness and systemic risk in the finance and insurance sectors. Journal of Finan- cial Economics, 104(3), 535–559. Social Blade (2020). Available at: https://socialblade.com/
  • Bollobas, B. (2001). Random graphs (2nd ed., Cambridge studies in advanced mathemat- ics). Cambridge: Cambridge University Press.
  • Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92(5), 1170–1182.
  • Borgatti, S. P. (2005). Centrality and network flow. Social Networks, 27(1), 55–71.
  • Borgatti, S. P., & Everett, M. G. (1999). Models of core/periphery structures. Social Net- works, 21, 375–395.
  • Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). Ucinet for windows: Software for social network analysis. Harvard, MA: Analytic Technologies.
  • Borghol, Y., Mitra, S., Ardon, S., Carlsson, N., Eager, D., & Mahanti, A. (2011). Character- izing and modelling popularity of user-generated videos. Performance Evaluation, 68(11), 1037–1055.
  • Brech, F. M., Messer, U., Vander Schee, B. A., Rauschnabel, P. A., & Ivens, B. S. (2017). Engag- ing fans and the community in social media: Interaction with institutions of higher education on Facebook. Journal of Marketing for Higher Education, 27(1), 112–130.
  • Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. In Proceedings of the 7th international world wide web conference (pp. 107−117).
  • Brusco, M., Stolze, H. J., Hoffman, M., & Steinley, D. (2017). A simulated annealing heu- ristic for maximum correlation core/periphery partitioning of binary networks. PloS One, 12,(5) e0170448.
  • Bullmore, E., & Sporns, O. (2009). Complex brain networks: Graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10(3), 186–198.
  • Caraiani, P. (2013). Using complex networks to characterize international business cycles. PloS One, 8(3), e58109.
  • Cunningham, S., Craig, D., & Silver, J. (2016). YouTube, multichannel networks and the accelerated evolution of the new screen ecology. Convergence, 22(4), 376–391.
  • Diks, C., & Panchenko, V. (2006). A new statistic and practical guidelines for nonpara- metric Granger causality testing. Journal of Economic Dynamics and Control, 30(9 −10), 1647–1669.
  • Du, W. B., Zhang, M. Y., Zhang, Y., Cao, X. B., & Zhang, J. (2018). Delay causality network in air transport systems. Transportation Research Part E: Logistics and Transportation Review, 118, 466–476.
  • Dutta, A. (2001). Telecommunications and economic activity: An analysis of Granger causality. Journal of Management Information Systems, 17(4), 71–95.
  • Figueiredo, F. (2013). On the prediction of popularity of trends and hits for user gener- ated videos. In Proceedings of the sixth ACM international conference on Web search and data mining (pp. 741−746).
  • Figueiredo, F., Benevenuto, F., & Almeida, J. M. (2011). The tube over time: Characteriz- ing popularity growth of youtube videos. In Proceedings of the fourth ACM interna- tional conference on Web search and data mining (pp. 745−754).
  • Gaenssle, S., & Budzinski, O. (2020). Stars in social media: New light through old win- dows? Journal of Media Business Studies, 18(2), 1–27.
  • Gonzalez-Maestre, M., & Martínez-Sanchez, F. (2015). Quality choice and advertising regulation in broadcasting markets. Journal of Economics, 114(2), 107–126.
  • Granger, C. W. (1969). Investigating causal relations by econometric models and cross- spectral methods. Econometrica: Journal of the Econometric Society, 424–438.
  • Guzman-Duque, A. P., & del Moral-Perez, M. E. (2014). Tendencias de uso de YouTube: Optimizando la comunicacion estrategica de las universidades iberoamericanas. Observatorio, 8(1), 69–94.
  • Han, Y., Lappas, T., & Sabnis, G. (2020). The importance of interactions between content characteristics and creator characteristics for studying virality in social media. Information Systems Research, 31(2), 576–588.
  • Hanneman, R. A., & Riddle, M. (2005). Introduction to social network methods. Riverside, CA: University of California, Riverside.
  • Huang, C., Wen, S., Li, M., Wen, F., & Yang, X. (2020). An empirical evaluation of the influential nodes for stock market network: Chinese A shares case. Finance Research Letters forthcoming.
  • Hue, S., Lucotte, Y., & Tokpavi, S. (2019). Measuring network systemic risk contribu- tions: A leave-one-out approach. Journal of Economic Dynamics and Control, 100, 86–114.
  • Ilyas, M. U., & Radha, H. (2011). Identifying influential nodes in online social networks using principal component centrality. 2011 IEEE international conference on com- munications (pp. 1−5).
  • Jiang, L., Miao, Y., Yang, Y., Lan, Z., & Hauptmann, A. G. (2014). Viral video style: A closer look at viral videos on YouTube. In Proceedings of international conference on multi- media retrieval (pp. 193−200).
  • Khan, G. F., & Vong, S. (2014). Virality over YouTube: An empirical analysis. Internet Research, 24(5), 629–647.
  • Kim, J. (2012). The institutionalization of YouTube: From user-generated content to professionally generated content. Media, Culture & Society, 34(1), 53–67.
  • Lovari, A., & Giglietto, F. (2012). Social media and Italian universities: An empirical study on the adoption and use of Facebook, Twitter and Youtube. Available at: http://ssrn. com/abstract=1978393 or https://doi.org/10.2139/ssrn.1978393.
  • Luca, M. (2015). User-generated content and social media. In handbook of media econom- ics, vol. 1B, edited by Simon Anderson, Joel Waldfogel, and David Str€omberg. (pp. 563 −592) Amsterdam: North-Holland.
  • Lund, B. (2019). Universities engaging social media users: An investigation of quantita- tive relationships between universities’ Facebook followers/interactions and uni- versity attributes. Journal of Marketing for Higher Education, 29(2), 251–267.
  • Mangani, A. (2003). Profit and audience maximization in broadcasting markets. Infor- mation Economics and Policy, 15(3), 305–315.
  • Martín-Gonzalez, M. Y., & Santamaría Llarena, R. (2017). Universidades espa~nolas en Youtube: Gestion de canales institucionales y de sus contenidos. Cuadernos de Doc- umentacion Multimedia, 28(2), 147–169.
  • Meseguer-Martínez, A., Ros-Galvez, A., & Rosa-García, A. (2017). Satisfaction with online teaching videos: A quantitative approach. Innovations in Education and Teaching International, 54(1), 62–67.
  • Meseguer-Martínez, A., Ros-Galvez, A., & Rosa-García, A. (2019b). Linking YouTube and university rankings: Research performance as predictor of online video impact. Telematics and Informatics, 43, 101264.
  • Meseguer-Martínez, A., Ros-Galvez, A., Rosa-García, A., & Catalan-Alarcon, J. A. (2019a). Online video impact of world class universities. Electronic Markets, 29(3), 519–532.
  • Mwenda, A. B., Sullivan, M., & Grand, A. (2019). How do Australian universities market STEM courses in YouTube videos? Journal of Marketing for Higher Education, 29(2), 191–208.
  • Oh, S., Baek, H., & Ahn, J. (2017). Predictive value of video-sharing behavior: Sharing of movie trailers and box-office revenue. Internet Research, 27(3), 691–708.
  • Papaioannou, G. P., Dikaiakos, C., Kaskouras, C., Evangelidis, G., & Georgakis, F. (2020). Granger causality network methods for analyzing cross-border electricity trading between Greece, Italy, and Bulgaria. Energies, 13(4), 900.
  • Park, J. H., Chang, W., & Song, J. W. (2020). Link prediction in the Granger causality net- work of the global currency market. Physica A: Statistical Mechanics and its Applica- tions, 553,(1) 124668.
  • Ros-Galvez, A., Meseguer-Martínez, A., & Lopez-Buenache, G. (2021). The dynamics of the university impact on YouTube: A comparative analysis. Social Network Analysis and Mining, 11(1), 9.
  • Rutz, O. J., Bucklin, R. E., & Sonnier, G. P. (2012). A latent instrumental variables approach to modeling keyword conversion in paid search advertising. Journal of Marketing Research, 49(3), 306–319.
  • Saurabh, S., & Gautam, S. (2019). Modelling and statistical analysis of YouTube’s educa- tional videos: A channel Owner’s perspective. Computers & Education, 128, 145– 158.
  • Schwemmer, C., & Ziewiecki, S. (2018). Social media sellout: The increasing role of product promotion on YouTube. Social Media + Society, 4,(3) 2056305118786720.
  • Shoufan, A. (2019a). Estimating the cognitive value of YouTube’s educational videos: A learning analytics approach. Computers in Human Behavior, 92, 450–458.
  • Shoufan, A. (2019b). What motivates university students to like or dislike an educa- tional online video? A sentimental framework. Computers & Education, 134, 132– 144.
  • Sugimoto, C. R., Work, S., Lariviere, V., & Haustein, S. (2017). Scholarly use of social media and altmetrics: A review of the literature. Journal of the Association for Infor- mation Science and Technology, 68(9), 2037–2062.
  • Susarla, A., Oh, J. H., & Tan, Y. (2016). Influentials, imitables, or susceptibles? Virality and word-of-mouth conversations in online social networks. Journal of Manage- ment Information Systems, 33(1), 139–170.
  • Telkmann, V. (2021). Broadcasters’ content distribution and programming decisions in multi-channel environments: A literature review. Journal of Media Business Studies, 18(2), 106–131.
  • Trzcinski, T., & Rokita, P. (2017). Predicting popularity of online videos using support vector regression. IEEE Transactions on Multimedia, 19(11), 2561–2570.
  • Tseng, C. H., & Huang, T. L. (2016). Internet advertising video facilitating health com- munication: Narrative and emotional perspectives. Internet Research, 26(1), 236– 264.
  • Ver Steeg, G., & Galstyan, A. (2012). Information transfer in social media. In Proceedings of the 21st international conference on World Wide Web (pp. 509−518).
  • Vyrost, T., Lyocsa, S., & Baum€ohl, E. (2015). Granger causality stock market networks: Temporal proximity and preferential attachment. Physica A: Statistical Mechanics and its Applications, 427, 262–276.
  • Xiao, C., Xue, Y., Li, Z., Luo, X., & Qin, Z. (2015). Measuring user influence based on mul- tiple metrics on YouTube. Algorithms and programming (PAAP), 2015 seventh inter- national symposium on parallel architectures (pp. 177−182).
Fuente de los datos: Dialnet