Skip to main navigation menu Skip to main content Skip to site footer

Research Articles

Vol. 22 No. 1 (2021): Vol. XXII No. 1 First Semester January - June 2021

Effects of the features of the videos on YouTube that increase their popularity: an empirical analysis

DOI
https://doi.org/10.22267/rtend.202102.153
Submitted
December 28, 2020
Published
2021-01-01

Abstract

YouTube is the most visited multimedia platform in the world; ideal setting for promoting products and services. The literature has been concerned with identifying elements that favor the popularity of videos on this platform; however, it is still rare. This research establishes the effects of the message strategy, brand consistency and technical elements of resolution and duration of the videos on popularity, understood as the volume of reproductions of each video published by mobile phone companies. Content analysis was used to identify the aforementioned characteristics and, using a negative binomial regression model, the hypotheses were tested. The findings showed that functional and emotional content strategies, brand consistency, and video resolution increase the volume of views. On the other hand, the length of the video decreases the reproduction rate. These results may be useful when developing strategies for the dissemination of advertising pieces on YouTube by mobile phone companies, however, future studies could analyze other industries to contrast the results obtained.

References

  1. (1) Abitbol, A., & Lee, S. (2017). Messages on CSR-dedicated Facebook pages: What works and what doesn’t. Public Relations Review, 43(4), 796-808. https://doi.org/10.1016/j.pubrev.2017.05.002
  2. (2) Ahmad, U., Zahid, A., Shoaib, M., & AlAmri, A. (2017). HarVis: An integrated social media content analysis framework for YouTube platform. Information Systems, 69, 25-39. https://doi.org/10.1016/j.is.2016.10.004
  3. (3) Akaike, H. (1974). A New Look at the Statistical Model Identification. IEEE Transactions on Automatic Control, 19(6), 716-723. https://doi,org/10.1109/TAC.1974.1100705
  4. (4) Borghol, N., Ardon, S., Carlsson, N., Eager, D., & Mahanti, A. (2012). The untold story of the clones: content-agnostic factors that impact YouTube video popularity. Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. https://doi.org/10.1145/2339530.2339717
  5. (5) Brasel, S. (2012). How focused identities can help brands navigate a changing media landscape. Business Horizons, 55(3), 283-291. Beijin, China. https://doi.org/10.1016/j.bushor.2012.01.005
  6. (6) Cameron, A., & Trivedi, P. (2013). Regression Analysis of Count Data (2.ªed.). Cambridge University Press.
  7. (7) Coxe, S., West, S., & Aiken, L. (2009). The Analysis of Count Data: A Gentle Introduction to Poisson Regression and Its Alternatives. Journal of Personality Assessment, 91(2), 121-136. https://doi.org/10.1080/00223890802634175
  8. (8) De Vries, L., Gensler, S., & Leeflang, P. (2012). Popularity of Brand Posts on Brand Fan Pages: An Investigation of the Effects of Social Media Marketing. Journal of Interactive Marketing, 26(2), 83-91. https://doi.org/10.1016/j.intmar.2012.01.003
  9. (9) Dehghani, M., Khorram, M., Ramezani, I., & Sali, R. (2016). Evaluating the influence of YouTube advertising for attraction of young customers. Computers in Human Behavior, 59, 165-172. https://doi.org/10.1016/j.chb.2016.01.037
  10. (10) eMarketer (2019). Key Digital Trends H2 2019. Five Trends to Track as We Head Into 2020. https://bit.ly/3bPRk8x
  11. (11) Figueiredo, F., Almeida, J., Gonçalves, M., & Benevenuto, F. (2014). On the dynamics of social media popularity: A YouTube case study. ACM Transactions on Internet Technology (TOIT), 14(4), 1-21. https://bit.ly/3bFEppz
  12. (12) Gupta, H., Singth, S., & Sinha, P. (2017). Multimedia tool as a predictor for social media advertising - a YouTube way. Multimedia Tools and Applications, 76(18), 18557–18568. https://doi.org/10.1007/s11042-016-4249-6
  13. (13) Hair, J., Black, W., Babin, B., & Anderson, R. (2013). Multivariate data analysis (7.ªed.). Prentice Hall.
  14. (14) Jáugueri, D. (2018, enero 9). Claro tiene la atención al cliente más deficiente de la telefonía móvil según encuesta de LR. La República. https://bit.ly/2zbc2lF
  15. (15) Kemp., S. (2019, 30 de enero). Digital 2019: Global Internet Use Accelerates. We Are Social Inc. https://bit.ly/3cK7n92
  16. (16) Kim, J., Guo, P., Seaton, D., Mitros, P., Gajos, K., & Miller, R. (2014). Understanding in-video dropouts and interaction peaks inonline lecture videos. Proceedings of the first ACM conference on Learning@ scale conference. Atlanta, USA. https://dl.acm.org/doi/10.1145/2556325.2566237
  17. (17) Krippendorff, K. (2018). Content analysis: An introduction to its methodology. Sage Publications.
  18. (18) Lee, C., & Ma, L. (2012). News sharing in social media: The effect of gratifications and prior experience. Computers in human behavior, 28(2), 331-339. https://doi.org/10.1016/j.chb.2011.10.002
  19. (19) Londoño-Silva, A. M., Osorio-Andrade, C. F. y Peláez-Muñoz, J. P. (2020). Efectos del lenguaje publicitario y del destino turístico usados en páginas comerciales de Facebook sobre la generación de boca a boca electrónico. Estudios Gerenciales, 36(156), 264-271. https://doi.org/10.18046/j.estger.2020.156.3895
  20. (20) Mudzanani, T. (2015). A review and analysis of the role of integrated marketing communication message typology in the development of communication strategies. African Journal of Marketing Management, 7(8), 90-97. https://doi.org/10.5897/AJMM2015.0475
  21. (21) Navarro, A., Utzet, F., Puig, P., Caminal, J. y Martín, M. (2001). La distribución binomial negativa frente a la de Poisson en el análisis de fenómenos recurrentes. Gaceta Sanitaria, 15(5), 447-452. https://doi.org/10.1016/S0213-9111(01)71599-3
  22. (22) Schultz, C. (2017). Proposing to your fans: Which brand post characteristics drive consumer engagement activities on social media brand pages? Electronic Commerce Research and Applications, 26, 23-24. https://doi.org/10.1016/j.elerap.2017.09.005
  23. (23) Scott, J., & Freese, J. (2014). Regression Models for Categorical Dependent Variables Using Stata (2.ªed.). Stata Press.
  24. (24) Shoufan, A., & Mohamed, F. (2017). On the likes and dislikes of youtube’s educational videos: A quantitative study. Proceedings of the 18th annual conference on information technology education. New York, USA. https://bit.ly/2TjCUqv
  25. (25) Socialbakers. (2019). YouTube stats - Brands in Colombia. https://bit.ly/3bKJIDU
  26. (26) Steffes, S., Lee, J., & Lee, S. (2014). Consumer-generated ads on Youtube: impacts of source credibility and need for cognition on attitudes, interactive behaviors, and ewom. Journal of Electronic Commerce Research, 15(3), 254-266. http://www.jecr.org/sites/default/files/Paper9.pdf
  27. (27) Tafesse, W. (2015). Content strategies and audience response on Facebook brand pages. Marketing Intelligence and Planning, 33(6), 927-943. https://doi.org/10.1108/MIP-07-2014-0135
  28. (28) Ten Hove, P., & Van Der Meij, H. (2015). Like It or Not. What Characterizes YouTube’s More Popular Instructional Videos? Technical Communication, 62(1), 48-62. https://bit.ly/3g1AjeH
  29. (29) Tuells, J., Martínez-Martínez, P., Duro-Torrijos, J., Caballero, P., Fraga-Freijeiro, P. y Navarro-López, V. (2015). Características de los vídeos en español publicados en Youtube sobre la vacuna contra el virus del papiloma humano. Revista Española de Salud Pública, 89(1), 107-115. https://doi.org/10.4321/S1135-57272015000100012
  30. (30) Voorveld, H., Neijens, P., & Smit, E. (2011). Opening the black box: Understanding cross-media effects. Journal of Marketing Communications, 17(2), 69-85. https://doi.org/10.1080/13527260903160460
  31. (31) Welbourne, D., & Grant, W. (2016). Science communication on YouTube: Factors that affect channel and video popularity. Public Understanding of Science, 25(6), 706-718. https://doi.org/10.1177%2F0963662515572068

Downloads

Download data is not yet available.