Recibido: 2017-01-09 / Aceptado: 2017-03-23

La relación entre el visionado y la evaluación del anuncio. Un análisis estructural de la publicidad no pagada en YouTube

Teresa Pintado, Joaquín Sánchez

DOI: 10.7764/cdi.40.1088

Resumen


Las redes sociales están siendo ampliamente estudiadas en el entorno publicitario. Sin embargo, escasean las investigaciones relevantes que analizan la estructura social digital formada por los anuncios y sus implicaciones. Para analizar este tópico se han seleccionado 387 campañas emitidas en la red social YouTube, junto con los votos y comentarios de 14.612 individuos. Los resultados muestran que anuncios con un número alto de visionados no tienen por qué ser los mejor valorados, y que la estructura de los anuncios sigue un patrón organizado en función de temas específicos. Tal estudio podría ser el punto de partida para trabajos centrados en tipologías concretas de anuncios o usuarios, y de utilidad para comprender mejor el proceso de planificación publicitaria online.

Palabras clave


publicidad online; redes sociales; YouTube; eWom

Como citar Pintado, T., & Sánchez, J. (2017). La relación entre el visionado y la evaluación del anuncio. Un análisis estructural de la publicidad no pagada en YouTube. Cuadernos.Info, (40), 189-202. https://doi.org/10.7764/cdi.40.1088

Referencias

  1. Abrahamson, E. & Rosenkopf, L. (1997). Social network effects on the extent of diffusion: A computer simulation. Organizational Science, 8(3), 289-309. https://doi.org/10.1287/orsc.8.3.289
  2. Bampo, M., Ewing, M. T., Mather, D. R., Stewart, D. & Wallace, M. (2008). The effects of the social structure of digital networks on viral marketing performance. Information Systems Research, 19(3), 273-290. https://doi.org/10.1287/isre.1070.0152
  3. Bandiera, O., Barankay, I. & Rasul, I. (2009). Social connections and incentives in the workplace: evidence from personnel data. Econometrica, 77(4), 1047-1094. https://doi.org/10.3982/ECTA6496
  4. Bertrand, M., Luttmer, E. F. P. & Mullainathan, S. (2000). Network effects and welfare cultures. Quarterly Journal of Economics, 115(3), 1019-1056. https://doi.org/10.1162/003355300554971
  5. Bramoullé, Y., Djebbari, H. & Fortin, B. (2009). Identification of peer effects through social networks. Journal of Econometrics, 150(1), 41-55. https://doi.org/10.1016/j.jeconom.2008.12.021
  6. Brock, W. A. & Durlauf, S. N. (2007). Identification of binary choice models with social interactions. Journal of Econometrics, 140(1), 52-75. https://doi.org/10.1016/j.jeconom.2006.09.002
  7. Cheung, C. M. K. & Lee, M. K. O. (2008). Information adoption in an online discussion forum, Proceedings of the International Joint Conference on e-Business and Telecommunications, Barcelona, Spain,
  8. -31 July.
  9. Chiu, H. C., Hsieh, Y. C., Kao, Y. H. & Lee, M. (2007). The determinants of email receivers’ disseminating behaviors on the Internet. Journal of Advertising Research, 47(4), 524-534. https://doi.org/10.2501/S0021849907070547
  10. Choi, H., Kim, S. H. & Lee, J. (2010). Role of network structure and network effects in diffusion of innovations. Industrial Marketing Management, 39(1), 170-177. https://doi.org/10.1016/j.indmarman.2008.08.006
  11. Chu, S. C. & Kim, Y. (2011). Determinants of consumer engagement in electronic word-of-mouth (eWOM) in social networking sites. International Journal of Advertising, 30(1), 47-75. https://doi.org/10.2501/IJA-30-1-047-075
  12. Cocktail Report (2016). VIII Observatorio de Redes Sociales [VIII Social Network Observatory]. The Cocktail Analysis. Retrieved from http://tcanalysis.com/blog/posts/viii-observatorio-de-redessociales
  13. Coleman, J. S., Katz, E. & Menzel, H. (1966). Medical innovation: A diffusion study. Indianapolis, IN: Bobbs-Merrill.
  14. De Bruyn, A., & Lilien, G. L. (2008). A multi-stage model of word-of-mouth influence through viral marketing. International Journal of Research in Marketing, 25(3), 151-163. https://doi.org/10.1016/j.ijresmar.2008.03.004
  15. Dehghani, M., Niaki, M. K., Ramezani, I. & Sali, R. (2016). Evaluating the influence of YouTube advertising for attraction of young customers. Computers in Human Behavior, 59( June), 165-172. https://doi.org/10.1016/j.chb.2016.01.037
  16. Dobele, A., Toleman, D. & Beverland, M. (2005). Controlled infection! Spreading the brand message through viral marketing. Business Horizons, 48(2), 143-149. https://doi.org/10.1016/j.bushor.2004.10.011
  17. Eccleston, D. & Griseri, L. (2008). How does Web 2.0 stretch traditional influencing patterns. International Journal of Market Research, 50(5), 591-161. https://doi.org/10.2501/S1470785308200055
  18. Ferguson, R. (2008). Word of mouth and viral marketing: taking the temperature of the hottest trends in marketing. Journal of Consumer Marketing, 25(3), 179-182. https://doi.org/10.1108/07363760810870671
  19. Feroz Khan, G. & Vong, S. (2014). Virality over YouTube: an empirical analysis. Internet Research, 24(5), 629-647. https://doi.org/10.1108/IntR-05-2013-0085
  20. Gladwell, M. (2002). The tipping point: How little things can make a big difference. Boston: Little, Brown and Company.
  21. Golan, G. J. & Zaidner, L. (2008). Creative Strategies in Viral Advertising: An Application of Taylor’s Six-Segment Message Strategy Wheel. Journal of Computer-Mediated Communication, 13(4), 959-972.
  22. https://doi.org/10.1111/j.1083-6101.2008.00426.x
  23. Goldenberg, J., Han, S., Lehmann, D. R., & Hong, J. W. (2009). The role of hubs in the adoption process. Journal of Marketing, 73(2), 1-13. https://doi.org/10.1509/jmkg.73.2.1
  24. Hartmann, W. R., Manchanda, P., Nair, H., Bothner, M., Dodds, P., Godes, D., Hosanaga, K. & Tucker, C. (2008). Modeling social interactions: identification, empirical methods and policy implications.
  25. Marketing Letters, 19(3). https://doi.org/10.1007/s11002-008-9048-z
  26. Hinz, O., Skiera, B., Barrot, C. & Becker, J. U. (2011). Seeding strategies for viral marketing: An empirical comparison. Journal of Marketing, 75(6), 55-71. https://doi.org/10.1509/jm.10.0088
  27. Ho, J. Y., & Dempsey, M. (2010). Viral marketing: Motivations to forward online content. Journal of Business Research, 63(9), 1000-1006. https://doi.org/10.1016/j.jbusres.2008.08.010
  28. Hong, T. (2006). The influence of structural and message features on web site credibility. Journal of the American Society for Information Science and Technology, 57(1), 114-127. https://doi.org/10.1002/asi.20258
  29. Hutcheon, L. (2000). A theory of parody: The teachings of twentieth-century art forms. Champaign, IL: University of Illinois Press.
  30. Kalyanam, D., McIntyre, S. y Masonis, J. T. (2007). Behind the scenes of a viral marketing campaign: How Plaxo Crossed the Tipping Point and Avoided the Fate of the Ebola Virus. Document: Plaxo JIM.
  31. Katz, E. & Lazarsfeld, P. F. (1955). Personal influence: The part played by people in the flow of mass communications. Glencoe, IL: Free Press.
  32. Kiss, C. & Bichler, M. (2008). Identification of influencers — Measuring influence in customer networks. Decision Support Systems, 46(1), 233-253. https://doi.org/10.1016/j.dss.2008.06.007
  33. Ko, D. G., Kirsch, L. J. & King, W.R. (2005). Antecedents of knowledge transfer from consultants to clients in enterprise system implementations. MIS Quarterly, 29(1), 59-85.
  34. Lee, J. & Hong, I. B. (2016). Predicting positive user responses to social media advertising: The roles of emotional appeal, informativeness, and creativity. International Journal of Information Management, 36(3), 360-373. https://doi.org/10.1016/j.ijinfomgt.2016.01.001
  35. Lee, M. K. O., Cheung, C. M. K., Lim, K.H. & Sia, C. L. (2006). Understanding customer knowledge sharing in web-based discussion boards: an exploratory study. Internet Research, 16(3), 289-303.
  36. https://doi.org/10.1108/10662240610673709
  37. Manski, C. F. (1993). Identification of endogenous social effects: the reflection problem. The Review of Economic Studies, 60(3), 531-542. https://doi.org/10.2307/2298123
  38. Mengze, S. (2003). Social network- based discriminatory pricing strategy. Marketing Letters, 14(4), 239-256. https://doi.org/10.1023/B:MARK.0000012470.94220.db
  39. Mohr, I. (2014). Going Viral: An Analysis of YouTube Videos. Journal of Marketing Development and Competitiveness, 8(3), 43. Retrieved from http://search.proquest.com/docview/1647066428?pqorigsite=gscholar
  40. Nair, H., Manchanda, P. & Bhatia, T. (2010). Asymmetric social interactions in physician prescription behavior: The role of opinion leaders. Journal of Marketing Research, 47(5), 883-895. https://doi.org/10.1509/jmkr.47.5.883
  41. Nardi, B. A., Whitaker, S. & Heirich, S. (2000). It’s not what you know, it’s who you know: work in the information age. First Monday. https://doi.org/10.5210/fm.v5i5.741
  42. Newman, M. E. & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113. APS. https://doi.org/10.1103/physreve.69.026113
  43. Niederhoffer, K., Mooth, R., Wiesenfeld, D. & Gordon, J. (2007). The origin and impact of CPG newproduct buzz: Emerging trends and implications. Journal of Advertising Research, 47(4), 420-426.
  44. https://doi.org/10.2501/S0021849907070432
  45. Paek, H., Kyongseok, K. & Hove, T. (2010). Content analysis of antismoking videos on YouTube: message sensation value, message appeals, and their relationships with viewer responses. Health Education
  46. Research, 25(6), 1085-1099. https://doi.org/10.1093/her/cyq063
  47. Phelps, J. E., Lewis, R., Mobilio, L., Perry, D. & Raman, N. (2004). Viral marketing or electronic word-ofmouth advertising: Examining consumer responses and motivations to pass along email. Journal of Advertising Research, 44(4), 333-348. https://doi.org/10.1017/S0021849904040371
  48. Pikas, B., Sorrentino, G. (2014). The Effectiveness of Online Advertising: Consumer’s Perceptions of Ads on Facebook, Twitter and YouTube. The Journal of Applied Business and Economics, 16(4), 70. Retrieved
  49. from http://m.www.na-businesspress.com/JABE/PikasB_Web16_4_.pdf
  50. Pornpitakpan, C (2004). The persuasiveness of source credibility. A critical review of five decades’evidence. Journal of Applied Social Phsycology, 34(2), 243-281. https://doi.org/10.1111/j.1559-1816.2004.tb02547.x
  51. Rogers, E. M. (2003). Diffusion of innovations (5th ed.). New York: Free Press.
  52. Sacerdote, B. (2001). Peer effects with random assignment: results for Dartmouth roommates. Quarterly Journal of Economics, 116, 681-704. https://doi.org/10.1162/00335530151144131
  53. Salganik, M. J., Dodds, P. S. & Watts, D. J. (2006). Experimental study of inequality and unpredictability in an artificial cultural market. Science, 311(5762), 854-856. https://doi.org/10.1126/science.1121066
  54. Sun, T., Youn, S., Wu, G. & Kuntaraporn, M. (2006). Online word-of-mouth (or mouse): An exploration of its antecedents and consequences. Journal of Computer-Mediated Communication, 11(4), 1104-1127. https://doi.org/10.1111/j.1083-6101.2006.00310.x
  55. Susarla, A., Oh, J. H. & Tan, Y. (2012). Social networks and the diffusion of user-generated content: Evidence from YouTube. Information Systems Research, 23(1), 23-41. https://doi.org/10.1287/isre.1100.0339
  56. Trogdon, J., Nonnemaker, J. & Pais, J. (2008). Peer effects in adolescent overweight. Journal of Health Economics, 27(5), 1388-1399. https://doi.org/10.1016/j.jhealeco.2008.05.003
  57. Trusov, M., Bodapati, A. V. & Bucklin, R. E. (2010). Determining influential users in internet social networks. Journal of Marketing Research, 47(4), 643-658. https://doi.org/10.1509/jmkr.47.4.643
  58. Valente, T. W. & Pumpuang, P. (2007). Identifying opinion leaders to promote behavior changes. Health Education & Behavior, 34, 881-896. https://doi.org/10.1177/1090198106297855
  59. Wang, X., Yu, C. & Wei, Y. (2012). Social media peer communication and impacts on purchase intentions: A consumer socialization framework. Journal of Interactive Marketing, 26(4), 198-208. https://doi.org/10.1016/j.intmar.2011.11.004
  60. Waters, R. D. & Jones, P. M. (2011). Using video to build an organization’s identity and brand: A content analysis of nonprofit organizations’ YouTube videos. Journal of Nonprofit & Public Sector Marketing, 23(3), 248-268. https://doi.org/10.1080/10495142.2011.594779
  61. Watts, D. J., & Dodds, P. S. (2007). Influentials, networks, and public opinion formation. Journal of Consumer Research, 34(4), 441-458. https://doi.org/10.1086/518527
  62. Yoganarasimhan, H. (2012). Impact of social network structure on content propagation: a study using YouTube data. Quantitative Marketing and Economics, 10, 111-150. https://doi.org/10.1007/s11129-011-9105-4
  63. Zhao, J. Wu, J. & Xu, K. (2010). Weak ties: Subtle role in the information diffusion of online social networks. Physical Review E, 82(1). https://doi.org/10.1103/PhysRevE.82.016105


Enlaces refback

  • No hay ningún enlace refback.