Digital advertising as a stimulant of basic emotional response in the audience
Article Sidebar
Main Article Content
Abstract
Advertising messages elicit emotional reactions in consumers. Attempts have been made to explain how advertising works within affective processes and the central position that information selection takes in consciousness. These breaches arise when examining emotions in advertising, especially in advertising's digital environments and media. Therefore, the objective of the research was to inquire into the digital advertising scenes of "Origen Ambateño 2022" from the facial expressions of the basic emotions that most attracted the audiences' attention. A quantitative approach, descriptive level, and Neuromarketing context were used, using the AffdexMe tool for university students of both sexes. As the main results, the emotions of joy and surprise were the most significant in the scenes. However, the most significant negative emotional state was disgust. However, disgust, anger, fear, and sadness presented low activations between the ascents and descents in each scene. The "Origen Ambateño 2022" campaign generated more positive than negative emotions. So, its benefits are reflected in the fact that digital advertising is more remembered by the audience, promotes, and encourages the consumption of local products, and makes visible all the productive capacity and territorial wealth.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
COPYRIGHT NOTICE
Authors who publish in the INNOVA Research Journal keeps copyright and guarantee the journal the right to be the first publication of the work under the Creative Commons License, Attribution-Non-Commercial 4.0 International (CC BY-NC 4.0). They can be copied, used, disseminated, transmitted and publicly exhibited, provided that: a) the authorship and original source of their publication (magazine, publisher, URL and DOI of the work) is cited; b) are not used for commercial purposes; c) the existence and specifications of this license of use are mentioned.
References
Aiolfi, S., Bellini, S., & Pellegrini, D. (2021). Data driven digital advertising: benefits and risks of online behavioral advertising. International Journal of Retail & Distribution Management, 49(7), 1-22. https://doi.org/10.1108/IJRDM-10-2020-0410
Alarcón, S. (2020). Análisis de tecnologías digitales para beneficiar el ejercicio profesional de los mediadores faniliares de Chile. Revista de Derecho Privado, (39), 33-60. https://dialnet.unirioja.es/servlet/articulo?codigo=7865433
Alawamleh, M., Twait, L., & Saht, G. (2020). The effect of online learning on communication between instructors and students during Covid-19 pandemic. Asian Education and Development, 1-22. https://doi.org/10.1108/AEDS-06-2020-0131
Appel, G., Grewal, L., Hadi, R., & Stephen, A. (2020). The future of social media in marketing. Journal of the Academy of Marketing Science, 48, 79-95. https://doi.org/10.1007/s11747-019-00695-1
Behera, R. G., Gupta, S., Kamboj, S., & Bala, P. (2019). Personalized digital marketing recommender engine. Journal of Retailing and Consumer Services, 1-24. https://doi.org/10.1016/j.jretconser.2019.03.026
Bhatia, T. (2019). Las emociones y el lenguaje en la publicidad. Journal Citation Reports, 435-449. https://doi.org/10.1111/weng.12420
Chen, Q., Feng, Y., Liu, L., & Tian, X. (2019). Understanding consumers’ reactance of online personalized advertising: A new scheme of rational choice from a perspective of negative effects. International Journal of Information Management, 44, 53-64. https://doi.org/10.1016/j.ijinfomgt.2018.09.001
Chen, S., & Lin, C. (2019). Understanding the effect of social media marketing activities: The mediationof social identification, perceived value, and satisfaction. Technological Forecasting & Social Change, 140, 22-32. https://doi.org/10.1016/j.techfore.2018.11.025
Choi, H., Carl, M., Balseiro, S., & Leary, A. (2020). Online Display Advertising Markets: A Literature Review and Future Directions. Information Systems Research, 1-20. https://doi.org/10.1287/isre.2019.0902
Cotán Fernández, A. (2020). El método etnográfico como construcción de conocimiento: un análisis descriptivo sobre su uso y conceptualización en ciencias sociales. Revista de Educación de la Universidad de Málaga, 1(1), 83-103. http://dx.doi.org/10.24310/mgnmar.v1i1.7241
Cowen, A., Keltner, D., Schroff, F., Jou, B., Hartwig, A., y Prasad, G. (2021). Dieciséis expresiones faciales ocurren en contextos similares en todo el mundo. Naturaleza, 589, 251-257. https://doi.org/10.1038/s41586-020-3037-7
Dafonte, A. (2018). The key elements of viral advertising. From motivation to emotion in the most shared videos. Media Education Reseacrh Journal, 22(43), 200-206. http://dx.doi.org/10.3916/C43-2014-20
Dang, H., Liu, F., Stehouer, J., Liu, X., & Jain, A. (2020). On the detection of digital face manipulation. CvF, 5781-5790.
Das, G., Wiener, H., & Kareklas, I. (2019). To emoji or not to emoji? Examining the influence of emoji on consumer reactions to advertising. Journal of Business Research, 96, 147-156. https://doi.org/10.1016/j.jbusres.2018.11.007
Dastane, O. (2020). Impact of digital marketing on online purchase intention: Mediation effect of customer relationship management. Journal of Asian Business Strategy, 10(1), 142-158. https://doi.org/10.18488/journal.1006.2020.101.142.158
Dolensek, N., Gehrlach, D., Klein, A., & Gogolla, N. (2020). Facial expressions of emotion states and their neuronal correlates in mice. Research Neuroscience, 368(6486), 89-94. https://doi.org/10.1126/science.aaz9468
Egger, M., Ley, M., & Hanke, S. (2019). Emotion recognition from physiological signal analysis: a review. Electronic Notes in Theoretical Computer Science, 343, 35-55. https://doi.org/10.1016/j.entcs.2019.04.009
Fernández, P., Peña, F., Ren, T., & Cunha, A. (2020). Human emotion recognition: review of sensors and methods. Sensors, 20(30), 2-40. https://doi.org/10.3390/s20030592
Fiestas Ambato. (2022). De origen Ambateño. De origen Ambateño: https://www.fiestasdeambato.com/origen
Gogic, I., Manhart, M., Pandzic, I., & Ahlberg, J. (2020). Fast facial expression recognition using local binary features and shallow neural networks. The Visual Computer, 36(1), 97-112. https://doi.org/10.1007/s00371-018-1585-8
Goldenberg, A., & Gross, J. (2020). Digital emotion contagion. CellPress Reviews, 1-13. https://doi.org/10.1016/j.tics.2020.01.009
Gutiérrez, G. (2019). El neuromarketing, como herramienta efectiva para la educación en las ventas y la publicidad. Revista Latina de Comunicación Social, (74), 1173-1189. https://doi.org/10.4185/RLCS-2019-1377
Heras, C., Sánchez, P., & Ignacio, J. (2020). Sentiment analysis and emotion understanding during the Covid 19 pandemic in Spain its impact on digital ecosystems. International journal of environmental research and public health, 1-22. https://doi.org/10.3390/ijerph17155542
Hernández-Ávila, C. E., y Carpio Escobar, N. A. (2019). Introducción a los tipos de muestreo. Revista Científica del Instituto Nacional de Salud, 2(1), 75-79. https://doi.org/10.5377/alerta.v2i1.7535
Karsten, L. (2022). Medición de la expresión facial de la emoción. Diálogos en Neurociencia Clínica, 17(4), 457-462. https://doi.org/10.31887/DCNS.2015.17.4/kwolf
Kietzmann, J., Lee, L., McCarthy, I., & Kietzmann, T. (2020). Deepfakes: Trick or treat? Business Horizons, 63(2), 135-146. https://doi.org/10.1016/j.bushor.2019.11.006
Kim, J., Kang, S., & Lee, K. (2019). Evolution of digital marketing communication: Bibliometric analysis and network visualization from key articles. Journal of Business Research, 130, 552-563. https://doi.org/10.1016/j.jbusres.2019.09.043
Kross, E., Verduyn, P., Sheppes, G., Costello, C., Jonides, J., & Ybarra, O. (2020). Social Media and Well-Being: Pitfalls, Progress, and Next Steps. Trends in Cognitive Sciences, 20(20), 1-12. https://doi.org/10.1016/j.tics.2020.10.005
Kyung, Y., y Sullivan, P. (2019). El branding emocional habla al corazón de los consumidores: el caso de las de moda. Moda y textiles, 6(2), 1-20. https://doi.org/10.1186/s40691-018-0164-y
Li, Y., & Xie, Y. (2020). Is a picture worth a thousand words? An empirical study of image content and social media engagement. Journal of Marketing Research, 57(1), 1-19. https://doi.org/10.1177/0022243719881113
Mafe, C., Bigné, E., & Currás, R. (2019). The effect of emotions, eWOM quality and online review sequence on consumer intention to followadvice obtained fromdigital services. Journal of Service Management, 1-23.https://doi.org/10.1108/JOSM-11-2018-0349
Medina, R. (06 de Septiembre de 2022). Branch Marketing Digital. Obtenido de Estadísticas de la situación Digital en Ecuador 2021-2022. https://branch.com.co/marketing-digital/estadisticas-de-la-situacion-digital-en-ecuador-2021-2022/
Meire, M., Hewett, K., Ballings, M., Kumar, & Poel, D. (2019). The role of marketer-generated content in customer engagement marketing. Journal of Marketing, 1-22. https://doi.org/10.1177/0022242919873903
Melo, E. (24 de mayo de 2018). Al día, una persona recibe entre 3.000 y 5.000 mensajes publicitarios. Obtenido de La república. https://www.larepublica.co/empresas/al-dia-una-persona-recibe-entre-3-000-y-5-000-mensajes-publicitarios-2730745
Nuñez, M., López, A., & Vela, D. (2021). Theoretical-scientific review of the conceptual framework of emotion and sentiment and its application to neuromarketing. Vivat Academia Revista de Comunicación, (154), 381-407. https://dialnet.unirioja.es/servlet/articulo?codigo=7931419
Otamendi, J., y Sutil, D. (2020). La efectividad emocional de la publicidad. Frontiers in Psychology, 11, 1-12. https://doi.org/10.3389/fpsyg.2020.02088
Piper, L. (2020). COVID-19 Information seeking on digital media and preventive behaviors: The mediation role of worry. Cyberpsychology, Behavior and Social Networking, 23(10), 1-6. https://doi.org/10.1089/cyber.2020.0250
Pobee, F. (2021). Non-probabilistic approach to e-banking adoption: the moderating impact of trialability. 47(2), 1-30. https://doi.org/10.1177/0258042X211054248
Poels, K., & Dewitte, S. (2019). The role of emotions in advertising: a call to action. Journal of Advertising, 48(1), 81-90. https://doi.org/10.1080/00913367.2019.1579688
Putri, E. (2022). An impact of the use instagram application towards students vocabulary. Pustakailmu, 2(22), 1-10. http://pustakailmu.id/index.php/pustakailmu/article/view/88
Qiyu, B., Qi, D., Zhe, M., & Maokun, Y. (2019). A systematic review of emoji: current research and future perspectives. Front. Psychol, 10, 1-36. https://doi.org/10.3389/fpsyg.2019.02221
Ramos, C. (2021). Diseño de investigación experimental. Ciencia América, 10(1), 1-7. https://doi.org/10.33210/ca.v10i1.356
Sanz, P., Jiménez, G., y Elías, R. (2019). La incorporación de la figura del influencer en las campañas publicitarias: Consecuencia para las agencias de publicidad española. Estrategias, Tendencias e Innovación en Comunicación, (18), 63-86. http://dx.doi.org/10.6035/2174-0992.2019.18.5
Saura, J. (2020). Using data sciences in digital marketing: framework, methods, and performance metrics. Journal of Innovation & Knowledge, 6(2), 92-102. https://doi.org/10.1016/j.jik.2020.08.001
Schreiner, M., Fischer, T., y Riedl, R. (20221). Impacto de las características del contenido y la emoción en el compromiso del comportamiento en las redes sociales: revisión de la literatura y agenda de investigación. Electronic Commerce Research, (21), 329-345. https://doi.org/10.1007/s10660-019-09353-8
Shahbaznezhad, H., Dolan, R., & Rashidirad. (2021). The role of social media content format and platform in users engagement behavior. Journal of Interactive Marketing, 53(1), 47-65. https://doi.org/10.1016/j.intmar.2020.05.001
Shen, C., Kasra, M., Wenjing, G., & Brien, J. (2018). Fake images: The effects of source, intermediary, and digital media literacy on contextual assessment of image credibility online. New media & society, 21(2), 1-6. https://doi.org/10.1177/1461444818799526
Smink, Frowijn, Reijmersdal, Noort, & Neijens. (2019). Try online before you buy: How does shopping with augmented reality affect brand responses and personal data disclosure. Electronic Commerce Research and Applications, 35, 1-10. https://doi.org/10.1016/j.elerap.2019.100854
Stroe, S., Sirén, C., Shepherd, D., & Wincent, J. (2019). The dualistic regulatory effect of passion on the relationship between fear of failure and negative affect: Insights from facialbetween fear of failure and negative affect: Insights from facial expression analysis. Journal of Business Venturing, 35(4), 1-20.https://doi.org/10.1016/j.jbusvent.2019.105948
Strohmaier, A., Mackay, K., Obersteiner, A., & Reiss, K. (2020). Eye-tracking methodology in mathematics education research: A systematic literature review. Educational Studies in Mathematics volume, 104, 147-200. https://doi.org/10.1007/s10649-020-09948-1
Szabo, S., & Webster, J. (2020). Perceived greenwashing: the effects of green marketing on environmental and product perceptions. Journal of Business Ethics, 171, 719-739. https://doi.org/10.1007/s10551-020-04461-0
Vaccari, C., & Chadwick, A. (2020). Deepfakes and disinformation: exploring the impact of synthetic political the impact of synthetic politicaland trust in news. Social Media + Society, 1-13. https://doi.org/10.1177/2056305120903408
Vieira, V., Severo, M., Agnihotri, R., & Correa, N. (2019). In pursuit of an effective B2B digital marketing strategy in an emerging market. Journal of the Academy of Marketing Science, 47, 1085-1108. https://doi.org/10.1007/s11747-019-00687-1
Wei, H., Hauer, R., & He, X. (2021). A forest experience does not always evoke positive emotion: A pilot study on unconscious facial expressions using the face reading technology. Forest Policy and Economics, 123. https://doi.org/10.1016/j.forpol.2020.102365
Westerlund, M. (2019). The emergence of deepfake technology: a review. Technology Innovation Management Review, 9(11), 39-52. https://timreview.ca/article/1282
Yue, L., Chen, W., Li, X., Zuo, W., & Yin, M. (2019). A survey of sentiment analysis in social media. Knowledge and Information Systems, 60, 617-663. https://doi.org/10.1007/s10115-018-1236-4
Zoya, K., Lartsev, S., Revelsky, A., & Buryak, A. (2021). Microextraction by packed sorbent optimized by statistical design of experiment as an approach to increase the sensitivity and selectivity of HPLC-UV determination of parabens in cosmetics. Journal of Pharmaceutical and Biomedical Analysis, 195, 32-40. https://doi.org/10.1016/j.jpba.2020.113843