An exploratory study on the impact of neuromarketing on virtual learning environments

Main Article Content

Luis Manuel Cerdá Suárez
Carmen Cristófol Rodriguez

Abstract

Neuromarketing is an essential topic in the current technological world, and it has experienced explosive growth in the last years as a tool for communication. Nowadays, neuromarketing subjects have greatly improved when classroom teaching is supported by adequate laboratory courses and experiments, following the ‘learning by doing’ paradigm, which provides students with a deeper understanding of theoretical lessons. However, many postgraduate programs do not teach their students about the use and applications of neuromarketing. It is believed that developments in neuromarketing will likely change the traditional practices in the classroom. The objective of this paper is to propose a mix of consumer-based technologies to develop a neuromarketing project into a laboratory activity. These technologies can make neuromarketing more appealing to students by enhancing the attractiveness of business administration curricula. This neuromarketing exploration project has been evaluated successfully based on the results and responses to questionnaires: students and experts rated the neuromarketing laboratory activity highly. Students found the laboratory activity learning in the neuromarketing exploration project to be very good or excellent. Moreover, the students obtained good academic outcomes. Within the specific context of a virtual private university, this work was oriented to design a neuromarketing workshop to develop certain generic competencies for improving educational processes at universities. The findings of this research will be relevant in decisions of educational policy, but also on the pedagogical theory and practice in the scope of this study.

Downloads

Download data is not yet available.

Article Details

How to Cite
Cerdá Suárez, Luis Manuel, and Carmen Cristófol Rodriguez. 2022. “An Exploratory Study on the Impact of Neuromarketing on Virtual Learning Environments”. Vivat Academia. Journal of Communication 155 (January):1-16. https://doi.org/10.15178/va.2022.155.e1391.
Section
Neuromarketing y análisis del comportamiento
Author Biographies

Luis Manuel Cerdá Suárez, Universidad Internacional De La Rioja

International University of La Rioja. Spain. His areas of work extend into disciplines of marketing, market research, business processes, leadership and business administration, information technology, and business management systems, in general. Furthermore, he has been a visiting professor in postdoctoral agreements with the Spanish Agency for International Development Cooperation (AECID) and has given courses and conferences in Spain, Mexico, Colombia, Ecuador, the United States, Chile, and Portugal. He has also published book chapters, books, articles in journals, and presentations at national and international conferences, actively collaborating in various scientific committees. Likewise, he has several national and international awards in recognition of his research work, accredited with a Six-year Research Period by the National Commission for the Evaluation of Research Activity, of the National Agency for Quality Assessment and Accreditation (ANECA).

Carmen Cristófol Rodriguez, Universidad Internacional De La Rioja

International University of La Rioja. Spain. Ph.D. in Communication and Bachelor of Advertising and Public Relations (UMA). She participates as MR in Teaching Innovation Projects, as an evaluator in prestigious journals, and as a member of thesis and final master's thesis tribunals. She is section editor of the Revista Mediterránea de Comunicación, researcher in the groups COMPUBES (Comunicación y Públicos Específicos) and IICCXXI (Industrias Culturales Hoy), both from the Universidad de Alicante. She is a member of the AEIC (section 10) and AIRRPP. She is part of the Interuniversity Doctoral Program. She has teaching experience in public and private universities and virtual and classroom teaching. For 17 years she has combined her teaching and research work with her facet as a media professional. She has a six-year research period.

References

Asrar-ul-Haq, M., Anwar, S. y Hassan, M. (2017). Impact of emotional intelligence on teacher׳s performance in higher education institutions of Pakistan. Future Business Journal, 3(2), 87-97; http://dx.doi.org/10.1016/j.fbj.2017.05.003

Avinash, T., Dikshant, L. y Seema, S. (2018). Methods of Neuromarketing and Implication of the Frontal Theta Asymmetry induced due to musical stimulus as choice modeling. Procedia Computer Science, 132, 55-67, https://doi.org/10.1016/j.procs.2018.05.059

Blömeke, S. y Olsen, R. V. (2019). Consistency of results regarding teacher effects across subjects, school levels, outcomes and countries. Teaching and Teacher Education, 77, 170-182. https://doi.org/10.1016/j.tate.2018.09.018

Casado-Aranda, L-A., Dimoka, A. y Sánchez-Fernández, J. (2019). Consumer Processing of Online Trust Signals: A Neuroimaging Study. Journal of Interactive Marketing, 47, 159-180. https://doi.org/10.1016/j.intmar.2019.02.006

Cerdá, L. M. (2016). Happiness in teaching: positive emotions for evaluating the relationship between leadership style and performance of the professor in the classroom. En Proceedings of 10th annual International Technology, Education and Development Conference INTED 2016. Valencia, March, 1396-1405.

Chen, J. (2016). Understanding teacher emotions: The development of a teacher emotion inventory. Teaching and Teacher Education, 55, 68–77.

Chihiro Watanabe, C., Naveed, K. y Neittaanmäki, P. (2017). Co-evolution between trust in teachers and higher education toward digitally-rich learning environments. 48, 70-96https://doi.org:10.1016/j.techsoc.2016.11.001.

Dirican, C. (2015). The Impacts of Robotics, Artificial Intelligence on Business and Economics. Procedia - Social and Behavioral Sciences, 195, 564-573; https://doi.org/10.1016/j.sbspro.2015.06.134

Folwarczny, M., Pawar, S., Sigurdsson, V. y Fagerstrøm, A. (2019). Using neuro-IS/consumer neuroscience tools to study healthy food choices: a review. Procedia Computer Science, 164, 532-537. https://doi.org/10.1016/j.procs.2019.12.216

Golnar-Nik, P., Farashi, S. y Safari, M.-S. (2019). The application of EEG power for the prediction and interpretation of consumer decision-making: A neuromarketing study. Physiology & Behavior 207, 90-98 https://doi.org/10.1016/j.physbeh.2019.04.025

Granziera, H. y Perera. H. N. (2019). Relations among teachers’ self-efficacy beliefs, engagement, and work satisfaction: A social cognitive view. Contemporary Educational Psychology, 58, 75-84. http://dx.doi.org/10.1016/j.cedpsych.2019.02.003

Gutiérrez, G. (2019). Neuromarketing as an effective tool for education in sales and advertising. Revista Latina de Comunicación Social, 74, 1173-1189. https:// dx.doi.org/10.4185/RLCS-2019-1377

Jang, H.-R. (2019). Teachers' intrinsic vs. extrinsic instructional goals predict their classroom motivating styles. Learning and Instruction, 60, 286-300; https://doi.org/10.1016/j.learninstruc.2017.11.001

Kaklauskas, A., Abraham, A., Dzemyda, G., Raslanas, S., Seniut, M., Ubarte, I., Kurasova, O., Binkyte-Veliene, A. y Cerkauskas, J. (2020). Emotional, affective and biometrical states analytics of a built environment. Engineering Applications of Artificial Intelligence. 91, 103621. https://doi.org/10.1016/j.engappai.2020.103621

Karakus, O., Howard-Jones, P. A. y Jay, T. (2015). Primary and Secondary School Teachers’ Knowledge and Misconceptions about the Brain in Turkey. Procedia - Social and Behavioral Sciences, 174, 1933-1940. https://doi.org/10.1016/j.sbspro.2015.01.858

Luiz, I., Annukka Kim Lindell, A. K. y Ekuni, R. (2020). Neurophilia is stronger for educators than students in Brazil. Trends in Neuroscience and Education, 20, 100136. https://doi.org/10.1016/j.tine.2020.100136

Mañas-Viniegra, L., Núñez-Gómez, P. y Tur-Viñes, V. (2020). Neuromarketing as a strategic tool for predicting how Instagramers have an influence on the personal identity of adolescents and young people in Spain. Heliyon, 6, (3), e03578. https://doi:10.1016/j.heliyon.2020.e03578

Moghadam, S. M. y Seyyedsalehi, S. A. (2018). Nonlinear analysis and synthesis of video images using deep dynamic bottleneck neural networks for face recognition. Neural Networks, 105, 304-315. https://doi.org/10.1016/j.neunet.2018.05.016

Nussbaum, P. A., Herrera, A., Joshi, R. y Hargraves, R. (2012). Analysis of Viewer EEG Data to Determine Categorization of Short Video Clip. Procedia Computer Science, 158-163. http://doi.org:10.1016/j.procs.2012.09.047

Papanastasiou, G., Drigas, A., Skianis, C. y Lytras, M. (2020). Brain computer interface based applications for training and rehabilitation of students with neurodevelopmental disorders. A literature review. Heliyon, 6(9), e04250; https://doi.org/10.1016/j.heliyon.2020.e04250

Salehzadeh, A., Calitz, A. P. y Greyling, J. (2020). Human activity recognition using deep electroencephalography learning. Biomedical Signal Processing and Control, 62, 102094; https://doi.org/10.1016/j.bspc.2020.102094

Seligman, M., Ernstb, R., Gillhamc, J., Reivicha, K y Linkins, M. (2009). Positive education: positive psychology and classroom interventions. Oxford Review of Education, 35(3), 293-311. https://doi.org/10.1080/03054980902934563

Siddiqui, N., Gorard, S. y See, B. H. (2019). Can learning beyond the classroom impact on social responsibility and academic attainment? An evaluation of the Children’s University youth social action programme. Studies in Educational Evaluation, 61, 74-82. http://doi.org:10.1016/j.stueduc.2019.03.004

Tshewang, R., Chandra, V. y Yeh, A. (2016). Students’ and teachers’ perceptions of classroom learning environment in Bhutanese eighth-grade mathematics classes. Learning Environments Research, 1(1), 1-20. https://doi.org/10.1007/s10984-016-9225-6

Wang, C.-C. y Hsu, M.-C. (2014). An exploratory study using inexpensive electroencephalography (EEG) to understand flow experience in computer-based instruction. Information & Management, 51(7), 912-923. https://doi.org/10.1016/j.im.2014.05.010

Zhang, J., Yin, Z., Chen, P. y Nichele, S. (2020). Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review. Information Fusion, 59, 103-126. https://doi.org:10.1016/j.inffus.2020.01.011

Most read articles by the same author(s)

Similar Articles

<< < 32 33 34 35 36 37 38 39 40 41 > >> 

You may also start an advanced similarity search for this article.