IDEAS home Printed from https://ideas.repec.org/a/gam/jscscx/v9y2020i9p162-d415989.html
   My bibliography  Save this article

An Adaptive Machine Learning Methodology Applied to Neuromarketing Analysis: Prediction of Consumer Behaviour Regarding the Key Elements of the Packaging Design of an Educational Toy

Author

Listed:
  • David Juárez-Varón

    (Department of Mechanical and Materials Engineering, Universitat Politècnica de València, Camino de Vera, s/n, 46022 Valencia, Spain)

  • Victoria Tur-Viñes

    (Department of Communication and Social Psychology, Universidad de Alicante, Carretera de San Vicente del Raspeig s/n, 03690 San Vicente del Raspeig, Spain)

  • Alejandro Rabasa-Dolado

    (Department of Statistics, Mathematics and Informatics, Miguel Hernández University, Avenida de la Universidad, s/n, 03202 Elche, Spain)

  • Kristina Polotskaya

    (Department of Statistics, Mathematics and Informatics, Miguel Hernández University, Avenida de la Universidad, s/n, 03202 Elche, Spain)

Abstract

This research is in response to the question of which aspects of package design are more relevant to consumers, when purchasing educational toys. Neuromarketing techniques are used, and we propose a methodology for predicting which areas attract the attention of potential customers. The aim of the present study was to propose a model that optimizes the communication design of educational toys’ packaging. The data extracted from the experiments was studied using new analytical models, based on machine learning techniques, to predict which area of packaging is observed in the first instance and which areas are never the focus of attention of potential customers. The results suggest that the most important elements are the graphic details of the packaging and the methodology fully analyzes and segments these areas, according to social circumstance and which consumer type is observing the packaging.

Suggested Citation

  • David Juárez-Varón & Victoria Tur-Viñes & Alejandro Rabasa-Dolado & Kristina Polotskaya, 2020. "An Adaptive Machine Learning Methodology Applied to Neuromarketing Analysis: Prediction of Consumer Behaviour Regarding the Key Elements of the Packaging Design of an Educational Toy," Social Sciences, MDPI, vol. 9(9), pages 1-23, September.
  • Handle: RePEc:gam:jscscx:v:9:y:2020:i:9:p:162-:d:415989
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2076-0760/9/9/162/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2076-0760/9/9/162/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Emmanuel Sirimal Silva & Hossein Hassani & Dag Øivind Madsen & Liz Gee, 2019. "Googling Fashion: Forecasting Fashion Consumer Behaviour Using Google Trends," Social Sciences, MDPI, vol. 8(4), pages 1-23, April.
    2. Vijay Victor & Jose Joy Thoppan & Robert Jeyakumar Nathan & Fekete Farkas Maria, 2018. "Factors Influencing Consumer Behavior and Prospective Purchase Decisions in a Dynamic Pricing Environment—An Exploratory Factor Analysis Approach," Social Sciences, MDPI, vol. 7(9), pages 1-14, September.
    3. Douglas M. Hawkins, 1980. "Critical Values for Identifying Outliers," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(1), pages 95-96, March.
    4. Dernoncourt, David & Hanczar, Blaise & Zucker, Jean-Daniel, 2014. "Analysis of feature selection stability on high dimension and small sample data," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 681-693.
    5. Alex Rabasa & Ciara Heavin, 2020. "An Introduction to Data Science and Its Applications," International Series in Operations Research & Management Science, in: Vincent Charles & Juan Aparicio & Joe Zhu (ed.), Data Science and Productivity Analytics, chapter 0, pages 57-81, Springer.
    6. Peres-Neto, Pedro R. & Jackson, Donald A. & Somers, Keith M., 2005. "How many principal components? stopping rules for determining the number of non-trivial axes revisited," Computational Statistics & Data Analysis, Elsevier, vol. 49(4), pages 974-997, June.
    7. Nopsaran Thuethongchai & Tatri Taiphapoon & Achara Chandrachai & Sipat Triukose, 2020. "Adopt Big-Data Analytics to Explore and Exploit the New Value for Service Innovation," Social Sciences, MDPI, vol. 9(3), pages 1-17, March.
    8. Andrea Lučić & Marina Dabić & John Finley, 2019. "Marketing innovation and up-and-coming product and process innovation," International Journal of Entrepreneurship and Small Business, Inderscience Enterprises Ltd, vol. 37(3), pages 434-448.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bhardwaj, Shikha & Rana, Gunjan A & Behl, Abhishek & Gallego de Caceres, Santiago Juan, 2023. "Exploring the boundaries of Neuromarketing through systematic investigation," Journal of Business Research, Elsevier, vol. 154(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kim, Hyun Hak & Swanson, Norman R., 2018. "Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods," International Journal of Forecasting, Elsevier, vol. 34(2), pages 339-354.
    2. Jonas Eberle & Renier Myburgh & Dirk Ahrens, 2014. "The Evolution of Morphospace in Phytophagous Scarab Chafers: No Competition - No Divergence?," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-16, May.
    3. Johann Fuchs & Doris Söhnlein & Brigitte Weber & Enzo Weber, 2018. "Stochastic Forecasting of Labor Supply and Population: An Integrated Model," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 37(1), pages 33-58, February.
    4. Hauck, Jana & Suess-Reyes, Julia & Beck, Susanne & Prügl, Reinhard & Frank, Hermann, 2016. "Measuring socioemotional wealth in family-owned and -managed firms: A validation and short form of the FIBER Scale," Journal of Family Business Strategy, Elsevier, vol. 7(3), pages 133-148.
    5. Karol Pilot & Alicja Ganczarek-Gamrot & Krzysztof Kania, 2024. "Dealing with Anomalies in Day-Ahead Market Prediction Using Machine Learning Hybrid Model," Energies, MDPI, vol. 17(17), pages 1-20, September.
    6. Volker G. Kuppelwieser & Aleksa-Carina Putinas & Marina Bastounis, 2019. "Toward Application and Testing of Measurement Scales and an Example," Sociological Methods & Research, , vol. 48(2), pages 326-349, May.
    7. Leise Kelli de Oliveira & Carla de Oliveira Leite Nascimento & Paulo Renato de Sousa & Paulo Tarso Vilela de Resende & Francisco Gildemir Ferreira da Silva, 2019. "Transport Service Provider Perception of Barriers and Urban Freight Policies in Brazil," Sustainability, MDPI, vol. 11(24), pages 1-17, December.
    8. Francesca Ieva & Anna Maria Paganoni, 2020. "Component-wise outlier detection methods for robustifying multivariate functional samples," Statistical Papers, Springer, vol. 61(2), pages 595-614, April.
    9. Dag Øivind Madsen, 2019. "The Emergence and Rise of Industry 4.0 Viewed through the Lens of Management Fashion Theory," Administrative Sciences, MDPI, vol. 9(3), pages 1-25, September.
    10. Mohammed Arshad Khan & Vivek & Syed Mohd Minhaj & Mohd Afzal Saifi & Shahid Alam & Asif Hasan, 2022. "Impact of Store Design and Atmosphere on Shoppers’ Purchase Decisions: An Empirical Study with Special Reference to Delhi-NCR," Sustainability, MDPI, vol. 15(1), pages 1-24, December.
    11. Andrzej Chmielowiec, 2021. "Algorithm for error-free determination of the variance of all contiguous subsequences and fixed-length contiguous subsequences for a sequence of industrial measurement data," Computational Statistics, Springer, vol. 36(4), pages 2813-2840, December.
    12. Marc Chataigner & Stéphane Crépey & Jiang Pu, 2020. "Nowcasting Networks," Post-Print hal-03910123, HAL.
    13. Greco, Salvatore & Ishizaka, Alessio & Tasiou, Menelaos & Torrisi, Gianpiero, 2019. "Sigma-Mu efficiency analysis: A methodology for evaluating units through composite indicators," European Journal of Operational Research, Elsevier, vol. 278(3), pages 942-960.
    14. Rahlff, Helen & Rinne, Ulf & Sonnabend, Hendrik, 2023. "COVID-19, School Closures and (Cyber)Bullying in Germany," IZA Discussion Papers 16650, Institute of Labor Economics (IZA).
    15. repec:jss:jstsof:46:i04 is not listed on IDEAS
    16. Zhongqiu Wang & Guan Yuan & Haoran Pei & Yanmei Zhang & Xiao Liu, 2020. "Unsupervised learning trajectory anomaly detection algorithm based on deep representation," International Journal of Distributed Sensor Networks, , vol. 16(12), pages 15501477209, December.
    17. repec:dgr:rugsom:14008-eef is not listed on IDEAS
    18. Psaradakis, Zacharias & Vávra, Marián, 2014. "On testing for nonlinearity in multivariate time series," Economics Letters, Elsevier, vol. 125(1), pages 1-4.
    19. Arata, Linda & Fabrizi, Enrico & Sckokai, Paolo, 2020. "A worldwide analysis of trend in crop yields and yield variability: Evidence from FAO data," Economic Modelling, Elsevier, vol. 90(C), pages 190-208.
    20. Qingyong Wang & Hong-Ning Dai & Hao Wang, 2017. "A Smart MCDM Framework to Evaluate the Impact of Air Pollution on City Sustainability: A Case Study from China," Sustainability, MDPI, vol. 9(6), pages 1-17, May.
    21. Wentao Yang & Huaxi He & Dongsheng Wei & Hao Chen, 2022. "Generating pseudo-absence samples of invasive species based on outlier detection in the geographical characteristic space," Journal of Geographical Systems, Springer, vol. 24(2), pages 261-279, April.
    22. Paweł Gajewski, 2017. "Sources of Regional Inflation in Poland," Eastern European Economics, Taylor & Francis Journals, vol. 55(3), pages 261-276, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jscscx:v:9:y:2020:i:9:p:162-:d:415989. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.