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Global Regionalization of Consumer Neuroscience Behavioral Qualities on Insights From Google Trends

Global Regionalization of Consumer Neuroscience Behavioral Qualities on Insights From Google Trends

Nepoleon Prabakaran, Harold Andrew Patrick, Alaulddin B. Jawad
Copyright: © 2024 |Pages: 17
ISBN13: 9798369313558|ISBN13 Softcover: 9798369346174|EISBN13: 9798369313565
DOI: 10.4018/979-8-3693-1355-8.ch016
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MLA

Prabakaran, Nepoleon, et al. "Global Regionalization of Consumer Neuroscience Behavioral Qualities on Insights From Google Trends." Explainable AI Applications for Human Behavior Analysis, edited by P. Paramasivan, et al., IGI Global, 2024, pp. 258-274. https://doi.org/10.4018/979-8-3693-1355-8.ch016

APA

Prabakaran, N., Patrick, H. A., & B. Jawad, A. (2024). Global Regionalization of Consumer Neuroscience Behavioral Qualities on Insights From Google Trends. In P. Paramasivan, S. Rajest, K. Chinnusamy, R. Regin, & F. John Joseph (Eds.), Explainable AI Applications for Human Behavior Analysis (pp. 258-274). IGI Global. https://doi.org/10.4018/979-8-3693-1355-8.ch016

Chicago

Prabakaran, Nepoleon, Harold Andrew Patrick, and Alaulddin B. Jawad. "Global Regionalization of Consumer Neuroscience Behavioral Qualities on Insights From Google Trends." In Explainable AI Applications for Human Behavior Analysis, edited by P. Paramasivan, et al., 258-274. Hershey, PA: IGI Global, 2024. https://doi.org/10.4018/979-8-3693-1355-8.ch016

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Abstract

This chapter uses Google Trends search query volume data to perception-based regionalize consumer neuroscience behavioural indicators. To determine consumer neuroscience behaviour, the study examined Scopus research from 2010 to 2023. The most common keywords were then analysed. The study found five behavioural variables: emotion, attention, memory, perception, and decision-making. Between October 2022 and 2023, global Google Trends data for five consumer neuroscience phrases was collected. The data was analysed using time series and geographic units. The analysis found correlations between each indicator using time series units. K-means clustering was used to propose global regionalization using Google Trends. The ideal four clusters were found using the elbow approach. Through a thorough analysis of terms from derived clusters 1 to 4, the study made significant discoveries and implications that would improve consumer neuroscience's behavioural knowledge. Finally, perception-based global regionalization was introduced. In conclusion, this novel method of classifying global regions using Google Trends data and people's perceptions of behavioural topics like emotion, attention, perception, memory, and decision-making provides valuable insights for consumer neuroscience research. Analyzing the importance of specific groups and indicators within each cluster improves research in this field.

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