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

David, José; Tur‐Viñes, Victoria; Rabasa, A.; Polotskaya, Kristina

doi:10.3390/socsci9090162

Cited by 16 publications

(13 citation statements)

References 50 publications

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“…Expanded access to education, where the main goal is to expand educational opportunities for students with special educational needs, from ethnic minorities, from disadvantaged groups. Within the framework of this direction, the educational policy is implemented through various social and economic forms of interaction with sponsors and partners in the digital environment [13].…”

Section: Materials and Methods Resultsmentioning

confidence: 99%

Visual techniques in the practice of implementing various inclusive approaches to learning in the context of digitalization

Byundyugova¹,

Kholina

Петрова

2021

E3S Web Conf.

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The article systematizes the main approaches and practical directions of implementation of inclusion in the educational environment (diagnostic, social, installation, resource, differentiated, dynamic, system, conductive, axiological approaches). Among the materials of the work there is a description of the key components of the implementation of inclusive theory and practice in the digital environment, an analysis of the key directions in working with students with special educational needs. The work contains the result of systematization of the use of inclusion in different approaches, taking into account all the subjects of the educational space in the context of different planes, including taking into account the possibilities of using visual technologies of work in the digital space. The article describes the results of the use of visual technologies (mind maps, scribing, crossence) in the digital environment in order to optimize the inclusive practices of the educational space, in particular, describes the results of native teaching of children without special educational needs and their parents to participate in inclusion, its support in mass schools. The use of visual technologies was effective – interest in the problem increased, and parents and children were actively involved in the discussion. All this allowed us to conclude that working in a digital environment using visual technologies is a simple and mass way to involve people in the problems of implementing inclusive practices.

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Section: Materials and Methods Resultsmentioning

confidence: 99%

Visual techniques in the practice of implementing various inclusive approaches to learning in the context of digitalization

Byundyugova¹,

Kholina

Петрова

2021

E3S Web Conf.

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“…´rez et al [32] employed an adaptive machine learning approach to conduct Neuromarketing Analysis and forecast consumer purchase behavior. Their dataset contained 375 records.…”

Section: Related Work On Predicting Purchase Behavior Using Machine L...mentioning

confidence: 99%

Predictive modeling of consumer purchase behavior on social media: Integrating theory of planned behavior and machine learning for actionable insights

Azad,

Khan,

Hossain

et al. 2023

PLoS ONE

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In recent times, it has been observed that social media exerts a favorable influence on consumer purchasing behavior. Many organizations are adopting the utilization of social media platforms as a means to promote products and services. Hence, it is crucial for enterprises to understand the consumer buying behavior in order to thrive. This article presents a novel approach that combines the theory of planned behavior (TPB) with machine learning techniques to develop accurate predictive models for consumer purchase behavior. This study examines three distinct factors of the theory of planned behavior (attitude, social norm, and perceived behavioral control) that provide insights into the primary determinants influencing online purchasing behavior. A total of eight machine learning algorithms, namely K-nearest neighbor, Decision Tree, Random Forest, Logistic Regression, Naive Bayes, Support Vector Machine, AdaBoost, and Gradient Boosting, were utilized in order to forecast consumer purchasing behavior. Empirical findings indicate that gradient boosting demonstrates superior performance in predicting customer buying behavior, with an accuracy rate of 0.91 and a macro F1 score of 0.91. This holds true when all factors, namely attitude (ATTD), social norm (SN), and perceived behavioral control (PBC), are included in the analysis. Furthermore, we incorporated Explainable AI (XAI), specifically LIME (Local Interpretable Model-Agnostic Explanations), to elucidate how the best machine learning model (i.e. gradient boosting) makes its prediction. The findings indicate that LIME has demonstrated a high level of confidence in accurately predicting the influence of low and high behavior. The outcome presented in this article has several implications. For instance, this article presents a novel way to combine the theory of planned behavior with machine learning techniques in order to predict consumer purchase behavior. This integration allows for a comprehensive analysis of factors influencing online purchasing decisions. Also, the incorporation of Explainable AI enhances the transparency and interpretability of the model. This feature is valuable for organizations seeking insights into factors driving predictions and the reasons behind certain outcomes. Moreover, these observations have the potential to offer valuable insights for businesses in customizing their marketing strategies to align with these influential factors.

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“…Neuromarketing analyzes the effectiveness of the stimuli created by the brand [32] and consumer behavior from the psychological perspective [48], combining neuroscience, psychology, and economics [49]. This discipline improves on traditional market research methods, limited by consumer behavior (their perceptions) [50,51].…”

Section: Research Instrumentmentioning

confidence: 99%

Emotional Impact of Dishes versus Wines on Restaurant Diners: From Haute Cuisine Open Innovation

Mengual-Recuerda

Tur‐Viñes

David

et al. 2021

Journal of Open Innovation: Technology, Market, and Complexity

Self Cite

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Haute cuisine is emblematic in the world of tourism and is of fundamental importance in the economic and social life in most countries worldwide. Haute cuisine gastronomic experiences play with the senses, involving the diner, thus generating a unique experience for the customer. This empirical study aims to analyze the influence on the consumer of the characteristic stimuli of a high-level gastronomic experience in a restaurant with two Michelin stars. Using neuromarketing biometrics, combined with a qualitative research technique, the objective of this research was to determine the emotional impact of the presentation and tasting of dishes compared to wines and to draw conclusions about each variable in the general experience. The results indicate that the dishes have a greater influence on the level of interest than the wines, and both have a different emotional impact at different moments of the experience due to its duration.

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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

Cited by 16 publications

References 50 publications

Visual techniques in the practice of implementing various inclusive approaches to learning in the context of digitalization

Visual techniques in the practice of implementing various inclusive approaches to learning in the context of digitalization

Predictive modeling of consumer purchase behavior on social media: Integrating theory of planned behavior and machine learning for actionable insights

Emotional Impact of Dishes versus Wines on Restaurant Diners: From Haute Cuisine Open Innovation

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