Risk Averseness and Emotional Stability in e-Commerce

Abstract

The study aims to examine the issue of the relationship between Emotional stability, one of the fundamental personality determinants, and users’ Risk Averseness, on the one hand, and user behavior in the field of e-Commerce, on the other hand. In the beginning, a brief overview of today’s primary benchmark for the measurement of human personality – the Big Five Model is proposed. A study with 226 participants has been conducted for the aim of the research, based on the TIPI test. The TIPI test is a validated and abridged version of the Five-Factor model. The result of the conducted survey confirms the existence of significant relationships between personality determinant Emotional stability and consumer’s Risk awareness, on one side, and some of the observed main functionalities of the online stores, on the other side. Two regression models of the field of Machine Learning (Linear Regression and Random Forest) have been implemented to make a reliable forecast about the user’s preferences in the process of online shopping. The conclusions made rely on the obtained results and analysis.

Artificial Intelligence and Behavioral Economics: A Bibliographic Analysis of Research Field

“…As a basic theme, it involves research topics that pertain to the use of machine learning and data analytics in various behavioral economics-related topics. These studies use AI techniques to classify consumer behavior [82], use latent Dirichlet allocation (LDA) to identify and analyze topics related to sustainable consumption behavior during the pandemic [83], evaluate the effectiveness of machine learning algorithms, particularly Support Vector Machines (SVM), for predicting consumer behavior and identifying its key influencing factors [84], examine the impact of risk averseness and emotional stability in e-commerce environment using machine learning models such as random forest [85], develop a blockchain-based framework for eSports using a combination of the theory of planned behavior (TPB) and machine learning [86], and use brain-computer interfaces (BCI) and electroencephalography (EEG) signals to predict consumers' choices in the context of neuromarketing [87]. Overall, the theme-related studies highlight the importance of using machine learning techniques to understand and predict consumer behavior in various contexts.…”

Modelling Activity of a Malicious User in Computer Networks

“…Mathematical modeling of psychological states and human reactions in different activities and circumstances are in the focus of researches in recent years. Risk averseness and emotional stability in e-commerce is discussed in [17,18]. Specific students' attitudes and reaction to e-learning processes are considered in [19,20].…”