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The rise of artificial intelligence (AI) and machine learning has largely promoted the emergence of “autonomous decision-making” (ADM). This paper aims to establish a personalized artificial intelligent housekeeper (AIH) that knows more about our hobbies, habits, personality traits, and shopping needs than ourselves and can replace us to do some habitual purchasing behavior.

We propose an AI decision-making method based on machine learning algorithm, a novel framework for personalized customer preference and purchase. First, the method uses interactive big data to predict a potential consumer’s decision possibility. Then, the method mines the correlation between consumer decision possibility and various factors affecting consumer behavior. Finally, the machine learning algorithm is used to estimate the consumer’s purchase decision according to the comprehensive influencing factors data of the target consumer.

The experimental results show that the method can predict the regular consumption behavior of consumers in advance and make accurate decision-making behavior. It can find correlations from a large amount of data to help predict many simple purchase decisions in our life, and become our AIH.

This study introduces a new approach that not only has the auxiliary decision-making function but also has the decision-making function. These findings contribute to the research on automated decision-making process of AI and on human–technology interaction by investigating how data attributes consumer purchase decision to AI.

This study aims to gain a new perspective on auditing by measuring investors’ fraud perception and to reveal the necessity of increasing individuals’ fraud perception by determining the effect of fraud perception on the intention to invest in crypto assets from the investor’s perspective.

As part of this quantitative research, a survey was conducted on individuals residing in Türkiye and aged 18 years and above through a convenience sampling method. A total of 446 participants were included in the study. The data collected was analyzed using the partial least squares-variance based structural equation modeling (PLS-SEM) method using the SmartPLS program.

Fraud perception causes individuals to be more risk-averse and reduces their intention to invest in crypto assets. At the same time, it has been observed that risk-averse individuals have lower intention to invest in crypto assets. According to the results of the mediating effect analysis, risk aversion behavior partially mediates between the fraud perception and the intention to invest in crypto assets. Among the emotions, only fear increases risk aversion behavior. Among the personality traits, extroversion and openness to experience personality traits reduce risk aversion behavior, whereas neuroticism personality traits increase the intention to invest in crypto assets.

In an environment where traditional auditing activities are insufficient, increasing investors’ perceptions of fraud can reduce fraud-related losses. In this context, to the best of the authors’ knowledge, the present study might be among the first to investigate the impact of individuals’ perceptions of fraud on their investment intentions in crypto assets.

While being integrated together conceptually and practically, the literature on game theory in the context of financial markets lacks a cohesive understanding. This study aims to systematically scrutinize and analyse the literature of game theory in the context of financial markets, through a systematic literature review.

A systematic literature-based approach, through the theories, context, characteristics and methodology (TCCM) framework has been applied to 97 articles, extracted and filtered from two databases, Scopus and Web of Science, for a comprehensive review and understanding of the intellectual development in the domain of game theory and financial markets.

The review highlighted the most utilitarian theories within the literature, the context of research in terms of countries and industries, four themes which characterize the literature, as well as the methodologies and research designs used in this research domain. The paper also uncovered certain essential areas that present scope for further research.

While two of the largest indexation databases have been used, some relevant articles may have been excluded due to the restriction of databases and screening criteria, which may lead to the less exploration of several facets of the domain.

Practically, the paper has implications for multiple stakeholders including traders, businesses and governments. For traders, this paper acts as a guide to entering and understanding the dynamics of financial markets. The review also covers decision-making from the perspective of firms, including venture capitalists. This paper would allow firms to understand how game theory can help influence or analyze the strategic interactions between their stakeholders in terms of information disclosure, or consumers and their behavior to stimuli from the firm’s actions.

To the best of the authors’ knowledge, this study is the first of its kind that attempts to comprehensively provide an overview of the literature on game theory in financial markets. In doing so, this study shall help assess the current state of knowledge in the said field and locate gaps in the literature to propose new research directions.