## 1 Introduction  
Phishing emails have become a common threat in cybersecurity, aiming to deceive users into giving up sensitive information such as personal credentials, financial details, or proprietary data [1]. These deceiving emails often serve as the initial phase of some more sophisticated cyberattacks, exploiting social engineering techniques to manipulate recipients [2, 3]. Despite significant advancements in email filtering, user awareness training, and phishing email detection tools, phishing remains a considerable threat, underscoring the need for a deeper understanding of the users' characteristics and the factors that make users susceptible to this kind of attack. While there has been much research on technical defenses against phishing, comparatively less attention has been paid to the human characteristics that contribute to users being vulnerable to such emails. Phishing exploits various psychological factors and social behaviors to bypass traditional security measures [4]. These attacks often exploit familiarity, urgency, and authority to create a sense of trust and prompt immediate action from the recipient. The diversity and complexity of these emails necessitate a comprehensive exploration of the factors that influence susceptibility, including individual user characteristics (such as demographic factors and technical skills), contextual elements (such as time pressure, distraction, and emotional state), and the specific attributes of the phishing emails themselves (technical characteristics of the email). The primary objective of this review article is to thoroughly examine the factors that contribute to users' susceptibility to phishing emails. Unlike technical analyses that focus on machine learning algorithms that detect phishing and protective measures against phishing, this review emphasizes the human factors and the personal characteristics of each user that make phishing email attacks effective. By combining findings from a variety of studies, we aim to identify the key susceptibility factors and the user characteristics that affect susceptibility, aiming to offer insights into potential mitigation strategies. This approach is particularly relevant given the persistent and evolving nature of phishing emails, which continue to adapt in response to the advancements of the defense measures. Previous research exploring user susceptibility to phishing has employed a variety of methodologies, including machine learning (ML), artificial intelligence (AI), and phishing simulations. Machine learning approaches have been widely used to analyze user behavior patterns and predict susceptibility by identifying subtle cues in user behavior when receiving such an email [5; 6; 7; 8]. Artificial intelligence techniques, such as natural language processing and neural networks, have also been applied to detect and categorize phishing attempts based on user response [9; 10; 11; 12]. Phishing simulations provide a practical approach by creating controlled environments where users' responses to simulated phishing emails are observed, allowing researchers to assess the effectiveness of training programs and the impact of various psychological and contextual factors [13; 14; 15]. These diverse methodologies have significantly contributed to understanding the multifaceted nature of phishing susceptibility and continue to offer valuable insights into enhancing user protection. The motivation for this review article comes from the need to enhance our understanding of user behavior in the context of phishing email attacks. By identifying and analyzing the characteristics that make users vulnerable to phishing, we can predict who is going to fall victim; thus, we can enhance the development of more effective user awareness programs (such as educational programs), improve security policies, and contribute to the evolution of the available technological solutions.