Cyber Behavior and Personality Nexus: Clustering Around Security Attitudes, FoMO, Problematic Social Media Use, and Cognitive and Personality Traits?

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Tourjana Islam Supti, Ala Yankouskaya, Mahmoud Barhmagi, Khaled M. Khan, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7090927/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Our research investigates whether different cyber behaviors can be classified based on a set of personality factors. The study involved 642 participants, comprising 314 from the UK and 328 from the Arab Gulf Cooperation Council (GCC) region. By analyzing the personality factors of Conscientiousness, Neuroticism, and Need for Cognition (NFC) along with the cyber behaviors of Problematic Social Media Use (PSMU), Fear of Missing Out (FoMO), and Security Attitude (SA), the study identifies three clusters in each cultural context, which were largely similar in characteristics. The clusters were then transformed into personas to enhance ease of interpretation and practical use. The UK personas included “Methodical Achievers,” “Reactive Explorers,” and “Engaged Seekers,” while the Arabic personas included “Analytical Protectors,” “Reactive Explorers,” and “Hyper-Connected Defenders.” Creating these user profiles and presenting them as visual behavioral personas was a major goal of this research. The clusters revealed consistent relationships between cyber behaviors and personal factors. For example, high Need for Cognition and Conscientiousness correlated with stronger security attitudes and lower levels of PSMU and FoMO, while higher Neuroticism showed the opposite trend. Our findings highlight the potential of clustering approaches that consider multiple cyber behaviors and their relationship to personal factors, offering a foundation for personalized interventions that address cyber safety comprehensively rather than focusing on one behavior at a time. Conscientiousness Neuroticism Need for Cognition Problematic Social Media Use Fear of Missing Out Security Attitude Clustering Persona Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction The digital age has transformed how individuals interact, communicate, and access information, creating new opportunities but also significant cybersecurity risks for both individuals and organizations [1]. As society becomes increasingly dependent on the internet, cybercrime remains a widespread and evolving threat, with its full scale and economic impact difficult to measure yet widely acknowledged by experts [2]. As people engage in various cyber behaviors, including social media use, cybersecurity practices, and risk-taking activities, their online interactions and vulnerability to cyber threats are influenced by these behaviors. Problematic social media use (PSMU), Fear of Missing Out (FoMO), and security attitudes are key behaviors that shape how individuals engage with digital platforms and manage cyber risks. Despite technological advancements to improve security systems, human behavior remains a critical vulnerability in cybersecurity [3], as individuals often are subject to cognitive biases that could expose them to threats. Social engineering tactics such as phishing and pretexting exploit psychological factors like trust, urgency, and fear, impairing rational decision-making and making individuals prime targets for manipulation [4, 5]. Employees prefer localized security support and assurance rather than rigid, centrally imposed instructions, highlighting the role of trust and social influence in shaping secure behaviors [6]. Understanding these psychological and behavioral factors is essential for developing effective interventions that mitigate cyber risks and promote safer, more responsible online behavior. Among the factors affecting cybersecurity behavior, excessive or problematic social media use, commonly known as “social media addiction” or “social media disorder” [7, 8], has gained significant attention. However, this terminology has faced criticism [9]. Problematic social media use (PSMU) refers to the excessive use of social media that leads to adverse effects on different areas of an individual's daily life [10]. Additionally, the psychological phenomenon of Fear of Missing Out (FoMO) has also been recognized for its influence on online behavior and risk perceptions. FoMO is a psychological condition marked by a constant anxiety that others are experiencing enjoyable or fulfilling events that one is not part of [11, 12]. These behaviors can create heightened emotional vulnerability, leading individuals to prioritize immediate gratification over long-term safety. A growing body of research highlights the significant role of PSMU and FoMO in shaping online behavior and influencing cybersecurity-related attitudes and behaviors. A study by Sindermann et al. shows that certain design elements of social media platforms encourage users to spend more time on these platforms, which is strongly linked to problematic social media use [13].PSMU is linked to factors such as depression, FoMO, the need to belong, and increased social media use [14]. A study by Weaver & Swank [15] reveals that higher levels of FoMO and PSMU are associated with lower self-esteem, mindfulness, and life satisfaction among undergraduates. During the pandemic, FoMO shifted from physical events to online activities, continuing to negatively impact well-being, including sleep deprivation and reduced focus [16]. Furthermore, FoMO has been shown to increase stress levels among social media users [17–19]. PSMU is also strongly correlated with cybercrime victimization, with increased levels raising the risk of becoming a victim [20]. The widespread sharing of information on social networks enhances the chances of cyberattacks by hackers [21]. A study by Deutrom et al. problematic internet use is associated with poorer cybersecurity behaviors [22]. Additionally, employees with higher FoMO levels tend to have lower information security awareness (ISA), demonstrating poorer knowledge, more negative attitudes, and riskier behaviors [23]. A cyclical relationship between FoMO-centric design and privacy-compromising behavior has also been observed, where individuals, despite expressing privacy concerns, engage in risky behaviors due to the pressure to participate [24]. Furthermore, an individual’s Security Attitude, belief in the importance of cybersecurity, and intention to adopt secure practices significantly influence protective behaviors. Those with a stronger belief in cybersecurity are likely to adopt protective behaviors despite the emotional pull of PSMU and FoMO. Individual differences, especially personality traits, play a key role in social media engagement, digital boundary management, and perceptions of security, yet their relationship with digital security remains largely underexplored. Personality traits are enduring characteristics that influence an individual's thinking, feeling, and behavior patterns. These traits are often described through frameworks like the Big Five Personality Traits (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) [25, 26]. Research has shown that certain personality traits are significantly associated with cybersecurity behaviors. Attitudes toward security recommendations are a multidimensional construct, with employees evaluating security policies based on their perceived legitimacy, effectiveness, and rigor [27]. For instance, conscientiousness, agreeableness, and openness are key traits influencing how individuals engage with digital security, with conscientiousness being the strongest predictor of secure behavior [28]. Neuroticism, on the other hand, has been linked to increased susceptibility to phishing attacks. At the same time, openness to experience is associated with more relaxed privacy settings and a greater tendency to share personal information, making individuals more vulnerable to privacy breaches [29]. Furthermore, conscientiousness, agreeableness, emotional stability, and risk-taking propensity influence Information Security Awareness (ISA) [30]. Specifically, conscientious individuals, who tend to be hardworking and detail-oriented, are more likely to engage in secure online practices [31]. Conscientiousness is strongly associated with secure online behaviors, as individuals high in this trait are likelier to follow cybersecurity practices diligently. In the context of social media use, neuroticism and impulsivity are indirectly associated with PSMU through fear of missing out (FoMO ) and the increased use of social media driven by design features [13]. People with high levels of neuroticism often engage with social media more than they realize, leading to greater dependency . Neuroticism is a significant predictor of PSMU severity, particularly among emotionally vulnerable individuals . A study by Alshakhsi et al. found that neuroticism is significantly associated with PSMU, with FoMO mediating this relationship fully in the UK and partially in the Arab region’s sample [34]. Additionally, neuroticism has been linked to FoMO, with studies revealing that it significantly increases susceptibility to social influence [35]. While neuroticism is strongly positively associated with FoMO, conscientiousness shows a small negative association with FoMO across various analyses [36]. This suggests that individuals with higher neuroticism are more likely to experience FoMO, potentially leading to increased social media use and reduced engagement with secure online practices. Personality traits such as neuroticism and conscientiousness significantly influence how individuals perceive and respond to both social media use and digital security practices. These findings highlight the need to consider individual differences when developing interventions aimed at promoting secure behavior and managing social media dependency. In addition to personality traits, the Need for Cognition (NFC) also plays a significant role in decision-making processes related to cybersecurity. NFC refers to an individual’s tendency to engage in and enjoy effortful cognitive activities, such as thinking critically and solving complex problems [37]. Likewise, cognitive styles, including Need for Cognition (NFC), Need for Affect (NFA), and Faith in Intuition (FII), also play a role in decision-making processes related to cybersecurity [38, 39]. People with high NFC, for example, are more likely to seek out detailed information, making them more cautious in decision-making [37], which can lead to adopting more secure measures. On the other hand, individuals with high reliance on intuition may make more impulsive decisions, relying on heuristics that overlook updated information [40], potentially increasing their online vulnerability. The NFC significantly influences decision-making in cybersecurity, as individuals high in NFC are more inclined to seek out detailed information and think critically before making decisions. This thoughtful approach leads to more secure online behaviors, as they are less likely to engage in risky actions without understanding the potential consequences. Additionally, individuals with high NFC are less susceptible to emotional impulses like FOMO and are better at managing PSMU. By prioritizing rational analysis over impulsivity, those with high NFC can reduce the impact of social media pressures and enhance their cybersecurity attitudes, making more informed and secure choices. While previous studies have provided valuable insights, they often examine these dimensions in isolation, overlooking their potential interplay. According to recent systematic literature reviews on cybersecurity and human factors [41] , as well as research on PSMU [42], the majority of studies in this area have focused on Western contexts which is acronymized as WEIRD (Western, Educated, Industrialized, Rich, and Democratic) [43], with limited exploration of cybersecurity attitudes and cyber behaviors within the Middle Eastern, particularly Arab, populations. Cultural norms, social influences, and varying levels of cybersecurity awareness can shape how individuals perceive vulnerability and engage in cyber behaviors. Research suggests that sociocultural factors such as individualism-collectivism and tightness-looseness influence risk perception and the use of base rate information in assessing cyber threats [44]. Individuals in cultures with high uncertainty avoidance may be more cautious and vigilant in adopting secure online practices than those in low uncertainty avoidance cultures [45]. By applying clustering techniques, this research aims to identify distinct cyber behavior profiles based on personality traits (conscientiousness, neuroticism), cognitive style (NFC), and behavioral factors (PSMU and FoMO) in the UK and Arab regions. The study examines whether different cyber behaviors can be grouped similarly across cultural contexts based on shared personal characteristics. Understanding these clusters provides deeper insights into the interplay of psychological and behavioral factors in cybersecurity, facilitating the development of more culturally sensitive interventions. Additionally, this research fills a gap in the literature by integrating cross-cultural differences into cybersecurity behavior analysis, particularly in non-WEIRD population samples. Based on this foundation, we can formulate the following research question, on both Arab and British samples: RQ1 : Can we identify distinct cyber behavior profiles among individuals based on their personality traits (conscientiousness and neuroticism), cognitive style (Need for Cognition) on the one hand and their cyber behavioral factors (Social Media Disorder, Fear of Missing Out, and Security Attitude) on the other? 2. Research Method Our study is part of a more extensive investigation examining the factors that shape cybersecurity attitudes and cyber behaviors. Both the Arabic and English versions of these scales and questions are available on the Open Science Framework (OSF), with the link provided in the “Supplementary Materials” section of this manuscript. 2.1 Participant and Procedure Participants for this study were recruited from the Gulf Cooperation Council (GCC) region and the United Kingdom (UK) through TGM Research (https://tgmresearch.com/), a company specializing in research data collection. These two cultural contexts were selected for their contrasting societal values and moral principles, offering a rich basis for comparative analysis [46]. The survey was designed and administered via SurveyMonkey (www.surveymonkey.com), a platform for creating and distributing questionnaires. To ensure the survey questions were clear and accurate, the research team followed an iterative development process. The survey was first drafted in English and then translated into Arabic by two team members using the recommended back-translation method [47]. A pilot test was then conducted with a small group of participants to identify and address any ambiguities or unclear wording. Eligibility criteria required participants to be over 18 years old and born and currently residing in either the UK (England, Scotland, Wales, and Northern Ireland) or one of the Arab GCC countries (Saudi Arabia, Qatar, Bahrain, Kuwait, Oman, and the UAE). Additionally, participants from the GCC region had to explicitly self-identify as Arabs in terms of cultural norms and values. Informed consent was obtained from all participants, who could withdraw from the survey at any time. To ensure data quality, attention checks were embedded within the survey, and participants who failed to complete the survey too quickly or provided monotonous answers were excluded. Those who successfully completed the survey were compensated for their time. Ethical approval for the study was granted by the Institutional Review Board (IRB) at the institution of the last author. Participants identifying as non-binary were excluded from the analysis due to their small sample size in both the Arab and UK datasets. Similarly, participants aged 60 and above were excluded from the UK sample to address the uneven age distribution across the two samples, as we could not get any participants in that age group from the Arab sample. While older individuals participated in the UK sample, only 7% of the population in many Arab countries falls within this age group [48]. Limiting the age range to 18–60 years ensured consistency. The final dataset included 642 participants, comprising 314 from the UK and 328 from the Arab GCC region. 2.2 Measure 2.2.1 Demographic Measure Participants were asked to provide their age and gender. Age was recorded as a continuous variable in years, while gender was collected through an open-text field and then coded accordingly. 2.2.2 Big Five Inventory (BFI-10) The study employed the BFI-10 to assess personality traits [49]. This abbreviated version of the Big Five Inventory evaluates five dimensions of personality: extraversion indicates the intensity of being outgoing and interactive; introverts, which is the opposite of extraversion; agreeableness identifies how friendly and trusting the person is. Neuroticism indicates the stability of emotion; openness measures how open to experience the user is; conscientiousness indicates that the person is focused on goals and determined with two items dedicated to each trait. Participants rated their agreement with each statement on a five-point Likert scale, ranging from “1= strongly disagree to 5 = strongly agree. Some items are reverse scored to control for response bias. The final score for each personality trait is calculated by summing the responses across all relevant items. In this study, we used only two personality traits: Conscientiousness and Neuroticism. The theoretical range for Conscientiousness and Neuroticism in the UK sample was 2 to 10, whereas in the Arab sample, the range was 3 to 10 for Conscientiousness and 2 to 10 for Neuroticism. 2.2.3 Need for Cognition (NFC) NFC was measured using the five-item subscale of the Rational Experiential Inventory (REI-10), which also includes a subscale for Faith in Intuition (FII) [50]. This scale is a shortened version of the original developed by Cacioppo and Petty [37], and participants rated their agreement with statements on a 5-point Likert scale ranging from “1 = Completely False” to “5 = Completely True”. The theoretical range was 6 to 25. An example item from the NFC scale is, “ I don’t like to have to do a lot of thinking. ” Several items were slightly adjusted for clarity and to ensure consistency between the English and Arabic versions of the scale. For example, the original NFC item “ Thinking hard and for a long time about something gives me little satisfaction ” was modified to “ Thinking hard and for a long time about something gives me some satisfaction ” to enhance its understanding of the study's cultural and linguistic context. The NFC total score is calculated by summing the responses to all scale items after reverse-scoring the negatively worded items, with higher scores indicating a greater preference for effortful thinking. The scale showed very good internal reliability across both samples, with Cronbach’s alpha of 0.82 in the UK sample and 0.70 in the Arab sample. 2.2.4 Problematic Social Media Use (PSMU) Problematic Social Media Use (PSMU) was measured using the original English version and a translated Arabic version of the “Social Media Disorder Scale” [51]. The scale consists of nine items, one of which is “ Have you ever found yourself unable to think of anything else but the moment when you’ll be able to use social media again? ” Participants responded to each item on a 5-point Likert scale, ranging from “1 = Never” to “5 = Always.” The theoretical range for total PSMU was from 9 to 40. The total PSMU score was calculated by summing participants’ responses, with higher scores indicating greater levels of social media disorder. In previous studies, the scale has shown very good internal consistency, with Cronbach’s alpha ranging from α = 0.76 to α = 0.82. In our study, Cronbach’s alpha was 0.87 for the UK sample and 0.84 for the Arab sample, indicating very good reliability. 2.2.5 Fear of Missing Out (FoMO) To measure the concept of Fear of Missing Out (FoMO), participants were provided a definition: “FoMO, the fear of missing out, refers to the fear of not being able to know what is happening (whether on social media or in real-world) and participate in it and taking opportunities.” Participants were then asked to rate their agreement with the statement: “ I experience FoMO regarding what is happening on social media. ” Responses were collected using a 10-point Likert scale, where “1 = Strongly Disagree” and “10 = Strongly Agree,” with higher scores indicating a stronger fear of missing out. 2.2.6 Security Attitude (SA-6) The Security Attitudes (SA-6) scale, developed by Faklaris [52] , is a validated six-item instrument to assess individuals' attitudes toward cybersecurity. Extensive empirical research supports the scale’s reliability and validity, revealing various responses. Participants rated their agreement with statements like “ I actively seek opportunities to learn about security measures that apply to me ” and “ I am highly motivated to take all necessary steps to protect my online data and accounts ” using a five-point Likert scale (1 = strongly disagree to 5 = strongly agree). The SA-6 scale has been shown to correlate significantly with both self-reported security intentions and actual secure behaviors, confirming its utility in measuring and comparing attitudes toward adopting recommended security practices. In the original validation study, the SA-6 scale demonstrated strong internal consistency with a Cronbach’s alpha of 0.84 [52]. In our study, the scale yielded a Cronbach's alpha of 0.87 for the UK participants and 0.79 for the Arab participants, reflecting very good internal consistency. The total security attitude score for each participant was calculated by summing the individual item scores, providing a standardized measure of security attitudes. 2.3 Data Pre-processing As an initial step in our data analysis, we examined the relationships among all the variables across both the UK and Arab samples. These correlations, which provided insights into the interplay between cyber behaviors and individual differences before clustering, are illustrated in the appendix (Figure 1S) in the OSF link provided in the “Supplementary Materials” section. Prior to conducting cluster analysis, we implemented a two-stage data preprocessing procedure. First, we standardized the datasets for both samples using the scale() function in R, which normalizes the numeric matrix by adjusting its columns. To determine whether the data were suitable for clustering, we assessed clustering tendency through visual analysis of two matrices. The first matrix represented correlation-based distances between data points in the original dataset, computed using the Spearman method via the get_dist() function in R. The second matrix, generated using randomly assigned values, maintained the same dimensions as the original dataset. A comparative visual inspection of these matrices confirmed that our data exhibited an appropriate structure for clustering, as presented in Appendix Figure 2S in the OSF link provided in the “Supplementary Materials” section. 2.4 Clustering Approach We applied partition clustering to group observations into distinct clusters based on their similarities within each sample. The variables used for clustering included conscientiousness, neuroticism, NFC, PSMU, FoMO, and SA. For this purpose, we implemented K-means clustering, an unsupervised machine learning algorithm that divides data into a set number of clusters, denoted by k. This algorithm aims to maximize the similarity within each cluster and minimize the dissimilarity between different clusters. To carry out this analysis, we utilized the Hartigan-Wong method [53], which minimizes the within-cluster variance by calculating the sum of squared Euclidean distances between observations and the centroids of their respective clusters. Each observation is assigned to a cluster so as to minimize the squared distance to its assigned centroid, which was computed using the k-means() function in R’s stats package. As K-means clustering requires a predefined number of clusters, we determined the optimal number of clusters, k, using the NbClust package in R. This package offers 30 indices to suggest the most suitable clustering solution by evaluating various combinations of k values, distance metrics, and clustering techniques [54]. To ensure reliable results, we undertook three additional steps. First, we tested different k-values to compare clustering outcomes and avoid arbitrary cluster selections. Second, in light of K-means’ sensitivity to the initial random placement of cluster centroids [55], we ran the clustering algorithm five times with different initial cluster center assignments, choosing the configuration with the lowest within-cluster sum of squares. For stability, we used 15, 25, 35, 45, and 55 random initializations with 1000 iterations per run. Lastly, recognizing the potential impact of outliers on clustering performance, we carefully examined the data for outliers prior to performing the clustering. 3. Results 3.1 Data Set Characteristics The overview of the UK and Arab sample participants is presented in Table 1 for this study. A Welch’s t-test was conducted to examine the null hypothesis that the means of the two groups are equal. Additionally, evidence for the alternative hypothesis was assessed using the Bayes Factor (BF10) with default priors [56]. Arab participants tended to score slightly higher in conscientiousness, indicating a more organized and goal-oriented approach compared to UK participants. This difference in conscientiousness may reflect contrasting ways of managing tasks and responsibilities. UK participants, on the other hand, were more neurotic and had higher levels of NFC, suggesting they may be more emotionally reactive and cognitively engaged in problem-solving. While UK participants exhibited a heightened cognitive focus, Arab participants showed stronger connections to PSMU, FoMO, and SA, highlighting differences in how each group approaches both emotional and cognitive aspects of their lives. Table 1: Descriptive Statistics Analysis of All the Variables for the UK and Arab Samples Variables UK (N=314) Arab (N=328) t-test (*W, p-value, **BF10) Gender Males 131 (42%) 185 (56%) Females 183(58%) 143 (44%) Age Males 38.99 (12.22) 37.88 (10.08) -0.85, p=.397, BF 10 =0.18 Females 36.95 (12.08) 32.64 (9.31) -3.64, p<.001, BF 10 =43.48 Conscientiousness 7.59 (1.75) 7.99 (1.62) 3.03, p=.003, BF 10 =7.85 Neuroticism 6.48 (2.09) 5.39 (2.02) -6.76, p<.001, BF 10 =14.19 NFC 17.44 (3.98) 16.21 (3.75) -4.02, p100 PSMU 16.83 (5.78) 22.75 (6.73) 11.98, p<.001, BF 10 =34.37 FoMO 4.01 (2.69) 5.40 (2.51) 6.79, p<.001, BF 10 = 15.86 SA 21.18 (4.48) 24.47(3.17) 10.70, p100 provides extreme evidence for the alternative hypothesis (H1), 30-100 – very strong evidence, 10-30 – strong evidence, 3-10 moderate evidence, 1-3 and 1/3-1– anecdotal evidence, 1 – no evidence. 3.2 Results of Clustering Analysis Our analysis sought to determine the optimal number of clusters, and based on the majority rule, we identified three clusters as the best solution for both samples (see Appendix for details in the OSF link provided in the “Supplementary Materials” section). Figures 1 and 2 present the defining characteristics of these clusters. For the UK sample, three distinct clusters were identified based on Conscientiousness, Neuroticism, NFC, PSMU, FoMO, and SA. Cluster 1 is characterized by high levels of Conscientiousness and NFC, coupled with low Neuroticism, PSMU, and FoMO. Members of this cluster display the strongest security attitudes (SA), suggesting a high level of vigilance and engagement with secure behaviors. Cluster 2 exhibits low Conscientiousness, high Neuroticism, and low NFC. Individuals in this cluster show moderate levels of PSMU and FoMO, indicating some dependence on social media but not at extreme levels. Their SA is the weakest among the three clusters, suggesting greater vulnerability to cybersecurity risks. Cluster 3 is defined by moderate levels of Conscientiousness, Neuroticism, and NFC, alongside high levels of PSMU and FoMO. This cluster shows average security attitudes (SA), balancing some secure behaviors with potential vulnerabilities due to their high engagement with social media and increased anxiety about missing out. Similarly, for the Arab sample, three distinct clusters were identified based on Conscientiousness, Neuroticism, NFC, PSMU, FoMO, and SA. Cluster 1 is characterized by high Conscientiousness and NFC, along with low Neuroticism, PSMU, and FoMO. Members of this cluster exhibit moderate SA, suggesting a reasonable level of engagement with secure behaviors. Cluster 2 displays low Conscientiousness, high Neuroticism, and low NFC. Individuals in this cluster report moderate levels of PSMU and FoMO, with weak SA, indicating a greater vulnerability to cybersecurity risks. Cluster 3 features moderate Conscientiousness, Neuroticism, and NFC, combined with high PSMU and FoMO. This cluster demonstrates strong SA, which may reflect their heightened engagement with cybersecurity practices despite significant social media dependency and fear of missing out. 3.2.1 Clustering Quality Evaluation To assess the distinct characteristics of each cluster, we performed a one-sample t-test, comparing the cluster values against a baseline of zero for both the UK and Arab samples (refer to Tables 2S and 3S in the appendix in the OSF link provided in the “Supplementary Materials” section). The primary goal was to highlight the key attributes within each cluster, specifically those with values notably above or below the average participant. These findings are further visualized in the appendix in the OSF link provided in the “Supplementary Materials” section through interval plots, accompanied by comprehensive statistical findings. For assessing the overall quality of clustering, we utilized the silhouette coefficient, which yielded values of 0.19 for the UK sample and 0.16 for the Arab sample, suggesting an adequate level of cluster cohesion and separation (see Figure 6S in the appendix in the OSF link provided in the “Supplementary Materials” section). 3.2.2 From Cluster to Persona Creation To enhance the clarity of the clustering results, each cluster is illustrated through a persona, an archetype that captures the key psychological and behavioral traits of individuals within that group. This method connects statistical analysis with practical relevance, providing an intuitive lens for understanding variations in cybersecurity attitudes and digital behavior. By portraying data-informed user types, personas make the findings more accessible to diverse stakeholders: designers can develop user-centered solutions, practitioners can customize interventions, and policymakers can design more effective awareness initiatives. 4. Discussion This paper introduces a novel, data-driven approach to constructing user personas based on psychological, emotional, and cognitive traits. Such personas offer a nuanced understanding of individual differences in digital behavior and have been widely applied in app development and policy design to identify target groups and tailor interventions. For example, personas have been used to model the mental frameworks of aging populations in China for health app development [58], and to address the specific needs of older adults with heart failure in user-centered design [59]. By grounding personas in empirical data, we aim to support user well-being and enable digital strategies that enhance social media awareness, strengthen security attitudes, and promote behavior change. 4.1 Analysis of User Profiles 4.1.1 UK Personas For the UK Clusters, three different user archetypes were identified, as shown in Figure 3. UK Cluster 1, termed the “Methodical Achiever,” this archetype, represented by a middle-aged man named Robert , reflects a persona defined by careful planning, emotional stability, and a proactive approach to online safety. Aligning with previous research, Robert’s high conscientiousness reflects strong organizational skills and a preference for structured, goal-oriented behaviors [60, 61], which is associated with adherence to best practices in managing digital security. Consistent with previous research, his low neuroticism indicates emotional stability, reducing his likelihood of experiencing stress or anxiety [62, 63] when confronted with online risks. This emotional resilience allows him to navigate the digital landscape confidently and clearly, avoiding impulsive or emotionally driven decisions. Robert’s high NFC reveals a strong preference for mentally stimulating tasks and a desire to engage deeply with complex information [37]. This cognitive engagement makes him more likely to critically evaluate online risks and adopt evidence-based solutions to protect his digital privacy and security. His intellectual curiosity aligns with findings from Cacioppo and Petty, who demonstrated that individuals with high NFC are more motivated to process detailed information thoroughly [37]. His low PSMU and low FoMO suggest reduced engagement in passive social media use, which aligns with Mao & Zhang (2023), who found that lower passive browsing may contribute to lower levels of trait-FoMO [64]. Robert uses digital tools purposefully rather than compulsively, maintaining control over his online habits. This minimizes his vulnerability to the emotional pressures and impulsive behaviors often linked to excessive social media use. According to Maathuis & Chockalingam, Robert’s high SA underscores his commitment to secure and responsible digital behavior [65]. He takes proactive steps to safeguard his data, such as using strong passwords, enabling multi-factor authentication, and staying informed about the latest cybersecurity practices. UK Cluster 2 termed “Reactive Explorer,” this archetype, represented by a middle-aged woman named Olivia, reflects a persona defined by emotional volatility and impulsive decision-making in the digital landscape. Olivia’s low conscientiousness reveals a tendency toward disorganization and difficulty with goal setting, which affects her ability to adopt structured or consistent security behaviors. Research on conscientiousness suggests that it plays a essential role in adherence to systematic approaches for managing risks, particularly in health-related behaviors [66] Given the parallels between structured risk management in health and digital security, individuals low in conscientiousness may similarly struggle with maintaining consistent cybersecurity practices. Aligning with findings by Thompson [67], her high neuroticism suggests frequent emotional instability, making her more likely to experience stress and anxiety, in situations involving online risks. Her low NFC means she prefers straightforward tasks and avoids cognitively demanding challenges, often relying on heuristics or impulsive responses in her decision-making. This aligns with findings from Cacioppo and Petty’s work on NFC, highlighting the reduced motivation to process complex information among individuals with low cognitive engagement [37]. As a result, Olivia is less likely to critically evaluate the risks of online interactions, increasing her susceptibility to phishing scams and other digital threats. Moderate levels of PSMU and FoMO reflect occasional reliance on social media and digital platforms for engagement but suggest that these behaviors do not completely consume Olivia. However, these moderate tendencies, combined with her high neuroticism, may amplify stress related to social comparisons or the fear of being left out, especially during emotionally charged situations. For instance, research by Settles demonstrates that individuals with similar traits, high neuroticism, and low conscientiousness are prone to negative urgency, leading to impulsive reactions under stress [68]. Olivia’s low SA signals limited attention to cybersecurity practices, such as using weak passwords or neglecting updates, which places her at further risk. Her approach to digital security reflects a reactive rather than proactive stance, driven by immediate emotional responses rather than long-term planning. UK Cluster 3 termed “Engaged Seeker,” this archetype, represented by a young woman named Emily , highlights a persona actively engaged with digital platforms but with mixed emotional and cognitive traits that influence her online behaviors. Emily’s moderate conscientiousness suggests a balanced approach to organization and planning. While she can be methodical in certain areas, she may occasionally struggle with maintaining consistency in her online security practices. Her moderate neuroticism indicates emotional variability, making her prone to stress or anxiety in uncertain situations but not to the extent of overwhelming instability. Emily’s moderate NFC reflects a willingness to engage in cognitively demanding tasks when necessary, though she may not consistently seek out challenging or thought-provoking activities. This aligns with her ability to analyze and adapt to new digital tools and risks, albeit somewhat cautiously. Aligning with a previous study, Emily’s high PSMU and high FoMO reveal a strong attachment to social media and digital interactions [69]. She frequently engages with social platforms, driven to stay connected and avoid missing out on social updates. These tendencies can sometimes lead to compulsive behaviors, such as over-checking notifications or spending excessive time online, potentially impacting her emotional well-being [70]. Research by Przybylski et al. highlights that high FoMO often correlates with increased social media use, contributing to feelings of dependency and digital fatigue. Her moderate SA suggests an awareness of cybersecurity practices but with inconsistent application. While Emily may understand the importance of secure online behavior, her high PSMU and FoMO could detract from her ability to implement these practices consistently. This creates a dynamic where emotional and social pressures might overshadow her rational understanding of online risks. A study found that individuals with higher trust in internet technology and vendors are more inclined to use social media and online shopping platforms, sometimes at the expense of rigorous security practices [71]. 4.1.2 Arab Personas Based on the characteristics of the three clusters in the Arab sample, we can identify distinct personas that reflect their behaviors, motivations, and challenges, as shown in Figure 4. Cluster 1, represented by “Sarah,” the persona we named Analytical Protector, a 36-year-old female characterized by high conscientiousness, low neuroticism, and a strong NFC. This profile aligns with previous research suggesting that highly conscientious individuals exhibit greater organizational skills and a more methodical approach to decision-making [72]. Sarah’s low neuroticism and high emotional stability further support findings that individuals with these traits are less likely to react impulsively or experience stress in the face of uncertainty, which might result in more rational processing of risks [73]. Sarah’s minimal reliance on social media (low PSMU) and limited susceptibility to FoMO are consistent with research linking lower levels of PSMU and FoMO with high self-control and a preference for more structured, predictable environments [74]. These traits also align with studies indicating that individuals less engaged with social media are less likely to experience heightened vulnerability to online threats or manipulation [75]. However, while Sarah demonstrates a moderate security attitude, her rational and emotionally stable nature might lead her to underestimate certain cybersecurity risks, as individuals with lower emotional reactivity may be less attuned to perceived threats. Existing literature on cybersecurity behavior suggests that individuals with high NFC tend to engage in more thoughtful analysis of security information [38], but they may prioritize efficiency over perceived risk. Thus, interventions for Sarah should focus on providing structured, logical guidelines that underscore the practical benefits of cybersecurity practices. Research by Ifinedo [76] has highlighted that individuals like Sarah, who are analytical and methodical, are more likely to adopt security measures when these measures emphasize tangible, logical advantages. Cluster 2, embodied by “Amina,” is the Reactive Explorer, a 35-year-old female who mirrors the characteristics seen in the UK cluster who struggles with organization and is prone to stress (low conscientiousness and high neuroticism). With moderate levels of social media dependency and FoMO, Amina's low-security attitude makes her more vulnerable to social engineering and phishing attacks. Effective strategies for Amina should focus on reducing her cognitive load by simplifying security practices and appealing to her emotions to underscore the importance of safety. Finally, Cluster 3, the “Hyper-Connected Defender” persona, exemplified by Amir, shares several key psychological traits that inform his cybersecurity behaviors. At 35 years old, Amir displays moderate conscientiousness, neuroticism, and NFC, which positions him as a balanced figure in the digital landscape. His moderate conscientiousness allows for some degree of organization but not to the extent of highly structured individuals. However, his moderate neuroticism suggests that he may still experience stress in online interactions, though it does not significantly detract from his cybersecurity practices. Amir’s high engagement with social media and intense FoMO align with high levels of PSMU [77]. This profile is similar to that of individuals who rely heavily on social platforms to maintain connections and stay informed [78]. In contrast to other personas, Amir balances this high digital engagement with a high SA. These findings contradict prior research that revealed users with 'careless' and 'carefree' attitudes, particularly those highly engaged on social media, tend to have lower security concerns and are more likely to explore and exploit various applications without stringent security considerations [79]. Amir might be an exception due to factors such as higher cybersecurity awareness, education, or intrinsic motivation to maintain secure behaviors despite high engagement. His proactive stance toward safeguarding his online presence reflects a more intentional approach to digital security, as he actively seeks to implement protective measures, such as strong passwords and regular software updates. The findings from the current research suggest that individuals like Amir, who exhibit high PSMU and FoMO, are often vulnerable to digital threats [20, 80] due to impulsive behaviors driven by emotional responses and social pressures. However, Amir’s high security attitude counters this tendency by motivating him to take deliberate actions to reduce risks. This aligns with existing research, which indicates that higher security attitudes are associated with proactive cybersecurity behaviors [81]. Similarly, studies suggest that individuals with high levels of PSMU and FoMO, like Amir, may experience increased susceptibility to online manipulation and phishing scams [80]. However, Amir’s elevated security awareness reduces his risk exposure. Studies show how education or targeted interventions can mitigate risks associated with high digital engagement [82]. A key contribution of this study is the practical application of the created personas to develop targeted cybersecurity interventions. Traditional, one-size-fits-all strategies often fail to address different user groups’ diverse needs and perceptions. By using personas that represent distinct segments of the population, this research allows for the design of more personalized and effective interventions tailored to specific emotional, cognitive, and behavioral profiles [83]. This approach enhances the effectiveness of cybersecurity strategies by directly addressing the unique risk perceptions and behaviors of each persona. The clustering approach offers context-specific insights, particularly when comparing UK and Arab populations. By accounting for cultural factors, this study enables the development of culturally sensitive interventions, recognizing how these variables intersect to shape cybersecurity behaviors [84]. Additionally, this study moves beyond demographic analysis by considering factors like the NFC, PSMU, and FoMO, providing a multidimensional perspective on how individuals interact with digital security behavior. This approach is essential for understanding how individuals respond to security challenges and interventions. Our clustering analysis challenges prior assumptions regarding the relationship between Conscientiousness, Neuroticism, PSMU, FoMO, and SA. While existing literature suggests that individuals with high PSMU and FoMO generally exhibit low SA [22, 23], our findings reveal a more nuanced structure. Specifically, only one cluster aligned with this expectation, whereas other clusters demonstrated moderate to high levels across all three dimensions. This indicates that high PSMU and FoMO do not universally predict weak security behaviors, suggesting the need for more tailored interventions that account for varying psychological profiles. Additionally, the distribution of cluster sizes was relatively comparable, highlighting that the assumed negative association between PSMU, FoMO, and SA is not as prevalent as previously thought. Unlike cyber behavior, which displayed cluster-specific variations, personality traits followed expected patterns, such as low conscientiousness and low NFC, which aligned with high neuroticism or vice versa. However, the way these personality profiles translated into cyber behaviors was not symmetrical; for example, individuals with low Conscientiousness, low NFC, and high Neuroticism did not exhibit cyber behaviors that were the direct inverse of those with high Conscientiousness, high NFC, and low Neuroticism. This underscores the complexity of how psychological traits interact with digital security behaviors, reinforcing the importance of moving beyond one-size-fits-all frameworks in cybersecurity interventions. This study has several limitations. First, the reliance on self-reported data introduces potential bias, even with measures to ensure anonymity and allow participant withdrawal. Additionally, while the UK and Arab samples provide valuable insights, the findings may not be generalizable to other cultural contexts. Finally, emphasizing quantitative data may overlook nuanced individual experiences that qualitative methods could better capture. 5. Conclusion This study employs clustering techniques to explore how personality traits, cognitive styles, and cyber behaviors intersect across cultures, identifying three unique personas for both the UK and Arab samples to provide a nuanced understanding of user profiles in the digital landscape. The personas, ranging from highly conscientious and cognitively engaged individuals to those more emotionally volatile or socially connected, highlight the diversity in cybersecurity behaviors and attitudes. The findings emphasize the critical role of traits like Conscientiousness and NFC in fostering proactive security practices, while behaviors driven by PSMU and FoMO often amplify vulnerabilities to cyber threats. The cultural distinctions observed with UK participants exhibiting stronger cognitive engagement but higher emotional volatility and Arab participants showing higher social media dependency yet stronger security attitudes underscore the importance of context-specific approaches to cybersecurity interventions. This research underscores the value of clustering techniques in creating actionable behavioral profiles, which can inform more targeted and culturally sensitive cybersecurity strategies. While this study focused on WEIRD populations, it provides foundational insights for further research. Future studies should integrate qualitative methods to capture more profound insights into individual experiences and employ experimental approaches to establish causal links between identified factors and cybersecurity behaviors. By leveraging these personas, stakeholders can develop tailored interventions that effectively address the psychological and behavioral dimensions of cybersecurity, ultimately promoting safer and more resilient online practices. Declarations Author Contribution The study was designed and supervised by RA, AE, MB, and KK, who also conceptualized the research. Statistical analysis was designed by TS, AY, and RA and performed by TS and AY. AY supervised the statistical analysis and contributed to the conceptualization and methodology of this paper. The initial draft of the manuscript was prepared by TS and then reviewed and revised by AY, MB, KK, AE and RA. Funding This work was supported by an NPRP-14-Cluster Grant # NPRP 14C-0916-210015 from the Qatar National Research Fund (a member of the Qatar Foundation). The findings herein reflect the work and are solely the authors' responsibility. Conflict of Interest The authors report that there are no competing interests to declare. 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Buglass, S.L., Binder, J.F., Betts, L.R., Underwood, J.D.M.: Motivators of online vulnerability: The impact of social network site use and FOMO. Comput. Hum. Behav. 66, 248–255 (2017). https://doi.org/10.1016/j.chb.2016.09.055. Adewusi, M., Adeshina, A., Odekeye, O.: Understanding Online Security Perceptions and Practices: A Qualitative Study. (2024). Bergdahl, N., Nouri, J., Fors, U.: Disengagement, engagement and digital skills in technology-enhanced learning. Educ. Inf. Technol. 25, 957–983 (2020). https://doi.org/10.1007/s10639-019-09998-w. Farooq, A., Alabed, A., Msefula, P.S., Tamime, R.A., Salminen, J., Jung, S., Jansen, B.J.: Representing Groups of Students as Personas: A Systematic Review of Persona Creation, Application, and Trends in the Educational Domain. Comput. Educ. Open. 100242 (2025). https://doi.org/10.1016/j.caeo.2025.100242. Bhatti-Sinclair, K.: Culturally Appropriate Interventions in Social Work. Int. Encycl. Soc. Behav. Sci. (2015). https://doi.org/10.1016/B978-0-08-097086-8.28023-9. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7090927","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":509222357,"identity":"a2e3ea01-935d-47d0-b6c3-9ed5321f3834","order_by":0,"name":"Tourjana Islam Supti","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYLACCTjLwAZNAAvgQVFxwCCNSC1wcIDhMGEt9uw9Zh8sau7IM/CfMXv8oeC87IYDzAdv8zBYy+O0heeM8QyJY88MGyRyzA0OGNw23nCALdmahyHdsAGXFokcYwYJtsOMDRI8ZhJALYkbDvCYSfMwAEXwavl32L4B6DCglnNALfzfQFrs8WqRbDuc2MCQA9JyAGQLG0hLIk4tZ44VM0j2HU5uk0grNzhjkGw88zCbseUcg/RkXFrY25s3M0t8O2zbz39424OKP3ayfcebH954U2Fti0sLCDCDooENjBgYGBuYQZQBMx4NQFUfIDRUC9QcvFpGwSgYBaNgRAEA4s1RFiwbF+IAAAAASUVORK5CYII=","orcid":"","institution":"Qatar University","correspondingAuthor":true,"prefix":"","firstName":"Tourjana","middleName":"Islam","lastName":"Supti","suffix":""},{"id":509222358,"identity":"969d8f7a-9585-4f32-aa5b-b43ae1a8d1a0","order_by":1,"name":"Ala Yankouskaya","email":"","orcid":"","institution":"Bournemouth University","correspondingAuthor":false,"prefix":"","firstName":"Ala","middleName":"","lastName":"Yankouskaya","suffix":""},{"id":509222359,"identity":"87026fe1-089c-43fd-bc32-2b95c607ee2b","order_by":2,"name":"Mahmoud Barhmagi","email":"","orcid":"","institution":"Qatar University","correspondingAuthor":false,"prefix":"","firstName":"Mahmoud","middleName":"","lastName":"Barhmagi","suffix":""},{"id":509222360,"identity":"2cffabce-1334-475c-a8a4-3d3fff4c6d3b","order_by":3,"name":"Khaled M. Khan","email":"","orcid":"","institution":"Qatar University","correspondingAuthor":false,"prefix":"","firstName":"Khaled","middleName":"M.","lastName":"Khan","suffix":""},{"id":509222361,"identity":"e00916ea-e442-428b-b2f0-b01376e8686b","order_by":4,"name":"Aiman Erbad","email":"","orcid":"","institution":"Qatar University","correspondingAuthor":false,"prefix":"","firstName":"Aiman","middleName":"","lastName":"Erbad","suffix":""},{"id":509222362,"identity":"8c6a187a-2b6b-4abf-ba36-207c72a3402c","order_by":5,"name":"Raian Ali","email":"","orcid":"","institution":"Hamad Bin Khalifa University","correspondingAuthor":false,"prefix":"","firstName":"Raian","middleName":"","lastName":"Ali","suffix":""}],"badges":[],"createdAt":"2025-07-10 08:53:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7090927/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7090927/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92616588,"identity":"d00a4de4-b55c-4918-bf60-c8ac972e8e96","added_by":"auto","created_at":"2025-10-01 17:40:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":204163,"visible":true,"origin":"","legend":"\u003cp\u003eCluster characteristics in the UK sample\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7090927/v1/0f13c5e68d49cdac1fa356f9.png"},{"id":92616587,"identity":"9972c303-4e29-4d63-aa06-336541ec57b8","added_by":"auto","created_at":"2025-10-01 17:40:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":255937,"visible":true,"origin":"","legend":"\u003cp\u003eCluster characteristics in the Arab sample\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7090927/v1/d2342706f644df9f16e00c61.png"},{"id":92617357,"identity":"3a1f1049-3acc-4340-9021-5be162ad94f3","added_by":"auto","created_at":"2025-10-01 17:48:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":246464,"visible":true,"origin":"","legend":"\u003cp\u003eUK Personas\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7090927/v1/4857b7182bf003929d64950c.png"},{"id":92616591,"identity":"9eba87f6-2dac-4b30-88a8-9fb0f42288c7","added_by":"auto","created_at":"2025-10-01 17:40:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":253907,"visible":true,"origin":"","legend":"\u003cp\u003eArab Personas\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e*Note: Profile pictures of each persona have been created by an AI Image generator, and the User Profiles are designed by the Canva website (https://www.canva.com/).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7090927/v1/3a49944181f93e9a416515f2.png"},{"id":92617974,"identity":"af134a7d-aa86-4571-8d33-1e2a285073f8","added_by":"auto","created_at":"2025-10-01 17:56:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1516114,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7090927/v1/82abd09b-c244-4b6d-bf15-534d8d38d696.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cyber Behavior and Personality Nexus: Clustering Around Security Attitudes, FoMO, Problematic Social Media Use, and Cognitive and Personality Traits?","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe digital age has transformed how individuals interact, communicate, and access information, creating new opportunities but also significant cybersecurity risks for both individuals and organizations [1]. As society becomes increasingly dependent on the internet, cybercrime remains a widespread and evolving threat, with its full scale and economic impact difficult to measure yet widely acknowledged by experts [2]. As people engage in various cyber behaviors, including social media use, cybersecurity practices, and risk-taking activities, their online interactions and vulnerability to cyber threats are influenced by these behaviors. Problematic social media use (PSMU), Fear of Missing Out (FoMO), and security attitudes are key behaviors that shape how individuals engage with digital platforms and manage cyber risks. Despite technological advancements to improve security systems, human behavior remains a critical vulnerability in cybersecurity [3], as individuals often are subject to cognitive biases that could expose them to threats. Social engineering tactics such as phishing and pretexting exploit psychological factors like trust, urgency, and fear, impairing rational decision-making and making individuals prime targets for manipulation [4, 5]. Employees prefer localized security support and assurance rather than rigid, centrally imposed instructions, highlighting the role of trust and social influence in shaping secure behaviors [6]. Understanding these psychological and behavioral factors is essential for developing effective interventions that mitigate cyber risks and promote safer, more responsible online behavior.\u003c/p\u003e\n\u003cp\u003eAmong the factors affecting cybersecurity behavior, excessive or problematic social media use, commonly known as “social media addiction” or “social media disorder” [7, 8], has gained significant attention. However, this terminology has faced criticism [9].\u0026nbsp;Problematic social media use (PSMU) refers to the excessive use of social media that leads to adverse effects on different areas of an individual's daily life\u0026nbsp;[10].\u0026nbsp;Additionally, the psychological phenomenon of Fear of Missing Out (FoMO) has also been recognized for its influence on online behavior and risk perceptions.\u0026nbsp; \u0026nbsp;FoMO is a psychological condition marked by a constant anxiety that others are experiencing enjoyable or fulfilling events that one is not part of\u0026nbsp;[11, 12].\u0026nbsp;\u0026nbsp;These behaviors can create heightened emotional vulnerability, leading individuals to prioritize immediate gratification over long-term safety.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA growing body of research highlights the significant role of PSMU and FoMO in shaping online behavior and influencing cybersecurity-related attitudes and behaviors. A study by\u0026nbsp;Sindermann et al.\u0026nbsp;shows that certain design elements of social media platforms encourage users to spend more time on these platforms, which is strongly linked to problematic social media use\u0026nbsp;[13].PSMU is linked to factors such as depression, FoMO, the need to belong, and increased social media use\u0026nbsp;[14]. \u0026nbsp; A study by Weaver \u0026amp; Swank\u0026nbsp;[15]\u0026nbsp;reveals that higher levels of FoMO and PSMU are associated with lower self-esteem, mindfulness, and life satisfaction among undergraduates. During the pandemic, FoMO shifted from physical events to online activities, continuing to negatively impact well-being, including sleep deprivation and reduced focus\u0026nbsp;[16]. Furthermore, FoMO has been shown to increase stress levels among social media users\u0026nbsp;[17–19]. PSMU is also strongly correlated with cybercrime victimization, with increased levels raising the risk of becoming a victim\u0026nbsp;[20]. The widespread sharing of information on social networks enhances the chances of cyberattacks by hackers\u0026nbsp;[21]. A study by Deutrom et al. problematic internet use is associated with poorer cybersecurity behaviors\u0026nbsp;[22]. \u0026nbsp; Additionally, employees with higher FoMO levels tend to have lower information security awareness (ISA), demonstrating poorer knowledge, more negative attitudes, and riskier behaviors\u0026nbsp;[23]. A cyclical relationship between FoMO-centric design and privacy-compromising behavior has also been observed, where individuals, despite expressing privacy concerns, engage in risky behaviors due to the pressure to participate\u0026nbsp;[24]. Furthermore, an individual’s Security Attitude, belief in the importance of cybersecurity, and intention to adopt secure practices significantly influence protective behaviors. Those with a stronger belief in cybersecurity are likely to adopt protective behaviors despite the emotional pull of PSMU and FoMO.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIndividual differences, especially personality traits, play a key role in social media engagement, digital boundary management, and perceptions of security, yet their relationship with digital security remains largely underexplored. Personality traits are enduring characteristics that influence an individual's thinking, feeling, and behavior patterns. These traits are often described through frameworks like the Big Five Personality Traits (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism)\u0026nbsp;[25, 26]. Research has shown that certain personality traits are significantly associated with cybersecurity behaviors. Attitudes toward security recommendations are a multidimensional construct, with employees evaluating security policies based on their perceived legitimacy, effectiveness, and rigor\u0026nbsp;[27]. \u0026nbsp;For instance, conscientiousness, agreeableness, and openness are key traits influencing how individuals engage with digital security, with conscientiousness being the strongest predictor of secure behavior\u0026nbsp;[28]. Neuroticism, on the other hand, has been linked to increased susceptibility to phishing attacks. At the same time, openness to experience is associated with more relaxed privacy settings and a greater tendency to share personal information, making individuals more vulnerable to privacy breaches\u0026nbsp;[29]. Furthermore, conscientiousness, agreeableness, emotional stability, and risk-taking propensity influence Information Security Awareness (ISA)\u0026nbsp;[30]. Specifically, conscientious individuals, who tend to be hardworking and detail-oriented, are more likely to engage in secure online practices\u0026nbsp;[31]. Conscientiousness is strongly associated with secure online behaviors, as individuals high in this trait are likelier to follow cybersecurity practices diligently.\u003c/p\u003e\n\u003cp\u003eIn the context of social media use, neuroticism and impulsivity are indirectly associated with PSMU through fear of missing out (FoMO\u003cstrong\u003e)\u003c/strong\u003e and the increased use of social media driven by design features\u0026nbsp;[13]. People with high levels of neuroticism often engage with social media more than they realize, leading to greater dependency . Neuroticism is a significant predictor of PSMU severity, particularly among emotionally vulnerable individuals . A study by\u0026nbsp;Alshakhsi et al.\u0026nbsp;found that neuroticism is significantly associated with PSMU, with FoMO mediating this relationship fully in the UK and partially in the Arab region’s sample\u0026nbsp;[34]. Additionally, neuroticism has been linked to FoMO, with studies revealing that it significantly increases susceptibility to social influence\u0026nbsp;[35]. While neuroticism is strongly positively associated with FoMO, conscientiousness shows a small negative association with FoMO across various analyses\u0026nbsp;[36]. This suggests that individuals with higher neuroticism are more likely to experience FoMO, potentially leading to increased social media use and reduced engagement with secure online practices. Personality traits such as neuroticism and conscientiousness significantly influence how individuals perceive and respond to both social media use and digital security practices. These findings highlight the need to consider individual differences when developing interventions aimed at promoting secure behavior and managing social media dependency.\u003c/p\u003e\n\u003cp\u003eIn addition to personality traits, the Need for Cognition (NFC) also plays a significant role in decision-making processes related to cybersecurity. NFC refers to an individual’s tendency to engage in and enjoy effortful cognitive activities, such as thinking critically and solving complex problems\u0026nbsp;[37]. Likewise, cognitive styles, including Need for Cognition (NFC), Need for Affect (NFA), and Faith in Intuition (FII), also play a role in decision-making processes related to cybersecurity\u0026nbsp;[38, 39]. People with high NFC, for example, are more likely to seek out detailed information, making them more cautious in decision-making\u0026nbsp;[37], which can lead to adopting more secure measures. On the other hand, individuals with high reliance on intuition may make more impulsive decisions, relying on heuristics that overlook updated information\u0026nbsp;[40], potentially increasing their online vulnerability. The NFC significantly influences decision-making in cybersecurity, as individuals high in NFC are more inclined to seek out detailed information and think critically before making decisions. This thoughtful approach leads to more secure online behaviors, as they are less likely to engage in risky actions without understanding the potential consequences. Additionally, individuals with high NFC are less susceptible to emotional impulses like FOMO and are better at managing PSMU. By prioritizing rational analysis over impulsivity, those with high NFC can reduce the impact of social media pressures and enhance their cybersecurity attitudes, making more informed and secure choices.\u003c/p\u003e\n\u003cp\u003eWhile previous studies have provided valuable insights, they often examine these dimensions in isolation, overlooking their potential interplay. According to recent systematic literature reviews on cybersecurity and human factors\u0026nbsp;[41]\u0026nbsp;, as well as research on PSMU\u0026nbsp;[42], the majority of studies in this area have focused on Western contexts which is acronymized as WEIRD (Western, Educated, Industrialized, Rich, and Democratic)\u0026nbsp;[43], with limited exploration of cybersecurity attitudes and cyber behaviors within the Middle Eastern, particularly Arab, populations. Cultural norms, social influences, and varying levels of cybersecurity awareness can shape how individuals perceive vulnerability and engage in cyber behaviors. Research suggests that sociocultural factors such as individualism-collectivism and tightness-looseness influence risk perception and the use of base rate information in assessing cyber threats\u0026nbsp;[44]. Individuals in cultures with high uncertainty avoidance may be more cautious and vigilant in adopting secure online practices than those in low uncertainty avoidance cultures\u0026nbsp;[45].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBy applying clustering techniques, this research aims to identify distinct cyber behavior profiles based on personality traits (conscientiousness, neuroticism), cognitive style (NFC), and behavioral factors (PSMU and FoMO) in the UK and Arab regions. The study examines whether different cyber behaviors can be grouped similarly across cultural contexts based on shared personal characteristics. Understanding these clusters provides deeper insights into the interplay of psychological and behavioral factors in cybersecurity, facilitating the development of more culturally sensitive interventions. Additionally, this research fills a gap in the literature by integrating cross-cultural differences into cybersecurity behavior analysis, particularly in non-WEIRD population samples.\u003c/p\u003e\n\u003cp\u003eBased on this foundation, we can formulate the following research question, on both Arab and British samples:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRQ1\u003c/strong\u003e: \u003cem\u003eCan we identify distinct cyber behavior profiles among individuals based on their personality traits (conscientiousness and neuroticism), cognitive style (Need for Cognition) on the one hand and their cyber behavioral factors (Social Media Disorder, Fear of Missing Out, and Security Attitude) on the other?\u003c/em\u003e\u003c/p\u003e"},{"header":"2. Research Method","content":"\u003cp\u003eOur study is part of a more extensive investigation examining the factors that shape cybersecurity attitudes and cyber behaviors. Both the Arabic and English versions of these scales and questions are available on the Open Science Framework (OSF), with the link provided in the \u0026ldquo;Supplementary Materials\u0026rdquo; section of this manuscript.\u003c/p\u003e\n\u003ch2\u003e2.1 Participant and Procedure \u003c/h2\u003e\n\u003cp\u003eParticipants for this study were recruited from the Gulf Cooperation Council (GCC) region and the United Kingdom (UK) through TGM Research (https://tgmresearch.com/), a company specializing in research data collection. These two cultural contexts were selected for their contrasting societal values and moral principles, offering a rich basis for comparative analysis [46]. The survey was designed and administered via SurveyMonkey (www.surveymonkey.com), a platform for creating and distributing questionnaires. To ensure the survey questions were clear and accurate, the research team followed an iterative development process. The survey was first drafted in English and then translated into Arabic by two team members using the recommended back-translation method [47]. A pilot test was then conducted with a small group of participants to identify and address any ambiguities or unclear wording.\u003c/p\u003e\n\u003cp\u003eEligibility criteria required participants to be over 18 years old and born and currently residing in either the UK (England, Scotland, Wales, and Northern Ireland) or one of the Arab GCC countries (Saudi Arabia, Qatar, Bahrain, Kuwait, Oman, and the UAE). Additionally, participants from the GCC region had to explicitly self-identify as Arabs in terms of cultural norms and values. Informed consent was obtained from all participants, who could withdraw from the survey at any time. To ensure data quality, attention checks were embedded within the survey, and participants who failed to complete the survey too quickly or provided monotonous answers were excluded. Those who successfully completed the survey were compensated for their time.\u003c/p\u003e\n\u003cp\u003eEthical approval for the study was granted by the Institutional Review Board (IRB) at the institution of the last author. Participants identifying as non-binary were excluded from the analysis due to their small sample size in both the Arab and UK datasets. Similarly, participants aged 60 and above were excluded from the UK sample to address the uneven age distribution across the two samples, as we could not get any participants in that age group from the Arab sample. While older individuals participated in the UK sample, only 7% of the population in many Arab countries falls within this age group [48]. Limiting the age range to 18\u0026ndash;60 years ensured consistency. The final dataset included 642 participants, comprising 314 from the UK and 328 from the Arab GCC region.\u003c/p\u003e\n\u003ch2\u003e2.2 Measure\u003c/h2\u003e\n\u003ch2\u003e2.2.1 Demographic Measure\u003c/h2\u003e\n\u003cp\u003eParticipants were asked to provide their age and gender. Age was recorded as a continuous variable in years, while gender was collected through an open-text field and then coded accordingly.\u003c/p\u003e\n\u003ch2\u003e2.2.2 Big Five Inventory (BFI-10) \u003c/h2\u003e\n\u003cp\u003eThe study employed the BFI-10 to assess personality traits [49]. This abbreviated version of the Big Five Inventory evaluates five dimensions of personality: extraversion indicates the intensity of being outgoing and interactive; introverts, which is the opposite of extraversion; agreeableness identifies how friendly and trusting the person is. Neuroticism indicates the stability of emotion; openness measures how open to experience the user is; conscientiousness indicates that the person is focused on goals and determined with two items dedicated to each trait. Participants rated their agreement with each statement on a five-point Likert scale, ranging from \u0026ldquo;1= strongly disagree to 5 = strongly agree. Some items are reverse scored to control for response bias. The final score for each personality trait is calculated by summing the responses across all relevant items. In this study, we used only two personality traits: Conscientiousness and Neuroticism. The theoretical range for Conscientiousness and Neuroticism in the UK sample was 2 to 10, whereas in the Arab sample, the range was 3 to 10 for Conscientiousness and 2 to 10 for Neuroticism.\u003c/p\u003e\n\u003ch2\u003e2.2.3 Need for Cognition (NFC)\u003c/h2\u003e\n\u003cp\u003eNFC was measured using the five-item subscale of the Rational Experiential Inventory (REI-10), which also includes a subscale for Faith in Intuition (FII) [50]. This scale is a shortened version of the original developed by Cacioppo and Petty [37], and participants rated their agreement with statements on a 5-point Likert scale ranging from \u0026ldquo;1 = Completely False\u0026rdquo; to \u0026ldquo;5 = Completely True\u0026rdquo;. The theoretical range was 6 to 25. An example item from the NFC scale is, \u0026ldquo;\u003cem\u003eI don\u0026rsquo;t like to have to do a lot of thinking.\u003c/em\u003e\u0026rdquo; Several items were slightly adjusted for clarity and to ensure consistency between the English and Arabic versions of the scale. For example, the original NFC item \u0026ldquo;\u003cem\u003eThinking hard and for a long time about something gives me little satisfaction\u003c/em\u003e\u0026rdquo; was modified to \u0026ldquo;\u003cem\u003eThinking hard and for a long time about something gives me some satisfaction\u003c/em\u003e\u0026rdquo; to enhance its understanding of the study\u0026apos;s cultural and linguistic context. The NFC total score is calculated by summing the responses to all scale items after reverse-scoring the negatively worded items, with higher scores indicating a greater preference for effortful thinking. The scale showed very good internal reliability across both samples, with Cronbach\u0026rsquo;s alpha of 0.82 in the UK sample and 0.70 in the Arab sample.\u003c/p\u003e\n\u003ch2\u003e2.2.4 Problematic Social Media Use (PSMU)\u003c/h2\u003e\n\u003cp\u003eProblematic Social Media Use (PSMU) was measured using the original English version and a translated Arabic version of the \u0026ldquo;Social Media Disorder Scale\u0026rdquo; [51]. The scale consists of nine items, one of which is \u0026ldquo;\u003cem\u003eHave you ever found yourself unable to think of anything else but the moment when you\u0026rsquo;ll be able to use social media again?\u003c/em\u003e\u0026rdquo; Participants responded to each item on a 5-point Likert scale, ranging from \u0026ldquo;1 = Never\u0026rdquo; to \u0026ldquo;5 = Always.\u0026rdquo; The theoretical range for total PSMU was from 9 to 40. The total PSMU score was calculated by summing participants\u0026rsquo; responses, with higher scores indicating greater levels of social media disorder. In previous studies, the scale has shown very good internal consistency, with Cronbach\u0026rsquo;s alpha ranging from \u0026alpha; = 0.76 to \u0026alpha; = 0.82. In our study, Cronbach\u0026rsquo;s alpha was 0.87 for the UK sample and 0.84 for the Arab sample, indicating very good reliability.\u003c/p\u003e\n\u003ch2\u003e2.2.5 Fear of Missing Out (FoMO)\u003c/h2\u003e\n\u003cp\u003eTo measure the concept of Fear of Missing Out (FoMO), participants were provided a definition: \u0026ldquo;FoMO, the fear of missing out, refers to the fear of not being able to know what is happening (whether on social media or in real-world) and participate in it and taking opportunities.\u0026rdquo; Participants were then asked to rate their agreement with the statement: \u0026ldquo;\u003cem\u003eI experience FoMO regarding what is happening on social media.\u003c/em\u003e\u0026rdquo; Responses were collected using a 10-point Likert scale, where \u0026ldquo;1 = Strongly Disagree\u0026rdquo; and \u0026ldquo;10 = Strongly Agree,\u0026rdquo; with higher scores indicating a stronger fear of missing out.\u003c/p\u003e\n\u003ch2\u003e2.2.6 Security Attitude (SA-6)\u003c/h2\u003e\n\u003cp\u003eThe Security Attitudes (SA-6) scale, developed by Faklaris [52] , is a validated six-item instrument to assess individuals\u0026apos; attitudes toward cybersecurity. Extensive empirical research supports the scale\u0026rsquo;s reliability and validity, revealing various responses. Participants rated their agreement with statements like \u0026ldquo;\u003cem\u003eI actively seek opportunities to learn about security measures that apply to me\u003c/em\u003e\u0026rdquo; and \u0026ldquo;\u003cem\u003eI am highly motivated to take all necessary steps to protect my online data and accounts\u003c/em\u003e\u0026rdquo; using a five-point Likert scale (1 = strongly disagree to 5 = strongly agree). The SA-6 scale has been shown to correlate significantly with both self-reported security intentions and actual secure behaviors, confirming its utility in measuring and comparing attitudes toward adopting recommended security practices. In the original validation study, the SA-6 scale demonstrated strong internal consistency with a Cronbach\u0026rsquo;s alpha of 0.84 [52]. In our study, the scale yielded a Cronbach\u0026apos;s alpha of 0.87 for the UK participants and 0.79 for the Arab participants, reflecting very good internal consistency. The total security attitude score for each participant was calculated by summing the individual item scores, providing a standardized measure of security attitudes.\u003c/p\u003e\n\u003ch2\u003e2.3 Data Pre-processing\u003c/h2\u003e\n\u003cp\u003eAs an initial step in our data analysis, we examined the relationships among all the variables across both the UK and Arab samples. These correlations, which provided insights into the interplay between cyber behaviors and individual differences before clustering, are illustrated in the appendix (Figure 1S) in the OSF link provided in the \u0026ldquo;Supplementary Materials\u0026rdquo; section. Prior to conducting cluster analysis, we implemented a two-stage data preprocessing procedure. First, we standardized the datasets for both samples using the scale() function in R, which normalizes the numeric matrix by adjusting its columns. To determine whether the data were suitable for clustering, we assessed clustering tendency through visual analysis of two matrices. The first matrix represented correlation-based distances between data points in the original dataset, computed using the Spearman method via the get_dist() function in R. The second matrix, generated using randomly assigned values, maintained the same dimensions as the original dataset. A comparative visual inspection of these matrices confirmed that our data exhibited an appropriate structure for clustering, as presented in Appendix Figure 2S in the OSF link provided in the \u0026ldquo;Supplementary Materials\u0026rdquo; section.\u003c/p\u003e\n\u003ch2\u003e2.4 Clustering Approach\u003c/h2\u003e\n\u003cp\u003eWe applied partition clustering to group observations into distinct clusters based on their similarities within each sample. The variables used for clustering included conscientiousness, neuroticism, NFC, PSMU, FoMO, and SA. For this purpose, we implemented K-means clustering, an unsupervised machine learning algorithm that divides data into a set number of clusters, denoted by k. This algorithm aims to maximize the similarity within each cluster and minimize the dissimilarity between different clusters. To carry out this analysis, we utilized the Hartigan-Wong method [53], which minimizes the within-cluster variance by calculating the sum of squared Euclidean distances between observations and the centroids of their respective clusters. Each observation is assigned to a cluster so as to minimize the squared distance to its assigned centroid, which was computed using the k-means() function in R\u0026rsquo;s stats package.\u003c/p\u003e\n\u003cp\u003eAs K-means clustering requires a predefined number of clusters, we determined the optimal number of clusters, k, using the NbClust package in R. This package offers 30 indices to suggest the most suitable clustering solution by evaluating various combinations of k values, distance metrics, and clustering techniques [54]. To ensure reliable results, we undertook three additional steps. First, we tested different k-values to compare clustering outcomes and avoid arbitrary cluster selections. Second, in light of K-means\u0026rsquo; sensitivity to the initial random placement of cluster centroids [55], we ran the clustering algorithm five times with different initial cluster center assignments, choosing the configuration with the lowest within-cluster sum of squares. For stability, we used 15, 25, 35, 45, and 55 random initializations with 1000 iterations per run. Lastly, recognizing the potential impact of outliers on clustering performance, we carefully examined the data for outliers prior to performing the clustering.\u003c/p\u003e"},{"header":"3. Results","content":"\u003ch2\u003e3.1 Data Set Characteristics\u003c/h2\u003e\n\u003cp\u003eThe overview of the UK and Arab sample participants is presented in Table 1 for this study. A Welch\u0026rsquo;s t-test was conducted to examine the null hypothesis that the means of the two groups are equal. Additionally, evidence for the alternative hypothesis was assessed using the Bayes Factor (BF10) with default priors [56]. Arab participants tended to score slightly higher in conscientiousness, indicating a more organized and goal-oriented approach compared to UK participants. This difference in conscientiousness may reflect contrasting ways of managing tasks and responsibilities. UK participants, on the other hand, were more neurotic and had higher levels of NFC, suggesting they may be more emotionally reactive and cognitively engaged in problem-solving. While UK participants exhibited a heightened cognitive focus, Arab participants showed stronger connections to PSMU, FoMO, and SA, highlighting differences in how each group approaches both emotional and cognitive aspects of their lives.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1:\u003c/strong\u003e Descriptive Statistics Analysis of All the Variables for the UK and Arab Samples\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"600\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eUK\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;(N=314)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eArab\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e(N=328)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et-test (*W, p-value, **BF10)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eMales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e131 (42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e185 (56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eFemales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e183(58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e143 (44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eMales\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e38.99 (12.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e37.88 (10.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e-0.85, p=.397, BF\u003csub\u003e10\u003c/sub\u003e=0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eFemales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e36.95 (12.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e32.64 (9.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e-3.64, p\u0026lt;.001, BF\u003csub\u003e10\u003c/sub\u003e=43.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eConscientiousness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e7.59 (1.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e7.99 (1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e3.03, p=.003, BF\u003csub\u003e10\u003c/sub\u003e=7.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eNeuroticism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e6.48 (2.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e5.39 (2.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e-6.76, p\u0026lt;.001, BF\u003csub\u003e10\u003c/sub\u003e=14.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eNFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e17.44 (3.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e16.21 (3.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e-4.02, p\u0026lt;.001, BF\u003csub\u003e10\u003c/sub\u003e\u0026gt;100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003ePSMU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e16.83 (5.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e22.75 (6.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e11.98, p\u0026lt;.001, BF\u003csub\u003e10\u003c/sub\u003e=34.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eFoMO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e4.01 (2.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e5.40 (2.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e6.79, p\u0026lt;.001, BF\u003csub\u003e10\u003c/sub\u003e= 15.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e21.18 (4.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e24.47(3.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e10.70, p\u0026lt;.001, BF\u003csub\u003e10\u003c/sub\u003e=37.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. * Negative values for Welch\u0026rsquo;s test indicate a smaller mean for the Arab sample than the UK sample. **According to Lee \u0026amp; Wagenmakers\u0026nbsp;[57]\u0026nbsp;Classification scheme, BF10 \u0026gt;100 provides extreme evidence for the alternative hypothesis (H1), 30-100 \u0026ndash; very strong evidence, 10-30 \u0026ndash; strong evidence, 3-10 moderate evidence, 1-3 and 1/3-1\u0026ndash; anecdotal evidence, 1 \u0026ndash; no evidence.\u003c/p\u003e\n\u003ch2\u003e3.2\u0026nbsp; Results of Clustering Analysis\u003c/h2\u003e\n\u003cp\u003eOur analysis sought to determine the optimal number of clusters, and based on the majority rule, we identified three clusters as the best solution for both samples (see Appendix for details in the OSF link provided in the \u0026ldquo;Supplementary Materials\u0026rdquo; section). Figures 1 and 2 present the defining characteristics of these clusters. For the UK sample, three distinct clusters were identified based on Conscientiousness, Neuroticism, NFC, PSMU, FoMO, and SA. Cluster 1 is characterized by high levels of Conscientiousness and NFC, coupled with low Neuroticism, PSMU, and FoMO. Members of this cluster display the strongest security attitudes (SA), suggesting a high level of vigilance and engagement with secure behaviors. Cluster 2 exhibits low Conscientiousness, high Neuroticism, and low NFC. Individuals in this cluster show moderate levels of PSMU and FoMO, indicating some dependence on social media but not at extreme levels. Their SA is the weakest among the three clusters, suggesting greater vulnerability to cybersecurity risks. Cluster 3 is defined by moderate levels of Conscientiousness, Neuroticism, and NFC, alongside high levels of PSMU and FoMO. This cluster shows average security attitudes (SA), balancing some secure behaviors with potential vulnerabilities due to their high engagement with social media and increased anxiety about missing out.\u003c/p\u003e\n\u003cp\u003eSimilarly, for the Arab sample, three distinct clusters were identified based on Conscientiousness, Neuroticism, NFC, PSMU, FoMO, and SA. Cluster 1 is characterized by high Conscientiousness and NFC, along with low Neuroticism, PSMU, and FoMO. Members of this cluster exhibit moderate SA, suggesting a reasonable level of engagement with secure behaviors. Cluster 2 displays low Conscientiousness, high Neuroticism, and low NFC. Individuals in this cluster report moderate levels of PSMU and FoMO, with weak SA, indicating a greater vulnerability to cybersecurity risks. Cluster 3 features moderate Conscientiousness, Neuroticism, and NFC, combined with high PSMU and FoMO. This cluster demonstrates strong SA, which may reflect their heightened engagement with cybersecurity practices despite significant social media dependency and fear of missing out.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e3.2.1 \u0026nbsp; \u0026nbsp;Clustering Quality Evaluation\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eTo assess the distinct characteristics of each cluster, we performed a one-sample t-test, comparing the cluster values against a baseline of zero for both the UK and Arab samples (refer to Tables 2S and 3S in the appendix in the OSF link provided in the \u0026ldquo;Supplementary Materials\u0026rdquo; section). The primary goal was to highlight the key attributes within each cluster, specifically those with values notably above or below the average participant. These findings are further visualized in the appendix in the OSF link provided in the \u0026ldquo;Supplementary Materials\u0026rdquo; section through interval plots, accompanied by comprehensive statistical findings. For assessing the overall quality of clustering, we utilized the silhouette coefficient, which yielded values of 0.19 for the UK sample and 0.16 for the Arab sample, suggesting an adequate level of cluster cohesion and separation (see Figure 6S in the appendix in the OSF link provided in the \u0026ldquo;Supplementary Materials\u0026rdquo; section).\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e3.2.2 \u0026nbsp; \u0026nbsp; From Cluster to Persona Creation\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eTo enhance the clarity of the clustering results, each cluster is illustrated through a persona, an archetype that captures the key psychological and behavioral traits of individuals within that group. This method connects statistical analysis with practical relevance, providing an intuitive lens for understanding variations in cybersecurity attitudes and digital behavior. By portraying data-informed user types, personas make the findings more accessible to diverse stakeholders: designers can develop user-centered solutions, practitioners can customize interventions, and policymakers can design more effective awareness initiatives.\u003c/p\u003e"},{"header":"4.\tDiscussion","content":"\u003cp\u003eThis paper introduces a novel, data-driven approach to constructing user personas based on psychological, emotional, and cognitive traits. Such personas offer a nuanced understanding of individual differences in digital behavior and have been widely applied in app development and policy design to identify target groups and tailor interventions. For example, personas have been used to model the mental frameworks of aging populations in China for health app development [58], and to address the specific needs of older adults with heart failure in user-centered design [59]. By grounding personas in empirical data, we aim to support user well-being and enable digital strategies that enhance social media awareness, strengthen security attitudes, and promote behavior change.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1 Analysis of User Profiles\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 4.1.1 UK Personas\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the UK Clusters, three different user archetypes were identified, as shown in Figure 3. UK Cluster 1, termed the \u0026ldquo;Methodical Achiever,\u0026rdquo; this archetype, represented by a middle-aged man named \u003cem\u003eRobert\u003c/em\u003e, reflects a persona defined by careful planning, emotional stability, and a proactive approach to online safety. Aligning with previous research, Robert\u0026rsquo;s high conscientiousness reflects strong organizational skills and a preference for structured, goal-oriented behaviors [60, 61], which is associated with adherence to best practices in managing digital security. Consistent with previous research, his low neuroticism indicates emotional stability, reducing his likelihood of experiencing stress or anxiety [62, 63] when confronted with online risks. This emotional resilience allows him to navigate the digital landscape confidently and clearly, avoiding impulsive or emotionally driven decisions. Robert\u0026rsquo;s high NFC reveals a strong preference for mentally stimulating tasks and a desire to engage deeply with complex information [37]. This cognitive engagement makes him more likely to critically evaluate online risks and adopt evidence-based solutions to protect his digital privacy and security. His intellectual curiosity aligns with findings from Cacioppo and Petty, who demonstrated that individuals with high NFC are more motivated to process detailed information thoroughly [37]. \u0026nbsp;His low PSMU and low FoMO suggest reduced engagement in passive social media use, which aligns with Mao \u0026amp; Zhang (2023), who found that lower passive browsing may contribute to lower levels of trait-FoMO [64]. Robert uses digital tools purposefully rather than compulsively, maintaining control over his online habits. This minimizes his vulnerability to the emotional pressures and impulsive behaviors often linked to excessive social media use. According to Maathuis \u0026amp; Chockalingam, Robert\u0026rsquo;s high SA underscores his commitment to secure and responsible digital behavior [65]. He takes proactive steps to safeguard his data, such as using strong passwords, enabling multi-factor authentication, and staying informed about the latest cybersecurity practices.\u003c/p\u003e\n\u003cp\u003eUK Cluster 2 termed \u0026ldquo;Reactive Explorer,\u0026rdquo; this archetype, represented by a middle-aged woman named Olivia, reflects a persona defined by emotional volatility and impulsive decision-making in the digital landscape. Olivia\u0026rsquo;s low conscientiousness reveals a tendency toward disorganization and difficulty with goal setting, which affects her ability to adopt structured or consistent security behaviors. \u0026nbsp;Research on conscientiousness suggests that it plays a essential role in adherence to systematic approaches for managing risks, particularly in health-related behaviors [66] \u0026nbsp;Given the parallels between structured risk management in health and digital security, individuals low in conscientiousness may similarly struggle with maintaining consistent cybersecurity practices. Aligning with findings by Thompson [67], her high neuroticism suggests frequent emotional instability, making her more likely to experience stress and anxiety, in situations involving online risks. Her low NFC means she prefers straightforward tasks and avoids cognitively demanding challenges, often relying on heuristics or impulsive responses in her decision-making. This aligns with findings from Cacioppo and Petty\u0026rsquo;s work on NFC, highlighting the reduced motivation to process complex information among individuals with low cognitive engagement [37]. As a result, Olivia is less likely to critically evaluate the risks of online interactions, increasing her susceptibility to phishing scams and other digital threats. Moderate levels of PSMU and FoMO reflect occasional reliance on social media and digital platforms for engagement but suggest that these behaviors do not completely consume Olivia. However, these moderate tendencies, combined with her high neuroticism, may amplify stress related to social comparisons or the fear of being left out, especially during emotionally charged situations. For instance, research by Settles demonstrates that individuals with similar traits, high neuroticism, and low conscientiousness are prone to negative urgency, leading to impulsive reactions under stress [68]. Olivia\u0026rsquo;s low SA signals limited attention to cybersecurity practices, such as using weak passwords or neglecting updates, which places her at further risk. Her approach to digital security reflects a reactive rather than proactive stance, driven by immediate emotional responses rather than long-term planning.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUK Cluster 3 termed \u0026ldquo;Engaged Seeker,\u0026rdquo; this archetype, represented by a young woman named \u003cem\u003eEmily\u003c/em\u003e, highlights a persona actively engaged with digital platforms but with mixed emotional and cognitive traits that influence her online behaviors. Emily\u0026rsquo;s moderate conscientiousness suggests a balanced approach to organization and planning. While she can be methodical in certain areas, she may occasionally struggle with maintaining consistency in her online security practices. Her moderate neuroticism indicates emotional variability, making her prone to stress or anxiety in uncertain situations but not to the extent of overwhelming instability. Emily\u0026rsquo;s moderate NFC reflects a willingness to engage in cognitively demanding tasks when necessary, though she may not consistently seek out challenging or thought-provoking activities. This aligns with her ability to analyze and adapt to new digital tools and risks, albeit somewhat cautiously. Aligning with a previous study, Emily\u0026rsquo;s high PSMU and high FoMO reveal a strong attachment to social media and digital interactions\u0026nbsp;[69]. She frequently engages with social platforms, driven to stay connected and avoid missing out on social updates. These tendencies can sometimes lead to compulsive behaviors, such as over-checking notifications or spending excessive time online, potentially impacting her emotional well-being\u0026nbsp;[70]. Research by Przybylski et al. highlights that high FoMO often correlates with increased social media use, contributing to feelings of dependency and digital fatigue. Her moderate SA suggests an awareness of cybersecurity practices but with inconsistent application. While Emily may understand the importance of secure online behavior, her high PSMU and FoMO could detract from her ability to implement these practices consistently. This creates a dynamic where emotional and social pressures might overshadow her rational understanding of online risks. A study found that individuals with higher trust in internet technology and vendors are more inclined to use social media and online shopping platforms, sometimes at the expense of rigorous security practices\u0026nbsp;[71].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 4.1.2 Arab Personas\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the characteristics of the three clusters in the Arab sample, we can identify distinct personas that reflect their behaviors, motivations, and challenges, as shown in Figure 4. Cluster 1, represented by \u0026ldquo;Sarah,\u0026rdquo; the persona we named Analytical Protector, a 36-year-old female characterized by high conscientiousness, low neuroticism, and a strong NFC. This profile aligns with previous research suggesting that highly conscientious individuals exhibit greater organizational skills and a more methodical approach to decision-making [72]. Sarah\u0026rsquo;s low neuroticism and high emotional stability further support findings that individuals with these traits are less likely to react impulsively or experience stress in the face of uncertainty, which might result in more rational processing of risks [73]. Sarah\u0026rsquo;s minimal reliance on social media (low PSMU) and limited susceptibility to FoMO are consistent with research linking lower levels of PSMU and FoMO with high self-control and a preference for more structured, predictable environments [74]. These traits also align with studies indicating that individuals less engaged with social media are less likely to experience heightened vulnerability to online threats or manipulation [75]. However, while Sarah demonstrates a moderate security attitude, her rational and emotionally stable nature might lead her to underestimate certain cybersecurity risks, as individuals with lower emotional reactivity may be less attuned to perceived threats. Existing literature on cybersecurity behavior suggests that individuals with high NFC tend to engage in more thoughtful analysis of security information [38], but they may prioritize efficiency over perceived risk. Thus, interventions for Sarah should focus on providing structured, logical guidelines that underscore the practical benefits of cybersecurity practices. Research by Ifinedo [76] has highlighted that individuals like Sarah, who are analytical and methodical, are more likely to adopt security measures when these measures emphasize tangible, logical advantages.\u003c/p\u003e\n\u003cp\u003eCluster 2, embodied by \u0026ldquo;Amina,\u0026rdquo; is the Reactive Explorer, a 35-year-old female who mirrors the characteristics seen in the UK cluster who struggles with organization and is prone to stress (low conscientiousness and high neuroticism). With moderate levels of social media dependency and FoMO, Amina\u0026apos;s low-security attitude makes her more vulnerable to social engineering and phishing attacks. Effective strategies for Amina should focus on reducing her cognitive load by simplifying security practices and appealing to her emotions to underscore the importance of safety.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, Cluster 3, the \u0026ldquo;Hyper-Connected Defender\u0026rdquo; persona, exemplified by Amir, shares several key psychological traits that inform his cybersecurity behaviors. At 35 years old, Amir displays moderate conscientiousness, neuroticism, and NFC, which positions him as a balanced figure in the digital landscape. His moderate conscientiousness allows for some degree of organization but not to the extent of highly structured individuals. However, his moderate neuroticism suggests that he may still experience stress in online interactions, though it does not significantly detract from his cybersecurity practices. Amir\u0026rsquo;s high engagement with social media and intense FoMO align with high levels of PSMU [77]. This profile is similar to that of individuals who rely heavily on social platforms to maintain connections and stay informed [78]. In contrast to other personas, Amir balances this high digital engagement with a high SA. These findings contradict prior research that revealed users with \u0026apos;careless\u0026apos; and \u0026apos;carefree\u0026apos; attitudes, particularly those highly engaged on social media, tend to have lower security concerns and are more likely to explore and exploit various applications without stringent security considerations [79]. Amir might be an exception due to factors such as higher cybersecurity awareness, education, or intrinsic motivation to maintain secure behaviors despite high engagement. His proactive stance toward safeguarding his online presence reflects a more intentional approach to digital security, as he actively seeks to implement protective measures, such as strong passwords and regular software updates. The findings from the current research suggest that individuals like Amir, who exhibit high PSMU and FoMO, are often vulnerable to digital threats [20, 80] \u0026nbsp;due to impulsive behaviors driven by emotional responses and social pressures. However, Amir\u0026rsquo;s high security attitude counters this tendency by motivating him to take deliberate actions to reduce risks. This aligns with existing research, which indicates that higher security attitudes are associated with proactive cybersecurity behaviors [81]. Similarly, studies suggest that individuals with high levels of PSMU and FoMO, like Amir, may experience increased susceptibility to online manipulation and phishing scams [80]. However, Amir\u0026rsquo;s elevated security awareness reduces his risk exposure. Studies show how education or targeted interventions can mitigate risks associated with high digital engagement [82].\u003c/p\u003e\n\u003cp\u003eA key contribution of this study is the practical application of the created personas to develop targeted cybersecurity interventions. Traditional, one-size-fits-all strategies often fail to address different user groups\u0026rsquo; diverse needs and perceptions. By using personas that represent distinct segments of the population, this research allows for the design of more personalized and effective interventions tailored to specific emotional, cognitive, and behavioral profiles\u0026nbsp;[83]. This approach enhances the effectiveness of cybersecurity strategies by directly addressing the unique risk perceptions and behaviors of each persona. The clustering approach offers context-specific insights, particularly when comparing UK and Arab populations. By accounting for cultural factors, this study enables the development of culturally sensitive interventions, recognizing how these variables intersect to shape cybersecurity behaviors\u0026nbsp;[84]. Additionally, this study moves beyond demographic analysis by considering factors like the NFC, PSMU, and FoMO, providing a multidimensional perspective on how individuals interact with digital security behavior. This approach is essential for understanding how individuals respond to security challenges and interventions.\u003c/p\u003e\n\u003cp\u003eOur clustering analysis challenges prior assumptions regarding the relationship between Conscientiousness, Neuroticism, PSMU, FoMO, and SA. While existing literature suggests that individuals with high PSMU and FoMO generally exhibit low SA\u0026nbsp;[22, 23], our findings reveal a more nuanced structure. Specifically, only one cluster aligned with this expectation, whereas other clusters demonstrated moderate to high levels across all three dimensions. This indicates that high PSMU and FoMO do not universally predict weak security behaviors, suggesting the need for more tailored interventions that account for varying psychological profiles. Additionally, the distribution of cluster sizes was relatively comparable, highlighting that the assumed negative association between PSMU, FoMO, and SA is not as prevalent as previously thought. Unlike cyber behavior, which displayed cluster-specific variations, personality traits followed expected patterns, such as low conscientiousness and low NFC, which aligned with high neuroticism or vice versa. However, the way these personality profiles translated into cyber behaviors was not symmetrical; for example, individuals with low Conscientiousness, low NFC, and high Neuroticism did not exhibit cyber behaviors that were the direct inverse of those with high Conscientiousness, high NFC, and low Neuroticism. This underscores the complexity of how psychological traits interact with digital security behaviors, reinforcing the importance of moving beyond one-size-fits-all frameworks in cybersecurity interventions.\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. First, the reliance on self-reported data introduces potential bias, even with measures to ensure anonymity and allow participant withdrawal. Additionally, while the UK and Arab samples provide valuable insights, the findings may not be generalizable to other cultural contexts. Finally, emphasizing quantitative data may overlook nuanced individual experiences that qualitative methods could better capture.\u003c/p\u003e"},{"header":"5.\tConclusion","content":"\u003cp\u003eThis study employs clustering techniques to explore how personality traits, cognitive styles, and cyber behaviors intersect across cultures, identifying three unique personas for both the UK and Arab samples to provide a nuanced understanding of user profiles in the digital landscape. The personas, ranging from highly conscientious and cognitively engaged individuals to those more emotionally volatile or socially connected, highlight the diversity in cybersecurity behaviors and attitudes. The findings emphasize the critical role of traits like Conscientiousness and NFC in fostering proactive security practices, while behaviors driven by PSMU and FoMO often amplify vulnerabilities to cyber threats. The cultural distinctions observed with UK participants exhibiting stronger cognitive engagement but higher emotional volatility and Arab participants showing higher social media dependency yet stronger security attitudes underscore the importance of context-specific approaches to cybersecurity interventions.\u003c/p\u003e\n\u003cp\u003eThis research underscores the value of clustering techniques in creating actionable behavioral profiles, which can inform more targeted and culturally sensitive cybersecurity strategies. While this study focused on WEIRD populations, it provides foundational insights for further research. Future studies should integrate qualitative methods to capture more profound insights into individual experiences and employ experimental approaches to establish causal links between identified factors and cybersecurity behaviors. By leveraging these personas, stakeholders can develop tailored interventions that effectively address the psychological and behavioral dimensions of cybersecurity, ultimately promoting safer and more resilient online practices.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAuthor Contribution\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study was designed and supervised by RA, AE, MB, and KK, who also conceptualized the research. Statistical analysis was designed by TS, AY, and RA and performed by TS and AY. AY supervised the statistical analysis and contributed to the conceptualization and methodology of this paper. The initial draft of the manuscript was prepared by TS\u0026nbsp;and then reviewed and revised by AY, MB, KK, AE and RA.\u003c/p\u003e\n\u003cp\u003eFunding\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis work was supported by an NPRP-14-Cluster Grant # NPRP 14C-0916-210015 from the Qatar National Research Fund (a member of the Qatar Foundation). The findings herein reflect the work and are solely the authors\u0026apos; responsibility.\u003c/p\u003e\n\u003cp\u003eConflict of Interest\u003c/p\u003e\n\u003cp\u003eThe authors report that there are no competing interests to declare.\u003c/p\u003e\n\u003cp\u003eSupplementary Materials\u003c/p\u003e\n\u003cp\u003eThe dataset associated with this work is uploaded alongside the appendix and other supplementary material for this article at: https://osf.io/b8ech/?view_only=93fbe92c82e24e8d96b75a3073bc12a6\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSlonopas, Dr.A.: What Is Cybersecurity? The Realities of the Digital Age | American Public University, https://www.apu.apus.edu/area-of-study/information-technology/resources/what-is-cybersecurity-the-realities-of-the-digital-age/, last accessed 2024/11/21.\u003c/li\u003e\n\u003cli\u003eRiek, M., Bohme, R., Moore, T.: Measuring the Influence of Perceived Cybercrime Risk on Online Service Avoidance. 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Health Addict. 22, 3975\u0026ndash;3989 (2024). https://doi.org/10.1007/s11469-023-01094-y.\u003c/li\u003e\n\u003cli\u003eHeimlich, R.: Using Social Media to Keep in Touch, https://www.pewresearch.org/short-reads/2011/12/22/using-social-media-to-keep-in-touch/, last accessed 2025/01/18.\u003c/li\u003e\n\u003cli\u003eMuhammad, S.S., Dey, B.L., Bala, H., Alwi, S.F., Asaad, Y.: A typology and model of privacy- and security-concerned users\u0026rsquo; attitudes towards digital footprints and consequent influence on their social media adaptation. J. Assoc. Inf. Syst. 25, 1240\u0026ndash;1273 (2024).\u003c/li\u003e\n\u003cli\u003eBuglass, S.L., Binder, J.F., Betts, L.R., Underwood, J.D.M.: Motivators of online vulnerability: The impact of social network site use and FOMO. Comput. Hum. Behav. 66, 248\u0026ndash;255 (2017). https://doi.org/10.1016/j.chb.2016.09.055.\u003c/li\u003e\n\u003cli\u003eAdewusi, M., Adeshina, A., Odekeye, O.: Understanding Online Security Perceptions and Practices: A Qualitative Study. (2024).\u003c/li\u003e\n\u003cli\u003eBergdahl, N., Nouri, J., Fors, U.: Disengagement, engagement and digital skills in technology-enhanced learning. Educ. Inf. Technol. 25, 957\u0026ndash;983 (2020). https://doi.org/10.1007/s10639-019-09998-w.\u003c/li\u003e\n\u003cli\u003eFarooq, A., Alabed, A., Msefula, P.S., Tamime, R.A., Salminen, J., Jung, S., Jansen, B.J.: Representing Groups of Students as Personas: A Systematic Review of Persona Creation, Application, and Trends in the Educational Domain. Comput. Educ. Open. 100242 (2025). https://doi.org/10.1016/j.caeo.2025.100242.\u003c/li\u003e\n\u003cli\u003eBhatti-Sinclair, K.: Culturally Appropriate Interventions in Social Work. Int. Encycl. Soc. Behav. Sci. (2015). https://doi.org/10.1016/B978-0-08-097086-8.28023-9.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Conscientiousness, Neuroticism, Need for Cognition, Problematic Social Media Use, Fear of Missing Out, Security Attitude, Clustering, Persona","lastPublishedDoi":"10.21203/rs.3.rs-7090927/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7090927/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Our research investigates whether different cyber behaviors can be classified based on a set of personality factors. The study involved 642 participants, comprising 314 from the UK and 328 from the Arab Gulf Cooperation Council (GCC) region. By analyzing the personality factors of Conscientiousness, Neuroticism, and Need for Cognition (NFC) along with the cyber behaviors of Problematic Social Media Use (PSMU), Fear of Missing Out (FoMO), and Security Attitude (SA), the study identifies three clusters in each cultural context, which were largely similar in characteristics. The clusters were then transformed into personas to enhance ease of interpretation and practical use. The UK personas included “Methodical Achievers,” “Reactive Explorers,” and “Engaged Seekers,” while the Arabic personas included “Analytical Protectors,” “Reactive Explorers,” and “Hyper-Connected Defenders.” Creating these user profiles and presenting them as visual behavioral personas was a major goal of this research. The clusters revealed consistent relationships between cyber behaviors and personal factors. For example, high Need for Cognition and Conscientiousness correlated with stronger security attitudes and lower levels of PSMU and FoMO, while higher Neuroticism showed the opposite trend. Our findings highlight the potential of clustering approaches that consider multiple cyber behaviors and their relationship to personal factors, offering a foundation for personalized interventions that address cyber safety comprehensively rather than focusing on one behavior at a time.","manuscriptTitle":"Cyber Behavior and Personality Nexus: Clustering Around Security Attitudes, FoMO, Problematic Social Media Use, and Cognitive and Personality Traits?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-01 17:40:24","doi":"10.21203/rs.3.rs-7090927/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7d8fa767-0b44-4cdc-84e7-90cdef2df96f","owner":[],"postedDate":"October 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-01T17:40:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-01 17:40:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7090927","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7090927","identity":"rs-7090927","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}