On the Role of Personality in Attitudes Toward AI: Do AI’s Freedom of Choice and Social Proof Matter?

On the Role of Personality in Attitudes Toward AI: Do AI’s Freedom of Choice and Social Proof Matter? | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article On the Role of Personality in Attitudes Toward AI: Do AI’s Freedom of Choice and Social Proof Matter? Mohammad Mominur Rahman, Sameha AlShakhsi, Areej Babiker, Ala Yankouskaya, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6565909/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Oct, 2025 Read the published version in World Wide Web → Version 1 posted 11 You are reading this latest preprint version Abstract Personal factors have been examined in recent literature for their impact on attitudes toward AI, often treating AI as a single, uniform concept and using samples from one cultural group. To address this, we differentiate between two modalities of AI operation: freedom of choice, referring to users' ability or inability to choose alternatives to AI, and social proof, reflecting whether AI has been widely used and accepted by others. We also include samples from two distinct populations, Arab and UK. This study investigates the influence of the Big Five personality traits, Need for Cognition (NFC), and Locus of Control (LOC) on attitudes toward AI across four combinations of these modalities. A total of 639 participants (316 UK, 323 Arab) completed a survey containing scenarios, validated scales and bespoke, face-validated questions. Using hierarchical multivariate multiple regression (MMR), we analyzed how these personal factors predict two key dimensions of AI attitudes: acceptance (perceived personal and social benefits) and fear (ethical concerns and risks). Agreeableness consistently predicted more favorable attitudes across both cultures, while neuroticism was linked to greater fear. Internal LOC and higher NFC were associated with greater acceptance, highlighting the role of perceived control and cognitive engagement. Cultural differences emerged, with conscientiousness being more influential in the Arab sample and openness in the UK. Overall, personality traits had a weaker impact than expected, aligning with previous research treating AI as a single concept. The modality of operation showed limited effect. This study adds to AI acceptance literature by emphasizing psychological and cultural variability in public attitudes. Artificial Intelligence Attitude Toward AI Freedom of Choice Social Proof Personality Traits Locus of Control Need for Cognition Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 10 1. Introduction In recent years, artificial intelligence (AI) has significantly impacted various sectors such as healthcare, education, finance, and entertainment [ 1 ]. As AI continues to shape society, understanding users' attitudes toward AI has become a critical area of research. One factor assumed to influence these attitudes is personality traits. The Big five-factor model (Neuroticism, Extraversion, Openness, Agreeableness, Conscientiousness) provides a validated framework for assessing personality structure and disorders, offering a dimensional alternative to categorical diagnosis in clinical settings [ 2 ]. Research has extensively examined how personality traits influence technology acceptance, as they help explain variations in individual behavior and preferences. For instance, individuals with high openness may be more receptive to AI due to their curiosity, while those with higher neuroticism may experience anxiety or fear towards it [ 3 ]. In addition to personality traits, psychological factors such as Need for Cognition (NFC), reflecting a person's willingness to engage in thinking and problem-solving, and Locus of Control (LOC), indicating whether individuals believe they (internal LOC) or environmental factors (external LOC) control their life events, can also significantly shape attitudes toward AI. The study [ 4 ] confirms NFC's role in shaping risk-benefit judgments, with high NFC linked to nuanced evaluations and low NFC to more polarized attitudes toward technologies. Similarly, individuals with an internal LOC are more likely to view AI positively, as they perceive it as a tool they can control, while those with an external LOC tend to fear AI, viewing it as a threat [ 5 ]. Beyond these factors, AI’s design and operation modalities such as freedom of choice and social proof also influence AI perceptions [ 6 ]. The concept of freedom of choice refers to whether AI’s users are enabled to choose between AI and human alternatives. Giving users such possibility can enhance their trust and satisfaction with AI systems, as it reinforces a sense of autonomy and control [ 6 ]. Studies show that users are more likely to trust and engage with AI when they feel they have control over their interaction with it, as seen in healthcare and customer service examples [ 7 ], [ 8 ]. Additionally, social proof, referring to the influence of how others view or react to something on an individual's decision, typically leading to conformity, plays a key role in technology adoption. Research indicates that people are more likely to accept AI when they observe others doing the same, as social influence shapes their perceptions of AI's benefits and trustworthiness [ 9 , 10 ]. Cultural differences significantly shape attitudes toward AI, as shown in a study where UK respondents exhibited gender and age gaps in AI concerns, with women and older adults being more wary, while Arab participants demonstrated consistent attitudes across demographics, highlighting distinct cultural drivers of AI acceptance, such as personal versus institutional trust [ 11 ]. While prior research has separately examined personal factors and AI design features, their combined influence on attitudes toward AI remains unexplored. Additionally, comparative studies across culturally distinct samples (e.g., UK vs. Arab) are scarce, limiting generalizability. This study addresses these gaps by investigating how psychological traits and operational modalities interact to shape AI acceptance, using parallel samples from both cultures for robust cross-validation. 2. Theoretical Underpinnings This section outlines the theoretical rationale for incorporating both personal factors and contextual features of AI design, specifically freedom of choice and social proof. By moving beyond a monolithic view of AI, we demonstrate how these personal factors and operational modalities actively shape user experiences, providing deeper insights into the interplay of individual differences and contextual factors in technology acceptance across cultures. 2.1. Modalities and Attitude toward AI Freedom of choice (FoC), the ability to choose alternatives not requiring interaction with AI (such as human interaction or manual oversight), influences user trust and satisfaction. According to Self-Determination Theory [ 12 ], user motivation to adopt a system is influenced by their perception of control over its use, their connection to its purpose, and the ability to make independent decisions about interacting with or adopting the system [ 13 , 14 ]. Allowing users the freedom to choose between AI or non-AI options reinforces a sense of autonomy, which, in turn, improves trust and satisfaction [ 15 ]. Research in customer service and healthcare supports this notion, showing that limiting interactions to AI systems, such as chatbots, can reduce user satisfaction and even increase negative perceptions [ 7 , 16 ]. In contrast, providing users with the option to choose, such as allowing patients to select between AI and human doctors, has been shown to increase trust in AI [ 16 ]. Similarly, IT consumerization studies reveal that offering users multiple technology options led to higher autonomy and engagement [ 17 ]. The Technology-to-Performance Chain (TPC) framework also emphasizes how voluntary use of technology, along with social norms, influences users' attitudes and adoption [ 18 ]. On the other hand, social proof (SP), which describes how individuals rely on others' actions in uncertain situations, also influences AI adoption. Grounded in social learning theory [ 19 ] and conformity theory [ 20 ], SP demonstrates that individuals often look to others when making technology adoption decisions [ 9 ]. Studies show that social influence can significantly affect AI acceptance, such as in AI-based tourism services [ 21 ] or AI-driven service delivery [ 22 ]. This aligns with Technology Acceptance Models (TAM2) and UTAUT, which highlight the importance of social influence and subjective norms in shaping attitudes toward new technologies [ 23 , 24 ]. Furthermore, the impact of social influence is moderated by voluntariness, where the effects are stronger in mandatory contexts than in voluntary ones [ 24 ]. Recent work by Alshakhsi [ 6 ] demonstrates that AI's operational modalities, particularly FoC and SP, fundamentally alter users' perceptions of both (1) contributions to personal/social good and (2) ethical concerns and risks. The study revealed significant variation in perceived AI risks and benefits across these modalities, establishing them as critical contextual boundaries for attitude formation. Building on this, we examine how psychological traits interact with these modalities to shape attitudes, a gap not yet addressed in prior research. Together, the modalities of FoC and SP demonstrate how different factors shape and influence user engagement and attitude towards AI, highlighting their importance in fostering AI acceptance and positive perceptions. Therefore, the following research questions are proposed: RQ1: How do individual differences influence attitudes toward AI, considering different combinations of SP and FoC? 2.2. Personality Traits and Attitudes Toward AI A growing body of research has examined how personality traits influence individuals’ perceptions of and attitudes toward AI. Central to this inquiry is the Big Five personality model, which offers a framework to understand consistent patterns of behavior, thoughts, and emotions. These traits are often linked to technology adoption and acceptance due to their predictive value in how individuals engage with innovations. One of the traits is openness to experience, characterized by intellectual curiosity, creativity, and openness to novelty, has been frequently associated with more favorable attitudes toward AI. Individuals scoring high on openness are more inclined to explore and accept emerging technologies, including AI systems, due to their appreciation for new experiences [ 25 , 26 ]. Conversely, individuals high in neuroticism, who tend to be emotionally reactive and prone to anxiety, are more likely to perceive AI as threatening or unsettling, resulting in negative attitudes toward its integration in daily life [ 27 ]. This trait has consistently been associated with increased fear and distrust toward AI technologies. The relationship between the remaining Big Five traits and AI attitudes appears more context-dependent. Extraversion, associated with sociability and assertiveness, has shown mixed outcomes. While it may lead to more positive views of AI in socially interactive contexts, it may have little or even negative impact in task-focused AI applications [ 28 ]. Agreeableness, linked with traits such as kindness and cooperation, generally predicts more favorable attitudes toward AI, especially in situations that emphasize interpersonal trust and harmony [ 29 ]. However, in some studies, agreeableness was also associated with higher levels of concern or fear, possibly due to heightened sensitivity to social disruption caused by AI [ 30 ]. Conscientiousness, reflecting self-discipline, organization, and a goal-oriented mindset, has been associated with both positive and cautious views. While conscientious individuals may appreciate AI’s efficiency and structure, concerns about automation’s impact on job security can contribute to ambivalence or negativity [ 28 ]. Empirical findings support these associations, though results vary across studies and cultural contexts. For instance, in a Turkish sample, agreeableness significantly predicted negative but not positive attitudes toward AI [ 30 ]. In contrast, South Korean participants showed links between agreeableness and both positive and negative emotions toward AI, while extraversion and neuroticism influenced emotional responses [ 28 ]. In the UK, introverted individuals reported more favorable attitudes toward AI, with agreeableness and conscientiousness linked to greater forgiveness of AI’s limitations [ 31 ]. Cross-cultural studies highlight further complexities. Among German and Chinese participants, neuroticism was associated with fear of AI in the German group, while agreeableness negatively predicted fear only in the Chinese group [ 32 ]. Similarly, openness and agreeableness predicted AI acceptance among German participants, but only agreeableness played a significant role in the Chinese sample. Existing research on the relationship between personality traits and attitudes toward AI has produced mixed findings, partly due to variations in measurement approaches. Studies employ multidimensional scales like the Attitudes Toward Artificial Intelligence (ATAI) [ 33 ], ATTARI-12 [ 29 ], General Attitudes toward AI Scale (GAAIS) [ 31 ], and AI Anxiety Scale (AIAS-4) [ 34 ], while others use domain-specific tools (e.g., Attitudes toward AI in the Workplace (AAAW) [ 35 ]) or single-item measures [ 36 ]. These methodological differences may contribute to inconsistencies, as scales emphasizing trust could yield divergent results from those focused-on fear. Additionally, research gaps persist: some studies examine narrow AI applications [ 37 ], while others analyze personality correlations with isolated AI attitude dimensions [ 38 ], limiting broader generalizations about overall AI perceptions. For example, openness was associated with more positive reactions to AI-generated art [ 26 ], while neuroticism was linked to reduced trust in AI [ 5 ] and increased support for AI regulation [ 39 ]. To address these inconsistencies, researchers should adopt standardized measurement tools across cultures while focusing on specific AI contexts—particularly its operational modalities. This approach will help clarify whether personality traits produce universal or context-dependent effects on AI attitudes, advancing both theoretical insights and practical applications. Therefore, the following research questions are proposed: RQ2: How do personality traits (Big Five) influence public attitudes towards AI, considering different combinations of social proof and freedom of choice? 2.3. NFC and Attitudes Toward AI NFC has emerged as an important psychological factor influencing individuals' attitudes towards AI. NFC refers to the tendency to engage in and enjoy effortful cognitive activities, such as analyzing, problem-solving, and deep thinking [ 40 ]. Research suggests that individuals with high NFC are more likely to form informed, reflective attitudes toward AI, as they are inclined to critically assess its potential benefits and risks [ 4 ]. These individuals tend to approach AI technologies with a balanced view, considering both the innovative aspects and the ethical implications of their use. In contrast, those with lower NFC, who are less likely to engage in deep cognitive processing, may form more superficial or polarized attitudes towards AI, often influenced by external factors such as popular opinions or media portrayals [ 41 ]. This lack of in-depth processing can lead to more reactive attitudes, where individuals either overestimate the potential benefits of AI or, conversely, succumb to exaggerated fears surrounding the technology. The study by Halttu and Oinas-Kukkonen [ 41 ] highlights that high NFC significantly influences how users engage with self-monitoring systems, such as those used in health and fitness contexts. Their research found that individuals with high NFC were more likely to trust and accept such systems due to their thoughtful evaluation of system credibility and effectiveness. While existing research confirms NFC's role in shaping AI attitudes, prior studies have not examined how this relationship varies across AI's operational contexts (particularly freedom of choice and social proof). Furthermore, the field lacks cross-cultural comparisons using standardized measures. To address these gaps, we propose the following research questions: RQ3: How does need for cognition influence public attitudes toward AI, considering different combinations of social proof and freedom of choice? 2.4. LOC and Attitudes Toward AI LOC influences attitudes toward AI based on individuals' beliefs about control over their lives. Those with an internal LOC, who feel they can control outcomes, tend to view AI positively, seeing it as a helpful tool for enhancing decision-making and productivity [ 5 ]. In contrast, individuals with an external LOC, who believe external forces control outcomes, are more likely to perceive AI as a threat or part of an uncontrollable system, leading to negative attitudes and greater fear of AI [ 5 ]. This effect extends to various domains, with external LOC individuals showing less willingness to adopt new technologies across different sectors, including agriculture [ 42 ]. Understanding LOC can help in designing AI strategies that address concerns and foster acceptance among those with an external LOC. Therefore, the following research questions are proposed: RQ4: How does locus of control influence public attitudes toward AI, considering different combinations of social proof and freedom of choice? The conceptual framework is illustrated in Fig. 1 . Previous research has demonstrated significant cultural variations in AI attitudes. Studies show that UK populations exhibit demographic-based differences in AI concerns (with women and older adults being more wary), while Arab samples display consistent attitudes across demographics, highlighting distinct cultural drivers like personal versus institutional trust [ 11 ]. Cross-cultural comparisons further reveal that personality traits predict AI attitudes differently across cultures, with neuroticism linked to AI fear in German samples but not Chinese samples, and agreeableness showing culture-specific effects [ 32 ]. Based on these established patterns, we examine all research questions across two cultural contexts (UK and Arab samples). 3. Method 3.1. Participant Recruitment and Selection The study recruited a total of 639 adult participants, with 323 individuals from the Arab GCC and 316 from the UK, ensuring a diverse cultural representation. Given the difficulty in recruiting Arab participants above the age of 60, the study restricted the sample to individuals between 18 and 60 years old. Eligibility criteria required participants to be at least 18 years old, be native to and currently residing in either the UK or the Arab GCC, have some familiarity with artificial intelligence (AI), and self-identify culturally as either British or Arab in terms of their cultural identity and norms. To confirm eligibility, participants completed a pre-screening questionnaire before proceeding to the main survey. Participant recruitment was facilitated by TGM Research, a global market research company with an extensive online panel spanning 130 countries [ 43 ]. To encourage participation, individuals received compensation upon survey completion, with incentives managed by TGM Research in accordance with ethical standards regarding voluntary participation and confidentiality. Ethical approval for the study was granted by the Institutional Review Board (IRB) of the lead author's institution. Informed consent was obtained from all participants, who were assured that no personally identifiable information, such as names or contact details, would be collected. Participants also had the option to withdraw from the study at any stage. The dataset used in this research is publicly available on the Open Science Framework (OSF), with links provided in the Data Availability Statements section. 3.2. Survey Methodology The survey was designed and administered through the SurveyMonkey platform and was available in both English and Arabic. The survey was conducted from late October 2023 to mid-December 2023 as part of a broader research project, with details accessible through the Open Science Framework (OSF). Participants were assigned different language versions based on their region: the Arabic version was provided to Arab participants, while the English version was given to UK participants. The Arabic version of the survey was developed using the back-translation method to ensure accuracy, with additional face validation conducted through trials with bilingual individuals to confirm clarity and cultural appropriateness [ 44 ]. A pilot study involving small groups from both the UK and Arab was conducted to identify and eliminate any unclear or ambiguous wording. To ensure data quality and response reliability, the survey incorporated attention checks to identify inattentive participants. Responses that failed these checks, contained inconsistencies, or were completed in less than half of the median survey duration were excluded from the analysis. The median response time was calculated after removing outliers, defined as participants whose completion time exceeded twice the expected duration, often due to completing the survey in multiple sessions. In line with experimental survey methodologies, this study employed vignette experiments to assess attitudes toward AI. Vignette experiments are a valuable survey research method that uses short descriptions of scenarios to elicit respondents' judgments. The method allows researchers to explore nuanced responses by presenting participants with realistic and relatable scenarios rather than abstract questions [ 45 ]. These experiments enhance the validity and reliability of survey responses by systematically varying characteristics of the vignettes and incorporating design elements such as confounded factorial designs, between-subjects factors, and anchoring vignettes [ 46 ]. The selection of vignette subsets can be either random or systematic, each with implications for how effects are interpreted [ 47 ]. Furthermore, fractional replication experimental designs enable researchers to vary a broad range of contextual factors while limiting the total number of vignette versions required [ 45 ]. Vignette experiments have been successfully used in studies exploring topics such as income fairness [ 46 ] and crime victim [ 45 ] perceptions supporting their utility across disciplines. 2.3. Questionnaire and Vignette Design and Measures The questionnaire began with demographic questions, including gender, age, education level, and employment status. Participants were then introduced to artificial intelligence with the following explanation: "Artificial Intelligence (AI) is centred around creating machines that possess the ability to accomplish activities typically necessitating human intelligence, including making recommendations, recognizing images, interpreting natural language, and the process of decision-making. For example, AI is used in self-driving cars, voice assistants like Siri, and recommendation systems like those on streaming platforms such as YouTube and Netflix.” Following this introduction, participants were presented with four AI-driven vignette scenarios designed to assess their perceptions of artificial intelligence under varying conditions. These scenarios underwent face-validation and multiple refinements to ensure clarity and effectiveness. Initially, a car-based AI scenario was considered, but due to concerns over safety perceptions, which could be more dominant on decision making than FoC and SP, chatbot agents were selected instead. Chatbots, widely used in customer service, were deemed more familiar and less likely to introduce bias. Each scenario was accompanied by a carefully designed image to represent the context accurately as shown in Fig. 2 . These images underwent several iterations based on participant feedback, and face validation was conducted with three UK and three Arab participants to ensure scenario comprehension. Adjustments included clarifying that human agents might not always be available, making the scenarios more realistic. To ensure participants understood key study concepts, they were provided with text explanations and visual illustrations of FoC) and SP. “The following section of the survey will introduce social characteristics related to AI, followed by scenarios of using advanced AI agent. In this scenario, you will interact with your internet provider about offers, bills, technical issues, or personal info changes. They offer an advanced AI agent that can interact through text, voice, and video, closely resembling human interaction. The agent is your primary point of conversation. Freedom of Choice Involves the option to use AI. Lack of choice occurs when AI is the only interaction, like AI customer service without a human alternative. Social Proof Reflects AI's successful use by others. For instance, limited adoption by people of driverless cars results in low social proof." After viewing each of the vignette scenarios, participants responded to a fixed set of questions measuring their perceptions of AI. The questions assessed trust (1–7 scale), enhancing wellbeing (0–10 scale), feeling pleasant (1–9 scale), recommend (0–10 scale), positive change (1–6 scale), ethical implications (0–10 scale), and perceived risk (1–6 scale), detailed scales available in Supplementary Materials. A. Big Five Inventory (BFI-10) This study employed the BFI-10 to assess personality traits, including openness, conscientiousness, extraversion, agreeableness, and neuroticism, with two items per trait [ 48 ]. Participants rated each statement on a five-point Likert scale (1 = strongly disagree to 5 = strongly agree). The BFI-10 was chosen for its validated effectiveness in large-scale studies and its ability to reduce survey fatigue while maintaining measurement reliability. To improve clarity, the openness item “ has a few artistic interests ” was revised to “ I have limited or no artistic interests ” to prevent misinterpretation, as the original phrasing was misunderstood in both languages. These modifications ensured cross-cultural consistency while maintaining the scale’s validity. The BFI-10 has demonstrated acceptable internal consistency in previous research [ 49 ]. B. Locus of Control We utilized the four-item short scale developed by Nieben [ 50 ] to assess LOC, comprising two subscales: internal and external LOC, each with two items. Participants rated their agreement on a five-point Likert scale (1 = does not apply at all to 5 = applies completely). To ensure cultural appropriateness, we modified the item “fate often gets in the way of my plans” to “circumstances often get in the way of my plans,” recognizing that in Arab culture, fate is not typically perceived as a hindrance. The scale demonstrated reliable validity in measuring both LOC dimensions [ 50 ]. C. Need for Cognition We used the six-item short scale developed by Cacioppo et al. to measure NFC [ 40 ], which reflects an individual's preference for engaging in and enjoying complex cognitive tasks. Participants rated each statement on a five-point Likert scale (1 = strongly disagree to 5 = strongly agree). The scale demonstrated adequate reliability and validity [ 40 ], with internal consistency scores of α = 0.86 for the UK sample and α = 0.66 for the Arab sample. D. Attitudes Toward Artificial Intelligence To evaluate individual differences in attitudes toward AI, we employed the five-item attitudes toward AI (ATAI) scale [ 33 ]. This scale captures two negatively correlated factors: (1) AI acceptance and (2) AI fear. Participants rated their agreement with AI-related statements on an 11-point Likert scale (0 = “strongly disagree” to 10 = “strongly agree”). ATAI acceptance was derived from responses related to trust and perceived benefits, while ATAI fear was based on concerns about AI-related risks, including destruction and job displacement. The scale demonstrated strong internal consistency, with reliability scores of α = 0.80 for both acceptance and fear in the UK sample, and α = 0.85 for acceptance and α = 0.75 for fear in the Arab sample. 2.4. Data Preprocessing Before conducting data analysis, preprocessing was performed to ensure consistency and suitability for statistical procedures. The datasets for both samples (Arab and UK) were standardized using the R base function scale (), transforming each variable to have a mean of 0 and a standard deviation of 1. To determine whether the questions measuring (Enhancing wellbeing, Feeling pleasant, Recommend, Trust, Positive change, Ethical implications, and Perceived risk) were suitable for factor analysis, the Kaiser-Meyer-Olkin (KMO) test and Bartlett’s test of sphericity were performed. The KMO values exceeded 0.88 for the UK sample and 0.86 for the Arab sample, demonstrating strong sampling adequacy. Additionally, Bartlett’s test of sphericity was highly significant (p < .001) for both groups, confirming that the dataset met the requirements for Exploratory Factor Analysis (EFA) [ 52 , 53 ]. Additionally, the correlation between modalities (dependent variables) was examined before conducting Hierarchical Multivariate Multiple Regression (MMR) to ensure their suitability for multivariate analysis. 2.5. Data Analysis Descriptive statistics were conducted for both samples, with all variables exhibiting skewness and kurtosis values between + 2 and − 2, indicating an approximately normal distribution (See Appendix A). Subsequently, EFA was performed to identify the underlying factor structure for each scenario. To examine relationships between dependent variables, Pearson’s correlation was used, while Spearman’s correlation was applied for all other variables. Additionally, a one-sample t-test was conducted for both samples to compare attitude toward AI acceptance and fear across different modalities. The hierarchical MMR analysis was then performed to assess how personality traits, NFC, and LOC predict factors across different modalities. The hierarchical MMR approach was chosen for its ability to assess multiple dependent variables simultaneously while controlling for other predictors, providing a clearer understanding of how individual differences influence AI perceptions [ 53 ]. To evaluate the relative contribution of each predictor, Appendix Tables 6S − 8S presents the effect sizes (partial eta-squared) for all variables across the UK and Arab sample, offering insight into which traits exert the strongest influence on each factor within different contextual modalities. Additionally, we compare our three model’s performance (Appendix C) to assess the validity of our method, ensuring a comprehensive evaluation of its predictive power. The hierarchical MMR were conducted using RStudio (version: 2024.12.1), while all other analyses were performed in JASP (version: 0.18.3) 4. Results 4.1. Descriptive Statistics for Demographics Participants provided information about their age, gender, education level, and employment status. A summarized demographic profile of both the UK and Arab groups is presented in Table 1. Table 1. Demographic Characteristics of Participants in the Arab and UK Samples Variables UK (N=316) Arab (N=323) Gender (%) Male 96 (30.38) 147 (45.51) Female 220 (69.62) 176 (54.49) Age M (SD) 40.81 (10.35) 33.07 (9.05) Rang 18 - 60 18 - 57 Education (%) No formal education - - Primary education (elementary) 0.63 0.62 Secondary education (high school) 24.68 14.86 Pursuing or completed vocational or technical education 22.47 4.03 Pursuing or completed undergraduate degree (bachelor’s) (*) 32.91 68.42 Pursuing or completed postgraduate degree (master’s, Ph.D., etc.) 19.31 12.07 Employment (%) Full time employment 53.16 54.80 Part time employment 17.40 11.45 Run my own business 4.75 6.50 Homemaker 6.96 9.91 Student 2.22 7.74 Retired 3.48 2.17 Unemployed 8.23 5.88 Other 3.80 1.55 (*) The differences observed between the UK and Arab regions are primarily attributed to the lower prevalence of vocational or technical education in the Arab region. 4.2. Factor Analysis of Reactions to FoC x SP Vignettes An EFA was conducted to identify the key factors influencing participants' perceptions across the different scenarios presenting combinations of FoC and SP as described in Section 2.1. Parallel analysis indicated that a two-factor solution was appropriate for all modality conditions in both samples. These factors were modality-specific acceptance factor 1: “perception of contributions to personal and social good” and modality-specific fear factor 2: “perceptions of ethical concerns and risks,” (Table 2). Table 2. EFA Factor Loadings for Modalities in the UK Sample NN Modality NY Modality YN Modality YY Modality Factor 1 Factor 2 Factor 1 Factor 2 Factor 1 Factor 2 Factor 1 Factor 2 Enhancing Wellbeing 0.940 0.962 0.977 0.961 Feeling pleasant 0.869 0.878 0.879 0.897 Recommend 0.891 0.913 0.889 0.890 Trust 0.849 0.762 0.807 0.809 Positive change 0.809 0.833 0.770 0.808 Ethical implications 0.823 0.827 0.824 0.788 Perceived risk 0.769 0.844 0.799 0.759 Note: NN - No FoC, No SP; NY - No FoC, Yes SP; YN - Yes FoC, No SP; YY - Yes FoC, Yes SP. For the UK sample, the two factors explained the following cumulative variance across modalities: NN (54.8% and 73.7%), NY (55.0% and 76.5%), YN (54.2% and 73.8%), and YY (55.2% and 73.0%). In the Arab sample, the factors accounted for: NN (54.2% and 69.2%), NY (51.3% and 67.1%), YN (49.8% and 65.3%), and YY (51.7% and 67.6%). This consistent pattern across all modality conditions indicates that the two-factor solution effectively captures the underlying structure of participants' responses, with the seven items reliably grouping into two distinct dimensions. Table 3. EFA Factor Loadings for Modalities in the Arab Sample NN Modality NY Modality YN Modality YY Modality Factor 1 Factor 2 Factor 1 Factor 2 Factor 1 Factor 2 Factor 1 Factor 2 Enhancing Wellbeing 0.975 0.920 0.966 0.979 Feeling pleasant 0.968 0.944 0.921 0.942 Recommend 0.907 0.895 0.820 0.869 Trust 0.842 0.853 0.879 0.818 Positive change 0.624 0.567 0.522 0.593 Ethical implications 0.716 0.741 0.867 0.741 Perceived risk 0.734 0.742 0.599 0.735 Note: NN - No FoC, No SP; NY - No FoC, Yes SP; YN - Yes FoC, No SP; YY - Yes FoC, Yes SP. Table 4 presents the Means and standard deviations (SD) for the two EFA factors across different modalities for both the UK and Arab samples, revealing notable regional differences in AI perceptions. For Personal Social Good Perception, the UK sample reported mean scores ranging from 3.123 to 5.237, while the Arab sample had higher mean scores, ranging from 5.497 to 6.811. In terms of Ethical Concern Risk Perception, the UK sample exhibited higher mean scores across all modalities, ranging from 3.778 to 5.078, whereas the Arab sample reported slightly lower scores, ranging from 3.991 to 4.511. Table 4. Means and SD for EFA Factors Across Different Modalities Modality UK M (SD) Arab M (SD) NN Factor 1 3.123 (1.804) 5.497 (2.107) Factor 2 5.078 (1.834) 4.511 (1.945) NY Factor 1 4.216 (1.826) 6.055 (1.768) Factor 2 4.296 (1.872) 4.393 (1.971) YN Factor 1 4.579 (1.743) 6.454 (1.542) Factor 2 4.141 (1.803) 3.991 (2.000) YY Factor 1 5.237 (1.789) 6.811 (1.520) Factor 2 3.778 (1.825) 3.859 (2.165) 4.3. Correlations between FoC x SP Modalities, Personality Traits, and Attitudes Factors A Pearson’s and Spearman's correlation analysis was conducted to examine relationships between attitudes toward AI (Factor 1 and Factor 2) across different modalities in both the UK and Arab samples (Table 5 and 6). In both UK and Arab samples, all factor 1 were positively correlated, as were all factor 2 variables. Factor 1 and factor 2 showed a strong inverse relationship, justifying the use of MMR for analysis. Additionally, relationships between personality traits, need for cognition (NFC), locus of control (LOC), and general attitudes toward AI (ATAI) are visually presented in Figures 3 and 4 (heatmaps). Figure 3's heatmap reveals consistent UK sample patterns: agreeableness/openness positively linked to general ATAI acceptance versus neuroticism's negative association. The Arab sample (Figure 4) showed similar patterns: conscientiousness and agreeableness positively correlated with general ATAI acceptance, while neuroticism showed negative associations. Factor 1 consistently aligned with general ATAI acceptance and factor 2 with general ATAI fear across modalities. Internal LOC and NFC also demonstrated positive relationships with general ATAI acceptance. Both in the UK and Arab samples, modality-specific attitude towards AI factor 1 tracked general ATAI acceptance, while factor 2 aligned with general ATAI fear, supporting our theoretical model. Table 5. Pearson’s Correlation Between Modality Specific Attitude Toward AI Factor 1 and 2 in UK Samples NN NY YN YY Factor 1 Factor 2 Factor 1 Factor 2 Factor 1 Factor 2 Factor 1 NN Factor 1 _ Factor 2 -0.47*** _ NY Factor 1 0.68*** -0.30*** _ Factor 2 -0.29*** 0.67*** -0.51*** _ YN Factor 1 0.63*** -0.29*** 0.68*** -0.33*** _ Factor 2 -0.20*** 0.62*** -0.31*** 0.72*** -0.44*** _ YY Factor 1 0.46*** -0.21*** 0.71*** -0.38*** 0.79*** -0.39*** _ Factor 2 -0.14* 0.57*** -0.35*** 0.71*** -0.37*** 0.79*** -0.44*** (* p < 0.05, ** p < 0.01, *** p < 0.001). Table 6. Pearson’s Correlation Between Modality Specific Attitude Toward AI Factor 1 and 2 in Arab Samples NN NY YN YY Factor 1 Factor 2 Factor 1 Factor 2 Factor 1 Factor 2 Factor 1 NN Factor 1 _ Factor 2 -0.27*** _ NY Factor 1 0.81*** -0.26*** _ Factor 2 -0.21*** 0.72*** -0.34*** _ YN Factor 1 0.61*** -0.29*** 0.63*** -0.28*** _ Factor 2 -0.09 0.68*** -0.17** 0.73*** -0.34*** _ YY Factor 1 0.43*** -0.25*** 0.64*** -0.34*** 0.74*** -0.36*** _ Factor 2 -0.03 0.60*** -0.17** 0.73*** -0.28*** 0.82*** -0.43*** (* p < 0.05, ** p < 0.01, *** p < 0.001). 4.4. Comparing Modality-Specific Attitude to General AI Attitude A one-sample t-test was conducted to determine whether modality-specific attitude toward AI significantly differed from the general attitude toward AI mean in both the UK and Arab sample, which are shown in Table 7 and 8. For factor 1, the UK sample showed significantly higher modality-specific acceptance in the YY modality (t(315) = 2.31, p < 0.05, d = 0.13). In the Arab sample, modality-specific acceptance was significantly higher in the NN (t(322) = 2.07, p < 0.05, d = 0.12) and YN (t(322) = 2.16, p < 0.05, d = 0.12) modalities. For factor 2, the UK sample showed significantly lower modality-specific fear ratings in the NY, YN, and YY modalities, while the Arab sample showed significantly higher modality-specific fear ratings in these same modalities. Table 7. One-Sample t-Tests Comparing Modality-Specific attitude toward AI Factor 1 to General attitude toward AI Acceptance in UK vs. Arab Samples UK Arab t (315) M(SD) Cohen’s d [95% CI] t (322) M(SD) Cohen’s d [95% CI] NN Factor 1 -0.17 -0.47 (0.79) -0.01 [-0.12, 0.10] 2.07* 0.56 (0.92) 0.12 [0.01, 0.23] NY Factor 1 1.58 -0.39 (0.89) 0.09 [-0.02, 0.20] 1.06 0.51 (0.86) 0.06 [-0.05, 0.17] YN Factor 1 1.12 -0.41 (0.89) 0.06 [-0.05, 0.17] 2.16* 0.55 (0.79) 0.12 [0.01, 0.23] YY Factor 1 2.31* -0.34 (0.94) 0.13 [0.02, 0.24] 0.66 0.49 (0.80) 0.06 [-0.07, 0.15] * p < 0.05, ** p < 0.01, *** p < 0.001 The general ATAI acceptance mean served as the test value for each sample: M = -0.47 (SD = 0.96) for the UK and M = 0.46 (SD = 0.82) for the Arab sample. Table 8. One-Sample t-Tests Comparing Modality-Specific attitude toward AI Factor 2 to General attitude toward AI Fear in UK vs. Arab Samples UK Arab t (315) M(SD) Cohen’s d [95% CI] t (322) M(SD) Cohen’s d [95% CI] NN Factor 2 -1.53 0.13 (0.96) -0.09 [-0.20, 0.03] 0.87 -0.16 (1.02) 0.05 [-0.06, 0.16] NY Factor 2 -4.89*** -0.05 (0.97) -0.28 [-0.39, -0.16] 3.74*** 0.00 (1.02) 0.21 [0.10, 0.32] YN Factor 2 -3.80*** 0.02 (0.94) -0.21 [-0.33, -0.10] 2.59** -0.06 (1.04) 0.14 [0.03, 0.25] YY Factor 2 -5.08*** -0.04 (0.91) -0.29 [-0.40, -0.17] 3.46*** -0.00 (1.08) 0.06 [0.08, 0.30] * p < 0.05, ** p < 0.01, *** p < 0.001 The general ATAI fear mean served as the test value for each sample: M = 0.22 (SD = 0.98) for the UK and M = -0.21 (SD = 0.98) for the Arab sample. 4.5. Examining Personality traits, NFC, and LOC as Predictors of Attitude Factors Across FoC x SP Modalities We tested the predictive power of personality traits, NFC, and LOC for factor 1: “perception of contributions to personal and social good” and factor 2: “perceptions of ethical concerns and risks” in the both UK and Arab sample using hierarchical MMR, which are shown in Table 2S, 3S, 4S and 5S (Appendix B). The results presented in these tables, summarized in Table 9, provide a detailed overview of the psychological factors influencing attitude factors, such as acceptance and fear, in both UK and Arab samples. Table 9. Summary of Psychological Predictors of Attitude Factors Across Modalities and Cultural Samples. Modality Cultural Sample Factor 1 (personal and social good) Factor 2 (ethical concerns and risks) NN UK · Agreeableness showed positive effect. (β = 0.10, t = 2.30*) · Internal LOC showed positive association with fear. (β = 0.20, t = 2.99*) Arab · Agreeableness positively predicted acceptance. (β= 0.12, t = 2.02*) · Neuroticism negatively predicted acceptance. (β = -0.20, t = -3.05**) · Internal LOC showed a strong positive effect. (β = 0.20, t = 3.67***) · External LOC showed a positive effect. (β = 0.28, t = 2.87**) · Neuroticism positively predicted. (β = 0.06, t = 2.17*) NY UK · Agreeableness showed a strong positive effect. (β = 0.16, t = 3.36***) · Openness also positively predicted acceptance. ((β = 0.01, t = 2.21*) · Internal LOC positively predicted fear. (β = 0.14, t = 2.11*) Arab · External LOC positively predicted acceptance. (β = 0.16, t = 3.26**) · Internal LOC strong positively predicted acceptance. (β = 0.44, t = 4.93) · Extraversion positively predicted fear. (β = 0.16, t = 2.11*) YN UK · Agreeableness positively predicted acceptance. (β = 0.15, t = 3.01**) · No significant predictors for fear. Arab · Internal LOC positively predicted acceptance. (β = 0.23, t = 2.65**) · No significant predictors for fear. YY UK · Agreeableness positively predicted acceptance. (β = 0.17, t = 3.21**) · Openness also positively predicted acceptance. (β = 0.10, t = 2.12*) · No significant predictors for fear. Arab · NFC positively predicted acceptance. (β = 0.10, t = 1.98*) · Internal LOC positively predicted acceptance. (β = 0.22, t = 2.48) · No significant predictors for fear. (* p < 0.05, ** p < 0.01, *** p < 0.001). 5. Discussion This study aimed to investigate how individual differences, particularly personality traits, cognitive styles, and locus of control, influence AI attitudes, with a focus on how these effects vary across different modalities of AI involvement. We examined psychological predictors of AI attitudes in parallel UK and Arab samples, identifying culture-specific patterns in how traits and cognitive factors relate to two factors (a) perceptions of AI's contributions to personal and societal well-being and (b) concerns about its ethical implications and potential risks. Additionally, we aimed to extend the research by comparing findings with Babiker’s study [ 54 ] on general attitudes toward AI and proposing a model to examine how personal factors and others' cognitive engagement correlate with attitudes toward AI in both general and modality-specific contexts. The analysis reveals, agreeableness and openness to experience (UK) or agreeableness and conscientiousness (Arab) were positively associated with general ATAI acceptance, suggesting that individuals who are cooperative, open to novelty, or goal-oriented tend to view AI more favourably. This is consistent with prior research indicating that people with higher agreeableness are more likely to view AI positively [ 29 ]. Conversely, neuroticism was negatively associated with general ATAI acceptance in both samples, and positively associated with modality-specific fear of AI for the Arab samples, indicating that individuals prone to anxiety or emotional instability may perceive AI as threatening or untrustworthy. Previous research showed that neuroticism was associated with negative emotions toward AI and higher perceived sociality, suggesting emotionally unstable individuals view AI as both threatening and socially capable [ 28 ]. This aligns with cross-cultural evidence showing neuroticism's consistent positive association with fear of AI in both German and Chinese samples, though with small effect sizes [ 32 ]. Additionally, the positive associations of NFC and internal LOC with general ATAI acceptance, particularly in the Arab sample, suggest that individuals who enjoy thinking deeply and believe in personal agency are more likely to engage with and accept AI technologies. This may reflect a proactive, curiosity-driven orientation toward AI as a tool for enhancing decision-making rather than replacing it. This aligns with prior findings that NFC plays a moderating role in the intention to use Web 2.0-based learning tools Personal Learning Environment approach, influencing how students engage with Web 2.0 tools for academic purposes [ 55 ]. The analysis provides further insight into how perceptions of AI differ across specific modality contexts (combinations of FoC and SP) compared to general ATAI. In the UK sample, significant differences for factor 1 (modality-specific AI acceptance) were observed only in the YY modality (where both FoC and SP were present). These results indicate that UK participants showed greater AI acceptance when both autonomy and social endorsement were present, suggesting perceived agency and peer validation jointly contribute to positive AI attitudes. These findings align with broader evidence that autonomy in technology use is consistently associated with more positive and less negative AI attitudes across European samples, demonstrating its important role in AI acceptance [ 56 ]. In contrast, the Arab sample showed significant increases in factor 1 for the NN and YN modalities. This could reflect greater receptiveness to AI’s benefits even when SP is absent, regardless of whether freedom of choice was available. It may also point to cultural differences in how autonomy and conformity influence technology perception, with Arab participants potentially placing more trust in authority or functionality over peer influence, a pattern consistent with the UAE COVID-19 study, where government communications were both widely consulted and highly trusted, surpassing peer/family influence in driving protective behavior adoption [ 57 ]. For factor 2 (modality-specific AI fear), significant differences were found in all modalities except NN in both samples. This indicates that perceived ethical concerns and risks are more salient when AI is socially endorsed or presented as a choice. Particularly in the UK, where participants showed lower fear in modalities, it suggests social comparison can modulate coping with fear, as demonstrated in a virtual reality study where upward assimilation led to reduced anxiety and fewer phobic symptoms [ 58 ]. Conversely, Arab participants showed increased fear across modalities, suggesting particular sensitivity to societal implications and ethical concerns when AI adoption becomes more visible. In the UK sample, agreeableness consistently predicted higher scores on factor 1 across all modalities, suggesting that individuals who value cooperation and social harmony are more likely to perceive AI as contributing positively, regardless of whether FoC or SP is present. Prior research indicates agreeable individuals tend to support stricter AI regulation, likely reflecting their prosocial concern for fairness and protection [ 39 ]. This pattern suggests individuals higher in agreeableness may consistently prioritize fairness and protection in technology governance. Additionally, openness emerged as a predictor in modalities that involved SP (NY, YY), aligning with the idea that open individuals are more receptive to new technologies, especially when endorsed by others. Partially aligning with our findings, Babiker et al. [ 54 ] found that agreeableness predicted more positive attitudes toward AI in a UK sample, although openness did not significantly predict AI attitudes. Their use of a general ATAI may suggest that the influence of openness could be context-dependent, emerging when social proof is present, as observed in our SP modalities. Further illustrating the context-dependent role of openness, Grassini and Koivisto [ 26 ], also using a UK sample, reported that openness predicted greater liking of AI-generated artworks, indicating a positive reception of AI-driven application. For Factor 2, NFC predicted increased concern only in the NN modality, possibly indicating that individuals who enjoy cognitive engagement become more skeptical when AI is imposed without choice or peer validation. Building on previous findings that high-NFC individuals resist misinformation through critical analysis (memory studies) [ 59 ], the current results similarly show their cognitive engagement increases skepticism toward AI when imposed without FoC or SP. However, this effect was diminished once LOC was included, with internal LOC becoming a significant predictor, particularly in NN and NY modalities. This suggests that people who believe they control their own outcomes are more sensitive to the ethical implications of AI, especially when freedom is limited, even in the presence of social pressure. This aligns with findings from aviation safety, where internal LOC correlated strongly with proactive risk mitigation (e.g., higher scores in civil/transport pilots) and was linked to education/service length, reinforcing its role as a cross-domain stabilizer of agency-driven decision-making [ 60 ]. In the Arab sample, agreeableness also positively predicted Factor 1 in the NN modality, while NFC was significant in the YY modality, where both FoC and SP were available. This pattern implies that cognitively engaged individuals in Arab cultures are most likely to embrace AI when it is both optional and widely accepted, suggesting a preference for thoughtful autonomy and social validation. Moreover, neuroticism negatively predicted Factor 1 in multiple modalities, aligning with the tendency of emotionally reactive individuals to view AI less favorably, a pattern consistent with pandemic findings where neuroticism amplified negative affect, crisis preoccupation, and emotional reactivity, suggesting this trait’s broad role in shaping threat perception across technological and health domains [ 61 ]. Moreover, Internal LOC was a significant predictor across all modalities, showing that individuals with a strong sense of personal control are more likely to perceive AI as beneficial. This aligns directly with prior findings that internal locus of control and perceived XAI availability jointly predicted greater AI acceptance [ 5 ]. External LOC was particularly influential in the NN and NY modalities, suggesting that reliance on external circumstances can shape positive evaluations of AI, especially when freedom is constrained. For Factor 2, neuroticism was a significant positive predictor of perceived ethical risks in the NN modality, consistent with higher sensitivity to AI threats in low-choice, low-social validation contexts. However, its influence diminished in other modalities after accounting for NFC and LOC. Extraversion was positively associated with ethical concern in the NY modality, suggesting that socially engaged individuals may be more attuned to AI’s societal implications when its usage is peer-endorsed, a pattern distinct from extraversion’s typical role in enhancing general social functioning in college students [ 62 ], revealing how this trait’s effects shift from social adaptation to critical evaluation in technology-governed contexts. Our findings suggest that modalities do influence the strength of personality traits’ effects on attitudes toward AI, although the overall effect sizes (η²p < 0.06) were generally small. As seen in previous studies [ 54 ], the effects of personality traits on AI attitudes were similarly weak. We summarized the relevant findings from [ 54 ] in Appendix D to facilitate a clearer evaluation alongside our results. These variations indicate that contextual factors (modalities) moderate how personality traits influence AI perceptions. That is, the same trait may have a greater or lesser impact on AI attitudes depending on whether individuals feel autonomous or socially supported in the decision context. This perspective aligns with the IMPACT model proposed by Montag et al. [ 63 ], which emphasizes the interplay between individual characteristics, modality, context, cultural/country-level influences, and transparency as critical components in shaping public attitudes toward AI. These results further support the idea that cultural context moderates the impact of modalities on AI attitudes. In line with prior research [ 6 ], autonomy had a stronger influence on ethical concerns in the UK, reflecting Western individualistic values, while both autonomy and SP shaped perceptions in the Arab sample, consistent with collectivist orientations and the role of social validation. 5.1. Practical Implications The findings of this study may inform developers, policymakers, and educators working toward responsible and culturally sensitive AI deployment. First, the consistent impact of agreeableness and internal LOC on AI acceptance suggests that messaging strategies should emphasize collaborative benefits and personal empowerment. A real-world application of this approach can be seen in Google’s ‘AI for Social Good’ initiative [ 64 ], which frames AI as a collaborative force for solving global challenges while also giving users control over AI-assisted tools like ‘Help Me Write’ in Google Docs [ 65 ]. Similarly, Microsoft’s ‘AI for Accessibility’ program emphasizes empowerment by developing assistive technologies (e.g., Seeing AI for the visually impaired) that enhance independence while fostering inclusivity [ 66 ]. By highlighting societal benefits alongside user control, these strategies effectively increase AI acceptance among individuals who prioritize harmony and self-direction. Second, the observed associations between neuroticism, external LOC, and heightened AI concerns (particularly in NN modality) suggest these psychological factors may influence risk perception. Such findings could inform communication strategies for addressing AI apprehensions in specific user groups. For example, IBM’s "Explainable AI" (XAI) framework provides clear, interpretable explanations for AI decisions (e.g., in loan approvals [ 67 ]), directly addressing transparency needs for anxious users [ 68 ]. Similarly, the EU’s General Data Protection Regulation (GDPR) mandates "right to explanation" clauses and human oversight options in automated systems, offering opt-out mechanisms to reinforce perceived control [ 69 ]. These approaches demonstrate how ethical safeguards and user-centric design can mitigate fears among emotionally sensitive or externally oriented individuals, particularly in high-stakes domains. Third, the study highlights the critical importance of modality design, specifically FoC and SP, in shaping public attitudes toward AI. Developers and organizations should design AI interfaces that integrate choice, such as toggling between chatbots and human agents, to enhance acceptance. Incorporating SP elements such as real-time feedback or success stories can reinforce positive AI perceptions, particularly among users sensitive to peer influence [ 70 ]. Policymakers, particularly in regions with evolving AI regulations like the Arab GCC, should enforce standards that guarantee autonomy in AI interactions. Aligning with frameworks such as the EU’s Ethics Guidelines for Trustworthy AI [ 71 ], regulations should require alternative interaction modes to foster both trust and ethical engagement. Moreover, while promoting autonomy, caution is necessary, as increased perceived control may inadvertently lead users to over-disclose sensitive information [ 72 ]. Fourth, the observed cultural differences underscore the importance of tailoring AI communication and policy frameworks to regional values and cognitive styles. In the UK, trust and acceptance increased significantly under conditions of high FoC and strong SP, indicating that British users are more responsive to peer influence and autonomy-supportive environments. For example, the NHS’s AI adoption strategy leverages clinician testimonials and opt-in pilot programs, which align with these preferences [ 73 ]. In contrast, Arab participants exhibited higher AI acceptance even in conditions lacking choice or social validation, highlighting a potentially greater reliance on perceived utility and institutional trust. This aligns with initiatives like the UAE’s National AI Strategy 2031 [ 74 ], which centrally promotes AI adoption through government-backed projects (e.g., smart city deployments in Dubai) without emphasizing individual choice. These differences indicate the need for localized engagement strategies, such as government-led AI endorsements in Arab countries and peer-driven adoption campaigns in Western contexts. 5.2. Limitations By investigating links between personality traits, contextual modalities and AI attitudes, this study provides data that may inform future research, though several limitations should be acknowledged. Firstly, the cross-sectional design restricts our ability to draw causal conclusions. While personality traits and cognitive variables showed measurable associations with AI attitudes in our analyses, causal interpretation demands longitudinal or experimental evidence to account for potential confounding and temporal effects. Secondly, the use of self-report measures to assess personality traits, LOC, NFC, and AI attitudes may introduce biases, including social desirability effects and inaccurate self-assessment. Despite implementing mitigation strategies, such as ensuring participant anonymity, providing clear definitions, visual aids, and face validation, some degree of response bias may still persist. Future studies should consider incorporating objective or behavioral assessments to complement self-reported data. Additionally, the study’s cultural scope was limited to participants from the UK and Arab countries. While this comparison offers valuable insights into individualistic versus collectivist cultural influences, the generalizability of findings to other global regions remains limited. Broader cross-cultural investigations are necessary to validate and expand these observations. The vignette scenarios used in this study were based on customer service chatbots, selected to ensure familiarity and reduce the confounding influence of domain criticality. However, this focus may limit the ecological validity of findings when applied to more sensitive or high-stakes applications of AI, such as medical diagnostics or autonomous vehicles. Despite visual aids and face validation processes, participants may still have varied in their interpretations of scenario realism or perceived autonomy, which could affect the consistency of responses. 6. Conclusion and Future work This study provides novel insights into how personality traits, NFC, and LOC influence public attitude towards AI across modalities (FoC and SP), with evidence from UK and Arab participants. Using hierarchical MMR, we identified that agreeableness consistently predicted modality specific attitude towards AI acceptance across both cultural contexts, while neuroticism was associated with lower positive attitudes and greater fear and ethical concerns in the Arab sample under the NN modality. Additionally, internal LOC and higher NFC were positively related to modality specific attitude towards AI acceptance, suggesting that individuals who perceive personal control and engage in deeper cognitive processing tend to view AI more favorably and thoughtfully. The analysis also revealed cultural distinctions in how these traits operate. For example, conscientiousness played a role in the Arab sample, while openness showed more relevance in the UK. Together, these findings highlight how individual psychological differences shape attitudes toward AI across cultural contexts, emphasizing the importance of considering psychological and cultural diversity in AI acceptance research. By examining both acceptance and fear dimensions of AI attitudes, the findings contribute to a more comprehensive understanding of how people engage with AI technologies. Future research should explore how these psychological traits interact with different AI application domains, such as healthcare or education, to determine whether the patterns observed here generalize across sectors. Studies could also incorporate longitudinal or experimental approaches to better understand how attitudes develop over time or in response to direct AI interaction. Furthermore, expanding the cultural scope beyond the UK and Arab would enhance the generalizability of these findings and allow for a more global perspective on AI acceptance. Finally, the integration of psychological profiling into AI design holds promising potential for developing human-centered, culturally adaptive AI systems that better align with public values and expectations. Declarations Funding Open Access funding is provided by the Qatar National Library. This publication was supported by NPRP 14 Cluster grant number NPRP 14C-0916–210015 from the Qatar National Research Fund (a member of Qatar Foundation). The findings herein reflect the work and are solely the responsibility of the authors. Conflict of Interest The authors declare no conflict of interest in this work. Consent to Participate All participants provided informed consent prior to participation. Human Ethics and Consent to Participate The study was conducted in accordance with the ethical standards set forth in the Declaration of Helsinki and was approved by the Institutional Review Board (IRB) of the fifth author (ID: IRB-2024-59). Informed consent was obtained from all individual participants included in the study. Clinical Trial Number : Not applicable. Authors Contribution Conceptualization : Mohammad Mominur Rahman, Raian Ali, Ala Yankouskaya, Sameha AlShakhsi and Areej Babiker; Data Curation : Sameha AlShakhsi, Areej Babiker, Raian Ali. Methodology : Mohammad Mominur Rahman, Ala Yankouskaya, Sameha AlShakhsi, Areej Babiker, and Raian Ali; Formal analysis and investigation : Mohammad Mominur Rahman; Validation : Ala Yankouskaya, Sameh a AlShakhsi, Areej Babiker ; Writing - original draft preparation : Mohammad Mominur Rahman; Writing - review and editing : Sameha AlShakhsi, Areej Babiker, Ala Yankouskaya, and Raian Ali; Supervision : Ala Yankouskaya and Raian Ali. Data Availability Statements The study design, the dataset, and the data dictionary can be found at the following Open Science Framework link: [https://osf.io/y24c6/?view_only=2eeb3fa17a1247f882418fe4a3eaaf50 ] and the Main research project: [https://osf.io/7ydwf/?view_only=f275ae745d334fc08c11243efb992140]. References Wang, W., Siau, K.: Artificial intelligence, machine learning, automation, robotics, future of work and future of humanity: A review and research agenda. J. Database Manage. (JDM). 30 (1), 61–79 (2019) Costa, P.T. Jr., McCrae, R.R.: The five-factor model of personality and its relevance to personality disorders. J. Pers. Disord. 6 (4), 343–359 (1992) Nov, O., Ye, C.: Personality and technology acceptance: Personal innovativeness in IT, openness and resistance to change, in Proceedings of the 41st annual Hawaii international conference on system sciences (HICSS IEEE, 2008, p. 448. (2008) Sleboda, C.L.P.: The inverse relation between risks and benefits The impact of individual differences in information processing style. PLoS One, (2021) Babiker, A., Alshakhsi, S., Al-Thani, D., Montag, C., Ali, R.: Attitude Towards AI: Potential Influence of Conspiracy Belief, XAI Experience and Locus of Control. Int. J. Hum. Comput. Interact. 1–13 (2024). https://doi.org/10.1080/10447318.2024.2401249 Alshakhsi, S., et al.: Designing AI to foster acceptance: do freedom to choose and social proof impact AI attitudes among British and Arab populations? Behav. Inform. Technol., pp. 1–19, (2025) Luo, X., Tong, S., Fang, Z., Qu, Z.: Frontiers: Machines vs. humans: The impact of artificial intelligence chatbot disclosure on customer purchases. Mark. Sci. 38 (6), 937–947 (2019) Juravle, G., Boudouraki, A., Terziyska, M., Rezlescu, C.: Trust in artificial intelligence for medical diagnoses. Prog Brain Res. 253 , 263–282 (2020) Cialdini, R.B., Cialdini, R.B.: Influence: The psychology of persuasion , vol. 55. Collins New York, (2007) Wang, Y., Meister, D.B., Gray, P.H.: Social influence and knowledge management systems use: Evidence from panel data. MIS Q., pp. 299–313, (2013) Rahman, M.M., Babiker, A., Ali, R.: Motivation, Concerns, and Attitudes Towards AI: Differences by Gender, Age, and Culture, in International Conference on Web Information Systems Engineering , Springer, pp. 375–391. (2024) Ryan, R.M., Deci, E.L.: Self-determination theory: Basic psychological needs in motivation, development, and wellness. Guilford (2017) Wang, Y., Meister, D.B., Gray, P.H.: Social influence and knowledge management systems use: Evidence from panel data. MIS Q., pp. 299–313, (2013) Kim, H.-W., Gupta, S.: A user empowerment approach to information systems infusion. IEEE Trans. Eng. Manag. 61 (4), 656–668 (2014) Balakrishnan, J., Dwivedi, Y.K.: Role of cognitive absorption in building user trust and experience. Psychol. Mark. 38 (4), 643–668 (2021) Juravle, G., Boudouraki, A., Terziyska, M., Rezlescu, C.: Trust in artificial intelligence for medical diagnoses. Prog Brain Res. 253 , 263–282 (2020) Fasolo, B., Misuraca, R., Reutskaja, E.: Choose as much as you wish: Freedom cues in the marketplace help consumers feel more satisfied with what they choose and improve customer experience. J. Exp. Psychol. Appl. 30 (1), 156 (2024) Staples, D.S., Seddon, P.: Testing the technology-to-performance chain model. J. Organizational End. User Comput. (JOEUC). 16 (4), 17–36 (2004) Bandura, A., Walters, R.H.: Social learning theory, vol. 1. Prentice hall Englewood Cliffs, NJ (1977) Asch, S.: Effects of Group Preassure the Modification and Distortion of Judgment. Groups Leadersh. Men, (1951) Chi, O.H., Gursoy, D., Chi, C.G.: Tourists’ attitudes toward the use of artificially intelligent (AI) devices in tourism service delivery: moderating role of service value seeking. J. Travel Res. 61 (1), 170–185 (2022) Gursoy, D., Chi, O.H., Lu, L., Nunkoo, R.: Consumers acceptance of artificially intelligent (AI) device use in service delivery. Int. J. Inf. Manage. 49 , 157–169 (2019) Venkatesh, V., Davis, F.D.: A theoretical extension of the technology acceptance model: Four longitudinal field studies. Manage. Sci. 46 (2), 186–204 (2000) Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D.: User acceptance of information technology: Toward a unified view. MIS Q., pp. 425–478, (2003) Bergdahl, J., et al.: Self-determination and attitudes toward artificial intelligence: Cross-national and longitudinal perspectives. Telematics Inform. 82 , 102013 (2023) Grassini, S., Koivisto, M.: Understanding how personality traits, experiences, and attitudes shape negative bias toward AI-generated artworks. Sci. Rep. 14 (1), 4113 (2024) Barnett, T., Pearson, A.W., Pearson, R., Kellermanns, F.W.: Five-factor model personality traits as predictors of perceived and actual usage of technology, European Journal of Information Systems , vol. 24, no. 4, pp. 374–390, Jul. (2015). 10.1057/ejis.2014.10 Park, J., Woo, S.E.: Who likes artificial intelligence? Personality predictors of attitudes toward artificial intelligence. J. Psychol. 156 (1), 68–94 (2022) Stein, J.-P., Messingschlager, T., Gnambs, T., Hutmacher, F., Appel, M.: Attitudes towards AI: measurement and associations with personality. Sci. Rep. 14 (1), 2909 (2024). 10.1038/s41598-024-53335-2 Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yetişensoy, O., Demir Kaya, M.: The roles of personality traits, AI anxiety, and demographic factors in attitudes toward artificial intelligence. Int. J. Hum. Comput. Interact. 40 (2), 497–514 (2024) Schepman, A., Rodway, P.: Initial validation of the general attitudes towards Artificial Intelligence Scale. Computers Hum. Behav. Rep. 1 , 100014 (2020) Sindermann, C., et al.: Acceptance and Fear of Artificial Intelligence: associations with personality in a German and a Chinese sample. Discover Psychol. 2 (1), 8 (2022) Sindermann, C., et al.: Assessing the attitude towards artificial intelligence: Introduction of a short measure in German, Chinese, and English language. KI-Künstliche intelligenz. 35 (1), 109–118 (2021) Grassini, S.: Development and validation of the AI attitude scale (AIAS-4): a brief measure of general attitude toward artificial intelligence. Front. Psychol. 14 , 1191628 (2023) Park, J., Woo, S.E., Kim, J.: Attitudes towards artificial intelligence at work: Scale development and validation. J. Occup. Organ. Psychol. 97 (3), 920–951 (2024) Brislin, R.W.: Back-translation for cross-cultural research. J. Cross Cult. Psychol. 1 (3), 185–216 (1970). 10.1177/135910457000100301 Qu, W., Sun, H., Ge, Y.: The effects of trait anxiety and the big five personality traits on self-driving car acceptance. Transp. (Amst). 48 (5), 2663–2679 (2021) Riedl, R.: Is trust in artificial intelligence systems related to user personality? Review of empirical evidence and future research directions. Electron. Markets. 32 (4), 2021–2051 (2022) Bartneck, C., Yogeeswaran, K., Sibley, C.G.: Personality and demographic correlates of support for regulating artificial intelligence. AI Ethics. 4 (2), 419–426 (2024) Cacioppo, J.T., Petty, R.E., Feng Kao, C.: The efficient assessment of need for cognition. J. Pers. Assess. 48 (3), 306–307 (1984) Halttu, K., Oinas-Kukkonen, H.: need for cognition among users of self-monitoring systems for physical activity: survey study. JMIR Form. Res. 5 (10), e23968 (2021) Taffesse, A.S., Tadesse, F.: Pathways less explored—Locus of control and technology adoption, J Afr Econ , vol. 26, no. suppl_1, pp. i36–i72, (2017) Laski, G., MOBILE panel Sample and ONLINE, Surveys, T.G.M., Research: Accessed: Jan. 09, 2024. [Online]. Available: https://tgmresearch.com/ Piçarra, N., Giger, J.-C.: Predicting intention to work with social robots at anticipation stage: Assessing the role of behavioral desire and anticipated emotions. Comput. Hum. Behav. 86 , 129–146 (2018). 10.1016/j.chb.2018.04.026 Alexander, C.S., Becker, H.J.: The use of vignettes in survey research. Public. Opin. Q. 42 (1), 93–104 (1978) Steiner, P.M., Atzmüller, C., Su, D.: Designing valid and reliable vignette experiments for survey research: A case study on the fair gender income gap. J. Methods Meas. Soc. Sci. 7 (2), 52–94 (2016) Atzmüller, C., Steiner, P.M.: Experimental vignette studies in survey research. Methodology, (2010) Rammstedt, B., John, O.P.: Measuring personality in one minute or less: A 10-item short version of the Big Five Inventory in English and German. J. Res. Pers. 41 (1), 203–212 (2007) Costa Mastrascusa, R., et al.: Evaluating the complete (44-item), short (20-item) and ultra-short (10-item) versions of the Big Five Inventory (BFI) in the Brazilian population. Sci. Rep. 13 (1), 7372 (2023) Nießen, D., Schmidt, I., Groskurth, K., Rammstedt, B., Lechner, C.M.: The Internal–External Locus of Control Short Scale–4 (IE-4): a comprehensive validation of the English-language adaptation. PLoS One. 17 (7), e0271289 (2022) Bartlett, M.S.: A note on the multiplying factors for various χ 2 approximations. J. Royal Stat. Soc. Ser. B (Methodological), pp. 296–298, (1954) Kaiser, H.F.: An index of factorial simplicity. Psychometrika. 39 (1), 31–36 (1974) Richardson, D.B., Hamra, G.B., MacLehose, R.F., Cole, S.R., Chu, H.: Hierarchical regression for analyses of multiple outcomes. Am. J. Epidemiol. 182 (5), 459–467 (2015) Babiker, A., Alshakhsi, S., Supti, T.I., Ali, R., Do Personality Traits Impact the Attitudes Towards Artificial Intelligence? in: 11th International Conference on Behavioural and Social Computing (BESC) , IEEE, 2024, pp. 1–8. (2024) del Barrio-García, S., Arquero, J.L., Romero-Frías, E.: Personal learning environments acceptance model: The role of need for cognition, e-learning satisfaction and students’ perceptions. J. Educ. Techno Soc. 18 (3), 129–141 (2015) Bergdahl, J., et al.: Self-determination and attitudes toward artificial intelligence: Cross-national and longitudinal perspectives. Telematics Inform. 82 , 102013 (2023) Figueiras, M.J., Ghorayeb, J., Coutinho, M.V.C., Marôco, J., Thomas, J.: Levels of trust in information sources as a predictor of protective health behaviors during COVID-19 pandemic: a UAE cross-sectional study. Front. Psychol. 12 , 633550 (2021) Kampmann, I.L., Meyer, T., Morina, N.: Social comparison modulates coping with fear in virtual environments. J. Anxiety Disord. 72 , 102226 (2020) Leding, J.K., Antonio, L.: Need for cognition and discrepancy detection in the misinformation effect. J. Cogn. Psychol. 31 (4), 409–415 (2019) Joseph, C., Ganesh, A.: Aviation safety locus of control in Indian aviators. Indian J. Aerosp. Med. 50 (1), 14–21 (2006) Kroencke, L., Geukes, K., Utesch, T., Kuper, N., Back, M.D.: Neuroticism and emotional risk during the COVID-19 pandemic. J. Res. Pers. 89 , 104038 (2020) Beard, J.B.: Identifying predictors of social functioning in college students: A Meta-analysis. University of Louisville (2011) Montag, C., Nakov, P., Ali, R.: Considering the IMPACT framework to understand the AI-well-being-complex from an interdisciplinary perspective. Telematics Inf. Rep. 13 , 100112 (2024) Jeff Dean, Fuller, J., AI for Social Good:. Accessed: Apr. 10, 2025. [Online]. Available: https://blog.google/outreach-initiatives/google-org/ai-social-good/ Felt, A.P.: Chrome’s new AI feature can help you write on the web. Accessed: Apr. 10, 2025. [Online]. Available: https://blog.google/products/chrome/google-chrome-ai-help-me-write/ AI for Accessibility: Accessed: Apr. 10, 2025. [Online]. Available: https://www.microsoft.com/en-us/accessibility/innovation Arya, V., et al.: Ai explainability 360 toolkit, in Proceedings of the 3rd ACM India joint international conference on data science & management of data (8th ACM IKDD CODS & 26th COMAD) , pp. 376–379. (2021) What is explainable AI? Accessed: Apr. 10, 2025. [Online]. Available: https://www.ibm.com/think/topics/explainable-ai General Data Protection Regulation,: Accessed: Apr. 10, 2025. [Online]. (2016). Available: https://gdpr-info.eu/ Gervazoni, A., Quaresma, M.: Trust in the system and human autonomy in customer service chatbots, in Service Design and Innovation Conference , pp. 1416–1430. (2023) Hleg, A.I.: Ethics guidelines for trustworthy AI, (2019) Brandimarte, L., Acquisti, A., Loewenstein, G.: Misplaced confidences: Privacy and the control paradox. Soc. Psychol. Personal Sci. 4 (3), 340–347 (2013) Artificial Intelligence in Health and Care Award: Accessed: Apr. 10, 2025. [Online]. Available: https://www.england.nhs.uk/aac/what-we-do/how-can-the-aac-help-me/ai-award/ MINISTERIAL FORWARD EXECUTIVE SUMMARY,, Oct: Accessed: Apr. 10, 2025. [Online]. (2017). Available: https://u.ae/en/about-the-uae/strategies-initiatives-and-awards/strategies-plans-and-visions/government-services-and-digital-transformation/uae-strategy-for-artificial-intelligence Additional Declarations No competing interests reported. Supplementary Files Appendix.docx Cite Share Download PDF Status: Published Journal Publication published 21 Oct, 2025 Read the published version in World Wide Web → Version 1 posted Editorial decision: Revision requested 16 Jun, 2025 Reviews received at journal 16 Jun, 2025 Reviews received at journal 13 Jun, 2025 Reviews received at journal 12 Jun, 2025 Reviewers agreed at journal 27 May, 2025 Reviewers agreed at journal 26 May, 2025 Reviewers agreed at journal 26 May, 2025 Reviewers invited by journal 09 May, 2025 Editor assigned by journal 07 May, 2025 Submission checks completed at journal 06 May, 2025 First submitted to journal 30 Apr, 2025 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-6565909","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":454624064,"identity":"a45d800f-1f16-461e-9c1b-9e0014602d42","order_by":0,"name":"Mohammad Mominur Rahman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYLCCDxU2cvzsPWyMDSDeASJ0MM44k2Ys2XOGBC3MvG2HEzfcyCFSi25778MPPGcOGzPcfHvs4cw2Bjm+GwmMN37g0WJ25rixhERFuhzj7Lx0w41tDMaSNxKYLXvwabmRxiBhcMbamFk6x0zyYRsD0IUJbBI8+LUw/0hsY05skzwD1lIP0iL5B78WNomDbc6JPRI8ZpJAhyUYALVI47XlzDE2ywZgIEvw5JgbzjgnYTjzzMNmaxl8Wo63Md/+A4xK++NnzB72lNnI8x1PPnjzDR4t6EACiBkbJEjQgaRxFIyCUTAKRgEMAAA/rlKjB17gEwAAAABJRU5ErkJggg==","orcid":"","institution":"Hamad Bin Khalifa University","correspondingAuthor":true,"prefix":"","firstName":"Mohammad","middleName":"Mominur","lastName":"Rahman","suffix":""},{"id":454624065,"identity":"36f050c1-d93f-4e62-8f21-e78925ae8af3","order_by":1,"name":"Sameha AlShakhsi","email":"","orcid":"","institution":"Hamad Bin Khalifa University","correspondingAuthor":false,"prefix":"","firstName":"Sameha","middleName":"","lastName":"AlShakhsi","suffix":""},{"id":454624066,"identity":"5a422002-a369-47dd-ba7e-9a3b35477733","order_by":2,"name":"Areej Babiker","email":"","orcid":"","institution":"Hamad Bin Khalifa University","correspondingAuthor":false,"prefix":"","firstName":"Areej","middleName":"","lastName":"Babiker","suffix":""},{"id":454624069,"identity":"1935602f-2d88-4dd0-8164-668b92f8c63a","order_by":3,"name":"Ala Yankouskaya","email":"","orcid":"","institution":"Bournemouth University","correspondingAuthor":false,"prefix":"","firstName":"Ala","middleName":"","lastName":"Yankouskaya","suffix":""},{"id":454624075,"identity":"adcf58fe-5d94-4a46-b632-0ff7a88a3cea","order_by":4,"name":"Raian Ali","email":"","orcid":"","institution":"Hamad Bin Khalifa University","correspondingAuthor":false,"prefix":"","firstName":"Raian","middleName":"","lastName":"Ali","suffix":""}],"badges":[],"createdAt":"2025-04-30 14:38:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6565909/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6565909/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11280-025-01372-w","type":"published","date":"2025-10-21T16:16:23+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83079131,"identity":"3ad19445-c49b-4573-8f5f-ddc8353bff84","added_by":"auto","created_at":"2025-05-19 18:56:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":151894,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual model examining how personality traits influence attitudes toward AI, moderated by need for cognition, locus of control, and the interaction of FoC x SP modalities, with cross-cultural comparison between UK and Arab samples.\u003c/p\u003e","description":"","filename":"floatimage138.png","url":"https://assets-eu.researchsquare.com/files/rs-6565909/v1/9c230f80d071dd4f71c5086d.png"},{"id":83079128,"identity":"626ffd0e-32ac-4f39-b3f1-daf76bbeb887","added_by":"auto","created_at":"2025-05-19 18:56:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":334030,"visible":true,"origin":"","legend":"\u003cp\u003eIllustration representing the concepts of FoC and SP\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6565909/v1/e7b8fa8a634115799039b38d.png"},{"id":83079339,"identity":"c15eb841-d445-4290-8fb1-d99f06d7f9ff","added_by":"auto","created_at":"2025-05-19 19:04:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":292135,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelational patterns between personality traits, NFC, LOC, modality specific AI factors and general ATAI in the UK sample (* p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6565909/v1/de73c03e28cac09bc78fd28b.png"},{"id":83079133,"identity":"a4754386-14b1-4823-8b50-e13d0c2f16bc","added_by":"auto","created_at":"2025-05-19 18:56:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":292554,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelational patterns between personality traits, NFC, LOC, modality specific AI factors and general ATAI in the Arab sample (* p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6565909/v1/01cd6c3b15ae03a1d646a13c.png"},{"id":83079831,"identity":"55ea3d85-ffe9-4ed5-8ae5-6f3efef877dc","added_by":"auto","created_at":"2025-05-19 19:12:02","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":292554,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelational patterns between personality traits, NFC, LOC, modality specific AI factors and general ATAI in the Arab sample (* p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6565909/v1/07f0bd0bda346afc697aea48.png"},{"id":94490391,"identity":"cbffe135-df95-42c4-bf3f-1378ffc21ee0","added_by":"auto","created_at":"2025-10-27 17:09:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2703677,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6565909/v1/7505f152-1923-4f18-a6f1-fdd409fc2df3.pdf"},{"id":83079336,"identity":"b9fb2722-1202-4f31-98f3-c12c4e3f82b3","added_by":"auto","created_at":"2025-05-19 19:04:02","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":88344,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-6565909/v1/9cff6d11b2a2d537b820a833.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"On the Role of Personality in Attitudes Toward AI: Do AI’s Freedom of Choice and Social Proof Matter?","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn recent years, artificial intelligence (AI) has significantly impacted various sectors such as healthcare, education, finance, and entertainment [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As AI continues to shape society, understanding users' attitudes toward AI has become a critical area of research. One factor assumed to influence these attitudes is personality traits. The Big five-factor model (Neuroticism, Extraversion, Openness, Agreeableness, Conscientiousness) provides a validated framework for assessing personality structure and disorders, offering a dimensional alternative to categorical diagnosis in clinical settings [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Research has extensively examined how personality traits influence technology acceptance, as they help explain variations in individual behavior and preferences. For instance, individuals with high openness may be more receptive to AI due to their curiosity, while those with higher neuroticism may experience anxiety or fear towards it [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition to personality traits, psychological factors such as Need for Cognition (NFC), reflecting a person's willingness to engage in thinking and problem-solving, and Locus of Control (LOC), indicating whether individuals believe they (internal LOC) or environmental factors (external LOC) control their life events, can also significantly shape attitudes toward AI. The study [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] confirms NFC's role in shaping risk-benefit judgments, with high NFC linked to nuanced evaluations and low NFC to more polarized attitudes toward technologies. Similarly, individuals with an internal LOC are more likely to view AI positively, as they perceive it as a tool they can control, while those with an external LOC tend to fear AI, viewing it as a threat [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBeyond these factors, AI\u0026rsquo;s design and operation modalities such as freedom of choice and social proof also influence AI perceptions [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The concept of freedom of choice refers to whether AI\u0026rsquo;s users are enabled to choose between AI and human alternatives. Giving users such possibility can enhance their trust and satisfaction with AI systems, as it reinforces a sense of autonomy and control [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Studies show that users are more likely to trust and engage with AI when they feel they have control over their interaction with it, as seen in healthcare and customer service examples [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Additionally, social proof, referring to the influence of how others view or react to something on an individual's decision, typically leading to conformity, plays a key role in technology adoption. Research indicates that people are more likely to accept AI when they observe others doing the same, as social influence shapes their perceptions of AI's benefits and trustworthiness [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Cultural differences significantly shape attitudes toward AI, as shown in a study where UK respondents exhibited gender and age gaps in AI concerns, with women and older adults being more wary, while Arab participants demonstrated consistent attitudes across demographics, highlighting distinct cultural drivers of AI acceptance, such as personal versus institutional trust [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile prior research has separately examined personal factors and AI design features, their combined influence on attitudes toward AI remains unexplored. Additionally, comparative studies across culturally distinct samples (e.g., UK vs. Arab) are scarce, limiting generalizability. This study addresses these gaps by investigating how psychological traits and operational modalities interact to shape AI acceptance, using parallel samples from both cultures for robust cross-validation.\u003c/p\u003e"},{"header":"2. Theoretical Underpinnings","content":"\u003cp\u003eThis section outlines the theoretical rationale for incorporating both personal factors and contextual features of AI design, specifically freedom of choice and social proof. By moving beyond a monolithic view of AI, we demonstrate how these personal factors and operational modalities actively shape user experiences, providing deeper insights into the interplay of individual differences and contextual factors in technology acceptance across cultures.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Modalities and Attitude toward AI\u003c/h2\u003e \u003cp\u003eFreedom of choice (FoC), the ability to choose alternatives not requiring interaction with AI (such as human interaction or manual oversight), influences user trust and satisfaction. According to Self-Determination Theory [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], user motivation to adopt a system is influenced by their perception of control over its use, their connection to its purpose, and the ability to make independent decisions about interacting with or adopting the system [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Allowing users the freedom to choose between AI or non-AI options reinforces a sense of autonomy, which, in turn, improves trust and satisfaction [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Research in customer service and healthcare supports this notion, showing that limiting interactions to AI systems, such as chatbots, can reduce user satisfaction and even increase negative perceptions [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In contrast, providing users with the option to choose, such as allowing patients to select between AI and human doctors, has been shown to increase trust in AI [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Similarly, IT consumerization studies reveal that offering users multiple technology options led to higher autonomy and engagement [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The Technology-to-Performance Chain (TPC) framework also emphasizes how voluntary use of technology, along with social norms, influences users' attitudes and adoption [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOn the other hand, social proof (SP), which describes how individuals rely on others' actions in uncertain situations, also influences AI adoption. Grounded in social learning theory [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and conformity theory [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], SP demonstrates that individuals often look to others when making technology adoption decisions [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Studies show that social influence can significantly affect AI acceptance, such as in AI-based tourism services [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] or AI-driven service delivery [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This aligns with Technology Acceptance Models (TAM2) and UTAUT, which highlight the importance of social influence and subjective norms in shaping attitudes toward new technologies [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Furthermore, the impact of social influence is moderated by voluntariness, where the effects are stronger in mandatory contexts than in voluntary ones [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent work by Alshakhsi [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] demonstrates that AI's operational modalities, particularly FoC and SP, fundamentally alter users' perceptions of both (1) contributions to personal/social good and (2) ethical concerns and risks. The study revealed significant variation in perceived AI risks and benefits across these modalities, establishing them as critical contextual boundaries for attitude formation. Building on this, we examine how psychological traits interact with these modalities to shape attitudes, a gap not yet addressed in prior research. Together, the modalities of FoC and SP demonstrate how different factors shape and influence user engagement and attitude towards AI, highlighting their importance in fostering AI acceptance and positive perceptions. Therefore, the following research questions are proposed:\u003c/p\u003e \u003cp\u003eRQ1: How do individual differences influence attitudes toward AI, considering different combinations of SP and FoC?\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Personality Traits and Attitudes Toward AI\u003c/h2\u003e \u003cp\u003eA growing body of research has examined how personality traits influence individuals\u0026rsquo; perceptions of and attitudes toward AI. Central to this inquiry is the Big Five personality model, which offers a framework to understand consistent patterns of behavior, thoughts, and emotions. These traits are often linked to technology adoption and acceptance due to their predictive value in how individuals engage with innovations.\u003c/p\u003e \u003cp\u003eOne of the traits is openness to experience, characterized by intellectual curiosity, creativity, and openness to novelty, has been frequently associated with more favorable attitudes toward AI. Individuals scoring high on openness are more inclined to explore and accept emerging technologies, including AI systems, due to their appreciation for new experiences [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Conversely, individuals high in neuroticism, who tend to be emotionally reactive and prone to anxiety, are more likely to perceive AI as threatening or unsettling, resulting in negative attitudes toward its integration in daily life [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This trait has consistently been associated with increased fear and distrust toward AI technologies.\u003c/p\u003e \u003cp\u003eThe relationship between the remaining Big Five traits and AI attitudes appears more context-dependent. Extraversion, associated with sociability and assertiveness, has shown mixed outcomes. While it may lead to more positive views of AI in socially interactive contexts, it may have little or even negative impact in task-focused AI applications [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Agreeableness, linked with traits such as kindness and cooperation, generally predicts more favorable attitudes toward AI, especially in situations that emphasize interpersonal trust and harmony [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. However, in some studies, agreeableness was also associated with higher levels of concern or fear, possibly due to heightened sensitivity to social disruption caused by AI [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Conscientiousness, reflecting self-discipline, organization, and a goal-oriented mindset, has been associated with both positive and cautious views. While conscientious individuals may appreciate AI\u0026rsquo;s efficiency and structure, concerns about automation\u0026rsquo;s impact on job security can contribute to ambivalence or negativity [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEmpirical findings support these associations, though results vary across studies and cultural contexts. For instance, in a Turkish sample, agreeableness significantly predicted negative but not positive attitudes toward AI [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In contrast, South Korean participants showed links between agreeableness and both positive and negative emotions toward AI, while extraversion and neuroticism influenced emotional responses [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In the UK, introverted individuals reported more favorable attitudes toward AI, with agreeableness and conscientiousness linked to greater forgiveness of AI\u0026rsquo;s limitations [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCross-cultural studies highlight further complexities. Among German and Chinese participants, neuroticism was associated with fear of AI in the German group, while agreeableness negatively predicted fear only in the Chinese group [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Similarly, openness and agreeableness predicted AI acceptance among German participants, but only agreeableness played a significant role in the Chinese sample.\u003c/p\u003e \u003cp\u003eExisting research on the relationship between personality traits and attitudes toward AI has produced mixed findings, partly due to variations in measurement approaches. Studies employ multidimensional scales like the Attitudes Toward Artificial Intelligence (ATAI) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], ATTARI-12 [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], General Attitudes toward AI Scale (GAAIS) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and AI Anxiety Scale (AIAS-4) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], while others use domain-specific tools (e.g., Attitudes toward AI in the Workplace (AAAW) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]) or single-item measures [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. These methodological differences may contribute to inconsistencies, as scales emphasizing trust could yield divergent results from those focused-on fear. Additionally, research gaps persist: some studies examine narrow AI applications [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], while others analyze personality correlations with isolated AI attitude dimensions [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], limiting broader generalizations about overall AI perceptions. For example, openness was associated with more positive reactions to AI-generated art [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], while neuroticism was linked to reduced trust in AI [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and increased support for AI regulation [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. To address these inconsistencies, researchers should adopt standardized measurement tools across cultures while focusing on specific AI contexts\u0026mdash;particularly its operational modalities. This approach will help clarify whether personality traits produce universal or context-dependent effects on AI attitudes, advancing both theoretical insights and practical applications. Therefore, the following research questions are proposed:\u003c/p\u003e \u003cp\u003eRQ2: How do personality traits (Big Five) influence public attitudes towards AI, considering different combinations of social proof and freedom of choice?\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. NFC and Attitudes Toward AI\u003c/h2\u003e \u003cp\u003eNFC has emerged as an important psychological factor influencing individuals' attitudes towards AI. NFC refers to the tendency to engage in and enjoy effortful cognitive activities, such as analyzing, problem-solving, and deep thinking [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Research suggests that individuals with high NFC are more likely to form informed, reflective attitudes toward AI, as they are inclined to critically assess its potential benefits and risks [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These individuals tend to approach AI technologies with a balanced view, considering both the innovative aspects and the ethical implications of their use.\u003c/p\u003e \u003cp\u003eIn contrast, those with lower NFC, who are less likely to engage in deep cognitive processing, may form more superficial or polarized attitudes towards AI, often influenced by external factors such as popular opinions or media portrayals [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. This lack of in-depth processing can lead to more reactive attitudes, where individuals either overestimate the potential benefits of AI or, conversely, succumb to exaggerated fears surrounding the technology.\u003c/p\u003e \u003cp\u003eThe study by Halttu and Oinas-Kukkonen [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] highlights that high NFC significantly influences how users engage with self-monitoring systems, such as those used in health and fitness contexts. Their research found that individuals with high NFC were more likely to trust and accept such systems due to their thoughtful evaluation of system credibility and effectiveness.\u003c/p\u003e \u003cp\u003eWhile existing research confirms NFC's role in shaping AI attitudes, prior studies have not examined how this relationship varies across AI's operational contexts (particularly freedom of choice and social proof). Furthermore, the field lacks cross-cultural comparisons using standardized measures. To address these gaps, we propose the following research questions:\u003c/p\u003e \u003cp\u003eRQ3: How does need for cognition influence public attitudes toward AI, considering different combinations of social proof and freedom of choice?\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. LOC and Attitudes Toward AI\u003c/h2\u003e \u003cp\u003eLOC influences attitudes toward AI based on individuals' beliefs about control over their lives. Those with an internal LOC, who feel they can control outcomes, tend to view AI positively, seeing it as a helpful tool for enhancing decision-making and productivity [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In contrast, individuals with an external LOC, who believe external forces control outcomes, are more likely to perceive AI as a threat or part of an uncontrollable system, leading to negative attitudes and greater fear of AI [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis effect extends to various domains, with external LOC individuals showing less willingness to adopt new technologies across different sectors, including agriculture [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Understanding LOC can help in designing AI strategies that address concerns and foster acceptance among those with an external LOC. Therefore, the following research questions are proposed:\u003c/p\u003e \u003cp\u003eRQ4: How does locus of control influence public attitudes toward AI, considering different combinations of social proof and freedom of choice?\u003c/p\u003e \u003cp\u003eThe conceptual framework is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePrevious research has demonstrated significant cultural variations in AI attitudes. Studies show that UK populations exhibit demographic-based differences in AI concerns (with women and older adults being more wary), while Arab samples display consistent attitudes across demographics, highlighting distinct cultural drivers like personal versus institutional trust [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Cross-cultural comparisons further reveal that personality traits predict AI attitudes differently across cultures, with neuroticism linked to AI fear in German samples but not Chinese samples, and agreeableness showing culture-specific effects [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Based on these established patterns, we examine all research questions across two cultural contexts (UK and Arab samples).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Method","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Participant Recruitment and Selection\u003c/h2\u003e \u003cp\u003eThe study recruited a total of 639 adult participants, with 323 individuals from the Arab GCC and 316 from the UK, ensuring a diverse cultural representation. Given the difficulty in recruiting Arab participants above the age of 60, the study restricted the sample to individuals between 18 and 60 years old. Eligibility criteria required participants to be at least 18 years old, be native to and currently residing in either the UK or the Arab GCC, have some familiarity with artificial intelligence (AI), and self-identify culturally as either British or Arab in terms of their cultural identity and norms.\u003c/p\u003e \u003cp\u003eTo confirm eligibility, participants completed a pre-screening questionnaire before proceeding to the main survey. Participant recruitment was facilitated by TGM Research, a global market research company with an extensive online panel spanning 130 countries [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. To encourage participation, individuals received compensation upon survey completion, with incentives managed by TGM Research in accordance with ethical standards regarding voluntary participation and confidentiality.\u003c/p\u003e \u003cp\u003eEthical approval\u003c/strong\u003e for the study was granted by the Institutional Review Board (IRB) of the lead author's institution. Informed consent was obtained from all participants, who were assured that no personally identifiable information, such as names or contact details, would be collected. Participants also had the option to withdraw from the study at any stage. The dataset used in this research is publicly available on the Open Science Framework (OSF), with links provided in the Data Availability Statements section.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Survey Methodology\u003c/h2\u003e \u003cp\u003eThe survey was designed and administered through the SurveyMonkey platform and was available in both English and Arabic. The survey was conducted from late October 2023 to mid-December 2023 as part of a broader research project, with details accessible through the Open Science Framework (OSF).\u003c/p\u003e \u003cp\u003eParticipants were assigned different language versions based on their region: the Arabic version was provided to Arab participants, while the English version was given to UK participants. The Arabic version of the survey was developed using the back-translation method to ensure accuracy, with additional face validation conducted through trials with bilingual individuals to confirm clarity and cultural appropriateness [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. A pilot study involving small groups from both the UK and Arab was conducted to identify and eliminate any unclear or ambiguous wording.\u003c/p\u003e \u003cp\u003eTo ensure data quality and response reliability, the survey incorporated attention checks to identify inattentive participants. Responses that failed these checks, contained inconsistencies, or were completed in less than half of the median survey duration were excluded from the analysis. The median response time was calculated after removing outliers, defined as participants whose completion time exceeded twice the expected duration, often due to completing the survey in multiple sessions.\u003c/p\u003e \u003cp\u003eIn line with experimental survey methodologies, this study employed vignette experiments to assess attitudes toward AI. Vignette experiments are a valuable survey research method that uses short descriptions of scenarios to elicit respondents' judgments. The method allows researchers to explore nuanced responses by presenting participants with realistic and relatable scenarios rather than abstract questions [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. These experiments enhance the validity and reliability of survey responses by systematically varying characteristics of the vignettes and incorporating design elements such as confounded factorial designs, between-subjects factors, and anchoring vignettes [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The selection of vignette subsets can be either random or systematic, each with implications for how effects are interpreted [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Furthermore, fractional replication experimental designs enable researchers to vary a broad range of contextual factors while limiting the total number of vignette versions required [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Vignette experiments have been successfully used in studies exploring topics such as income fairness [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] and crime victim [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] perceptions supporting their utility across disciplines.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Questionnaire and Vignette Design and Measures\u003c/h2\u003e \u003cp\u003eThe questionnaire began with demographic questions, including gender, age, education level, and employment status. Participants were then introduced to artificial intelligence with the following explanation:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003e\"Artificial Intelligence (AI) is centred around creating machines that possess the ability to accomplish activities typically necessitating human intelligence, including making recommendations, recognizing images, interpreting natural language, and the process of decision-making. For example, AI is used in self-driving cars, voice assistants like Siri, and recommendation systems like those on streaming platforms such as YouTube and Netflix.\u0026rdquo;\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eFollowing this introduction, participants were presented with four AI-driven vignette scenarios designed to assess their perceptions of artificial intelligence under varying conditions. These scenarios underwent face-validation and multiple refinements to ensure clarity and effectiveness. Initially, a car-based AI scenario was considered, but due to concerns over safety perceptions, which could be more dominant on decision making than FoC and SP, chatbot agents were selected instead. Chatbots, widely used in customer service, were deemed more familiar and less likely to introduce bias. Each scenario was accompanied by a carefully designed image to represent the context accurately as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. These images underwent several iterations based on participant feedback, and face validation was conducted with three UK and three Arab participants to ensure scenario comprehension. Adjustments included clarifying that human agents might not always be available, making the scenarios more realistic. To ensure participants understood key study concepts, they were provided with text explanations and visual illustrations of FoC) and SP.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003e\u0026ldquo;The following section of the survey will introduce social characteristics related to AI, followed by scenarios of using advanced AI agent. In this scenario, you will interact with your internet provider about offers, bills, technical issues, or personal info changes. They offer an advanced AI agent that can interact through text, voice, and video, closely resembling human interaction. The agent is your primary point of conversation.\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFreedom of Choice\u003c/strong\u003e \u003cp\u003e \u003cem\u003eInvolves the option to use AI. Lack of choice occurs when AI is the only interaction, like AI customer service without a human alternative.\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSocial Proof\u003c/strong\u003e \u003cp\u003e \u003cem\u003eReflects AI's successful use by others. For instance, limited adoption by people of driverless cars results in low social proof.\"\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAfter viewing each of the vignette scenarios, participants responded to a fixed set of questions measuring their perceptions of AI. The questions assessed trust (1\u0026ndash;7 scale), enhancing wellbeing (0\u0026ndash;10 scale), feeling pleasant (1\u0026ndash;9 scale), recommend (0\u0026ndash;10 scale), positive change (1\u0026ndash;6 scale), ethical implications (0\u0026ndash;10 scale), and perceived risk (1\u0026ndash;6 scale), detailed scales available in Supplementary Materials.\u003c/p\u003e \u003cp\u003e\u003cp\u003e \u003cb\u003eA. Big Five Inventory (BFI-10)\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThis study employed the BFI-10 to assess personality traits, including openness, conscientiousness, extraversion, agreeableness, and neuroticism, with two items per trait [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Participants rated each statement on a five-point Likert scale (1\u0026thinsp;=\u0026thinsp;strongly disagree to 5\u0026thinsp;=\u0026thinsp;strongly agree). The BFI-10 was chosen for its validated effectiveness in large-scale studies and its ability to reduce survey fatigue while maintaining measurement reliability.\u003c/p\u003e \u003cp\u003eTo improve clarity, the openness item \u0026ldquo;\u003cem\u003ehas a few artistic interests\u003c/em\u003e\u0026rdquo; was revised to \u0026ldquo;\u003cem\u003eI have limited or no artistic interests\u003c/em\u003e\u0026rdquo; to prevent misinterpretation, as the original phrasing was misunderstood in both languages. These modifications ensured cross-cultural consistency while maintaining the scale\u0026rsquo;s validity. The BFI-10 has demonstrated acceptable internal consistency in previous research [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cp\u003e \u003cb\u003eB. Locus of Control\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eWe utilized the four-item short scale developed by Nieben [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] to assess LOC, comprising two subscales: internal and external LOC, each with two items. Participants rated their agreement on a five-point Likert scale (1\u0026thinsp;=\u0026thinsp;does not apply at all to 5\u0026thinsp;=\u0026thinsp;applies completely).\u003c/p\u003e \u003cp\u003eTo ensure cultural appropriateness, we modified the item \u003cem\u003e\u0026ldquo;fate often gets in the way of my plans\u0026rdquo;\u003c/em\u003e to \u003cem\u003e\u0026ldquo;circumstances often get in the way of my plans,\u0026rdquo;\u003c/em\u003e recognizing that in Arab culture, fate is not typically perceived as a hindrance. The scale demonstrated reliable validity in measuring both LOC dimensions [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eC. Need for Cognition\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eWe used the six-item short scale developed by Cacioppo et al. to measure NFC [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], which reflects an individual's preference for engaging in and enjoying complex cognitive tasks. Participants rated each statement on a five-point Likert scale (1\u0026thinsp;=\u0026thinsp;strongly disagree to 5\u0026thinsp;=\u0026thinsp;strongly agree). The scale demonstrated adequate reliability and validity [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], with internal consistency scores of α\u0026thinsp;=\u0026thinsp;0.86 for the UK sample and α\u0026thinsp;=\u0026thinsp;0.66 for the Arab sample.\u003c/p\u003e \u003cp\u003e \u003cb\u003eD. Attitudes Toward Artificial Intelligence\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eTo evaluate individual differences in attitudes toward AI, we employed the five-item attitudes toward AI (ATAI) scale [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This scale captures two negatively correlated factors: (1) AI acceptance and (2) AI fear. Participants rated their agreement with AI-related statements on an 11-point Likert scale (0 = \u0026ldquo;strongly disagree\u0026rdquo; to 10 = \u0026ldquo;strongly agree\u0026rdquo;).\u003c/p\u003e \u003cp\u003eATAI acceptance was derived from responses related to trust and perceived benefits, while ATAI fear was based on concerns about AI-related risks, including destruction and job displacement. The scale demonstrated strong internal consistency, with reliability scores of α\u0026thinsp;=\u0026thinsp;0.80 for both acceptance and fear in the UK sample, and α\u0026thinsp;=\u0026thinsp;0.85 for acceptance and α\u0026thinsp;=\u0026thinsp;0.75 for fear in the Arab sample.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Data Preprocessing\u003c/h2\u003e \u003cp\u003eBefore conducting data analysis, preprocessing was performed to ensure consistency and suitability for statistical procedures. The datasets for both samples (Arab and UK) were standardized using the R base function scale (), transforming each variable to have a mean of 0 and a standard deviation of 1.\u003c/p\u003e \u003cp\u003eTo determine whether the questions measuring (Enhancing wellbeing, Feeling pleasant, Recommend, Trust, Positive change, Ethical implications, and Perceived risk) were suitable for factor analysis, the Kaiser-Meyer-Olkin (KMO) test and Bartlett\u0026rsquo;s test of sphericity were performed. The KMO values exceeded 0.88 for the UK sample and 0.86 for the Arab sample, demonstrating strong sampling adequacy. Additionally, Bartlett\u0026rsquo;s test of sphericity was highly significant (p\u0026thinsp;\u0026lt;\u0026thinsp;.001) for both groups, confirming that the dataset met the requirements for Exploratory Factor Analysis (EFA) [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdditionally, the correlation between modalities (dependent variables) was examined before conducting Hierarchical Multivariate Multiple Regression (MMR) to ensure their suitability for multivariate analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Data Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics were conducted for both samples, with all variables exhibiting skewness and kurtosis values between +\u0026thinsp;2 and \u0026minus;\u0026thinsp;2, indicating an approximately normal distribution (See Appendix A). Subsequently, EFA was performed to identify the underlying factor structure for each scenario.\u003c/p\u003e \u003cp\u003eTo examine relationships between dependent variables, Pearson\u0026rsquo;s correlation was used, while Spearman\u0026rsquo;s correlation was applied for all other variables. Additionally, a one-sample t-test was conducted for both samples to compare attitude toward AI acceptance and fear across different modalities.\u003c/p\u003e \u003cp\u003eThe hierarchical MMR analysis was then performed to assess how personality traits, NFC, and LOC predict factors across different modalities. The hierarchical MMR approach was chosen for its ability to assess multiple dependent variables simultaneously while controlling for other predictors, providing a clearer understanding of how individual differences influence AI perceptions [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. To evaluate the relative contribution of each predictor, Appendix Tables\u0026nbsp;6S \u0026minus;\u0026thinsp;8S presents the effect sizes (partial eta-squared) for all variables across the UK and Arab sample, offering insight into which traits exert the strongest influence on each factor within different contextual modalities. Additionally, we compare our three model\u0026rsquo;s performance (Appendix C) to assess the validity of our method, ensuring a comprehensive evaluation of its predictive power.\u003c/p\u003e \u003cp\u003eThe hierarchical MMR were conducted using RStudio (version: 2024.12.1), while all other analyses were performed in JASP (version: 0.18.3)\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003ch2\u003e\u003cem\u003e4.1. Descriptive Statistics for\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cem\u003eDemographics\u003c/em\u003e\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eParticipants provided information about their age, gender, education level, and employment status. A summarized demographic profile of both the UK and Arab groups is presented in Table 1.\u003c/p\u003e\n\u003cp\u003eTable 1. Demographic Characteristics of Participants in the Arab and UK Samples\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUK (N=316)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eArab (N=323)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Male\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e96 (30.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e147 (45.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Female\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e220 (69.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e176 (54.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; M (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e40.81 (10.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e33.07 (9.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 360px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Rang\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e18 - 60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e18 - 57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003eNo formal education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003ePrimary education (elementary)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003eSecondary education (high school)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e24.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e14.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003ePursuing or completed vocational or technical education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e22.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e4.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003ePursuing or completed undergraduate degree (bachelor\u0026rsquo;s)\u003csup\u003e\u0026nbsp;(*)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e32.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e68.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003ePursuing or completed postgraduate degree (master\u0026rsquo;s, Ph.D., etc.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e19.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e12.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmployment (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003eFull time employment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e53.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e54.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003ePart time employment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e17.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e11.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003eRun my own business\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e4.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e6.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003eHomemaker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e6.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e9.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003eStudent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e7.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003eRetired\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e3.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e8.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e5.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e3.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e(*)\u0026nbsp;\u003c/sup\u003eThe differences observed between the UK and Arab regions are primarily attributed to the lower prevalence of vocational or technical education in the Arab region.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e4.2. Factor Analysis of Reactions to FoC x SP Vignettes \u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eAn EFA was conducted to identify the key factors influencing participants\u0026apos; perceptions across the different scenarios presenting combinations of FoC and SP as described in Section 2.1.\u0026nbsp;Parallel analysis indicated that a two-factor solution was appropriate for all modality conditions in both samples. These factors were modality-specific acceptance factor 1: \u0026ldquo;perception of contributions to personal and social good\u0026rdquo; and modality-specific fear factor 2: \u0026ldquo;perceptions of ethical concerns and risks,\u0026rdquo; (Table 2).\u003c/p\u003e\n\u003cp\u003eTable 2. EFA Factor Loadings for Modalities in the UK Sample\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 123px;\"\u003e\n \u003cp\u003eNN Modality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 123px;\"\u003e\n \u003cp\u003eNY Modality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 123px;\"\u003e\n \u003cp\u003eYN Modality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 123px;\"\u003e\n \u003cp\u003eYY Modality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eEnhancing Wellbeing\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eFeeling pleasant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eRecommend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eTrust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003ePositive change\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eEthical implications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.788\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003ePerceived risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.759\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: \u003cstrong\u003eNN\u003c/strong\u003e- No FoC, No SP; \u003cstrong\u003eNY\u003c/strong\u003e- No FoC, Yes SP; \u003cstrong\u003eYN\u003c/strong\u003e- Yes FoC, No SP; \u003cstrong\u003eYY\u003c/strong\u003e- Yes FoC, Yes SP.\u003c/p\u003e\n\u003cp\u003eFor the UK sample, the two factors explained the following cumulative variance across modalities: NN (54.8% and 73.7%), NY (55.0% and 76.5%), YN (54.2% and 73.8%), and YY (55.2% and 73.0%). In the Arab sample, the factors accounted for: NN (54.2% and 69.2%), NY (51.3% and 67.1%), YN (49.8% and 65.3%), and YY (51.7% and 67.6%). This consistent pattern across all modality conditions indicates that the two-factor solution effectively captures the underlying structure of participants\u0026apos; responses, with the seven items reliably grouping into two distinct dimensions.\u003c/p\u003e\n\u003cp\u003eTable 3. EFA Factor Loadings for Modalities in the Arab Sample\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNN Modality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNY Modality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYN Modality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYY Modality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eEnhancing Wellbeing\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eFeeling pleasant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eRecommend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eTrust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003ePositive change\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eEthical implications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.741\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003ePerceived risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.735\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: \u003cstrong\u003eNN\u003c/strong\u003e- No FoC, No SP; \u003cstrong\u003eNY\u003c/strong\u003e- No FoC, Yes SP; \u003cstrong\u003eYN\u003c/strong\u003e- Yes FoC, No SP; \u003cstrong\u003eYY\u003c/strong\u003e- Yes FoC, Yes SP.\u003c/p\u003e\n\u003cp\u003eTable 4 presents the Means and standard deviations (SD) for the two EFA factors across different modalities for both the UK and Arab samples, revealing notable regional differences in AI perceptions. For Personal Social Good Perception, the UK sample reported mean scores ranging from 3.123 to 5.237, while the Arab sample had higher mean scores, ranging from 5.497 to 6.811. In terms of Ethical Concern Risk Perception, the UK sample exhibited higher mean scores across all modalities, ranging from 3.778 to 5.078, whereas the Arab sample reported slightly lower scores, ranging from 3.991 to 4.511.\u003c/p\u003e\n\u003cp\u003eTable 4. Means and SD for EFA Factors Across Different Modalities\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"434\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 232px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUK\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eM (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eArab\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eM (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e3.123 (1.804)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e5.497 (2.107)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e5.078 (1.834)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e4.511 (1.945)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNY\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e4.216 (1.826)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e6.055 (1.768)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e4.296 (1.872)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e4.393 (1.971)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e4.579 (1.743)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e6.454 (1.542)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e4.141 (1.803)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e3.991 (2.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYY\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e5.237 (1.789)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e6.811 (1.520)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e3.778 (1.825)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e3.859 (2.165)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003cbr\u003e\n\u003ch2\u003e\u003cem\u003e4.3.\u0026nbsp;Correlations between FoC x SP Modalities, Personality Traits, and Attitudes Factors\u003c/em\u003e\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eA Pearson\u0026rsquo;s and Spearman\u0026apos;s correlation analysis was conducted to examine relationships between attitudes toward AI (Factor 1 and Factor 2) across different modalities in both the UK and Arab samples (Table 5 and 6). In both UK and Arab samples, all factor 1 were positively correlated, as were all factor 2 variables. Factor 1 and factor 2 showed a strong inverse relationship, justifying the use of MMR for analysis.\u003c/p\u003e\n\u003cp\u003eAdditionally, relationships between personality traits, need for cognition (NFC), locus of control (LOC), and general attitudes toward AI (ATAI) are visually presented in Figures 3 and 4 (heatmaps). Figure 3\u0026apos;s heatmap reveals consistent UK sample patterns: agreeableness/openness positively linked to general ATAI acceptance versus neuroticism\u0026apos;s negative association. \u0026nbsp;The Arab sample (Figure 4) showed similar patterns: conscientiousness and agreeableness positively correlated with general ATAI acceptance, while neuroticism showed negative associations. Factor 1 consistently aligned with general ATAI acceptance and factor 2 with general ATAI fear across modalities. Internal LOC and NFC also demonstrated positive relationships with general ATAI acceptance. Both in the UK and Arab samples, modality-specific attitude towards AI factor 1 tracked general ATAI acceptance, while factor 2 aligned with general ATAI fear, supporting our theoretical model. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 5. Pearson\u0026rsquo;s Correlation Between Modality Specific Attitude Toward AI Factor 1 and 2 in UK Samples\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eNN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eNY\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eYN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eYY\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eNN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.47***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eNY\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.68***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.30***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.29***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.67***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.51***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eYN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.63***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.29***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.68***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.33***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.20***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.62***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.31***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.72***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.44***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eYY\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.46***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.21***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.71***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.38***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.79***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.39***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.14*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.57***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.35***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.71***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.37***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.79***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.44***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e(* p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eTable 6. Pearson\u0026rsquo;s Correlation Between Modality Specific Attitude Toward AI Factor 1 and 2 in Arab Samples\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eNN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eNY\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eYN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eYY\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eNN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.27***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eNY\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.81***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.26***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.21***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.72***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.34***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eYN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.61***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.29***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.63***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.28***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.68***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.17**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.73***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.34***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eYY\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.43***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.25***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.64***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.34***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.74***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.36***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.60***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.17**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.73***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.28***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.82***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.43***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e(* p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e4.4.\u0026nbsp;Comparing Modality-Specific Attitude to General AI Attitude\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA one-sample t-test was conducted to determine whether modality-specific attitude toward AI significantly differed from the general attitude toward AI mean in both the UK and Arab sample, which are shown in Table 7 and 8. For factor 1, the UK sample showed significantly higher modality-specific acceptance in the YY modality (t(315) = 2.31, p \u0026lt; 0.05, d = 0.13). In the Arab sample, modality-specific acceptance was significantly higher in the NN (t(322) = 2.07, p \u0026lt; 0.05, d = 0.12) and YN (t(322) = 2.16, p \u0026lt; 0.05, d = 0.12) modalities.\u003c/p\u003e\n\u003cp\u003eFor factor 2, the UK sample showed significantly lower modality-specific fear ratings in the NY, YN, and YY modalities, while the Arab sample showed significantly higher modality-specific fear ratings in these same modalities.\u003c/p\u003e\n\u003cp\u003eTable 7. One-Sample t-Tests Comparing Modality-Specific attitude toward AI Factor 1 to General attitude toward AI Acceptance in UK vs. Arab Samples\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eUK\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eArab\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003et (315)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eM(SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCohen\u0026rsquo;s d [95% CI]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003et (322)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eM(SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCohen\u0026rsquo;s d [95% CI]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNN Factor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.47 (0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.01 [-0.12, 0.10]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.07*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.56 (0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.12 [0.01, 0.23]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNY Factor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.39 (0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.09 [-0.02, 0.20]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.51 (0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.06 [-0.05, 0.17]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYN Factor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.41 (0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.06 [-0.05, 0.17]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.16*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.55 (0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.12 [0.01, 0.23]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYY Factor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.31*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.34 (0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.13 [0.02, 0.24]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.49 (0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.06 [-0.07, 0.15]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e* p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001\u003c/p\u003e\n\u003cp\u003eThe general ATAI acceptance mean served as the test value for each sample: M = -0.47 (SD = 0.96) for the UK and M = 0.46 (SD = 0.82) for the Arab sample.\u003c/p\u003e\n\u003cp\u003eTable 8. One-Sample t-Tests Comparing Modality-Specific attitude toward AI Factor 2 to General attitude toward AI Fear in UK vs. Arab Samples\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eUK\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eArab\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003et (315)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eM(SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCohen\u0026rsquo;s d [95% CI]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003et (322)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eM(SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCohen\u0026rsquo;s d [95% CI]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNN Factor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.13 (0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.09 [-0.20, 0.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.16 (1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.05 [-0.06, 0.16]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNY Factor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-4.89***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.05 (0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.28 [-0.39, -0.16]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.74***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00 (1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.21 [0.10, 0.32]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYN Factor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-3.80***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02 (0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.21 [-0.33, -0.10]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.59**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.06 (1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.14 [0.03, 0.25]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYY Factor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-5.08***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.04 (0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.29 [-0.40, -0.17]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.46***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.00 (1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.06 [0.08, 0.30]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e* p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001\u003c/p\u003e\n\u003cp\u003eThe general ATAI fear mean served as the test value for each sample: M = 0.22 (SD = 0.98) for the UK and M = -0.21 (SD = 0.98) for the Arab sample.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e4.5.\u0026nbsp;Examining Personality traits, NFC, and LOC as Predictors of Attitude Factors\u003c/em\u003e\u003c/strong\u003e \u003cstrong\u003e\u003cem\u003eAcross\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eFoC x SP\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eModalities\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe tested the predictive power of personality traits, NFC, and LOC for factor 1: \u0026ldquo;perception of contributions to personal and social good\u0026rdquo; and factor 2: \u0026ldquo;perceptions of ethical concerns and risks\u0026rdquo; in the both UK and Arab sample using hierarchical MMR, which are shown in Table 2S, 3S, 4S and 5S (Appendix B). The results presented in these tables, summarized in Table 9, provide a detailed overview of the psychological factors influencing attitude factors, such as acceptance and fear, in both UK and Arab samples.\u003c/p\u003e\n\u003cp\u003eTable 9. Summary of Psychological Predictors of Attitude Factors Across Modalities and Cultural Samples.\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCultural Sample\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFactor 1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(personal and social good)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFactor 2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(ethical concerns and risks)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003eNN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eUK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003e\u0026middot; Agreeableness showed positive effect. (\u0026beta; = 0.10, t = 2.30*)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026middot; Internal LOC showed positive association with fear. (\u0026beta; = 0.20, t = 2.99*)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eArab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003e\u0026middot; Agreeableness positively predicted acceptance. (\u0026beta;= 0.12, t = 2.02*)\u003c/p\u003e\n \u003cp\u003e\u0026middot; Neuroticism negatively predicted acceptance. (\u0026beta; = -0.20, t = -3.05**)\u003c/p\u003e\n \u003cp\u003e\u0026middot; Internal LOC showed a strong positive effect. (\u0026beta; = 0.20, t = 3.67***)\u003c/p\u003e\n \u003cp\u003e\u0026middot; External LOC showed a positive effect. (\u0026beta; = 0.28, t = 2.87**)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026middot; Neuroticism positively predicted. (\u0026beta; = 0.06, t = 2.17*)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003eNY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eUK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003e\u0026middot; Agreeableness showed a strong positive effect. (\u0026beta; = 0.16, t = 3.36***)\u003c/p\u003e\n \u003cp\u003e\u0026middot; Openness also positively predicted acceptance. ((\u0026beta; = 0.01, t = 2.21*)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026middot; Internal LOC positively predicted fear. (\u0026beta; = 0.14, t = 2.11*)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eArab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003e\u0026middot; External LOC positively predicted acceptance. (\u0026beta; = 0.16, t = 3.26**)\u003c/p\u003e\n \u003cp\u003e\u0026middot; Internal LOC strong positively predicted acceptance. (\u0026beta; = 0.44, t = 4.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026middot; Extraversion positively predicted fear. (\u0026beta; = 0.16, t = 2.11*)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003eYN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eUK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003e\u0026middot; Agreeableness positively predicted acceptance. (\u0026beta; = 0.15, t = 3.01**)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026middot; No significant predictors for fear.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eArab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003e\u0026middot; Internal LOC positively predicted acceptance. (\u0026beta; = 0.23, t = 2.65**)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026middot; No significant predictors for fear.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003eYY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eUK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003e\u0026middot; Agreeableness positively predicted acceptance. (\u0026beta; = 0.17, t = 3.21**)\u003c/p\u003e\n \u003cp\u003e\u0026middot; Openness also positively predicted acceptance. (\u0026beta; = 0.10, t = 2.12*)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026middot; No significant predictors for fear.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eArab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003e\u0026middot; NFC positively predicted acceptance. (\u0026beta; = 0.10, t = 1.98*)\u003c/p\u003e\n \u003cp\u003e\u0026middot; Internal LOC positively predicted acceptance. (\u0026beta; = 0.22, t = 2.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026middot; No significant predictors for fear.\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(* p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001).\u003c/p\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThis study aimed to investigate how individual differences, particularly personality traits, cognitive styles, and locus of control, influence AI attitudes, with a focus on how these effects vary across different modalities of AI involvement. We examined psychological predictors of AI attitudes in parallel UK and Arab samples, identifying culture-specific patterns in how traits and cognitive factors relate to two factors (a) perceptions of AI's contributions to personal and societal well-being and (b) concerns about its ethical implications and potential risks. Additionally, we aimed to extend the research by comparing findings with Babiker\u0026rsquo;s study [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] on general attitudes toward AI and proposing a model to examine how personal factors and others' cognitive engagement correlate with attitudes toward AI in both general and modality-specific contexts.\u003c/p\u003e \u003cp\u003eThe analysis reveals, agreeableness and openness to experience (UK) or agreeableness and conscientiousness (Arab) were positively associated with general ATAI acceptance, suggesting that individuals who are cooperative, open to novelty, or goal-oriented tend to view AI more favourably. This is consistent with prior research indicating that people with higher agreeableness are more likely to view AI positively [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Conversely, neuroticism was negatively associated with general ATAI acceptance in both samples, and positively associated with modality-specific fear of AI for the Arab samples, indicating that individuals prone to anxiety or emotional instability may perceive AI as threatening or untrustworthy. Previous research showed that neuroticism was associated with negative emotions toward AI and higher perceived sociality, suggesting emotionally unstable individuals view AI as both threatening and socially capable [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This aligns with cross-cultural evidence showing neuroticism's consistent positive association with fear of AI in both German and Chinese samples, though with small effect sizes [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Additionally, the positive associations of NFC and internal LOC with general ATAI acceptance, particularly in the Arab sample, suggest that individuals who enjoy thinking deeply and believe in personal agency are more likely to engage with and accept AI technologies. This may reflect a proactive, curiosity-driven orientation toward AI as a tool for enhancing decision-making rather than replacing it. This aligns with prior findings that NFC plays a moderating role in the intention to use Web 2.0-based learning tools Personal Learning Environment approach, influencing how students engage with Web 2.0 tools for academic purposes [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe analysis provides further insight into how perceptions of AI differ across specific modality contexts (combinations of FoC and SP) compared to general ATAI. In the UK sample, significant differences for factor 1 (modality-specific AI acceptance) were observed only in the YY modality (where both FoC and SP were present). These results indicate that UK participants showed greater AI acceptance when both autonomy and social endorsement were present, suggesting perceived agency and peer validation jointly contribute to positive AI attitudes. These findings align with broader evidence that autonomy in technology use is consistently associated with more positive and less negative AI attitudes across European samples, demonstrating its important role in AI acceptance [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. In contrast, the Arab sample showed significant increases in factor 1 for the NN and YN modalities. This could reflect greater receptiveness to AI\u0026rsquo;s benefits even when SP is absent, regardless of whether freedom of choice was available. It may also point to cultural differences in how autonomy and conformity influence technology perception, with Arab participants potentially placing more trust in authority or functionality over peer influence, a pattern consistent with the UAE COVID-19 study, where government communications were both widely consulted and highly trusted, surpassing peer/family influence in driving protective behavior adoption [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor factor 2 (modality-specific AI fear), significant differences were found in all modalities except NN in both samples. This indicates that perceived ethical concerns and risks are more salient when AI is socially endorsed or presented as a choice. Particularly in the UK, where participants showed lower fear in modalities, it suggests social comparison can modulate coping with fear, as demonstrated in a virtual reality study where upward assimilation led to reduced anxiety and fewer phobic symptoms [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Conversely, Arab participants showed increased fear across modalities, suggesting particular sensitivity to societal implications and ethical concerns when AI adoption becomes more visible.\u003c/p\u003e \u003cp\u003eIn the UK sample, agreeableness consistently predicted higher scores on factor 1 across all modalities, suggesting that individuals who value cooperation and social harmony are more likely to perceive AI as contributing positively, regardless of whether FoC or SP is present. Prior research indicates agreeable individuals tend to support stricter AI regulation, likely reflecting their prosocial concern for fairness and protection [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. This pattern suggests individuals higher in agreeableness may consistently prioritize fairness and protection in technology governance. Additionally, openness emerged as a predictor in modalities that involved SP (NY, YY), aligning with the idea that open individuals are more receptive to new technologies, especially when endorsed by others. Partially aligning with our findings, Babiker et al. [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] found that agreeableness predicted more positive attitudes toward AI in a UK sample, although openness did not significantly predict AI attitudes. Their use of a general ATAI may suggest that the influence of openness could be context-dependent, emerging when social proof is present, as observed in our SP modalities. Further illustrating the context-dependent role of openness, Grassini and Koivisto [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], also using a UK sample, reported that openness predicted greater liking of AI-generated artworks, indicating a positive reception of AI-driven application.\u003c/p\u003e \u003cp\u003eFor Factor 2, NFC predicted increased concern only in the NN modality, possibly indicating that individuals who enjoy cognitive engagement become more skeptical when AI is imposed without choice or peer validation. Building on previous findings that high-NFC individuals resist misinformation through critical analysis (memory studies) [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], the current results similarly show their cognitive engagement increases skepticism toward AI when imposed without FoC or SP. However, this effect was diminished once LOC was included, with internal LOC becoming a significant predictor, particularly in NN and NY modalities. This suggests that people who believe they control their own outcomes are more sensitive to the ethical implications of AI, especially when freedom is limited, even in the presence of social pressure. This aligns with findings from aviation safety, where internal LOC correlated strongly with proactive risk mitigation (e.g., higher scores in civil/transport pilots) and was linked to education/service length, reinforcing its role as a cross-domain stabilizer of agency-driven decision-making [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the Arab sample, agreeableness also positively predicted Factor 1 in the NN modality, while NFC was significant in the YY modality, where both FoC and SP were available. This pattern implies that cognitively engaged individuals in Arab cultures are most likely to embrace AI when it is both optional and widely accepted, suggesting a preference for thoughtful autonomy and social validation. Moreover, neuroticism negatively predicted Factor 1 in multiple modalities, aligning with the tendency of emotionally reactive individuals to view AI less favorably, a pattern consistent with pandemic findings where neuroticism amplified negative affect, crisis preoccupation, and emotional reactivity, suggesting this trait\u0026rsquo;s broad role in shaping threat perception across technological and health domains [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Moreover, Internal LOC was a significant predictor across all modalities, showing that individuals with a strong sense of personal control are more likely to perceive AI as beneficial. This aligns directly with prior findings that internal locus of control and perceived XAI availability jointly predicted greater AI acceptance [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. External LOC was particularly influential in the NN and NY modalities, suggesting that reliance on external circumstances can shape positive evaluations of AI, especially when freedom is constrained.\u003c/p\u003e \u003cp\u003eFor Factor 2, neuroticism was a significant positive predictor of perceived ethical risks in the NN modality, consistent with higher sensitivity to AI threats in low-choice, low-social validation contexts. However, its influence diminished in other modalities after accounting for NFC and LOC. Extraversion was positively associated with ethical concern in the NY modality, suggesting that socially engaged individuals may be more attuned to AI\u0026rsquo;s societal implications when its usage is peer-endorsed, a pattern distinct from extraversion\u0026rsquo;s typical role in enhancing general social functioning in college students [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], revealing how this trait\u0026rsquo;s effects shift from social adaptation to critical evaluation in technology-governed contexts.\u003c/p\u003e \u003cp\u003eOur findings suggest that modalities do influence the strength of personality traits\u0026rsquo; effects on attitudes toward AI, although the overall effect sizes (η\u0026sup2;p\u0026thinsp;\u0026lt;\u0026thinsp;0.06) were generally small. As seen in previous studies [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], the effects of personality traits on AI attitudes were similarly weak. We summarized the relevant findings from [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] in Appendix D to facilitate a clearer evaluation alongside our results. These variations indicate that contextual factors (modalities) moderate how personality traits influence AI perceptions. That is, the same trait may have a greater or lesser impact on AI attitudes depending on whether individuals feel autonomous or socially supported in the decision context. This perspective aligns with the IMPACT model proposed by Montag et al. [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], which emphasizes the interplay between individual characteristics, modality, context, cultural/country-level influences, and transparency as critical components in shaping public attitudes toward AI. These results further support the idea that cultural context moderates the impact of modalities on AI attitudes. In line with prior research [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], autonomy had a stronger influence on ethical concerns in the UK, reflecting Western individualistic values, while both autonomy and SP shaped perceptions in the Arab sample, consistent with collectivist orientations and the role of social validation.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e5.1. Practical Implications\u003c/h2\u003e \u003cp\u003eThe findings of this study may inform developers, policymakers, and educators working toward responsible and culturally sensitive AI deployment. First, the consistent impact of agreeableness and internal LOC on AI acceptance suggests that messaging strategies should emphasize collaborative benefits and personal empowerment. A real-world application of this approach can be seen in Google\u0026rsquo;s \u0026lsquo;AI for Social Good\u0026rsquo; initiative [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], which frames AI as a collaborative force for solving global challenges while also giving users control over AI-assisted tools like \u0026lsquo;Help Me Write\u0026rsquo; in Google Docs [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Similarly, Microsoft\u0026rsquo;s \u0026lsquo;AI for Accessibility\u0026rsquo; program emphasizes empowerment by developing assistive technologies (e.g., Seeing AI for the visually impaired) that enhance independence while fostering inclusivity [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. By highlighting societal benefits alongside user control, these strategies effectively increase AI acceptance among individuals who prioritize harmony and self-direction.\u003c/p\u003e \u003cp\u003eSecond, the observed associations between neuroticism, external LOC, and heightened AI concerns (particularly in NN modality) suggest these psychological factors may influence risk perception. Such findings could inform communication strategies for addressing AI apprehensions in specific user groups. For example, IBM\u0026rsquo;s \"Explainable AI\" (XAI) framework provides clear, interpretable explanations for AI decisions (e.g., in loan approvals [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]), directly addressing transparency needs for anxious users [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Similarly, the EU\u0026rsquo;s General Data Protection Regulation (GDPR) mandates \"right to explanation\" clauses and human oversight options in automated systems, offering opt-out mechanisms to reinforce perceived control [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. These approaches demonstrate how ethical safeguards and user-centric design can mitigate fears among emotionally sensitive or externally oriented individuals, particularly in high-stakes domains.\u003c/p\u003e \u003cp\u003eThird, the study highlights the critical importance of modality design, specifically FoC and SP, in shaping public attitudes toward AI. Developers and organizations should design AI interfaces that integrate choice, such as toggling between chatbots and human agents, to enhance acceptance. Incorporating SP elements such as real-time feedback or success stories can reinforce positive AI perceptions, particularly among users sensitive to peer influence [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Policymakers, particularly in regions with evolving AI regulations like the Arab GCC, should enforce standards that guarantee autonomy in AI interactions. Aligning with frameworks such as the EU\u0026rsquo;s Ethics Guidelines for Trustworthy AI [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e], regulations should require alternative interaction modes to foster both trust and ethical engagement. Moreover, while promoting autonomy, caution is necessary, as increased perceived control may inadvertently lead users to over-disclose sensitive information [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFourth, the observed cultural differences underscore the importance of tailoring AI communication and policy frameworks to regional values and cognitive styles. In the UK, trust and acceptance increased significantly under conditions of high FoC and strong SP, indicating that British users are more responsive to peer influence and autonomy-supportive environments. For example, the NHS\u0026rsquo;s AI adoption strategy leverages clinician testimonials and opt-in pilot programs, which align with these preferences [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. In contrast, Arab participants exhibited higher AI acceptance even in conditions lacking choice or social validation, highlighting a potentially greater reliance on perceived utility and institutional trust. This aligns with initiatives like the UAE\u0026rsquo;s National AI Strategy 2031 [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e], which centrally promotes AI adoption through government-backed projects (e.g., smart city deployments in Dubai) without emphasizing individual choice. These differences indicate the need for localized engagement strategies, such as government-led AI endorsements in Arab countries and peer-driven adoption campaigns in Western contexts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e5.2. Limitations\u003c/h2\u003e \u003cp\u003eBy investigating links between personality traits, contextual modalities and AI attitudes, this study provides data that may inform future research, though several limitations should be acknowledged. Firstly, the cross-sectional design restricts our ability to draw causal conclusions. While personality traits and cognitive variables showed measurable associations with AI attitudes in our analyses, causal interpretation demands longitudinal or experimental evidence to account for potential confounding and temporal effects. Secondly, the use of self-report measures to assess personality traits, LOC, NFC, and AI attitudes may introduce biases, including social desirability effects and inaccurate self-assessment. Despite implementing mitigation strategies, such as ensuring participant anonymity, providing clear definitions, visual aids, and face validation, some degree of response bias may still persist. Future studies should consider incorporating objective or behavioral assessments to complement self-reported data.\u003c/p\u003e \u003cp\u003eAdditionally, the study\u0026rsquo;s cultural scope was limited to participants from the UK and Arab countries. While this comparison offers valuable insights into individualistic versus collectivist cultural influences, the generalizability of findings to other global regions remains limited. Broader cross-cultural investigations are necessary to validate and expand these observations. The vignette scenarios used in this study were based on customer service chatbots, selected to ensure familiarity and reduce the confounding influence of domain criticality. However, this focus may limit the ecological validity of findings when applied to more sensitive or high-stakes applications of AI, such as medical diagnostics or autonomous vehicles. Despite visual aids and face validation processes, participants may still have varied in their interpretations of scenario realism or perceived autonomy, which could affect the consistency of responses.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusion and Future work","content":"\u003cp\u003eThis study provides novel insights into how personality traits, NFC, and LOC influence public attitude towards AI across modalities (FoC and SP), with evidence from UK and Arab participants. Using hierarchical MMR, we identified that agreeableness consistently predicted modality specific attitude towards AI acceptance across both cultural contexts, while neuroticism was associated with lower positive attitudes and greater fear and ethical concerns in the Arab sample under the NN modality. Additionally, internal LOC and higher NFC were positively related to modality specific attitude towards AI acceptance, suggesting that individuals who perceive personal control and engage in deeper cognitive processing tend to view AI more favorably and thoughtfully.\u003c/p\u003e \u003cp\u003eThe analysis also revealed cultural distinctions in how these traits operate. For example, conscientiousness played a role in the Arab sample, while openness showed more relevance in the UK. Together, these findings highlight how individual psychological differences shape attitudes toward AI across cultural contexts, emphasizing the importance of considering psychological and cultural diversity in AI acceptance research. By examining both acceptance and fear dimensions of AI attitudes, the findings contribute to a more comprehensive understanding of how people engage with AI technologies.\u003c/p\u003e \u003cp\u003eFuture research should explore how these psychological traits interact with different AI application domains, such as healthcare or education, to determine whether the patterns observed here generalize across sectors. Studies could also incorporate longitudinal or experimental approaches to better understand how attitudes develop over time or in response to direct AI interaction. Furthermore, expanding the cultural scope beyond the UK and Arab would enhance the generalizability of these findings and allow for a more global perspective on AI acceptance. Finally, the integration of psychological profiling into AI design holds promising potential for developing human-centered, culturally adaptive AI systems that better align with public values and expectations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOpen Access funding is provided by the Qatar National Library. This publication was supported by NPRP 14 Cluster grant number NPRP 14C-0916\u0026ndash;210015 from the Qatar National Research Fund (a member of Qatar Foundation). The findings herein reflect the work and are solely the responsibility of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest in this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants provided informed consent prior to participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the ethical standards set forth in the Declaration of Helsinki and was approved by the Institutional Review Board (IRB) of the fifth author (ID: IRB-2024-59). Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e: Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConceptualization\u003c/strong\u003e: Mohammad Mominur Rahman, Raian Ali, Ala Yankouskaya, Sameha AlShakhsi and Areej Babiker; \u003cstrong\u003eData Curation\u003c/strong\u003e: Sameha AlShakhsi, Areej Babiker, Raian Ali. \u003cstrong\u003eMethodology\u003c/strong\u003e: Mohammad Mominur Rahman, Ala Yankouskaya, Sameha AlShakhsi, Areej Babiker, and Raian Ali; \u003cstrong\u003eFormal analysis and investigation\u003c/strong\u003e: Mohammad Mominur Rahman; \u003cstrong\u003eValidation\u003c/strong\u003e: Ala Yankouskaya, Sameh a AlShakhsi, Areej Babiker\u003cstrong\u003e; Writing - original draft preparation\u003c/strong\u003e: Mohammad Mominur Rahman; \u003cstrong\u003eWriting - review and editing\u003c/strong\u003e: Sameha AlShakhsi, Areej Babiker, Ala Yankouskaya, and Raian Ali; \u003cstrong\u003eSupervision\u003c/strong\u003e: Ala Yankouskaya and Raian Ali.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study design, the dataset, and the data dictionary can be found at the following Open Science Framework link: [https://osf.io/y24c6/?view_only=2eeb3fa17a1247f882418fe4a3eaaf50 ] and the Main research project: [https://osf.io/7ydwf/?view_only=f275ae745d334fc08c11243efb992140].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWang, W., Siau, K.: Artificial intelligence, machine learning, automation, robotics, future of work and future of humanity: A review and research agenda. J. Database Manage. (JDM). \u003cb\u003e30\u003c/b\u003e(1), 61\u0026ndash;79 (2019)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCosta, P.T. Jr., McCrae, R.R.: The five-factor model of personality and its relevance to personality disorders. J. Pers. Disord. \u003cb\u003e6\u003c/b\u003e(4), 343\u0026ndash;359 (1992)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNov, O., Ye, C.: Personality and technology acceptance: Personal innovativeness in IT, openness and resistance to change, in \u003cem\u003eProceedings of the 41st annual Hawaii international conference on system sciences (HICSS\u003c/em\u003e IEEE, 2008, p. 448. (2008)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSleboda, C.L.P.: The inverse relation between risks and benefits The impact of individual differences in information processing style. PLoS One, (2021)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBabiker, A., Alshakhsi, S., Al-Thani, D., Montag, C., Ali, R.: Attitude Towards AI: Potential Influence of Conspiracy Belief, XAI Experience and Locus of Control. Int. J. Hum. Comput. Interact. 1\u0026ndash;13 (2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/10447318.2024.2401249\u003c/span\u003e\u003cspan address=\"10.1080/10447318.2024.2401249\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlshakhsi, S., et al.: Designing AI to foster acceptance: do freedom to choose and social proof impact AI attitudes among British and Arab populations? Behav. Inform. Technol., pp. 1\u0026ndash;19, (2025)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo, X., Tong, S., Fang, Z., Qu, Z.: Frontiers: Machines vs. humans: The impact of artificial intelligence chatbot disclosure on customer purchases. Mark. Sci. \u003cb\u003e38\u003c/b\u003e(6), 937\u0026ndash;947 (2019)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJuravle, G., Boudouraki, A., Terziyska, M., Rezlescu, C.: Trust in artificial intelligence for medical diagnoses. Prog Brain Res. \u003cb\u003e253\u003c/b\u003e, 263\u0026ndash;282 (2020)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCialdini, R.B., Cialdini, R.B.: \u003cem\u003eInfluence: The psychology of persuasion\u003c/em\u003e, vol. 55. Collins New York, (2007)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, Y., Meister, D.B., Gray, P.H.: Social influence and knowledge management systems use: Evidence from panel data. MIS Q., pp. 299\u0026ndash;313, (2013)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahman, M.M., Babiker, A., Ali, R.: Motivation, Concerns, and Attitudes Towards AI: Differences by Gender, Age, and Culture, in \u003cem\u003eInternational Conference on Web Information Systems Engineering\u003c/em\u003e, Springer, pp. 375\u0026ndash;391. (2024)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRyan, R.M., Deci, E.L.: Self-determination theory: Basic psychological needs in motivation, development, and wellness. Guilford (2017)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, Y., Meister, D.B., Gray, P.H.: Social influence and knowledge management systems use: Evidence from panel data. MIS Q., pp. 299\u0026ndash;313, (2013)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim, H.-W., Gupta, S.: A user empowerment approach to information systems infusion. IEEE Trans. Eng. Manag. \u003cb\u003e61\u003c/b\u003e(4), 656\u0026ndash;668 (2014)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalakrishnan, J., Dwivedi, Y.K.: Role of cognitive absorption in building user trust and experience. Psychol. Mark. \u003cb\u003e38\u003c/b\u003e(4), 643\u0026ndash;668 (2021)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJuravle, G., Boudouraki, A., Terziyska, M., Rezlescu, C.: Trust in artificial intelligence for medical diagnoses. Prog Brain Res. \u003cb\u003e253\u003c/b\u003e, 263\u0026ndash;282 (2020)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFasolo, B., Misuraca, R., Reutskaja, E.: Choose as much as you wish: Freedom cues in the marketplace help consumers feel more satisfied with what they choose and improve customer experience. J. Exp. Psychol. Appl. \u003cb\u003e30\u003c/b\u003e(1), 156 (2024)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStaples, D.S., Seddon, P.: Testing the technology-to-performance chain model. J. Organizational End. User Comput. (JOEUC). \u003cb\u003e16\u003c/b\u003e(4), 17\u0026ndash;36 (2004)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBandura, A., Walters, R.H.: Social learning theory, vol. 1. Prentice hall Englewood Cliffs, NJ (1977)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsch, S.: Effects of Group Preassure the Modification and Distortion of Judgment. Groups Leadersh. Men, (1951)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChi, O.H., Gursoy, D., Chi, C.G.: Tourists\u0026rsquo; attitudes toward the use of artificially intelligent (AI) devices in tourism service delivery: moderating role of service value seeking. J. Travel Res. \u003cb\u003e61\u003c/b\u003e(1), 170\u0026ndash;185 (2022)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGursoy, D., Chi, O.H., Lu, L., Nunkoo, R.: Consumers acceptance of artificially intelligent (AI) device use in service delivery. Int. J. Inf. Manage. \u003cb\u003e49\u003c/b\u003e, 157\u0026ndash;169 (2019)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVenkatesh, V., Davis, F.D.: A theoretical extension of the technology acceptance model: Four longitudinal field studies. Manage. Sci. \u003cb\u003e46\u003c/b\u003e(2), 186\u0026ndash;204 (2000)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVenkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D.: User acceptance of information technology: Toward a unified view. MIS Q., pp. 425\u0026ndash;478, (2003)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBergdahl, J., et al.: Self-determination and attitudes toward artificial intelligence: Cross-national and longitudinal perspectives. Telematics Inform. \u003cb\u003e82\u003c/b\u003e, 102013 (2023)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrassini, S., Koivisto, M.: Understanding how personality traits, experiences, and attitudes shape negative bias toward AI-generated artworks. Sci. Rep. \u003cb\u003e14\u003c/b\u003e(1), 4113 (2024)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarnett, T., Pearson, A.W., Pearson, R., Kellermanns, F.W.: Five-factor model personality traits as predictors of perceived and actual usage of technology, \u003cem\u003eEuropean Journal of Information Systems\u003c/em\u003e, vol. 24, no. 4, pp. 374\u0026ndash;390, Jul. (2015). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1057/ejis.2014.10\u003c/span\u003e\u003cspan address=\"10.1057/ejis.2014.10\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark, J., Woo, S.E.: Who likes artificial intelligence? Personality predictors of attitudes toward artificial intelligence. J. Psychol. \u003cb\u003e156\u003c/b\u003e(1), 68\u0026ndash;94 (2022)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStein, J.-P., Messingschlager, T., Gnambs, T., Hutmacher, F., Appel, M.: Attitudes towards AI: measurement and associations with personality. Sci. Rep. \u003cb\u003e14\u003c/b\u003e(1), 2909 (2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-024-53335-2\u003c/span\u003e\u003cspan address=\"10.1038/s41598-024-53335-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaya, F., Aydin, F., Schepman, A., Rodway, P., Yetişensoy, O., Demir Kaya, M.: The roles of personality traits, AI anxiety, and demographic factors in attitudes toward artificial intelligence. Int. J. Hum. Comput. Interact. \u003cb\u003e40\u003c/b\u003e(2), 497\u0026ndash;514 (2024)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchepman, A., Rodway, P.: Initial validation of the general attitudes towards Artificial Intelligence Scale. Computers Hum. Behav. Rep. \u003cb\u003e1\u003c/b\u003e, 100014 (2020)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSindermann, C., et al.: Acceptance and Fear of Artificial Intelligence: associations with personality in a German and a Chinese sample. Discover Psychol. \u003cb\u003e2\u003c/b\u003e(1), 8 (2022)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSindermann, C., et al.: Assessing the attitude towards artificial intelligence: Introduction of a short measure in German, Chinese, and English language. KI-K\u0026uuml;nstliche intelligenz. \u003cb\u003e35\u003c/b\u003e(1), 109\u0026ndash;118 (2021)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrassini, S.: Development and validation of the AI attitude scale (AIAS-4): a brief measure of general attitude toward artificial intelligence. Front. Psychol. \u003cb\u003e14\u003c/b\u003e, 1191628 (2023)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark, J., Woo, S.E., Kim, J.: Attitudes towards artificial intelligence at work: Scale development and validation. J. Occup. Organ. Psychol. \u003cb\u003e97\u003c/b\u003e(3), 920\u0026ndash;951 (2024)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrislin, R.W.: Back-translation for cross-cultural research. J. Cross Cult. Psychol. \u003cb\u003e1\u003c/b\u003e(3), 185\u0026ndash;216 (1970). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/135910457000100301\u003c/span\u003e\u003cspan address=\"10.1177/135910457000100301\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQu, W., Sun, H., Ge, Y.: The effects of trait anxiety and the big five personality traits on self-driving car acceptance. Transp. (Amst). \u003cb\u003e48\u003c/b\u003e(5), 2663\u0026ndash;2679 (2021)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiedl, R.: Is trust in artificial intelligence systems related to user personality? Review of empirical evidence and future research directions. Electron. Markets. \u003cb\u003e32\u003c/b\u003e(4), 2021\u0026ndash;2051 (2022)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBartneck, C., Yogeeswaran, K., Sibley, C.G.: Personality and demographic correlates of support for regulating artificial intelligence. AI Ethics. \u003cb\u003e4\u003c/b\u003e(2), 419\u0026ndash;426 (2024)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCacioppo, J.T., Petty, R.E., Feng Kao, C.: The efficient assessment of need for cognition. J. Pers. Assess. \u003cb\u003e48\u003c/b\u003e(3), 306\u0026ndash;307 (1984)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHalttu, K., Oinas-Kukkonen, H.: need for cognition among users of self-monitoring systems for physical activity: survey study. JMIR Form. Res. \u003cb\u003e5\u003c/b\u003e(10), e23968 (2021)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaffesse, A.S., Tadesse, F.: Pathways less explored\u0026mdash;Locus of control and technology adoption, \u003cem\u003eJ Afr Econ\u003c/em\u003e, vol. 26, no. suppl_1, pp. i36\u0026ndash;i72, (2017)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaski, G., MOBILE panel Sample and ONLINE, Surveys, T.G.M., Research: Accessed: Jan. 09, 2024. [Online]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tgmresearch.com/\u003c/span\u003e\u003cspan address=\"https://tgmresearch.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePi\u0026ccedil;arra, N., Giger, J.-C.: Predicting intention to work with social robots at anticipation stage: Assessing the role of behavioral desire and anticipated emotions. Comput. Hum. Behav. \u003cb\u003e86\u003c/b\u003e, 129\u0026ndash;146 (2018). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.chb.2018.04.026\u003c/span\u003e\u003cspan address=\"10.1016/j.chb.2018.04.026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlexander, C.S., Becker, H.J.: The use of vignettes in survey research. Public. Opin. Q. \u003cb\u003e42\u003c/b\u003e(1), 93\u0026ndash;104 (1978)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteiner, P.M., Atzm\u0026uuml;ller, C., Su, D.: Designing valid and reliable vignette experiments for survey research: A case study on the fair gender income gap. J. Methods Meas. Soc. Sci. \u003cb\u003e7\u003c/b\u003e(2), 52\u0026ndash;94 (2016)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAtzm\u0026uuml;ller, C., Steiner, P.M.: Experimental vignette studies in survey research. Methodology, (2010)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRammstedt, B., John, O.P.: Measuring personality in one minute or less: A 10-item short version of the Big Five Inventory in English and German. J. Res. Pers. \u003cb\u003e41\u003c/b\u003e(1), 203\u0026ndash;212 (2007)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCosta Mastrascusa, R., et al.: Evaluating the complete (44-item), short (20-item) and ultra-short (10-item) versions of the Big Five Inventory (BFI) in the Brazilian population. Sci. Rep. \u003cb\u003e13\u003c/b\u003e(1), 7372 (2023)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNie\u0026szlig;en, D., Schmidt, I., Groskurth, K., Rammstedt, B., Lechner, C.M.: The Internal\u0026ndash;External Locus of Control Short Scale\u0026ndash;4 (IE-4): a comprehensive validation of the English-language adaptation. PLoS One. \u003cb\u003e17\u003c/b\u003e(7), e0271289 (2022)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBartlett, M.S.: A note on the multiplying factors for various χ 2 approximations. J. Royal Stat. Soc. Ser. B (Methodological), pp. 296\u0026ndash;298, (1954)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaiser, H.F.: An index of factorial simplicity. Psychometrika. \u003cb\u003e39\u003c/b\u003e(1), 31\u0026ndash;36 (1974)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRichardson, D.B., Hamra, G.B., MacLehose, R.F., Cole, S.R., Chu, H.: Hierarchical regression for analyses of multiple outcomes. Am. J. Epidemiol. \u003cb\u003e182\u003c/b\u003e(5), 459\u0026ndash;467 (2015)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBabiker, A., Alshakhsi, S., Supti, T.I., Ali, R., Do Personality Traits Impact the Attitudes Towards Artificial Intelligence? in: \u003cem\u003e11th International Conference on Behavioural and Social\u003c/em\u003e Computing \u003cem\u003e(BESC)\u003c/em\u003e, IEEE, 2024, pp. 1\u0026ndash;8. (2024)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003edel Barrio-Garc\u0026iacute;a, S., Arquero, J.L., Romero-Fr\u0026iacute;as, E.: Personal learning environments acceptance model: The role of need for cognition, e-learning satisfaction and students\u0026rsquo; perceptions. J. Educ. Techno Soc. \u003cb\u003e18\u003c/b\u003e(3), 129\u0026ndash;141 (2015)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBergdahl, J., et al.: Self-determination and attitudes toward artificial intelligence: Cross-national and longitudinal perspectives. Telematics Inform. \u003cb\u003e82\u003c/b\u003e, 102013 (2023)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFigueiras, M.J., Ghorayeb, J., Coutinho, M.V.C., Mar\u0026ocirc;co, J., Thomas, J.: Levels of trust in information sources as a predictor of protective health behaviors during COVID-19 pandemic: a UAE cross-sectional study. Front. Psychol. \u003cb\u003e12\u003c/b\u003e, 633550 (2021)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKampmann, I.L., Meyer, T., Morina, N.: Social comparison modulates coping with fear in virtual environments. J. Anxiety Disord. \u003cb\u003e72\u003c/b\u003e, 102226 (2020)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeding, J.K., Antonio, L.: Need for cognition and discrepancy detection in the misinformation effect. J. Cogn. Psychol. \u003cb\u003e31\u003c/b\u003e(4), 409\u0026ndash;415 (2019)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJoseph, C., Ganesh, A.: Aviation safety locus of control in Indian aviators. Indian J. Aerosp. Med. \u003cb\u003e50\u003c/b\u003e(1), 14\u0026ndash;21 (2006)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKroencke, L., Geukes, K., Utesch, T., Kuper, N., Back, M.D.: Neuroticism and emotional risk during the COVID-19 pandemic. J. Res. Pers. \u003cb\u003e89\u003c/b\u003e, 104038 (2020)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeard, J.B.: Identifying predictors of social functioning in college students: A Meta-analysis. University of Louisville (2011)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMontag, C., Nakov, P., Ali, R.: Considering the IMPACT framework to understand the AI-well-being-complex from an interdisciplinary perspective. Telematics Inf. Rep. \u003cb\u003e13\u003c/b\u003e, 100112 (2024)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJeff Dean, Fuller, J., AI for Social Good:. Accessed: Apr. 10, 2025. [Online]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://blog.google/outreach-initiatives/google-org/ai-social-good/\u003c/span\u003e\u003cspan address=\"https://blog.google/outreach-initiatives/google-org/ai-social-good/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFelt, A.P.: Chrome\u0026rsquo;s new AI feature can help you write on the web. Accessed: Apr. 10, 2025. [Online]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://blog.google/products/chrome/google-chrome-ai-help-me-write/\u003c/span\u003e\u003cspan address=\"https://blog.google/products/chrome/google-chrome-ai-help-me-write/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAI for Accessibility: Accessed: Apr. 10, 2025. [Online]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.microsoft.com/en-us/accessibility/innovation\u003c/span\u003e\u003cspan address=\"https://www.microsoft.com/en-us/accessibility/innovation\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArya, V., et al.: Ai explainability 360 toolkit, in \u003cem\u003eProceedings of the 3rd ACM India joint international conference on data science \u0026amp; management of data (8th ACM IKDD CODS \u0026amp; 26th COMAD)\u003c/em\u003e, pp. 376\u0026ndash;379. (2021)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhat is explainable AI? Accessed: Apr. 10, 2025. [Online]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ibm.com/think/topics/explainable-ai\u003c/span\u003e\u003cspan address=\"https://www.ibm.com/think/topics/explainable-ai\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeneral Data Protection Regulation,: Accessed: Apr. 10, 2025. [Online]. (2016). Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gdpr-info.eu/\u003c/span\u003e\u003cspan address=\"https://gdpr-info.eu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGervazoni, A., Quaresma, M.: Trust in the system and human autonomy in customer service chatbots, in \u003cem\u003eService Design and Innovation Conference\u003c/em\u003e, pp. 1416\u0026ndash;1430. (2023)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHleg, A.I.: Ethics guidelines for trustworthy AI, (2019)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrandimarte, L., Acquisti, A., Loewenstein, G.: Misplaced confidences: Privacy and the control paradox. Soc. Psychol. Personal Sci. \u003cb\u003e4\u003c/b\u003e(3), 340\u0026ndash;347 (2013)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArtificial Intelligence in Health and Care Award: Accessed: Apr. 10, 2025. [Online]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.england.nhs.uk/aac/what-we-do/how-can-the-aac-help-me/ai-award/\u003c/span\u003e\u003cspan address=\"https://www.england.nhs.uk/aac/what-we-do/how-can-the-aac-help-me/ai-award/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMINISTERIAL FORWARD EXECUTIVE SUMMARY,, Oct: Accessed: Apr. 10, 2025. [Online]. (2017). Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://u.ae/en/about-the-uae/strategies-initiatives-and-awards/strategies-plans-and-visions/government-services-and-digital-transformation/uae-strategy-for-artificial-intelligence\u003c/span\u003e\u003cspan address=\"https://u.ae/en/about-the-uae/strategies-initiatives-and-awards/strategies-plans-and-visions/government-services-and-digital-transformation/uae-strategy-for-artificial-intelligence\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"world-wide-web","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wwwj","sideBox":"Learn more about [World Wide Web](http://link.springer.com/journal/11280)","snPcode":"11280","submissionUrl":"https://submission.nature.com/new-submission/11280/3","title":"World Wide Web","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Artificial Intelligence, Attitude Toward AI, Freedom of Choice, Social Proof, Personality Traits, Locus of Control, Need for Cognition","lastPublishedDoi":"10.21203/rs.3.rs-6565909/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6565909/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePersonal factors have been examined in recent literature for their impact on attitudes toward AI, often treating AI as a single, uniform concept and using samples from one cultural group. To address this, we differentiate between two modalities of AI operation: freedom of choice, referring to users' ability or inability to choose alternatives to AI, and social proof, reflecting whether AI has been widely used and accepted by others. We also include samples from two distinct populations, Arab and UK. This study investigates the influence of the Big Five personality traits, Need for Cognition (NFC), and Locus of Control (LOC) on attitudes toward AI across four combinations of these modalities. A total of 639 participants (316 UK, 323 Arab) completed a survey containing scenarios, validated scales and bespoke, face-validated questions. Using hierarchical multivariate multiple regression (MMR), we analyzed how these personal factors predict two key dimensions of AI attitudes: acceptance (perceived personal and social benefits) and fear (ethical concerns and risks). Agreeableness consistently predicted more favorable attitudes across both cultures, while neuroticism was linked to greater fear. Internal LOC and higher NFC were associated with greater acceptance, highlighting the role of perceived control and cognitive engagement. Cultural differences emerged, with conscientiousness being more influential in the Arab sample and openness in the UK. Overall, personality traits had a weaker impact than expected, aligning with previous research treating AI as a single concept. The modality of operation showed limited effect. This study adds to AI acceptance literature by emphasizing psychological and cultural variability in public attitudes.\u003c/p\u003e","manuscriptTitle":"On the Role of Personality in Attitudes Toward AI: Do AI’s Freedom of Choice and Social Proof Matter?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-19 18:55:58","doi":"10.21203/rs.3.rs-6565909/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-16T17:29:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-16T17:22:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-13T10:23:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-12T08:07:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"236975703151586050783960090945284845004","date":"2025-05-27T14:57:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"69703432324537054577682708738236808753","date":"2025-05-26T22:46:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"125193354688050100869139672758648061174","date":"2025-05-26T12:50:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-09T15:16:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-07T05:48:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-06T11:30:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"World Wide Web","date":"2025-04-30T14:22:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"world-wide-web","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wwwj","sideBox":"Learn more about [World Wide Web](http://link.springer.com/journal/11280)","snPcode":"11280","submissionUrl":"https://submission.nature.com/new-submission/11280/3","title":"World Wide Web","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c31d8531-eb54-4a29-ae08-243dc61263b2","owner":[],"postedDate":"May 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-27T16:25:09+00:00","versionOfRecord":{"articleIdentity":"rs-6565909","link":"https://doi.org/10.1007/s11280-025-01372-w","journal":{"identity":"world-wide-web","isVorOnly":false,"title":"World Wide Web"},"publishedOn":"2025-10-21 16:16:23","publishedOnDateReadable":"October 21st, 2025"},"versionCreatedAt":"2025-05-19 18:55:58","video":"","vorDoi":"10.1007/s11280-025-01372-w","vorDoiUrl":"https://doi.org/10.1007/s11280-025-01372-w","workflowStages":[]},"version":"v1","identity":"rs-6565909","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6565909","identity":"rs-6565909","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}