Modeling Digital Banking Adoption in Saudi Arabia: An Integrated C-TAM-TPB Framework Examining the Role of Perceived Security-Based Trust

Modeling Digital Banking Adoption in Saudi Arabia: An Integrated C-TAM-TPB Framework Examining the Role of Perceived Security-Based Trust | 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 Article Modeling Digital Banking Adoption in Saudi Arabia: An Integrated C-TAM-TPB Framework Examining the Role of Perceived Security-Based Trust Reem Abdalla This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6670162/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study creates and tests a model for digital banking adoption in Saudi Arabia that combines the Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB), and Perceived Security-Based Trust (PSBT). Analyzing data from 353 valid online survey responses using Partial Least Squares Structural Equation Modeling (PLS-SEM), the model explains 67.3% of the variance in Behavioral Intention (R² = 0.673). It shows strong predictive relevance, with Q²predict values over 0.4. Key findings reveal that Perceived Ease of Use (β = 0.578) and PSBT (β = 0.748) significantly influence Perceived Usefulness and trust. Trust impacts both Subjective Norms (β = 0.691) and Perceived Behavioral Control (β = 0.693), highlighting its role in facilitating social and self-regulatory pathways to adoption. Although PU and Attitude mediate PEU and PSBT effects on Behavioral Intention, Subjective Norms show no direct influence, differing from typical TPB expectations. This research highlights trust-related mechanisms as primary drivers of digital adoption in cautious cultures, enhancing theoretical understanding of TAM-TPB integration in security-sensitive contexts and offering practical guidance for banks to improve usability and trust. Business and commerce/Business and management Business and commerce/Finance Business and commerce/Information systems and information technology Social science/Business and management Social science/Finance Social science/Science technology and society Digital Banking Adoption Perceived Security-Based Trust Technology Acceptance Model (TAM) Theory of Planned Behavior (TPB) Behavioral Intention Partial Least Squares (PLS-SEM) Saudi Arabia Trust in Technology Financial Technology Adoption Figures Figure 1 Figure 2 1. Introduction Recent developments in technology, globalization, and competition have led banks worldwide to adopt innovative technologies. Internet Banking (IB) has emerged as a widely accepted solution, providing customers with secure 24-hour access to their accounts for various financial activities [1]. This advancement offers banks an opportunity to deliver services more efficiently. Saudi Arabia's banking sector was one of the first in the Middle East to introduce electronic banking services (EBS), extending their reach through remote and electronic channels. [2]. The Kingdom of Saudi Arabia is undergoing a remarkable evolution in its banking industry, driven by the ambitious goals outlined in Vision 2030 and the swift progress of digital technologies. [3], [4]. This initiative seeks to revitalize the financial ecosystem, broaden the spectrum of economic activities, and elevate the overall wellbeing of its residents [5]. Within this context, digital banking has emerged as an essential driver, offering a variety of benefits that include enhanced convenience, reduced costs, and better access to financial services. In spite of significant financial commitments to enhance technology and infrastructure, the adoption of digital banking services in Saudi Arabia remains markedly less than that observed in other advanced nations [6] This inconsistency underscores an urgent need to explore the basic factors that shape consumer behavior in this area. Additionally, various insufficient measures and influences on the demand side persistently obstruct the adoption of cashless technology [7], [8]. The potential of digital banking relies significantly on creating a strong and secure atmosphere that nurtures confidence among users. This is particularly important since online financial transactions are often viewed as more precarious compared to conventional banking practices [9], [10]. The apprehension surrounding this risk, along with worries about the safeguarding of personal information and potential security violations, can notably hinder the widespread acceptance of digital banking services.[11]. This highlights the essential importance of perceived trust within digital financial environments. Therefore, it becomes crucial to explore how trust and perceived security influence the intention of Saudi consumers to embrace digital banking. The concept of Perceived Security-Based Trust (PSBT) within Digital Environments holds significant importance. Previous research has primarily centered around the Technology Acceptance Model and the Theory of Planned Behavior to elucidate the process of technology adoption. However, there exists a need for more in-depth exploration of the intricate aspects of trust and security specifically related to digital banking in Saudi Arabia [8]. The presence of rust significantly impacts the customer's perception and willingness to engage with internet banking services.[12]. In the realm of technological services, trust emerges as a fundamental aspect that shapes the relationship between providers and their customers. The level of trust perceived by customers directly correlates with the frequency with which they utilize these services [13], [14]; the greater the trust, the higher the consumption rates [15]. Within the context of digital banking, the concept of Perceived Security-Based Trust encompasses the beliefs surrounding safety and reliability associated with digital platforms. It is crucial for customers to feel confident that the services offered are secure and devoid of risk, while also benefiting from robust data protection measures [16]. This necessity explains the relatively slow uptake of mobile technology by both banks and clients, despite the widespread use of smartphones. There exists a pressing demand for enhanced IT resources and aesthetically pleasing, functional digital platforms, as these advancements would likely facilitate a greater acceptance and integration of digital services [17]. However, few models currently provide a comprehensive approach to fully incorporate the element of trust within this framework’s constructs into TAM-TPB frameworks for financial services in developing digital economies. To address these gaps, this research aims to develop and test an integrated model that combines the core constructs of the Technology Acceptance Model and the Theory of Planned Behavior with Perceived Security-Based Trust, resulting in a comprehensive understanding of the factors driving digital banking adoption in Saudi Arabia. This study aims to bridge this gap by providing a holistic understanding of the key factors that influence the adoption of digital banking in Saudi Arabia, with a particular focus on the role of trust and perceived security [18]. 2. Literature Review Trust is a multifaceted and complex concept that has been studied by multiple fields and disciplines. The concept is rooted in the behavioral confidence that reflects an actor’s intention to depend on another party’s expectation of future actions. Trust reduces the perceived risk involved in the interaction [19], thereby the actor believes that another party should behave in a certain condition. Factors affecting trust can be grouped into four categories based on two different dimensions: entity and trustor [20]. The trustor dimension consists of ability, benevolence and integrity trust factors, while the entity characterizes a person or group, an organization or institution, and the situation of a particular event [21]. Trust is an emergent future-oriented belief expressed as a subjective probability. 2.1. Digital Banking Service and Trust in Financial Services Digital banking allows customers to access, order, or subscribe to services through devices over a public network or online remotely, without needing bank staff's physical presence [22], Consumers can perform banking transactions, receive advice, and obtain account statements using computers or mobile devices with adequate network connectivity [23], [24]. Users access internet service banks through public computers or ATMs, which raises access barriers related to trust. Trust issues are critical for stakeholders as they comply with service, ethics, and economic development standards [25]. Understanding the factors influencing consumer trust in e-commerce, including privacy, policy presentation, and lender reliability, is essential. In financial services, perceived security is crucial for mitigating transaction risks. Banks strive to assure customers that their transaction risks are minimal, facilitating safe information sharing during transactions and collaborations [26], [27]. Trust has gained prominence in the IS and business sectors, as stakeholders seek to comply with various services, ethics, and economic development codes [28], [29]. Notable research has examined what builds and impacts consumer trust in e-commerce, emphasizing the roles of privacy, policy presentation, and lenders [30]. In financial services, perceived security ensures the elimination of divestiture risk in transactions. Banks strive to provide customers with the lowest transaction risks. Essentially, security trust signifies the belief in safe information sharing during transactions and collaborations [31]. 2.2. Technology Acceptance Model (TAM) The Technology Acceptance Model (TAM) proposed an information systems success model to ascertain how new technologies are recognized. The TAM postulates two core constructs being perceived ease of use (PEU) and perceived usefulness (PU) that form another fleet of constructs, like attitude to use (ATU), behavioral intention (BI), actual system use, and the external variables [32]. 2.2.1. Perceived Ease of Use (PEU) A user's perception of a system's ability to reduce effort and simplify task complexities, while also enhancing its ease of use, is indicated [33]. Users generally view e-banking as user-friendly; consequently, it is recommended that banks streamline their security features. [34]. 2.2.2. Perceived Usefulness (PU) refers to the capability of an information system to analyze data and provide accurate responses to user inquiries [35]. This concept includes elements of both information quality and system quality. Even if a system is easy to use, users may hesitate to engage with it if they do not consider it useful [36], [37]. 2.2.3. Attitude to Use (ATU) refers to a comprehensive set of attitudinal frameworks aimed at evaluating the behaviors of others or their support for a specific behavior [38], [39]. For instance, an individual may assess their own interaction with a system, the operation of that system, or the effectiveness of a speech understanding system. People can hold either favorable or unfavorable attitudes and may communicate these assessments with varying degrees of intensity [40]. 2.3. Perceived Security in Digital Transactions While the digital banking form may initially seem convenient and efficient, a considerable number of customers continue to harbor significant concerns regarding its actual security and reliability [41]. In fact, the United States is at the forefront of the world in terms of e-commerce development, which can provide invaluable insights drawn from its varied experiences [42]. Digital banking and stock trading institutions typically strive to anticipate the diverse needs and objectives of their customers, as well as the essential technology that underpins secure transactions [22]. Nonetheless, these institutions must diligently address the growing anxiety among their customers that stem from perceived risks associated with engaging in this relatively new and evolving channel [43]. Digital technology led to increased security breaches, causing users to worry about digital transaction safety. Ensuring data integrity is crucial, necessitating that digital format be immutable and access limited to authorized parties [44], [45]. Transparent tracking of transactions should be established, allowing customers to analyze their activity and spot potential security issues or unfair practices that could harm their finances [46]. By building an environment of trust, digital banking institutions can alleviate customers' anxiety. To encourage the adoption of these innovative channels, it is vital to assure customers regarding the physical and procedural security of their investments, which includes up-to-date technology solutions and effective management systems safeguarding data [10]. 2.4. Theory of Planned Behavior (TPB) The Theory of Planned Behavior (TPB) elucidates how specific factors can predict behavior based on intentions. In TPB, attitude toward the behavior, perceived behavioral control (PBC), and subjective norm (SN) constructs directly influence intention. The concept of Attitude Toward Behavior plays a vital role in comprehending the way people develop their intentions to embrace particular services. This construct includes the various beliefs and assessments that individuals possess about the potential results associated with the service [47], [48]. Subjective Norms (SN): This concept examines the societal pressures that shape a person's actions, informed by their perceptions of what important people in their lives believe [49]. It encompasses descriptive norms, which reflect an individual's understanding that their behavior should correspond to the actions of others, as well as injunctive norms, which pertain to the anticipated behaviors that the reference group expects from them [50]. Perceived Behavioral Control (PBC): PBC indicates how easy or difficult an individual perceives a behavior to be and is based on past experiences and anticipated challenges [51], [52]. Control can arise from external factors or intentions in the presence of enabling or constraining conditions [51]. 2.5. Integration of C-TAM and TPB The C-TAM-TPB framework, which combines the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB), provides a clear method to predict and understand how people accept information systems today [53], [54]. To elaborate, TAM first introduced by [55] primarily focuses on cognitive beliefs, particularly emphasizing two key constructs that drive technology adoption: Perceived Usefulness (PU) and Perceived Ease of Use (PEU) [56], [57]. This foundational model serves as a cornerstone for assessing how users feel about technology’s value and how easy they find it to utilize [58]. The Theory of Planned Behavior (TPB), created by [59], broadens this idea by adding more factors that influence how users act, specifically Subjective Norms (SN) and Perceived Behavioral Control (PBC). These components act as essential social influences and aspects of control that significantly affect Behavioral Intention (BI), thereby providing a more holistic understanding of user motivations [52], [60]. The C-TAM-TPB model, developed by [61], effectively combines the attitudes from TAM and the social and control beliefs from TPB [62], enhancing its ability to explain outcomes in fields like health informatics, e-commerce, and digital banking [63], [64]. Research shows that C-TAM-TPB consistently outperforms TAM or TPB used separately, due to its thorough incorporation of motivational factors such as attitudes, perceived usefulness, and ease of use, along with key external influences like social impact and perceived control over technology Studies by[64], [65] further support this model's relevance. The integrative nature of C-TAM-TPB is especially important in areas where security, trust, and social acceptability are crucial, making it a strong framework for understanding behavioral intentions in adopting digital banking solutions [63]. 2.6. Hypotheses Development 1. Perceived Ease of Use and its Impacts The Technology Acceptance Model (TAM) , developed by Davis, (1989), is one of the most widely adopted frameworks for understanding user acceptance of information systems. At the core of TAM is the construct Perceived Ease of Use (PEU) , defined as the degree to which a person believes that using a system will be free of effort. Prior research consistently supports that PEU positively affects Perceived Usefulness (PU) is ([55], [66], as systems that are easier to use are more likely to be perceived as beneficial. Moreover, PEU has been shown to significantly influence Attitude Toward Use , which reflects a user’s overall evaluative response to system adoption [67], [68]. Recently, in the realm of online banking and electronic services, Perceived Experiential Usefulness (PEU) has been associated with Perceived Security-Based Trust (PSBT) [69], as a system that is easy to understand and open typically improves user trust in its security components [70] Hypotheses: H1a : Perceived Ease of Use (PEU) positively influences Perceived Usefulness (PU). H1b : Perceived Ease of Use (PEU) positively influences Attitude to Use. H1c : Perceived Ease of Use (PEU) positively influences Perceived Security-Based Trust (PSBT). 2. Perceived Usefulness as a Cognitive Driver Perceived Usefulness (PU) is defined as the degree to which a person feels that utilizing a specific system improves their job performance or increases the effectiveness of their decision-making processes. [55]. Several studies (e.g., [71], [72] confirmed that Perceived Usefulness (PU) has a direct influence on both the Attitude Toward Use and the Behavioral Intention (BI) to utilize a system[68], [73]. This connection is especially compelling in scenarios such as digital banking, where the value of the service like time saving features and seamless transactions greatly influences user dedication. Consequently, a positive perception of the system's benefits not only fosters favorable attitudes but also directly drives users' intentions to engage further. Hypotheses: H2a : Perceived Usefulness (PU) positively influences Attitude to Use. H2b : Perceived Usefulness (PU) positively influences Behavioral Intention to use digital banking. 3. Perceived Security-Based Trust (PSBT) in Digital Environments Trust plays a vital role in the success of digital banking [74], as users are required to provide sensitive financial information. Perceived Security-Based Trust (PSBT) represents the confidence users have in the technical security of the system and the trustworthiness of the institution involved [69], [75]. Prior literature by [76], [77]. highlights the multidimensional influence of PSBT across different acceptance factors. Furthermore, in alignment with the Theory of Planned Behavior (TPB) , PSBT influences Subjective Norms and Perceived Behavioral Control, indicating that trust enhances confidence in one's abilities to use a system and promotes support from social norms[78]. Hypotheses: H3a : Perceived Security-Based Trust (PSBT) positively influences Perceived Usefulness (PU). H3b : Perceived Security-Based Trust (PSBT) positively influences Attitude to Use. H3c : Perceived Security-Based Trust (PSBT) positively influences Subjective Norms (SN). H3d : Perceived Security-Based Trust (PSBT) positively influences Perceived Behavioral Control (PBC). 4. Attitude to Use and Behavioral Intention In both the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB), Attitude Toward Use serves as an essential mediating factor that links cognitive perceptions to behavioral intentions. Studies by [61], [79], [80] suggest that positive attitudes increase the likelihood of system adoption. The strong empirical evidence supporting the connection between Attitude and Behavioral Intention (BI) in various contexts, such as banking, healthcare, and mobile applications, suggests that a comparable association is likely to exist within digital banking settings as well [81]. Hypothesis: H4 : Attitude to Use positively influences Behavioral Intention to use digital banking. 5. Social and Control Factors from TPB theory of Planned Behavior (TPB) enhances the comprehension of human behavior by incorporating the concepts of Subjective Norms (SN) and Perceived Behavioral Control (PBC) [59]. Subjective Norms represent the social pressures individuals feel from their peers, family, or society regarding the performance of certain behaviors. On the other hand, Perceived Behavioral Control pertains to an individual's assessment of how easy or challenging it is to engage in a behavior, which is often associated with their self-efficacy and available resources[52], [82]. Both constructs have been validated in studies of online service adoption such as [82], [83], [84]; especially in contexts involving risk or complexity, like digital banking. Hypotheses: H5a : Subjective Norms (SN) positively influence Behavioral Intention to use digital banking. H5b : Perceived Behavioral Control (PBC) positively influences Behavioral Intention to use digital banking. 3. Research Methodology Measurement: A structured and validated questionnaire was developed to examine key factors influencing digital banking adoption in Saudi Arabia. It incorporates components from the C-TAM-TPB framework, focusing on Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Perceived Security-Based Trust (PSBT), Subjective Norms (SN), Perceived Behavioral Control (PBC), Attitude to Use, and Digital Banking Adoption Intention (DBAI), ensuring construct validity and measurement reliability. Questionnaire Development: The questionnaire had multiple sections aligned with the integrated C-TAM-TPB model, grounded in prior research for relevance. Perceived Usefulness (PU) was evaluated with four items from [85]. focusing on digital banking benefits. Perceived Ease of Use (PEOU) used three items to assess system clarity and simplicity, based on[85]. Perceived Security-Based Trust (PSBT) employed three adapted items to gauge trust in digital transaction security. Attitude to Use, Subjective Norms, and Perceived Behavioral Control were assessed with three to four items each, primarily from [86]. Digital Banking Adoption Intention (DBAI) involved three statements derived from prior TAM studies. This design enabled a multidimensional evaluation of behavioral drivers for digital banking uptake. Measurement Model Modification: Minor linguistic modifications were made to adapt the original items for the Saudi banking environment and general users. Perceived Usefulness (PU): Language was localized to highlight utility in personal financial management instead of organizational productivity. Perceived Ease of Use (PEOU): Items stayed unchanged due to their broad applicability. Perceived Security-Based Trust (PSBT): Wording was altered to address public concerns about online security and data privacy. Subjective Norms and Perceived Behavioral Control were adapted to reflect influences from family and peers and access to technology in Saudi Arabia. Adoption Intention and Attitude were slightly adjusted for clarity while maintaining theoretical integrity. All changes were validated through pre-testing and expert review. Data Collection: An extensive online survey was carried out through Google Forms with the aim of gathering insights into the behavioral perceptions of people throughout Saudi Arabia. The survey consisted of various sections, featuring Likert scale items alongside multiple-choice questions to collect demographic details and contextual information. The G*Power software played a crucial role in determining the minimum sample size needed for the study [87]. Focusing on a maximum of four predictors and aiming for a medium effect size of 0.15, the analysis revealed that at least 85 observations were required [88]. To bolster the reliability and generalizability of the findings, the researcher purposefully gathered a greater volume of data than the minimum requirement. Response Rate: A comprehensive collection of 361 responses was submitted. Following the meticulous process of filtering out incomplete or invalid entries, 353 completed responses were gathered. The entire dataset was preserved for further examination. This favorable response rate surpassed the established minimum threshold, thereby ensuring the viability of rigorous statistical analysis Measures: Each construct was assessed utilizing a 5-point Likert scale, which spanned from 1 (Strongly Disagree) to 5 (Strongly Agree). This methodology facilitated a detailed examination of the participants' perspectives, experiences, and intentions regarding their behaviors. Sampling Method: The method of convenience sampling was utilized for the study. Initially, the survey link was disseminated via email, WhatsApp, LinkedIn, and other various professional networks dedicated to banking topics. Participants were actively invited to circulate the link among their acquaintances, which facilitated an expansion of the sample and incorporated a wider range of demographics, including different regions, age brackets, and educational backgrounds. This approach effectively ensured that the survey captured a representative cross-section of the general banking population in Saudi Arabia, while also considering practical aspects of reaching respondents and maintaining accessibility. Challenges During Data Collection Key challenges included motivating less digitally literate participants, ensuring data accuracy, and engaging users in remote or conservative areas. Solutions involved sending reminders, clarifying survey instructions, and utilizing endorsements from experts and digital banking influences. 4. Results and Discussion The analysis of the measurement and structural model was conducted with the aid of SmartPLS version 4 [89]. This software is particularly beneficial as it does not depend on the assumption of normality, which is a significant advantage for survey research that commonly features non-normal distribution patterns [90]. 4-1 Common Method Bias As the data from single source Standard method bias was required [91] . Recent investigations have highlighted significant limitations associated with Harman's Single-Factor Test in effectively pinpointing CMB in research that relies on surveys. A contemporary study by [92], [93] revealed that this commonly utilized method has a restricted capacity to detect CMB, so the Full Collinearity technique was selected as a more effective approach than Harman's Single-Factor Test to tackle this problem of common method bias (CMB). Such insights imply that researchers could be misled into believing they possess accurate findings when, in fact, they do not. A VIF value of ≤ 3. indicated an absence of bias [94], [95]. The analysis confirmed that the VIF was <3.3, as documented in Table 1, demonstrating that no bias was identified (See Table 1). Table 1 . Full Collinearity Testing Construct AI ATU PBC PEU PU Trust SN VIF 1.401 1.424 2.543 2.259 2.855 1.274 2.145 4-2 Model Assessment We employed the recommendations outlined by Anderson & Gerbing [96] to examine the model through a two-step process. Initially, a comprehensive assessment of the measurement model was carried out to ascertain the precision and reliability of the instruments [97], [98]. This was succeeded by an in-depth analysis of the structural model aimed at validating our hypotheses. 4-2-1 Step 1: Measurement Model In the process of assessing the measurement model, it is crucial to examine four distinct forms of validity. The reliability of the indicators is evaluated through loadings, while the convergence validity is gauged by the Average Variance Extracted (AVE) [99], [100], [101]. Additionally, the internal consistency reliability can be determined through Composite Reliability (CR). Initially, we assessed the loadings, Average Variance Extracted (AVE), and Composite Reliability (CR) metrics. To ensure robustness, these values should meet or exceed 0.708 for loadings, 0.5 for AVE, and 0.7 for CR, as stipulated by the evaluation criteria of [97]. As illustrated in Table 2, every indicator's outer loading surpassed the critical threshold of 0.708, thereby confirming the convergent credibility at the indicator level. Furthermore, our findings indicated that all constructs achieved AVE values exceeding 0.5, which substantiates the convergent validity at the construct level. Lastly, every indicator within the measurement models adhered to the required composite reliability benchmarks. The results confirmed that all constructs exhibited internal consistency and reliability throughout (See Table 2). Subsequently, we examined discriminant validity to determine how effectively a construct is uniquely separate from the other constructs present in the model. Discriminant validity was assessed using [102] , where ensuring that AVE value was higher than its highest squared correlations with any other construct. (See Table 3). The two test ensured that the measurement model is valid and reliable. Table 2 . Measurement model assessment Constructs and their items Loading CR AVE Adoption intention [85] 0.904 0.759 AI1 0.891 AI2 0.862 AI3 0.86 Attitude toward the behavior [85] 0.879 0.707 ATU1 0.857 ATU2 0.846 ATU3 0.819 Perceived behavioral control (PBC) [86] 0.906 0.708 PBC1 0.841 PBC2 0.879 PBC3 0.835 PBC4 0.81 Ease of Use [85] 0.879 0.708 PEU1 0.83 PEU2 0.82 PEU3 0.873 Usefulness [85] 0.899 0.749 PU1 0.873 PU2 0.879 PU3 0.844 Trust [85] Trust 0.778 0.883 0.717 Trust 0.877 Trust 0.881 Subjective norm (SN) [86] 0.881 0.649 SN1 0.778 SN2 0.811 SN3 0.853 SN4 0.779 Table 3 . Discriminant Validity Variables AI ATU PBC PEU PU SN Trust AI 0.871 ATU 0.752 0.841 PBC 0.726 0.698 0.841 PEU 0.768 0.743 0.719 0.841 PU 0.753 0.74 0.773 0.776 0.865 SN 0.642 0.644 0.761 0.692 0.72 0.806 Trust 0.621 0.621 0.694 0.749 0.698 0.69 0.847 4-2-2 Step 2: Structural Model 4-2-2-1 Path Coefficient Bootstrapping enhanced the stability and precision of the estimates, leading to the generation of more dependable confidence intervals for the path coefficients. This methodology is regarded as superior to approaches utilizing smaller sample sizes (e.g., 500 or 1,000), as it mitigates the standard error while augmenting the accuracy of the estimates. Consequently, a bootstrapping technique utilizing 10,000 samples was implemented [97], and we reported the path coefficients, standard errors, t-values, and p-values associated with the structural model in alignment with [97], [103]. Moreover, in light of the critiques regarding the reliance on p-values by [104], it is advisable to supplement p-values with confidence intervals and effect sizes as additional metrics for assessing the significance of the hypothesis. The findings of the hypothesis testing—covering both direct and indirect effects—are presented in Table 4. Direct Effects We further assessed the model’s explanatory power by examining the R² values of the endogenous constructs. The R² value for Behavioral Intention (AI) was 0.673 , indicating that the predictors accounted for 67.3% of the variance in digital banking adoption intention. The associated t-value was 13.748 (p < 0.001), confirming the model's statistical significance in explaining this construction (See Table 4 & Figure 2). For Attitude to Use (ATU) , the R² value was 0.620 , showing that the predictors explained 62% of the variance. The t-value of 11.549 (p < 0.001) supports this result as statistically significant. The model explained 48.1% of the variance in Perceived Behavioral Control (PBC) , with an R² of 0.481 and a t-value of 8.158 (p < 0.001). Perceived Usefulness (PU) had an R² value of 0.633 , indicating that the predictors explained 63.3% of its variance. This relationship was statistically significant, with a t-value of 12.261 (p < 0.001). Subjective Norms (SN) showed an R² of 0.476 , meaning that the model explained 47.6% of its variance. The corresponding t-value was 9.463 (p < 0.001). Lastly, Perceived Security-Based Trust (Trust) had an R² value of 0.561 , with a t-value of 11.176 (p < 0.001), confirming a substantial level of variance explained by the predictors. Indirect Effects To examine the mediating mechanisms embedded within the integrated C-TAM-TPB framework, a comprehensive analysis of indirect effects was conducted. The results uncovered several significant multi-path mediations , emphasizing the complex interrelationships among perceived ease of use (PEU), perceived usefulness (PU), perceived trust, and behavioral intention (AI) (See Table 5 & Figure 2). One of the most noteworthy findings was the indirect influence of PEU on Behavioral Intention (AI) . Although the direct path from PEU to AI was not modeled, the analysis revealed multiple significant indirect pathways . Specifically, PEU → Attitude to Use → AI (β = 0.140, t = 3.119, p = 0.002) emerged as a prominent mediation chain, suggesting that ease of use leads to more favorable attitudes, which in turn heighten the intention to adopt digital banking services. Similarly, PEU → PU → Attitude to Use → AI (β = 0.062, t = 2.884, p = 0.004) highlights a sequential cognitive-affective route, underlining PU’s role as a pivotal bridge between ease perceptions and attitudinal commitment. Another compelling chain is PEU → Trust → Attitude to Use → AI (β = 0.023, t = 2.468, p = 0.014), which reveals that trust, when nurtured through usability, indirectly fosters behavioral intention via attitudinal shifts. This layered pathway not only validates trust as a mediator but positions PEU as an enabler of trust , with implications for interface design and user onboarding in fintech platforms. Trust, in itself, also demonstrated significant indirect pathways . While the direct path from Trust to AI was significant (β = 0.306, t = 4.590, p < 0.001), it was further complemented by Trust → PU → Attitude to Use → AI (β = 0.028, t = 2.889, p = 0.004), indicating a dual-pathway influence — both direct and cognitive-affective. This points to trust's dual nature: an emotional state and a logical appraisal based on perceived performance benefits. In contrast, Subjective Norms (SN) failed to significantly mediate the relationship between Trust and AI (β = 0.020, t = 1.445, p = 0.149), suggesting that while trust fosters normative pressure, these social cues may not translate effectively into behavioral intentions in the context of digital banking in Saudi Arabia — possibly due to rising financial autonomy or individualism among users. Additional indirect effects included PEU → Trust → PU → Attitude to Use → AI , a longer path that, although statistically modest, underscores the compounded effect of multiple mediators. These deep-chain mediations reflect a cascading influence , where initial usability perceptions trigger trust and perceived utility, which then generate attitudinal and behavioral outcomes. Altogether, these findings illuminate the multi-layered and interdependent mechanisms driving digital banking adoption. Rather than acting in isolation, key predictors like PEU and Trust function through indirect, serial mediation , showcasing the value of adopting a sophisticated, path-analytic perspective rather than relying on bivariate relationships. Table 4 . Structural model assessment: Hypotheses testing (direct relationships Hypothesis Direct Relationships STd. Beta Std Dev. t-Value P- values PCI LL f² H1a PEU -> PU 0.578 0.059 9.715 p ATU 0.4 0.077 5.239 p Trust 0.748 0.034 22.22 p AI 0.282 0.077 3.728 p ATU 0.398 0.071 5.625 p PU 0.265 0.057 4.617 p ATU 0.043 0.065 0.642 0.521 [-0.085, 0.171] 0.002 H3c Trust -> SN 0.691 0.036 19.17 p PBC 0.693 0.042 16.65 p AI 0.361 0.072 5.014 p AI 0.03 0.06 0.456 0.648 [-0.089, 0.147] 0.001 H1b PBC -> AI 0.233 0.087 2.651 0.008 [0.056, 039] 0.049 Table 5 . Structural model assessment: (indirect relationships) Influencing Relationships STd. Beta Std Dev. t-Value P- values [PCI, LL] STd. Beta t-Value P- values Pathways to Significant Indirect Effects PEU -> AI 0.625 0.048 13.09 0 [0.522, 0.709] 0.147 3.119 0.002 PEU -> ATU -> AI 0.011 0.65 0.516 PEU -> Trust -> ATU -> AI 0.122 2.493 0.013 PEU -> Trust -> PBC -> AI 0.083 3.501 p PU -> ATU -> AI 0.055 3.316 0.001 PEU -> Trust -> PU -> AI 0.015 0.455 0.649 PEU -> Trust -> SN -> AI 0.164 3.159 0.002 PEU -> PU -> AI 0.028 2.882 0.004 PEU -> Trust -> PU -> ATU -> AI PEU -> ATU 0.341 0.064 5.357 0 [0.222, 0.472] 0.078 3.693 p Trust -> PU -> ATU 0.033 0.641 0.522 PEU -> Trust -> ATU 0.23 4.68 p PU -> ATU PEU -> PBC 0.519 0.048 10.91 0 [0.421, 0.606] — — — PEU -> Trust -> PBC PEU -> PU 0.198 0.042 4.682 0 [0.116, 0.281] — — — PEU -> Trust -> PU PEU -> SN 0.517 0.041 12.46 0 0.43, 0.595 — — — PEU -> Trust -> SN PU -> AI 0.144 0.038 3.757 0 0.077, 0.232 — — — PU -> ATU -> AI Trust -> AI 0.308 0.067 4.594 0 0.173, 0.437 0.021 0.454 0.65 Trust -> SN -> AI 0.073 3.351 0.001 Trust -> PU -> AI 0.162 2.555 0.011 Trust -> PBC -> AI 0.014 0.651 0.515 Trust -> ATU -> AI 0.038 2.899 0.004 Trust -> PU -> ATU -> AI Trust -> ATU 0.105 0.028 3.728 0 0.058, 0.171 — — — Trust -> PU -> ATU 4-2-2-2 Testing Coefficient of Determination, Effect Sizes, and Predictive Performance As per the recommendation of [97] the in sample and out of sample to prediction techniques, first examined the in-sample prediction to evaluate the model’s explanatory power through the coefficient of determination (R²), followed by an assessment of effect sizes (f²) and predictive performance using PLS-Predict for out-of-sample analysis. Coefficient of Determination (R²) The R² values were evaluated to determine the level of variance explained by the structural model. According to [100], R² values of 0.25, 0.50, and 0.75 are considered weak, moderate, and substantial, respectively. In our model, the R² for Behavioral Intention (AI) was 0.673, indicating that 67.3% of the variance in AI is explained by Attitude, Subjective Norms, Perceived Behavioral Control, PU, and Trust—demonstrating a substantial level of explanatory power. For Attitude to Use (ATU), the R² value was 0.620, indicating that the model explains 62% of the variance based on PEU, PU, and Trust. Similarly, Perceived Usefulness (PU) showed an R² value of 0.633, while Perceived Behavioral Control (PBC) was moderately explained with an R² of 0.481. Subjective Norms (SN) and Trust were also moderately explained, with R² values of 0.476 and 0.561, respectively. Effect Size (f²) To understand the relative contribution of exogenous constructs, f² values were assessed. Values of 0.02, 0.15, and 0.35 correspond to weak, medium, and strong effects, respectively [105], [106]. The strongest effect was observed for PEU → Trust (f² = 1.278), signifying a large influence. Other notable medium-to-large effects included PEU → PU (f² = 0.399), Trust → PBC (f² = 0.928), and Trust → SN (f² = 0.512). Meanwhile, PU → Attitude (f² = 0.153) and Attitude → AI (f² = 0.162) demonstrated medium effects. Paths such as Trust → Attitude (f² = 0.002) and SN → AI (f² = 0.001) were negligible, suggesting weak or no practical significance. Predictive Performance (PLS-Predict) To assess out-of-sample predictive performance, we employed PLS-Predict [107], using a 10-fold cross-validation procedure. Table results show the Q²predict values for the reflective indicators of Behavioral Intention (AI) were 0.477 (AI1), 0.404 (AI2), and 0.403 (AI3), all above the 0 value, indicating medium to strong predictive relevance ( See Table 6). In terms of prediction error, the PLS-SEM_RMSE values (AI1 = 0.628; AI2 = 0.634; AI3 = 0.658) were all lower than their corresponding LM_RMSE counterparts (AI1 = 0.611; AI2 = 0.633; AI3 = 0.654), with positive SEM-RMSE margins of 0.017, 0.001, and 0.004, respectively. Since most of the indicators showed lower RMSE in the PLS-SEM model compared to the linear model (LM), the model’s predictive accuracy is confirmed [107]. These findings indicate that the integrated C-TAM-TPB model not only explains substantial variance in behavioral intention but also demonstrates strong out-of-sample predictive performance, which supports its reliability in predicting user adoption of digital banking technologies in the Saudi context. Table 6 . PLSpredict Q²predict PLS-SEM_RMSE LM_RMSE SEM-RMSE AI1 0.477 0.628 0.611 0.017 AI2 0.404 0.634 0.633 0.001 AI3 0.403 0.658 0.654 0.004 Endogenous variables Q²predict R² Std Dev. t-Value P- values AI 0.428 0.673 0.049 13.748 p< 0.001 ATU 0.387 0.62 0.053 11.549 p< 0.001 PBC 0.331 0.481 0.059 8.158 p< 0.001 PU 0.445 0.633 0.052 12.261 p< 0.001 SN 0.287 0.476 0.05 9.463 p< 0.001 Trust 0.395 0.561 0.05 11.176 p< 0.001 5. Conclusion This research enhances the existing literature on digital banking adoption by substantiating an integrated framework based on the Combined-Technology Acceptance Model (C-TAM) and the Theory of Planned Behavior (TPB), emphasizing Perceived Security-Based Trust (PSBT) as a key explanatory element. The empirical findings indicate that Perceived Ease of Use (PEU) significantly affects both Perceived Usefulness (PU) and PSBT, while Trust has a crucial influence on behavioral control and normative expectations. These outcomes provide partial validation for earlier studies conducted by [55], [59][72], which highlight the importance of cognitive evaluations in determining intention. Nonetheless, this research diverges from earlier models, notably those by [59][61], by revealing a reduced significance of social norms, which did not demonstrate a substantial direct impact on Behavioral Intention. This shift may reflect the evolving nature of financial decision-making in the Saudi Arabian context, where individual autonomy and security of digital platforms are becoming more decisive than peer influences. The inclusion of PSBT, which has often been neglected in standard TAM-TPB frameworks, is highlighted as a potent factor that shapes behavioral outcomes both directly and indirectly via PU and Perceived Behavioral Control (PBC). This finding contrasts with earlier literature such as [71],[75], which primarily positioned trust as a secondary antecedent rather than a central integrating mechanism between TPB and TAM constructs. Furthermore, the observed multi-step mediation effects, particularly the sequential influence of PEU through PU and Attitude, illustrate the complex nature of adoption behavior. These interconnected effects imply that in environments characterized by low trust or developing digital frameworks, the coalescence of usability and trust is essential to establish both cognitive credibility and behavioral confidence. This complexity is frequently overlooked in simplistic adoption models, highlighting the necessity of employing more sophisticated modeling approaches in emerging markets. In conclusion, this study not only reaffirms established findings from the TAM and TPB literature but also broadens the theoretical landscape by demonstrating how trust serves as a foundational element that can stabilize individual control beliefs and perceived utility, particularly in contexts where social pressures are minimal or fragmented. This positions PSBT as a critical enabler across different constructs in adoption theory, presenting significant implications for strategies related to digital banking, policy formulation, and user education initiatives. 6. Implications and limitations 6.1 Implications The findings provide practical guidance for banks and fintech developers in cautious or transitional environments. Simplifying user interfaces and onboarding can enhance trust and ease of use for less tech-savvy individuals. Communication strategies should highlight security credentials and data protection features to strengthen perceived safety, indirectly boosting adoption. Additionally, enhancing users' control perceptions through tutorials, live support, and responsive design can increase engagement confidence. For policymakers, building trust through regulations and public digital literacy initiatives is crucial. Improving cybersecurity standards and enforcing transparent data policies can enhance user confidence and foster greater adoption. 6.2 Limitations and Future Research This study has several limitations. It relies on self-reported data, which may be affected by common method variance, despite strong statistical controls. The diverse sample was obtained through convenience sampling, possibly missing rural or lower-income populations. Additionally, the cross-sectional design restricts the inference of causality and tracking of changing attitudes over time. Future research should focus on longitudinal designs to assess behavioral changes as digital infrastructure evolves. Comparative studies among Gulf countries or other emerging digital economies could better contextualize trust and perceived control in technology adoption. Incorporating emotion-related factors, like perceived risk or enjoyment, may provide deeper insights into non-rational aspects of adoption behavior. 7. Declarations Author Contributions: Conceptualization, R.A.; methodology, R.A.; software, R.A.; validation, R.A.; formal analysis, R.A.; investigation, R.A.; resources, R.A.; data curation, R.A.; writing—original draft preparation, R.A.; writing—review and editing, R.A.; visualization, R.A.; supervision, R.A.; project administration, R.A. The sole author has read and agreed to the published version of the manuscript. Data Availability Statement The information contained in this research can be obtained by contacting the corresponding author. Funding The researchers did not obtain any financial assistance for the research, writing, and/or dissemination of this article. Acknowledgements I Dr. Reem Abdalla the single author from the University of Technology in Bahrain, would like to wholeheartedly thank the peers' and colleagues' moral support that has been priceless. I would also like to thank my friends and family for their unconditional support and tolerance throughout the process. I would also like to thank the anonymous editors and reviewers; their helpful comments have significantly improved the final manuscript. Ethical Approval Statement This study received ethical clearance from the Ethical Committee of the College of Administrative and Financial Sciences at the University of Technology Bahrain (UTB). The approval was granted under the reference number UTB2025CAFSEC026, dated 10 March 2025. The committee reviewed the research plan, which involved a questionnaire targeting adult bank customers in Saudi Arabia and confirmed its alignment with the institution’s research ethics policy. Moreover, the study was designed in line with international principles governing research involving human subjects, including the Declaration of Helsinki. No clinical intervention or personally sensitive information was collected. Informed Consent Statement Before starting the questionnaire, participants were shown a short introduction explaining what the study was about, why it was being conducted, and what their rights were to decide if they chose to take part. The form made it clear that their participation was voluntary and that no personal or identifying information would be collected at any point. To continue, each participant had to check a box confirming they agreed to take part. Without this step, the form would not proceed. This served as their informed consent. The survey was conducted online using Google Forms, and responses were collected over the course of four weeks from December 25, 2024 to January 22, 2025 . Participants could leave the survey at any moment without having to explain, and no names, emails, or contact details were recorded. Conflicts of Interest The authors declare no conflict of interest in publishing this manuscript. They have fully adhered to ethical issues, including plagiarism, informed consent, misconduct, and data fabrication . References A. Jalal, M. Al Mubarak, and F. Durani, “Financial Technology (Fintech),” Studies in Systems, Decision and Control , vol. 487, pp. 525–536, 2024, doi: 10.1007/978-3-031-35828-9_45. M. Al-Husein and M. Asad Sadi, “Preference on the Perception of Mobile Banking: A Saudi Arabian Perspective,” European Online Journal of Natural and Social Sciences , vol. 4, no. 1, pp. 161–172, 2015, Accessed: May 05, 2025. [Online]. Available: https://european-science.com/eojnss/article/view/2627/pdf A. Council, S. Grand, and K. Wolff, “ASSESSING SAUDI VISION 2030: A 2020 REVIEW”. S. A. Al-Somali, R. Gholami, and B. Clegg, “An investigation into the acceptance of online banking in Saudi Arabia,” Technovation , vol. 29, no. 2, pp. 130–141, Feb. 2009, doi: 10.1016/J.TECHNOVATION.2008.07.004. A. Eskandarany, “Adoption of artificial intelligence and machine learning in banking systems: a qualitative survey of board of directors,” Front Artif Intell , vol. 7, p. 1440051, Nov. 2024, doi: 10.3389/FRAI.2024.1440051/BIBTEX. H. A. Alnemer, “Determinants of digital banking adoption in the Kingdom of Saudi Arabia: A technology acceptance model approach,” Digital Business , vol. 2, no. 2, p. 100037, Jan. 2022, doi: 10.1016/J.DIGBUS.2022.100037. J. W. Tang and P. H. Tsai, “Exploring critical determinants influencing businesses’ continuous usage of mobile payment in post-pandemic era: Based on the UTAUT2 perspective,” Technol Soc , vol. 77, p. 102554, Jun. 2024, doi: 10.1016/J.TECHSOC.2024.102554. T. Wen Ni, “Factors Influencing Behavioural Intention towards Adoption of Digital Banking Services in Malaysia,” International Journal of Asian Social Science , vol. 10, no. 8, pp. 450–457, Jul. 2020, doi: 10.18488/JOURNAL.1.2020.108.450.457. V. Melnyk, “Transforming the nature of trust between banks and young clients: from traditional to digital banking,” Qualitative Research in Financial Markets , vol. 16, no. 4, pp. 618–635, Jun. 2024, doi: 10.1108/QRFM-08-2022-0129. S. Chauhan, A. Akhtar, and A. Gupta, “Customer experience in digital banking: a review and future research directions,” International Journal of Quality and Service Sciences , vol. 14, no. 2, pp. 311–348, May 2022, doi: 10.1108/IJQSS-02-2021-0027. W. A. Alkhowaiter, “Digital payment and banking adoption research in Gulf countries: A systematic literature review,” Int J Inf Manage , vol. 53, p. 102102, Aug. 2020, doi: 10.1016/J.IJINFOMGT.2020.102102. P. Agus et al. , “TECHNOLOGY ACCEPTANCE MODEL AND TRUST IN EXPLAINING CUSTOMER INTENTION TO USE INTERNET BANKING,” RJOAS , vol. 7, no. 91, 2019, doi: 10.18551/rjoas.2019-07.29. S. M. Noble and M. Mende, “The future of artificial intelligence and robotics in the retail and service sector: Sketching the field of consumer-robot-experiences,” J Acad Mark Sci , vol. 51, no. 4, pp. 747–756, Jul. 2023, doi: 10.1007/S11747-023-00948-0. W. Ozuem, S. Ranfagni, M. Willis, G. Salvietti, and K. Howell, “Exploring the relationship between chatbots, service failure recovery and customer loyalty: A frustration–aggression perspective,” Psychol Mark , Oct. 2024, doi: 10.1002/MAR.22051. R. Hasan, R. Shams, and M. Rahman, “Consumer trust and perceived risk for voice-controlled artificial intelligence: The case of Siri,” J Bus Res , vol. 131, pp. 591–597, Jul. 2021, doi: 10.1016/J.JBUSRES.2020.12.012. H. Joshi and D. Chawla, “Impact of security on wallet adoption: multiple and serial mediating roles of trust and attitude and gender as a moderator,” International Journal of Bank Marketing , vol. 42, no. 5, pp. 870–896, Jul. 2024, doi: 10.1108/IJBM-02-2023-0118/FULL/XML. S. Ahmed and S. Sur, “Change in the uses pattern of digital banking services by Indian rural MSMEs during demonetization and Covid-19 pandemic-related restrictions,” Vilakshan - XIMB Journal of Management , vol. 20, no. 1, pp. 166–192, Feb. 2021, doi: 10.1108/XJM-09-2020-0138. K. Kantika, F. Kurniasari, and M. Mulyono, “The Factors Affecting Digital Bank Services Adoption Using Trust as Mediating Variable,” Journal of Business and Management Review , vol. 3, no. 10, pp. 690–704, Oct. 2022, doi: 10.47153/JBMR310.4882022. Z. Kenesei et al. , “Trust and perceived risk: How different manifestations affect the adoption of autonomous vehicles,” Transp Res Part A Policy Pract , vol. 164, pp. 379–393, Oct. 2022, doi: 10.1016/J.TRA.2022.08.022. A. M. Baabdullah, A. A. Alalwan, N. P. Rana, H. Kizgin, and P. Patil, “Consumer use of mobile banking (M-Banking) in Saudi Arabia: Towards an integrated model,” Int J Inf Manage , vol. 44, pp. 38–52, Feb. 2019, doi: 10.1016/J.IJINFOMGT.2018.09.002. M. A. Afandi, R. B. Hartarto, and H. Usman, “Effects of ability, benevolence, and integrity on banking trust during the COVID-19 pandemic in Indonesia,” International Journal of Business and Emerging Markets , vol. 16, no. 2, pp. 207–221, 2024, doi: 10.1504/IJBEM.2024.137664;JOURNAL:JOURNAL:IJBEM;WGROUP:STRING:PUBLICATION. S. Jit Kaur, L. Ali, M. Kabir Hassan, and M. Al-Emran, “Adoption of digital banking channels in an emerging economy: exploring the role of in-branch efforts,” Journal of Financial Services Marketing , vol. 26, pp. 107–121, 123AD, doi: 10.1057/s41264-020-00082-w. K. Khando, M. S. Islam, and S. Gao, “The Emerging Technologies of Digital Payments and Associated Challenges: A Systematic Literature Review,” Future Internet , vol. 15, no. 1, Jan. 2023, doi: 10.3390/FI15010021. A. K. Tyagi, S. Dananjayan, D. Agarwal, and H. F. Thariq Ahmed, “Blockchain—Internet of Things Applications: Opportunities and Challenges for Industry 4.0 and Society 5.0,” Sensors , vol. 23, no. 2, Jan. 2023, doi: 10.3390/S23020947. R. Chan, I. Troshani, S. Rao Hill, and A. Hoffmann, “Towards an understanding of consumers’ FinTech adoption: the case of Open Banking,” International Journal of Bank Marketing , vol. 40, no. 4, pp. 886–917, Apr. 2022, doi: 10.1108/IJBM-08-2021-0397/FULL/XML. Adetunji Paul Adejumo and Chinonso Peter Ogburie, “Strengthening finance with cybersecurity: Ensuring safer digital transactions,” World Journal of Advanced Research and Reviews , vol. 25, no. 3, pp. 1527–1541, Mar. 2025, doi: 10.30574/WJARR.2025.25.3.0908. M. Ali, S. A. Raza, B. Khamis, C. H. Puah, and H. Amin, “How perceived risk, benefit and trust determine user Fintech adoption: a new dimension for Islamic finance,” Foresight , vol. 23, no. 4, pp. 403–420, 2021, doi: 10.1108/FS-09-2020-0095. T. Khan, M. M. H. Emon, and S. A. J. Siam, “Impact of Green Supply Chain Practices on Sustainable Development in Bangladesh,” Malaysian Business Management Journal , vol. 3, no. 2, pp. 73–83, Apr. 2024, doi: 10.26480/MBMJ.02.2024.73.83. S. L. Cabrera-Luján, D. J. Sánchez-Lima, S. A. Guevara-Flores, D. Y. Millones-Liza, E. E. García-Salirrosas, and M. Villar-Guevara, “Impact of Corporate Social Responsibility, Business Ethics and Corporate Reputation on the Retention of Users of Third-Sector Institutions,” Sustainability (Switzerland) , vol. 15, no. 3, Feb. 2023, doi: 10.3390/SU15031781. T. Roh, Y. S. Yang, S. Xiao, and B. Il Park, “What makes consumers trust and adopt fintech? An empirical investigation in China,” Electronic Commerce Research , vol. 24, no. 1, pp. 3–35, Mar. 2024, doi: 10.1007/S10660-021-09527-3. E. L. M. Bettinazzi and E. R. Feldman, “Stakeholder orientation and divestiture activity,” Academy of Management Journal , vol. 64, no. 4, pp. 1078–1096, Sep. 2021, doi: 10.5465/AMJ.2019.0627. S. R. Natasia, Y. T. Wiranti, and A. Parastika, “Acceptance analysis of NUADU as e-learning platform using the Technology Acceptance Model (TAM) approach,” Procedia Comput Sci , vol. 197, pp. 512–520, 2021, doi: 10.1016/J.PROCS.2021.12.168. N. F. S. Jeffri and D. R. Awang Rambli, “A review of augmented reality systems and their effects on mental workload and task performance,” Heliyon , vol. 7, no. 3, Mar. 2021, doi: 10.1016/J.HELIYON.2021.E06277. C. S. Dewi and Z. Zulkifli, “E-Banking Technology: A Comprehensive Study on Customer Satisfaction and Bank Services,” International Journal of Business, Law, and Education , vol. 5, no. 2, pp. 1655–1665, Jun. 2024, doi: 10.56442/IJBLE.V5I2.708. A. T. Neumann, Y. Yin, S. Sowe, S. Decker, and M. Jarke, “An LLM-Driven Chatbot in Higher Education for Databases and Information Systems,” IEEE Transactions on Education , 2024, doi: 10.1109/TE.2024.3467912. M. A. Almaiah et al. , “Investigating the Effect of Perceived Security, Perceived Trust, and Information Quality on Mobile Payment Usage through Near-Field Communication (NFC) in Saudi Arabia,” Electronics (Switzerland) , vol. 11, no. 23, Dec. 2022, doi: 10.3390/ELECTRONICS11233926. Y. Chen, S. Jensen, L. J. Albert, S. Gupta, and T. Lee, “Artificial Intelligence (AI) Student Assistants in the Classroom: Designing Chatbots to Support Student Success,” Information Systems Frontiers , vol. 25, no. 1, pp. 161–182, Feb. 2023, doi: 10.1007/S10796-022-10291-4. J. E. Andrews, H. Ward, and J. W. Yoon, “UTAUT as a Model for Understanding Intention to Adopt AI and Related Technologies among Librarians,” Journal of Academic Librarianship , vol. 47, no. 6, Dec. 2021, doi: 10.1016/J.ACALIB.2021.102437. A. E. Demirel, S. M. Top, Z. Y. İlerisoy, and E. B. Kınacı, “Evaluation of architecture and engineering students’ attitudes towards online education in Türkiye with technology acceptance model,” Open House International , 2025, doi: 10.1108/OHI-07-2024-0255. S. J. Moon, “Investigating beliefs, attitudes, and intentions regarding green restaurant patronage: An application of the extended theory of planned behavior with moderating effects of gender and age,” Int J Hosp Manag , vol. 92, Jan. 2021, doi: 10.1016/J.IJHM.2020.102727. R. K, S. G, S. Kumar, and S. T, “A Study of Cyber Security Challenges and its Emerging Trends on Latest Technologies,” International Journal of Innovative Research in Advanced Engineering , vol. 10, no. 06, pp. 379–382, Jun. 2023, doi: 10.26562/ijirae.2023.v1006.25. O. Odeyemi et al. , “AI in E-commerce: Reviewing developments in the USA and their global influence,” International Journal of Science and Research Archive , vol. 11, no. 1, pp. 1460–1468, Feb. 2024, doi: 10.30574/IJSRA.2024.11.1.0232. V. Kaushal and R. Yadav, “Learning successful implementation of Chatbots in businesses from B2B customer experience perspective,” Concurr Comput , vol. 35, no. 1, Jan. 2023, doi: 10.1002/CPE.7450. K. Hughes-Lartey, M. Li, F. E. Botchey, and Z. Qin, “Human factor, a critical weak point in the information security of an organization’s Internet of things,” Heliyon , vol. 7, no. 3, p. e06522, Mar. 2021, doi: 10.1016/J.HELIYON.2021.E06522. A. S. Malik, S. Acharya, and S. Humane, “Exploring the Impact of Security Technologies on Mental Health: A Comprehensive Review,” Cureus , vol. 16, no. 2, Feb. 2024, doi: 10.7759/CUREUS.53664. L. Albshaier, S. Almarri, and M. M. Hafizur Rahman, “A Review of Blockchain’s Role in E-Commerce Transactions: Open Challenges, and Future Research Directions,” Computers , vol. 13, no. 1, Jan. 2024, doi: 10.3390/COMPUTERS13010027. Y. Zhong, S. Oh, and H. C. Moon, “Service transformation under industry 4.0: Investigating acceptance of facial recognition payment through an extended technology acceptance model,” Technol Soc , vol. 64, Feb. 2021, doi: 10.1016/J.TECHSOC.2020.101515. Md. T. A. Himel, S. Ashraf, T. A. Bappy, M. T. Abir, M. K. Morshed, and Md. N. Hossain, “Users’ attitude and intention to use mobile financial services in Bangladesh: an empirical study,” South Asian Journal of Marketing , vol. 2, no. 1, pp. 72–96, Sep. 2021, doi: 10.1108/SAJM-02-2021-0015. E. S. Quaye, L. E. K. Ameyibor, K. Mokgethi, and Y. Kabeya Saini, “Healthy lifestyle and behavioural intentions: the role of self-identity, self-efficacy, subjective norms, and attitudes,” International Journal of Spa and Wellness , vol. 7, no. 2, pp. 176–196, 2024, doi: 10.1080/24721735.2024.2374588. Y. Xu et al. , “Effects of Subjective Norms and Environmental Mechanism on Green Purchase Behavior: An Extended Model of Theory of Planned Behavior,” Front Environ Sci , vol. 10, Feb. 2022, doi: 10.3389/FENVS.2022.779629. O. Fawehinmi, M. Y. Yusliza, M. I. Tanveer, and M. S. Abdullahi, “Influence of green human resource management on employee green behavior: The sequential mediating effect of perceived behavioral control and attitude toward corporate environmental policy,” Corp Soc Responsib Environ Manag , vol. 31, no. 3, pp. 2514–2536, May 2024, doi: 10.1002/CSR.2707;WGROUP:STRING:PUBLICATION. M. S. Hagger, M. W. L. Cheung, I. Ajzen, and K. Hamilton, “Perceived Behavioral Control Moderating Effects in the Theory of Planned Behavior: A Meta-Analysis,” Health Psychology , Feb. 2022, doi: 10.1037/HEA0001153. G. Yang, V. Caiati, S. Rasouli, and J. Chen, “Understanding Drivers’ Behavioral attitudes and Intentions to Use Guidance Systems in Urban Complex Parking Lots Based on the C-TAM-TPB Framework,” Transp Res Rec , Nov. 2024, doi: 10.1177/03611981241242064. I. Triutomo, P. Putra, and I. Heruwasto, “Factors affecting consumer intention to use QRIS during the Covid-19 pandemic by using C-TAM-TPB,” Proceeding of the International Conference on Family Business and Entrepreneurship , vol. 3, no. 1, Jul. 2022, doi: 10.33021/ICFBE.V3I1.3783. F. D. Davis, “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology,” MIS Quarterly , vol. 13, no. 3, p. 319, Sep. 1989, doi: 10.2307/249008. M. Alshurideh et al. , “Factors affecting ChatGPT use in education employing TAM: A Jordanian universities’ perspective,” International Journal of Data and Network Science , vol. 8, no. 3, pp. 1599–1606, Jun. 2024, doi: 10.5267/J.IJDNS.2024.3.007. R. Andy, A. C. Dewi, and M. As’adi, “An Empirical Study to Validate The Technology Acceptance Model (TAM) In Evaluating ‘Desa Digital’ Applications,” IOP Conf Ser Mater Sci Eng , vol. 1125, no. 1, p. 012055, May 2021, doi: 10.1088/1757-899X/1125/1/012055. K. F. Yuen, L. Cai, G. Qi, and X. Wang, “Factors influencing autonomous vehicle adoption: an application of the technology acceptance model and innovation diffusion theory,” Technol Anal Strateg Manag , vol. 33, no. 5, pp. 505–519, 2021, doi: 10.1080/09537325.2020.1826423. I. Ajzen, “The theory of planned behavior,” Organ Behav Hum Decis Process , vol. 50, no. 2, pp. 179–211, 1991. F. La Barbera and I. Ajzen, “Moderating role of perceived behavioral control in the theory of planned behavior: A preregistered study,” Journal of Theoretical Social Psychology , vol. 5, no. 1, pp. 35–45, Jan. 2021, doi: 10.1002/JTS5.83. S. Taylor and P. A. Todd, “Understanding information technology usage: A test of competing models,” Information Systems Research , vol. 6, no. 2, pp. 144–176, Jun. 1995, doi: 10.1287/ISRE.6.2.144;WEBSITE:WEBSITE:INFORMS-SITE. I. B. Ohanu, T. O. Shodipe, C. M. G. Ohanu, and J. E. Anene-Okeakwa, “System quality, technology acceptance model and theory of planned behaviour models: Agents for adopting blended learning tools,” E-Learning and Digital Media , vol. 20, no. 3, pp. 255–281, May 2023, doi: 10.1177/20427530221108031. S. Kaur and S. Arora, “Understanding customers’ usage behavior towards online banking services: an integrated risk–benefit framework,” Journal of Financial Services Marketing , vol. 28, no. 1, pp. 74–98, Mar. 2023, doi: 10.1057/S41264-022-00140-5. T. Hewavitharana, S. Nanayakkara, A. Perera, and P. Perera, “Modifying the unified theory of acceptance and use of technology (UTAUT) model for the digital transformation of the construction industry from the user perspective,” Informatics , vol. 8, no. 4, Dec. 2021, doi: 10.3390/INFORMATICS8040081. I. U. Khan, Z. Hameed, S. N. Khan, S. U. Khan, and M. T. Khan, “Exploring the Effects of Culture on Acceptance of Online Banking: A Comparative Study of Pakistan and Turkey by Using the Extended UTAUT Model,” Journal of Internet Commerce , vol. 21, no. 2, pp. 183–216, 2022, doi: 10.1080/15332861.2021.1882749;JOURNAL:JOURNAL:WICO20. V. Venkatesh and F. D. Davis, “A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies,” Manage Sci , vol. 46, no. 2, pp. 186–204, Feb. 2000, doi: 10.1287/mnsc.46.2.186.11926. Y. Lee, K. A. Kozar, and K. R. T. Larsen, “The Technology Acceptance Model: Past, Present, and Future,” Communications of the Association for Information Systems , vol. 12, 2003, doi: 10.17705/1CAIS.01250. D. I. Prastiawan, S. Aisjah, and R. Rofiaty, “The Effect of Perceived Usefulness, Perceived Ease of Use, and Social Influence on the Use of Mobile Banking through the Mediation of Attitude Toward Use,” Asia Pacific Management and Business Application , vol. 009, no. 03, pp. 243–260, Apr. 2021, doi: 10.21776/UB.APMBA.2021.009.03.4. S. Yousafzai, J. Pallister, and G. Foxall, “Multi-dimensional role of trust in Internet banking adoption,” Service Industries Journal , vol. 29, no. 5, pp. 591–605, 2009, doi: 10.1080/02642060902719958. M. Ramezaninia, F. Panahifar, and N. H. Sarhangi, “Significant factors affecting m-banking adoption case study: higher education institutions in Tehran,” International Journal of Electronic Business , vol. 17, no. 1, pp. 61–86, 2022, doi: 10.1504/IJEB.2022.120014. D. Gefen, E. Karahanna, and D. W. Straub, “Trust and tam in online shopping: AN integrated model,” MIS Q , vol. 27, no. 1, pp. 51–90, 2003, doi: 10.2307/30036519. V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, “User acceptance of information technology: Toward a unified view,” MIS Q , vol. 27, no. 3, pp. 425–478, 2003, doi: 10.2307/30036540. H. Siagian, Z. J. H. Tarigan, S. R. Basana, and R. Basuki, “The effect of perceived security, perceived ease of use, and perceived usefulness on consumer behavioral intention through trust in digital payment platform,” International Journal of Data and Network Science , vol. 6, no. 3, pp. 861–874, Jun. 2022, doi: 10.5267/J.IJDNS.2022.2.010. D. H. McKnight, V. Choudhury, and C. Kacmar, “Developing and validating trust measures for e-commerce: An integrative typology,” Information Systems Research , vol. 13, no. 3, pp. 334–359, Sep. 2002, doi: 10.1287/ISRE.13.3.334.81;REQUESTEDJOURNAL:JOURNAL:ISRE;WGROUP:STRING:PUBLICATION. P. A. Pavlou, “Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model,” International Journal of Electronic Commerce , vol. 7, no. 3, pp. 101–134, 2003, doi: 10.1080/10864415.2003.11044275;WEBSITE:WEBSITE:TFOPB;PAGEGROUP:STRING:PUBLICATION. I. M. Shaikh and H. Amin, “Influence of innovation diffusion factors on non-users’ adoption of digital banking services in the banking 4.0 era,” Inf Discov Deliv , Jan. 2024, doi: 10.1108/IDD-05-2023-0044. E. K. Ghani, M. M. Ali, M. N. R. Musa, and A. A. Omonov, “The Effect of Perceived Usefulness, Reliability, and COVID-19 Pandemic on Digital Banking Effectiveness: Analysis Using Technology Acceptance Model,” Sustainability 2022, Vol. 14, Page 11248 , vol. 14, no. 18, p. 11248, Sep. 2022, doi: 10.3390/SU141811248. Z. Cao, J. Zheng, and R. Liu, “Factors Affecting Users’ Continuous Usage in Online Health Communities: An Integrated Framework of SCT and TPB,” Healthcare 2023, Vol. 11, Page 1238 , vol. 11, no. 9, p. 1238, Apr. 2023, doi: 10.3390/HEALTHCARE11091238. K. Mathieson, “Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior,” Information Systems Research , vol. 2, no. 3, pp. 173–191, Sep. 1991, doi: 10.1287/ISRE.2.3.173;CTYPE:STRING:JOURNAL. M. Rahman, T. H. Ming, T. A. Baigh, and M. Sarker, “Adoption of artificial intelligence in banking services: an empirical analysis,” International Journal of Emerging Markets , vol. 18, no. 10, pp. 4270–4300, Nov. 2023, doi: 10.1108/IJOEM-06-2020-0724. V. Saprikis, G. Avlogiaris, and A. Katarachia, “A Comparative Study of Users versus Non-Users’ Behavioral Intention towards M-Banking Apps’ Adoption,” Information (Switzerland) , vol. 13, no. 1, Jan. 2022, doi: 10.3390/INFO13010030. F. La Barbera and I. Ajzen, “Moderating role of perceived behavioral control in the theory of planned behavior: A preregistered study,” Journal of Theoretical Social Psychology , vol. 5, no. 1, pp. 35–45, Jan. 2021, doi: 10.1002/JTS5.83. J. F. George, “The theory of planned behavior and Internet purchasing,” Internet Research , vol. 14, no. 3, pp. 198–212, Jul. 2004, doi: 10.1108/10662240410542634/FULL/XML. C. K. Riemenschneider, D. A. Harrison, and P. P. Mykytyn, “Understanding it adoption decisions in small business: integrating current theories,” Information & Management , vol. 40, no. 4, pp. 269–285, Mar. 2003, doi: 10.1016/S0378-7206(02)00010-1. T. C. E. Cheng, D. Y. C. Lam, and A. C. L. Yeung, “Adoption of internet banking: An empirical study in Hong Kong,” Decis Support Syst , vol. 42, no. 3, pp. 1558–1572, Dec. 2006, doi: 10.1016/j.dss.2006.01.002. J. Sharifuddin, L. Ramalingam, Z. Mohamed, and G. Rezai, “Factors affecting intention to purchase edible bird’s nest products: the case of Malaysian consumers,” Journal of Food Products Marketing , vol. 20, no. sup1, pp. 75–84, 2014. R. J. Harris, A Primer of Multivariate Statistics . Psychology Press, 2001. doi: 10.4324/9781410600455. J. Keshminder and P. Río, “The missing links? The indirect impacts of drivers on eco‐innovation,” Corp Soc Responsib Environ Manag , 2019, [Online]. Available: https://api.semanticscholar.org/CorpusID:197894847 C. M. Ringle, S. Wende, and J.-M. Becker, “SmartPLS 4. Oststeinbek: SmartPLS GmbH,” 2022. W. W. Chin, B. L. Marcolin, and P. R. Newsted, “A Partial Least Squares Latent Variable Modeling Approach for Measuring Interaction Effects: Results from a Monte Carlo Simulation Study and an Electronic-Mail Emotion/Adoption Study,” 2003. [Online]. Available: http://www.informs.org/Pubs/ S. Fuchs, “Common Method Variance Analysis of Structural Models,” Understanding Psychological Bonds between Individuals and Organizations , pp. 119–165, 2012, doi: 10.1057/9781137033970_6. M. I. Aguirre-Urreta and J. Hu, “Detecting Common Method Bias,” ACM SIGMIS Database: the DATABASE for Advances in Information Systems , vol. 50, no. 2, pp. 45–70, May 2019, doi: 10.1145/3330472.3330477. M. C. Howard and J. Henderson, “A review of exploratory factor analysis in tourism and hospitality research: Identifying current practices and avenues for improvement,” J Bus Res , vol. 154, p. 113328, Jan. 2023, doi: 10.1016/j.jbusres.2022.113328. N. Kock and G. S. Lynn, “Lateral Collinearity and Misleading Results in Variance-Based SEM: An Illustration and Recommendations,” J Assoc Inf Syst , vol. 13, no. 7, p. 547, 2012. N. Kock, “Common method bias in PLS-SEM: A full collinearity assessment approach,” International Journal of e-Collaboration , vol. 11, no. 4, pp. 1–10, 2015, doi: 10.4018/ijec.2015100101. J. C. Anderson and D. W. Gerbing, “Structural equation modeling in practice: A review and recommended two-step approach.,” Psychol Bull , vol. 103, no. 3, pp. 411–423, May 1988, doi: 10.1037/0033-2909.103.3.411. M. Sarstedt, C. M. Ringle, and J. F. Hair, “Partial Least Squares Structural Equation Modeling,” Handbook of Market Research , pp. 587–632, Jan. 2022, doi: 10.1007/978-3-319-57413-4_15. T. Ramayah, J.-H. Cheah, F. Chuah, and H. Ting, “PLS-SEM using SmartPLS 3.0: Chapter 13: Assessment of Moderation Analysis,” 2018. [Online]. Available: https://www.researchgate.net/publication/341357609 J. F. Hair, G. T. M. Hult, C. M. Ringle, and M. Sarstedt, A Primer on Partial Least Squares Structural Equation Modeling PLS-SEM , vol. 3. 2022. J. F. Hair, J. J. Risher, M. Sarstedt, and C. M. Ringle, “When to use and how to report the results of PLS-SEM,” Jan. 14, 2019, Emerald Group Publishing Ltd. doi: 10.1108/EBR-11-2018-0203. N. Darsono, A. Yahya, A. Muzammil, S. Musnadi, C. Anwar, and W. Irawati, “Consumer Actual Purchase Behavior for Organic Products In Aceh, Indonesia,” pp. 265–275, Jan. 2019, doi: 10.2991/AGC-18.2019.43. C. Fornell and D. F.Larker, “36. Fornell & Larcker 1981”. J. M. Becker, J. H. Cheah, R. Gholamzade, C. M. Ringle, and M. Sarstedt, “PLS-SEM’s most wanted guidance,” Jan. 02, 2023, Emerald Publishing . doi: 10.1108/IJCHM-04-2022-0474. E. D. Hahn and S. H. Ang, “From the editors: New directions in the reporting of statistical results in the Journal of World Business,” Journal of World Business , vol. 52, no. 2, pp. 125–126, Feb. 2017, doi: 10.1016/j.jwb.2016.12.003. J. Cohen, Statistical Power Analysis for the Behavioral Sciences . Routledge, 2013. doi: 10.4324/9780203771587. J. C. Daly and J. Cohen, “Statistical Power Analysis For The Behavioral Sciences Revised Edition,” 1987. [Online]. Available: https://api.semanticscholar.org/CorpusID:123646481 G. Shmueli et al. , “Predictive model assessment in PLS-SEM: guidelines for using PLSpredict,” Eur J Mark , vol. 53, no. 11, pp. 2322–2347, Sep. 2019, doi: 10.1108/EJM-02-2019-0189. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-6670162","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":468521161,"identity":"57b99b96-a4b1-4671-81c2-5a672542d228","order_by":0,"name":"Reem Abdalla","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYBAC+wYGNgYeBgsZBvYGBmagAGMDAw8bXi0GB8BaJHgYeA4wNpOoRSKBaC3Mzx68qZHg4Z/5xvxxAYON7IYDvMce4PcLm7nhnGMSPBK3cwybZzCkGW84wJdugE+LHQODmTQPG9BhIC08DIcTNxzgMZPAp8WYgf2bNM8/CR75m2dAWv4T1mLYwGMmzdsmwWNwgwek5QBhLQaHecoN5/ZJ8BieSSucPcMg2XjmYb40/FqOt2978OabjZzc8cMbPhdU2Mn2He89hlcLOMaRTMAQGQWjYBSMglFADgAA0LdE0HV1Sf4AAAAASUVORK5CYII=","orcid":"","institution":"University of Technology Bahrain","correspondingAuthor":true,"prefix":"","firstName":"Reem","middleName":"","lastName":"Abdalla","suffix":""}],"badges":[],"createdAt":"2025-05-15 07:53:25","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6670162/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6670162/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84279239,"identity":"d9a5b31d-df28-4d92-b79c-8d3b2a8fb9e6","added_by":"auto","created_at":"2025-06-10 06:17:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":95257,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6670162/v1/24dd1a1ecae7bbd90bb18b1a.png"},{"id":84279984,"identity":"3873a5aa-f539-41e8-b237-af5eee546a3b","added_by":"auto","created_at":"2025-06-10 06:25:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":105718,"visible":true,"origin":"","legend":"\u003cp\u003ePath diagram based on the data analysis\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6670162/v1/668d4783b1f31c77b0862f65.png"},{"id":86441246,"identity":"98921897-8a39-4959-839e-02133ab4948f","added_by":"auto","created_at":"2025-07-10 16:31:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2114636,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6670162/v1/c0c4fe16-2b4b-4dc6-aab7-41d5a97d5564.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Modeling Digital Banking Adoption in Saudi Arabia: An Integrated C-TAM-TPB Framework Examining the Role of Perceived Security-Based Trust","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRecent developments in technology, globalization, and competition have led banks worldwide to adopt innovative technologies. Internet Banking (IB) has emerged as a widely accepted solution, providing customers with secure 24-hour access to their accounts for various financial activities [1]. This advancement offers banks an opportunity to deliver services more efficiently. Saudi Arabia's banking sector was one of the first in the Middle East to introduce electronic banking services (EBS), extending their reach through remote and electronic channels. [2]. The Kingdom of Saudi Arabia is undergoing a remarkable evolution in its banking industry, driven by the ambitious goals outlined in Vision 2030 and the swift progress of digital technologies. [3], [4]. This initiative seeks to revitalize the financial ecosystem, broaden the spectrum of economic activities, and elevate the overall wellbeing of its residents [5]. Within this context, digital banking has emerged as an essential driver, offering a variety of benefits that include enhanced convenience, reduced costs, and better access to financial services. In spite of significant financial commitments to enhance technology and infrastructure, the adoption of digital banking services in Saudi Arabia remains markedly less than that observed in other advanced nations [6] This inconsistency underscores an urgent need to explore the basic factors that shape consumer behavior in this area. Additionally, various insufficient measures and influences on the demand side persistently obstruct the adoption of cashless technology [7], [8]. The potential of digital banking relies significantly on creating a strong and secure atmosphere that nurtures confidence among users. This is particularly important since online financial transactions are often viewed as more precarious compared to conventional banking practices [9], [10]. The apprehension surrounding this risk, along with worries about the safeguarding of personal information and potential security violations, can notably hinder the widespread acceptance of digital banking services.[11]. This highlights the essential importance of perceived trust within digital financial environments. Therefore, it becomes crucial to explore how trust and perceived security influence the intention of Saudi consumers to embrace digital banking.\u003c/p\u003e\n\u003cp\u003eThe concept of Perceived Security-Based Trust (PSBT) within Digital Environments holds significant importance. Previous research has primarily centered around the Technology Acceptance Model and the Theory of Planned Behavior to elucidate the process of technology adoption. However, there exists a need for more in-depth exploration of the intricate aspects of trust and security specifically related to digital banking in Saudi Arabia [8]. The presence of rust significantly impacts the customer's perception and willingness to engage with internet banking services.[12]. In the realm of technological services, trust emerges as a fundamental aspect that shapes the relationship between providers and their customers. The level of trust perceived by customers directly correlates with the frequency with which they utilize these services [13], [14]; the greater the trust, the higher the consumption rates [15]. Within the context of digital banking, the concept of Perceived Security-Based Trust encompasses the beliefs surrounding safety and reliability associated with digital platforms. It is crucial for customers to feel confident that the services offered are secure and devoid of risk, while also benefiting from robust data protection measures [16]. This necessity explains the relatively slow uptake of mobile technology by both banks and clients, despite the widespread use of smartphones. There exists a pressing demand for enhanced IT resources and aesthetically pleasing, functional digital platforms, as these advancements would likely facilitate a greater acceptance and integration of digital services [17]. However, few models currently provide a comprehensive approach to fully incorporate the element of trust within this framework’s constructs into TAM-TPB frameworks for financial services in developing digital economies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo address these gaps, this research aims to develop and test an integrated model that combines the core constructs of the Technology Acceptance Model and the Theory of Planned Behavior with Perceived Security-Based Trust, resulting in a comprehensive understanding of the factors driving digital banking adoption in Saudi Arabia. This study aims to bridge this gap by providing a holistic understanding of the key factors that influence the adoption of digital banking in Saudi Arabia, with a particular focus on the role of trust and perceived security [18].\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eTrust is a multifaceted and complex concept that has been studied by multiple fields and disciplines. The concept is rooted in the behavioral confidence that reflects an actor’s intention to depend on another party’s expectation of future actions. Trust reduces the perceived risk involved in the interaction [19], thereby the actor believes that another party should behave in a certain condition. Factors affecting trust can be grouped into four categories based on two different dimensions: entity and trustor [20]. The trustor dimension consists of ability, benevolence and integrity trust factors, while the entity characterizes a person or group, an organization or institution, and the situation of a particular event [21]. Trust is an emergent future-oriented belief expressed as a subjective probability.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1. Digital Banking Service and Trust in Financial Services\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDigital banking allows customers to access, order, or subscribe to services through devices over a public network or online remotely, without needing bank staff's physical presence [22], Consumers can perform banking transactions, receive advice, and obtain account statements using computers or mobile devices with adequate network connectivity [23], [24]. Users access internet service banks through public computers or ATMs, which raises access barriers related to trust. Trust issues are critical for stakeholders as they comply with service, ethics, and economic development standards [25]. Understanding the factors influencing consumer trust in e-commerce, including privacy, policy presentation, and lender reliability, is essential. In financial services, perceived security is crucial for mitigating transaction risks. Banks strive to assure customers that their transaction risks are minimal, facilitating safe information sharing during transactions and collaborations [26], [27].\u003c/p\u003e\n\u003cp\u003eTrust has gained prominence in the IS and business sectors, as stakeholders seek to comply with various services, ethics, and economic development codes [28], [29]. Notable research has examined what builds and impacts consumer trust in e-commerce, emphasizing the roles of privacy, policy presentation, and lenders [30].\u003c/p\u003e\n\u003cp\u003eIn financial services, perceived security ensures the elimination of divestiture risk in transactions. Banks strive to provide customers with the lowest transaction risks. Essentially, security trust signifies the belief in safe information sharing during transactions and collaborations [31].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2. Technology Acceptance Model (TAM)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Technology Acceptance Model (TAM) proposed an information systems success model to ascertain how new technologies are recognized. The TAM postulates two core constructs being perceived ease of use (PEU) and perceived usefulness (PU) that form another fleet of constructs, like attitude to use (ATU), behavioral intention (BI), actual system use, and the external variables [32].\u003c/p\u003e\n\u003cp\u003e2.2.1. Perceived Ease of Use (PEU) A user's perception of a system's ability to reduce effort and simplify task complexities, while also enhancing its ease of use, is indicated [33]. Users generally view e-banking as user-friendly; consequently, it is recommended that banks streamline their security features. [34].\u003c/p\u003e\n\u003cp\u003e2.2.2. Perceived Usefulness (PU) refers to the capability of an information system to analyze data and provide accurate responses to user inquiries [35]. This concept includes elements of both information quality and system quality. Even if a system is easy to use, users may hesitate to engage with it if they do not consider it useful [36], [37].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.2.3. Attitude to Use (ATU) refers to a comprehensive set of attitudinal frameworks aimed at evaluating the behaviors of others or their support for a specific behavior [38], [39]. For instance, an individual may assess their own interaction with a system, the operation of that system, or the effectiveness of a speech understanding system. People can hold either favorable or unfavorable attitudes and may communicate these assessments with varying degrees of intensity [40].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3. Perceived Security in Digital Transactions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhile the digital banking form may initially seem convenient and efficient, a considerable number of customers continue to harbor significant concerns regarding its actual security and reliability [41]. In fact, the United States is at the forefront of the world in terms of e-commerce development, which can provide invaluable insights drawn from its varied experiences [42]. Digital banking and stock trading institutions typically strive to anticipate the diverse needs and objectives of their customers, as well as the essential technology that underpins secure transactions [22]. Nonetheless, these institutions must diligently address the growing anxiety among their customers that stem from perceived risks associated with engaging in this relatively new and evolving channel [43]. Digital technology led to increased security breaches, causing users to worry about digital transaction safety. Ensuring data integrity is crucial, necessitating that digital format be immutable and access limited to authorized parties [44], [45]. Transparent tracking of transactions should be established, allowing customers to analyze their activity and spot potential security issues or unfair practices that could harm their finances [46]. By building an environment of trust, digital banking institutions can alleviate customers' anxiety. To encourage the adoption of these innovative channels, it is vital to assure customers regarding the physical and procedural security of their investments, which includes up-to-date technology solutions and effective management systems safeguarding data [10].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4. Theory of Planned Behavior (TPB)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Theory of Planned Behavior (TPB) elucidates how specific factors can predict behavior based on intentions. In TPB, attitude toward the behavior, perceived behavioral control (PBC), and subjective norm (SN) constructs directly influence intention.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe concept of Attitude Toward Behavior plays a vital role in comprehending the way people develop their intentions to embrace particular services. This construct includes the various beliefs and assessments that individuals possess about the potential results associated with the service [47], [48].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSubjective Norms (SN): This concept examines the societal pressures that shape a person's actions, informed by their perceptions of what important people in their lives believe [49]. It encompasses descriptive norms, which reflect an individual's understanding that their behavior should correspond to the actions of others, as well as injunctive norms, which pertain to the anticipated behaviors that the reference group expects from them [50].\u003c/p\u003e\n\u003cp\u003ePerceived Behavioral Control (PBC): PBC indicates how easy or difficult an individual perceives a behavior to be and is based on past experiences and anticipated challenges [51], [52]. Control can arise from external factors or intentions in the presence of enabling or constraining conditions [51].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5. Integration of C-TAM and TPB\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe C-TAM-TPB framework, which combines the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB), provides a clear method to predict and understand how people accept information systems today [53], [54]. To elaborate, TAM first introduced by [55] primarily focuses on cognitive beliefs, particularly emphasizing two key constructs that drive technology adoption: Perceived Usefulness (PU) and Perceived Ease of Use (PEU) [56], [57]. This foundational model serves as a cornerstone for assessing how users feel about technology’s value and how easy they find it to utilize [58]. The Theory of Planned Behavior (TPB), created by [59], broadens this idea by adding more factors that influence how users act, specifically Subjective Norms (SN) and Perceived Behavioral Control (PBC). These components act as essential social influences and aspects of control that significantly affect Behavioral Intention (BI), thereby providing a more holistic understanding of user motivations [52], [60]. The C-TAM-TPB model, developed by \u0026nbsp;[61], effectively combines the attitudes from TAM and the social and control beliefs from TPB [62], enhancing its ability to explain outcomes in fields like health informatics, e-commerce, and digital banking [63], [64]. Research shows that C-TAM-TPB consistently outperforms TAM or TPB used separately, due to its thorough incorporation of motivational factors such as attitudes, perceived usefulness, and ease of use, along with key external influences like social impact and perceived control over technology Studies by[64], [65] further support this model's relevance. The integrative nature of C-TAM-TPB is especially important in areas where security, trust, and social acceptability are crucial, making it a strong framework for understanding behavioral intentions in adopting digital banking solutions [63].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6. Hypotheses Development\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1. Perceived Ease of Use and its Impacts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe \u003cstrong\u003eTechnology Acceptance Model (TAM)\u003c/strong\u003e, developed by Davis, (1989), is one of the most widely adopted frameworks for understanding user acceptance of information systems. At the core of TAM is the construct \u003cstrong\u003ePerceived Ease of Use (PEU)\u003c/strong\u003e, defined as the degree to which a person believes that using a system will be free of effort. Prior research consistently supports that PEU positively affects \u003cstrong\u003ePerceived Usefulness (PU)\u003c/strong\u003e is ([55], [66], as systems that are easier to use are more likely to be perceived as beneficial. Moreover, PEU has been shown to significantly influence \u003cstrong\u003eAttitude Toward Use\u003c/strong\u003e, which reflects a user’s overall evaluative response to system adoption \u0026nbsp;[67], [68]. Recently, in the realm of online banking and electronic services, Perceived Experiential Usefulness (PEU) has been associated with Perceived Security-Based Trust (PSBT) \u0026nbsp;[69], as a system that is easy to understand and open typically improves user trust in its security components [70]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypotheses:\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eH1a\u003c/strong\u003e: Perceived Ease of Use (PEU) positively influences Perceived Usefulness (PU).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eH1b\u003c/strong\u003e: Perceived Ease of Use (PEU) positively influences Attitude to Use.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eH1c\u003c/strong\u003e: Perceived Ease of Use (PEU) positively influences Perceived Security-Based Trust (PSBT).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e2. Perceived Usefulness as a Cognitive Driver\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePerceived Usefulness (PU) is defined as the degree to which a person feels that utilizing a specific system improves their job performance or increases the effectiveness of their decision-making processes. [55]. Several studies (e.g., [71], [72] confirmed that Perceived Usefulness (PU) has a direct influence on both the Attitude Toward Use and the Behavioral Intention (BI) to utilize a system[68], [73].\u003c/p\u003e\n\u003cp\u003eThis connection is especially compelling in scenarios such as digital banking, where the value of the service like time saving features and seamless transactions greatly influences user dedication. Consequently, a positive perception of the system's benefits not only fosters favorable attitudes but also directly drives users' intentions to engage further.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypotheses:\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eH2a\u003c/strong\u003e: Perceived Usefulness (PU) positively influences Attitude to Use.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eH2b\u003c/strong\u003e: Perceived Usefulness (PU) positively influences Behavioral Intention to use digital banking.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e3. Perceived Security-Based Trust (PSBT) in Digital Environments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTrust plays a vital role in the success of digital banking\u0026nbsp;[74], as users are required to provide sensitive financial information. Perceived Security-Based Trust (PSBT) represents the confidence users have in the technical security of the system and the trustworthiness of the institution involved\u0026nbsp;[69], [75]. Prior literature by [76], [77]. highlights the multidimensional influence of PSBT across different acceptance factors. \u0026nbsp;Furthermore, in alignment with the \u003cstrong\u003eTheory of Planned Behavior (TPB)\u003c/strong\u003e, PSBT influences Subjective Norms and Perceived Behavioral Control, indicating that trust enhances confidence in one's abilities to use a system and promotes support from social norms[78].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypotheses:\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eH3a\u003c/strong\u003e: Perceived Security-Based Trust (PSBT) positively influences Perceived Usefulness (PU).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eH3b\u003c/strong\u003e: Perceived Security-Based Trust (PSBT) positively influences Attitude to Use.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eH3c\u003c/strong\u003e: Perceived Security-Based Trust (PSBT) positively influences Subjective Norms (SN).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eH3d\u003c/strong\u003e: Perceived Security-Based Trust (PSBT) positively influences Perceived Behavioral Control (PBC).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e4. Attitude to Use and Behavioral Intention\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn both the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB), Attitude Toward Use serves as an essential mediating factor that links cognitive perceptions to behavioral intentions. Studies by \u0026nbsp;[61], [79], [80] suggest that positive attitudes increase the likelihood of system adoption.\u003c/p\u003e\n\u003cp\u003eThe strong empirical evidence supporting the connection between Attitude and Behavioral Intention (BI) in various contexts, such as banking, healthcare, and mobile applications, suggests that a comparable association is likely to exist within digital banking settings as well [81].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis:\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eH4\u003c/strong\u003e: Attitude to Use positively influences Behavioral Intention to use digital banking.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e5. Social and Control Factors from TPB\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003etheory of Planned Behavior (TPB) enhances the comprehension of human behavior by incorporating the concepts of Subjective Norms (SN) and Perceived Behavioral Control (PBC) [59]. Subjective Norms represent the social pressures individuals feel from their peers, family, or society regarding the performance of certain behaviors. On the other hand, Perceived Behavioral Control pertains to an individual's assessment of how easy or challenging it is to engage in a behavior, which is often associated with their self-efficacy and available resources[52], [82].\u003c/p\u003e\n\u003cp\u003eBoth constructs have been validated in studies of online service adoption such as \u0026nbsp;[82], [83], [84]; especially in contexts involving risk or complexity, like digital banking.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypotheses:\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eH5a\u003c/strong\u003e: Subjective Norms (SN) positively influence Behavioral Intention to use digital banking.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eH5b\u003c/strong\u003e: Perceived Behavioral Control (PBC) positively influences Behavioral Intention to use digital banking.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"3. Research Methodology","content":"\u003cp\u003e\u003cstrong\u003eMeasurement: A\u003c/strong\u003e structured and validated questionnaire was developed to examine key factors influencing digital banking adoption in Saudi Arabia. It incorporates components from the C-TAM-TPB framework, focusing on Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Perceived Security-Based Trust (PSBT), Subjective Norms (SN), Perceived Behavioral Control (PBC), Attitude to Use, and Digital Banking Adoption Intention (DBAI), ensuring construct validity and measurement reliability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuestionnaire Development:\u0026nbsp;\u003c/strong\u003eThe questionnaire had multiple sections aligned with the integrated C-TAM-TPB model, grounded in prior research for relevance. Perceived Usefulness (PU) was evaluated with four items from [85]. focusing on digital banking benefits. Perceived Ease of Use (PEOU) used three items to assess system clarity and simplicity, based on[85]. Perceived Security-Based Trust (PSBT) employed three adapted items to gauge trust in digital transaction security. Attitude to Use, Subjective Norms, and Perceived Behavioral Control were assessed with three to four items each, primarily from [86]. Digital Banking Adoption Intention (DBAI) involved three statements derived from prior TAM studies. This design enabled a multidimensional evaluation of behavioral drivers for digital banking uptake.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurement Model Modification:\u0026nbsp;\u003c/strong\u003eMinor linguistic modifications were made to adapt the original items for the Saudi banking environment and general users. Perceived Usefulness (PU): Language was localized to highlight utility in personal financial management instead of organizational productivity. Perceived Ease of Use (PEOU): Items stayed unchanged due to their broad applicability. Perceived Security-Based Trust (PSBT): Wording was altered to address public concerns about online security and data privacy. Subjective Norms and Perceived Behavioral Control were adapted to reflect influences from family and peers and access to technology in Saudi Arabia. Adoption Intention and Attitude were slightly adjusted for clarity while maintaining theoretical integrity. All changes were validated through pre-testing and expert review.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection:\u0026nbsp;\u003c/strong\u003eAn extensive online survey was carried out through Google Forms with the aim of gathering insights into the behavioral perceptions of people throughout Saudi Arabia. The survey consisted of various sections, featuring Likert scale items alongside multiple-choice questions to collect demographic details and contextual information.\u003c/p\u003e\n\u003cp\u003eThe G*Power software played a crucial role in determining the minimum sample size needed for the study [87]. Focusing on a maximum of four predictors and aiming for a medium effect size of 0.15, the analysis revealed that at least 85 observations were required [88]. To bolster the reliability and generalizability of the findings, the researcher purposefully gathered a greater volume of data than the minimum requirement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResponse Rate:\u0026nbsp;\u003c/strong\u003eA comprehensive collection of 361 responses was submitted. Following the meticulous process of filtering out incomplete or invalid entries, 353 completed responses were gathered. The entire dataset was preserved for further examination. This favorable response rate surpassed the established minimum threshold, thereby ensuring the viability of rigorous statistical analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasures:\u0026nbsp;\u003c/strong\u003eEach construct was assessed utilizing a 5-point Likert scale, which spanned from 1 (Strongly Disagree) to 5 (Strongly Agree). This methodology facilitated a detailed examination of the participants\u0026apos; perspectives, experiences, and intentions regarding their behaviors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSampling Method:\u0026nbsp;\u003c/strong\u003eThe method of convenience sampling was utilized for the study. Initially, the survey link was disseminated via email, WhatsApp, LinkedIn, and other various professional networks dedicated to banking topics. Participants were actively invited to circulate the link among their acquaintances, which facilitated an expansion of the sample and incorporated a wider range of demographics, including different regions, age brackets, and educational backgrounds. This approach effectively ensured that the survey captured a representative cross-section of the general banking population in Saudi Arabia, while also considering practical aspects of reaching respondents and maintaining accessibility.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChallenges During Data Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKey challenges included motivating less digitally literate participants, ensuring data accuracy, and engaging users in remote or conservative areas. Solutions involved sending reminders, clarifying survey instructions, and utilizing endorsements from experts and digital banking influences.\u003c/p\u003e"},{"header":"4. Results and Discussion","content":"\u003cp\u003eThe analysis of the measurement and structural model was conducted with the aid of SmartPLS version 4 [89]. This software is particularly beneficial as it does not depend on the assumption of normality, which is a significant advantage for survey research that commonly features non-normal distribution patterns [90].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4-1 Common Method Bias\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs the data from single source Standard method bias was required [91] . Recent investigations have highlighted significant limitations associated with Harman\u0026apos;s Single-Factor Test in effectively pinpointing CMB in research that relies on surveys. A contemporary study by [92], [93] revealed that this commonly utilized method has a restricted capacity to detect CMB, so the Full Collinearity technique was selected as a more effective approach than Harman\u0026apos;s Single-Factor Test to tackle this problem of common method bias (CMB). Such insights imply that researchers could be misled into believing they possess accurate findings when, in fact, they do not. A VIF value of \u0026le; 3. indicated an absence of bias [94], [95]. The analysis confirmed that the VIF was \u0026lt;3.3, as documented in Table 1, demonstrating that no bias was identified (See Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e Full Collinearity Testing\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"503\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConstruct\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eATU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePBC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePEU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrust\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVIF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e1.401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e1.424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e2.543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e2.259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e2.855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e1.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e2.145\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\u003cstrong\u003e4-2 Model Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe employed the recommendations outlined by Anderson \u0026amp; Gerbing [96] to examine the model through a two-step process. Initially, a comprehensive assessment of the measurement model was carried out to ascertain the precision and reliability of the instruments [97], [98]. This was succeeded by an in-depth analysis of the structural model aimed at validating our hypotheses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4-2-1 \u0026nbsp;Step 1: Measurement Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the process of assessing the measurement model, it is crucial to examine four distinct forms of validity. The reliability of the indicators is evaluated through loadings, while the convergence validity is gauged by the Average Variance Extracted (AVE) [99], [100], [101]. Additionally, the internal consistency reliability can be determined through Composite Reliability (CR).\u003c/p\u003e\n\u003cp\u003eInitially, we assessed the loadings, Average Variance Extracted (AVE), and Composite Reliability (CR) metrics. To ensure robustness, these values should meet or exceed 0.708 for loadings, 0.5 for AVE, and 0.7 for CR, as stipulated by the evaluation criteria of [97]. As illustrated in Table 2, every indicator\u0026apos;s outer loading surpassed the critical threshold of 0.708, thereby confirming the convergent credibility at the indicator level. Furthermore, our findings indicated that all constructs achieved AVE values exceeding 0.5, which substantiates the convergent validity at the construct level. Lastly, every indicator within the measurement models adhered to the required composite reliability benchmarks. The results confirmed that all constructs exhibited internal consistency and reliability throughout (See Table 2).\u003c/p\u003e\n\u003cp\u003eSubsequently, we examined discriminant validity to determine how effectively a construct is uniquely separate from the other constructs present in the model. Discriminant validity was assessed using \u0026nbsp;[102] , where ensuring that AVE value was higher than its highest squared correlations with any other construct. (See Table 3).\u003c/p\u003e\n\u003cp\u003eThe two test ensured that the measurement model is valid and reliable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e Measurement model assessment\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eConstructs and their items\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLoading\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAVE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdoption intention\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e[85]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.759\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAttitude toward the behavior\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e[85]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.707\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eATU1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eATU2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eATU3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePerceived behavioral control (PBC)\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e[86]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.708\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePBC1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePBC2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePBC3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePBC4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEase of Use\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e[85]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.708\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePEU1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePEU2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePEU3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUsefulness\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e[85]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.749\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePU1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePU2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePU3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrust\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e[85]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrust\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.717\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrust\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrust\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubjective norm (SN)\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e[86]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSN1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSN2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSN3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSN4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\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\u0026nbsp;\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e Discriminant Validity\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"512\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eATU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePBC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePEU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrust\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eATU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePBC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePEU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrust\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.847\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\u003cstrong\u003e4-2-2 Step 2: Structural Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4-2-2-1 Path Coefficient\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBootstrapping enhanced the stability and precision of the estimates, leading to the generation of more dependable confidence intervals for the path coefficients. This methodology is regarded as superior to approaches utilizing smaller sample sizes (e.g., 500 or 1,000), as it mitigates the standard error while augmenting the accuracy of the estimates. Consequently, a bootstrapping technique utilizing 10,000 samples was implemented [97], and we reported the path coefficients, standard errors, t-values, and p-values associated with the structural model in alignment with \u0026nbsp;[97], [103]. Moreover, in light of the critiques regarding the reliance on p-values by [104], it is advisable to supplement p-values with confidence intervals and effect sizes as additional metrics for assessing the significance of the hypothesis. The findings of the hypothesis testing\u0026mdash;covering both direct and indirect effects\u0026mdash;are presented in Table 4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDirect Effects\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe further assessed the model\u0026rsquo;s explanatory power by examining the \u003cstrong\u003eR\u0026sup2; values\u003c/strong\u003e of the endogenous constructs. The R\u0026sup2; value for \u003cstrong\u003eBehavioral Intention (AI)\u003c/strong\u003e was \u003cstrong\u003e0.673\u003c/strong\u003e, indicating that the predictors accounted for \u003cstrong\u003e67.3%\u003c/strong\u003e of the variance in digital banking adoption intention. The associated t-value was \u003cstrong\u003e13.748\u003c/strong\u003e (p \u0026lt; 0.001), confirming the model\u0026apos;s statistical significance in explaining this construction (See Table 4 \u0026amp; Figure 2).\u003c/p\u003e\n\u003cp\u003eFor \u003cstrong\u003eAttitude to Use (ATU)\u003c/strong\u003e, the R\u0026sup2; value was \u003cstrong\u003e0.620\u003c/strong\u003e, showing that the predictors explained \u003cstrong\u003e62%\u003c/strong\u003e of the variance. The t-value of \u003cstrong\u003e11.549\u003c/strong\u003e (p \u0026lt; 0.001) supports this result as statistically significant.\u003c/p\u003e\n\u003cp\u003eThe model explained \u003cstrong\u003e48.1%\u003c/strong\u003e of the variance in \u003cstrong\u003ePerceived Behavioral Control (PBC)\u003c/strong\u003e, with an R\u0026sup2; of \u003cstrong\u003e0.481\u003c/strong\u003e and a t-value of \u003cstrong\u003e8.158\u003c/strong\u003e (p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePerceived Usefulness (PU)\u003c/strong\u003e had an R\u0026sup2; value of \u003cstrong\u003e0.633\u003c/strong\u003e, indicating that the predictors explained \u003cstrong\u003e63.3%\u003c/strong\u003e of its variance. This relationship was statistically significant, with a t-value of \u003cstrong\u003e12.261\u003c/strong\u003e (p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubjective Norms (SN)\u003c/strong\u003e showed an R\u0026sup2; of \u003cstrong\u003e0.476\u003c/strong\u003e, meaning that the model explained \u003cstrong\u003e47.6%\u003c/strong\u003e of its variance. The corresponding t-value was \u003cstrong\u003e9.463\u003c/strong\u003e (p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eLastly, \u003cstrong\u003ePerceived Security-Based Trust (Trust)\u003c/strong\u003e had an R\u0026sup2; value of \u003cstrong\u003e0.561\u003c/strong\u003e, with a t-value of \u003cstrong\u003e11.176\u003c/strong\u003e (p \u0026lt; 0.001), confirming a substantial level of variance explained by the predictors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIndirect Effects\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo examine the mediating mechanisms embedded within the integrated C-TAM-TPB framework, a comprehensive analysis of indirect effects was conducted. The results uncovered \u003cstrong\u003eseveral significant multi-path mediations\u003c/strong\u003e, emphasizing the complex interrelationships among perceived ease of use (PEU), perceived usefulness (PU), perceived trust, and behavioral intention (AI) (See Table 5 \u0026amp; Figure 2).\u003c/p\u003e\n\u003cp\u003eOne of the most noteworthy findings was the \u003cstrong\u003eindirect influence of PEU on Behavioral Intention (AI)\u003c/strong\u003e. Although the \u003cstrong\u003edirect path from PEU to AI\u003c/strong\u003e was not modeled, the analysis revealed \u003cstrong\u003emultiple significant indirect pathways\u003c/strong\u003e. Specifically, \u003cstrong\u003ePEU \u0026rarr; Attitude to Use \u0026rarr; AI\u003c/strong\u003e (\u0026beta; = 0.140, t = 3.119, p = 0.002) emerged as a prominent mediation chain, suggesting that ease of use leads to more favorable attitudes, which in turn heighten the intention to adopt digital banking services. Similarly, \u003cstrong\u003ePEU \u0026rarr; PU \u0026rarr; Attitude to Use \u0026rarr; AI\u003c/strong\u003e (\u0026beta; = 0.062, t = 2.884, p = 0.004) highlights a sequential cognitive-affective route, underlining PU\u0026rsquo;s role as a pivotal bridge between ease perceptions and attitudinal commitment.\u003c/p\u003e\n\u003cp\u003eAnother compelling chain is \u003cstrong\u003ePEU \u0026rarr; Trust \u0026rarr; Attitude to Use \u0026rarr; AI\u003c/strong\u003e (\u0026beta; = 0.023, t = 2.468, p = 0.014), which reveals that trust, when nurtured through usability, indirectly fosters behavioral intention via attitudinal shifts. This layered pathway not only validates trust as a mediator but positions \u003cstrong\u003ePEU as an enabler of trust\u003c/strong\u003e, with implications for interface design and user onboarding in fintech platforms.\u003c/p\u003e\n\u003cp\u003eTrust, in itself, also demonstrated \u003cstrong\u003esignificant indirect pathways\u003c/strong\u003e. While the \u003cstrong\u003edirect path from Trust to AI\u003c/strong\u003e was significant (\u0026beta; = 0.306, t = 4.590, p \u0026lt; 0.001), it was further complemented by \u003cstrong\u003eTrust \u0026rarr; PU \u0026rarr; Attitude to Use \u0026rarr; AI\u003c/strong\u003e (\u0026beta; = 0.028, t = 2.889, p = 0.004), indicating a dual-pathway influence \u0026mdash; both direct and cognitive-affective. This points to trust\u0026apos;s dual nature: an emotional state and a logical appraisal based on perceived performance benefits.\u003c/p\u003e\n\u003cp\u003eIn contrast, \u003cstrong\u003eSubjective Norms (SN)\u003c/strong\u003e failed to significantly mediate the relationship between Trust and AI (\u0026beta; = 0.020, t = 1.445, p = 0.149), suggesting that while trust fosters normative pressure, these social cues may not translate effectively into behavioral intentions in the context of digital banking in Saudi Arabia \u0026mdash; possibly due to rising financial autonomy or individualism among users.\u003c/p\u003e\n\u003cp\u003eAdditional indirect effects included \u003cstrong\u003ePEU \u0026rarr; Trust \u0026rarr; PU \u0026rarr; Attitude to Use \u0026rarr; AI\u003c/strong\u003e, a longer path that, although statistically modest, underscores the compounded effect of multiple mediators. These deep-chain mediations reflect a \u003cstrong\u003ecascading influence\u003c/strong\u003e, where initial usability perceptions trigger trust and perceived utility, which then generate attitudinal and behavioral outcomes.\u003c/p\u003e\n\u003cp\u003eAltogether, these findings illuminate the \u003cstrong\u003emulti-layered and interdependent mechanisms\u003c/strong\u003e driving digital banking adoption. Rather than acting in isolation, key predictors like PEU and Trust function through \u003cstrong\u003eindirect, serial mediation\u003c/strong\u003e, showcasing the value of adopting a sophisticated, path-analytic perspective rather than relying on bivariate relationships.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e Structural model assessment: Hypotheses testing (direct relationships\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"552\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypothesis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDirect Relationships\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSTd. Beta\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd Dev.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP- values\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePCI LL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ef\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH1a\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePEU -\u0026gt; PU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e9.715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003ep\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e[0.452, 0.688]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.399\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH1b\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePEU -\u0026gt; ATU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e5.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003ep\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e[0.25, 0.549]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH1c\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePEU -\u0026gt; Trust\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e22.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003ep\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e[0.676, 0.807]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e1.278\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH2a\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePU -\u0026gt; AI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e3.728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003ep\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e[0.143,0.45]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH2b\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePU -\u0026gt; ATU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e5.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003ep\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e[0.25, 0.533]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH3a\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrust -\u0026gt; PU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e4.617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003ep\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e[0.155, 0.378]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH3b\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrust -\u0026gt; ATU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0.521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e[-0.085, 0.171]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH3c\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrust -\u0026gt; SN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e19.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003ep\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e[0.613, 0.754]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.909\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH3d\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrust -\u0026gt; PBC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e16.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003ep\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e[0.601, 0.766]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.928\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eATU -\u0026gt; AI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e5.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003ep\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e[0.216, 0.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH1d\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSN -\u0026gt; AI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0.648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e[-0.089, 0.147]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH1b\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePBC -\u0026gt; AI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e2.651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e[0.056, 039]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e Structural model assessment: \u0026nbsp;(indirect relationships)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"737\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInfluencing Relationships\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSTd. Beta\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd Dev.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP- values\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e[PCI, LL]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSTd. Beta\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP- values\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePathways to Significant Indirect Effects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"8\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePEU -\u0026gt; AI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"8\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"8\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"8\" style=\"width: 48px;\"\u003e\n \u003cp\u003e13.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"8\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"8\" style=\"width: 90px;\"\u003e\n \u003cp\u003e[0.522, 0.709]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e3.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003ePEU -\u0026gt; ATU -\u0026gt; AI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003ePEU -\u0026gt; Trust -\u0026gt; ATU -\u0026gt; AI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e2.493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003ePEU -\u0026gt; Trust -\u0026gt; PBC -\u0026gt; AI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e3.501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003ep\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003ePEU -\u0026gt; PU -\u0026gt; ATU -\u0026gt; AI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e3.316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003ePEU -\u0026gt; Trust -\u0026gt; PU -\u0026gt; AI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003ePEU -\u0026gt; Trust -\u0026gt; SN -\u0026gt; AI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e3.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003ePEU -\u0026gt; PU -\u0026gt; AI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e2.882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003ePEU -\u0026gt; Trust -\u0026gt; PU -\u0026gt; ATU -\u0026gt; AI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePEU -\u0026gt; ATU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 48px;\"\u003e\n \u003cp\u003e5.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 90px;\"\u003e\n \u003cp\u003e[0.222, 0.472]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e3.693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003ep\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003ePEU -\u0026gt; Trust -\u0026gt; PU -\u0026gt; ATU\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003ePEU -\u0026gt; Trust -\u0026gt; ATU\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e4.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003ep\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003ePEU -\u0026gt; PU -\u0026gt; ATU\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePEU -\u0026gt; PBC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e10.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e[0.421, 0.606]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003ePEU -\u0026gt; Trust -\u0026gt; PBC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePEU -\u0026gt; PU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e4.682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e[0.116, 0.281]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003ePEU -\u0026gt; Trust -\u0026gt; PU\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePEU -\u0026gt; SN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e12.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.43, 0.595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003ePEU -\u0026gt; Trust -\u0026gt; SN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePU -\u0026gt; AI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e3.757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.077, 0.232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003ePU -\u0026gt; ATU -\u0026gt; AI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrust -\u0026gt; AI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 44px;\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4.594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.173, 0.437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eTrust -\u0026gt; SN -\u0026gt; AI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e3.351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eTrust -\u0026gt; PU -\u0026gt; AI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e2.555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eTrust -\u0026gt; PBC -\u0026gt; AI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eTrust -\u0026gt; ATU -\u0026gt; AI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e2.899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eTrust -\u0026gt; PU -\u0026gt; ATU -\u0026gt; AI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrust -\u0026gt; ATU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e3.728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.058, 0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eTrust -\u0026gt; PU -\u0026gt; ATU\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e4-2-2-2 Testing Coefficient of Determination, Effect Sizes, and Predictive Performance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs per the recommendation of [97] the in sample and out of sample to prediction techniques, first examined the in-sample prediction to evaluate the model\u0026rsquo;s explanatory power through the coefficient of determination (R\u0026sup2;), followed by an assessment of effect sizes (f\u0026sup2;) and predictive performance using PLS-Predict for out-of-sample analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCoefficient of Determination (R\u0026sup2;)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe R\u0026sup2; values were evaluated to determine the level of variance explained by the structural model. According to [100], R\u0026sup2; values of 0.25, 0.50, and 0.75 are considered weak, moderate, and substantial, respectively. In our model, the R\u0026sup2; for Behavioral Intention (AI) was 0.673, indicating that 67.3% of the variance in AI is explained by Attitude, Subjective Norms, Perceived Behavioral Control, PU, and Trust\u0026mdash;demonstrating a substantial level of explanatory power.\u003c/p\u003e\n\u003cp\u003eFor Attitude to Use (ATU), the R\u0026sup2; value was 0.620, indicating that the model explains 62% of the variance based on PEU, PU, and Trust. Similarly, Perceived Usefulness (PU) showed an R\u0026sup2; value of 0.633, while Perceived Behavioral Control (PBC) was moderately explained with an R\u0026sup2; of 0.481. Subjective Norms (SN) and Trust were also moderately explained, with R\u0026sup2; values of 0.476 and 0.561, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEffect Size (f\u0026sup2;)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo understand the relative contribution of exogenous constructs, f\u0026sup2; values were assessed. Values of 0.02, 0.15, and 0.35 correspond to weak, medium, and strong effects, respectively [105], [106]. The strongest effect was observed for PEU \u0026rarr; Trust (f\u0026sup2; = 1.278), signifying a large influence. Other notable medium-to-large effects included PEU \u0026rarr; PU (f\u0026sup2; = 0.399), Trust \u0026rarr; PBC (f\u0026sup2; = 0.928), and Trust \u0026rarr; SN (f\u0026sup2; = 0.512). Meanwhile, PU \u0026rarr; Attitude (f\u0026sup2; = 0.153) and Attitude \u0026rarr; AI (f\u0026sup2; = 0.162) demonstrated medium effects. Paths such as Trust \u0026rarr; Attitude (f\u0026sup2; = 0.002) and SN \u0026rarr; AI (f\u0026sup2; = 0.001) were negligible, suggesting weak or no practical significance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredictive Performance (PLS-Predict)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess out-of-sample predictive performance, we employed PLS-Predict [107], using a 10-fold cross-validation procedure. Table results show the Q\u0026sup2;predict values for the reflective indicators of Behavioral Intention (AI) were 0.477 (AI1), 0.404 (AI2), and 0.403 (AI3), all above the 0 value, indicating medium to strong predictive relevance ( See Table 6).\u003c/p\u003e\n\u003cp\u003eIn terms of prediction error, the PLS-SEM_RMSE values (AI1 = 0.628; AI2 = 0.634; AI3 = 0.658) were all lower than their corresponding LM_RMSE counterparts (AI1 = 0.611; AI2 = 0.633; AI3 = 0.654), with positive SEM-RMSE margins of 0.017, 0.001, and 0.004, respectively. Since most of the indicators showed lower RMSE in the PLS-SEM model compared to the linear model (LM), the model\u0026rsquo;s predictive accuracy is confirmed [107].\u003c/p\u003e\n\u003cp\u003eThese findings indicate that the integrated C-TAM-TPB model not only explains substantial variance in behavioral intention but also demonstrates strong out-of-sample predictive performance, which supports its reliability in predicting user adoption of digital banking technologies in the Saudi context.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e6\u003c/strong\u003e. PLSpredict\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ\u0026sup2;predict\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePLS-SEM_RMSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLM_RMSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSEM-RMSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.004\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\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"473\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEndogenous variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ\u0026sup2;predict\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd Dev.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP- values\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e13.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003ep\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eATU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e11.549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003ep\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePBC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e8.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003ep\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e12.261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003ep\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e9.463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003ep\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrust\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e11.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003ep\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis research enhances the existing literature on digital banking adoption by substantiating an integrated framework based on the Combined-Technology Acceptance Model (C-TAM) and the Theory of Planned Behavior (TPB), emphasizing Perceived Security-Based Trust (PSBT) as a key explanatory element. The empirical findings indicate that Perceived Ease of Use (PEU) significantly affects both Perceived Usefulness (PU) and PSBT, while Trust has a crucial influence on behavioral control and normative expectations. These outcomes provide partial validation for earlier studies conducted by [55], [59][72], which highlight the importance of cognitive evaluations in determining intention. Nonetheless, this research diverges from earlier models, notably those by [59][61], by revealing a reduced significance of social norms, which did not demonstrate a substantial direct impact on Behavioral Intention. This shift may reflect the evolving nature of financial decision-making in the Saudi Arabian context, where individual autonomy and security of digital platforms are becoming more decisive than peer influences. The inclusion of PSBT, which has often been neglected in standard TAM-TPB frameworks, is highlighted as a potent factor that shapes behavioral outcomes both directly and indirectly via PU and Perceived Behavioral Control (PBC). This finding contrasts with earlier literature such as [71],[75], which primarily positioned trust as a secondary antecedent rather than a central integrating mechanism between TPB and TAM constructs. Furthermore, the observed multi-step mediation effects, particularly the sequential influence of PEU through PU and Attitude, illustrate the complex nature of adoption behavior. These interconnected effects imply that in environments characterized by low trust or developing digital frameworks, the coalescence of usability and trust is essential to establish both cognitive credibility and behavioral confidence. This complexity is frequently overlooked in simplistic adoption models, highlighting the necessity of employing more sophisticated modeling approaches in emerging markets. In conclusion, this study not only reaffirms established findings from the TAM and TPB literature but also broadens the theoretical landscape by demonstrating how trust serves as a foundational element that can stabilize individual control beliefs and perceived utility, particularly in contexts where social pressures are minimal or fragmented. This positions PSBT as a critical enabler across different constructs in adoption theory, presenting significant implications for strategies related to digital banking, policy formulation, and user education initiatives.\u003c/p\u003e"},{"header":"6. Implications and limitations","content":"\u003cp\u003e\u003cstrong\u003e6.1 Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe findings provide practical guidance for banks and fintech developers in cautious or transitional environments. Simplifying user interfaces and onboarding can enhance trust and ease of use for less tech-savvy individuals. Communication strategies should highlight security credentials and data protection features to strengthen perceived safety, indirectly boosting adoption. Additionally, enhancing users\u0026apos; control perceptions through tutorials, live support, and responsive design can increase engagement confidence. For policymakers, building trust through regulations and public digital literacy initiatives is crucial. Improving cybersecurity standards and enforcing transparent data policies can enhance user confidence and foster greater adoption.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.2 Limitations and Future Research\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. It relies on self-reported data, which may be affected by common method variance, despite strong statistical controls. The diverse sample was obtained through convenience sampling, possibly missing rural or lower-income populations. Additionally, the cross-sectional design restricts the inference of causality and tracking of changing attitudes over time.\u003c/p\u003e\n\u003cp\u003eFuture research should focus on longitudinal designs to assess behavioral changes as digital infrastructure evolves. Comparative studies among Gulf countries or other emerging digital economies could better contextualize trust and perceived control in technology adoption. Incorporating emotion-related factors, like perceived risk or enjoyment, may provide deeper insights into non-rational aspects of adoption behavior.\u003c/p\u003e"},{"header":"7. Declarations","content":"\u003cp\u003eAuthor Contributions:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConceptualization, R.A.; methodology, R.A.; software, R.A.; validation, R.A.; formal analysis, R.A.; investigation, R.A.; resources, R.A.; data curation, R.A.; writing—original draft preparation, R.A.; writing—review and editing, R.A.; visualization, R.A.; supervision, R.A.; project administration, R.A. The sole author has read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003eData Availability Statement\u003c/p\u003e\n\u003cp\u003eThe information contained in this research can be obtained by contacting the corresponding author.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThe researchers did not obtain any financial assistance for the research, writing, and/or dissemination of this article.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eI Dr. Reem Abdalla the single author from the University of Technology in Bahrain, would like to wholeheartedly thank the peers' and colleagues' moral support that has been priceless. I would also like to thank my friends and family for their unconditional support and tolerance throughout the process. I would also like to thank the anonymous editors and reviewers; their helpful comments have significantly improved the final manuscript.\u003c/p\u003e\n\u003cp\u003eEthical Approval Statement\u003c/p\u003e\n\u003cp\u003eThis study received ethical clearance from the Ethical Committee of the College of Administrative and Financial Sciences at the University of Technology Bahrain (UTB). The approval was granted under the reference number UTB2025CAFSEC026, dated 10 March 2025. The committee reviewed the research plan, which involved a questionnaire targeting adult bank customers in Saudi Arabia and confirmed its alignment with the institution’s research ethics policy. Moreover, the study was designed in line with international principles governing research involving human subjects, including the Declaration of Helsinki. No clinical intervention or personally sensitive information was collected.\u003c/p\u003e\n\u003cp\u003eInformed Consent Statement\u003c/p\u003e\n\u003cp\u003eBefore starting the questionnaire, participants were shown a short introduction explaining what the study was about, why it was being conducted, and what their rights were to decide if they chose to take part. The form made it clear that their participation was voluntary and that no personal or identifying information would be collected at any point.\u003c/p\u003e\n\u003cp\u003eTo continue, each participant had to check a box confirming they agreed to take part. Without this step, the form would not proceed. This served as their informed consent.\u003c/p\u003e\n\u003cp\u003eThe survey was conducted online using Google Forms, and responses were collected over the course of four weeks from \u003cstrong\u003eDecember 25, 2024 to January 22, 2025\u003c/strong\u003e. Participants could leave the survey at any moment without having to explain, and no names, emails, or contact details were recorded.\u003c/p\u003e\n\u003cp\u003eConflicts of Interest\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest in publishing this manuscript. They have fully adhered to ethical issues, including plagiarism, informed consent, misconduct, and data fabrication\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eA. Jalal, M. Al Mubarak, and F. Durani, \u0026ldquo;Financial Technology (Fintech),\u0026rdquo; \u003cem\u003eStudies in Systems, Decision and Control\u003c/em\u003e, vol. 487, pp. 525\u0026ndash;536, 2024, doi: 10.1007/978-3-031-35828-9_45.\u003c/li\u003e\n\u003cli\u003eM. Al-Husein and M. Asad Sadi, \u0026ldquo;Preference on the Perception of Mobile Banking: A Saudi Arabian Perspective,\u0026rdquo; \u003cem\u003eEuropean Online Journal of Natural and Social Sciences\u003c/em\u003e, vol. 4, no. 1, pp. 161\u0026ndash;172, 2015, Accessed: May 05, 2025. [Online]. Available: https://european-science.com/eojnss/article/view/2627/pdf\u003c/li\u003e\n\u003cli\u003eA. Council, S. Grand, and K. Wolff, \u0026ldquo;ASSESSING SAUDI VISION 2030: A 2020 REVIEW\u0026rdquo;.\u003c/li\u003e\n\u003cli\u003eS. A. Al-Somali, R. Gholami, and B. Clegg, \u0026ldquo;An investigation into the acceptance of online banking in Saudi Arabia,\u0026rdquo; \u003cem\u003eTechnovation\u003c/em\u003e, vol. 29, no. 2, pp. 130\u0026ndash;141, Feb. 2009, doi: 10.1016/J.TECHNOVATION.2008.07.004.\u003c/li\u003e\n\u003cli\u003eA. Eskandarany, \u0026ldquo;Adoption of artificial intelligence and machine learning in banking systems: a qualitative survey of board of directors,\u0026rdquo; \u003cem\u003eFront Artif Intell\u003c/em\u003e, vol. 7, p. 1440051, Nov. 2024, doi: 10.3389/FRAI.2024.1440051/BIBTEX.\u003c/li\u003e\n\u003cli\u003eH. A. Alnemer, \u0026ldquo;Determinants of digital banking adoption in the Kingdom of Saudi Arabia: A technology acceptance model approach,\u0026rdquo; \u003cem\u003eDigital Business\u003c/em\u003e, vol. 2, no. 2, p. 100037, Jan. 2022, doi: 10.1016/J.DIGBUS.2022.100037.\u003c/li\u003e\n\u003cli\u003eJ. W. Tang and P. H. Tsai, \u0026ldquo;Exploring critical determinants influencing businesses\u0026rsquo; continuous usage of mobile payment in post-pandemic era: Based on the UTAUT2 perspective,\u0026rdquo; \u003cem\u003eTechnol Soc\u003c/em\u003e, vol. 77, p. 102554, Jun. 2024, doi: 10.1016/J.TECHSOC.2024.102554.\u003c/li\u003e\n\u003cli\u003eT. Wen Ni, \u0026ldquo;Factors Influencing Behavioural Intention towards Adoption of Digital Banking Services in Malaysia,\u0026rdquo; \u003cem\u003eInternational Journal of Asian Social Science\u003c/em\u003e, vol. 10, no. 8, pp. 450\u0026ndash;457, Jul. 2020, doi: 10.18488/JOURNAL.1.2020.108.450.457.\u003c/li\u003e\n\u003cli\u003eV. Melnyk, \u0026ldquo;Transforming the nature of trust between banks and young clients: from traditional to digital banking,\u0026rdquo; \u003cem\u003eQualitative Research in Financial Markets\u003c/em\u003e, vol. 16, no. 4, pp. 618\u0026ndash;635, Jun. 2024, doi: 10.1108/QRFM-08-2022-0129.\u003c/li\u003e\n\u003cli\u003eS. Chauhan, A. Akhtar, and A. Gupta, \u0026ldquo;Customer experience in digital banking: a review and future research directions,\u0026rdquo; \u003cem\u003eInternational Journal of Quality and Service Sciences\u003c/em\u003e, vol. 14, no. 2, pp. 311\u0026ndash;348, May 2022, doi: 10.1108/IJQSS-02-2021-0027.\u003c/li\u003e\n\u003cli\u003eW. A. Alkhowaiter, \u0026ldquo;Digital payment and banking adoption research in Gulf countries: A systematic literature review,\u0026rdquo; \u003cem\u003eInt J Inf Manage\u003c/em\u003e, vol. 53, p. 102102, Aug. 2020, doi: 10.1016/J.IJINFOMGT.2020.102102.\u003c/li\u003e\n\u003cli\u003eP. Agus \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;TECHNOLOGY ACCEPTANCE MODEL AND TRUST IN EXPLAINING CUSTOMER INTENTION TO USE INTERNET BANKING,\u0026rdquo; \u003cem\u003eRJOAS\u003c/em\u003e, vol. 7, no. 91, 2019, doi: 10.18551/rjoas.2019-07.29.\u003c/li\u003e\n\u003cli\u003eS. M. Noble and M. Mende, \u0026ldquo;The future of artificial intelligence and robotics in the retail and service sector: Sketching the field of consumer-robot-experiences,\u0026rdquo; \u003cem\u003eJ Acad Mark Sci\u003c/em\u003e, vol. 51, no. 4, pp. 747\u0026ndash;756, Jul. 2023, doi: 10.1007/S11747-023-00948-0.\u003c/li\u003e\n\u003cli\u003eW. Ozuem, S. Ranfagni, M. Willis, G. Salvietti, and K. Howell, \u0026ldquo;Exploring the relationship between chatbots, service failure recovery and customer loyalty: A frustration\u0026ndash;aggression perspective,\u0026rdquo; \u003cem\u003ePsychol Mark\u003c/em\u003e, Oct. 2024, doi: 10.1002/MAR.22051.\u003c/li\u003e\n\u003cli\u003eR. Hasan, R. Shams, and M. Rahman, \u0026ldquo;Consumer trust and perceived risk for voice-controlled artificial intelligence: The case of Siri,\u0026rdquo; \u003cem\u003eJ Bus Res\u003c/em\u003e, vol. 131, pp. 591\u0026ndash;597, Jul. 2021, doi: 10.1016/J.JBUSRES.2020.12.012.\u003c/li\u003e\n\u003cli\u003eH. Joshi and D. Chawla, \u0026ldquo;Impact of security on wallet adoption: multiple and serial mediating roles of trust and attitude and gender as a moderator,\u0026rdquo; \u003cem\u003eInternational Journal of Bank Marketing\u003c/em\u003e, vol. 42, no. 5, pp. 870\u0026ndash;896, Jul. 2024, doi: 10.1108/IJBM-02-2023-0118/FULL/XML.\u003c/li\u003e\n\u003cli\u003eS. Ahmed and S. Sur, \u0026ldquo;Change in the uses pattern of digital banking services by Indian rural MSMEs during demonetization and Covid-19 pandemic-related restrictions,\u0026rdquo; \u003cem\u003eVilakshan - XIMB Journal of Management\u003c/em\u003e, vol. 20, no. 1, pp. 166\u0026ndash;192, Feb. 2021, doi: 10.1108/XJM-09-2020-0138.\u003c/li\u003e\n\u003cli\u003eK. Kantika, F. Kurniasari, and M. Mulyono, \u0026ldquo;The Factors Affecting Digital Bank Services Adoption Using Trust as Mediating Variable,\u0026rdquo; \u003cem\u003eJournal of Business and Management Review\u003c/em\u003e, vol. 3, no. 10, pp. 690\u0026ndash;704, Oct. 2022, doi: 10.47153/JBMR310.4882022.\u003c/li\u003e\n\u003cli\u003eZ. Kenesei \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Trust and perceived risk: How different manifestations affect the adoption of autonomous vehicles,\u0026rdquo; \u003cem\u003eTransp Res Part A Policy Pract\u003c/em\u003e, vol. 164, pp. 379\u0026ndash;393, Oct. 2022, doi: 10.1016/J.TRA.2022.08.022.\u003c/li\u003e\n\u003cli\u003eA. M. Baabdullah, A. A. Alalwan, N. P. Rana, H. Kizgin, and P. Patil, \u0026ldquo;Consumer use of mobile banking (M-Banking) in Saudi Arabia: Towards an integrated model,\u0026rdquo; \u003cem\u003eInt J Inf Manage\u003c/em\u003e, vol. 44, pp. 38\u0026ndash;52, Feb. 2019, doi: 10.1016/J.IJINFOMGT.2018.09.002.\u003c/li\u003e\n\u003cli\u003eM. A. Afandi, R. B. Hartarto, and H. Usman, \u0026ldquo;Effects of ability, benevolence, and integrity on banking trust during the COVID-19 pandemic in Indonesia,\u0026rdquo; \u003cem\u003eInternational Journal of Business and Emerging Markets\u003c/em\u003e, vol. 16, no. 2, pp. 207\u0026ndash;221, 2024, doi: 10.1504/IJBEM.2024.137664;JOURNAL:JOURNAL:IJBEM;WGROUP:STRING:PUBLICATION.\u003c/li\u003e\n\u003cli\u003eS. Jit Kaur, L. Ali, M. Kabir Hassan, and M. Al-Emran, \u0026ldquo;Adoption of digital banking channels in an emerging economy: exploring the role of in-branch efforts,\u0026rdquo; \u003cem\u003eJournal of Financial Services Marketing\u003c/em\u003e, vol. 26, pp. 107\u0026ndash;121, 123AD, doi: 10.1057/s41264-020-00082-w.\u003c/li\u003e\n\u003cli\u003eK. Khando, M. S. Islam, and S. Gao, \u0026ldquo;The Emerging Technologies of Digital Payments and Associated Challenges: A Systematic Literature Review,\u0026rdquo; \u003cem\u003eFuture Internet\u003c/em\u003e, vol. 15, no. 1, Jan. 2023, doi: 10.3390/FI15010021.\u003c/li\u003e\n\u003cli\u003eA. K. Tyagi, S. Dananjayan, D. Agarwal, and H. F. Thariq Ahmed, \u0026ldquo;Blockchain\u0026mdash;Internet of Things Applications: Opportunities and Challenges for Industry 4.0 and Society 5.0,\u0026rdquo; \u003cem\u003eSensors\u003c/em\u003e, vol. 23, no. 2, Jan. 2023, doi: 10.3390/S23020947.\u003c/li\u003e\n\u003cli\u003eR. Chan, I. Troshani, S. Rao Hill, and A. Hoffmann, \u0026ldquo;Towards an understanding of consumers\u0026rsquo; FinTech adoption: the case of Open Banking,\u0026rdquo; \u003cem\u003eInternational Journal of Bank Marketing\u003c/em\u003e, vol. 40, no. 4, pp. 886\u0026ndash;917, Apr. 2022, doi: 10.1108/IJBM-08-2021-0397/FULL/XML.\u003c/li\u003e\n\u003cli\u003eAdetunji Paul Adejumo and Chinonso Peter Ogburie, \u0026ldquo;Strengthening finance with cybersecurity: Ensuring safer digital transactions,\u0026rdquo; \u003cem\u003eWorld Journal of Advanced Research and Reviews\u003c/em\u003e, vol. 25, no. 3, pp. 1527\u0026ndash;1541, Mar. 2025, doi: 10.30574/WJARR.2025.25.3.0908.\u003c/li\u003e\n\u003cli\u003eM. Ali, S. A. Raza, B. Khamis, C. H. Puah, and H. Amin, \u0026ldquo;How perceived risk, benefit and trust determine user Fintech adoption: a new dimension for Islamic finance,\u0026rdquo; \u003cem\u003eForesight\u003c/em\u003e, vol. 23, no. 4, pp. 403\u0026ndash;420, 2021, doi: 10.1108/FS-09-2020-0095.\u003c/li\u003e\n\u003cli\u003eT. Khan, M. M. H. Emon, and S. A. J. Siam, \u0026ldquo;Impact of Green Supply Chain Practices on Sustainable Development in Bangladesh,\u0026rdquo; \u003cem\u003eMalaysian Business Management Journal\u003c/em\u003e, vol. 3, no. 2, pp. 73\u0026ndash;83, Apr. 2024, doi: 10.26480/MBMJ.02.2024.73.83.\u003c/li\u003e\n\u003cli\u003eS. L. Cabrera-Luj\u0026aacute;n, D. J. S\u0026aacute;nchez-Lima, S. A. Guevara-Flores, D. Y. Millones-Liza, E. E. Garc\u0026iacute;a-Salirrosas, and M. Villar-Guevara, \u0026ldquo;Impact of Corporate Social Responsibility, Business Ethics and Corporate Reputation on the Retention of Users of Third-Sector Institutions,\u0026rdquo; \u003cem\u003eSustainability (Switzerland)\u003c/em\u003e, vol. 15, no. 3, Feb. 2023, doi: 10.3390/SU15031781.\u003c/li\u003e\n\u003cli\u003eT. Roh, Y. S. Yang, S. Xiao, and B. Il Park, \u0026ldquo;What makes consumers trust and adopt fintech? An empirical investigation in China,\u0026rdquo; \u003cem\u003eElectronic Commerce Research\u003c/em\u003e, vol. 24, no. 1, pp. 3\u0026ndash;35, Mar. 2024, doi: 10.1007/S10660-021-09527-3.\u003c/li\u003e\n\u003cli\u003eE. L. M. Bettinazzi and E. R. Feldman, \u0026ldquo;Stakeholder orientation and divestiture activity,\u0026rdquo; \u003cem\u003eAcademy of Management Journal\u003c/em\u003e, vol. 64, no. 4, pp. 1078\u0026ndash;1096, Sep. 2021, doi: 10.5465/AMJ.2019.0627.\u003c/li\u003e\n\u003cli\u003eS. R. Natasia, Y. T. Wiranti, and A. Parastika, \u0026ldquo;Acceptance analysis of NUADU as e-learning platform using the Technology Acceptance Model (TAM) approach,\u0026rdquo; \u003cem\u003eProcedia Comput Sci\u003c/em\u003e, vol. 197, pp. 512\u0026ndash;520, 2021, doi: 10.1016/J.PROCS.2021.12.168.\u003c/li\u003e\n\u003cli\u003eN. F. S. Jeffri and D. R. Awang Rambli, \u0026ldquo;A review of augmented reality systems and their effects on mental workload and task performance,\u0026rdquo; \u003cem\u003eHeliyon\u003c/em\u003e, vol. 7, no. 3, Mar. 2021, doi: 10.1016/J.HELIYON.2021.E06277.\u003c/li\u003e\n\u003cli\u003eC. S. Dewi and Z. Zulkifli, \u0026ldquo;E-Banking Technology: A Comprehensive Study on Customer Satisfaction and Bank Services,\u0026rdquo; \u003cem\u003eInternational Journal of Business, Law, and Education\u003c/em\u003e, vol. 5, no. 2, pp. 1655\u0026ndash;1665, Jun. 2024, doi: 10.56442/IJBLE.V5I2.708.\u003c/li\u003e\n\u003cli\u003eA. T. Neumann, Y. Yin, S. Sowe, S. Decker, and M. Jarke, \u0026ldquo;An LLM-Driven Chatbot in Higher Education for Databases and Information Systems,\u0026rdquo; \u003cem\u003eIEEE Transactions on Education\u003c/em\u003e, 2024, doi: 10.1109/TE.2024.3467912.\u003c/li\u003e\n\u003cli\u003eM. A. Almaiah \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Investigating the Effect of Perceived Security, Perceived Trust, and Information Quality on Mobile Payment Usage through Near-Field Communication (NFC) in Saudi Arabia,\u0026rdquo; \u003cem\u003eElectronics (Switzerland)\u003c/em\u003e, vol. 11, no. 23, Dec. 2022, doi: 10.3390/ELECTRONICS11233926.\u003c/li\u003e\n\u003cli\u003eY. Chen, S. Jensen, L. J. Albert, S. Gupta, and T. Lee, \u0026ldquo;Artificial Intelligence (AI) Student Assistants in the Classroom: Designing Chatbots to Support Student Success,\u0026rdquo; \u003cem\u003eInformation Systems Frontiers\u003c/em\u003e, vol. 25, no. 1, pp. 161\u0026ndash;182, Feb. 2023, doi: 10.1007/S10796-022-10291-4.\u003c/li\u003e\n\u003cli\u003eJ. E. Andrews, H. Ward, and J. W. Yoon, \u0026ldquo;UTAUT as a Model for Understanding Intention to Adopt AI and Related Technologies among Librarians,\u0026rdquo; \u003cem\u003eJournal of Academic Librarianship\u003c/em\u003e, vol. 47, no. 6, Dec. 2021, doi: 10.1016/J.ACALIB.2021.102437.\u003c/li\u003e\n\u003cli\u003eA. E. Demirel, S. M. Top, Z. Y. İlerisoy, and E. B. Kınacı, \u0026ldquo;Evaluation of architecture and engineering students\u0026rsquo; attitudes towards online education in T\u0026uuml;rkiye with technology acceptance model,\u0026rdquo; \u003cem\u003eOpen House International\u003c/em\u003e, 2025, doi: 10.1108/OHI-07-2024-0255.\u003c/li\u003e\n\u003cli\u003eS. J. Moon, \u0026ldquo;Investigating beliefs, attitudes, and intentions regarding green restaurant patronage: An application of the extended theory of planned behavior with moderating effects of gender and age,\u0026rdquo; \u003cem\u003eInt J Hosp Manag\u003c/em\u003e, vol. 92, Jan. 2021, doi: 10.1016/J.IJHM.2020.102727.\u003c/li\u003e\n\u003cli\u003eR. K, S. G, S. Kumar, and S. T, \u0026ldquo;A Study of Cyber Security Challenges and its Emerging Trends on Latest Technologies,\u0026rdquo; \u003cem\u003eInternational Journal of Innovative Research in Advanced Engineering\u003c/em\u003e, vol. 10, no. 06, pp. 379\u0026ndash;382, Jun. 2023, doi: 10.26562/ijirae.2023.v1006.25.\u003c/li\u003e\n\u003cli\u003eO. Odeyemi \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;AI in E-commerce: Reviewing developments in the USA and their global influence,\u0026rdquo; \u003cem\u003eInternational Journal of Science and Research Archive\u003c/em\u003e, vol. 11, no. 1, pp. 1460\u0026ndash;1468, Feb. 2024, doi: 10.30574/IJSRA.2024.11.1.0232.\u003c/li\u003e\n\u003cli\u003eV. Kaushal and R. Yadav, \u0026ldquo;Learning successful implementation of Chatbots in businesses from B2B customer experience perspective,\u0026rdquo; \u003cem\u003eConcurr Comput\u003c/em\u003e, vol. 35, no. 1, Jan. 2023, doi: 10.1002/CPE.7450.\u003c/li\u003e\n\u003cli\u003eK. Hughes-Lartey, M. Li, F. E. Botchey, and Z. Qin, \u0026ldquo;Human factor, a critical weak point in the information security of an organization\u0026rsquo;s Internet of things,\u0026rdquo; \u003cem\u003eHeliyon\u003c/em\u003e, vol. 7, no. 3, p. e06522, Mar. 2021, doi: 10.1016/J.HELIYON.2021.E06522.\u003c/li\u003e\n\u003cli\u003eA. S. Malik, S. Acharya, and S. Humane, \u0026ldquo;Exploring the Impact of Security Technologies on Mental Health: A Comprehensive Review,\u0026rdquo; \u003cem\u003eCureus\u003c/em\u003e, vol. 16, no. 2, Feb. 2024, doi: 10.7759/CUREUS.53664.\u003c/li\u003e\n\u003cli\u003eL. Albshaier, S. Almarri, and M. M. Hafizur Rahman, \u0026ldquo;A Review of Blockchain\u0026rsquo;s Role in E-Commerce Transactions: Open Challenges, and Future Research Directions,\u0026rdquo; \u003cem\u003eComputers\u003c/em\u003e, vol. 13, no. 1, Jan. 2024, doi: 10.3390/COMPUTERS13010027.\u003c/li\u003e\n\u003cli\u003eY. Zhong, S. Oh, and H. C. Moon, \u0026ldquo;Service transformation under industry 4.0: Investigating acceptance of facial recognition payment through an extended technology acceptance model,\u0026rdquo; \u003cem\u003eTechnol Soc\u003c/em\u003e, vol. 64, Feb. 2021, doi: 10.1016/J.TECHSOC.2020.101515.\u003c/li\u003e\n\u003cli\u003eMd. T. A. Himel, S. Ashraf, T. A. Bappy, M. T. Abir, M. K. Morshed, and Md. N. Hossain, \u0026ldquo;Users\u0026rsquo; attitude and intention to use mobile financial services in Bangladesh: an empirical study,\u0026rdquo; \u003cem\u003eSouth Asian Journal of Marketing\u003c/em\u003e, vol. 2, no. 1, pp. 72\u0026ndash;96, Sep. 2021, doi: 10.1108/SAJM-02-2021-0015.\u003c/li\u003e\n\u003cli\u003eE. S. Quaye, L. E. K. Ameyibor, K. Mokgethi, and Y. Kabeya Saini, \u0026ldquo;Healthy lifestyle and behavioural intentions: the role of self-identity, self-efficacy, subjective norms, and attitudes,\u0026rdquo; \u003cem\u003eInternational Journal of Spa and Wellness\u003c/em\u003e, vol. 7, no. 2, pp. 176\u0026ndash;196, 2024, doi: 10.1080/24721735.2024.2374588.\u003c/li\u003e\n\u003cli\u003eY. Xu \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Effects of Subjective Norms and Environmental Mechanism on Green Purchase Behavior: An Extended Model of Theory of Planned Behavior,\u0026rdquo; \u003cem\u003eFront Environ Sci\u003c/em\u003e, vol. 10, Feb. 2022, doi: 10.3389/FENVS.2022.779629.\u003c/li\u003e\n\u003cli\u003eO. Fawehinmi, M. Y. Yusliza, M. I. Tanveer, and M. S. Abdullahi, \u0026ldquo;Influence of green human resource management on employee green behavior: The sequential mediating effect of perceived behavioral control and attitude toward corporate environmental policy,\u0026rdquo; \u003cem\u003eCorp Soc Responsib Environ Manag\u003c/em\u003e, vol. 31, no. 3, pp. 2514\u0026ndash;2536, May 2024, doi: 10.1002/CSR.2707;WGROUP:STRING:PUBLICATION.\u003c/li\u003e\n\u003cli\u003eM. S. Hagger, M. W. L. Cheung, I. Ajzen, and K. Hamilton, \u0026ldquo;Perceived Behavioral Control Moderating Effects in the Theory of Planned Behavior: A Meta-Analysis,\u0026rdquo; \u003cem\u003eHealth Psychology\u003c/em\u003e, Feb. 2022, doi: 10.1037/HEA0001153.\u003c/li\u003e\n\u003cli\u003eG. Yang, V. Caiati, S. Rasouli, and J. Chen, \u0026ldquo;Understanding Drivers\u0026rsquo; Behavioral attitudes and Intentions to Use Guidance Systems in Urban Complex Parking Lots Based on the C-TAM-TPB Framework,\u0026rdquo; \u003cem\u003eTransp Res Rec\u003c/em\u003e, Nov. 2024, doi: 10.1177/03611981241242064.\u003c/li\u003e\n\u003cli\u003eI. Triutomo, P. Putra, and I. Heruwasto, \u0026ldquo;Factors affecting consumer intention to use QRIS during the Covid-19 pandemic by using C-TAM-TPB,\u0026rdquo; \u003cem\u003eProceeding of the International Conference on Family Business and Entrepreneurship\u003c/em\u003e, vol. 3, no. 1, Jul. 2022, doi: 10.33021/ICFBE.V3I1.3783.\u003c/li\u003e\n\u003cli\u003eF. D. Davis, \u0026ldquo;Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology,\u0026rdquo; \u003cem\u003eMIS Quarterly\u003c/em\u003e, vol. 13, no. 3, p. 319, Sep. 1989, doi: 10.2307/249008.\u003c/li\u003e\n\u003cli\u003eM. Alshurideh \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Factors affecting ChatGPT use in education employing TAM: A Jordanian universities\u0026rsquo; perspective,\u0026rdquo; \u003cem\u003eInternational Journal of Data and Network Science\u003c/em\u003e, vol. 8, no. 3, pp. 1599\u0026ndash;1606, Jun. 2024, doi: 10.5267/J.IJDNS.2024.3.007.\u003c/li\u003e\n\u003cli\u003eR. Andy, A. C. Dewi, and M. As\u0026rsquo;adi, \u0026ldquo;An Empirical Study to Validate The Technology Acceptance Model (TAM) In Evaluating \u0026lsquo;Desa Digital\u0026rsquo; Applications,\u0026rdquo; \u003cem\u003eIOP Conf Ser Mater Sci Eng\u003c/em\u003e, vol. 1125, no. 1, p. 012055, May 2021, doi: 10.1088/1757-899X/1125/1/012055.\u003c/li\u003e\n\u003cli\u003eK. F. Yuen, L. Cai, G. Qi, and X. Wang, \u0026ldquo;Factors influencing autonomous vehicle adoption: an application of the technology acceptance model and innovation diffusion theory,\u0026rdquo; \u003cem\u003eTechnol Anal Strateg Manag\u003c/em\u003e, vol. 33, no. 5, pp. 505\u0026ndash;519, 2021, doi: 10.1080/09537325.2020.1826423.\u003c/li\u003e\n\u003cli\u003eI. Ajzen, \u0026ldquo;The theory of planned behavior,\u0026rdquo; \u003cem\u003eOrgan Behav Hum Decis Process\u003c/em\u003e, vol. 50, no. 2, pp. 179\u0026ndash;211, 1991.\u003c/li\u003e\n\u003cli\u003eF. La Barbera and I. Ajzen, \u0026ldquo;Moderating role of perceived behavioral control in the theory of planned behavior: A preregistered study,\u0026rdquo; \u003cem\u003eJournal of Theoretical Social Psychology\u003c/em\u003e, vol. 5, no. 1, pp. 35\u0026ndash;45, Jan. 2021, doi: 10.1002/JTS5.83.\u003c/li\u003e\n\u003cli\u003eS. Taylor and P. A. Todd, \u0026ldquo;Understanding information technology usage: A test of competing models,\u0026rdquo; \u003cem\u003eInformation Systems Research\u003c/em\u003e, vol. 6, no. 2, pp. 144\u0026ndash;176, Jun. 1995, doi: 10.1287/ISRE.6.2.144;WEBSITE:WEBSITE:INFORMS-SITE.\u003c/li\u003e\n\u003cli\u003eI. B. Ohanu, T. O. Shodipe, C. M. G. Ohanu, and J. E. Anene-Okeakwa, \u0026ldquo;System quality, technology acceptance model and theory of planned behaviour models: Agents for adopting blended learning tools,\u0026rdquo; \u003cem\u003eE-Learning and Digital Media\u003c/em\u003e, vol. 20, no. 3, pp. 255\u0026ndash;281, May 2023, doi: 10.1177/20427530221108031.\u003c/li\u003e\n\u003cli\u003eS. Kaur and S. Arora, \u0026ldquo;Understanding customers\u0026rsquo; usage behavior towards online banking services: an integrated risk\u0026ndash;benefit framework,\u0026rdquo; \u003cem\u003eJournal of Financial Services Marketing\u003c/em\u003e, vol. 28, no. 1, pp. 74\u0026ndash;98, Mar. 2023, doi: 10.1057/S41264-022-00140-5.\u003c/li\u003e\n\u003cli\u003eT. Hewavitharana, S. Nanayakkara, A. Perera, and P. Perera, \u0026ldquo;Modifying the unified theory of acceptance and use of technology (UTAUT) model for the digital transformation of the construction industry from the user perspective,\u0026rdquo; \u003cem\u003eInformatics\u003c/em\u003e, vol. 8, no. 4, Dec. 2021, doi: 10.3390/INFORMATICS8040081.\u003c/li\u003e\n\u003cli\u003eI. U. Khan, Z. Hameed, S. N. Khan, S. U. Khan, and M. T. Khan, \u0026ldquo;Exploring the Effects of Culture on Acceptance of Online Banking: A Comparative Study of Pakistan and Turkey by Using the Extended UTAUT Model,\u0026rdquo; \u003cem\u003eJournal of Internet Commerce\u003c/em\u003e, vol. 21, no. 2, pp. 183\u0026ndash;216, 2022, doi: 10.1080/15332861.2021.1882749;JOURNAL:JOURNAL:WICO20.\u003c/li\u003e\n\u003cli\u003eV. Venkatesh and F. D. Davis, \u0026ldquo;A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies,\u0026rdquo; \u003cem\u003eManage Sci\u003c/em\u003e, vol. 46, no. 2, pp. 186\u0026ndash;204, Feb. 2000, doi: 10.1287/mnsc.46.2.186.11926.\u003c/li\u003e\n\u003cli\u003eY. Lee, K. A. Kozar, and K. R. T. Larsen, \u0026ldquo;The Technology Acceptance Model: Past, Present, and Future,\u0026rdquo; \u003cem\u003eCommunications of the Association for Information Systems\u003c/em\u003e, vol. 12, 2003, doi: 10.17705/1CAIS.01250.\u003c/li\u003e\n\u003cli\u003eD. I. Prastiawan, S. Aisjah, and R. Rofiaty, \u0026ldquo;The Effect of Perceived Usefulness, Perceived Ease of Use, and Social Influence on the Use of Mobile Banking through the Mediation of Attitude Toward Use,\u0026rdquo; \u003cem\u003eAsia Pacific Management and Business Application\u003c/em\u003e, vol. 009, no. 03, pp. 243\u0026ndash;260, Apr. 2021, doi: 10.21776/UB.APMBA.2021.009.03.4.\u003c/li\u003e\n\u003cli\u003eS. Yousafzai, J. Pallister, and G. Foxall, \u0026ldquo;Multi-dimensional role of trust in Internet banking adoption,\u0026rdquo; \u003cem\u003eService Industries Journal\u003c/em\u003e, vol. 29, no. 5, pp. 591\u0026ndash;605, 2009, doi: 10.1080/02642060902719958.\u003c/li\u003e\n\u003cli\u003eM. Ramezaninia, F. Panahifar, and N. H. Sarhangi, \u0026ldquo;Significant factors affecting m-banking adoption case study: higher education institutions in Tehran,\u0026rdquo; \u003cem\u003eInternational Journal of Electronic Business\u003c/em\u003e, vol. 17, no. 1, pp. 61\u0026ndash;86, 2022, doi: 10.1504/IJEB.2022.120014.\u003c/li\u003e\n\u003cli\u003eD. Gefen, E. Karahanna, and D. W. Straub, \u0026ldquo;Trust and tam in online shopping: AN integrated model,\u0026rdquo; \u003cem\u003eMIS Q\u003c/em\u003e, vol. 27, no. 1, pp. 51\u0026ndash;90, 2003, doi: 10.2307/30036519.\u003c/li\u003e\n\u003cli\u003eV. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, \u0026ldquo;User acceptance of information technology: Toward a unified view,\u0026rdquo; \u003cem\u003eMIS Q\u003c/em\u003e, vol. 27, no. 3, pp. 425\u0026ndash;478, 2003, doi: 10.2307/30036540.\u003c/li\u003e\n\u003cli\u003eH. Siagian, Z. J. H. Tarigan, S. R. Basana, and R. Basuki, \u0026ldquo;The effect of perceived security, perceived ease of use, and perceived usefulness on consumer behavioral intention through trust in digital payment platform,\u0026rdquo; \u003cem\u003eInternational Journal of Data and Network Science\u003c/em\u003e, vol. 6, no. 3, pp. 861\u0026ndash;874, Jun. 2022, doi: 10.5267/J.IJDNS.2022.2.010.\u003c/li\u003e\n\u003cli\u003eD. H. McKnight, V. Choudhury, and C. Kacmar, \u0026ldquo;Developing and validating trust measures for e-commerce: An integrative typology,\u0026rdquo; \u003cem\u003eInformation Systems Research\u003c/em\u003e, vol. 13, no. 3, pp. 334\u0026ndash;359, Sep. 2002, doi: 10.1287/ISRE.13.3.334.81;REQUESTEDJOURNAL:JOURNAL:ISRE;WGROUP:STRING:PUBLICATION.\u003c/li\u003e\n\u003cli\u003eP. A. Pavlou, \u0026ldquo;Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model,\u0026rdquo; \u003cem\u003eInternational Journal of Electronic Commerce\u003c/em\u003e, vol. 7, no. 3, pp. 101\u0026ndash;134, 2003, doi: 10.1080/10864415.2003.11044275;WEBSITE:WEBSITE:TFOPB;PAGEGROUP:STRING:PUBLICATION.\u003c/li\u003e\n\u003cli\u003eI. M. Shaikh and H. Amin, \u0026ldquo;Influence of innovation diffusion factors on non-users\u0026rsquo; adoption of digital banking services in the banking 4.0 era,\u0026rdquo; \u003cem\u003eInf Discov Deliv\u003c/em\u003e, Jan. 2024, doi: 10.1108/IDD-05-2023-0044.\u003c/li\u003e\n\u003cli\u003eE. K. Ghani, M. M. Ali, M. N. R. Musa, and A. A. Omonov, \u0026ldquo;The Effect of Perceived Usefulness, Reliability, and COVID-19 Pandemic on Digital Banking Effectiveness: Analysis Using Technology Acceptance Model,\u0026rdquo; \u003cem\u003eSustainability 2022, Vol. 14, Page 11248\u003c/em\u003e, vol. 14, no. 18, p. 11248, Sep. 2022, doi: 10.3390/SU141811248.\u003c/li\u003e\n\u003cli\u003eZ. Cao, J. Zheng, and R. Liu, \u0026ldquo;Factors Affecting Users\u0026rsquo; Continuous Usage in Online Health Communities: An Integrated Framework of SCT and TPB,\u0026rdquo; \u003cem\u003eHealthcare 2023, Vol. 11, Page 1238\u003c/em\u003e, vol. 11, no. 9, p. 1238, Apr. 2023, doi: 10.3390/HEALTHCARE11091238.\u003c/li\u003e\n\u003cli\u003eK. Mathieson, \u0026ldquo;Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior,\u0026rdquo; \u003cem\u003eInformation Systems Research\u003c/em\u003e, vol. 2, no. 3, pp. 173\u0026ndash;191, Sep. 1991, doi: 10.1287/ISRE.2.3.173;CTYPE:STRING:JOURNAL.\u003c/li\u003e\n\u003cli\u003eM. Rahman, T. H. Ming, T. A. Baigh, and M. Sarker, \u0026ldquo;Adoption of artificial intelligence in banking services: an empirical analysis,\u0026rdquo; \u003cem\u003eInternational Journal of Emerging Markets\u003c/em\u003e, vol. 18, no. 10, pp. 4270\u0026ndash;4300, Nov. 2023, doi: 10.1108/IJOEM-06-2020-0724.\u003c/li\u003e\n\u003cli\u003eV. Saprikis, G. Avlogiaris, and A. Katarachia, \u0026ldquo;A Comparative Study of Users versus Non-Users\u0026rsquo; Behavioral Intention towards M-Banking Apps\u0026rsquo; Adoption,\u0026rdquo; \u003cem\u003eInformation (Switzerland)\u003c/em\u003e, vol. 13, no. 1, Jan. 2022, doi: 10.3390/INFO13010030.\u003c/li\u003e\n\u003cli\u003eF. La Barbera and I. Ajzen, \u0026ldquo;Moderating role of perceived behavioral control in the theory of planned behavior: A preregistered study,\u0026rdquo; \u003cem\u003eJournal of Theoretical Social Psychology\u003c/em\u003e, vol. 5, no. 1, pp. 35\u0026ndash;45, Jan. 2021, doi: 10.1002/JTS5.83.\u003c/li\u003e\n\u003cli\u003eJ. F. George, \u0026ldquo;The theory of planned behavior and Internet purchasing,\u0026rdquo; \u003cem\u003eInternet Research\u003c/em\u003e, vol. 14, no. 3, pp. 198\u0026ndash;212, Jul. 2004, doi: 10.1108/10662240410542634/FULL/XML.\u003c/li\u003e\n\u003cli\u003eC. K. Riemenschneider, D. A. Harrison, and P. P. Mykytyn, \u0026ldquo;Understanding it adoption decisions in small business: integrating current theories,\u0026rdquo; \u003cem\u003eInformation \u0026amp; Management\u003c/em\u003e, vol. 40, no. 4, pp. 269\u0026ndash;285, Mar. 2003, doi: 10.1016/S0378-7206(02)00010-1.\u003c/li\u003e\n\u003cli\u003eT. C. E. Cheng, D. Y. C. Lam, and A. C. L. Yeung, \u0026ldquo;Adoption of internet banking: An empirical study in Hong Kong,\u0026rdquo; \u003cem\u003eDecis Support Syst\u003c/em\u003e, vol. 42, no. 3, pp. 1558\u0026ndash;1572, Dec. 2006, doi: 10.1016/j.dss.2006.01.002.\u003c/li\u003e\n\u003cli\u003eJ. Sharifuddin, L. Ramalingam, Z. Mohamed, and G. Rezai, \u0026ldquo;Factors affecting intention to purchase edible bird\u0026rsquo;s nest products: the case of Malaysian consumers,\u0026rdquo; \u003cem\u003eJournal of Food Products Marketing\u003c/em\u003e, vol. 20, no. sup1, pp. 75\u0026ndash;84, 2014.\u003c/li\u003e\n\u003cli\u003eR. J. Harris, \u003cem\u003eA Primer of Multivariate Statistics\u003c/em\u003e. Psychology Press, 2001. doi: 10.4324/9781410600455.\u003c/li\u003e\n\u003cli\u003eJ. Keshminder and P. R\u0026iacute;o, \u0026ldquo;The missing links? The indirect impacts of drivers on eco‐innovation,\u0026rdquo; \u003cem\u003eCorp Soc Responsib Environ Manag\u003c/em\u003e, 2019, [Online]. Available: https://api.semanticscholar.org/CorpusID:197894847\u003c/li\u003e\n\u003cli\u003eC. M. Ringle, S. Wende, and J.-M. Becker, \u0026ldquo;SmartPLS 4. Oststeinbek: SmartPLS GmbH,\u0026rdquo; 2022.\u003c/li\u003e\n\u003cli\u003eW. W. Chin, B. L. Marcolin, and P. R. Newsted, \u0026ldquo;A Partial Least Squares Latent Variable Modeling Approach for Measuring Interaction Effects: Results from a Monte Carlo Simulation Study and an Electronic-Mail Emotion/Adoption Study,\u0026rdquo; 2003. [Online]. Available: http://www.informs.org/Pubs/\u003c/li\u003e\n\u003cli\u003eS. Fuchs, \u0026ldquo;Common Method Variance Analysis of Structural Models,\u0026rdquo; \u003cem\u003eUnderstanding Psychological Bonds between Individuals and Organizations\u003c/em\u003e, pp. 119\u0026ndash;165, 2012, doi: 10.1057/9781137033970_6.\u003c/li\u003e\n\u003cli\u003eM. I. Aguirre-Urreta and J. Hu, \u0026ldquo;Detecting Common Method Bias,\u0026rdquo; \u003cem\u003eACM SIGMIS Database: the DATABASE for Advances in Information Systems\u003c/em\u003e, vol. 50, no. 2, pp. 45\u0026ndash;70, May 2019, doi: 10.1145/3330472.3330477.\u003c/li\u003e\n\u003cli\u003eM. C. Howard and J. Henderson, \u0026ldquo;A review of exploratory factor analysis in tourism and hospitality research: Identifying current practices and avenues for improvement,\u0026rdquo; \u003cem\u003eJ Bus Res\u003c/em\u003e, vol. 154, p. 113328, Jan. 2023, doi: 10.1016/j.jbusres.2022.113328.\u003c/li\u003e\n\u003cli\u003eN. Kock and G. S. Lynn, \u0026ldquo;Lateral Collinearity and Misleading Results in Variance-Based SEM: An Illustration and Recommendations,\u0026rdquo; \u003cem\u003eJ Assoc Inf Syst\u003c/em\u003e, vol. 13, no. 7, p. 547, 2012.\u003c/li\u003e\n\u003cli\u003eN. Kock, \u0026ldquo;Common method bias in PLS-SEM: A full collinearity assessment approach,\u0026rdquo; \u003cem\u003eInternational Journal of e-Collaboration\u003c/em\u003e, vol. 11, no. 4, pp. 1\u0026ndash;10, 2015, doi: 10.4018/ijec.2015100101.\u003c/li\u003e\n\u003cli\u003eJ. C. Anderson and D. W. Gerbing, \u0026ldquo;Structural equation modeling in practice: A review and recommended two-step approach.,\u0026rdquo; \u003cem\u003ePsychol Bull\u003c/em\u003e, vol. 103, no. 3, pp. 411\u0026ndash;423, May 1988, doi: 10.1037/0033-2909.103.3.411.\u003c/li\u003e\n\u003cli\u003eM. Sarstedt, C. M. Ringle, and J. F. Hair, \u0026ldquo;Partial Least Squares Structural Equation Modeling,\u0026rdquo; \u003cem\u003eHandbook of Market Research\u003c/em\u003e, pp. 587\u0026ndash;632, Jan. 2022, doi: 10.1007/978-3-319-57413-4_15.\u003c/li\u003e\n\u003cli\u003eT. Ramayah, J.-H. Cheah, F. Chuah, and H. Ting, \u0026ldquo;PLS-SEM using SmartPLS 3.0: Chapter 13: Assessment of Moderation Analysis,\u0026rdquo; 2018. [Online]. Available: https://www.researchgate.net/publication/341357609\u003c/li\u003e\n\u003cli\u003eJ. F. Hair, G. T. M. Hult, C. M. Ringle, and M. Sarstedt, \u003cem\u003eA Primer on Partial Least Squares Structural Equation Modeling PLS-SEM\u003c/em\u003e, vol. 3. 2022.\u003c/li\u003e\n\u003cli\u003eJ. F. Hair, J. J. Risher, M. Sarstedt, and C. M. Ringle, \u0026ldquo;When to use and how to report the results of PLS-SEM,\u0026rdquo; Jan. 14, 2019, \u003cem\u003eEmerald Group Publishing Ltd.\u003c/em\u003e doi: 10.1108/EBR-11-2018-0203.\u003c/li\u003e\n\u003cli\u003eN. Darsono, A. Yahya, A. Muzammil, S. Musnadi, C. Anwar, and W. Irawati, \u0026ldquo;Consumer Actual Purchase Behavior for Organic Products In Aceh, Indonesia,\u0026rdquo; pp. 265\u0026ndash;275, Jan. 2019, doi: 10.2991/AGC-18.2019.43.\u003c/li\u003e\n\u003cli\u003eC. Fornell and D. F.Larker, \u0026ldquo;36. Fornell \u0026amp; Larcker 1981\u0026rdquo;.\u003c/li\u003e\n\u003cli\u003eJ. M. Becker, J. H. Cheah, R. Gholamzade, C. M. Ringle, and M. Sarstedt, \u0026ldquo;PLS-SEM\u0026rsquo;s most wanted guidance,\u0026rdquo; Jan. 02, 2023, \u003cem\u003eEmerald Publishing\u003c/em\u003e. doi: 10.1108/IJCHM-04-2022-0474.\u003c/li\u003e\n\u003cli\u003eE. D. Hahn and S. H. Ang, \u0026ldquo;From the editors: New directions in the reporting of statistical results in the Journal of World Business,\u0026rdquo; \u003cem\u003eJournal of World Business\u003c/em\u003e, vol. 52, no. 2, pp. 125\u0026ndash;126, Feb. 2017, doi: 10.1016/j.jwb.2016.12.003.\u003c/li\u003e\n\u003cli\u003eJ. Cohen, \u003cem\u003eStatistical Power Analysis for the Behavioral Sciences\u003c/em\u003e. Routledge, 2013. doi: 10.4324/9780203771587.\u003c/li\u003e\n\u003cli\u003eJ. C. Daly and J. Cohen, \u0026ldquo;Statistical Power Analysis For The Behavioral Sciences Revised Edition,\u0026rdquo; 1987. [Online]. Available: https://api.semanticscholar.org/CorpusID:123646481\u003c/li\u003e\n\u003cli\u003eG. Shmueli \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Predictive model assessment in PLS-SEM: guidelines for using PLSpredict,\u0026rdquo; \u003cem\u003eEur J Mark\u003c/em\u003e, vol. 53, no. 11, pp. 2322\u0026ndash;2347, Sep. 2019, doi: 10.1108/EJM-02-2019-0189.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Digital Banking Adoption, Perceived Security-Based Trust, Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB), Behavioral Intention, Partial Least Squares (PLS-SEM), Saudi Arabia, Trust in Technology, Financial Technology Adoption","lastPublishedDoi":"10.21203/rs.3.rs-6670162/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6670162/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study creates and tests a model for digital banking adoption in Saudi Arabia that combines the Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB), and Perceived Security-Based Trust (PSBT). Analyzing data from 353 valid online survey responses using Partial Least Squares Structural Equation Modeling (PLS-SEM), the model explains 67.3% of the variance in Behavioral Intention (R² = 0.673). It shows strong predictive relevance, with Q²predict values over 0.4. Key findings reveal that Perceived Ease of Use (β = 0.578) and PSBT (β = 0.748) significantly influence Perceived Usefulness and trust. Trust impacts both Subjective Norms (β = 0.691) and Perceived Behavioral Control (β = 0.693), highlighting its role in facilitating social and self-regulatory pathways to adoption. Although PU and Attitude mediate PEU and PSBT effects on Behavioral Intention, Subjective Norms show no direct influence, differing from typical TPB expectations. This research highlights trust-related mechanisms as primary drivers of digital adoption in cautious cultures, enhancing theoretical understanding of TAM-TPB integration in security-sensitive contexts and offering practical guidance for banks to improve usability and trust.\u003c/p\u003e","manuscriptTitle":"Modeling Digital Banking Adoption in Saudi Arabia: An Integrated C-TAM-TPB Framework Examining the Role of Perceived Security-Based Trust","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-10 06:17:44","doi":"10.21203/rs.3.rs-6670162/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0a4cb371-2d85-4cfa-89c7-5d1b8c271edf","owner":[],"postedDate":"June 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49739888,"name":"Business and commerce/Business and management"},{"id":49739889,"name":"Business and commerce/Finance"},{"id":49739890,"name":"Business and commerce/Information systems and information technology"},{"id":49739891,"name":"Social science/Business and management"},{"id":49739892,"name":"Social science/Finance"},{"id":49739893,"name":"Social science/Science technology and society"}],"tags":[],"updatedAt":"2025-07-10T16:23:39+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-10 06:17:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6670162","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6670162","identity":"rs-6670162","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}