Exploring the Adoption of Financial Technology (FinTech) by Banking and Insurance Companies Customers in Ghana Using an Extended UTAUT2 Model

Exploring the Adoption of Financial Technology (FinTech) by Banking and Insurance Companies Customers in Ghana Using an Extended UTAUT2 Model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploring the Adoption of Financial Technology (FinTech) by Banking and Insurance Companies Customers in Ghana Using an Extended UTAUT2 Model Kwaku Okyere Danquah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8843096/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 The rapid growth of financial technology (FinTech) has transformed the delivery of financial services globally, yet customer adoption remains uneven, particularly within developing economies. This study investigates the adoption of FinTech by banking and insurance company customers in Ghana using an extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. A quantitative research approach was employed, adopting a descriptive cross-sectional survey design. Data were collected from 409 active FinTech users through a structured questionnaire administered using both online and face-to-face methods. Partial Least Squares Structural Equation Modelling (PLS-SEM) was utilised to analyse the data. The findings reveal that behavioural intention significantly predicts actual FinTech use behaviour. Effort expectancy, facilitating conditions, and hedonic motivation positively and significantly influence behavioural intention, while habit, perceived trust, and social influence exhibit significant negative effects. Performance expectancy and personal innovativeness show positive but statistically insignificant relationships with behavioural intention; however, personal innovativeness directly and significantly predicts use behaviour. Ethical perceptions strongly predict perceived trust, which in turn significantly influences both behavioural intention and actual use. The model explains a substantial proportion of variance in behavioural intention and use behaviour, demonstrating strong predictive relevance. The study contributes to the FinTech literature by extending the UTAUT2 model to include ethics and trust within the Ghanaian banking and insurance context. Practically, the findings provide insights for financial institutions and policymakers to design user-centred, trustworthy, and ethically grounded FinTech solutions that enhance customer adoption and sustained usage. Banking and Insurance Financial Technology FinTech Adoption Ghana PLS-SEM UTAUT2 Figures Figure 1 Figure 2 Figure 3 Introduction The Fourth Industrial Revolution has intensified the demand for digital transformation across industries, with financial institutions experiencing some of the most profound changes. The convergence of intelligent automation, data-driven decision-making, and interconnected digital platforms has fundamentally altered how financial services are designed, delivered, and consumed. In the digital era, financial institutions increasingly rely on technology-enabled business models to enhance efficiency, expand service reach, and improve customer experience (Jünger & Mietzner, 2020 ). As a result, technology has become deeply embedded in all aspects of financial service provision, reshaping traditional banking and insurance operations (Bureshaid et al., 2021 ). Within this evolving landscape, Financial Technology (FinTech) has emerged as a transformative force that integrates financial services with advanced information technologies. FinTech encompasses a wide range of technology-driven innovations, including mobile payments, digital banking, peer-to-peer transfers, and data-driven financial services, aimed at improving accessibility, convenience, and system efficiency (Hwang et al., 2021 ; Roh et al., 2021). Although FinTech remains in a relatively early stage of development, scholars and practitioners increasingly view it as a defining element of the future financial ecosystem. Its rapid diffusion has been supported by the widespread use of mobile technologies and cloud-based platforms, enabling customers to manage financial activities seamlessly across time and space. Globally, investment in FinTech has expanded significantly, reflecting growing confidence in its disruptive potential. According to Statista ( 2021 ), global investment in FinTech firms rose dramatically from USD 9 billion to over USD 210 billion by 2021. Similarly, global awareness and usage of FinTech services have increased substantially, with EY Global (2021) reporting that 96% of individuals are familiar with at least one FinTech service and nearly two-thirds actively use such services. These trends underscore the mainstream acceptance of FinTech and its growing relevance across both developed and emerging economies. The banking and insurance sectors have been particularly affected by FinTech-driven innovations. FinTech solutions have enabled financial institutions to improve operational efficiency, reduce transaction costs, and deliver personalized services that enhance customer satisfaction (Giglio, 2021 ; Hu et al., 2019 ). In emerging markets, digital banking adoption has accelerated rapidly, reaching levels comparable to those of developed economies (Ahmed & Sur, 2023 ). The integration of technologies such as artificial intelligence, big data analytics, blockchain, and mobile internet has further expanded the scope and functionality of FinTech services, challenging traditional financial service models and intensifying competition (Chikri & Kassou, 2024 ). In Sub-Saharan Africa, FinTech has become a key driver of financial inclusion and economic participation. Ghana, in particular, has emerged as one of Africa’s fastest-growing digital markets, alongside countries such as Nigeria, Kenya, and South Africa (Ayakwah et al., 2021 ; Geiger et al., 2019 ). FinTech firms and digital platforms have increasingly complemented and in some cases disrupted traditional banking and insurance services by offering customer-centric, technology-enabled financial solutions (Nelaturu et al., 2022 ). Despite these developments, the success of FinTech initiatives ultimately depends on customers’ willingness to adopt and continuously use these technologies. The COVID-19 pandemic further accelerated FinTech adoption by reinforcing the need for contactless, remote, and efficient financial services (Slawinski, 2021 ). While a growing body of research has examined FinTech adoption across different contexts, much of the existing literature focuses on developed economies or specific sectors such as banking, with limited attention to insurance services and emerging economies such as Ghana. Moreover, empirical studies rarely integrate ethical considerations, trust mechanisms, and behavioural continuance factors within a single explanatory framework. Against this backdrop, the present study investigates the adoption of FinTech by banking and insurance customers in Ghana by extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) to incorporate perceived ethics, trust, personal innovativeness, and use behaviour. Empirical studies on FinTech adoption consistently demonstrate that behavioural intention is influenced by a combination of technological, social, and psychological factors, although findings vary across contexts and sectors. Research grounded in UTAUT, UTAUT2, and TAM has identified performance expectancy, effort expectancy, facilitating conditions, social influence, hedonic motivation, habit, and trust as significant predictors of FinTech adoption in emerging and developed economies (Bommer et al., 2023 ; Hassan et al., 2022 ; Mulyana et al., 2020 ; Sultana et al., 2023 ). Extensions of these models have further highlighted the roles of personal innovativeness, perceived risk, perceived security, and ethical considerations in shaping adoption intentions and usage behaviour (Almashhadani et al., 2023 ; Chawla et al., 2023 ; Zhang et al., 2023 ). However, empirical evidence remains fragmented, with inconsistent effects reported for core constructs such as social influence, performance expectancy, and perceived risk across different cultural and institutional settings (Hoang et al., 2021 ; Tang et al., 2020 ). Notably, most prior studies focus on single financial sectors, predominantly banking, while overlooking insurance services and the joint behaviour of banking and insurance customers. Furthermore, limited research integrates perceived ethics and trust as antecedents linking technological perceptions to actual use behaviour, particularly in Sub-Saharan African contexts. Consequently, there is a clear gap in the literature for a comprehensive, context-specific model that extends UTAUT2 by incorporating ethical perceptions, trust, personal innovativeness, and actual usage behaviour to explain FinTech adoption among banking and insurance customers in Ghana. Table 1 shows a summary of empirical studies and identified research gaps addressed by the current study. Table 1 Summary of Empirical Studies on FinTech Adoption and Identified Research Gaps Addressed by the Current Study Empirical Focus in Prior Studies Key Evidence from Literature Identified Limitation / Gap How the Current Study Addresses the Gap Core UTAUT / TAM constructs Performance expectancy, effort expectancy, facilitating conditions widely examined (Mulyana et al., 2020 ; Sultana et al., 2023 ) Inconsistent findings across sectors and contexts Re-tests core UTAUT2 paths within Ghana’s banking and insurance context Sectoral focus Predominantly banking or Islamic finance (Hassan et al., 2023 ; Hoang et al., 2021 ) Insurance customers largely underrepresented Integrates banking and insurance customers in a single model Behavioural intention focus Majority stop at BI as outcome (Bommer et al., 2023 ) Limited explanation of actual use behaviour Examines BI → USE and PI → USE , strengthening behavioural realism Trust as mediator Trust mediates risk–intention link (Al Nawayseh, 2020; Ali et al., 2021 ) Trust often treated in isolation from ethics Models Perceived Ethics → Trust → BI / USE Perceived risk emphasis Risk effects mixed and context-dependent (Tang et al., 2020 ; Zhang et al., 2023 ) Overemphasis on risk, neglect of ethical perceptions Shifts focus from risk dominance to ethical trust-building mechanisms Personal innovativeness Found significant in some contexts (Almashhadani et al., 2023 ) Rarely linked to actual use behaviour Tests PI → BI and PI → USE simultaneously Social influence Significant in emerging markets, insignificant elsewhere Contextual inconsistency not well explained Re-examines SI within Ghana’s collectivist-social environment Hedonic motivation & habit Strong predictors in meta-analyses (Bommer et al., 2023 ) Underexplored in African contexts Includes HM → BI and HT → BI in Ghana Ethical considerations Rarely included in adoption models Ethical perceptions overlooked in FinTech research Introduces Perceived Ethics (ET) as a novel antecedent Geographic coverage Dominated by Asia & Middle East Limited Sub-Saharan African evidence Provides context-specific empirical evidence from Ghana User groups Students, Islamic bank users, tech-savvy consumers General customers underrepresented Focuses on actual banking and insurance customers Theoretical Foundation and Hypotheses Development This study is grounded in the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), an extension of the original UTAUT model developed by Venkatesh et al. ( 2003 ) and later refined to explain consumer technology adoption behaviour (Venkatesh et al., 2012 ). UTAUT2 synthesises constructs from foundational theories such as the Technology Acceptance Model (TAM) and the Theory of Reasoned Action (TRA), providing a comprehensive socio-psychological framework for explaining behavioural intention and technology use. UTAUT2 posits that performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, and habit jointly influence behavioural intention and use behaviour. The model has been extensively validated across multiple technology contexts, including digital finance, mobile banking, and electronic payments (Chauhan & Jaiswal, 2016 ; Dowdy, 2020; Khalilzadeh et al., 2017 ). Its predictive robustness and adaptability make it particularly suitable for investigating FinTech adoption in emerging economies. However, despite its strengths, UTAUT2 has been criticised for insufficiently accounting for trust-based and ethical considerations, which are particularly salient in financial technologies where perceived risk, data privacy, and transparency concerns remain prominent (Bagozzi, 2007 ; Raihan & Rachmawati, 2019 ). In response, this study extends UTAUT2 by incorporating perceived ethics, perceived trust, and personal innovativeness, thereby enhancing the model’s explanatory power within the Ghanaian FinTech context. Hypotheses Development Use Behaviour (Actual Use of FinTech Services) Use behaviour, also referred to as actual usage, represents the extent to which individuals translate their intentions into concrete actions through the sustained utilisation of FinTech services. Within technology adoption research, actual use is regarded as the ultimate behavioural outcome, reflecting not only acceptance but also the practical integration of technology into users’ daily financial activities. Unlike behavioural intention, which captures users’ motivational readiness, actual use provides empirical evidence of engagement with FinTech platforms in real-world settings. Drawing from behavioural theories such as the Theory of Reasoned Action and the Unified Theory of Acceptance and Use of Technology, actual use behaviour is conceptualised as a direct consequence of behavioural intention (Sharma & Munjal, 2024 ; Venkatesh et al., 2003 ). This intention–behaviour linkage is critical in FinTech contexts, where favourable intentions do not always translate into sustained usage due to trust concerns, usability challenges, or ethical apprehensions. Examining actual use therefore enables a more comprehensive understanding of FinTech adoption beyond attitudinal dispositions. Empirical evidence consistently supports a positive relationship between behavioural intention and actual use of FinTech services (Hassan et al., 2023 ; Senyo & Osabutey, 2020 ; Thusi & Maduku, 2020 ). These studies suggest that individuals who exhibit stronger intentions are more likely to engage in consistent FinTech usage. Consequently, this study incorporates actual use behaviour as a key endogenous construct, allowing for a more robust assessment of FinTech adoption among banking and insurance customers in Ghana. Based on the foregoing theoretical and empirical evidence, it was therefore postulated that: H1 “Behavioural intention has a positive and significant effect on actual use of FinTech services”. Effort Expectancy and Behavioural Intention Effort expectancy refers to the degree to which individuals perceive FinTech services as easy to use and free of complexity. When FinTech platforms are perceived as user-friendly, customers are more likely to develop favourable intentions towards adoption. Empirical studies consistently demonstrate that effort expectancy significantly influences behavioural intention, particularly in emerging economies where digital literacy varies widely (Mulyana et al., 2020 ; Sultana et al., 2023 ). Based on this reasoning, it was therefore formulated that: H2 “Effort expectancy has a positive and significant effect on behavioural intention to use FinTech services”. Performance Expectancy and Behavioural Intention Performance expectancy reflects users’ beliefs regarding the extent to which FinTech services enhance efficiency, convenience, and financial management. In financial contexts, perceived usefulness remains a critical determinant of adoption, as users prioritise technologies that deliver tangible benefits. Several empirical studies report a positive association between performance expectancy and behavioural intention, although findings vary across sectors (Bommer et al., 2023 ; Mulyana et al., 2020 ; Parviz & Arthur, 2025 ). Accordingly, it was postulated that: H3 “Performance expectancy has a positive and significant effect on behavioural intention to use FinTech services”. Facilitating Conditions and Behavioural Intention Facilitating conditions capture users’ perceptions of the availability of technical, organisational, and infrastructural support necessary for FinTech usage. Adequate internet connectivity, digital devices, and user support systems enhance customers’ confidence and readiness to adopt FinTech services. Prior studies have confirmed the importance of facilitating conditions in shaping adoption intentions (Acquah et al., 2024 ; Hassan et al., 2022 ; Sultana et al., 2023 ). Thus, it was formulated that: H4 “Facilitating conditions have a positive and significant effect on behavioural intention to use FinTech services”. Social Influence and Behavioural Intention Social influence reflects the extent to which individuals perceive that “important others” believe they should use FinTech services (Hoque et al., 2024 ). Given the relatively novel nature of FinTech in many emerging economies, individuals often rely on social cues and peer experiences when forming adoption decisions (Chan et al., 2022 ; Firmansyah et al., 2022 ; Vardari & Hameli, 2025 ). Empirical findings provide mixed but context-dependent evidence regarding the role of social influence. Based on this perspective, it was proposed that: H5 “Social influence has a positive and significant effect on behavioural intention to use FinTech services”. Hedonic Motivation and Behavioural Intention Hedonic motivation refers to the enjoyment or pleasure derived from using FinTech services (Amnas et al., 2023 ). Beyond functional benefits, enjoyable user experiences can strengthen emotional attachment and enhance adoption intentions. Meta-analytic evidence suggests that hedonic motivation is among the strongest predictors of FinTech usage intention (Bommer et al., 2023 ; Kashyap et al., 2026; Sharma et al., 2025 ). It was therefore postulated that: H6 “Hedonic motivation has a positive and significant effect on behavioural intention to use FinTech services”. Habit and Behavioural Intention Habit represents the extent to which individuals perform behaviours automatically due to prior experience (Acquah et al., 2024 ). In digital finance, repeated exposure to FinTech platforms can lead to habitual usage patterns that reinforce adoption intentions. Empirical studies confirm habit as a significant predictor of behavioural intention in FinTech contexts (Bommer et al., 2023 ; Venkatesh et al., 2012 ). Accordingly, it was formulated that: H7 “Habit has a positive and significant effect on behavioural intention to use FinTech services”. Perceived Ethics and Perceived Trust Perceived ethics refers to users’ perceptions of fairness, transparency, and responsible conduct by FinTech service providers. Ethical practices play a critical role in trust formation, particularly in financial environments characterised by information asymmetry. Empirical studies highlight trust as a key mechanism through which ethical perceptions influence adoption decisions (Ali et al., 2021 ; Chawla et al., 2023 ). Based on this argument, it was postulated that: H8 “Perceived ethics has a positive and significant effect on perceived trust in FinTech services”. Perceived Trust, Behavioural Intention, and Actual Use Trust reflects users’ confidence in the reliability, security, and integrity of FinTech services. Trust has been widely recognised as a central determinant of both intention and actual usage behaviour (Al Nawayseh, 2020; Hassan et al., 2022 ; Zhao et al., 2024 ). Users who trust FinTech platforms are more likely to intend to use them and translate these intentions into sustained usage. Thus, it was formulated that: H9 “Perceived trust has a positive and significant effect on behavioural intention to use FinTech services”. H10 “Perceived trust has a positive and significant effect on actual use of FinTech services”. Personal Innovativeness, Behavioural Intention, and Actual Use Personal innovativeness captures individuals’ willingness to experiment with new technologies (Salifu et al., 2025 ). Highly innovative users are more inclined to adopt disruptive technologies such as FinTech earlier and more frequently (Almashhadani et al., 2023 ; Leong et al., 2020). Empirical evidence supports the direct influence of personal innovativeness on both intention and actual usage (Salifu et al., 2025 ). Accordingly, it was proposed that: H11 “Personal innovativeness has a positive and significant effect on behavioural intention to use FinTech services”. H12 “Personal innovativeness has a positive and significant effect on actual use of FinTech services”. Conceptual Model The conceptual model of the study based on the formulated hypotheses is displayed by Fig. 1. Note “BI = Behavioural Intention; EE = Effort Expectancy, ET = Perceived Ethics; FC = Facilitating Conditions; HM = Hedonic Motivation; HT = Habit; PE = Performance Expectancy; PI = Personal Innovativeness; PT = Perceived Trust; SI = Social Influence; and USE = Use behaviour” Figure 1: Conceptual model Source: Authors’ own work Methodology Research Design The research design provides the methodological framework that guides how a study is systematically planned, executed, and analysed in order to address its stated objectives. In quantitative research, the choice of design is largely determined by the nature of the research problem, the theoretical orientation of the study, and the type of data required to test the proposed relationships among variables. Broadly, research designs may be classified as experimental or non-experimental. While experimental designs involve manipulation and control of variables to establish causality, non-experimental designs focus on observing existing phenomena without researcher intervention. This study adopted a descriptive cross-sectional survey design to investigate the determinants of financial technology adoption among banking and insurance customers in Ghana. The choice of this design is consistent with the objective of capturing respondents’ perceptions, intentions, and usage behaviour at a single point in time, without manipulating any study variables (Abreh et al., 2025 ; Almashhadani et al., 2023 ). A cross-sectional approach is particularly appropriate for technology adoption studies, as it enables the examination of prevailing attitudes, behaviours, and relationships among constructs within a defined population (Hassan et al., 2023 ). Additionally, the cross-sectional survey approach supports the efficient collection of data from a large and diverse sample, thereby enhancing the external validity and generalisability of the findings (Hassan et al., 2022 ). Given the quantitative orientation of the study and the need to empirically test multiple hypotheses derived from the extended UTAUT2 framework, this design was deemed both methodologically appropriate and practically feasible. Sample The sample for this study consisted of customers of banking and insurance companies in Ghana who actively use financial technology (FinTech) services. The selection of this group was intentional, as the study sought to obtain insights from respondents with direct experience and sufficient familiarity with FinTech platforms used for banking and insurance transactions. Focusing on actual users ensured that responses reflected informed perceptions, behavioural intentions, and real usage behaviour rather than hypothetical adoption tendencies. A purposive sampling approach was employed to identify respondents who met the inclusion criteria, namely individuals who had previously used or were currently using FinTech services offered by banking or insurance institutions. This approach is appropriate for FinTech adoption studies, as it allows researchers to target participants with relevant knowledge and practical exposure to digital financial services (Almashhadani et al., 2023 ; Sultana et al., 2023 ). Given the dispersed nature of FinTech users across Ghana, purposive sampling enabled efficient access to respondents most capable of providing meaningful and reliable data aligned with the objectives of the study. The minimum sample size was determined using G*Power software, which indicated a requirement of 172 respondents based on a model with a maximum of 10 predictors, an effect size of 0.15, a significance level of 0.05, and a statistical power of 0.95. To strengthen the robustness of the analysis and improve the generalisability of the findings, the study exceeded this minimum threshold by targeting a larger sample. Out of the 500 questionnaires administered, 409 were successfully retrieved, representing a response rate of 81.8%, which is considered adequate for quantitative survey research. This high return rate enhanced the reliability of the data and provided a sufficient basis for subsequent statistical analyses and interpretation. The expanded sample size enhanced the statistical power of the analysis and improved the reliability of the results, particularly for the application of Partial Least Squares Structural Equation Modelling. Instrument Data for the study were collected using a structured questionnaire, which served as the primary research instrument. The use of a questionnaire is well suited to quantitative research, as it enables the systematic collection of standardised data that can be readily quantified and subjected to statistical analysis. Questionnaires are particularly effective for technology adoption studies because they allow researchers to capture respondents’ perceptions, attitudes, and behavioural intentions across a wide range of constructs within a relatively short period. The questionnaire was adapted from previously validated instruments used in FinTech and technology adoption research to ensure content relevance, measurement accuracy, and contextual suitability. Adapting established scales allows researchers to retain the psychometric strengths of existing instruments while modifying item wording to reflect the cultural and institutional context of the study. This approach enhances both validity and reliability while ensuring alignment with the study’s objectives. The instrument was organised into two main sections. The first section collected demographic information, including gender, age, educational qualification, monthly income, and respondents’ familiarity with FinTech services. This section provided background information necessary for profiling respondents and for testing the moderating effects of demographic variables. The second section consisted of items measuring the core study constructs: effort expectancy, social influence, perceived risk, behavioural intention, use behaviour, facilitating conditions, perceived trust, hedonic motivation, personal innovativeness, habit, and ethics. All items were measured using a “five-point Likert scale ranging from strongly disagree (1) to strongly agree (5)”. Each construct was operationalised using multiple items to allow for a more comprehensive and nuanced assessment of respondents’ perceptions and behaviours. Data Collection Data collection was carried out using a mixed-mode approach, combining face-to-face administration and online survey distribution. This approach was adopted to accommodate differences in geographical location, internet accessibility, and technological proficiency among respondents across Ghana. The online questionnaire was administered using Google Forms and distributed through digital platforms, including email and social media channels related to banking and financial services. This method enabled the researcher to reach respondents across multiple regions efficiently and cost-effectively. In addition to the online survey, face-to-face data collection was conducted to improve response rates and to include respondents who may have limited access to digital platforms. This method also allowed for direct interaction with participants, providing opportunities to clarify questions where necessary and ensuring more complete responses. Trained research assistants supported the administration of questionnaires in selected locations, including banks, insurance companies, and public spaces. Prior to data collection, introductory letters were submitted to the management of selected banking institutions and insurance companies to seek formal approval. These letters outlined the purpose of the study, assured confidentiality, and explained how the data would be used. Ethical considerations were strictly observed throughout the data collection process, including obtaining informed consent, ensuring voluntary participation, and maintaining respondent anonymity. The combination of online and face-to-face methods enhanced coverage, reduced sampling bias, and contributed to the robustness of the dataset. Analysis Strategy The data analysis process followed a structured and sequential approach. Initially, the collected data were entered into the Statistical Package for the Social Sciences (SPSS) for data screening, coding, and preliminary analysis. This stage involved checking for missing values, identifying outliers, and generating descriptive statistics to summarise respondents’ demographic characteristics and overall response patterns. Following data cleaning, the dataset was converted into a comma-separated values format and imported into SmartPLS 4 for further analysis. The primary analytical technique employed was “Partial Least Squares Structural Equation Modelling (PLS-SEM)”. PLS-SEM is particularly appropriate for studies involving complex models with multiple latent constructs and indicators, as well as for predictive and theory-building research (Acquah et al., 2025 ; Arthur et al., 2025 ; Hair et al., 2021; Hair et al., 2022). The method allows for the simultaneous assessment of the measurement model and the structural model, providing comprehensive insights into both construct validity and hypothesised relationships. The measurement model was evaluated by examining indicator reliability, internal consistency reliability, convergent validity, and discriminant validity using established criteria such as “factor loadings, Cronbach’s alpha, composite reliability, Average Variance Extracted, and the HTMT ratio”. The structural model assessment involved analysing path coefficients, significance levels, coefficients of determination, and predictive relevance. Bootstrapping procedures were applied to test the significance of hypothesised relationships. This analytical strategy enabled a rigorous examination of the determinants of FinTech adoption, the role of behavioural intention in predicting actual use, and the moderating influence of demographic factors. By combining SPSS and SmartPLS, the study ensured both statistical precision and theoretical robustness in addressing the research objectives. Results Measurement model The outer loadings of all indicators on their respective latent constructs met the recommended threshold of 0.70 (see Table 2 and Appendix A), with a few slightly below but still within acceptable limits (Hair et al., 2021). Table 2 presents the “outer loadings, Cronbach’s alpha (CA), composite reliability (CR), average variance extracted (AVE), and outer VIF values”. All CR values exceeded the 0.70 threshold, indicating good internal consistency reliability. Similarly, AVE values for all constructs were above 0.50, confirming convergent validity. Outer VIF values were below the critical value of 3.3 (Legate et al., 2023 ), indicating no multicollinearity concerns among indicators. The algorithm results of the PLS-SEM are displayed by Fig. 2 . Table 2 Measurement Model Results (Outer Loadings, CA, CR, AVE, and VIF) Construct Indicator Loading VIF CA CR AVE BI BI1 0.660 1.385 0.714 0.823 0.539 BI2 0.779 1.576 BI3 0.744 1.504 BI4 0.747 1.505 EE EE1 0.767 1.767 0.786 0.86 0.606 EE2 0.825 2.216 EE3 0.801 1.933 EE4 0.717 1.206 ET ET1 0.843 1.961 0.864 0.906 0.707 ET2 0.868 2.201 ET3 0.855 2.513 ET4 0.797 2.18 FC FC1 0.749 1.608 0.823 0.871 0.576 FC2 0.821 1.864 FC3 0.75 1.998 FC4 0.709 1.923 FC5 0.761 2.128 HM HM1 0.644 1.284 0.76 0.847 0.583 HM2 0.754 1.469 HM3 0.846 1.824 HM4 0.795 1.781 HT HT1 0.853 1.912 0.807 0.875 0.702 HT2 0.93 1.833 HT3 0.718 1.603 PE PE1 0.853 2.074 0.818 0.878 0.644 PE2 0.72 1.57 PE3 0.779 1.543 PE4 0.849 1.914 PI PI1 0.848 1.802 0.771 0.868 0.687 PI2 0.851 1.867 PI3 0.785 1.368 PT PT1 0.889 1.987 0.822 0.893 0.736 PT2 0.812 1.707 PT3 0.871 1.908 SI SI1 0.782 1.8 0.834 0.882 0.601 SI2 0.77 1.591 SI3 0.765 1.902 SI4 0.83 1.962 SI5 0.725 1.581 USE USE1 0.758 1.542 0.772 0.854 0.595 USE2 0.745 1.502 USE3 0.829 1.952 USE4 0.75 1.672 Note: “BI = Behavioural Intention; EE = Effort Expectancy, ET = Perceived Ethics; FC = Facilitating Conditions; HM = Hedonic Motivation; HT = Habit; PE = Performance Expectancy; PI = Personal Innovativeness; PT = Perceived Trust; SI = Social Influence; and USE = Use behaviour” Discriminant Validity Discriminant validity was evaluated using the HTMT ratio (see Table 3 ). All HTMT values among BI, EE, ET, FC, HM, HT, PE, PI, PT, SI, and USE were below the threshold of 0.90, indicating satisfactory discriminant validity (Hair et al., 2025 ). This confirms that the constructs are empirically distinct and do not exhibit multicollinearity concerns. Structural Model Based on the results presented in Table 4 , a structural model assessment was carried out to examine the hypothesized relationships among the latent constructs. The evaluation included the examination of “path coefficients (β), standard deviations (SD), t-statistics, p-values, confidence intervals, and effect sizes (f²)”, as well as the “coefficient of determination (R²)” for endogenous constructs. The analysis employed a bootstrapping procedure with 10,000 subsamples to assess the significance of path relationships, and multicollinearity diagnostics were conducted using VIF values. The VIF values across the paths ranged between 1.000 and 4.147, indicating that multicollinearity was not a concern, as all values were below the conservative threshold of 5. The results indicated that BI significantly predicted USE (β = 0.444, t = 12.325, p < 0.001, f² = 0.434), accounting for 61.8% of the variance in USE (R² = 0.618). EE significantly influenced BI (β = 0.324, t = 3.329, p < 0.001, f² = 0.053), with a VIF of 3.722, suggesting no multicollinearity. Similarly, FC positively predicted BI (β = 0.410, t = 3.761, p < 0.001, f² = 0.076), and HM was also a strong predictor of BI (β = 0.536, t = 7.971, p < 0.001, f² = 0.249). Conversely, HT had a significant negative effect on BI (β = -0.332, t = 3.696, p < 0.001, f² = 0.053), as did PT (β = -0.286, t = 4.768, p < 0.001, f² = 0.069) and SI (β = -0.157, t = 2.294, p = 0.011, f² = 0.014). Although PE (β = 0.087, t = 1.077, p = 0.141, f² = 0.004) and PI (β = 0.097, t = 1.378, p = 0.084, f² = 0.012) showed positive associations with BI, their effects were not statistically significant. However, PI had a significant direct effect on USE (β = 0.165, t = 4.005, p < 0.001, f² = 0.057). Moreover, ET showed a very strong and significant predictive effect on PT (β = 0.702, t = 37.457, p < 0.001, f² = 0.970), accounting for 49.2% of the variance in PT (R² = 0.492). PT also had a direct and significant influence on USE (β = 0.428, t = 11.746, p < 0.001, f² = 0.407), underscoring its importance as a predictor. Figure 3 shows the PLS-SEM bootstrapping results. Table 4 Structural model Path β M SD T Statistics P Values VIF 5.0% 95.0% f 2 R 2 BI -> USE 0.444 0.445 0.036 12.325 0.000 1.189 0.381 0.500 0.434 0.618 EE -> BI 0.324 0.326 0.097 3.329 0.000 3.722 0.171 0.489 0.053 0.465 ET -> PT 0.702 0.703 0.019 37.457 0.000 1.000 0.668 0.730 0.970 0.492 FC -> BI 0.410 0.404 0.109 3.761 0.000 4.147 0.236 0.594 0.076 HM -> BI 0.536 0.535 0.067 7.971 0.000 2.162 0.427 0.646 0.249 HT -> BI -0.332 -0.322 0.090 3.696 0.000 3.897 -0.482 -0.191 0.053 PE -> BI 0.087 0.080 0.081 1.077 0.141 3.331 -0.044 0.220 0.004 PI -> BI 0.097 0.098 0.070 1.378 0.084 1.502 -0.023 0.209 0.012 PI -> USE 0.165 0.164 0.041 4.005 0.000 1.244 0.099 0.234 0.057 PT -> BI -0.286 -0.287 0.060 4.768 0.000 2.209 -0.384 -0.186 0.069 PT -> USE 0.428 0.430 0.036 11.746 0.000 1.178 0.368 0.487 0.407 SI -> BI -0.157 -0.150 0.068 2.294 0.011 3.302 -0.278 -0.053 0.014 Note: M = Sample mean, SD = Standard deviation; R 2 adjusted for BI, PT and USE are 0.454, 0.491 and 0.615 respectively. Predictive Relevance Table 5 presents the results of the “Stone–Geisser predictive relevance (Q²)” assessment for the endogenous constructs in the proposed FinTech adoption model. The Q² values were obtained using the blindfolding procedure by comparing the “sum of squares observed (SSO)” with the “sum of squares error (SSE)”. A Q² value greater than zero indicates that the model has predictive relevance for a given endogenous construct, whereas values equal to zero suggest no predictive relevance. As shown in Table 5 , “behavioural intention (BI)” records a Q² value of 0.239, indicating that the model demonstrates acceptable predictive relevance for customers’ intention to adopt FinTech services. This suggests that the explanatory constructs included in the extended UTAUT2 framework such as “effort expectancy, facilitating conditions, hedonic motivation, habit, personal innovativeness, perceived trust, social influence, and perceived ethics”, meaningfully predict customers’ behavioural intentions within the Ghanaian banking and insurance context. Similarly, perceived trust (PT) yields a Q² value of 0.351, reflecting strong predictive relevance. This result underscores the central role of trust in FinTech adoption, particularly as influenced by ethical perceptions and related antecedents. The finding suggests that the model effectively explains variations in customers’ trust in FinTech services, reinforcing the importance of trust-building mechanisms in digital financial environments. Furthermore, use behaviour (USE) records a Q² value of 0.358, indicating substantial predictive relevance of the model in explaining actual FinTech usage. This implies that behavioural intention and personal innovativeness meaningfully translate into real usage behaviour among customers of banking and insurance companies in Ghana. In contrast, constructs such as “effort expectancy (EE), perceived ethics (ET), facilitating conditions (FC), hedonic motivation (HM), habit (HT), performance expectancy (PE), personal innovativeness (PI), and social influence (SI)” report Q² values of zero, as they are modelled as exogenous constructs. Consequently, predictive relevance is not assessed for these variables. The results confirm that the proposed extended UTAUT2 model exhibits adequate predictive relevance, particularly for behavioural intention, perceived trust, and use behaviour. This provides empirical support for the model’s suitability in predicting FinTech adoption among banking and insurance customers in Ghana. Table 5 Predictive Relevance (Q²) of Endogenous Constructs in the FinTech Adoption Model Construct SSO SSE Q² (= 1-SSE/SSO) BI 1636 1245.572 0.239 EE 1636 1636 ET 1636 1636 FC 2045 2045 HM 1636 1636 HT 1227 1227 PE 1636 1636 PI 1227 1227 PT 1227 795.78 0.351 SI 2045 2045 USE 1636 1051.11 0.358 Discussion The current study examined the adoption of FinTech by banking and insurance company customers in Ghana using an extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. The findings of this study demonstrate that behavioural intention strongly predicts the actual use of FinTech services among banking and insurance customers in Ghana. This aligns with the theoretical propositions of UTAUT2 and the Theory of Reasoned Action, which posit that behavioural intention is the immediate antecedent of technology usage (Venkatesh et al., 2012 ; Sharma et al., 2020). In the Ghanaian context, the strong intention–use relationship may reflect growing awareness and familiarity with digital financial services, alongside increased reliance on mobile money and banking apps in everyday financial transactions. Empirical studies in other emerging economies similarly report that strong intentions translate into sustained adoption (Senyo & Osabutey, 2020 ; Thusi & Maduku, 2020 ), reinforcing the universal applicability of this relationship while highlighting its relevance in Ghana’s FinTech landscape. Effort expectancy positively influenced adoption intentions, suggesting that perceived ease of use is a key driver of FinTech adoption in Ghana. Customers appear more willing to adopt FinTech services when platforms are intuitive and require minimal technical skills. This finding corroborates earlier studies in emerging economies where digital literacy varies, such as those by Mulyana et al. ( 2020 ) and Sultana et al. ( 2023 ). In Ghana, where some users may have limited experience with digital finance, platforms that reduce cognitive load and simplify processes likely enhance users’ willingness to engage. Facilitating conditions and hedonic motivation were also significant predictors of adoption intentions. The availability of reliable infrastructure, internet connectivity, and supportive customer service appears crucial for fostering positive adoption behaviours. Similarly, the enjoyment and satisfaction derived from using FinTech services reinforce intentions to adopt, aligning with Bommer et al. ( 2023 ), who noted that hedonic experiences strengthen emotional attachment to financial technologies. In Ghana, where digital financial services increasingly compete for attention, enjoyable and seamless experiences can differentiate providers and encourage sustained usage. Interestingly, certain constructs such as habit, social influence, and performance expectancy exerted weaker or negative effects on adoption intentions in the current study. This could reflect the context-specific dynamics of Ghanaian FinTech adoption. For example, habitual usage may be less influential because FinTech platforms are relatively new and customers are still exploring different services rather than relying on automatic routines. Similarly, social influence may be weaker as adoption decisions are often guided more by personal convenience and trust in the platform than by peers. Performance expectancy showed limited direct influence on intention, possibly because users in Ghana prioritise practical accessibility, security, and ethical practices over abstract efficiency gains, reflecting a contextual sensitivity to perceived risks in digital finance. Perceived ethics emerged as a strong determinant of trust, which, in turn, influenced both adoption intentions and actual usage. This finding underscores the central role of ethical perceptions in financial technology adoption, particularly in Ghana where concerns about fraud, data privacy, and transparency are prevalent. Customers are more likely to adopt FinTech services when they perceive providers as ethical and trustworthy, corroborating the argument of Ali et al. ( 2021 ) and Chawla et al. ( 2023 ) that ethical practices directly shape trust and adoption behaviour. Finally, personal innovativeness directly influenced actual use of FinTech services, highlighting the role of individual differences in shaping adoption. Customers who are more willing to experiment with new technologies are more likely to explore and maintain engagement with FinTech platforms, even in the absence of strong behavioural intentions. This finding aligns with prior studies (Leong et al., 2020; Almashhadani et al., 2023 ) and is particularly relevant in Ghana, where early adopters often pave the way for broader community acceptance of digital financial solutions. The findings suggest that the UTAUT2 model, when extended with perceived ethics, perceived trust, and personal innovativeness, is a robust framework for explaining FinTech adoption in Ghana. While core constructs like behavioural intention, effort expectancy, facilitating conditions, and hedonic motivation significantly drive adoption, context-specific factors such as emerging usage patterns, trust concerns, and ethical perceptions shape the nuanced adoption behaviour observed among Ghanaian customers. These insights provide practical guidance for FinTech providers seeking to enhance adoption and sustained use in Ghana’s unique socio-economic and technological landscape. Implications of the Study The findings of this study offer important theoretical, practical, and policy implications. Theoretically, the study extends the UTAUT2 framework by incorporating perceived ethics, perceived trust, and personal innovativeness, demonstrating that these factors significantly enhance the model’s explanatory power in the Ghanaian FinTech context. Practically, the results provide guidance for FinTech providers on strategies to improve adoption and sustained usage. Emphasizing user-friendly interfaces, reliable support systems, and ethical business practices can increase customer confidence and engagement. Additionally, the study highlights the importance of targeting innovative early adopters to encourage wider uptake. Policymakers and regulatory bodies such as the Bank of Ghana and the National Insurance Commission can leverage these insights to develop guidelines that enhance transparency, security, and ethical standards in digital financial services, thereby fostering greater trust among users. Limitations and Future Studies While this study provides valuable insights, it has several limitations. The cross-sectional research design restricts the ability to infer causal relationships, suggesting that longitudinal or experimental studies could provide stronger evidence of temporal effects. The study was conducted in selected urban areas in Ghana, which may limit the generalizability of the findings to rural communities or other emerging economies. Additionally, self-reported data may be subject to social desirability or response biases. Future studies could expand the scope by including diverse geographic regions, examining sector-specific FinTech adoption patterns, or integrating qualitative methods to capture deeper insights into user experiences. Researchers may also explore additional contextual factors such as financial literacy, risk perception, and cultural influences to further refine models of FinTech adoption in Ghana. Conclusion This study examined the adoption of FinTech services by banking and insurance customers in Ghana using an extended UTAUT2 model. The findings confirm that behavioural intention, effort expectancy, facilitating conditions, hedonic motivation, perceived ethics, perceived trust, and personal innovativeness play significant roles in predicting actual use of FinTech services. Contextual factors in Ghana, including trust concerns and ethical considerations, significantly shape customer adoption behaviour. The study contributes to both theory and practice by extending UTAUT2 and providing actionable recommendations for FinTech providers, regulators, and policymakers. Ultimately, enhancing usability, trust, and ethical standards can promote greater financial inclusion and technology adoption in Ghana. Declarations CRediT authorship contribution statement Kwaku Okyere Danquah: Conceptualization, Data curation, Investigation, Writing – original draft, Writing – review & editing, Resources, Software, Supervision, Methodology, Validation, Visualization. The author reviewed the manuscript and approved it for final submission. Ethics Statement The study was conducted in accordance with ethical standards and all applicable institutional guidelines and regulations. Ethical approval letter was waived by the ethics committee of the Sumy State University. Informed consent was obtained from all participants. All procedures followed in the study complied with the principles outlined in the “Declaration of Helsinki”. Clinical Trial Number : Not Applicable Consent of Participants Informed consent was obtained from all participants before data collection. Data Availability Data will be made available upon request. Conflict of interest The author declare that they have no conflicts of interest. Funding The author did not receive any fund for this study. References Abreh MK, Arthur F, Akwetey FA, Nortey SA. Modelling STEM students’ intention to learn artificial intelligence (AI) in Ghana: a PLS-SEM and fsQCA approach. Discover Artif Intell. 2025;5(1):223. Acquah BYS, Arthur F, Salifu I, Mensah E, Opoku E, Nortey SA, Tetteh SA. Modelling economics students’ use of ChatGPT and academic performance: insights from self-determination theory and epistemic curiosity. Discover Artif Intell. 2025;5(1):1–24. Acquah BYS, Arthur F, Salifu I, Quayson E, Nortey SA. Preservice teachers’ behavioural intention to use artificial intelligence in lesson planning: A dual-staged PLS-SEM-ANN approach. Computers Education: Artif Intell. 2024;7:100307. Ahmed S, Sur S. Change in the uses pattern of digital banking services by Indian rural MSMEs during demonetization and Covid-19 pandemic-related restrictions. Vilakshan-XIMB J Manage. 2023;20(1):166–92. Ali M, Raza SA, Khamis B, Puah CH, Amin H. (2021). How perceived risk, benefit and trust determine user Fintech adoption: a new dimension for Islamic finance. foresight , 23 (4), 403–420. Almashhadani IS, Abuhashesh M, Bany Mohammad A, Masa’deh RE, Al-Khasawneh M. Exploring the determinants of FinTech adoption and intention to use in Jordan: The impact of COVID-19. Cogent Social Sci. 2023;9(2):2256536. Amnas MB, Selvam M, Raja M, Santhoshkumar S, Parayitam S. Understanding the determinants of FinTech adoption: Integrating UTAUT2 with trust theoretic model. J risk financial Manage. 2023;16(12):505. Arthur F, Salifu I, Abam Nortey S. Predictors of higher education students’ behavioural intention and usage of ChatGPT: the moderating roles of age, gender and experience. Interact Learn Environ. 2025;33(2):993–1019. Ayakwah A, Damoah IS, Osabutey EL. Digitalization in Africa: The case of public programs in Ghana. Business in Africa in the Era of Digital Technology: Essays in Honour of Professor William Darley. Cham: Springer International Publishing; 2021. pp. 7–25. Bagozzi RP. The legacy of the technology acceptance model and a proposal for a paradigm shift. J association Inform Syst. 2007;8(4):3. Bommer WH, Milevoj E, Rana S. A meta-analytic examination of the antecedents explaining the intention to use fintech. Industrial Manage Data Syst. 2023;123(3):886–909. Bureshaid N, Lu K, Sarea A. (2021). Adoption of fintech services in the banking industry. Appl Artif Intell Bus Educ Healthc, 125–38. Chan R, Troshani I, Rao Hill S, Hoffmann A. Towards an understanding of consumers’ FinTech adoption: The case of Open Banking. Int J Bank Mark. 2022;40(4):886–917. Chauhan S, Jaiswal M. Determinants of acceptance of ERP software training in business schools: Empirical investigation using UTAUT model. Int J Manage Educ. 2016;14(3):248–62. Chawla U, Mohnot R, Singh HV, Banerjee A. The mediating effect of perceived trust in the adoption of cutting-edge financial technology among digital natives in the Post-COVID-19 Era. Economies. 2023;11(12):286. Chikri H, Kassou M. Financial revolution: Innovation powered by FinTech and artificial intelligence. J Theoretical Appl Inform Technol. 2024;102(9):4145–57. Firmansyah EA, Masri M, Anshari M, Besar MHA. Factors affecting fintech adoption: a systematic literature review. FinTech. 2022;2(1):21–33. Geiger MT, Kwakye KG, Vicente CL, Wiafe BM, Boakye Adjei NY. Fourth Ghana economic update: Enhancing financial inclusion - Africa region (English). Ghana Economic Update. Volume 4. World Bank; 2019. Giglio F. Fintech: A literature review. Eur Res Stud J. 2021;24(2B):600–27. Hair JF, Babin BJ, Ringle CM, Sarstedt M, Becker JM. (2025). Covariance-based structural equation modeling (CB-SEM): a SmartPLS 4 software tutorial. J Market Anal 13, 709–724 (2025). https://doi.org/10.1057/s41270-025-00414-6 Hassan MS, Islam MA, Sobhani FA, Nasir H, Mahmud I, Zahra FT. Drivers influencing the adoption intention towards mobile fintech services: a study on the emerging Bangladesh market. Information. 2022;13(7):349. Hassan MS, Islam MA, Yusof MF, Nasir H. (2023). Users’ fintech services acceptance: A cross-sectional study on Malaysian Insurance & Takaful industry. Heliyon, 9 (11). Hoang YH, Nguyen DT, Tran LH, Nguyen NT, Vu NB. Customers’ adoption of financial services offered by banks and fintechs partnerships: evidence of a transitional economy. Data Sci Finance Econ. 2021;1(1):77–95. Hoque MZ, Chowdhury NJ, Hossain AA, Tabassum T. (2024). Social and facilitating influences in fintech user intention and the fintech gender gap. Heliyon, 10 (1). Hu Z, Ding S, Li S, Chen L, Yang S. Adoption intention of fintech services for bank users: An empirical examination with an extended technology acceptance model. Symmetry. 2019;11(3):340. Hwang Y, Park S, Shin N. Sustainable development of a mobile payment security environment using fintech solutions. Sustainability. 2021;13(15):8375. Jünger M, Mietzner M. Banking goes digital: The adoption of FinTech services by German households. Finance Res Lett. 2020;34:101260. Kashyap AK, Priyanka, Gupta P. (2026). Do cyber fraud experiences matter? Revisiting user motivations and satisfaction in driving FinTech continuance. J Financial Crime, 1–20. Khalilzadeh J, Ozturk AB, Bilgihan A. Security-related factors in extended UTAUT model for NFC based mobile payment in the restaurant industry. Comput Hum Behav. 2017;70:460–74. Legate AE, Hair Jr JF, Chretien JL, Risher JJ. PLS-SEM: Prediction‐oriented solutions for HRD researchers. Hum Res Dev Q. 2023;34(1):91–109. Mulyana A, Disman D, Wibowo L, Hurriyati R. (2020). Application of customer behavior in using fintech as business media based on the unified theory of acceptance and use of technology model. In 3rd Global conference on business, management, and entrepreneurship (GCBME 2018) (pp. 69–75). Atlantis Press. Nelaturu K, Du H, Le DP. A Review of Blockchain in Fintech: Taxonomy, Challenges, and Future Directions. Cryptography. 2022;6(2):18. Parviz M, Arthur F. Exploring EFL Teachers’ Behavioral Intentions to Integrate GenAI Applications: Insights From PLS-SEM and fsQCA. Hum Behav Emerg Technol. 2025;2025(1):5582099. Raihan T, Rachmawati I. (2019). Analyzing Factors Influencing Continuance Intention Of E-Wallet Adoption Using UTAUT 2 Model (A Case Study Of Dana In Indonesia). eProceedings of Management , 6 (2). Roh T, Yang YS, Xiao S, Park BI. (2022). What makes consumers trust and adopt fintech? An empirical investigation in China. Electron Commer Res, 1–33. Salifu I, Arthur F, Acquah BYS, Opoku E, Nortey SA, Boateng E. Exploring graduate students’ use of generative artificial intelligence in Ghana: insights from an extended UTAUT2 model, PLS-SEM, IPMA and fsQCA. Discover Educ. 2025;4(1):305. Senyo PK, Osabutey EL. Unearthing antecedents to financial inclusion through FinTech innovations. Technovation. 2020;98:102155. Sharma A, Mohan A, Johri A, Asif M. Determinants of financial technology (FinTech) adoption by the farmers in agrarian economy. Social Sci Humanit Open. 2025;11:101370. Sharma D, Munjal P. Determining the key drivers of FinTech adoption in India. Int J Process Manage Benchmarking. 2024;16(4):533–54. Slawinski S. (2021). Fintechs in South Korea see the launch of a new virtual sandbox. Global Fintech News . Statista. (2021). www.statista.com, [Online]. Available: https://www.statista.com/statistics/219339/us-prices-of-cement/#::text = In%202020%2C%20the%20cost%20of,highest%20in%20the%20last%20years [Accessed 01 March 2021]. Sultana N, Chowdhury RS, Haque A. (2023). Gravitating towards Fintech: A study on Undergraduates using extended UTAUT model. Heliyon, 9 (10). Tang KL, Ooi CK, Chong JB. Perceived risk factors affect intention to use FinTech. J Acc finance Emerg economies. 2020;6(2):453–63. Thusi P, Maduku DK. South African millennials’ acceptance and use of retail mobile banking apps: An integrated perspective. Comput Hum Behav. 2020;111:106405. Vardari L, Hameli K. (2025). Perceived benefits, trust and social influence in FinTech: pathways to adoption and satisfaction in a developing economy. Global Knowledge, Memory and Communication . Venkatesh V, Morris MG, Davis GB, Davis FD. (2003). User acceptance of information technology: Toward a unified view. MIS Q, 425–78. Venkatesh V, Thong JYL, Xu X. Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Q. 2012;36(1):157–78. Zhang W, Siyal S, Riaz S, Ahmad R, Hilmi MF, Li Z. Data security, customer trust and intention for adoption of fintech services: An empirical analysis from commercial bank users in Pakistan. SAGE Open. 2023;13(3):21582440231181388. Zhao H, Khaliq N, Li C, Rehman FU, Popp J. (2024). Exploring trust determinants influencing the intention to use fintech via SEM approach: Evidence from Pakistan. Heliyon, 10 (8). Additional Declarations No competing interests reported. Supplementary Files AppendixA.docx 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. 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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-8843096","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":595642369,"identity":"abacbaec-7075-4ce0-b0db-2358d82e40aa","order_by":0,"name":"Kwaku Okyere Danquah","email":"data:image/png;base64,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","orcid":"","institution":"Sumy State University","correspondingAuthor":true,"prefix":"","firstName":"Kwaku","middleName":"Okyere","lastName":"Danquah","suffix":""}],"badges":[],"createdAt":"2026-02-10 16:10:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8843096/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8843096/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103329433,"identity":"69c72e27-9648-45fe-9d56-9c68584f06ac","added_by":"auto","created_at":"2026-02-24 13:29:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":34925,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual model\u003c/p\u003e\n\u003cp\u003eNote: “BI = Behavioural Intention; EE = Effort Expectancy, ET = Perceived Ethics; FC = Facilitating Conditions; HM = Hedonic Motivation; HT = Habit; PE = Performance Expectancy; PI = Personal Innovativeness; PT = Perceived Trust; SI = Social Influence; and USE = Use behaviour”\u003c/p\u003e\n\u003cp\u003eSource: Authors’ own work\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8843096/v1/1e3e6d4dfa49bebd796ed9c9.png"},{"id":103329436,"identity":"b643766e-d049-425b-8d1c-1fe2d9967bd5","added_by":"auto","created_at":"2026-02-24 13:29:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":182823,"visible":true,"origin":"","legend":"\u003cp\u003e“Algorithm results of PLS-SEM”\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8843096/v1/399a8bf1811c99ea1b373f4d.png"},{"id":103329435,"identity":"9d8b4d3c-af64-4168-bf9c-e52f7f83f0ed","added_by":"auto","created_at":"2026-02-24 13:29:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":171572,"visible":true,"origin":"","legend":"\u003cp\u003e“Bootstrapping results of PLS-SEM”\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8843096/v1/44be21b3b384e5270934285d.png"},{"id":106974236,"identity":"e071cc55-7cb8-4256-90bf-aeaadcb02d00","added_by":"auto","created_at":"2026-04-15 10:31:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1675712,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8843096/v1/8e4bc82d-3209-4616-97a4-00e6819538b9.pdf"},{"id":103506655,"identity":"5fe895cd-e527-445f-a035-69d2f629041c","added_by":"auto","created_at":"2026-02-26 13:38:29","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22646,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixA.docx","url":"https://assets-eu.researchsquare.com/files/rs-8843096/v1/930a13b8c8ef4150d70dce4c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the Adoption of Financial Technology (FinTech) by Banking and Insurance Companies Customers in Ghana Using an Extended UTAUT2 Model","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe Fourth Industrial Revolution has intensified the demand for digital transformation across industries, with financial institutions experiencing some of the most profound changes. The convergence of intelligent automation, data-driven decision-making, and interconnected digital platforms has fundamentally altered how financial services are designed, delivered, and consumed. In the digital era, financial institutions increasingly rely on technology-enabled business models to enhance efficiency, expand service reach, and improve customer experience (J\u0026uuml;nger \u0026amp; Mietzner, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As a result, technology has become deeply embedded in all aspects of financial service provision, reshaping traditional banking and insurance operations (Bureshaid et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWithin this evolving landscape, Financial Technology (FinTech) has emerged as a transformative force that integrates financial services with advanced information technologies. FinTech encompasses a wide range of technology-driven innovations, including mobile payments, digital banking, peer-to-peer transfers, and data-driven financial services, aimed at improving accessibility, convenience, and system efficiency (Hwang et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Roh et al., 2021). Although FinTech remains in a relatively early stage of development, scholars and practitioners increasingly view it as a defining element of the future financial ecosystem. Its rapid diffusion has been supported by the widespread use of mobile technologies and cloud-based platforms, enabling customers to manage financial activities seamlessly across time and space.\u003c/p\u003e \u003cp\u003eGlobally, investment in FinTech has expanded significantly, reflecting growing confidence in its disruptive potential. According to Statista (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), global investment in FinTech firms rose dramatically from USD 9\u0026nbsp;billion to over USD 210\u0026nbsp;billion by 2021. Similarly, global awareness and usage of FinTech services have increased substantially, with EY Global (2021) reporting that 96% of individuals are familiar with at least one FinTech service and nearly two-thirds actively use such services. These trends underscore the mainstream acceptance of FinTech and its growing relevance across both developed and emerging economies.\u003c/p\u003e \u003cp\u003eThe banking and insurance sectors have been particularly affected by FinTech-driven innovations. FinTech solutions have enabled financial institutions to improve operational efficiency, reduce transaction costs, and deliver personalized services that enhance customer satisfaction (Giglio, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hu et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In emerging markets, digital banking adoption has accelerated rapidly, reaching levels comparable to those of developed economies (Ahmed \u0026amp; Sur, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The integration of technologies such as artificial intelligence, big data analytics, blockchain, and mobile internet has further expanded the scope and functionality of FinTech services, challenging traditional financial service models and intensifying competition (Chikri \u0026amp; Kassou, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Sub-Saharan Africa, FinTech has become a key driver of financial inclusion and economic participation. Ghana, in particular, has emerged as one of Africa\u0026rsquo;s fastest-growing digital markets, alongside countries such as Nigeria, Kenya, and South Africa (Ayakwah et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Geiger et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). FinTech firms and digital platforms have increasingly complemented and in some cases disrupted traditional banking and insurance services by offering customer-centric, technology-enabled financial solutions (Nelaturu et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Despite these developments, the success of FinTech initiatives ultimately depends on customers\u0026rsquo; willingness to adopt and continuously use these technologies.\u003c/p\u003e \u003cp\u003eThe COVID-19 pandemic further accelerated FinTech adoption by reinforcing the need for contactless, remote, and efficient financial services (Slawinski, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). While a growing body of research has examined FinTech adoption across different contexts, much of the existing literature focuses on developed economies or specific sectors such as banking, with limited attention to insurance services and emerging economies such as Ghana. Moreover, empirical studies rarely integrate ethical considerations, trust mechanisms, and behavioural continuance factors within a single explanatory framework. Against this backdrop, the present study investigates the adoption of FinTech by banking and insurance customers in Ghana by extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) to incorporate perceived ethics, trust, personal innovativeness, and use behaviour.\u003c/p\u003e \u003cp\u003eEmpirical studies on FinTech adoption consistently demonstrate that behavioural intention is influenced by a combination of technological, social, and psychological factors, although findings vary across contexts and sectors. Research grounded in UTAUT, UTAUT2, and TAM has identified performance expectancy, effort expectancy, facilitating conditions, social influence, hedonic motivation, habit, and trust as significant predictors of FinTech adoption in emerging and developed economies (Bommer et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hassan et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mulyana et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sultana et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Extensions of these models have further highlighted the roles of personal innovativeness, perceived risk, perceived security, and ethical considerations in shaping adoption intentions and usage behaviour (Almashhadani et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Chawla et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, empirical evidence remains fragmented, with inconsistent effects reported for core constructs such as social influence, performance expectancy, and perceived risk across different cultural and institutional settings (Hoang et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Notably, most prior studies focus on single financial sectors, predominantly banking, while overlooking insurance services and the joint behaviour of banking and insurance customers. Furthermore, limited research integrates perceived ethics and trust as antecedents linking technological perceptions to actual use behaviour, particularly in Sub-Saharan African contexts. Consequently, there is a clear gap in the literature for a comprehensive, context-specific model that extends UTAUT2 by incorporating ethical perceptions, trust, personal innovativeness, and actual usage behaviour to explain FinTech adoption among banking and insurance customers in Ghana. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows a summary of empirical studies and identified research gaps addressed by the current study.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of Empirical Studies on FinTech Adoption and Identified Research Gaps Addressed by the Current Study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmpirical Focus in Prior Studies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKey Evidence from Literature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIdentified Limitation / Gap\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHow the Current Study Addresses the Gap\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCore UTAUT / TAM constructs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerformance expectancy, effort expectancy, facilitating conditions widely examined (Mulyana et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sultana et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInconsistent findings across sectors and contexts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRe-tests core UTAUT2 paths within Ghana\u0026rsquo;s banking and insurance context\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSectoral focus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredominantly banking or Islamic finance (Hassan et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hoang et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInsurance customers largely underrepresented\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntegrates \u003cb\u003ebanking and insurance customers\u003c/b\u003e in a single model\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavioural intention focus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMajority stop at BI as outcome (Bommer et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLimited explanation of actual use behaviour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExamines \u003cb\u003eBI \u0026rarr; USE\u003c/b\u003e and \u003cb\u003ePI \u0026rarr; USE\u003c/b\u003e, strengthening behavioural realism\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust as mediator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrust mediates risk\u0026ndash;intention link (Al Nawayseh, 2020; Ali et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTrust often treated in isolation from ethics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModels \u003cb\u003ePerceived Ethics \u0026rarr; Trust \u0026rarr; BI / USE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived risk emphasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRisk effects mixed and context-dependent (Tang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOveremphasis on risk, neglect of ethical perceptions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eShifts focus from risk dominance to \u003cb\u003eethical trust-building mechanisms\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersonal innovativeness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFound significant in some contexts (Almashhadani et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRarely linked to actual use behaviour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTests \u003cb\u003ePI \u0026rarr; BI\u003c/b\u003e and \u003cb\u003ePI \u0026rarr; USE\u003c/b\u003e simultaneously\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial influence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignificant in emerging markets, insignificant elsewhere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContextual inconsistency not well explained\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRe-examines SI within \u003cb\u003eGhana\u0026rsquo;s collectivist-social environment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHedonic motivation \u0026amp; habit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrong predictors in meta-analyses (Bommer et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnderexplored in African contexts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncludes \u003cb\u003eHM \u0026rarr; BI\u003c/b\u003e and \u003cb\u003eHT \u0026rarr; BI\u003c/b\u003e in Ghana\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthical considerations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRarely included in adoption models\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEthical perceptions overlooked in FinTech research\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntroduces \u003cb\u003ePerceived Ethics (ET)\u003c/b\u003e as a novel antecedent\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeographic coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDominated by Asia \u0026amp; Middle East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLimited Sub-Saharan African evidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProvides \u003cb\u003econtext-specific empirical evidence from Ghana\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUser groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudents, Islamic bank users, tech-savvy consumers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGeneral customers underrepresented\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFocuses on \u003cb\u003eactual banking and insurance customers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eTheoretical Foundation and Hypotheses Development\u003c/h3\u003e\n\u003cp\u003eThis study is grounded in the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), an extension of the original UTAUT model developed by Venkatesh et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) and later refined to explain consumer technology adoption behaviour (Venkatesh et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). UTAUT2 synthesises constructs from foundational theories such as the Technology Acceptance Model (TAM) and the Theory of Reasoned Action (TRA), providing a comprehensive socio-psychological framework for explaining behavioural intention and technology use. UTAUT2 posits that performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, and habit jointly influence behavioural intention and use behaviour. The model has been extensively validated across multiple technology contexts, including digital finance, mobile banking, and electronic payments (Chauhan \u0026amp; Jaiswal, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Dowdy, 2020; Khalilzadeh et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Its predictive robustness and adaptability make it particularly suitable for investigating FinTech adoption in emerging economies.\u003c/p\u003e \u003cp\u003eHowever, despite its strengths, UTAUT2 has been criticised for insufficiently accounting for trust-based and ethical considerations, which are particularly salient in financial technologies where perceived risk, data privacy, and transparency concerns remain prominent (Bagozzi, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Raihan \u0026amp; Rachmawati, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In response, this study extends UTAUT2 by incorporating perceived ethics, perceived trust, and personal innovativeness, thereby enhancing the model\u0026rsquo;s explanatory power within the Ghanaian FinTech context.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eHypotheses Development\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eUse Behaviour (Actual Use of FinTech Services)\u003c/h2\u003e \u003cp\u003eUse behaviour, also referred to as actual usage, represents the extent to which individuals translate their intentions into concrete actions through the sustained utilisation of FinTech services. Within technology adoption research, actual use is regarded as the ultimate behavioural outcome, reflecting not only acceptance but also the practical integration of technology into users\u0026rsquo; daily financial activities. Unlike behavioural intention, which captures users\u0026rsquo; motivational readiness, actual use provides empirical evidence of engagement with FinTech platforms in real-world settings.\u003c/p\u003e \u003cp\u003eDrawing from behavioural theories such as the Theory of Reasoned Action and the Unified Theory of Acceptance and Use of Technology, actual use behaviour is conceptualised as a direct consequence of behavioural intention (Sharma \u0026amp; Munjal, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Venkatesh et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). This intention\u0026ndash;behaviour linkage is critical in FinTech contexts, where favourable intentions do not always translate into sustained usage due to trust concerns, usability challenges, or ethical apprehensions. Examining actual use therefore enables a more comprehensive understanding of FinTech adoption beyond attitudinal dispositions.\u003c/p\u003e \u003cp\u003eEmpirical evidence consistently supports a positive relationship between behavioural intention and actual use of FinTech services (Hassan et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Senyo \u0026amp; Osabutey, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Thusi \u0026amp; Maduku, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These studies suggest that individuals who exhibit stronger intentions are more likely to engage in consistent FinTech usage. Consequently, this study incorporates actual use behaviour as a key endogenous construct, allowing for a more robust assessment of FinTech adoption among banking and insurance customers in Ghana. Based on the foregoing theoretical and empirical evidence, it was therefore postulated that:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH1\u003c/strong\u003e \u003cp\u003e\u0026ldquo;Behavioural intention has a positive and significant effect on actual use of FinTech services\u0026rdquo;.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eEffort Expectancy and Behavioural Intention\u003c/h3\u003e\n\u003cp\u003eEffort expectancy refers to the degree to which individuals perceive FinTech services as easy to use and free of complexity. When FinTech platforms are perceived as user-friendly, customers are more likely to develop favourable intentions towards adoption. Empirical studies consistently demonstrate that effort expectancy significantly influences behavioural intention, particularly in emerging economies where digital literacy varies widely (Mulyana et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sultana et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Based on this reasoning, it was therefore formulated that:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH2\u003c/strong\u003e \u003cp\u003e\u0026ldquo;Effort expectancy has a positive and significant effect on behavioural intention to use FinTech services\u0026rdquo;.\u003c/p\u003e \u003c/p\u003e\n\u003ch3\u003ePerformance Expectancy and Behavioural Intention\u003c/h3\u003e\n\u003cp\u003ePerformance expectancy reflects users\u0026rsquo; beliefs regarding the extent to which FinTech services enhance efficiency, convenience, and financial management. In financial contexts, perceived usefulness remains a critical determinant of adoption, as users prioritise technologies that deliver tangible benefits. Several empirical studies report a positive association between performance expectancy and behavioural intention, although findings vary across sectors (Bommer et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mulyana et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Parviz \u0026amp; Arthur, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Accordingly, it was postulated that:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH3\u003c/strong\u003e \u003cp\u003e\u0026ldquo;Performance expectancy has a positive and significant effect on behavioural intention to use FinTech services\u0026rdquo;.\u003c/p\u003e \u003c/p\u003e\n\u003ch3\u003eFacilitating Conditions and Behavioural Intention\u003c/h3\u003e\n\u003cp\u003eFacilitating conditions capture users\u0026rsquo; perceptions of the availability of technical, organisational, and infrastructural support necessary for FinTech usage. Adequate internet connectivity, digital devices, and user support systems enhance customers\u0026rsquo; confidence and readiness to adopt FinTech services. Prior studies have confirmed the importance of facilitating conditions in shaping adoption intentions (Acquah et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Hassan et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sultana et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Thus, it was formulated that:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH4\u003c/strong\u003e \u003cp\u003e\u0026ldquo;Facilitating conditions have a positive and significant effect on behavioural intention to use FinTech services\u0026rdquo;.\u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSocial Influence and Behavioural Intention\u003c/h2\u003e \u003cp\u003eSocial influence reflects the extent to which individuals perceive that \u0026ldquo;important others\u0026rdquo; believe they should use FinTech services (Hoque et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Given the relatively novel nature of FinTech in many emerging economies, individuals often rely on social cues and peer experiences when forming adoption decisions (Chan et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Firmansyah et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Vardari \u0026amp; Hameli, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Empirical findings provide mixed but context-dependent evidence regarding the role of social influence. Based on this perspective, it was proposed that:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH5\u003c/strong\u003e \u003cp\u003e\u0026ldquo;Social influence has a positive and significant effect on behavioural intention to use FinTech services\u0026rdquo;.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eHedonic Motivation and Behavioural Intention\u003c/h3\u003e\n\u003cp\u003eHedonic motivation refers to the enjoyment or pleasure derived from using FinTech services (Amnas et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Beyond functional benefits, enjoyable user experiences can strengthen emotional attachment and enhance adoption intentions. Meta-analytic evidence suggests that hedonic motivation is among the strongest predictors of FinTech usage intention (Bommer et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kashyap et al., 2026; Sharma et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). It was therefore postulated that:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH6\u003c/strong\u003e \u003cp\u003e\u0026ldquo;Hedonic motivation has a positive and significant effect on behavioural intention to use FinTech services\u0026rdquo;.\u003c/p\u003e \u003c/p\u003e\n\u003ch3\u003eHabit and Behavioural Intention\u003c/h3\u003e\n\u003cp\u003eHabit represents the extent to which individuals perform behaviours automatically due to prior experience (Acquah et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In digital finance, repeated exposure to FinTech platforms can lead to habitual usage patterns that reinforce adoption intentions. Empirical studies confirm habit as a significant predictor of behavioural intention in FinTech contexts (Bommer et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Venkatesh et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Accordingly, it was formulated that:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH7\u003c/strong\u003e \u003cp\u003e\u0026ldquo;Habit has a positive and significant effect on behavioural intention to use FinTech services\u0026rdquo;.\u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePerceived Ethics and Perceived Trust\u003c/h2\u003e \u003cp\u003ePerceived ethics refers to users\u0026rsquo; perceptions of fairness, transparency, and responsible conduct by FinTech service providers. Ethical practices play a critical role in trust formation, particularly in financial environments characterised by information asymmetry. Empirical studies highlight trust as a key mechanism through which ethical perceptions influence adoption decisions (Ali et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Chawla et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Based on this argument, it was postulated that:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH8\u003c/strong\u003e \u003cp\u003e\u0026ldquo;Perceived ethics has a positive and significant effect on perceived trust in FinTech services\u0026rdquo;.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePerceived Trust, Behavioural Intention, and Actual Use\u003c/h2\u003e \u003cp\u003eTrust reflects users\u0026rsquo; confidence in the reliability, security, and integrity of FinTech services. Trust has been widely recognised as a central determinant of both intention and actual usage behaviour (Al Nawayseh, 2020; Hassan et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Users who trust FinTech platforms are more likely to intend to use them and translate these intentions into sustained usage. Thus, it was formulated that:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH9\u003c/strong\u003e \u003cp\u003e\u0026ldquo;Perceived trust has a positive and significant effect on behavioural intention to use FinTech services\u0026rdquo;.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH10\u003c/strong\u003e \u003cp\u003e\u0026ldquo;Perceived trust has a positive and significant effect on actual use of FinTech services\u0026rdquo;.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePersonal Innovativeness, Behavioural Intention, and Actual Use\u003c/h2\u003e \u003cp\u003ePersonal innovativeness captures individuals\u0026rsquo; willingness to experiment with new technologies (Salifu et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Highly innovative users are more inclined to adopt disruptive technologies such as FinTech earlier and more frequently (Almashhadani et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Leong et al., 2020). Empirical evidence supports the direct influence of personal innovativeness on both intention and actual usage (Salifu et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Accordingly, it was proposed that:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH11\u003c/strong\u003e \u003cp\u003e\u0026ldquo;Personal innovativeness has a positive and significant effect on behavioural intention to use FinTech services\u0026rdquo;.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH12\u003c/strong\u003e \u003cp\u003e\u0026ldquo;Personal innovativeness has a positive and significant effect on actual use of FinTech services\u0026rdquo;.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eConceptual Model\u003c/h2\u003e \u003cp\u003eThe conceptual model of the study based on the formulated hypotheses is displayed by Fig.\u0026nbsp;1.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003e\u0026ldquo;BI\u0026thinsp;=\u0026thinsp;Behavioural Intention; EE\u0026thinsp;=\u0026thinsp;Effort Expectancy, ET\u0026thinsp;=\u0026thinsp;Perceived Ethics; FC\u0026thinsp;=\u0026thinsp;Facilitating Conditions; HM\u0026thinsp;=\u0026thinsp;Hedonic Motivation; HT\u0026thinsp;=\u0026thinsp;Habit; PE\u0026thinsp;=\u0026thinsp;Performance Expectancy; PI\u0026thinsp;=\u0026thinsp;Personal Innovativeness; PT\u0026thinsp;=\u0026thinsp;Perceived Trust; SI\u0026thinsp;=\u0026thinsp;Social Influence; and USE\u0026thinsp;=\u0026thinsp;Use behaviour\u0026rdquo;\u003c/p\u003e \u003c/p\u003e \u003cp\u003eFigure 1: Conceptual model\u003c/p\u003e \u003cp\u003eSource: Authors\u0026rsquo; own work\u003c/p\u003e \u003c/div\u003e "},{"header":"Methodology","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003eResearch Design\u003c/h2\u003e \u003cp\u003eThe research design provides the methodological framework that guides how a study is systematically planned, executed, and analysed in order to address its stated objectives. In quantitative research, the choice of design is largely determined by the nature of the research problem, the theoretical orientation of the study, and the type of data required to test the proposed relationships among variables. Broadly, research designs may be classified as experimental or non-experimental. While experimental designs involve manipulation and control of variables to establish causality, non-experimental designs focus on observing existing phenomena without researcher intervention.\u003c/p\u003e \u003cp\u003eThis study adopted a descriptive cross-sectional survey design to investigate the determinants of financial technology adoption among banking and insurance customers in Ghana. The choice of this design is consistent with the objective of capturing respondents\u0026rsquo; perceptions, intentions, and usage behaviour at a single point in time, without manipulating any study variables (Abreh et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Almashhadani et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). A cross-sectional approach is particularly appropriate for technology adoption studies, as it enables the examination of prevailing attitudes, behaviours, and relationships among constructs within a defined population (Hassan et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Additionally, the cross-sectional survey approach supports the efficient collection of data from a large and diverse sample, thereby enhancing the external validity and generalisability of the findings (Hassan et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Given the quantitative orientation of the study and the need to empirically test multiple hypotheses derived from the extended UTAUT2 framework, this design was deemed both methodologically appropriate and practically feasible.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eSample\u003c/h2\u003e \u003cp\u003eThe sample for this study consisted of customers of banking and insurance companies in Ghana who actively use financial technology (FinTech) services. The selection of this group was intentional, as the study sought to obtain insights from respondents with direct experience and sufficient familiarity with FinTech platforms used for banking and insurance transactions. Focusing on actual users ensured that responses reflected informed perceptions, behavioural intentions, and real usage behaviour rather than hypothetical adoption tendencies.\u003c/p\u003e \u003cp\u003eA purposive sampling approach was employed to identify respondents who met the inclusion criteria, namely individuals who had previously used or were currently using FinTech services offered by banking or insurance institutions. This approach is appropriate for FinTech adoption studies, as it allows researchers to target participants with relevant knowledge and practical exposure to digital financial services (Almashhadani et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sultana et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Given the dispersed nature of FinTech users across Ghana, purposive sampling enabled efficient access to respondents most capable of providing meaningful and reliable data aligned with the objectives of the study. The minimum sample size was determined using G*Power software, which indicated a requirement of 172 respondents based on a model with a maximum of 10 predictors, an effect size of 0.15, a significance level of 0.05, and a statistical power of 0.95. To strengthen the robustness of the analysis and improve the generalisability of the findings, the study exceeded this minimum threshold by targeting a larger sample. Out of the 500 questionnaires administered, 409 were successfully retrieved, representing a response rate of 81.8%, which is considered adequate for quantitative survey research. This high return rate enhanced the reliability of the data and provided a sufficient basis for subsequent statistical analyses and interpretation. The expanded sample size enhanced the statistical power of the analysis and improved the reliability of the results, particularly for the application of Partial Least Squares Structural Equation Modelling.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eInstrument\u003c/h2\u003e \u003cp\u003eData for the study were collected using a structured questionnaire, which served as the primary research instrument. The use of a questionnaire is well suited to quantitative research, as it enables the systematic collection of standardised data that can be readily quantified and subjected to statistical analysis. Questionnaires are particularly effective for technology adoption studies because they allow researchers to capture respondents\u0026rsquo; perceptions, attitudes, and behavioural intentions across a wide range of constructs within a relatively short period.\u003c/p\u003e \u003cp\u003eThe questionnaire was adapted from previously validated instruments used in FinTech and technology adoption research to ensure content relevance, measurement accuracy, and contextual suitability. Adapting established scales allows researchers to retain the psychometric strengths of existing instruments while modifying item wording to reflect the cultural and institutional context of the study. This approach enhances both validity and reliability while ensuring alignment with the study\u0026rsquo;s objectives.\u003c/p\u003e \u003cp\u003eThe instrument was organised into two main sections. The first section collected demographic information, including gender, age, educational qualification, monthly income, and respondents\u0026rsquo; familiarity with FinTech services. This section provided background information necessary for profiling respondents and for testing the moderating effects of demographic variables. The second section consisted of items measuring the core study constructs: effort expectancy, social influence, perceived risk, behavioural intention, use behaviour, facilitating conditions, perceived trust, hedonic motivation, personal innovativeness, habit, and ethics. All items were measured using a \u0026ldquo;five-point Likert scale ranging from strongly disagree (1) to strongly agree (5)\u0026rdquo;. Each construct was operationalised using multiple items to allow for a more comprehensive and nuanced assessment of respondents\u0026rsquo; perceptions and behaviours.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eData Collection\u003c/h2\u003e \u003cp\u003eData collection was carried out using a mixed-mode approach, combining face-to-face administration and online survey distribution. This approach was adopted to accommodate differences in geographical location, internet accessibility, and technological proficiency among respondents across Ghana. The online questionnaire was administered using Google Forms and distributed through digital platforms, including email and social media channels related to banking and financial services. This method enabled the researcher to reach respondents across multiple regions efficiently and cost-effectively.\u003c/p\u003e \u003cp\u003eIn addition to the online survey, face-to-face data collection was conducted to improve response rates and to include respondents who may have limited access to digital platforms. This method also allowed for direct interaction with participants, providing opportunities to clarify questions where necessary and ensuring more complete responses. Trained research assistants supported the administration of questionnaires in selected locations, including banks, insurance companies, and public spaces.\u003c/p\u003e \u003cp\u003ePrior to data collection, introductory letters were submitted to the management of selected banking institutions and insurance companies to seek formal approval. These letters outlined the purpose of the study, assured confidentiality, and explained how the data would be used. Ethical considerations were strictly observed throughout the data collection process, including obtaining informed consent, ensuring voluntary participation, and maintaining respondent anonymity. The combination of online and face-to-face methods enhanced coverage, reduced sampling bias, and contributed to the robustness of the dataset.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis Strategy\u003c/h2\u003e \u003cp\u003eThe data analysis process followed a structured and sequential approach. Initially, the collected data were entered into the Statistical Package for the Social Sciences (SPSS) for data screening, coding, and preliminary analysis. This stage involved checking for missing values, identifying outliers, and generating descriptive statistics to summarise respondents\u0026rsquo; demographic characteristics and overall response patterns.\u003c/p\u003e \u003cp\u003eFollowing data cleaning, the dataset was converted into a comma-separated values format and imported into SmartPLS 4 for further analysis. The primary analytical technique employed was \u0026ldquo;Partial Least Squares Structural Equation Modelling (PLS-SEM)\u0026rdquo;. PLS-SEM is particularly appropriate for studies involving complex models with multiple latent constructs and indicators, as well as for predictive and theory-building research (Acquah et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Arthur et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Hair et al., 2021; Hair et al., 2022). The method allows for the simultaneous assessment of the measurement model and the structural model, providing comprehensive insights into both construct validity and hypothesised relationships.\u003c/p\u003e \u003cp\u003eThe measurement model was evaluated by examining indicator reliability, internal consistency reliability, convergent validity, and discriminant validity using established criteria such as \u0026ldquo;factor loadings, Cronbach\u0026rsquo;s alpha, composite reliability, Average Variance Extracted, and the HTMT ratio\u0026rdquo;. The structural model assessment involved analysing path coefficients, significance levels, coefficients of determination, and predictive relevance. Bootstrapping procedures were applied to test the significance of hypothesised relationships. This analytical strategy enabled a rigorous examination of the determinants of FinTech adoption, the role of behavioural intention in predicting actual use, and the moderating influence of demographic factors. By combining SPSS and SmartPLS, the study ensured both statistical precision and theoretical robustness in addressing the research objectives.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement model\u003c/h2\u003e \u003cp\u003eThe outer loadings of all indicators on their respective latent constructs met the recommended threshold of 0.70 (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Appendix A), with a few slightly below but still within acceptable limits (Hair et al., 2021). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the \u0026ldquo;outer loadings, Cronbach\u0026rsquo;s alpha (CA), composite reliability (CR), average variance extracted (AVE), and outer VIF values\u0026rdquo;. All CR values exceeded the 0.70 threshold, indicating good internal consistency reliability. Similarly, AVE values for all constructs were above 0.50, confirming convergent validity. Outer VIF values were below the critical value of 3.3 (Legate et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), indicating no multicollinearity concerns among indicators. The algorithm results of the PLS-SEM are displayed by Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMeasurement Model Results (Outer Loadings, CA, CR, AVE, and VIF)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLoading\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBI1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.539\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBI2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBI3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBI4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.606\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEE4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eET1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eET2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eET3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eET4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.576\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHM3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHM4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.702\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.644\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePE4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePI1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.687\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePI2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePI3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.736\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSI1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSI2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSI3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSI4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSI5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSE4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: \u0026ldquo;BI\u0026thinsp;=\u0026thinsp;Behavioural Intention; EE\u0026thinsp;=\u0026thinsp;Effort Expectancy, ET\u0026thinsp;=\u0026thinsp;Perceived Ethics; FC\u0026thinsp;=\u0026thinsp;Facilitating Conditions; HM\u0026thinsp;=\u0026thinsp;Hedonic Motivation; HT\u0026thinsp;=\u0026thinsp;Habit; PE\u0026thinsp;=\u0026thinsp;Performance Expectancy; PI\u0026thinsp;=\u0026thinsp;Personal Innovativeness; PT\u0026thinsp;=\u0026thinsp;Perceived Trust; SI\u0026thinsp;=\u0026thinsp;Social Influence; and USE\u0026thinsp;=\u0026thinsp;Use behaviour\u0026rdquo;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003eDiscriminant Validity\u003c/h2\u003e \u003cp\u003eDiscriminant validity was evaluated using the HTMT ratio (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). All HTMT values among BI, EE, ET, FC, HM, HT, PE, PI, PT, SI, and USE were below the threshold of 0.90, indicating satisfactory discriminant validity (Hair et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This confirms that the constructs are empirically distinct and do not exhibit multicollinearity concerns.\u003c/p\u003e \u003cp\u003e\u003cimg 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width=\"609\" height=\"475\"\u003e\u003c/p\u003e\u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003eStructural Model\u003c/h2\u003e \u003cp\u003eBased on the results presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, a structural model assessment was carried out to examine the hypothesized relationships among the latent constructs. The evaluation included the examination of \u0026ldquo;path coefficients (β), standard deviations (SD), t-statistics, p-values, confidence intervals, and effect sizes (f\u0026sup2;)\u0026rdquo;, as well as the \u0026ldquo;coefficient of determination (R\u0026sup2;)\u0026rdquo; for endogenous constructs. The analysis employed a bootstrapping procedure with 10,000 subsamples to assess the significance of path relationships, and multicollinearity diagnostics were conducted using VIF values. The VIF values across the paths ranged between 1.000 and 4.147, indicating that multicollinearity was not a concern, as all values were below the conservative threshold of 5.\u003c/p\u003e \u003cp\u003eThe results indicated that BI significantly predicted USE (β\u0026thinsp;=\u0026thinsp;0.444, t\u0026thinsp;=\u0026thinsp;12.325, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, f\u0026sup2; = 0.434), accounting for 61.8% of the variance in USE (R\u0026sup2; = 0.618). EE significantly influenced BI (β\u0026thinsp;=\u0026thinsp;0.324, t\u0026thinsp;=\u0026thinsp;3.329, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, f\u0026sup2; = 0.053), with a VIF of 3.722, suggesting no multicollinearity. Similarly, FC positively predicted BI (β\u0026thinsp;=\u0026thinsp;0.410, t\u0026thinsp;=\u0026thinsp;3.761, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, f\u0026sup2; = 0.076), and HM was also a strong predictor of BI (β\u0026thinsp;=\u0026thinsp;0.536, t\u0026thinsp;=\u0026thinsp;7.971, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, f\u0026sup2; = 0.249). Conversely, HT had a significant negative effect on BI (β = -0.332, t\u0026thinsp;=\u0026thinsp;3.696, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, f\u0026sup2; = 0.053), as did PT (β = -0.286, t\u0026thinsp;=\u0026thinsp;4.768, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, f\u0026sup2; = 0.069) and SI (β = -0.157, t\u0026thinsp;=\u0026thinsp;2.294, p\u0026thinsp;=\u0026thinsp;0.011, f\u0026sup2; = 0.014).\u003c/p\u003e \u003cp\u003eAlthough PE (β\u0026thinsp;=\u0026thinsp;0.087, t\u0026thinsp;=\u0026thinsp;1.077, p\u0026thinsp;=\u0026thinsp;0.141, f\u0026sup2; = 0.004) and PI (β\u0026thinsp;=\u0026thinsp;0.097, t\u0026thinsp;=\u0026thinsp;1.378, p\u0026thinsp;=\u0026thinsp;0.084, f\u0026sup2; = 0.012) showed positive associations with BI, their effects were not statistically significant. However, PI had a significant direct effect on USE (β\u0026thinsp;=\u0026thinsp;0.165, t\u0026thinsp;=\u0026thinsp;4.005, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, f\u0026sup2; = 0.057). Moreover, ET showed a very strong and significant predictive effect on PT (β\u0026thinsp;=\u0026thinsp;0.702, t\u0026thinsp;=\u0026thinsp;37.457, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, f\u0026sup2; = 0.970), accounting for 49.2% of the variance in PT (R\u0026sup2; = 0.492). PT also had a direct and significant influence on USE (β\u0026thinsp;=\u0026thinsp;0.428, t\u0026thinsp;=\u0026thinsp;11.746, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, f\u0026sup2; = 0.407), underscoring its importance as a predictor. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the PLS-SEM bootstrapping results.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStructural model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePath\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eT Statistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP Values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.0%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e95.0%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ef\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBI -\u0026gt; USE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.618\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEE -\u0026gt; BI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.465\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eET -\u0026gt; PT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFC -\u0026gt; BI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHM -\u0026gt; BI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHT -\u0026gt; BI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE -\u0026gt; BI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePI -\u0026gt; BI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePI -\u0026gt; USE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT -\u0026gt; BI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT -\u0026gt; USE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSI -\u0026gt; BI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eNote: M\u0026thinsp;=\u0026thinsp;Sample mean, SD\u0026thinsp;=\u0026thinsp;Standard deviation; R\u003csup\u003e2\u003c/sup\u003e adjusted for BI, PT and USE are 0.454, 0.491 and 0.615 respectively.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003ePredictive Relevance\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the results of the \u0026ldquo;Stone\u0026ndash;Geisser predictive relevance (Q\u0026sup2;)\u0026rdquo; assessment for the endogenous constructs in the proposed FinTech adoption model. The Q\u0026sup2; values were obtained using the blindfolding procedure by comparing the \u0026ldquo;sum of squares observed (SSO)\u0026rdquo; with the \u0026ldquo;sum of squares error (SSE)\u0026rdquo;. A Q\u0026sup2; value greater than zero indicates that the model has predictive relevance for a given endogenous construct, whereas values equal to zero suggest no predictive relevance.\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, \u0026ldquo;behavioural intention (BI)\u0026rdquo; records a Q\u0026sup2; value of 0.239, indicating that the model demonstrates acceptable predictive relevance for customers\u0026rsquo; intention to adopt FinTech services. This suggests that the explanatory constructs included in the extended UTAUT2 framework such as \u0026ldquo;effort expectancy, facilitating conditions, hedonic motivation, habit, personal innovativeness, perceived trust, social influence, and perceived ethics\u0026rdquo;, meaningfully predict customers\u0026rsquo; behavioural intentions within the Ghanaian banking and insurance context.\u003c/p\u003e \u003cp\u003eSimilarly, perceived trust (PT) yields a Q\u0026sup2; value of 0.351, reflecting strong predictive relevance. This result underscores the central role of trust in FinTech adoption, particularly as influenced by ethical perceptions and related antecedents. The finding suggests that the model effectively explains variations in customers\u0026rsquo; trust in FinTech services, reinforcing the importance of trust-building mechanisms in digital financial environments.\u003c/p\u003e \u003cp\u003eFurthermore, use behaviour (USE) records a Q\u0026sup2; value of 0.358, indicating substantial predictive relevance of the model in explaining actual FinTech usage. This implies that behavioural intention and personal innovativeness meaningfully translate into real usage behaviour among customers of banking and insurance companies in Ghana. In contrast, constructs such as \u0026ldquo;effort expectancy (EE), perceived ethics (ET), facilitating conditions (FC), hedonic motivation (HM), habit (HT), performance expectancy (PE), personal innovativeness (PI), and social influence (SI)\u0026rdquo; report Q\u0026sup2; values of zero, as they are modelled as exogenous constructs. Consequently, predictive relevance is not assessed for these variables.\u003c/p\u003e \u003cp\u003eThe results confirm that the proposed extended UTAUT2 model exhibits adequate predictive relevance, particularly for behavioural intention, perceived trust, and use behaviour. This provides empirical support for the model\u0026rsquo;s suitability in predicting FinTech adoption among banking and insurance customers in Ghana.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePredictive Relevance (Q\u0026sup2;) of Endogenous Constructs in the FinTech Adoption Model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSSO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eQ\u0026sup2; (=\u0026thinsp;1-SSE/SSO)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1245.572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e795.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1051.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe current study examined the adoption of FinTech by banking and insurance company customers in Ghana using an extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. The findings of this study demonstrate that behavioural intention strongly predicts the actual use of FinTech services among banking and insurance customers in Ghana. This aligns with the theoretical propositions of UTAUT2 and the Theory of Reasoned Action, which posit that behavioural intention is the immediate antecedent of technology usage (Venkatesh et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Sharma et al., 2020). In the Ghanaian context, the strong intention\u0026ndash;use relationship may reflect growing awareness and familiarity with digital financial services, alongside increased reliance on mobile money and banking apps in everyday financial transactions. Empirical studies in other emerging economies similarly report that strong intentions translate into sustained adoption (Senyo \u0026amp; Osabutey, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Thusi \u0026amp; Maduku, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), reinforcing the universal applicability of this relationship while highlighting its relevance in Ghana\u0026rsquo;s FinTech landscape.\u003c/p\u003e \u003cp\u003eEffort expectancy positively influenced adoption intentions, suggesting that perceived ease of use is a key driver of FinTech adoption in Ghana. Customers appear more willing to adopt FinTech services when platforms are intuitive and require minimal technical skills. This finding corroborates earlier studies in emerging economies where digital literacy varies, such as those by Mulyana et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Sultana et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In Ghana, where some users may have limited experience with digital finance, platforms that reduce cognitive load and simplify processes likely enhance users\u0026rsquo; willingness to engage.\u003c/p\u003e \u003cp\u003eFacilitating conditions and hedonic motivation were also significant predictors of adoption intentions. The availability of reliable infrastructure, internet connectivity, and supportive customer service appears crucial for fostering positive adoption behaviours. Similarly, the enjoyment and satisfaction derived from using FinTech services reinforce intentions to adopt, aligning with Bommer et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who noted that hedonic experiences strengthen emotional attachment to financial technologies. In Ghana, where digital financial services increasingly compete for attention, enjoyable and seamless experiences can differentiate providers and encourage sustained usage.\u003c/p\u003e \u003cp\u003eInterestingly, certain constructs such as habit, social influence, and performance expectancy exerted weaker or negative effects on adoption intentions in the current study. This could reflect the context-specific dynamics of Ghanaian FinTech adoption. For example, habitual usage may be less influential because FinTech platforms are relatively new and customers are still exploring different services rather than relying on automatic routines. Similarly, social influence may be weaker as adoption decisions are often guided more by personal convenience and trust in the platform than by peers. Performance expectancy showed limited direct influence on intention, possibly because users in Ghana prioritise practical accessibility, security, and ethical practices over abstract efficiency gains, reflecting a contextual sensitivity to perceived risks in digital finance.\u003c/p\u003e \u003cp\u003ePerceived ethics emerged as a strong determinant of trust, which, in turn, influenced both adoption intentions and actual usage. This finding underscores the central role of ethical perceptions in financial technology adoption, particularly in Ghana where concerns about fraud, data privacy, and transparency are prevalent. Customers are more likely to adopt FinTech services when they perceive providers as ethical and trustworthy, corroborating the argument of Ali et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Chawla et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) that ethical practices directly shape trust and adoption behaviour.\u003c/p\u003e \u003cp\u003eFinally, personal innovativeness directly influenced actual use of FinTech services, highlighting the role of individual differences in shaping adoption. Customers who are more willing to experiment with new technologies are more likely to explore and maintain engagement with FinTech platforms, even in the absence of strong behavioural intentions. This finding aligns with prior studies (Leong et al., 2020; Almashhadani et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and is particularly relevant in Ghana, where early adopters often pave the way for broader community acceptance of digital financial solutions.\u003c/p\u003e \u003cp\u003eThe findings suggest that the UTAUT2 model, when extended with perceived ethics, perceived trust, and personal innovativeness, is a robust framework for explaining FinTech adoption in Ghana. While core constructs like behavioural intention, effort expectancy, facilitating conditions, and hedonic motivation significantly drive adoption, context-specific factors such as emerging usage patterns, trust concerns, and ethical perceptions shape the nuanced adoption behaviour observed among Ghanaian customers. These insights provide practical guidance for FinTech providers seeking to enhance adoption and sustained use in Ghana\u0026rsquo;s unique socio-economic and technological landscape.\u003c/p\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003eImplications of the Study\u003c/h2\u003e \u003cp\u003eThe findings of this study offer important theoretical, practical, and policy implications. Theoretically, the study extends the UTAUT2 framework by incorporating perceived ethics, perceived trust, and personal innovativeness, demonstrating that these factors significantly enhance the model\u0026rsquo;s explanatory power in the Ghanaian FinTech context. Practically, the results provide guidance for FinTech providers on strategies to improve adoption and sustained usage. Emphasizing user-friendly interfaces, reliable support systems, and ethical business practices can increase customer confidence and engagement. Additionally, the study highlights the importance of targeting innovative early adopters to encourage wider uptake. Policymakers and regulatory bodies such as the Bank of Ghana and the National Insurance Commission can leverage these insights to develop guidelines that enhance transparency, security, and ethical standards in digital financial services, thereby fostering greater trust among users.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and Future Studies\u003c/h2\u003e \u003cp\u003eWhile this study provides valuable insights, it has several limitations. The cross-sectional research design restricts the ability to infer causal relationships, suggesting that longitudinal or experimental studies could provide stronger evidence of temporal effects. The study was conducted in selected urban areas in Ghana, which may limit the generalizability of the findings to rural communities or other emerging economies. Additionally, self-reported data may be subject to social desirability or response biases. Future studies could expand the scope by including diverse geographic regions, examining sector-specific FinTech adoption patterns, or integrating qualitative methods to capture deeper insights into user experiences. Researchers may also explore additional contextual factors such as financial literacy, risk perception, and cultural influences to further refine models of FinTech adoption in Ghana.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study examined the adoption of FinTech services by banking and insurance customers in Ghana using an extended UTAUT2 model. The findings confirm that behavioural intention, effort expectancy, facilitating conditions, hedonic motivation, perceived ethics, perceived trust, and personal innovativeness play significant roles in predicting actual use of FinTech services. Contextual factors in Ghana, including trust concerns and ethical considerations, significantly shape customer adoption behaviour. The study contributes to both theory and practice by extending UTAUT2 and providing actionable recommendations for FinTech providers, regulators, and policymakers. Ultimately, enhancing usability, trust, and ethical standards can promote greater financial inclusion and technology adoption in Ghana.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKwaku Okyere Danquah: Conceptualization, Data curation, Investigation, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing, Resources, Software, Supervision, Methodology, Validation, Visualization. The author reviewed the manuscript and approved it for final submission.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with ethical standards and all applicable institutional guidelines and regulations. Ethical approval letter was waived by the ethics committee of the Sumy State University. Informed consent was obtained from all participants. All procedures followed in the study complied with the principles outlined in the \u0026ldquo;Declaration of Helsinki\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot Applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent of Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all participants before data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available upon request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author did not receive any fund for this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbreh MK, Arthur F, Akwetey FA, Nortey SA. Modelling STEM students\u0026rsquo; intention to learn artificial intelligence (AI) in Ghana: a PLS-SEM and fsQCA approach. Discover Artif Intell. 2025;5(1):223.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAcquah BYS, Arthur F, Salifu I, Mensah E, Opoku E, Nortey SA, Tetteh SA. Modelling economics students\u0026rsquo; use of ChatGPT and academic performance: insights from self-determination theory and epistemic curiosity. Discover Artif Intell. 2025;5(1):1\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAcquah BYS, Arthur F, Salifu I, Quayson E, Nortey SA. Preservice teachers\u0026rsquo; behavioural intention to use artificial intelligence in lesson planning: A dual-staged PLS-SEM-ANN approach. Computers Education: Artif Intell. 2024;7:100307.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmed S, Sur S. Change in the uses pattern of digital banking services by Indian rural MSMEs during demonetization and Covid-19 pandemic-related restrictions. Vilakshan-XIMB J Manage. 2023;20(1):166\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAli M, Raza SA, Khamis B, Puah CH, Amin H. (2021). How perceived risk, benefit and trust determine user Fintech adoption: a new dimension for Islamic finance. \u003cem\u003eforesight\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(4), 403\u0026ndash;420.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlmashhadani IS, Abuhashesh M, Bany Mohammad A, Masa\u0026rsquo;deh RE, Al-Khasawneh M. Exploring the determinants of FinTech adoption and intention to use in Jordan: The impact of COVID-19. Cogent Social Sci. 2023;9(2):2256536.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmnas MB, Selvam M, Raja M, Santhoshkumar S, Parayitam S. Understanding the determinants of FinTech adoption: Integrating UTAUT2 with trust theoretic model. J risk financial Manage. 2023;16(12):505.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArthur F, Salifu I, Abam Nortey S. Predictors of higher education students\u0026rsquo; behavioural intention and usage of ChatGPT: the moderating roles of age, gender and experience. Interact Learn Environ. 2025;33(2):993\u0026ndash;1019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAyakwah A, Damoah IS, Osabutey EL. Digitalization in Africa: The case of public programs in Ghana. Business in Africa in the Era of Digital Technology: Essays in Honour of Professor William Darley. Cham: Springer International Publishing; 2021. pp. 7\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBagozzi RP. The legacy of the technology acceptance model and a proposal for a paradigm shift. J association Inform Syst. 2007;8(4):3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBommer WH, Milevoj E, Rana S. A meta-analytic examination of the antecedents explaining the intention to use fintech. Industrial Manage Data Syst. 2023;123(3):886\u0026ndash;909.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBureshaid N, Lu K, Sarea A. (2021). Adoption of fintech services in the banking industry. Appl Artif Intell Bus Educ Healthc, 125\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChan R, Troshani I, Rao Hill S, Hoffmann A. Towards an understanding of consumers\u0026rsquo; FinTech adoption: The case of Open Banking. Int J Bank Mark. 2022;40(4):886\u0026ndash;917.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChauhan S, Jaiswal M. Determinants of acceptance of ERP software training in business schools: Empirical investigation using UTAUT model. Int J Manage Educ. 2016;14(3):248\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChawla U, Mohnot R, Singh HV, Banerjee A. The mediating effect of perceived trust in the adoption of cutting-edge financial technology among digital natives in the Post-COVID-19 Era. Economies. 2023;11(12):286.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChikri H, Kassou M. Financial revolution: Innovation powered by FinTech and artificial intelligence. J Theoretical Appl Inform Technol. 2024;102(9):4145\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFirmansyah EA, Masri M, Anshari M, Besar MHA. Factors affecting fintech adoption: a systematic literature review. FinTech. 2022;2(1):21\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeiger MT, Kwakye KG, Vicente CL, Wiafe BM, Boakye Adjei NY. Fourth Ghana economic update: Enhancing financial inclusion - Africa region (English). Ghana Economic Update. Volume 4. World Bank; 2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGiglio F. Fintech: A literature review. Eur Res Stud J. 2021;24(2B):600\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHair JF, Babin BJ, Ringle CM, Sarstedt M, Becker JM. (2025). Covariance-based structural equation modeling (CB-SEM): a SmartPLS 4 software tutorial. \u003cem\u003eJ Market Anal\u003c/em\u003e 13, 709\u0026ndash;724 (2025). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1057/s41270-025-00414-6\u003c/span\u003e\u003cspan address=\"10.1057/s41270-025-00414-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHassan MS, Islam MA, Sobhani FA, Nasir H, Mahmud I, Zahra FT. Drivers influencing the adoption intention towards mobile fintech services: a study on the emerging Bangladesh market. Information. 2022;13(7):349.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHassan MS, Islam MA, Yusof MF, Nasir H. (2023). Users\u0026rsquo; fintech services acceptance: A cross-sectional study on Malaysian Insurance \u0026amp; Takaful industry. Heliyon, \u003cem\u003e9\u003c/em\u003e(11).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoang YH, Nguyen DT, Tran LH, Nguyen NT, Vu NB. Customers\u0026rsquo; adoption of financial services offered by banks and fintechs partnerships: evidence of a transitional economy. Data Sci Finance Econ. 2021;1(1):77\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoque MZ, Chowdhury NJ, Hossain AA, Tabassum T. (2024). Social and facilitating influences in fintech user intention and the fintech gender gap. Heliyon, \u003cem\u003e10\u003c/em\u003e(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu Z, Ding S, Li S, Chen L, Yang S. Adoption intention of fintech services for bank users: An empirical examination with an extended technology acceptance model. Symmetry. 2019;11(3):340.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHwang Y, Park S, Shin N. Sustainable development of a mobile payment security environment using fintech solutions. Sustainability. 2021;13(15):8375.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ\u0026uuml;nger M, Mietzner M. Banking goes digital: The adoption of FinTech services by German households. Finance Res Lett. 2020;34:101260.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKashyap AK, Priyanka, Gupta P. (2026). Do cyber fraud experiences matter? Revisiting user motivations and satisfaction in driving FinTech continuance. J Financial Crime, 1\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhalilzadeh J, Ozturk AB, Bilgihan A. Security-related factors in extended UTAUT model for NFC based mobile payment in the restaurant industry. Comput Hum Behav. 2017;70:460\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLegate AE, Hair Jr JF, Chretien JL, Risher JJ. PLS-SEM: Prediction‐oriented solutions for HRD researchers. Hum Res Dev Q. 2023;34(1):91\u0026ndash;109.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMulyana A, Disman D, Wibowo L, Hurriyati R. (2020). Application of customer behavior in using fintech as business media based on the unified theory of acceptance and use of technology model. In \u003cem\u003e3rd Global conference on business, management, and entrepreneurship (GCBME 2018)\u003c/em\u003e (pp. 69\u0026ndash;75). Atlantis Press.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNelaturu K, Du H, Le DP. A Review of Blockchain in Fintech: Taxonomy, Challenges, and Future Directions. Cryptography. 2022;6(2):18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParviz M, Arthur F. Exploring EFL Teachers\u0026rsquo; Behavioral Intentions to Integrate GenAI Applications: Insights From PLS-SEM and fsQCA. Hum Behav Emerg Technol. 2025;2025(1):5582099.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaihan T, Rachmawati I. (2019). Analyzing Factors Influencing Continuance Intention Of E-Wallet Adoption Using UTAUT 2 Model (A Case Study Of Dana In Indonesia). \u003cem\u003eeProceedings of Management\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoh T, Yang YS, Xiao S, Park BI. (2022). What makes consumers trust and adopt fintech? An empirical investigation in China. Electron Commer Res, 1\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalifu I, Arthur F, Acquah BYS, Opoku E, Nortey SA, Boateng E. Exploring graduate students\u0026rsquo; use of generative artificial intelligence in Ghana: insights from an extended UTAUT2 model, PLS-SEM, IPMA and fsQCA. Discover Educ. 2025;4(1):305.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSenyo PK, Osabutey EL. Unearthing antecedents to financial inclusion through FinTech innovations. Technovation. 2020;98:102155.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma A, Mohan A, Johri A, Asif M. Determinants of financial technology (FinTech) adoption by the farmers in agrarian economy. Social Sci Humanit Open. 2025;11:101370.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma D, Munjal P. Determining the key drivers of FinTech adoption in India. Int J Process Manage Benchmarking. 2024;16(4):533\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSlawinski S. (2021). Fintechs in South Korea see the launch of a new virtual sandbox. \u003cem\u003eGlobal Fintech News\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStatista. (2021). www.statista.com, [Online]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.statista.com/statistics/219339/us-prices-of-cement/#::text\u0026thinsp;=\u0026thinsp;In%202020%2C%20the%20cost%20of,highest%20in%20the%20last%20years\u003c/span\u003e\u003cspan address=\"https://www.statista.com/statistics/219339/us-prices-of-cement/#::text\u0026thinsp;=\u0026thinsp;In%202020%2C%20the%20cost%20of,highest%20in%20the%20last%20years\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e [Accessed 01 March 2021].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSultana N, Chowdhury RS, Haque A. (2023). Gravitating towards Fintech: A study on Undergraduates using extended UTAUT model. Heliyon, \u003cem\u003e9\u003c/em\u003e(10).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang KL, Ooi CK, Chong JB. Perceived risk factors affect intention to use FinTech. J Acc finance Emerg economies. 2020;6(2):453\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThusi P, Maduku DK. South African millennials\u0026rsquo; acceptance and use of retail mobile banking apps: An integrated perspective. Comput Hum Behav. 2020;111:106405.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVardari L, Hameli K. (2025). Perceived benefits, trust and social influence in FinTech: pathways to adoption and satisfaction in a developing economy. \u003cem\u003eGlobal Knowledge, Memory and Communication\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVenkatesh V, Morris MG, Davis GB, Davis FD. (2003). User acceptance of information technology: Toward a unified view. MIS Q, 425\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVenkatesh V, Thong JYL, Xu X. Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Q. 2012;36(1):157\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang W, Siyal S, Riaz S, Ahmad R, Hilmi MF, Li Z. Data security, customer trust and intention for adoption of fintech services: An empirical analysis from commercial bank users in Pakistan. SAGE Open. 2023;13(3):21582440231181388.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao H, Khaliq N, Li C, Rehman FU, Popp J. (2024). Exploring trust determinants influencing the intention to use fintech via SEM approach: Evidence from Pakistan. Heliyon, \u003cem\u003e10\u003c/em\u003e(8).\u003c/span\u003e\u003c/li\u003e\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":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Banking and Insurance, Financial Technology, FinTech Adoption, Ghana, PLS-SEM, UTAUT2","lastPublishedDoi":"10.21203/rs.3.rs-8843096/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8843096/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rapid growth of financial technology (FinTech) has transformed the delivery of financial services globally, yet customer adoption remains uneven, particularly within developing economies. This study investigates the adoption of FinTech by banking and insurance company customers in Ghana using an extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. A quantitative research approach was employed, adopting a descriptive cross-sectional survey design. Data were collected from 409 active FinTech users through a structured questionnaire administered using both online and face-to-face methods. Partial Least Squares Structural Equation Modelling (PLS-SEM) was utilised to analyse the data. The findings reveal that behavioural intention significantly predicts actual FinTech use behaviour. Effort expectancy, facilitating conditions, and hedonic motivation positively and significantly influence behavioural intention, while habit, perceived trust, and social influence exhibit significant negative effects. Performance expectancy and personal innovativeness show positive but statistically insignificant relationships with behavioural intention; however, personal innovativeness directly and significantly predicts use behaviour. Ethical perceptions strongly predict perceived trust, which in turn significantly influences both behavioural intention and actual use. The model explains a substantial proportion of variance in behavioural intention and use behaviour, demonstrating strong predictive relevance. The study contributes to the FinTech literature by extending the UTAUT2 model to include ethics and trust within the Ghanaian banking and insurance context. Practically, the findings provide insights for financial institutions and policymakers to design user-centred, trustworthy, and ethically grounded FinTech solutions that enhance customer adoption and sustained usage.\u003c/p\u003e","manuscriptTitle":"Exploring the Adoption of Financial Technology (FinTech) by Banking and Insurance Companies Customers in Ghana Using an Extended UTAUT2 Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-24 13:29:31","doi":"10.21203/rs.3.rs-8843096/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":"5d69469d-29b5-4bef-bd6c-9f088e8b6c94","owner":[],"postedDate":"February 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-15T10:21:05+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-24 13:29:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8843096","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8843096","identity":"rs-8843096","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}