Extending Unified Theory of Acceptance and Use of Technology with Information System Quality Constructs to Predict Pre-Service Teachers Mobile Learning Adoption and Continuance Behaviour

Extending Unified Theory of Acceptance and Use of Technology with Information System Quality Constructs to Predict Pre-Service Teachers Mobile Learning Adoption and Continuance Behaviour | 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 Extending Unified Theory of Acceptance and Use of Technology with Information System Quality Constructs to Predict Pre-Service Teachers Mobile Learning Adoption and Continuance Behaviour REBECCA OJOCHIDE MARTINS, OLABANJI TAIWO SHODIPE, OLUSOLA AYINLA OYAGBOLA, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9169158/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract The adoption of mobile learning to education eliminates barriers to teaching and learning activities. It introduces a new belief that teaching and learning activities can be executed without being limited by location and time. So, there is a general belief that if adopted among pre-service teachers, will improve usage and academic performance. The study employed a stratified sampling technique to select 475 pre-service teachers from Technical Vocational Education and Training (TVET) institutions. Data were collected through structured questionnaire designed on a five-point Likert scale, which captured respondents’ perception on UTAUT and IS quality construct. Data were analyzed using Structural Equation Modelling with Partial Least Squares (SEM-PLS) through SmartPLS 3.0. The results revealed that student’s intentions to use mobile learning significantly influenced their behaviour. Additionally, facilitating condition, social influence, performance expectancy and information quality influenced students’ intentions to use mobile learning. The study indicated that mobile learning usage is highly predicted by its antecedents. Furthermore, the study suggests that the unified theory of acceptance and use of technology (UTAUT) can be enhanced by incorporating quality constructs to influence students’ behaviour and promote adoption of mobile learning. UTAUT Mobile learning System quality Information quality Service quality Pre-Service teachers Figures Figure 1 Figure 2 Figure 3 Introduction The adoption of mobile learning removes barriers to teaching and learning (Shodipe & Ohanu, 2021 ), supporting traditional instruction, e-learning, and evaluation while adding value to educational systems. It represents an innovative shift in higher education (Casebourne, 2024; Ohanu & Chukwuone, 2018 ). Mobile learning involves accessing content across locations and times using devices such as tablets, laptops, and phones through wireless networks (Oberer & Erkollar, 2013 ). These devices such as PDAs, mobile phones, laptops, PCs, and e-books enable learning without time or location constraints (Şad & Göktaş, 2014). In higher education, mobile learning fosters collaboration and knowledge sharing via wireless networks (Sophonhiranrak, 2021 ). Mobile learning also delivers information conveniently to learners and educators and supports personalized learning (Al-Emran et al., 2016 ). It enables learners to set goals, select resources, and access materials flexibly for personalized learning (Sophonhiranrak, 2021 ). Many countries, such as Italy, the Netherlands, New Zealand, the USA, Portugal, and Australia, invest heavily in educational technologies (Chow, 2013 ). These tools support collaborative, independent, and lifelong learning, however, there are varying challenges integrating mobile learning by culture, Japanese students showed low mobile learning engagement due to perceived low utility (Bull & Reid, 2004 ).), Malaysian students struggled with transfer of learning (Ramli et al., 2010 ), and information quality limited adoption in Finland (Koivumäki et al., 2008 ). Because mobile learning does not guarantee success in all settings, understanding user acceptance is essential (Amadin et al., 2018 ). Despite technological growth, engagement remains low in some countries and institution most especially in developing countries (Kumar & Bervell, 2019 ). Factors influencing students’ intentions to use mobile learning therefore require further investigation. Previous studies extended the Technology Acceptance Model (Ohanu et al., 2022 ) and Theory of Planned Behaviour (Cheung & Vogel, 2013 ). The UTAUT model identifies four constructs that directly influence technology acceptance (Venkatesh et al., 2003 ), and further extensions include self-management, innovativeness, playfulness, enjoyment, service quality, and ubiquity (Badwelan et al., 2016 ; Huan et al., 2015 ). Teacher education programmes play a critical role in preparing pre-service teachers with the pedagogical knowledge, technological competence, and professional skills required for effective classroom practice (Ogbuaynya & Shodipe, 2022; Shodipe & Ogbuanya, 2024 ). Pre-service teachers are exposed to courses that integrate pedagogy, subject content knowledge, and educational technologies to enhance teaching and learning (Kuo & Kuo, 2020 ; Thomas & O’Bannon, 2013 ). Through teaching practice and technology-supported learning environments, pre-service teachers develop competencies in lesson planning, instructional delivery, classroom management, and the use of digital tools for teaching. Mobile learning has increasingly become an important component of teacher preparation because it allows pre-service teachers to access instructional materials, collaborate with peers, and engage in continuous learning beyond the traditional classroom environment. The Unified Theory of Acceptance and Use of Technology (UTAUT) model is suitable for studying mobile learning adoption (Nwibe & Ogbuanya, 2025) among pre-service teachers because it explains how UTAUT factors influence individuals’ acceptance and use of technology. This study extends the UTAUT model with the information systems success model (system quality, information quality, and service quality) to examine factors influencing pre-service teacher’s intentions and usage of mobile learning. UTAUT explains behavioural intentions (Venkatesh et al., 2003 ), while quality constructs further strengthen motivation to use mobile technologies (DeLone & McLean, 2003 ). There is an increased intention to continue using mobile learning and an increased performance when users perceive high quality of the technologies, increased quality of the information provided and quality services delivery (Sitar-Taut & Mican, 2021 ). Morealso, several researches on mobile learning had focused on the area of higher education, pre-school education, undergraduate learning, content delivery (Sophonhiranrak, 2021 ; Drigas et al., 2016 ) but little has focused on skilled area of education, technical education or career and technical education. This study therefore focuses on mobile learning adoption among pre-service teachers in technical vocational education and training (TVET). Theoretical framework and hypotheses Unified theory of acceptance and use of technology (UTAUT) Unified theory of acceptance and use of technology (UTAUT) is a derived framework from eight models (Chang, 2012 ) that describes how people accept and use technology systems (Venkatesh, et al., 2003 ). UTAUT contained four major constructs that influence usage intentions and actual usage of mobile learning technologies. These constructs are facilitating condition, effort expectancy, performance expectancy and social influence (Venkatesh, et al., 2003 ). Despite its several applications to information system, marketing, tourism and purchasing (Venkatesh, et al., 2011 ; Chang et al., 2016 ). Therefore, there is need verify its influence on technical vocational education and training (TVET) pre-service teachers in Nigeria. User’s behavior and behavioural intentions Usage intentions is the “desire or an individual readiness to use certain mobile learning system within specific circumstances” (Hyman et al., 2014 ). It is a construct in UTAUT that predicts continuous or actual usage of mobile learning technology (Kumar & Bervell, 2019 ). The behavioural theorists believe that user’s behavioural intentions should culminate to user’s actual usage of technology devices (Ajzen, 1991 ; Bandura, 1997 ). Sometimes, this ideal may not be ascertained, Gollwitzer and Sheeran ( 2006 ) argued that intentions may not necessarily guarantee actual behaviour if the person fails to deal with self-regulatory problems. Previous literatures had found significant relationship between intention and actual or continuous usage behaviour (Maican et al., 2018 ) while some studies found significant relationship between intentions and its antecedents (Ohanu et al., 2023 ; Kaium et al., 2020 ) H1. Intentions to use mobile learning positively influence continuance user’s behaviour Facilitating condition Facilitating condition is the extent to which a mobile learning user believes that an institution and technical infrastructure exists to enhance system usage, and are typically operationalized to include aspects of the environment that are designed to remove barriers to use (Venkatesh et al., 2003 ; Tosuntas¸ et al., 2015). Facilitating condition is also known as perceived behavioural control from decomposed TPB, C-TPB-TAM, MPCU and IDT (Hoi, 2019 ). Venkatesh et al. ( 2003 ) had found facilitating condition to conveniently predict intentions with the exclusion of effort expectancy from the model but Dwivedi et al. ( 2019 ) found significance with the relationship between the two constructs in the phase of effort expectancy. Unlike some studies that found an insignificant relationship between facilitating condition and user’s behaviour (Botero et al., 2019 ). Some other literature found a significant relationship between the constructs (Li, et al., 2021 ; Blaise, et al., 2018 ). With various in-conclusions in previous literature, it was tentatively proposed that: H2. Facilitating conditions positively influences user’s intentions to use mobile learning. H3. Facilitating conditions positively influences user’s behaviour to continue using mobile learning. H4. The positive relationship between user’s intentions and continuance use behaviour is mediated by facilitating condition. Social influence Social influence is the degree to which a person perceives that important people think he or she should use the mobile learning system, similar to subjective norms in C-TPB-TAM (Ho, et al., 2013 ; Kumar & Bervell, 2019 ). Users are subjected to the pressures of social interactions and will, in social contexts such as the school environment, colleagues, and so on, considering not only their own perception but also the opinions and perceptions of others, particularly individuals who they consider to be important in the given context (Isaias, et al., 2017 ). In literature, social influence had been considered to have significant relationship with intentions across various fields of application (Tan, 2013 ; Isaias et al., 2017 ) but Tan et al., ( 2014 ) findings deviated from the general belief with an insignificant relationship. Beyond the general belief about social influence of mobile learning across culture and region, we propose to validate social influence among pre-service teachers in Nigeria, hence, it was hypothesized that: H5. Social influence positively influences user’s intentions towards mobile learning usage. Effort expectancy Effort expectancy is the “degree of ease associated with learner’s use of mobile technology devices without much effort” (Venkatesh et al. 2012 ). It is analogous to perceived ease of use in TAM (Ho, et al., 2013 ) and it positively influences performance expectancy. When learners feel that mobile technologies are easy to use and do not require much effort, they would have high intentions to use (Tam et al., 2018 ) and when learners feel difficulty in using mobile technologies, they will have low intention to use (Prasanna and Huggins, 2016 ). Previous studies found significant relationship between effort expectancy and intentions (Rahi, et al., 2019 ). Hence in this study, it was hypothesized that: H6. Effort expectancy positively influences user’s intentions towards mobile learning usage. Performance expectancy Performance expectancy is the “degree to which an individual believes that using mobile learning devices will enhance learning performance” (Venkatesh, et al. 2003 ). Performance expectation stirs the belief that using mobile learning devices in teaching and learning activities will improve learner’s academic performance. Performance expectancy is defined as the degree to which learner’s believes that the perceived usefulness of utilizing a particular mobile technology device will assist in improving his performance (Engotoit et al., 2016 ). Previous literature had found significant relationship between performance expectancy and intentions among farmers, flight ticket bookings, accountants, and food delivery system (Jeon et al., 2018 ; Gunden et al, 2020 ). Hence, it was hypothesized that: H7. Performance expectancy positively influences user’s intentions towards mobile learning usage. Extended UTAUT with DeLone and McLean quality constructs The Unified Theory of Acceptance and Use of Technology (UTAUT) explains users’ technology adoption through four key constructs: performance expectancy, effort expectancy, social influence, and facilitating conditions (Venkatesh et al., 2003 ). However, it does not directly address the quality of the technology being adopted (Alazab et al., 2021 ). The DeLone and McLean information systems success model fills this gap by emphasizing six quality constructs system quality, information quality, service quality, use, user satisfaction, and net benefits (DeLone & McLean, 2003 ). Integrating these quality constructs into UTAUT provides a more comprehensive explanation of users’ adoption and usage behaviour. As shown in Fig. 1 , combining both models reveals how quality factors shape users’ perceptions related to the core UTAUT constructs. Service quality Service quality is the “desirable characteristics of the system outputs; that is, management reports and Web pages. For example: relevance, understandability, accuracy, conciseness, completeness, understandability, currency, timeliness, and usability” (Peter et al., 2008 ). Gorla, et al., ( 2010 ) stated that it is the degree of discrepancy between user’s distinct expectations for services and their views of service performance. Mohammadi ( 2015 ) stipulated that the service quality influences users’ satisfaction and intention to use, leading to enhanced users’ usage of the mobile learning technologies. Some previous studies had found a significant relationship between service quality and customer satisfaction and continuance intentions (Oghuma, et al., 2016 ). Therefore, it was hypothesized that: H8. Service quality positively influences user’s intentions towards mobile learning usage. Information quality Information quality is the extent to which information exchange is facilitated by a mobile learning deviceS or the degree of user’s evaluation of information sharing in data exchanges (Nicolaou & McKnight, 2006 ). Information quality is an individual's evaluation of the mobile learning technology performance in providing information based on user’s experience of using the devices (Todd & Barbara, 2005). Gu et al. ( 2007 ) noted that low quality information confuses because it excites users' search and increases information processing costs. Quality time and resources could be wasted on reading irrelevant or out-of-date posts. This makes information search difficult for users (Zheng et al., 2013 ). Several previous researches have examined the influence of information quality of a mobile technology on user’s satisfaction (Janda et al., 2002 ; Koivumäki et al., 2008 ). H9. Information quality positively influences user’s intentions to use mobile learning. H10. Information quality positively influences user’s behaviour to continue using mobile learning. H11. The positive relationship between user’s intentions and continuance use behaviour is mediated by information quality. System quality System quality is the degree of functionality of an instructional system (DeLone & McLean, 2003 ; Ohanu et al., 2022 ; 2023 ). It is the “desirable characteristics of an information system, i.e ease of use, system flexibility, system reliability and ease of learning, as well as system features of intuitiveness, sophistication, flexibility and response times” (Peter et al., 2008 ). It represents the quality of the information system processing itself, which includes software, data and hardware component that measures the extent to which the system is technically sound (Gorla et al., 2010 ). It is one of the major determiners of the success of information system (DeLone & McLean 1992 , 2003 ). System quality has been applied to improve undergraduate students’ skills, warehousing success, organizational improvement (Ohanu et al., 2022 ; Gorla, et al., 2010 ). Hence, it was hypothesized that: H12. System quality positively influences user’s intentions to use mobile learning. H13. System quality positively influences user’s behaviour to continue using mobile learning. H14. The positive relationship between user’s intentions and continuance use behaviour is mediated by system quality. Methodology Sample The study population comprised 1,200 pre-service teachers from three Nigerian universities. Using a stratified sampling technique, 475 valid responses were obtained after removing incomplete, repeated, and atypical questionnaires. Participation was voluntary, as indicated in the consent form. Ethical considerations were strictly observed throughout the study. Participants were adequately informed about the purpose of the research and their right to withdraw at any stage without penalty. Informed consent was obtained before questionnaire administration, and respondents’ anonymity and confidentiality were assured by not collecting personally identifiable information. Questionnaires with missing values were excluded to avoid inaccuracies, resulting in a 95% valid return rate. The demography of the respondents is indicated in Table 1 . Table 1 Demography of Respondents Variable Category Frequency Percentage (%) Age (Year) 16–18 56 11.6 19–21 69 14.7 22–24 150 31.6 25–28 170 35.8 Above 28 30 6.3 Gender Male 298 62.7 Female 177 37.3 Level of study Year 1 161 33.9 Year 2 87 18.3 Year 3 80 16.8 Year 4 147 31.0 Mobile Learning Device Android Phone 239 50.3 Laptop 180 37.9 iPad 56 11.8 Mobile Learning Applications Zoom 59 12.4 Google Meet 45 9.5 Telegram 150 31.6 WhatsApp 190 40.0 YouTube 31 6.5 Instrument for data collection A five-point Likert scale research instrument was collated from several reviewed literature that applied UTAUT to the adoption and utilization of mobile and blended learning tools as shown in Table 2 . The constructs used are Performance expectancy with a statement “I find mobile learning useful in my learning”, Effort expectancy (EE) with a statement “I am skillful at using mobile learning”, Social influence (SI) with a statement “People who are important to me think that I should use mobile learning”, Facilitating condition (FC) with a statement “I have the resources necessary to use mobile learning, Intention to use (IU) with a statement “I intend to use mobile learning in the future, Usage behaviour (AU) with a statement “I consider myself a regular user of mobile learning”. System quality (SQ) with a statement “Mobile learning allows me to have control over my learning actively, Information quality (IQ) with a statement “Mobile learning provides required content and information, Service quality (SEQ) with a statement “Mobile learning application provides learning service anywhere. Table 2 Instrument S/N Construct Abbreviation No. of Items Cronbach’s Alpha (α) Source 1 Performance Expectancy PE 4 0.88 Viswanath Venkatesh et al. ( 2003 ; 2012 ) 2 Effort Expectancy EE 4 0.91 Viswanath Venkatesh et al. ( 2003 ; 2012 ) 3 Social Influence SI 4 0.82 Viswanath Venkatesh et al. ( 2003 ; 2012 ) 4 Facilitating Conditions FC 4 0.75 Viswanath Venkatesh et al. ( 2003 ; 2012 ) 5 Intention to Use IU 4 0.93 Viswanath Venkatesh et al. ( 2012 ) 6 Usage Behaviour AU 4 0.70 Viswanath Venkatesh et al. ( 2012 ) 7 System Quality SQ 7 0.77 William H. DeLone & Ephraim R. McLean (2003); Tarek Alsabawy et al. (2016) 8 Information Quality IQ 6 0.93 William H. DeLone & Ephraim R. McLean (2003); Tarek Alsabawy et al. (2016) 9 Service Quality SEQ 4 0.91 William H. DeLone & Ephraim R. McLean (2003); Tarek Alsabawy et al. (2016) Data analysis The hypotheses were evaluated using confirmatory and exploratory factor analysis through the structural equation modelling partial least squares (SEM-PLS) approach, implemented in SmartPLS 3.0 (Ringle et al., 2015 ). This method is appropriate for testing causal effects, predicting relationships, and validating theoretical frameworks. In the model, system, information, and service quality, along with performance expectancy, effort expectancy, social influence, and facilitating conditions, were treated as predictors of behavioural intention. In the second stage, system quality, information quality, and facilitating conditions served as predictors of use behaviour, meeting the requirements for establishing cause-and-effect relationships (Sánchez-Franco et al., 2014 ). Convergent validity was assessed using factor loadings, composite reliability (CR), and average variance extracted (AVE). Factor loadings above 0.70 are acceptable. Composite reliability exceeded 0.70, and AVE was above 0.50 (Hair Jr. et al., 2014 ). The rho_A was determined following Dijkstra &Henseler’s (2015) approach as shown in Table 3 . Table 3 Cross loadings, Reliabilities and Validities of the constructs Constructs Factor loadings Composite Reliability Average Variance Extracted Discriminant Validity rho_A Cronbach’s Alpha Effort Expectancies EE1 0.879 0.915 0.781 0.884 1.027 0.872 EE3 0.845 EE4 0.926 Faciliatating Conditions FC2 0.942 0.936 0.879 0.938 0.865 0.863 FC3 0.933 Information Quality IQ2 0.814 0.918 0.736 0.858 0.900 0.881 IQ3 0.902 IQ4 0.870 IQ5 0.843 Intention to Use IU2 0.965 0.913 0.840 0.916 1.087 0.826 IU3 0.865 Performance Expectation ໿PE1 0.957 0.898 0.748 0.865 0.719 0.887 PE2 0.916 PE3 0.701 Service Quality SEQ1 0.892 0.895 0.809 0.900 0.768 0.765 SEQ2 0.907 Social Influence SI2 0.877 0.902 0.821 0.906 0.837 0.787 SI3 0.935 System Quality SQ1 0.989 0.887 0.799 0.894 2.351 0.815 SQ2 0.787 Use Behaviour AU1 0.866 0.892 0.735 0.857 0.841 0.821 AU2 0.898 AU3 0.804 Findings and results Measurement and model testing To ascertain the validity and reliability of the instrument, the factor loading exceeded the threshold, Cronbach alpha measurement exceeded 0.50, composite reliability exceeded 0.7 with C.R range from 0.887–0.936 and average variance extracted exceeded 0.50 as indicated in Fig. 2 . Composite reliability is adequate with an average variance extracted of 0.5 (Hair Jr. et al., 2014 ). Rho_A should be greater than 0.7 (Dijkstra &Henseler’s 2015). Table 4 Heterotrait-Monotrait Ratio (HTMT) Constructs 1 2 3 4 5 6 6 7 8 Effort Expectancies Facilitating Condition 0.040 Information Quality 0.030 0.064 Intention to use 0.042 0.147 0.027 Performance Expectation 0.510 0.066 0.036 0.042 Service Quality 0.116 0.110 0.213 0.084 0.083 Social Influence 0.035 0.107 0.090 0.091 0.058 0.030 System Quality 0.067 0.034 0.101 0.051 0.024 0.023 0.133 Use Behaviour 0.046 0.105 0.455 0.051 0.025 0.057 0.064 0.149 The discriminant validity of the instrument was confirmed as the square root of the average variance extracted exceeded the cross-loadings of the correlation matrix (Lowry & Gaskin, 2014 ), as shown in Table 4 , and further supported through the Heterotrait-Monotrait Ratio (HTMT). Following the recommended HTMT threshold of 0.90 (Teo et al., 2008 ), the values presented in Table 2 were all below this limit, indicating satisfactory discriminant validity. Table 5 Effect size (f 2 ) and Collinearity test (VIF) Hyp Path Effect size (f 2 ) Collinearity test (VIF) Decision 1 Int. to use -> Use behaviour 0.011 1.018 Small effect 2 Facil. cond. -> Int. to use 0.022 1.027 Small effect 3 Facil. cond. -> Use behaviour 0.016 1.020 Small effect 4 Social infl. -> Int. to use 0.012 1.020 Small effect 5 Effort exp. -> int. to use 0.020 1.218 Small effect 6 Perform. exp. -> Int. to use 0.011 1.212 Small effect 7 Service qual. -> Int. to use 0.018 1.056 Small effect 8 Inform. qual. -> Int. to use 0.012 1.053 Small effect 9 Inform. qual. -> Use behaviour 0.179 1.011 Medium effect 10 System qual. -> Int. to use 0.010 1.017 Small effect 11 System qual. -> Use behaviour 0.020 1.009 Small effect In addition, the lateral collinearity test (VIF), which measures the correlational significance between independent variables showed values ranging from 1.02 to 1.21, well below the 3.3 threshold suggested by Cenfetelli and Bassellier ( 2009 ), confirming the absence of multicollinearity (Hair Jr et al., 2014 ). The coefficient of determination (R²) for the endogenous variables was 0.039 for intention to use and 0.178 for usage behaviour (Nitzl, 2016 ), indicating modest explanatory power. Effect size (f²) analysis, using thresholds of 0.02 (small), 0.15 (medium), and 0.35 (large) (Hair Jr et al., 2014 ) in Table 5 , revealed that the exogenous variables exerted small effects on their corresponding endogenous constructs, except for information quality, which demonstrated a medium effect on usage behaviour. The predictive relevance of the model, assessed using cross-validated redundancy (Q²), showed values greater than zero, 0.015 for intention to use and 0.121 for usage behaviour indicating that the model possesses adequate predictive capability (Sarstedt et al., 2014 ). Reflective-Formative Measurement Model Assessment The reflective–formative measurement model presented in Fig. 3 and Table 6 evaluates the extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework. The results indicate that information quality and system quality demonstrate statistically significant relationships with the higher-order construct. Specifically, information quality exhibits a strong positive outer loading (β = 0.911), accompanied by a substantial outer weight and a highly significant t-value, confirming its robustness as a key formative indicator. In contrast, system quality shows a negative outer loading (β = −0.274) but retains a positive outer weight and a statistically significant t-value, suggesting a complex but meaningful contribution to the construct. Conversely, effort expectancy, performance expectancy, social influence, facilitating conditions, and service quality display relatively low outer loadings and weights, with non-significant t-values. This indicates that these indicators contribute less substantially to the formative construct within the current model context. To assess potential multicollinearity issues, a lateral collinearity test was conducted. The variance inflation factor (VIF) values ranged from 1.017 to 1.218, which are well below the recommended threshold of 3.3 (Hair et al., 2014 ), confirming the absence of lateral multicollinearity concerns among the formative indicators. Furthermore, the discriminant validity of the model was evaluated using the Fornell–Larcker criterion. The results demonstrate that the square root of the average variance extracted (AVE) for intention to use (0.903) and use behaviour (0.857) exceeds the inter-construct correlations, thereby establishing adequate discriminant validity. Table 6 Formative measurement model Construct Items Scale Weight Loadings t-values VIF Extended UTAUT Effort expectancy Formative 0.475 0.051 0.459 1.218 Facilitating condition 1.317 0.163 1.228 1.027 Information quality 5.541 0.911 14.639* 1.053 Performance expectation 0.570 -0.065 0.601 1.212 Service quality 0.241 -0.026 0.225 1.056 Social influence 0.678 -0.083 0.685 1.020 System quality 2.302 -0.274 2.650* 1.017 • Indicates a significant t-value Predictive Relevance and Model Performance (PLSpredict) The predictive performance of the model was assessed using the PLSpredict procedure, following the guidelines of Shmueli et al. ( 2019 ). A 10-fold cross-validation procedure (K = 10) with 10 repetitions (r = 10) was employed to ensure robustness of the prediction results. Model predictive accuracy was evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), where lower values indicate higher predictive accuracy. Additionally, Q²_predict values were examined to compare the predictive performance of the PLS-SEM model against a naïve linear regression (LM) benchmark. The results in Table 7 indicate that the PLS-SEM model generally demonstrates superior predictive performance, as evidenced by lower RMSE and MAE values relative to the linear regression benchmark across most indicators. Although some Q²_predict values are marginal or slightly negative, the overall pattern suggests that the model achieves acceptable to high predictive relevance, particularly for use behaviour indicators. Importantly, a positive Q²_predict value implies that the prediction error of the PLS-SEM model is lower than that of the naïve benchmark model. In this study, the predominance of lower prediction errors in the PLS-SEM model confirms its strong out-of-sample predictive capability, thereby supporting the robustness and practical applicability of the extended UTAUT framework. Table 7 Manifest variable (MV) prediction Composite Indicator RMSE MAE MAPE Q2 RMSE MAE MAPE Q2 PLS-SEM model Linear regression model Intention to use IU3 0.948 0.753 24.234 -0.004 0.953 0.766 24.535 -0.013 IU2 0.969 0.789 24.661 -0.006 0.976 0.792 24.327 0.021 Use behaviour AU3 1.133 0.918 40.682 0.071 1.149 0.934 40.788 0.074 AU2 1.132 0.911 43.621 0.094 1.161 0.934 43.671 0.096 AU1 1.136 0.943 43.116 0.152 1.155 0.957 42.178 0.155 Hypothesis testing The estimates used in the study allowed the acceptance or rejection of the hypotheses with a direct effect measure of the constructs using the bootstrapping method. With a t–values 1.96 and p–values 0.05, the hypothesis is accepted. Figure 2 and Table 8 indicate through the outer loadings and model path coefficient that eight out of the eleven hypotheses measuring direct relationship have significant relationship with their dependent variables, hence were accepted. Mediation effect In a mediation analysis, a statistically significant indirect effect is considered as an acceptable mediation effect between the constructs (Preacher & Hayes, 2004). Also, if the confidence interval for the indirect effect based on 5000 bootstrapping samples, does not straddle a zero in between, this supports the presence of mediation effect and vice versa (Memon et al., 2018). In Table 8 , the hypotheses stating the indirect relationship between the constructs were rejected. Discussion This study applied UTAUT to examine mobile learning adoption among pre-service teachers in Nigerian universities. The findings show that several conditions must be in place for mobile learning to effectively support teaching and learning, yet Nigeria’s developmental challenges hinder its adoption. To address this, the study evaluated students’ exposure to facilitating conditions, expectancies (performance and effort), and quality factors (system, information, and service) to understand how these shape their intentions and continued use of mobile learning for better academic performance. The result reveals that intentions to use mobile learning have a negative but significant influence on users’ continuance behaviour, thus supporting hypothesis H1. This suggests that while learners may initially intend to use mobile learning, certain factors such as usability challenges, system Table 8 Result of the direct effect and mediating effect of the constructs Hyp Path Coefficient Std error Confidence interval t-Values P-Values Decision 2.5% 97.5% 1 Intention. to use -> Use behaviour -0.030 0.044 -0.117 0.055 1.987 0.007 Accepted 2 Facilitating cond. -> Intention to use 0.146 0.047 0.054 0.242 3.106 0.002 Accepted 3 Facilitating cond. -> Use behaviour 0.070 0.043 -0.018 0.149 1.645 0.101 Rejected 5 Social influence -> Intention to use -0.108 0.051 -0.196 0.029 2.109 0.035 Accepted 6 Effort expectancy -> Intention to use -0.012 0.062 -0.124 0.114 0.193 0.847 Rejected 7 Performance expectancy -> Intention to use -0.028 0.012 -0.132 0.134 2.253 0.008 Accepted 8 Service quality -> Intention to use 0.093 0.047 -0.025 0.183 1.968 0.040 Accepted 9 Inform. quality -> Intention to use -0.046 0.014 -0.130 0.049 3.246 0.000 Accepted 10 Inform. quality-> Use behaviour 0.386 0.040 0.308 0.461 9.657 0.000 Accepted 12 System quality -> Intention to use 0.028 0.056 -0.109 0.116 0.497 0.619 Rejected 13 System quality -> Use behaviour -0.112 0.043 -0.196 -0.032 2.625 0.009 Accepted Mediating effect of the constructs 4 Facilitating cond. -> Intention to use -> Use behavour -0.004 0.007 -0.02 0.008 0.635 0.526 Rejected 11 Inform. quality -> Intention to use -> Use behavior 0.001 0.003 -0.003 0.009 0.446 0.655 Rejected 14 System quality -> Intention to use -> Use behavior -0.001 0.003 -0.007 0.006 0.275 0.783 Rejected R 2 = Intention to use – 0.039; Use behaviour – 0.178; Q 2 = Intention to use – 0.015; Use behavour – 0.121 fatigue, or unmet expectations may lead to a decline in continued use over time. Nonetheless, the significant relationship aligns with previous findings (Hoi, 2019 ; Tosuntas et al., 2015 ), which confirm that behavioural intention remains a strong predictor of usage behaviour. As Botero et al. ( 2019 ) noted, when mobile learning technologies are effectively integrated into teaching and learning, they can enhance engagement and sustain users’ participation indicating that improving the quality and relevance of mobile learning experiences is essential for maintaining continued use. The result indicates that facilitating conditions have a positive and significant influence on learners’ intentions to use mobile learning, thereby supporting hypothesis H2. This implies that when learners perceive that sufficient technical infrastructure, institutional support, and necessary resources are available, they are more likely to adopt and continue using mobile learning platforms. This finding aligns with previous studies (Teo, 2011 ; Venkatesh et al., 2003 ; He & Wei, 2007), which emphasize that perceived availability, accessibility, and timeliness of support systems strengthen learners’ confidence and motivation to use mobile technologies for learning. Essentially, conducive facilitating conditions reduce perceived barriers and enhance learners’ readiness to integrate mobile learning into their educational activities. There is no significant influence between facilitating conditions and users’ continuance behaviour toward mobile learning; therefore, H3 is rejected. This suggests that conditions shaping intentions do not always translate into actual behaviour. The result contrasts with earlier studies reporting a significant relationship (Prasad et al., 2018 ). Kumar and Bervell ( 2019 ), Sultana ( 2020 ), and Yadegaridehkordi et al. ( 2020 ), noted that the insignificant relationship may stem from learners being required to use mobile learning regardless of available support such as internet access or technical assistance. This finding implies that when mobile learning becomes mandatory, students may continue using it even without strong institutional or infrastructural support, and that their behaviour may be driven more by internal motivation and perceived usefulness than by external facilitating conditions. The result yielded a negative and non-significant mediating influence of facilitating conditions on the relationship between user’s intentions and continuance use behaviour. This result does not allow H4 to be accepted. The insignificant mediating effect occurs when users perceive that the system is very useful and easy to use, even when the external conditions are less than optimal (Chawla & Joshi, 2020 ; Sitar-Tăut, 2021 ). This suggests that once learners are convinced of the usefulness and ease of mobile learning technologies, their intentions and behaviour may become less dependent on infrastructural support or institutional provisions. In such situations, intrinsic motivation and perceived system value may override the absence of strong facilitating conditions. However, this does not diminish the relevance of facilitating conditions in general, as they may still play a more decisive role in contexts where students are less confident in their abilities or where system usability is more complex. The findings show a negative but significant influence of social influence on pre-service teachers intentions to use mobile learning; thus, H5 is accepted. This aligns with prior studies (Yadegaridehkordi et al., 2020 ; Madan & Yadav, 2016 ), which demonstrate that learners’ intentions are shaped by the social structures around them. Tan and Teo ( 2000 ) also noted that friends, peers, family, and other referents can influence learners’ adoption decisions, as users may develop stronger intentions when considering others’ expectations (Venkatesh & Morris, 2000 ). In this study, the negative direction may reflect cultural or institutional contexts where external pressure leads to resistance rather than motivation. This suggests that while social influence remains important, its effect can vary depending on social norms, peer dynamics, and the broader educational culture surrounding technology adoption. Effort expectancy has a negative and insignificant influence on users’ intentions to use mobile learning; thus, H6 is rejected. The insignificant effect may stem from challenges associated with adopting mobile learning, such as infrastructural deficits, institutional policy constraints, and limited pedagogical capacity for mobile-based instruction (Madan & Yadav, 2016 ). Also, anti-mobile phone sentiments and concerns about distraction while technical limitations such as low processing power, small screens, limited storage, and short battery life further complicate usage (Joo et al., 2016 ). These barriers may overshadow perceived ease of use, reducing the influence of effort expectancy on intention. The findings suggest that without addressing infrastructural and design issues, students may not view mobile learning as convenient or effortless despite its academic potential. Performance expectancy significantly influence user’s intentions to use mobile learning yielded a negative but significant relationship. This is an indication that H7 is accepted. The result of the study is line with the belief that the usefulness or crops of benefit accrued from system usage towards performing a task influences the user’s intention toward continuous usage (Venkatesh et al., 2003 ). This implies that quality academic performance is a major goal for learners and they perceive that mobile learning technologies would enhance these goals (Abbad 2021 ). The finding further suggests that when students strongly believe mobile learning contributes to efficiency, knowledge acquisition, and improved outcomes, their motivation to sustain its use becomes stronger. In other words, any gap between expected performance benefits and actual learning outcomes may create dissatisfaction, reinforcing the importance of aligning system features with learners’ academic needs. Thus, performance expectancy remains a central driver of behavioural intention, especially in educational contexts where achievement is highly valued The service quality and user’s intentions to use mobile learning yielded a positive and significant relationship. This is an indication that quality service delivery is paramount and shapes certain human activities with the intentions to behave in certain ways. Hence H8 is accepted. The result is consistent with previous literatures that found significant relationship between system quality and continuance intentions (Oghuma, et al., 2016 ). In this situation, the quality of service delivered by technology devices will influence user’s intention to adopt and continued usage for better performance (DeLone & McLean, 2003 ). Moreover, timely responsiveness, technical support, and overall reliability of service features can enhance users’ confidence and satisfaction, thereby strengthening their intention to continue engaging with mobile learning. This further suggests that sustained adoption is not only a matter of system or information quality but also depends heavily on the perceived dependability and effectiveness of the services that support the technology. The result of the findings indicated that the quality nature of information received and presented by technology devices (mobile learning devices) towards lesson delivery will negatively influence user’s intentions to continue using mobile learning. Hence, H9 is accepted. This result of the findings relied on previous literature with a significant relationship between information quality and intentions to use (Wu & Zhang, 2014 ). This is an indication that an improvement in the information quality of mobile technology devices increases user’s intention to use. Furthermore, when learners perceive the information as accurate, comprehensive, and aligned with their learning needs, they are more inclined to adopt and sustain the use of such technologies. Conversely, insufficient, outdated, or poorly structured content can discourage learners from continued engagement, thereby weakening their intention to use mobile learning. Thus, sufficient and updated information provided by the technology device will definitely result in enhancing learner’s behavioural intention (Ramayah et al., 2010 ). Information quality has positive and significant relationship on user’s behaviour to continue to use mobile learning. Hence, H10 is accepted. This is an indication that quality information in a large extent can influence certain human behaviour to a large extent (Mohammadi, 2015 ). The result signifies that the provision of complete, sufficient and supporting learning contents on mobile learning application will enhance continuous use behaviour of mobile learning devices (Almaiah & Alismaiel, 2018 ). Arain et al. ( 2019 ) identified organizing informative workshop and seminar to educate learners as a potent tool to stimulate the learner’s adoption and usage of mobile learning devices. This finding further implies that when students perceive the information provided as accurate, up-to-date, and relevant to their academic and practical needs, they are more likely to remain committed to using mobile learning. It also highlights the centrality of content quality in shaping learner trust and satisfaction, which ultimately translates into sustained engagement with mobile learning platforms. Information quality has a positive but insignificant mediating effect on the relationship between users’ intentions and continuance use behaviour; therefore, H11 is rejected. Unlike previous studies, this result suggests a contradictory view on the mediating role of information system quality in linking user intention and usage behaviour (Zheng et al., 2013 ). It indicates that while the quality of information may shape initial perceptions, it does not necessarily sustain intentions or continuance behaviour. This may be due to other underlying factors, such as satisfaction with the system (Liang et al., 2011 ). Additionally, students may consider information quality a baseline expectation rather than a decisive factor, making continuance behaviour more influenced by motivational constructs like performance expectancy, social influence, or facilitating conditions. System quality has a positive but not significant influence on intentions to use mobile learning. Therefore, H12 is rejected. The result is consistent with the findings of Kaium et al. ( 2020 ) among mHealth users but inconsistent with the general belief on the significance of the two constructs (Mohammadi, 2015 ). This outcome may be attributed to challenges such as new system interfaces and poor screen visibility (Joo et al., 2016 ), which limit students’ ability to fully engage with the technology. In addition, many students may prioritize the relevance and accuracy of information over the technical features of the system, thereby reducing the weight of system quality in shaping their behavioural intentions. It is also possible that infrastructural barriers, such as unstable power supply or limited internet connectivity, further dilute the perceived importance of system quality, as external factors overshadow system-related functionalities. System quality negatively influenced user’s behaviour to continue to use mobile learning despite a significant relationship with their intention to use. Hence, H13 is accepted. The result of the finding coincides with the findings of Lin ( 2007 ) whose study found significant relationship between system quality and actual usage. This is an indication that access, convenience, reliability and ease of use will influence learners to exhibit adequate user’s behaviour (Ho et al., 2013 ). Almaiah and Alismaiel, ( 2018 ) explained that mobile learning technologies allow teaching and learning flexibility and most especially quality interactions between the learners. Since information sharing is flexible among the learners, they perceive ease of use of mobile learning technologies. System quality does not mediate the positive relationship between user’s intentions and continuance use behaviour. Therefore, H14 is rejected. The result obtained from this study is in contrary with the suggestion from literatures where the mediation effects of quality dimensions have significant influence on other variable (Arain, et al., 2019 ; Taqdees, et al., 2023 ). The insignificant mediating effect may be as a result of the stronger significant influence by other contingent variables. The result indicate that students place greater emphasis on the reliability and accuracy of learning content rather than the underlying system features, most especially in technical vocational education and training where practical based learning outcomes are prioritized. This finding suggests that while system quality remains relevant, it may not serve as a decisive mechanism through which intentions are translated into actual usage, particularly when other constructs demonstrate stronger predictive power. Implications of the study The findings of this study offer significant theoretical and practical implications within the framework of the Unified Theory of Acceptance and Use of Technology (UTAUT) and the context of mobile learning adoption in technical vocational education and training. Theoretically, the study reaffirms UTAUT’s explanatory strength by showing that key constructs (performance expectancy, effort expectancy, social influence, and facilitating conditions) significantly influence behavioural intention and actual usage of mobile learning among pre-service teachers, thereby validating the model’s applicability to a domain that has received limited attention. It further reveals that the influence of UTAUT constructs is context-dependent, with performance expectancy and social influence exerting stronger effects than effort expectancy, while also confirming that behavioural intention reliably predicts actual usage in developing countries with infrastructural challenges. Practically, the study underscores the need to enhance system quality and information quality in mobile learning technologies, ensuring that devices are durable, efficient, user-friendly, and supported with accurate, clear, and relevant content. Administrators and educational institutions should develop strategies that promote effective integration of mobile learning into the curriculum, maintain robust and user-friendly systems, provide continuous training for facilitators, and ensure stable internet connectivity, reliable power supply, and a supportive learning environment. Workshops, training sessions, and the use of social networking applications can further strengthen students’ engagement and adoption, while ongoing capacity building for education practitioners remains essential for maximizing the pedagogical potential of mobile learning in Nigerian universities. Conclusion This study provides valuable insights into the adoption and use of mobile learning technologies among pre-service teachers in Nigerian universities, demonstrating through an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model that behavioural intention remains a strong determinant of actual usage, while information quality and system quality add significant explanatory power. The findings confirm that students’ decisions to adopt mobile learning are shaped not only by perceptions of usefulness, ease of use, social influence, and facilitating conditions, but also by the quality of information and system support embedded in the platforms. Beyond validating UTAUT in technical vocational education and training, the study underscores the need to address contextual realities such as infrastructure deficits, institutional support, and pedagogical design to ensure sustainable mobile learning adoption. The results highlight that the effectiveness of mobile learning hinges on balancing motivational factors with the quality dimensions of the technology itself, positioning mobile learning as a potentially transformative tool for teaching and learning provided that institutions and policymakers invest in the necessary infrastructure, training, and system design. Limitation of the study Although this study provides useful insights into mobile learning adoption among pre-service teachers in technical vocational education and training, it is not without limitations. The study was conducted within selected Nigerian universities, and as such, the findings may not be fully generalizable to other disciplines, institutions, or countries with different technological infrastructures and educational systems. In addition, the study employed a cross-sectional survey design, which captures students’ perceptions and behaviour at a single point in time. This limits the ability to examine changes in adoption behaviour over time or to establish stronger causal inferences between the UTAUT constructs and actual mobile learning usage. Furthermore, the study relied on self-reported data, which may be subject to response biases such as social desirability or overestimation of usage behaviour. Despite these limitations, the study offers a strong basis for understanding the predictors of mobile learning adoption in technical and vocational education and provides directions for future research. Declarations Disclosure statement The authors report there are no competing interests to declare Ethical statement The research protocol for this study was approved by the Research Ethics Committee of Yaba College of Technology in accordance with institutional ethical guidelines and national regulations for research involving human participants. The study was carried out following the approved guidelines and in compliance with the principles of the 1964 Declaration of Helsinki to ensure ethical standards in participant recruitment, informed consent, confidentiality, and data handling. All participants voluntarily consented to take part in the study, and their anonymity and rights were fully protected throughout the research process. Consent to participate Informed consent to participate in this study was obtained from all participant prior to data collection. Participation was voluntary, and respondents were adequately informed about the purpose of the study, their right to withdraw at any time without penalty, and the confidentiality of their responses. Consent to publish Informed consent to publish was obtained from all participants involved in the study. Participants were informed that the data collected would be used strictly for academic and publication purposes, and no personally identifiable information would be disclosed in any form. Funding The authors declared that the research received no funding Author Contribution R.O - Data curation and data collectionO.T - Wrote the initial manuscriptO.A - Data analysisA.T - Conceptual and Theoretical modelsAll authors reviewed the manuscript Acknowledgement I appreciate all the research assistance engaged in this study Data Availability The data associated to this research study is available on reasonable request from the corresponding author. References Abbad MMM. Using the UTAUT model to understand students’ usage of e-learning systems in developing countries. Educ Inform Technol. 2021;26:7205–24. https://doi.org/10.1007/s10639-021-10573-5 . Ajzen I. The theory of planned behavior. Organ Behav Hum Decis Process. 1991;50(2):179–211. https://doi.org/10.1016/0749-5978(91)90020-T . 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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-9169158","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":627742924,"identity":"0de55344-e7cf-4237-a6fd-d156309ccb4b","order_by":0,"name":"REBECCA OJOCHIDE MARTINS","email":"","orcid":"","institution":"Yaba College of Technology","correspondingAuthor":false,"prefix":"","firstName":"REBECCA","middleName":"OJOCHIDE","lastName":"MARTINS","suffix":""},{"id":627742925,"identity":"61fc301f-7b25-48ce-9266-1ef9141f05f5","order_by":1,"name":"OLABANJI TAIWO SHODIPE","email":"data:image/png;base64,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","orcid":"","institution":"Yaba College of Technology","correspondingAuthor":true,"prefix":"","firstName":"OLABANJI","middleName":"TAIWO","lastName":"SHODIPE","suffix":""},{"id":627742926,"identity":"4fe1b337-17f2-4a52-805f-d1e1a07b60fa","order_by":2,"name":"OLUSOLA AYINLA OYAGBOLA","email":"","orcid":"","institution":"University of Lagos","correspondingAuthor":false,"prefix":"","firstName":"OLUSOLA","middleName":"AYINLA","lastName":"OYAGBOLA","suffix":""},{"id":627742938,"identity":"88d99413-314a-40b5-9940-a0af373ef941","order_by":3,"name":"ANIKE TEMITOPE SHODIPE","email":"","orcid":"","institution":"University of Lagos","correspondingAuthor":false,"prefix":"","firstName":"ANIKE","middleName":"TEMITOPE","lastName":"SHODIPE","suffix":""}],"badges":[],"createdAt":"2026-03-19 11:54:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9169158/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9169158/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108229164,"identity":"2546f086-2378-4623-9f34-f936824c24d6","added_by":"auto","created_at":"2026-04-30 17:05:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":38649,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTheoretical framework and hypotheses formulation\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9169158/v1/7cf7cf9e8cc01f8eca084794.png"},{"id":108491625,"identity":"76009aac-9ad2-4664-b9bb-23a4f3631d1f","added_by":"auto","created_at":"2026-05-05 09:54:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":104755,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOuter loading and model path coefficient\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9169158/v1/6421c18c91ac7b8150ca0894.png"},{"id":108229166,"identity":"a48d06ca-2c66-4709-9736-ef4da11d34b4","added_by":"auto","created_at":"2026-04-30 17:05:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":55450,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReflective-Formative model path\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9169158/v1/57984101559e5bad8b72bc40.png"},{"id":108496618,"identity":"6d22c977-477c-4ebf-883b-72831949fa2b","added_by":"auto","created_at":"2026-05-05 10:12:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":983740,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9169158/v1/c1bff63b-ac42-487a-890b-2f8408a6a9a5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Extending Unified Theory of Acceptance and Use of Technology with Information System Quality Constructs to Predict Pre-Service Teachers Mobile Learning Adoption and Continuance Behaviour","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe adoption of mobile learning removes barriers to teaching and learning (Shodipe \u0026amp; Ohanu, \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), supporting traditional instruction, e-learning, and evaluation while adding value to educational systems. It represents an innovative shift in higher education (Casebourne, 2024; Ohanu \u0026amp; Chukwuone, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Mobile learning involves accessing content across locations and times using devices such as tablets, laptops, and phones through wireless networks (Oberer \u0026amp; Erkollar, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). These devices such as PDAs, mobile phones, laptops, PCs, and e-books enable learning without time or location constraints (Şad \u0026amp; G\u0026ouml;ktaş, 2014). In higher education, mobile learning fosters collaboration and knowledge sharing via wireless networks (Sophonhiranrak, \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMobile learning also delivers information conveniently to learners and educators and supports personalized learning (Al-Emran et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). It enables learners to set goals, select resources, and access materials flexibly for personalized learning (Sophonhiranrak, \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Many countries, such as Italy, the Netherlands, New Zealand, the USA, Portugal, and Australia, invest heavily in educational technologies (Chow, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). These tools support collaborative, independent, and lifelong learning, however, there are varying challenges integrating mobile learning by culture, Japanese students showed low mobile learning engagement due to perceived low utility (Bull \u0026amp; Reid, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).), Malaysian students struggled with transfer of learning (Ramli et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), and information quality limited adoption in Finland (Koivum\u0026auml;ki et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Because mobile learning does not guarantee success in all settings, understanding user acceptance is essential (Amadin et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite technological growth, engagement remains low in some countries and institution most especially in developing countries (Kumar \u0026amp; Bervell, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Factors influencing students\u0026rsquo; intentions to use mobile learning therefore require further investigation. Previous studies extended the Technology Acceptance Model (Ohanu et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Theory of Planned Behaviour (Cheung \u0026amp; Vogel, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The UTAUT model identifies four constructs that directly influence technology acceptance (Venkatesh et al., \u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), and further extensions include self-management, innovativeness, playfulness, enjoyment, service quality, and ubiquity (Badwelan et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Huan et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTeacher education programmes play a critical role in preparing pre-service teachers with the pedagogical knowledge, technological competence, and professional skills required for effective classroom practice (Ogbuaynya \u0026amp; Shodipe, 2022; Shodipe \u0026amp; Ogbuanya, \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Pre-service teachers are exposed to courses that integrate pedagogy, subject content knowledge, and educational technologies to enhance teaching and learning (Kuo \u0026amp; Kuo, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Thomas \u0026amp; O\u0026rsquo;Bannon, \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Through teaching practice and technology-supported learning environments, pre-service teachers develop competencies in lesson planning, instructional delivery, classroom management, and the use of digital tools for teaching. Mobile learning has increasingly become an important component of teacher preparation because it allows pre-service teachers to access instructional materials, collaborate with peers, and engage in continuous learning beyond the traditional classroom environment. The Unified Theory of Acceptance and Use of Technology (UTAUT) model is suitable for studying mobile learning adoption (Nwibe \u0026amp; Ogbuanya, 2025) among pre-service teachers because it explains how UTAUT factors influence individuals\u0026rsquo; acceptance and use of technology.\u003c/p\u003e \u003cp\u003eThis study extends the UTAUT model with the information systems success model (system quality, information quality, and service quality) to examine factors influencing pre-service teacher\u0026rsquo;s intentions and usage of mobile learning. UTAUT explains behavioural intentions (Venkatesh et al., \u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), while quality constructs further strengthen motivation to use mobile technologies (DeLone \u0026amp; McLean, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). There is an increased intention to continue using mobile learning and an increased performance when users perceive high quality of the technologies, increased quality of the information provided and quality services delivery (Sitar-Taut \u0026amp; Mican, \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Morealso, several researches on mobile learning had focused on the area of higher education, pre-school education, undergraduate learning, content delivery (Sophonhiranrak, \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Drigas et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) but little has focused on skilled area of education, technical education or career and technical education. This study therefore focuses on mobile learning adoption among pre-service teachers in technical vocational education and training (TVET).\u003c/p\u003e"},{"header":"Theoretical framework and hypotheses","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eUnified theory of acceptance and use of technology (UTAUT)\u003c/h2\u003e \u003cp\u003eUnified theory of acceptance and use of technology (UTAUT) is a derived framework from eight models (Chang, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) that describes how people accept and use technology systems (Venkatesh, et al., \u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). UTAUT contained four major constructs that influence usage intentions and actual usage of mobile learning technologies. These constructs are facilitating condition, effort expectancy, performance expectancy and social influence (Venkatesh, et al., \u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Despite its several applications to information system, marketing, tourism and purchasing (Venkatesh, et al., \u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Chang et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Therefore, there is need verify its influence on technical vocational education and training (TVET) pre-service teachers in Nigeria.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eUser’s behavior and behavioural intentions\u003c/h3\u003e\n\u003cp\u003eUsage intentions is the \u0026ldquo;desire or an individual readiness to use certain mobile learning system within specific circumstances\u0026rdquo; (Hyman et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). It is a construct in UTAUT that predicts continuous or actual usage of mobile learning technology (Kumar \u0026amp; Bervell, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The behavioural theorists believe that user\u0026rsquo;s behavioural intentions should culminate to user\u0026rsquo;s actual usage of technology devices (Ajzen, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Bandura, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Sometimes, this ideal may not be ascertained, Gollwitzer and Sheeran (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) argued that intentions may not necessarily guarantee actual behaviour if the person fails to deal with self-regulatory problems. Previous literatures had found significant relationship between intention and actual or continuous usage behaviour (Maican et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) while some studies found significant relationship between intentions and its antecedents (Ohanu et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kaium et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eH1. Intentions to use mobile learning positively influence continuance user\u0026rsquo;s behaviour\u003c/p\u003e\n\u003ch3\u003eFacilitating condition\u003c/h3\u003e\n\u003cp\u003eFacilitating condition is the extent to which a mobile learning user believes that an institution and technical infrastructure exists to enhance system usage, and are typically operationalized to include aspects of the environment that are designed to remove barriers to use (Venkatesh et al., \u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Tosuntas\u0026cedil; et al., 2015). Facilitating condition is also known as perceived behavioural control from decomposed TPB, C-TPB-TAM, MPCU and IDT (Hoi, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Venkatesh et al. (\u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) had found facilitating condition to conveniently predict intentions with the exclusion of effort expectancy from the model but Dwivedi et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) found significance with the relationship between the two constructs in the phase of effort expectancy. Unlike some studies that found an insignificant relationship between facilitating condition and user\u0026rsquo;s behaviour (Botero et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Some other literature found a significant relationship between the constructs (Li, et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Blaise, et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). With various in-conclusions in previous literature, it was tentatively proposed that:\u003c/p\u003e \u003cp\u003eH2. Facilitating conditions positively influences user\u0026rsquo;s intentions to use mobile learning.\u003c/p\u003e \u003cp\u003eH3. Facilitating conditions positively influences user\u0026rsquo;s behaviour to continue using mobile learning.\u003c/p\u003e \u003cp\u003eH4. The positive relationship between user\u0026rsquo;s intentions and continuance use behaviour is mediated by facilitating condition.\u003c/p\u003e\n\u003ch3\u003eSocial influence\u003c/h3\u003e\n\u003cp\u003eSocial influence is the degree to which a person perceives that important people think he or she should use the mobile learning system, similar to subjective norms in C-TPB-TAM (Ho, et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kumar \u0026amp; Bervell, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Users are subjected to the pressures of social interactions and will, in social contexts such as the school environment, colleagues, and so on, considering not only their own perception but also the opinions and perceptions of others, particularly individuals who they consider to be important in the given context (Isaias, et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In literature, social influence had been considered to have significant relationship with intentions across various fields of application (Tan, \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Isaias et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) but Tan et al., (\u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) findings deviated from the general belief with an insignificant relationship. Beyond the general belief about social influence of mobile learning across culture and region, we propose to validate social influence among pre-service teachers in Nigeria, hence, it was hypothesized that:\u003c/p\u003e \u003cp\u003eH5. Social influence positively influences user\u0026rsquo;s intentions towards mobile learning usage.\u003c/p\u003e\n\u003ch3\u003eEffort expectancy\u003c/h3\u003e\n\u003cp\u003eEffort expectancy is the \u0026ldquo;degree of ease associated with learner\u0026rsquo;s use of mobile technology devices without much effort\u0026rdquo; (Venkatesh et al. \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). It is analogous to perceived ease of use in TAM (Ho, et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and it positively influences performance expectancy. When learners feel that mobile technologies are easy to use and do not require much effort, they would have high intentions to use (Tam et al., \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and when learners feel difficulty in using mobile technologies, they will have low intention to use (Prasanna and Huggins, \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Previous studies found significant relationship between effort expectancy and intentions (Rahi, et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Hence in this study, it was hypothesized that:\u003c/p\u003e \u003cp\u003eH6. Effort expectancy positively influences user\u0026rsquo;s intentions towards mobile learning usage.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePerformance expectancy\u003c/h2\u003e \u003cp\u003ePerformance expectancy is the \u0026ldquo;degree to which an individual believes that using mobile learning devices will enhance learning performance\u0026rdquo; (Venkatesh, et al. \u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Performance expectation stirs the belief that using mobile learning devices in teaching and learning activities will improve learner\u0026rsquo;s academic performance. Performance expectancy is defined as the degree to which learner\u0026rsquo;s believes that the perceived usefulness of utilizing a particular mobile technology device will assist in improving his performance (Engotoit et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Previous literature had found significant relationship between performance expectancy and intentions among farmers, flight ticket bookings, accountants, and food delivery system (Jeon et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Gunden et al, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Hence, it was hypothesized that:\u003c/p\u003e \u003cp\u003eH7. Performance expectancy positively influences user\u0026rsquo;s intentions towards mobile learning usage.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExtended UTAUT with DeLone and McLean quality constructs\u003c/h3\u003e\n\u003cp\u003eThe Unified Theory of Acceptance and Use of Technology (UTAUT) explains users\u0026rsquo; technology adoption through four key constructs: performance expectancy, effort expectancy, social influence, and facilitating conditions (Venkatesh et al., \u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). However, it does not directly address the quality of the technology being adopted (Alazab et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The DeLone and McLean information systems success model fills this gap by emphasizing six quality constructs system quality, information quality, service quality, use, user satisfaction, and net benefits (DeLone \u0026amp; McLean, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Integrating these quality constructs into UTAUT provides a more comprehensive explanation of users\u0026rsquo; adoption and usage behaviour. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, combining both models reveals how quality factors shape users\u0026rsquo; perceptions related to the core UTAUT constructs.\u003c/p\u003e\n\u003ch3\u003eService quality\u003c/h3\u003e\n\u003cp\u003eService quality is the “desirable characteristics of the system outputs; that is, management reports and Web pages. For example: relevance, understandability, accuracy, conciseness, completeness, understandability, currency, timeliness, and usability” (Peter et al., \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e). Gorla, et al., (\u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e) stated that it is the degree of discrepancy between user’s distinct expectations for services and their views of service performance. Mohammadi (\u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e) stipulated that the service quality influences users’ satisfaction and intention to use, leading to enhanced users’ usage of the mobile learning technologies. Some previous studies had found a significant relationship between service quality and customer satisfaction and continuance intentions (Oghuma, et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). Therefore, it was hypothesized that:\u003c/p\u003e \u003cp\u003eH8. Service quality positively influences user’s intentions towards mobile learning usage.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eInformation quality\u003c/h2\u003e \u003cp\u003eInformation quality is the extent to which information exchange is facilitated by a mobile learning deviceS or the degree of user’s evaluation of information sharing in data exchanges (Nicolaou \u0026amp; McKnight, \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). Information quality is an individual's evaluation of the mobile learning technology performance in providing information based on user’s experience of using the devices (Todd \u0026amp; Barbara, 2005). Gu et al. (\u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e) noted that low quality information confuses because it excites users' search and increases information processing costs. Quality time and resources could be wasted on reading irrelevant or out-of-date posts. This makes information search difficult for users (Zheng et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). Several previous researches have examined the influence of information quality of a mobile technology on user’s satisfaction (Janda et al., \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e; Koivumäki et al., \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eH9. Information quality positively influences user’s intentions to use mobile learning.\u003c/p\u003e \u003cp\u003eH10. Information quality positively influences user’s behaviour to continue using mobile learning.\u003c/p\u003e \u003cp\u003eH11. The positive relationship between user’s intentions and continuance use behaviour is mediated by information quality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSystem quality\u003c/h2\u003e \u003cp\u003eSystem quality is the degree of functionality of an instructional system (DeLone \u0026amp; McLean, \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e; Ohanu et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). It is the “desirable characteristics of an information system, i.e ease of use, system flexibility, system reliability and ease of learning, as well as system features of intuitiveness, sophistication, flexibility and response times” (Peter et al., \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e). It represents the quality of the information system processing itself, which includes software, data and hardware component that measures the extent to which the system is technically sound (Gorla et al., \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). It is one of the major determiners of the success of information system (DeLone \u0026amp; McLean \u003cspan class=\"CitationRef\"\u003e1992\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e). System quality has been applied to improve undergraduate students’ skills, warehousing success, organizational improvement (Ohanu et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Gorla, et al., \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). Hence, it was hypothesized that:\u003c/p\u003e \u003cp\u003eH12. System quality positively influences user’s intentions to use mobile learning.\u003c/p\u003e \u003cp\u003eH13. System quality positively influences user’s behaviour to continue using mobile learning.\u003c/p\u003e \u003cp\u003eH14. The positive relationship between user’s intentions and continuance use behaviour is mediated by system quality.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e "},{"header":"Methodology","content":"\u003cp\u003e \u003cb\u003eSample\u003c/b\u003e \u003c/p\u003e\u003cp\u003eThe study population comprised 1,200 pre-service teachers from three Nigerian universities. Using a stratified sampling technique, 475 valid responses were obtained after removing incomplete, repeated, and atypical questionnaires. Participation was voluntary, as indicated in the consent form. Ethical considerations were strictly observed throughout the study. Participants were adequately informed about the purpose of the research and their right to withdraw at any stage without penalty. Informed consent was obtained before questionnaire administration, and respondents’ anonymity and confidentiality were assured by not collecting personally identifiable information. Questionnaires with missing values were excluded to avoid inaccuracies, resulting in a 95% valid return rate. The demography of the respondents is indicated in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab1\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemography of Respondents\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eAge (Year)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e16–18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e19–21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e14.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e22–24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e31.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e25–28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e35.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAbove 28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e62.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e37.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eLevel of study\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eYear 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e33.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eYear 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e18.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eYear 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e16.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eYear 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e31.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eMobile Learning Device\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAndroid Phone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e50.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eLaptop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e37.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eiPad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eMobile Learning Applications\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eZoom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e12.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGoogle Meet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e9.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eTelegram\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e31.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eWhatsApp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e40.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eYouTube\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eInstrument for data collection\u003c/h2\u003e\u003cp\u003eA five-point Likert scale research instrument was collated from several reviewed literature that applied UTAUT to the adoption and utilization of mobile and blended learning tools as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The constructs used are Performance expectancy with a statement “I find mobile learning useful in my learning”, Effort expectancy (EE) with a statement “I am skillful at using mobile learning”, Social influence (SI) with a statement “People who are important to me think that I should use mobile learning”, Facilitating condition (FC) with a statement “I have the resources necessary to use mobile learning, Intention to use (IU) with a statement “I intend to use mobile learning in the future, Usage behaviour (AU) with a statement “I consider myself a regular user of mobile learning”. System quality (SQ) with a statement “Mobile learning allows me to have control over my learning actively, Information quality (IQ) with a statement “Mobile learning provides required content and information, Service quality (SEQ) with a statement “Mobile learning application provides learning service anywhere.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab2\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInstrument\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eS/N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eAbbreviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eNo. of Items\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eCronbach’s Alpha (α)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePerformance Expectancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eViswanath Venkatesh et al. (\u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e; \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eEffort Expectancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eEE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eViswanath Venkatesh et al. (\u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e; \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSocial Influence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eViswanath Venkatesh et al. (\u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e; \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eFacilitating Conditions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eViswanath Venkatesh et al. (\u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e; \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eIntention to Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eIU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eViswanath Venkatesh et al. (\u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eUsage Behaviour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eViswanath Venkatesh et al. (\u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSystem Quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eWilliam H. DeLone \u0026amp; Ephraim R. McLean (2003); Tarek Alsabawy et al. (2016)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eInformation Quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eIQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eWilliam H. DeLone \u0026amp; Ephraim R. McLean (2003); Tarek Alsabawy et al. (2016)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eService Quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSEQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eWilliam H. DeLone \u0026amp; Ephraim R. McLean (2003); Tarek Alsabawy et al. (2016)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eThe hypotheses were evaluated using confirmatory and exploratory factor analysis through the structural equation modelling partial least squares (SEM-PLS) approach, implemented in SmartPLS 3.0 (Ringle et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). This method is appropriate for testing causal effects, predicting relationships, and validating theoretical frameworks. In the model, system, information, and service quality, along with performance expectancy, effort expectancy, social influence, and facilitating conditions, were treated as predictors of behavioural intention. In the second stage, system quality, information quality, and facilitating conditions served as predictors of use behaviour, meeting the requirements for establishing cause-and-effect relationships (Sánchez-Franco et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). Convergent validity was assessed using factor loadings, composite reliability (CR), and average variance extracted (AVE). Factor loadings above 0.70 are acceptable. Composite reliability exceeded 0.70, and AVE was above 0.50 (Hair Jr. et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). The rho_A was determined following Dijkstra \u0026amp;Henseler’s (2015) approach as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab3\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCross loadings, Reliabilities and Validities of the constructs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eConstructs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eFactor loadings\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eComposite Reliability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eAverage Variance Extracted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eDiscriminant Validity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003erho_A\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eCronbach’s Alpha\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eEffort Expectancies\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eEE1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cem\u003e0.879\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"3\"\u003e \u003cp\u003e0.915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"3\"\u003e \u003cp\u003e0.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"3\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"3\"\u003e \u003cp\u003e1.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"3\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eEE3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cem\u003e0.845\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eEE4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cem\u003e0.926\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eFaciliatating Conditions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eFC2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cem\u003e0.942\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"2\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"2\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"2\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"2\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"2\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eFC3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cem\u003e0.933\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eInformation Quality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eIQ2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cem\u003e0.814\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"4\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"4\"\u003e \u003cp\u003e0.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"4\"\u003e \u003cp\u003e0.858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"4\"\u003e \u003cp\u003e0.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"4\"\u003e \u003cp\u003e0.881\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eIQ3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cem\u003e0.902\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eIQ4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cem\u003e0.870\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eIQ5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cem\u003e0.843\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eIntention to Use\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eIU2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cem\u003e0.965\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"2\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"2\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"2\"\u003e \u003cp\u003e0.916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"2\"\u003e \u003cp\u003e1.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"2\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eIU3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cem\u003e0.865\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003ePerformance Expectation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003e໿PE1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cem\u003e0.957\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"3\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"3\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"3\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"3\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"3\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003ePE2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cem\u003e0.916\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003ePE3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cem\u003e0.701\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eService Quality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eSEQ1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cem\u003e0.892\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"2\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"2\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"2\"\u003e \u003cp\u003e0.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"2\"\u003e \u003cp\u003e0.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"2\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eSEQ2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cem\u003e0.907\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eSocial Influence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eSI2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cem\u003e0.877\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"2\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"2\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"2\"\u003e \u003cp\u003e0.906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"2\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"2\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eSI3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cem\u003e0.935\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eSystem Quality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eSQ1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cem\u003e0.989\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"2\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"2\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"2\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"2\"\u003e \u003cp\u003e2.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"2\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eSQ2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cem\u003e0.787\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eUse Behaviour\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eAU1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cem\u003e0.866\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"3\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"3\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"3\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"3\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" rowspan=\"3\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eAU2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cem\u003e0.898\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eAU3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cem\u003e0.804\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e"},{"header":"Findings and results","content":"\u003cp\u003e \u003cb\u003eMeasurement and model testing\u003c/b\u003e \u003c/p\u003e\u003cp\u003eTo ascertain the validity and reliability of the instrument, the factor loading exceeded the threshold, Cronbach alpha measurement exceeded 0.50, composite reliability exceeded 0.7 with C.R range from 0.887–0.936 and average variance extracted exceeded 0.50 as indicated in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Composite reliability is adequate with an average variance extracted of 0.5 (Hair Jr. et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). Rho_A should be greater than 0.7 (Dijkstra \u0026amp;Henseler’s 2015).\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab4\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeterotrait-Monotrait Ratio (HTMT)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eConstructs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eEffort Expectancies\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eFacilitating Condition\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eInformation Quality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eIntention to use\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003ePerformance Expectation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eService Quality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eSocial Influence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eSystem Quality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eUse Behaviour\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe discriminant validity of the instrument was confirmed as the square root of the average variance extracted exceeded the cross-loadings of the correlation matrix (Lowry \u0026amp; Gaskin, \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e), as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, and further supported through the Heterotrait-Monotrait Ratio (HTMT). Following the recommended HTMT threshold of 0.90 (Teo et al., \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e), the values presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e were all below this limit, indicating satisfactory discriminant validity.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab5\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect size (f\u003csup\u003e2\u003c/sup\u003e) and Collinearity test (VIF)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eHyp\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003ePath\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eEffect size (f\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eCollinearity test (VIF)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eDecision\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eInt. to use -\u0026gt; Use behaviour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSmall effect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eFacil. cond. -\u0026gt; Int. to use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSmall effect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eFacil. cond. -\u0026gt; Use behaviour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSmall effect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSocial infl. -\u0026gt; Int. to use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSmall effect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eEffort exp. -\u0026gt; int. to use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSmall effect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePerform. exp. -\u0026gt; Int. to use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSmall effect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eService qual. -\u0026gt; Int. to use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSmall effect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eInform. qual. -\u0026gt; Int. to use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSmall effect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eInform. qual. -\u0026gt; Use behaviour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMedium effect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSystem qual. -\u0026gt; Int. to use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSmall effect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSystem qual. -\u0026gt; Use behaviour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSmall effect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn addition, the lateral collinearity test (VIF), which measures the correlational significance between independent variables showed values ranging from 1.02 to 1.21, well below the 3.3 threshold suggested by Cenfetelli and Bassellier (\u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e), confirming the absence of multicollinearity (Hair Jr et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cp\u003eThe coefficient of determination (R²) for the endogenous variables was 0.039 for intention to use and 0.178 for usage behaviour (Nitzl, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e), indicating modest explanatory power. Effect size (f²) analysis, using thresholds of 0.02 (small), 0.15 (medium), and 0.35 (large) (Hair Jr et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e) in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, revealed that the exogenous variables exerted small effects on their corresponding endogenous constructs, except for information quality, which demonstrated a medium effect on usage behaviour. The predictive relevance of the model, assessed using cross-validated redundancy (Q²), showed values greater than zero, 0.015 for intention to use and 0.121 for usage behaviour indicating that the model possesses adequate predictive capability (Sarstedt et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003eReflective-Formative Measurement Model Assessment\u003c/h2\u003e\u003cp\u003eThe reflective–formative measurement model presented in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e evaluates the extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework. The results indicate that information quality and system quality demonstrate statistically significant relationships with the higher-order construct. Specifically, information quality exhibits a strong positive outer loading (β = 0.911), accompanied by a substantial outer weight and a highly significant t-value, confirming its robustness as a key formative indicator. In contrast, system quality shows a negative outer loading (β = −0.274) but retains a positive outer weight and a statistically significant t-value, suggesting a complex but meaningful contribution to the construct.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cp\u003eConversely, effort expectancy, performance expectancy, social influence, facilitating conditions, and service quality display relatively low outer loadings and weights, with non-significant t-values. This indicates that these indicators contribute less substantially to the formative construct within the current model context.\u003c/p\u003e\u003cp\u003eTo assess potential multicollinearity issues, a lateral collinearity test was conducted. The variance inflation factor (VIF) values ranged from 1.017 to 1.218, which are well below the recommended threshold of 3.3 (Hair et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e), confirming the absence of lateral multicollinearity concerns among the formative indicators.\u003c/p\u003e\u003cp\u003eFurthermore, the discriminant validity of the model was evaluated using the Fornell–Larcker criterion. The results demonstrate that the square root of the average variance extracted (AVE) for intention to use (0.903) and use behaviour (0.857) exceeds the inter-construct correlations, thereby establishing adequate discriminant validity.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab6\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFormative measurement model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eScale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eLoadings\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003et-values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"7\"\u003e \u003cp\u003e\u003cb\u003eExtended UTAUT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eEffort expectancy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" rowspan=\"7\"\u003e \u003cp\u003eFormative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.218\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eFacilitating condition\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eInformation quality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e5.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e14.639*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003ePerformance expectation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.212\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eService quality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eSocial influence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eSystem quality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e2.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e2.650*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e• Indicates a significant t-value\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003ePredictive Relevance and Model Performance (PLSpredict)\u003c/h2\u003e\u003cp\u003e The predictive performance of the model was assessed using the PLSpredict procedure, following the guidelines of Shmueli et al. (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). A 10-fold cross-validation procedure (K = 10) with 10 repetitions (r = 10) was employed to ensure robustness of the prediction results.\u003c/p\u003e\u003cp\u003eModel predictive accuracy was evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), where lower values indicate higher predictive accuracy. Additionally, Q²_predict values were examined to compare the predictive performance of the PLS-SEM model against a naïve linear regression (LM) benchmark.\u003c/p\u003e\u003cp\u003eThe results in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e indicate that the PLS-SEM model generally demonstrates superior predictive performance, as evidenced by lower RMSE and MAE values relative to the linear regression benchmark across most indicators. Although some Q²_predict values are marginal or slightly negative, the overall pattern suggests that the model achieves acceptable to high predictive relevance, particularly for use behaviour indicators.\u003c/p\u003e\u003cp\u003eImportantly, a positive Q²_predict value implies that the prediction error of the PLS-SEM model is lower than that of the naïve benchmark model. In this study, the predominance of lower prediction errors in the PLS-SEM model confirms its strong out-of-sample predictive capability, thereby supporting the robustness and practical applicability of the extended UTAUT framework.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab7\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eManifest variable (MV) prediction\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003c/colgroup\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eComposite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eRMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMAE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMAPE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eRMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMAE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMAPE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\"\u003e \u003cp\u003ePLS-SEM model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\"\u003e \u003cp\u003eLinear regression model\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"2\"\u003e \u003cp\u003eIntention to use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eIU3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e24.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e24.535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e-0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eIU2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e24.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e24.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"3\"\u003e \u003cp\u003eUse behaviour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAU3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e40.682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e40.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAU2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e43.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e43.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAU1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e43.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e42.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eHypothesis testing\u003c/h2\u003e\u003cp\u003eThe estimates used in the study allowed the acceptance or rejection of the hypotheses with a direct effect measure of the constructs using the bootstrapping method. With a t–values 1.96 and p–values 0.05, the hypothesis is accepted. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e indicate through the outer loadings and model path coefficient that eight out of the eleven hypotheses measuring direct relationship have significant relationship with their dependent variables, hence were accepted.\u003c/p\u003e\u003ch2\u003eMediation effect\u003c/h2\u003e\u003cp\u003eIn a mediation analysis, a statistically significant indirect effect is considered as an acceptable mediation effect between the constructs (Preacher \u0026amp; Hayes, 2004). Also, if the confidence interval for the indirect effect based on 5000 bootstrapping samples, does not straddle a zero in between, this supports the presence of mediation effect and vice versa (Memon et al., 2018). In Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e, the hypotheses stating the indirect relationship between the constructs were rejected.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study applied UTAUT to examine mobile learning adoption among pre-service teachers in Nigerian universities. The findings show that several conditions must be in place for mobile learning to effectively support teaching and learning, yet Nigeria\u0026rsquo;s developmental challenges hinder its adoption. To address this, the study evaluated students\u0026rsquo; exposure to facilitating conditions, expectancies (performance and effort), and quality factors (system, information, and service) to understand how these shape their intentions and continued use of mobile learning for better academic performance.\u003c/p\u003e \u003cp\u003eThe result reveals that intentions to use mobile learning have a negative but significant influence on users\u0026rsquo; continuance behaviour, thus supporting hypothesis H1. This suggests that while learners may initially intend to use mobile learning, certain factors such as usability challenges, system\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResult of the direct effect and mediating effect of the constructs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHyp\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePath\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eConfidence interval\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003et-Values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP-Values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDecision\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.5%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97.5%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIntention. to use -\u0026gt; Use behaviour\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAccepted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFacilitating cond. -\u0026gt; Intention to use\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAccepted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFacilitating cond. -\u0026gt; Use behaviour\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRejected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSocial influence -\u0026gt; Intention to use\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAccepted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEffort expectancy -\u0026gt; Intention to use\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRejected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePerformance expectancy -\u0026gt; Intention to use\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAccepted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eService quality -\u0026gt; Intention to use\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAccepted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eInform. quality -\u0026gt; Intention to use\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAccepted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eInform. quality-\u0026gt; Use behaviour\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAccepted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSystem quality -\u0026gt; Intention to use\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRejected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSystem quality -\u0026gt; Use behaviour\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAccepted\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\u003e\u003cb\u003eMediating effect of the constructs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFacilitating cond. -\u0026gt; Intention to use -\u0026gt; Use behavour\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRejected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eInform. quality -\u0026gt; Intention to use -\u0026gt; Use behavior\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRejected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSystem quality -\u0026gt; Intention to use -\u0026gt; Use behavior\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRejected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;Intention to use \u0026ndash; 0.039; Use behaviour \u0026ndash; 0.178;\u003c/p\u003e \u003cp\u003eQ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;Intention to use \u0026ndash; 0.015; Use behavour \u0026ndash; 0.121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003efatigue, or unmet expectations may lead to a decline in continued use over time. Nonetheless, the significant relationship aligns with previous findings (Hoi, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tosuntas et al., \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), which confirm that behavioural intention remains a strong predictor of usage behaviour. As Botero et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) noted, when mobile learning technologies are effectively integrated into teaching and learning, they can enhance engagement and sustain users\u0026rsquo; participation indicating that improving the quality and relevance of mobile learning experiences is essential for maintaining continued use.\u003c/p\u003e \u003cp\u003eThe result indicates that facilitating conditions have a positive and significant influence on learners\u0026rsquo; intentions to use mobile learning, thereby supporting hypothesis H2. This implies that when learners perceive that sufficient technical infrastructure, institutional support, and necessary resources are available, they are more likely to adopt and continue using mobile learning platforms. This finding aligns with previous studies (Teo, \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Venkatesh et al., \u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; He \u0026amp; Wei, 2007), which emphasize that perceived availability, accessibility, and timeliness of support systems strengthen learners\u0026rsquo; confidence and motivation to use mobile technologies for learning. Essentially, conducive facilitating conditions reduce perceived barriers and enhance learners\u0026rsquo; readiness to integrate mobile learning into their educational activities.\u003c/p\u003e \u003cp\u003eThere is no significant influence between facilitating conditions and users\u0026rsquo; continuance behaviour toward mobile learning; therefore, H3 is rejected. This suggests that conditions shaping intentions do not always translate into actual behaviour. The result contrasts with earlier studies reporting a significant relationship (Prasad et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Kumar and Bervell (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), Sultana (\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and Yadegaridehkordi et al. (\u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), noted that the insignificant relationship may stem from learners being required to use mobile learning regardless of available support such as internet access or technical assistance. This finding implies that when mobile learning becomes mandatory, students may continue using it even without strong institutional or infrastructural support, and that their behaviour may be driven more by internal motivation and perceived usefulness than by external facilitating conditions.\u003c/p\u003e \u003cp\u003eThe result yielded a negative and non-significant mediating influence of facilitating conditions on the relationship between user\u0026rsquo;s intentions and continuance use behaviour. This result does not allow H4 to be accepted. The insignificant mediating effect occurs when users perceive that the system is very useful and easy to use, even when the external conditions are less than optimal (Chawla \u0026amp; Joshi, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sitar-Tăut, \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This suggests that once learners are convinced of the usefulness and ease of mobile learning technologies, their intentions and behaviour may become less dependent on infrastructural support or institutional provisions. In such situations, intrinsic motivation and perceived system value may override the absence of strong facilitating conditions. However, this does not diminish the relevance of facilitating conditions in general, as they may still play a more decisive role in contexts where students are less confident in their abilities or where system usability is more complex.\u003c/p\u003e \u003cp\u003eThe findings show a negative but significant influence of social influence on pre-service teachers intentions to use mobile learning; thus, H5 is accepted. This aligns with prior studies (Yadegaridehkordi et al., \u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Madan \u0026amp; Yadav, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), which demonstrate that learners\u0026rsquo; intentions are shaped by the social structures around them. Tan and Teo (\u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) also noted that friends, peers, family, and other referents can influence learners\u0026rsquo; adoption decisions, as users may develop stronger intentions when considering others\u0026rsquo; expectations (Venkatesh \u0026amp; Morris, \u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). In this study, the negative direction may reflect cultural or institutional contexts where external pressure leads to resistance rather than motivation. This suggests that while social influence remains important, its effect can vary depending on social norms, peer dynamics, and the broader educational culture surrounding technology adoption.\u003c/p\u003e \u003cp\u003eEffort expectancy has a negative and insignificant influence on users\u0026rsquo; intentions to use mobile learning; thus, H6 is rejected. The insignificant effect may stem from challenges associated with adopting mobile learning, such as infrastructural deficits, institutional policy constraints, and limited pedagogical capacity for mobile-based instruction (Madan \u0026amp; Yadav, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Also, anti-mobile phone sentiments and concerns about distraction while technical limitations such as low processing power, small screens, limited storage, and short battery life further complicate usage (Joo et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These barriers may overshadow perceived ease of use, reducing the influence of effort expectancy on intention. The findings suggest that without addressing infrastructural and design issues, students may not view mobile learning as convenient or effortless despite its academic potential.\u003c/p\u003e \u003cp\u003ePerformance expectancy significantly influence user\u0026rsquo;s intentions to use mobile learning yielded a negative but significant relationship. This is an indication that H7 is accepted. The result of the study is line with the belief that the usefulness or crops of benefit accrued from system usage towards performing a task influences the user\u0026rsquo;s intention toward continuous usage (Venkatesh et al., \u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). This implies that quality academic performance is a major goal for learners and they perceive that mobile learning technologies would enhance these goals (Abbad \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The finding further suggests that when students strongly believe mobile learning contributes to efficiency, knowledge acquisition, and improved outcomes, their motivation to sustain its use becomes stronger. In other words, any gap between expected performance benefits and actual learning outcomes may create dissatisfaction, reinforcing the importance of aligning system features with learners\u0026rsquo; academic needs. Thus, performance expectancy remains a central driver of behavioural intention, especially in educational contexts where achievement is highly valued\u003c/p\u003e \u003cp\u003eThe service quality and user\u0026rsquo;s intentions to use mobile learning yielded a positive and significant relationship. This is an indication that quality service delivery is paramount and shapes certain human activities with the intentions to behave in certain ways. Hence H8 is accepted. The result is consistent with previous literatures that found significant relationship between system quality and continuance intentions (Oghuma, et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In this situation, the quality of service delivered by technology devices will influence user\u0026rsquo;s intention to adopt and continued usage for better performance (DeLone \u0026amp; McLean, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Moreover, timely responsiveness, technical support, and overall reliability of service features can enhance users\u0026rsquo; confidence and satisfaction, thereby strengthening their intention to continue engaging with mobile learning. This further suggests that sustained adoption is not only a matter of system or information quality but also depends heavily on the perceived dependability and effectiveness of the services that support the technology.\u003c/p\u003e \u003cp\u003eThe result of the findings indicated that the quality nature of information received and presented by technology devices (mobile learning devices) towards lesson delivery will negatively influence user\u0026rsquo;s intentions to continue using mobile learning. Hence, H9 is accepted. This result of the findings relied on previous literature with a significant relationship between information quality and intentions to use (Wu \u0026amp; Zhang, \u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This is an indication that an improvement in the information quality of mobile technology devices increases user\u0026rsquo;s intention to use. Furthermore, when learners perceive the information as accurate, comprehensive, and aligned with their learning needs, they are more inclined to adopt and sustain the use of such technologies. Conversely, insufficient, outdated, or poorly structured content can discourage learners from continued engagement, thereby weakening their intention to use mobile learning. Thus, sufficient and updated information provided by the technology device will definitely result in enhancing learner\u0026rsquo;s behavioural intention (Ramayah et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInformation quality has positive and significant relationship on user\u0026rsquo;s behaviour to continue to use mobile learning. Hence, H10 is accepted. This is an indication that quality information in a large extent can influence certain human behaviour to a large extent (Mohammadi, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The result signifies that the provision of complete, sufficient and supporting learning contents on mobile learning application will enhance continuous use behaviour of mobile learning devices (Almaiah \u0026amp; Alismaiel, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Arain et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) identified organizing informative workshop and seminar to educate learners as a potent tool to stimulate the learner\u0026rsquo;s adoption and usage of mobile learning devices. This finding further implies that when students perceive the information provided as accurate, up-to-date, and relevant to their academic and practical needs, they are more likely to remain committed to using mobile learning. It also highlights the centrality of content quality in shaping learner trust and satisfaction, which ultimately translates into sustained engagement with mobile learning platforms.\u003c/p\u003e \u003cp\u003eInformation quality has a positive but insignificant mediating effect on the relationship between users\u0026rsquo; intentions and continuance use behaviour; therefore, H11 is rejected. Unlike previous studies, this result suggests a contradictory view on the mediating role of information system quality in linking user intention and usage behaviour (Zheng et al., \u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). It indicates that while the quality of information may shape initial perceptions, it does not necessarily sustain intentions or continuance behaviour. This may be due to other underlying factors, such as satisfaction with the system (Liang et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Additionally, students may consider information quality a baseline expectation rather than a decisive factor, making continuance behaviour more influenced by motivational constructs like performance expectancy, social influence, or facilitating conditions.\u003c/p\u003e \u003cp\u003eSystem quality has a positive but not significant influence on intentions to use mobile learning. Therefore, H12 is rejected. The result is consistent with the findings of Kaium et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) among mHealth users but inconsistent with the general belief on the significance of the two constructs (Mohammadi, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This outcome may be attributed to challenges such as new system interfaces and poor screen visibility (Joo et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), which limit students\u0026rsquo; ability to fully engage with the technology. In addition, many students may prioritize the relevance and accuracy of information over the technical features of the system, thereby reducing the weight of system quality in shaping their behavioural intentions. It is also possible that infrastructural barriers, such as unstable power supply or limited internet connectivity, further dilute the perceived importance of system quality, as external factors overshadow system-related functionalities.\u003c/p\u003e \u003cp\u003eSystem quality negatively influenced user\u0026rsquo;s behaviour to continue to use mobile learning despite a significant relationship with their intention to use. Hence, H13 is accepted. The result of the finding coincides with the findings of Lin (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) whose study found significant relationship between system quality and actual usage. This is an indication that access, convenience, reliability and ease of use will influence learners to exhibit adequate user\u0026rsquo;s behaviour (Ho et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Almaiah and Alismaiel, (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) explained that mobile learning technologies allow teaching and learning flexibility and most especially quality interactions between the learners. Since information sharing is flexible among the learners, they perceive ease of use of mobile learning technologies.\u003c/p\u003e \u003cp\u003eSystem quality does not mediate the positive relationship between user\u0026rsquo;s intentions and continuance use behaviour. Therefore, H14 is rejected. The result obtained from this study is in contrary with the suggestion from literatures where the mediation effects of quality dimensions have significant influence on other variable (Arain, et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Taqdees, et al., \u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The insignificant mediating effect may be as a result of the stronger significant influence by other contingent variables. The result indicate that students place greater emphasis on the reliability and accuracy of learning content rather than the underlying system features, most especially in technical vocational education and training where practical based learning outcomes are prioritized. This finding suggests that while system quality remains relevant, it may not serve as a decisive mechanism through which intentions are translated into actual usage, particularly when other constructs demonstrate stronger predictive power.\u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eImplications of the study\u003c/h2\u003e \u003cp\u003eThe findings of this study offer significant theoretical and practical implications within the framework of the Unified Theory of Acceptance and Use of Technology (UTAUT) and the context of mobile learning adoption in technical vocational education and training. Theoretically, the study reaffirms UTAUT\u0026rsquo;s explanatory strength by showing that key constructs (performance expectancy, effort expectancy, social influence, and facilitating conditions) significantly influence behavioural intention and actual usage of mobile learning among pre-service teachers, thereby validating the model\u0026rsquo;s applicability to a domain that has received limited attention. It further reveals that the influence of UTAUT constructs is context-dependent, with performance expectancy and social influence exerting stronger effects than effort expectancy, while also confirming that behavioural intention reliably predicts actual usage in developing countries with infrastructural challenges. Practically, the study underscores the need to enhance system quality and information quality in mobile learning technologies, ensuring that devices are durable, efficient, user-friendly, and supported with accurate, clear, and relevant content. Administrators and educational institutions should develop strategies that promote effective integration of mobile learning into the curriculum, maintain robust and user-friendly systems, provide continuous training for facilitators, and ensure stable internet connectivity, reliable power supply, and a supportive learning environment. Workshops, training sessions, and the use of social networking applications can further strengthen students\u0026rsquo; engagement and adoption, while ongoing capacity building for education practitioners remains essential for maximizing the pedagogical potential of mobile learning in Nigerian universities.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides valuable insights into the adoption and use of mobile learning technologies among pre-service teachers in Nigerian universities, demonstrating through an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model that behavioural intention remains a strong determinant of actual usage, while information quality and system quality add significant explanatory power. The findings confirm that students\u0026rsquo; decisions to adopt mobile learning are shaped not only by perceptions of usefulness, ease of use, social influence, and facilitating conditions, but also by the quality of information and system support embedded in the platforms. Beyond validating UTAUT in technical vocational education and training, the study underscores the need to address contextual realities such as infrastructure deficits, institutional support, and pedagogical design to ensure sustainable mobile learning adoption. The results highlight that the effectiveness of mobile learning hinges on balancing motivational factors with the quality dimensions of the technology itself, positioning mobile learning as a potentially transformative tool for teaching and learning provided that institutions and policymakers invest in the necessary infrastructure, training, and system design.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003eLimitation of the study\u003c/h2\u003e \u003cp\u003eAlthough this study provides useful insights into mobile learning adoption among pre-service teachers in technical vocational education and training, it is not without limitations. The study was conducted within selected Nigerian universities, and as such, the findings may not be fully generalizable to other disciplines, institutions, or countries with different technological infrastructures and educational systems. In addition, the study employed a cross-sectional survey design, which captures students\u0026rsquo; perceptions and behaviour at a single point in time. This limits the ability to examine changes in adoption behaviour over time or to establish stronger causal inferences between the UTAUT constructs and actual mobile learning usage. Furthermore, the study relied on self-reported data, which may be subject to response biases such as social desirability or overestimation of usage behaviour. Despite these limitations, the study offers a strong basis for understanding the predictors of mobile learning adoption in technical and vocational education and provides directions for future research.\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eDisclosure statement\u003c/h2\u003e \u003cp\u003eThe authors report there are no competing interests to declare\u003c/p\u003e \u003c/div\u003e\u003cp\u003e \u003ch2\u003eEthical statement\u003c/h2\u003e \u003cp\u003e The research protocol for this study was approved by the Research Ethics Committee of Yaba College of Technology in accordance with institutional ethical guidelines and national regulations for research involving human participants. The study was carried out following the approved guidelines and in compliance with the principles of the 1964 Declaration of Helsinki to ensure ethical standards in participant recruitment, informed consent, confidentiality, and data handling. All participants voluntarily consented to take part in the study, and their anonymity and rights were fully protected throughout the research process.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConsent to participate\u003c/h2\u003e \u003cp\u003eInformed consent to participate in this study was obtained from all participant prior to data collection. Participation was voluntary, and respondents were adequately informed about the purpose of the study, their right to withdraw at any time without penalty, and the confidentiality of their responses.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to publish\u003c/strong\u003e \u003cp\u003e Informed consent to publish was obtained from all participants involved in the study. Participants were informed that the data collected would be used strictly for academic and publication purposes, and no personally identifiable information would be disclosed in any form.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe authors declared that the research received no funding\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eR.O - Data curation and data collectionO.T - Wrote the initial manuscriptO.A - Data analysisA.T - Conceptual and Theoretical modelsAll authors reviewed the manuscript\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eI appreciate all the research assistance engaged in this study\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data associated to this research study is available on reasonable request from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbbad MMM. 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Technol Soc. 2021;67:101767. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.techsoc.2021.101767\u003c/span\u003e\u003cspan address=\"10.1016/j.techsoc.2021.101767\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diedu","sideBox":"Learn more about [Discover Education](https://www.springer.com/journal/44217)","snPcode":"44217","submissionUrl":"https://submission.nature.com/new-submission/44217/3","title":"Discover Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"UTAUT, Mobile learning, System quality, Information quality, Service quality, Pre-Service teachers","lastPublishedDoi":"10.21203/rs.3.rs-9169158/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9169158/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe adoption of mobile learning to education eliminates barriers to teaching and learning activities. It introduces a new belief that teaching and learning activities can be executed without being limited by location and time. So, there is a general belief that if adopted among pre-service teachers, will improve usage and academic performance. The study employed a stratified sampling technique to select 475 pre-service teachers from Technical Vocational Education and Training (TVET) institutions. Data were collected through structured questionnaire designed on a five-point Likert scale, which captured respondents\u0026rsquo; perception on UTAUT and IS quality construct. Data were analyzed using Structural Equation Modelling with Partial Least Squares (SEM-PLS) through SmartPLS 3.0. The results revealed that student\u0026rsquo;s intentions to use mobile learning significantly influenced their behaviour. Additionally, facilitating condition, social influence, performance expectancy and information quality influenced students\u0026rsquo; intentions to use mobile learning. The study indicated that mobile learning usage is highly predicted by its antecedents. Furthermore, the study suggests that the unified theory of acceptance and use of technology (UTAUT) can be enhanced by incorporating quality constructs to influence students\u0026rsquo; behaviour and promote adoption of mobile learning.\u003c/p\u003e","manuscriptTitle":"Extending Unified Theory of Acceptance and Use of Technology with Information System Quality Constructs to Predict Pre-Service Teachers Mobile Learning Adoption and Continuance Behaviour","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-30 17:04:59","doi":"10.21203/rs.3.rs-9169158/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-14T07:57:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"160573634492982942374686473593504210980","date":"2026-05-14T07:45:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-22T09:15:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-05T10:40:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-01T12:28:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-27T19:03:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Education","date":"2026-03-27T18:57:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diedu","sideBox":"Learn more about [Discover Education](https://www.springer.com/journal/44217)","snPcode":"44217","submissionUrl":"https://submission.nature.com/new-submission/44217/3","title":"Discover Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"53b6dccc-0197-4b3e-909d-3ef2becd2be5","owner":[],"postedDate":"April 30th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-14T07:57:38+00:00","index":59,"fulltext":""},{"type":"reviewerAgreed","content":"160573634492982942374686473593504210980","date":"2026-05-14T07:45:50+00:00","index":58,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-30T17:04:59+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-30 17:04:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9169158","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9169158","identity":"rs-9169158","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}