Medical students’ acceptance and use of the Wi-Fi system at hostels as a learning tool: an investigation based on the 'unified theory of acceptance and use of technology'

Medical students’ acceptance and use of the Wi-Fi system at hostels as a learning tool: an investigation based on the 'unified theory of acceptance and use of technology' | 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 Medical students’ acceptance and use of the Wi-Fi system at hostels as a learning tool: an investigation based on the 'unified theory of acceptance and use of technology' Samankumara Hettige, Indrajith Solangaarahchi, Dileepa Senajith Ediriweera This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4732428/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Universities use Wi-Fi networks to provide internet access to enhance students’ learning experience. This study evaluated the factors that might effect students’ intention to use a specific hostel Wi-Fi system for learning purposes at the Faculty of Medicine, University of Kelaniya, Sri Lanka. Methods: A cross-sectional study was conducted at the Faculty of Medicine, University of Kelaniya. The unified theory of acceptance and use of technology (UTAUT), proposed by Venkatesh et al., which explains users’ behavioral intentions to use technology, was used to design the conceptual framework. The covariance-based structural equation modelling technique was employed to analyse data collected from 310 medical students. Results: The model assessments of validity and reliability were acceptable. Among the factors studied, only performance expectancy (PE) (β = 0.284, P ≤ 0.001), social influence (SI) (β = 0.222, P ≤ 0.001), and facilitating conditions (FC) (β = 0.615, P ≤ 0.001) significantly effected on students’ intention to use the Wi-Fi system for learning, while effort expectancy (EE) (β = -0.184, P ≤ 0.130) did not. Moreover, behavioral intention (BI) (β = 0.533, P ≤ 0.001) and FC (β = 0.320, P ≤ 0.001) had significant effects on the students’ actual use of the Wi-Fi system for learning. Conclusions: This study enhances our understanding of the factors effecting medical students’ Wi-Fi access on campuses for learning purposes. Among these factors, the influence of FC was very strong. This highlights the importance of the FC, among other factors, in providing Wi-Fi network initiatives on campuses. Furthermore, university administrators can use the findings of this study to identify the requirements for the successful integration of network technologies in educational settings. Wi-Fi Medical students UTAUT SEM Education Learning Figures Figure 1 Figure 2 Background Technology has deeply infiltrated various aspects of life, becoming a crucial indicator of a country’s progress(1). Technology has also been integrated into diverse educational systems, leading to the use of technology in education(2). Numerous educational institutions, including schools, colleges, and universities, have focused on information and communication technology (ICT) as a method of achieving educational goals (3,4). Integrating ICT into education has created numerous opportunities for students, providing a platform and tools for searching, collecting, and analysing information. It also works as a communication medium for teaching and collaborative learning (2,5). According to Khan et al., technology generates new knowledge, enhances problem-solving skills, and improves people’s capacity to work efficiently(6). Many institutions, researchers, and governments view ICT development within an organization as a vital investment for the future (7,8). Therefore, higher education institutions prioritize investing in ICT infrastructure to support teaching and learning in this digital world (9,10). The rise of mobile technology has also led to increased use of portable devices among students (11–13). At the same time, the concept of accessing the wireless internet to learn via these mobile devices has become popular (12,14). Students can access information and participate in online learning activities anytime and anywhere through portable devices such as mobile phones, tablets, and laptops with internet connectivity (15,16). Establishing wireless campus environments enables this connectivity, forming a virtually interconnected network that enhances academic interactions (17) and increases productivity (15). The influence of Wi-Fi technology on students' academic orientation has increased to the extent that it has become an essential part of university students' daily lives (16). Hence, providing campus Wi-Fi facilities to offer a digital learning experience is a primary concern (14,16) that requires substantial investment from educational providers (15). Numerous advantages associated with using ICT in educational settings have been identified in the literature (18). However, substantial investment in ICT alone does not guarantee higher returns. Necessary factors such as proper planning, management involvement, teamwork, a collaborative environment, and technical support must support the investment (19–21). Thus, it is crucial to examine how university students adopt and utilize ICT facilities to identify the factors influencing their usage for educational purposes. Over the years, several technology acceptance models have been developed to understand the factors influencing the acceptance and use of a system or technology (22,23). To provide a unified view of user acceptance and identify the most significant influences, Venkatesh et al.(24) reviewed and compared eight prominent technology acceptance models and introduced the unified theory of acceptance and use of technology (UTAUT)(25). The UTAUT is a significant and modern form of the technology acceptance model, designed to analyse users' behavioral intention to use technology (26). The key idea of the UTAUT is that several factors lead to the behavioral intention to accept and use a system or technology, which, combined with facilitating conditions, leads to the actual use of the system or technology (27). This model has been used to analyse user acceptance and use of systems or technologies in different settings (5,28–30), including healthcare (25,31,32). The “unified theory of acceptance and use of technology” (UTAUT) The UTAUT model (Figure 1) is popular among information system researchers studying system acceptance and usage behavior (33). Proposed by Venkatesh et al., the UTAUT (24) helps to understand the adoption of information technology. It is a theoretical model developed based on the Theory of Reasoned Action, Theory of Planned Behavior, Technology Acceptance Model, etc. (31). The UTAUT suggests that four core constructs act as determinants of behavioral intention (BI) and use behavior (UB). They are performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC). These four constructs are defined as follows in the UTAUT: PE is the extent to which a prospective user believes that using technology will improve work performance; EE refers to the perceived ease of use of the technology; SI is defined as the extent of one’s perception that people who are important to him or her believe that he or she should use new technology; and FC is the perception of technical support and resources provided by the organization to assist in using the technology (24,34). According to the UTAUT, the first three constructs are the main determinants of BI, while FC directly influences UB (Figure 1). The evidence shows that UTAUT explains up to 70% of the variance in users' behavioral intention to adopt a technology (24) . Additionally, the model also indicates that gender, age, experience and voluntariness of use significantly impact the acceptance and use of technology, moderating the key relationships either increasing or decreasing the effects of the independent variables on the dependent variables (Figure 1)(24). The UTAUT assists educational researchers and practitioners in assessing new technologies' success and helps them understand the driving force of acceptance to design effective user interventions (35). The theoretical foundation and study goals This study investigated medical students’ acceptance and use of a specific hostel Wi-Fi system for learning purposes at the Faculty of Medicine, University of Kelaniya, Sri Lanka. A conceptual framework based on the UTAUT model was employed to examine the factors effecting students' acceptance and use of the Wi-Fi system for learning. Therefore, the study is guided by the following objectives. To investigate whether PE, EE, SI and FC effect students’ BI to use Wi-Fi system at hostel premises. To investigate whether BI and FC effect students’ UB. To determine the nature and strength of the relationships among these factors and clarify which factors influence the decision to use the Wi-Fi system for learning. To make recommendations to university administrators for successful Wi-Fi technology adoption among medical students. To our knowledge, there are no studies in the published literature on the use of the UTAUT model to analyse medical students’ acceptance of technology in Sri Lanka. Therefore, the outcome of this study contributes to the literature, and it is hoped that this study will inspire further research in this area at medical schools. The findings of this study will also be helpful for university administrators in identifying the crucial factors influencing students’ adoption and use of technologies for learning. Conceptual Farmwork and Research Hypotheses The UTAUT model is often adapted to fit the study context (36–39). The original model did not include the direct relationship between FC and BI (Figure 1) (24). However, a review of 174 studies incorporating the UTAUT revealed that 48 original studies specifically investigated the direct relationship between FC and BI, and 32 of those studies reported significant positive effects (32). Additionally, previous studies in healthcare settings (25,32,40) reported significant relations between FC and BI. We believe that FC, such as the resources necessary to use the faculty Wi-Fi system, uninterrupted connectivity, and technical support, will influence users’ BI. Therefore, the conceptual model (Figure 2) of this study presumes that FC has a strong effect on BI (25,32,34–36) in addition to its direct effect on UB. Hence, this study proposes the following hypothesis to examine the direct effects among the major components of the UTAUT model. H1: PE has a positive effect on students’ BI to use the Wi-Fi system. H2: EE has a positive effect on students’ BI to use the Wi-Fi system. H3: SI has a positive effect on students’ BI to use the Wi-Fi system. H4: FC has a positive effect on students’ BI to use the Wi-Fi system. H5: FC has a positive effect on students’ UB. H6: BI has a positive effect on students’ UB. Method A cross-sectional study at the Faculty of Medicine, University of Kelaniya, Sri Lanka, was conducted from 1st January 2023 to 28th February 2023. The study population included all the students who had hostel accommodations and who were able to access Wi-Fi through the Wi-Fi zone, which was created to interconnect all the hostels. The data were collected using a self-administered online questionnaire that included questions to collect students’ demographic information and five-point Likert scale questions to assess the UTAUT model concerning the acceptance and use of the Wi-Fi system for learning (supplementary material 1). For each construct in the research model, the students were asked to select a five-point Likert scale to which extent they agreed with the statement, ranging from “strongly disagree” to “strongly agree”. These survey questions were mainly based on questions that were already tested in the original UTAUT model by Venkatesh et al. (2003) ( 24 ) and relevant literature ( 28 , 41 , 42 ). Furthermore, expert opinions were obtained to ensure the content validity of the questionnaire, and ethical approval for the study was obtained from the Faculty of Medicine, University of Kelaniya. The questionnaire was implemented in the REDCap web platform. The link to the REDCap online survey form was sent to all hostel students via email with a brief study introduction. Students were informed that participation in this survey was completely voluntary, and reading the information provided with the survey and submitting the consent form implied their consent to participate in this study. Data analysis Descriptive statistics on students’ characteristics and background information are presented. Structural equation modelling (SEM) techniques were used to analyse the data and test the research hypotheses. We employed the two-step modelling approach developed by Anderson and Gerbing (1988) ( 43 ) in the analysis. The measurement model was first verified, and then the structural model was tested. A confirmatory factor analysis (CFA) was performed to examine the reliability and validity of the measurement model, and the structural model was tested to estimate the hypothesized relationships between constructs. CFA was computed using AMOS. The Ratio of Chi-square statistic to its degree of freedom (CMIN/df), Goodness of Fit Index (GFI), Comparative Fit Index (CFI), Tucker Lewis Index (TLI), Standardized Root Mean Square Residual (SRMR), and Root Mean Square Error of Approximation (RMSEA) were used to assess the model’s overall goodness of fit ( 44 , 45 ). Then, we assessed the reliability and two types of validity, Convergent Validity (CV) and Discriminant Validity (DV), of the measurement model. Reliability was assessed using Cronbach’s Alpha (CA) and Composite Reliability (CR). The recommended cut-off of 0.7 for both CA and CR was tested ( 44 , 46 ) CV was estimated using the Average Variance Extracted (AVE) and CR. AVE threshold value of 0.50 ( 46 ) and CR > AVE were tested for each construct to establish CV ( 44 , 47 ). DV was established when the square root of the AVE for a construct was greater than its correlation with the other constructs in the study ( 48 ). The goodness of fit of the structural model was assessed using the same criteria used for the measurement model. The critical ratio and standardized path coefficients were used to analyse the structural model's relationship between the dependent and independent constructs. The critical ratio and path coefficients were calculated by bootstrapping. A p-value of less than 0.05 was used to assess the predictors’ statistical significance. Results Of the 716 distributed online questionnaires, 318 (44.4%) responses were received, and 8 incomplete responses were rejected, leaving 310 valid questionnaires for data analysis. The data (supplementary material 2) revealed that there were 197 (63.5%) female students. The mean age of the students was 23.7 years ± 1.9 SD. The number of students who responded to the questionnaire from the first year to the final year with the percentage for each year is as follows: 42 (13.5%), 45 (14.5%), 64 (20.6%), 83 (26.8%) and 76 (24.5%). Measurement model CFA based on AMOS was assessed to test the measurement model. One item (FC3) was found to have low factor loading (< 0.5) and was removed from the initial measurement model. The factor loadings of the modified model ranged from 0.63 to 0.95 (Table 2 ) and thus were higher than the recommended levels ( 44 ). Fit indices (CMIN/df, GFI, CFI, TLI, SRMR, and RMSEA) that should be considered to assess the model goodness-of-fit were tested ( 44 , 45 ). This model-generated fit indices are given in Table 1 . All values were within their respective common acceptance levels. Table 1 Fix indices for the measurement and structural models Fix index Recommended value Measurement model Structural Model CMIN/df 0.9( 44 ) 0.907 0.906 CFI > 0.9( 49 ) 0.963 0.963 TLI > 0.9( 49 ) 0.951 0.953 SRMR < 0.08( 50 ) 0.059 0.060 RMSEA < 0.08( 50 ) 0.075 0.074 The internal reliability of the model was evaluated by examining CA and CR values. The CA reliability coefficients and CRs for all the constructs were greater than 0.70, the minimal cut-off recommended by Hair et al.( 44 ) and Fornell et al. ( 46 ) (Table 2 ). The CR and AVE were above 0.70 and 0.50, respectively, for all the constructs (Table 2 ). According to these results, the AVE and CR values exceeded the recommended thresholds ( 44 , 47 ); hence, the CV was at the required level. Table 2 Construct Reliability results Construct No. of factors Factor loading CA AVE CR Performance Expectancy (PE) 3 0.88–0.93 0.93 0.851 0.945 Effort Expectancy (EE) 3 0.79–0.89 0.87 0.821 0.932 Social Influence (SI) 3 0.63–0.93 0.84 0.736 0.893 Facilitating conditions (FC) 2 0.89–0.90 0.89 0.676 0.859 Behavioral Intention (BI) 3 0.90–0.95 0.94 0.799 0.888 Use Behavior (UB) 3 0.82–0.88 0.89 0.725 0.888 The DV results are presented in Table 3 . This index was evaluated using the square roots of the AVE. To establish the DV, for each construct, the square root of the AVE (shown diagonally with bold values) should exceed the inter-construct correlations (the off-diagonal values in the corresponding columns and rows) ( 48 ). According to the results, the measurement model had the appropriate level of DV. Table 3 Discriminant Validity results Construct PE EE SI FC BI UB PE 0.906 EE 0.839 0.858 SI 0.514 0.599 0.822 FC 0.676 0.817 0.626 0.894 BI 0.659 0.690 0.643 0.797 0.922 UB 0.614 0.648 0.573 0.741 0.786 0.851 Structural Model The goodness-of-fit of the structural model was evaluated using the same criteria applied to the measurement model. The results indicated a good fit of the data (Table 1 ); thus, we proceeded to examine the hypothesized relationships within the model. As shown in Table 4 , the PE (β = 0.28, P ≤ 0.001), SI (β = 0.22, P ≤ 0.001), and FC (β = 0.62, P ≤ 0.001) constructs had a significantly positive effect on the students’ BI to use the Wi-Fi facility. However, EE had an insignificant negative impact on BI (β = -0.18, P = 0.13). Table 4: Hypothesis testing results. Furthermore, the results showed that both FC (β = 0.32, P ≤ 0.001) and BI (β = 0.53, P ≤ 0.001) significantly influenced the actual usage of the Wi-Fi system for learning. The squared multiple correlations for BI and UB were 0.69 and 0.66, respectively. Hence, PE, SI and FC explained 69 percent of the variance in BI, with FC contributing more to BI than to the other constructs, and FC and BI jointly accounted for 66 percent of the variance in UB. Discussion The main aim of this study was to explore medical students’ acceptance and use of Wi-Fi for learning purposes based on the unified theory of acceptance and use of technology (UTAUT). While the UTAUT was initially developed and tested outside healthcare settings ( 24 , 25 ), our findings demonstrate its applicability in understanding medical students' behavioral intentions and using the specific hostel Wi-Fi system for learning. Most of the path coefficients in the proposed model (Fig. 2 ) were statistically significant, except for the path from effort expectancy (EE) to behavioural intention (BI). Among the significant paths, facilitating conditions (FC) was the most prominent construct that influenced BI to use the Wi-Fi system. In the proposed model, both FC and BI were significant constructs of the actual usage of the Wi-Fi system for learning. Additionally, the model achieved an acceptable fit and explained 69% of the variance in BI, close to the 70% variance in usage intention explained by the original UTAUT model ( 24 ). BI and FC directly contributed to the actual use of the Wi-Fi system, explaining 66% of the variance in actual use. However, the study revealed no significant relationship between EE and BI, indicating that EE does not have an effect on students' BI. This finding contradicts those of previous studies ( 24 , 51 , 52 ) but aligns with some other studies ( 12 ) ( 25 ). This is a surprising result according to the UTAUT model. EE becomes a core construct of the model and is considered a determining factor in BI. One possible reason for this is the ease of accessing Wi-Fi for students. Once a device is connected to the Wi-Fi system, it can automatically reconnect the next time. Hence, the effect of EE is less prominent in the Wi-Fi system. The results revealed that performance expectancy (PE) had a significantly positive effect on BI. This survey reaffirms the clear impact of PE on the intention to use hostel Wi-Fi system in the educational process. Our results are consistent with previous research ( 26 , 31 , 51 , 52 ). When students find the system useful for education, they are more likely to have a better perception of using the technology. Recent studies have revealed that many students frequently utilize internet-based devices to enhance their knowledge ( 53 ). Wireless connectivity in learning environments allows students to access academic resources on the Web and engage in academic interaction with peers without any hindrance, and improves productivity and effectiveness in the learning process ( 17 ). The study also found a significant positive relationship between social influence (SI) and BI, indicating that medical students consider SI a factor in accepting Wi-Fi as a learning tool. This finding aligns with previous studies ( 12 , 52 ). As Venkatesh et al. ( 24 ) explained, the user's BI to use technology is influenced by the opinions of important people around them. In this context, it is important that faculty lectures can encourage students to use the Wi-Fi system for learning. Research on m-learning adaptation in higher education ( 54 ) has shown that lecturers’ acceptance and attitudes toward m-learning influence students’ perceptions of this new technology and motivate them to adopt it. In the Sri Lankan context, teachers' recommendations are particularly influential. As Sri Lanka offers free education, university administrators can also highlight the financial challenges of providing free technology to motivate students to use it effectively for education. FC was the strongest construct in effecting BI to use the Wi-Fi system. Although this path was not included in the original UTAUT model ( 24 ), later research that tested the effect of FC on BI found the positive influence. Among the 174 studies that incorporated the UTAUT, 48 original studies specifically examined the direct relationship between FC and BI. Of these, 32 studies reported significant positive effects ( 32 ). In particular, there are previous studies, even in healthcare settings ( 25 , 32 , 40 ), that reported significant relations between FC and BI. Our findings indicate that FC plays a crucial role in influencing medical students’ BI to use Wi-Fi for learning. According to the study done by Abbad ( 41 ), students use a learning management system if they believe that the necessary resources and technical support are available. Therefore, the availability of devices, digital skills, and support to resolve technical Wi-Fi issues significantly impact students' BI to use the Wi-Fi system in hostels. With respect to technical support, uninterrupted Wi-Fi connectivity is essential in an environment in which students use the Internet for knowledge acquisition ( 53 ). Connectivism, a learning theory, developed for e-learning environments, also emphasizes the necessity of uninterrupted network connectivity to benefit from Internet technologies for learning and sharing information. ( 55 ). To have such benefits for students, administrators must ensure a reliable network environment to support the learning process. Our findings showed that BI substantially influences the actual use of the Wi-Fi system. Furthermore, these findings coincide with previous studies involving university students and different technology use for education ( 24 , 56 , 57 ). These studies further emphasize that individuals with high BI also exhibit high usage. Our study also revealed that FC significantly influences the actual use of the Wi-Fi system for learning. Students believe that if technological infrastructures and all necessary support are available, they will be more motivated to use Wi-Fi for learning ( 58 ). This finding aligns with prior studies on students’ use of technologies ( 5 , 41 , 59 , 60 ). To the best of the authors’ knowledge, this research is the first to apply the UTAUT model to understand Sri Lankan medical students' acceptance and use of technology. This study provides insights into the factors affecting students' BI in using the Wi-Fi system for learning, offering valuable information for university administrators to address challenges related to technology acceptance and use in education. Limitations This study has several limitations. This study was conducted at a single institution in Sri Lanka, which may limit the generalizability of the results to other institutions using similar systems for learning. This study analysed self-reported data to assess students’ acceptance and use of the Wi-Fi system for learning, which might have affected the accuracy of the results. The research model included only direct effects and excluded moderator effects and tested only the constructs of the original UTAUT model. The other factors that could encourage students to accept and use the hostel Wi-Fi system were not included in the model. Also, the study used only quantitative methods to describe the results. Conclusions Our study revealed that the UTAUT-based model can identify factors associated with medical students' acceptance and use of the Wi-Fi system for learning ( 26 , 47 ). This study serves as a valuable reference for future research using UTAUT to predict technology usage among medical students, particularly in the Sri Lankan context. PE, SI, and FC significantly influenced students' intention to use the Wi-Fi system for learning, but the influence of EE was not significant. Both BI and FC directly impacted usage. Among the three influencing constructs on BI, FC had more significant prediction power of intention to use the Wi-Fi system than did the other constructs. Thus, FC is a major factor influencing BI when using the Wi-Fi system, even though it is not included in the original UTAUT model ( 24 ). Abbreviations UTAUT - Unified Theory of Acceptance and Use of Technology PE - Performance Expectancy EE - Effort Expectancy SI - Social Influence FC - Facilitating Conditions BI - Behavioral Intention UB - Use Behavior (UB). ICT - Information and Communication Technology SEM - Structural Equation Modelling CFA - Confirmatory Factor Analysis CMIN/df - Chi-square divided by degrees of freedom GFI - Goodness of Fit Index CFI - Comparative Fit Index TLI - Tucker Lewis Index SRMR - Standardized Root Mean Square Residual RMSEA - Root Mean Square Error of Approximation CV - Convergent Validity CA - Cronbach’s Alpha DV - Discriminant Validity CR - Composite Reliability AVE - Average Variance Extracted Declarations Ethics approval and consent to participate This study was approved by the Ethics Review Committee of the Faculty of Medicine, University of Kelaniya. Students were asked to submit the consent form, sent with online questionnaire to imply their consent to participate in this study. Consent for publication Not applicable Availability of data and material All the data analysed during this study are included in this published article as a supplementary information file. Competing interests The authors declare that they have no competing interests. Funding No funding was used. Authors' contributions SH, DE and IS contributed to the design and implementation of the study. SH drafted the manuscript. IS was involved in the acquisition of the data. SH and DE engaged in the analysis and interpretation of the data. All authors have read and approved the manuscript. Acknowledgements We thank all the students who voluntarily participated in the research study. Endnotes Endnotes were not used in the article. References Al-Mamary YHS. Understanding the use of learning management systems by undergraduate university students using the UTAUT model: Credible evidence from Saudi Arabia. Int J Inf Manag Data Insights. 2022;2(2):100092. Cabaleiro-Cerviño G, Vera C. 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Acceptance of clinical decision support system to prevent venous thromboembolism among nurses: an extension of the UTAUT model. BMC Med Inform Decis Mak. 2022;22:221. Isaac O, Abdullah Z, Aldholay AH, Abdulbaqi Ameen A. Antecedents and outcomes of internet usage within organisations in Yemen: An extension of the Unified Theory of Acceptance and Use of Technology (UTAUT) model. Asia Pac Manag Rev. 2019;24(4):335–54. Yeboah D, Nyagorme P. Students’ acceptance of WhatsApp as teaching and learning tool in distance higher education in sub-Saharan Africa. Cogent Educ. 2022;9:2077045. Zhang Z, Cao T, Shu J, Liu H. Identifying key factors affecting college students’ adoption of the e-learning system in mandatory blended learning environments. Interact Learn Environ. 2022;30(8):1388–401. Zuiderwijk A, Janssen M, Dwivedi YK. Acceptance and use predictors of open data technologies: Drawing upon the unified theory of acceptance and use of technology. Gov Inf Q. 2015;32(4):429–40. Curtis L, Edwards C, Fraser KL, Gudelsky S, Holmquist J, Thornton K, et al. Adoption of social media for public relations by nonprofit organizations. Public Relat Rev. 2010;36(1):90–2. Venkatesh V, Thong JYL, Chan FKY, Hu PJH, Brown SA. Extending the two-stage information systems continuance model: incorporating UTAUT predictors and the role of context. Inf Syst J. 2011;21(6):527–55. Duyck P, Pynoo B, Devolder P, Voet T, Adang L, Vercruysse J. User Acceptance of a Picture Archiving and Communication System. Methods Inf Med. 2008;47(2):149–56. Chau PY, Hu PJH. Investigating healthcare professionals’ decisions to accept telemedicine technology: an empirical test of competing theories. Inf Manage. 2002;39(4):297–311. Abbad MMM. Using the UTAUT model to understand students’ usage of e-learning systems in developing countries. Educ Inf Technol. 2021;26(6):7205–24. Decman M. Modeling the acceptance of e-learning in mandatory environments of higher education: The influence of previous education and gender. Comput Hum Behav. 2015 Aug 31;49. Anderson JC, Gerbing DW. Structural equation modeling in practice: A review and recommended two-step approach. Psychol Bull. 1988;103(3):411. Hair J, Black WC, Babin BJ, Anderson RE. Multivariate Data Analysis. 7th ed. New York: Pearson Educación; 2010. Kline R. Principles and Practice of Structural Equation Modeling. 2nd ed. New York: The Guilford Press; 2005. Fornell C, Larcker DF. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. J Mark Res. 1981;18(1):39–50. Tarhini A, El-Masri M, Ali M, Serrano A. Extending the UTAUT model to understand the customers’ acceptance and use of internet banking in Lebanon: A structural equation modeling approach. Inf Technol People. 2016;29(4):830–49. Azizi SM, Khatony A. Investigating factors affecting on medical sciences students’ intention to adopt mobile learning. BMC Med Educ. 2019;19:381. Bentler PM. Comparative fit indexes in structural models. Psychol Bull. 1990;107(2):238–46. Hu L tze, Bentler PM. Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychol Methods. 1998;3(4):424–53. Venkatesh V, Zhang X. Unified Theory of Acceptance and Use of Technology: U.S. Vs. China. J Glob Inf Technol Manag. 2010 Jan 1;13(1):5–27. Chao CM. Factors Determining the Behavioral Intention to Use Mobile Learning: An Application and Extension of the UTAUT Model. Front Psychol. 2019;10:1652. Islam AYMA, Mok MMC, Xiuxiu Q, Leng CH. Factors influencing students’ satisfaction in using wireless internet in higher education: Cross-validation of TSM. Electron Libr. 2018;36(1):2–20. Abu-Al-Aish A, Love S. Factors influencing students’ acceptance of m-learning: An investigation in higher education. Int Rev Res Open Distrib Learn. 2013;14(5):82–107. Goldie JGS. Connectivism: A knowledge learning theory for the digital age? Med Teach. 2016;38(10):1064–9. Raza SA, Qazi W, Khan KA, Salam J. Social Isolation and Acceptance of the Learning Management System (LMS) in the time of COVID-19 Pandemic: An Expansion of the UTAUT Model. J Educ Comput Res. 2021;59(2):183–208. Han S, Mustonen P, Seppanen M, Kallio M. Physicians’ acceptance of mobile communication technology: an exploratory study. Int J Mob Commun. 2006;4(2):210. Sun Y, Wang N, Guo X, Peng J. Understanding the acceptance of mobile health services: A comparison and integration of alternative models. J Electron Commer Res. 2013;14:183–200. Maduku DK. An empirical investigation of students’ behavioural intention to use e-books. Manag Dyn J South Afr Inst Manag Sci. 2015;24(3):3–20. Khechine H, Lakhal S, Pascot D, Bytha A. UTAUT Model for Blended Learning: The Role of Gender and Age in the Intention to Use Webinars. Interdiscip J E-Ski Lifelong Learn. 2014;10:033–52. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial1.docx Supplementarymaterial2.csv Additionalfile.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4732428","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":327739332,"identity":"5f5a462e-e828-403d-92e6-e09d27651754","order_by":0,"name":"Samankumara Hettige","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYBACAxCRwMAgJ8EOEZCRIKyFmbEBqMVYghkiwEOcFiCdOINoLebs/ccfPPhlkz6zmfcAw48aBh7JBgJaLHsOMzYk9qXlzmbmS2DsOcbAI03QYTeSgVp6DufOY+YxYOBtYOCRI6jl/mOQlv/pckAtjH+J0nIDFGI/DiRIA7Uwg2wh7LAzyYYzEhuSDWc28xgcljkmQdj7BscPPvj444+dvMTxHsOHb2ps5CQOELIGBBjbIDRQMcFYgYE/xCocBaNgFIyCEQkAJ9c5wLR5sS8AAAAASUVORK5CYII=","orcid":"","institution":"University of Kelaniya","correspondingAuthor":true,"prefix":"","firstName":"Samankumara","middleName":"","lastName":"Hettige","suffix":""},{"id":327739333,"identity":"fa33cda7-d540-4125-b281-12668660207d","order_by":1,"name":"Indrajith Solangaarahchi","email":"","orcid":"","institution":"University of 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model\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4732428/v1/9814fd170014cca4262786bc.png"},{"id":60550293,"identity":"dc480e1b-7f06-485c-8784-3b750f0b3b1c","added_by":"auto","created_at":"2024-07-18 05:00:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":39690,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual model\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4732428/v1/29ecfadb01feff4e4bccb14f.png"},{"id":60550296,"identity":"05c18a94-06ec-4eee-849e-55687e7e6893","added_by":"auto","created_at":"2024-07-18 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technology'","fulltext":[{"header":"Background","content":"\u003cp\u003eTechnology has deeply infiltrated various aspects of life, becoming a crucial indicator of a country\u0026rsquo;s progress(1). Technology has also\u0026nbsp;been\u0026nbsp;integrated into diverse educational systems, leading to\u0026nbsp;the use of\u0026nbsp;technology in education(2). Numerous educational institutions, including schools, colleges, and universities, have focused on information and communication technology (ICT) as a method of achieving educational goals\u0026nbsp;(3,4).\u003c/p\u003e\n\u003cp\u003eIntegrating ICT into education has created numerous opportunities for students, providing a platform and tools for searching, collecting, and analysing information. It also works as a communication medium for teaching and collaborative learning\u0026nbsp;(2,5).\u0026nbsp; According to\u0026nbsp;Khan et al., technology generates new knowledge, enhances problem-solving skills, and improves people\u0026rsquo;s capacity to work efficiently(6).\u0026nbsp;\u0026nbsp;Many institutions, researchers, and governments view ICT development within\u0026nbsp;an\u0026nbsp;organization as a vital investment for the future\u0026nbsp;(7,8).\u0026nbsp;Therefore,\u0026nbsp;higher education institutions prioritize investing in ICT infrastructure to support teaching and learning in this digital world\u0026nbsp;(9,10).\u003c/p\u003e\n\u003cp\u003eThe rise of mobile technology has also led to increased use of portable devices among students\u0026nbsp;(11\u0026ndash;13). At the same time, the concept of accessing\u0026nbsp;the\u0026nbsp;wireless internet to learn via these mobile devices has become popular\u0026nbsp;(12,14). Students can access information and participate in online learning activities anytime and anywhere through portable devices\u0026nbsp;such as\u0026nbsp;mobile phones, tablets, and laptops with internet connectivity\u0026nbsp;(15,16).\u003c/p\u003e\n\u003cp\u003eEstablishing wireless campus environments enables this connectivity, forming a virtually interconnected network that enhances academic interactions\u0026nbsp;(17)\u0026nbsp;and increases productivity\u0026nbsp;(15).\u0026nbsp;The influence of Wi-Fi technology on students\u0026apos; academic orientation has\u0026nbsp;increased\u0026nbsp;to the extent that it has become an essential part of university students\u0026apos; daily lives\u0026nbsp;(16). Hence, providing campus Wi-Fi facilities to offer a digital learning experience is a primary concern\u0026nbsp;(14,16)\u0026nbsp;that requires substantial investment from educational providers\u0026nbsp;(15).\u003c/p\u003e\n\u003cp\u003eNumerous advantages associated with using ICT in educational settings have been\u0026nbsp;identified\u0026nbsp;in the literature\u0026nbsp;(18).\u0026nbsp;However, substantial investment in ICT alone does not guarantee higher returns. Necessary factors such as proper planning, management involvement, teamwork, a collaborative environment, and technical support must support the investment\u0026nbsp;(19\u0026ndash;21). Thus, it is crucial to examine how university students adopt and utilize ICT facilities to identify the factors influencing their usage for educational purposes.\u003c/p\u003e\n\u003cp\u003eOver the years, several technology acceptance models have been developed to understand the factors influencing the acceptance and use of a system or technology\u0026nbsp;(22,23).\u0026nbsp;To provide a unified view of user acceptance and identify the most significant influences, Venkatesh et al.(24)\u0026nbsp;reviewed and compared eight prominent technology acceptance models and introduced the\u0026nbsp;unified theory of acceptance and use of technology\u0026nbsp;(UTAUT)(25).\u0026nbsp;The UTAUT is a significant and modern form of the technology acceptance model, designed to analyse users\u0026apos; behavioral intention to use technology\u0026nbsp;(26).\u0026nbsp;The key idea of the UTAUT is that several factors lead to the behavioral intention to accept and use a system or technology, which, combined with facilitating conditions, leads to the actual use of the system or technology\u0026nbsp;(27).\u0026nbsp;This model has been used to analyse user acceptance and use of systems or technologies in different settings\u0026nbsp;(5,28\u0026ndash;30), including healthcare\u0026nbsp;(25,31,32).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe \u0026ldquo;unified theory of acceptance and use of technology\u0026rdquo; (UTAUT)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe UTAUT model (Figure 1) is popular among information system researchers studying system acceptance and usage behavior\u0026nbsp;(33). Proposed by Venkatesh et al., the UTAUT\u0026nbsp;(24)\u0026nbsp;helps to understand the adoption of information technology. It is a theoretical model developed based on the Theory of Reasoned Action, Theory of Planned Behavior, Technology Acceptance Model, etc.\u0026nbsp;(31). The UTAUT suggests that four core constructs act as determinants of behavioral intention (BI) and use behavior (UB). They are performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC). These four constructs are defined as follows in the UTAUT: PE is the extent to which a prospective user believes that using technology will improve work performance; EE refers to the perceived ease of use of the technology; SI is defined as the extent of one\u0026rsquo;s perception that people who are important to him or her believe that he or she should use new technology; and FC is the perception of technical support and resources provided by the organization to assist in using the technology\u0026nbsp;(24,34). According to the UTAUT, the first three constructs are the main determinants of BI, while FC directly influences UB (Figure 1). The evidence shows that UTAUT explains up to 70% of the variance in users\u0026apos; behavioral intention to adopt a technology\u0026nbsp;(24)\u003cem\u003e.\u0026nbsp;\u003c/em\u003eAdditionally, the model also indicates that gender, age, experience and voluntariness of use significantly impact the acceptance and use of technology, moderating the key relationships either increasing or decreasing the effects of the independent variables on the dependent variables (Figure 1)(24). The UTAUT assists educational researchers and practitioners in assessing new technologies\u0026apos; success and helps them understand the driving force of acceptance to design effective user interventions (35).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe theoretical foundation and study goals\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study investigated\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003emedical students\u0026rsquo; acceptance and use of a specific hostel Wi-Fi system for learning purposes at the Faculty of Medicine, University of Kelaniya, Sri Lanka. \u0026nbsp;A conceptual framework based on the UTAUT model was employed to examine the factors effecting students\u0026apos; acceptance and use of the Wi-Fi system for learning. Therefore, the study is guided by the following objectives.\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eTo investigate whether PE, EE, SI and FC effect students\u0026rsquo; BI to use Wi-Fi system at hostel premises.\u003c/li\u003e\n \u003cli\u003eTo investigate whether BI and FC effect students\u0026rsquo; UB.\u003c/li\u003e\n \u003cli\u003eTo determine the nature and strength of the relationships among these factors and clarify which factors influence the decision to use the Wi-Fi system for learning.\u003c/li\u003e\n \u003cli\u003eTo make recommendations to university administrators for successful Wi-Fi technology adoption among medical students.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTo our knowledge, there are no studies in the published literature on the use of the UTAUT model to analyse medical students\u0026rsquo; acceptance of technology in Sri Lanka. Therefore, the outcome of this study contributes to the literature, and it is hoped that this study will inspire further research in this area at medical schools.\u0026nbsp;The findings of this study will also be helpful for university administrators in identifying the crucial factors influencing students\u0026rsquo; adoption and use of technologies for learning.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConceptual Farmwork and Research Hypotheses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe UTAUT model is often adapted to fit the study context (36\u0026ndash;39). \u0026nbsp;The original model did not include the direct relationship between FC and BI (Figure 1) (24). However, a review of 174 studies incorporating the UTAUT revealed that 48 original studies specifically investigated the direct relationship between FC and BI, and 32 of those studies reported significant positive effects (32). \u0026nbsp;Additionally, previous studies in healthcare settings (25,32,40) reported significant relations between FC and BI. We believe that FC, such as the resources necessary to use the faculty Wi-Fi system, uninterrupted connectivity, and technical support, will influence users\u0026rsquo; BI. Therefore, the conceptual model (Figure 2) of this study presumes that FC has a strong effect on BI (25,32,34\u0026ndash;36) in addition to its direct effect on UB. Hence, this study proposes the following hypothesis to examine the direct effects among the major components of the UTAUT model.\u003c/p\u003e\n\u003cp\u003eH1: PE has a positive effect on students\u0026rsquo; BI to use the Wi-Fi system.\u003c/p\u003e\n\u003cp\u003eH2: EE has a positive effect on students\u0026rsquo; BI to use the Wi-Fi system.\u003c/p\u003e\n\u003cp\u003eH3: SI has a positive effect on students\u0026rsquo; BI to use the Wi-Fi system.\u003c/p\u003e\n\u003cp\u003eH4: FC has a positive effect on students\u0026rsquo; BI to use the Wi-Fi system.\u003c/p\u003e\n\u003cp\u003eH5: FC has a positive effect on students\u0026rsquo; UB.\u003c/p\u003e\n\u003cp\u003eH6: BI has a positive effect on students\u0026rsquo; UB.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003eA cross-sectional study at the Faculty of Medicine, University of Kelaniya, Sri Lanka, was conducted from 1st January 2023 to 28th February 2023. The study population included all the students who had hostel accommodations and who were able to access Wi-Fi through the Wi-Fi zone, which was created to interconnect all the hostels.\u003c/p\u003e \u003cp\u003eThe data were collected using a self-administered online questionnaire that included questions to collect students\u0026rsquo; demographic information and five-point Likert scale questions to assess the UTAUT model concerning the acceptance and use of the Wi-Fi system for learning (supplementary material 1). For each construct in the research model, the students were asked to select a five-point Likert scale to which extent they agreed with the statement, ranging from \u0026ldquo;strongly disagree\u0026rdquo; to \u0026ldquo;strongly agree\u0026rdquo;. These survey questions were mainly based on questions that were already tested in the original UTAUT model by Venkatesh et al. (2003) (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) and relevant literature (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Furthermore, expert opinions were obtained to ensure the content validity of the questionnaire, and ethical approval for the study was obtained from the Faculty of Medicine, University of Kelaniya.\u003c/p\u003e \u003cp\u003eThe questionnaire was implemented in the REDCap web platform. The link to the REDCap online survey form was sent to all hostel students via email with a brief study introduction. Students were informed that participation in this survey was completely voluntary, and reading the information provided with the survey and submitting the consent form implied their consent to participate in this study.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics on students\u0026rsquo; characteristics and background information are presented. Structural equation modelling (SEM) techniques were used to analyse the data and test the research hypotheses. We employed the two-step modelling approach developed by Anderson and Gerbing (1988) (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e) in the analysis. The measurement model was first verified, and then the structural model was tested. A confirmatory factor analysis (CFA) was performed to examine the reliability and validity of the measurement model, and the structural model was tested to estimate the hypothesized relationships between constructs.\u003c/p\u003e \u003cp\u003eCFA was computed using AMOS. The Ratio of Chi-square statistic to its degree of freedom (CMIN/df), Goodness of Fit Index (GFI), Comparative Fit Index (CFI), Tucker Lewis Index (TLI), Standardized Root Mean Square Residual (SRMR), and Root Mean Square Error of Approximation (RMSEA) were used to assess the model\u0026rsquo;s overall goodness of fit (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThen, we assessed the reliability and two types of validity, Convergent Validity (CV) and Discriminant Validity (DV), of the measurement model. Reliability was assessed using Cronbach\u0026rsquo;s Alpha (CA) and Composite Reliability (CR). The recommended cut-off of 0.7 for both CA and CR was tested (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eCV was estimated using the Average Variance Extracted (AVE) and CR. AVE threshold value of 0.50 (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e) and CR\u0026thinsp;\u0026gt;\u0026thinsp;AVE were tested for each construct to establish CV (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). DV was established when the square root of the AVE for a construct was greater than its correlation with the other constructs in the study (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe goodness of fit of the structural model was assessed using the same criteria used for the measurement model. The critical ratio and standardized path coefficients were used to analyse the structural model's relationship between the dependent and independent constructs. The critical ratio and path coefficients were calculated by bootstrapping. A p-value of less than 0.05 was used to assess the predictors\u0026rsquo; statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eOf the 716 distributed online questionnaires, 318 (44.4%) responses were received, and 8 incomplete responses were rejected, leaving 310 valid questionnaires for data analysis.\u003c/p\u003e\n\u003cp\u003eThe data (supplementary material 2) revealed that there were 197 (63.5%) female students. The mean age of the students was 23.7 years\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9 SD. The number of students who responded to the questionnaire from the first year to the final year with the percentage for each year is as follows: 42 (13.5%), 45 (14.5%), 64 (20.6%), 83 (26.8%) and 76 (24.5%).\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eMeasurement model\u003c/h2\u003e\n \u003cp\u003eCFA based on AMOS was assessed to test the measurement model. One item (FC3) was found to have low factor loading (\u0026lt;\u0026thinsp;0.5) and was removed from the initial measurement model. The factor loadings of the modified model ranged from 0.63 to 0.95 (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) and thus were higher than the recommended levels (\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eFit indices (CMIN/df, GFI, CFI, TLI, SRMR, and RMSEA) that should be considered to assess the model goodness-of-fit were tested (\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e). This model-generated fit indices are given in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. All values were within their respective common acceptance levels.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFix indices for the measurement and structural models\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFix index\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRecommended value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMeasurement model\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStructural Model\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCMIN/df\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;3(\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.679\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.9(\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.906\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.9(\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.963\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTLI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.9(\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.953\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSRMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.08(\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRMSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.08(\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe internal reliability of the model was evaluated by examining CA and CR values. The CA reliability coefficients and CRs for all the constructs were greater than 0.70, the minimal cut-off recommended by Hair et al.(\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e) and Fornell et al. (\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The CR and AVE were above 0.70 and 0.50, respectively, for all the constructs (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). According to these results, the AVE and CR values exceeded the recommended thresholds (\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e,\u0026nbsp;\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e); hence, the CV was at the required level.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eConstruct Reliability results\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConstruct\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo. of factors\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFactor loading\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAVE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerformance Expectancy (PE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.88\u0026ndash;0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.945\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEffort Expectancy (EE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.79\u0026ndash;0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSocial Influence (SI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.63\u0026ndash;0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.893\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFacilitating conditions (FC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.89\u0026ndash;0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.859\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBehavioral Intention (BI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.90\u0026ndash;0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.888\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUse Behavior (UB)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.82\u0026ndash;0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.888\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe DV results are presented in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. This index was evaluated using the square roots of the AVE. To establish the DV, for each construct, the square root of the AVE (shown diagonally with bold values) should exceed the inter-construct correlations (the off-diagonal values in the corresponding columns and rows) (\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e). According to the results, the measurement model had the appropriate level of DV.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDiscriminant Validity results\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConstruct\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUB\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.906\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.858\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.822\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.894\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.922\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.851\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eStructural Model\u003c/h2\u003e\n \u003cp\u003eThe goodness-of-fit of the structural model was evaluated using the same criteria applied to the measurement model. The results indicated a good fit of the data (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e); thus, we proceeded to examine the hypothesized relationships within the model.\u003c/p\u003e\n \u003cp\u003eAs shown in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, the PE (\u0026beta;\u0026thinsp;=\u0026thinsp;0.28, P\u0026thinsp;\u0026le;\u0026thinsp;0.001), SI (\u0026beta;\u0026thinsp;=\u0026thinsp;0.22, P\u0026thinsp;\u0026le;\u0026thinsp;0.001), and FC (\u0026beta;\u0026thinsp;=\u0026thinsp;0.62, P\u0026thinsp;\u0026le;\u0026thinsp;0.001) constructs had a significantly positive effect on the students\u0026rsquo; BI to use the Wi-Fi facility. However, EE had an insignificant negative impact on BI (\u0026beta; = -0.18, P\u0026thinsp;=\u0026thinsp;0.13).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003eTable 4: Hypothesis testing results.\u003c/div\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cimg 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\"\u003e\u003c/div\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003eFurthermore, the results showed that both FC (\u0026beta;\u0026thinsp;=\u0026thinsp;0.32, P\u0026thinsp;\u0026le;\u0026thinsp;0.001) and BI (\u0026beta;\u0026thinsp;=\u0026thinsp;0.53, P\u0026thinsp;\u0026le;\u0026thinsp;0.001) significantly influenced the actual usage of the Wi-Fi system for learning. The squared multiple correlations for BI and UB were 0.69 and 0.66, respectively. Hence, PE, SI and FC explained 69 percent of the variance in BI, with FC contributing more to BI than to the other constructs, and FC and BI jointly accounted for 66 percent of the variance in UB.\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe main aim of this study was to explore medical students\u0026rsquo; acceptance and use of Wi-Fi for learning purposes based on the unified theory of acceptance and use of technology (UTAUT). While the UTAUT was initially developed and tested outside healthcare settings (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), our findings demonstrate its applicability in understanding medical students' behavioral intentions and using the specific hostel Wi-Fi system for learning. Most of the path coefficients in the proposed model (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) were statistically significant, except for the path from effort expectancy (EE) to behavioural intention (BI). Among the significant paths, facilitating conditions (FC) was the most prominent construct that influenced BI to use the Wi-Fi system.\u003c/p\u003e \u003cp\u003eIn the proposed model, both FC and BI were significant constructs of the actual usage of the Wi-Fi system for learning. Additionally, the model achieved an acceptable fit and explained 69% of the variance in BI, close to the 70% variance in usage intention explained by the original UTAUT model (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). BI and FC directly contributed to the actual use of the Wi-Fi system, explaining 66% of the variance in actual use.\u003c/p\u003e \u003cp\u003eHowever, the study revealed no significant relationship between EE and BI, indicating that EE does not have an effect on students' BI. This finding contradicts those of previous studies (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e) but aligns with some other studies (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). This is a surprising result according to the UTAUT model. EE becomes a core construct of the model and is considered a determining factor in BI. One possible reason for this is the ease of accessing Wi-Fi for students. Once a device is connected to the Wi-Fi system, it can automatically reconnect the next time. Hence, the effect of EE is less prominent in the Wi-Fi system.\u003c/p\u003e \u003cp\u003eThe results revealed that performance expectancy (PE) had a significantly positive effect on BI. This survey reaffirms the clear impact of PE on the intention to use hostel Wi-Fi system in the educational process. Our results are consistent with previous research (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). When students find the system useful for education, they are more likely to have a better perception of using the technology.\u003c/p\u003e \u003cp\u003eRecent studies have revealed that many students frequently utilize internet-based devices to enhance their knowledge (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Wireless connectivity in learning environments allows students to access academic resources on the Web and engage in academic interaction with peers without any hindrance, and improves productivity and effectiveness in the learning process (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe study also found a significant positive relationship between social influence (SI) and BI, indicating that medical students consider SI a factor in accepting Wi-Fi as a learning tool. This finding aligns with previous studies (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). As Venkatesh et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) explained, the user's BI to use technology is influenced by the opinions of important people around them. In this context, it is important that faculty lectures can encourage students to use the Wi-Fi system for learning. Research on m-learning adaptation in higher education (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e) has shown that lecturers\u0026rsquo; acceptance and attitudes toward m-learning influence students\u0026rsquo; perceptions of this new technology and motivate them to adopt it. In the Sri Lankan context, teachers' recommendations are particularly influential. As Sri Lanka offers free education, university administrators can also highlight the financial challenges of providing free technology to motivate students to use it effectively for education.\u003c/p\u003e \u003cp\u003eFC was the strongest construct in effecting BI to use the Wi-Fi system. Although this path was not included in the original UTAUT model (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), later research that tested the effect of FC on BI found the positive influence. Among the 174 studies that incorporated the UTAUT, 48 original studies specifically examined the direct relationship between FC and BI. Of these, 32 studies reported significant positive effects (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). In particular, there are previous studies, even in healthcare settings (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e), that reported significant relations between FC and BI.\u003c/p\u003e \u003cp\u003eOur findings indicate that FC plays a crucial role in influencing medical students\u0026rsquo; BI to use Wi-Fi for learning. According to the study done by Abbad (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e), students use a learning management system if they believe that the necessary resources and technical support are available. Therefore, the availability of devices, digital skills, and support to resolve technical Wi-Fi issues significantly impact students' BI to use the Wi-Fi system in hostels.\u003c/p\u003e \u003cp\u003eWith respect to technical support, uninterrupted Wi-Fi connectivity is essential in an environment in which students use the Internet for knowledge acquisition (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Connectivism, a learning theory, developed for e-learning environments, also emphasizes the necessity of uninterrupted network connectivity to benefit from Internet technologies for learning and sharing information. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). To have such benefits for students, administrators must ensure a reliable network environment to support the learning process.\u003c/p\u003e \u003cp\u003eOur findings showed that BI substantially influences the actual use of the Wi-Fi system. Furthermore, these findings coincide with previous studies involving university students and different technology use for education (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). These studies further emphasize that individuals with high BI also exhibit high usage.\u003c/p\u003e \u003cp\u003eOur study also revealed that FC significantly influences the actual use of the Wi-Fi system for learning. Students believe that if technological infrastructures and all necessary support are available, they will be more motivated to use Wi-Fi for learning (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). This finding aligns with prior studies on students\u0026rsquo; use of technologies (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo the best of the authors\u0026rsquo; knowledge, this research is the first to apply the UTAUT model to understand Sri Lankan medical students' acceptance and use of technology. This study provides insights into the factors affecting students' BI in using the Wi-Fi system for learning, offering valuable information for university administrators to address challenges related to technology acceptance and use in education.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations. This study was conducted at a single institution in Sri Lanka, which may limit the generalizability of the results to other institutions using similar systems for learning. This study analysed self-reported data to assess students\u0026rsquo; acceptance and use of the Wi-Fi system for learning, which might have affected the accuracy of the results. The research model included only direct effects and excluded moderator effects and tested only the constructs of the original UTAUT model. The other factors that could encourage students to accept and use the hostel Wi-Fi system were not included in the model. Also, the study used only quantitative methods to describe the results.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study revealed that the UTAUT-based model can identify factors associated with medical students' acceptance and use of the Wi-Fi system for learning (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). This study serves as a valuable reference for future research using UTAUT to predict technology usage among medical students, particularly in the Sri Lankan context. PE, SI, and FC significantly influenced students' intention to use the Wi-Fi system for learning, but the influence of EE was not significant. Both BI and FC directly impacted usage. Among the three influencing constructs on BI, FC had more significant prediction power of intention to use the Wi-Fi system than did the other constructs. Thus, FC is a major factor influencing BI when using the Wi-Fi system, even though it is not included in the original UTAUT model (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eUTAUT - Unified Theory of Acceptance and Use of Technology\u003c/p\u003e\n\u003cp\u003ePE - Performance Expectancy\u003c/p\u003e\n\u003cp\u003eEE - Effort Expectancy\u003c/p\u003e\n\u003cp\u003eSI - Social Influence\u003c/p\u003e\n\u003cp\u003eFC - Facilitating Conditions\u003c/p\u003e\n\u003cp\u003eBI - Behavioral Intention\u003c/p\u003e\n\u003cp\u003eUB - Use Behavior (UB).\u003c/p\u003e\n\u003cp\u003eICT - Information and Communication Technology\u003c/p\u003e\n\u003cp\u003eSEM - Structural Equation Modelling\u003c/p\u003e\n\u003cp\u003eCFA - Confirmatory Factor Analysis\u003c/p\u003e\n\u003cp\u003eCMIN/df - Chi-square divided by degrees of freedom\u003c/p\u003e\n\u003cp\u003eGFI - Goodness of Fit Index\u003c/p\u003e\n\u003cp\u003eCFI - Comparative Fit Index\u003c/p\u003e\n\u003cp\u003eTLI - Tucker Lewis Index\u003c/p\u003e\n\u003cp\u003eSRMR - Standardized Root Mean Square Residual\u003c/p\u003e\n\u003cp\u003eRMSEA - Root Mean Square Error of Approximation\u003c/p\u003e\n\u003cp\u003eCV - Convergent Validity\u003c/p\u003e\n\u003cp\u003eCA - Cronbach’s Alpha\u003c/p\u003e\n\u003cp\u003eDV - Discriminant Validity\u003c/p\u003e\n\u003cp\u003eCR - Composite Reliability\u003c/p\u003e\n\u003cp\u003eAVE - Average Variance Extracted\u003c/p\u003e"},{"header":"Declarations","content":"\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis study was approved by the Ethics Review Committee of the Faculty of Medicine, University of Kelaniya. Students were asked to submit the consent form, sent with online questionnaire to imply their consent to participate in this study.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAll the data analysed during this study are included in this published article as a supplementary information file.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNo funding was used.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSH, DE and IS contributed to the design and implementation of the study. SH drafted the manuscript. IS was involved in the acquisition of the data. SH and DE engaged in the analysis and interpretation of the data. \u0026nbsp; All authors have read and approved the manuscript.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWe thank all the students who voluntarily participated in the research study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEndnotes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEndnotes were not used in the article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAl-Mamary YHS. Understanding the use of learning management systems by undergraduate university students using the UTAUT model: Credible evidence from Saudi Arabia. Int J Inf Manag Data Insights. 2022;2(2):100092.\u003c/li\u003e\n \u003cli\u003eCabaleiro-Cervi\u0026ntilde;o G, Vera C. 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Intention to use electronic medical record and its predictors among health care providers at referral hospitals, north-West Ethiopia, 2019: using unified theory of acceptance and use technology 2(UTAUT2) model. BMC Med Inform Decis Mak. 2020;20:207.\u003c/li\u003e\n \u003cli\u003eZha H, Liu K, Tang T, Yin YH, Dou B, Jiang L, et al. Acceptance of clinical decision support system to prevent venous thromboembolism among nurses: an extension of the UTAUT model. BMC Med Inform Decis Mak. 2022;22:221.\u003c/li\u003e\n \u003cli\u003eIsaac O, Abdullah Z, Aldholay AH, Abdulbaqi Ameen A. Antecedents and outcomes of internet usage within organisations in Yemen: An extension of the Unified Theory of Acceptance and Use of Technology (UTAUT) model. Asia Pac Manag Rev. 2019;24(4):335\u0026ndash;54.\u003c/li\u003e\n \u003cli\u003eYeboah D, Nyagorme P. Students\u0026rsquo; acceptance of WhatsApp as teaching and learning tool in distance higher education in sub-Saharan Africa. Cogent Educ. 2022;9:2077045.\u003c/li\u003e\n \u003cli\u003eZhang Z, Cao T, Shu J, Liu H. Identifying key factors affecting college students\u0026rsquo; adoption of the e-learning system in mandatory blended learning environments. Interact Learn Environ. 2022;30(8):1388\u0026ndash;401.\u003c/li\u003e\n \u003cli\u003eZuiderwijk A, Janssen M, Dwivedi YK. Acceptance and use predictors of open data technologies: Drawing upon the unified theory of acceptance and use of technology. Gov Inf Q. 2015;32(4):429\u0026ndash;40.\u003c/li\u003e\n \u003cli\u003eCurtis L, Edwards C, Fraser KL, Gudelsky S, Holmquist J, Thornton K, et al. Adoption of social media for public relations by nonprofit organizations. Public Relat Rev. 2010;36(1):90\u0026ndash;2.\u003c/li\u003e\n \u003cli\u003eVenkatesh V, Thong JYL, Chan FKY, Hu PJH, Brown SA. Extending the two-stage information systems continuance model: incorporating UTAUT predictors and the role of context. Inf Syst J. 2011;21(6):527\u0026ndash;55.\u003c/li\u003e\n \u003cli\u003eDuyck P, Pynoo B, Devolder P, Voet T, Adang L, Vercruysse J. User Acceptance of a Picture Archiving and Communication System. Methods Inf Med. 2008;47(2):149\u0026ndash;56.\u003c/li\u003e\n \u003cli\u003eChau PY, Hu PJH. Investigating healthcare professionals\u0026rsquo; decisions to accept telemedicine technology: an empirical test of competing theories. Inf Manage. 2002;39(4):297\u0026ndash;311.\u003c/li\u003e\n \u003cli\u003eAbbad MMM. Using the UTAUT model to understand students\u0026rsquo; usage of e-learning systems in developing countries. Educ Inf Technol. 2021;26(6):7205\u0026ndash;24.\u003c/li\u003e\n \u003cli\u003eDecman M. Modeling the acceptance of e-learning in mandatory environments of higher education: The influence of previous education and gender. Comput Hum Behav. 2015 Aug 31;49.\u003c/li\u003e\n \u003cli\u003eAnderson JC, Gerbing DW. Structural equation modeling in practice: A review and recommended two-step approach. Psychol Bull. 1988;103(3):411.\u003c/li\u003e\n \u003cli\u003eHair J, Black WC, Babin BJ, Anderson RE. Multivariate Data Analysis. 7th ed. New York: Pearson Educaci\u0026oacute;n; 2010.\u003c/li\u003e\n \u003cli\u003eKline R. Principles and Practice of Structural Equation Modeling. 2nd ed. New York: The Guilford Press; 2005.\u003c/li\u003e\n \u003cli\u003eFornell C, Larcker DF. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. J Mark Res. 1981;18(1):39\u0026ndash;50.\u003c/li\u003e\n \u003cli\u003eTarhini A, El-Masri M, Ali M, Serrano A. Extending the UTAUT model to understand the customers\u0026rsquo; acceptance and use of internet banking in Lebanon: A structural equation modeling approach. Inf Technol People. 2016;29(4):830\u0026ndash;49.\u003c/li\u003e\n \u003cli\u003eAzizi SM, Khatony A. Investigating factors affecting on medical sciences students\u0026rsquo; intention to adopt mobile learning. BMC Med Educ. 2019;19:381.\u003c/li\u003e\n \u003cli\u003eBentler PM. Comparative fit indexes in structural models. Psychol Bull. 1990;107(2):238\u0026ndash;46.\u003c/li\u003e\n \u003cli\u003eHu L tze, Bentler PM. Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychol Methods. 1998;3(4):424\u0026ndash;53.\u003c/li\u003e\n \u003cli\u003eVenkatesh V, Zhang X. Unified Theory of Acceptance and Use of Technology: U.S. Vs. China. J Glob Inf Technol Manag. 2010 Jan 1;13(1):5\u0026ndash;27.\u003c/li\u003e\n \u003cli\u003eChao CM. Factors Determining the Behavioral Intention to Use Mobile Learning: An Application and Extension of the UTAUT Model. Front Psychol. 2019;10:1652.\u003c/li\u003e\n \u003cli\u003eIslam AYMA, Mok MMC, Xiuxiu Q, Leng CH. Factors influencing students\u0026rsquo; satisfaction in using wireless internet in higher education: Cross-validation of TSM. Electron Libr. 2018;36(1):2\u0026ndash;20.\u003c/li\u003e\n \u003cli\u003eAbu-Al-Aish A, Love S. Factors influencing students\u0026rsquo; acceptance of m-learning: An investigation in higher education. Int Rev Res Open Distrib Learn. 2013;14(5):82\u0026ndash;107.\u003c/li\u003e\n \u003cli\u003eGoldie JGS. Connectivism: A knowledge learning theory for the digital age? Med Teach. 2016;38(10):1064\u0026ndash;9.\u003c/li\u003e\n \u003cli\u003eRaza SA, Qazi W, Khan KA, Salam J. Social Isolation and Acceptance of the Learning Management System (LMS) in the time of COVID-19 Pandemic: An Expansion of the UTAUT Model. J Educ Comput Res. 2021;59(2):183\u0026ndash;208.\u003c/li\u003e\n \u003cli\u003eHan S, Mustonen P, Seppanen M, Kallio M. Physicians\u0026rsquo; acceptance of mobile communication technology: an exploratory study. Int J Mob Commun. 2006;4(2):210.\u003c/li\u003e\n \u003cli\u003eSun Y, Wang N, Guo X, Peng J. Understanding the acceptance of mobile health services: A comparison and integration of alternative models. J Electron Commer Res. 2013;14:183\u0026ndash;200.\u003c/li\u003e\n \u003cli\u003eMaduku DK. An empirical investigation of students\u0026rsquo; behavioural intention to use e-books. Manag Dyn J South Afr Inst Manag Sci. 2015;24(3):3\u0026ndash;20.\u003c/li\u003e\n \u003cli\u003eKhechine H, Lakhal S, Pascot D, Bytha A. UTAUT Model for Blended Learning: The Role of Gender and Age in the Intention to Use Webinars. Interdiscip J E-Ski Lifelong Learn. 2014;10:033\u0026ndash;52.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Wi-Fi, Medical students, UTAUT, SEM, Education, Learning","lastPublishedDoi":"10.21203/rs.3.rs-4732428/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4732428/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground:\u003c/b\u003e\u003c/p\u003e \u003cp\u003eUniversities use Wi-Fi networks to provide internet access to enhance students\u0026rsquo; learning experience. This study evaluated the factors that might effect students\u0026rsquo; intention to use a specific hostel Wi-Fi system for learning purposes at the Faculty of Medicine, University of Kelaniya, Sri Lanka.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods:\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA cross-sectional study was conducted at the Faculty of Medicine, University of Kelaniya. The unified theory of acceptance and use of technology (UTAUT), proposed by Venkatesh et al., which explains users\u0026rsquo; behavioral intentions to use technology, was used to design the conceptual framework. The covariance-based structural equation modelling technique was employed to analyse data collected from 310 medical students.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults:\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe model assessments of validity and reliability were acceptable. Among the factors studied, only performance expectancy (PE) (β\u0026thinsp;=\u0026thinsp;0.284, P\u0026thinsp;\u0026le;\u0026thinsp;0.001), social influence (SI) (β\u0026thinsp;=\u0026thinsp;0.222, P\u0026thinsp;\u0026le;\u0026thinsp;0.001), and facilitating conditions (FC) (β\u0026thinsp;=\u0026thinsp;0.615, P\u0026thinsp;\u0026le;\u0026thinsp;0.001) significantly effected on students\u0026rsquo; intention to use the Wi-Fi system for learning, while effort expectancy (EE) (β = -0.184, P\u0026thinsp;\u0026le;\u0026thinsp;0.130) did not. Moreover, behavioral intention (BI) (β\u0026thinsp;=\u0026thinsp;0.533, P\u0026thinsp;\u0026le;\u0026thinsp;0.001) and FC (β\u0026thinsp;=\u0026thinsp;0.320, P\u0026thinsp;\u0026le;\u0026thinsp;0.001) had significant effects on the students\u0026rsquo; actual use of the Wi-Fi system for learning.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions:\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis study enhances our understanding of the factors effecting medical students\u0026rsquo; Wi-Fi access on campuses for learning purposes. Among these factors, the influence of FC was very strong. This highlights the importance of the FC, among other factors, in providing Wi-Fi network initiatives on campuses. Furthermore, university administrators can use the findings of this study to identify the requirements for the successful integration of network technologies in educational settings.\u003c/p\u003e","manuscriptTitle":"Medical students’ acceptance and use of the Wi-Fi system at hostels as a learning tool: an investigation based on the 'unified theory of acceptance and use of technology'","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-18 04:44:21","doi":"10.21203/rs.3.rs-4732428/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c0588379-13a5-4a31-8749-e11cbac030a2","owner":[],"postedDate":"July 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-30T08:18:20+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-18 04:44:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4732428","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4732428","identity":"rs-4732428","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}