Acceptance and Use of Cloud-Based Virtual Platforms by Higher Education Vocational School Students: Application of the UTAUT Model with a PLS-SEM Approach

The COVID-19 lockdown has temporarily switched the education system from face-to-face to virtual instruction (Torrás, 2021; Vásquez et al., 2023). The first occurrence was reported in Wuhan, China, on February 22nd, 2020, and the first case was discovered in Turkey on March 11th, 2020 (Republic of Turkey Ministry of Health, 2020). The Turkish government has implemented stringent regulations in relation to higher education, with peak instances beginning in the middle of 2021. For safety reasons, the spring 2019-2020 term and the fall 2020-2021 term were launched online (Bautista et al., 2022). According to the council of higher education  (2021), the institutions had the authority to choose whether students would receive their education remotely, virtually, or in person, depending on whether the region was under threat. The requirement of the vaccine for students, academics, and administrative personnel also shaped the educational atmosphere (the council of higher education, 2021). Since the COVID-19 epidemic began to spread in July 2022, it was necessary to make research to determine how cloud-based learning platforms would be used effectively if students were online away from college.

The UTAUT is a significant theory for examining people’s intentions regarding the acceptance and use of technology (Venkatesh et al., 2003). Several studies have successfully used UTAUT in understanding the technology acceptance and use across various technologies, containing AL integrated CRM systems (Chatterjee et al., 2021), document management systems (Ayaz, & Yanartas, 2020), eWallet (Bommer et al, 2022), accounting information systems (Lutfi, 2022), online technology (Chayomchai et al, 2020), e-commerce (Chan et al., 2021), open online courses (Li, & Zhao, 2021), internet banking services (Al-Fahim et al., 2021), smart city (Popova, & Zagulova, 2022), digital technology (Akinnuwesi et al., 2022), online knowledge paying behavior (Yu et al., 2021), mobile payments (Ariffin et al., 2020; Namahoot & Jantasri, 2022; Nur, & Gosal, 2021), mobile health or ehealth applications (Magsamen-Conrad et al., 2019; Semiz, & Semiz, 2021), ERP systems (Thottoli, & Thomas, 2022), e-government (Zeebaree et al., 2022), streaming media (Limna et al., 2022) and online learning (Ahmed et al., 2022; Alvi, 2021; Alwahaishi, 2021; Jameel et al., 2022; Marandu et al., 2022; Sabayleh et al., 2020; Şimşek, & Ateş, 2022; Sukarya et al., 2020; Tussardi et al., 2021), m-learning (Alyoussef, 2021; Ikhsan et al., 2021). However, the COVID-19 period has seen a limited number of studies on behavioral intentions to use cloud-based learning platforms, and the majority of the extant research has focused on a four-year higher education (Adanir & Cinar, 2011; Batucan et al., 2022; Serttas, & Kasabali, 2020). The dearth of research in the literature today makes it even more important to examine the influences of effort expectancy, behavioral intentions, and facilitating conditions on the adoption of cloud-based learning platforms during the COVID-19 period in the two-year higher education program context.

In light of this, it is intended to study students’ behavioral intentions with regard to using cloud-based learning platforms during COVID-19 from vocational school and comprehend what are hurdles of cloud-based learning platforms adoption usage using UTAUT theory. The following are covered under the research questions: What are the factors that influence vocational school students’ intentions to use cloud-based virtual platforms? How did performance expectations and social influence affect their behavioral intentions? How did effort expectations, behavioral intentions, and performance expectations interact with one another?

This study will contribute to the educational institutions taking the required actions and creating user-friendly features to make preferable virtual learning platforms and guide their students in using cloud-based virtual platforms during COVID-19. The details of materials and methods to address the aim of this study are given in the next section.

The research primarily proposes a UTAUT theory to understand how cloud-based virtual platforms are used and accepted, which are Blackboard, Microsoft, Zoom, Edmodo, Sakai and Moodle. In order to comprehend the behavioral intentions of higher education students at vocational schools, the quantitative study methodology was used. Table 1 displays the five demographic questions that were developed to profile the universities’ cloud based virtual platform usage of for higher education.

TABLE 1. Descriptive statistics for the participants.

2.1. Design and data collection

The UTAUT investigates people’s effort, performance, social influence, facilitating conditions regarding behavioral intention, and the user behavior of technologies. This model was used because this was the only mixture theory of combined psychological and sociological studies, measuring acceptance and use of disruptive technologies and including the combined technology acceptance model (TAM) for social cognitive theory (SCT) towards cloud-based virtual platforms acceptance and use during COVID-19. The unique feature of the UTAUT theory discovered was that it was successful in explaining students’ spontaneous and instantaneous actions toward cloud-based virtual platforms during COVID-19 by measuring the impact of effort expectancy directly on technology adoption and use. In this study, the UTAUT was applied by obtaining Effort expectancy (EE), Performance expectancy (PE), Social influence (SI), Facilitating condition (FC), Behavioural intention (BI), and Use behavior (UB), as the hypothesis illustrated in Fig 1. EE (H1) served to examine perceived ease of use and complexity. PE and SI were applied to analyze perceived usefulness and subjective norms, respectively, as H2a and H2b. FC (H3) was also examined to seek perceived behavioral control and compatibility.

FIGURE 1. The Research Model.

2.2. Participants

In the present study, an online survey was administered to 313 vocational higher education students in the spring of 2021/between mid-March and mid-April 2021, during the partial lockdown, and lockdown.

2.3. Instrument and hypothesis

The students’ use and acceptance intention to adopt cloud-based virtual platforms were evaluated using a 5-point Likert scale from 1 (totally disagree) to 5 (totally agree). Six constructs and fifteen items were used in the structural model, and accept and use of cloud-based virtual platforms were used and accepted as a dependent variable, which had 2 items. There are 5 independent variables: Effort expectancy (EE), Performance expectancy (PE), Social influence (SI), Facilitating condition (FC), and Behavioural intention (BI), each of which has 4, 3, 2, and 3 items, respectively. Assessments were made in accordance with the hypothesis put forth as the study’s starting premise after the results were elicited.

• H1. Effort expectancy (EE) has a positive impact on the acceptance and use (AUB) of cloud-based virtual platforms.
• H2. Behavioral intention (BI) has a positive impact on the acceptance and use (AUB) of cloud-based virtual platforms.
• H2a. Performance expectancy (PE) has a positive impact on behavioral intention (BI) to adopt cloud-based virtual platforms.
• H2b. Social influence (SI) has a positive impact on behavioral intention (BI) to adopt cloud-based virtual platforms.
• H3. Facilitating conditions (FC) has a positive impact on the acceptance and use (AUB) of cloud-based virtual platforms.

2.4. Data analysis procedures and techniques

To create a structural model for the research domains, 15 questions were applied to SmartPLS 4.0 for six factors. The structural model was examined using confirmatory factor analysis (CFA). To test the theory, structural equation modeling (SEM) was applied. Standard Loadings, Cronbach’s Alpha, Composite reliability (CR), Average variances extracted (AVE), and Variance inflation factor (VIF) were acquired to check the validity and reliability of data items. The Fornell-Larcker criterion and cross loadings analysis were formed to check the discriminant validity among constructs. After ensuring the reliability and validity, the structural model assessment was exhibited to assess the model’s effect, explanatory and predictive power. The structural model was formed, deriving from the UTAUT theory to test the hypothesis.

Six constructs were used to create the UTAUT theory: EE, PE, SI, FC, BI, and AUB. The Items were also illustrated in Appendix 1. The section was divided into two subsections: Reliability and Validity, and Model Evaluation.

3.1. Reliability and Validity

SRMR value for the structural model was 0.080, which MacCallum et al. (1996) noted was a significant fit for the range of 0.08 to 0.10. Nunnally and Bernstein (1994) stated that all Cronbach’s Alpha values of the EE, PE, SI, FC, BI, and AUB ranging from 0.635 to 0.956, which is more than 0.6, were considered fit. According to Hair et al. (2017), all AVE values should be more than 0.5 and were all valid. According to Hair et al. (2010), the threshold of value was 0.7, and all CR values were fit. Table 2 shows that EE has four components, with a CR of 0.925, and an AVE of 0.755. PE consists of three items with a CR of 0.944, and an AVE of 0.849. SI has 2 items with a CR of 0.845, and AVE of 0.732. FC has two items holding with a CR of 0.854, and an AVE of 0.745. BI has three elements, with factor loadings ranging from 0.950 to 0.967, a CR of 0.971, and an AVE of 0.919. AUB has 2 components with a CR of 0.893, and an AVE of 0.807. According to Hair (1995), the VIF value, which was below 10, was acceptable. It was shown in Table 2 that all items have no multicollinearity issues.

TABLE 2. Items, Cronbach’s Alpha, Composite reliability (CR), Average variances extracted (AVE) and Variance Inflation Factors (VIF).

The distinctness and uniqueness of the construct were evaluated using the Forner-Lacker criterion, as proposed by Hair et al. (2017), in this work. Every latent variable should have an AVE square root that is higher than other latent variable correlation coefficients (Fornell, & Lacker, 1981). In Table 3, EE, PE, SI, FC, BI, and AUB consist of four, three, two, two, three, two components, with standardized factor loadings, ranging from 0.771 to 0.910, 0.902 to 0.913, 0.838 to 0.873, 0.841 to 0.885, 0.950 to 0.967, and 0.888 to 0.909, respectively. According to Hair et al. (2010), all factor loadings should be more than 0.7, which all were satisfied. Additionally, it was observed that the cross-loadings for each construct are low, indicating strong discriminant validity. The discriminant validity assessments’ valid Fornell-Larcker criterion and cross-loading results were shown in Table 3. The structural model was assessed in the next section when it was determined that the construct measurements were accurate and valid.

TABLE 3. Discriminant Validity Measurement through Fornell-Larcker Criterion and Cross-loadings.

3.2. Model Evaluation

The model was assessed by obtaining f2 (effect size of the model), Q2 (predictive relevance), and R2 (coefficient of determination), path coefficients, and T-values.

As Table 4 illustrated, The effect size (f square) of the model was defined as large, which was above 0.35, medium, which was above 0.15, and small was above 0.02. (Cohen, 1988). Every hypothesis has separate values: the effect of BI on AUB (H2) was 0.436 (large); the effect of EE on AUB (H1) was 0.191 (medium); the effect of SI on BI (H2b) was 0.190 (medium); the effect of PE on BI (H2a) was 0.118 (small). The predictive relevance (Q square) of the model was described as 0.02, 0.15, 0.32 for weak moderate, and strong (Hair et al., 2010). The influence of both EE on AUB (H1) and PE on BI (H2a) hypotheses were strong with 0.670, and 0.450, respectively.

TABLE 4. Structural model’s effect, explanatory, and results.

As shown in Figure 1, path coefficients of effort expectancy and acceptance and use of cloud-based virtual platforms (H1), behavioral intention and acceptance and use of cloud-based virtual platforms (H2), performance expectancy and behavioral intention (H2a), social influence and behavioral intention (H2b), and facilitating conditions and accept and use of cloud-based virtual platforms (H3) were 0.272, 0.437, 0.329, 0.419, and 0.284 with the T-value of 4.916, 8.096, 4.891, 6.024, and 4.662, respectively. The hypotheses were supported as T-value exceeds 1.96 (Hair et al., 2010). As understood from explained variable as variance (R square), the variance of PE and SI explained 46.30% of BI of cloud-based learning platforms. The results exhibited that the variance of EE, BI, and FC explained 76.00% of the variance of AUB.

FIGURE 2. The structural model with Path Coefficients, T- values and R square.

By utilizing cloud-based learning platforms like Blackboard, Microsoft, Zoom, Edmodo, and Sakai, education has transitioned from traditional classroom instruction to online instruction. According to Kacan and Gelen (2020), 50% of institutions have chosen to adopt Moodle as a platform for conducting business. Blackboard is the platform of choice in Turkish higher education, according to Ozbay (2015). Additionally, the UZEM platform was extensively used for history and e-MBA programs, according to Kirkan and Kalelioglu (2017). According to Kacan and Gelen (2020), 15% of Turkish universities have created their software. Izmir Katip Celebi University, Ataturk University, Bilecik Seyh Edebali University, and Dokuz Eylul University, respectively, produced the software known as Canvas, ALU, SOFRA, and DEOYS. This demonstrated how the research and development division became creative to sustain education.

The council of higher education (2021) emphasized that the entire class will continue to engage in hybrid learning based on COVID-19 commission regulations and COVID-19 cases. By using the council of higher education’s guide, the government has continued to specify the standardized learning ecosystem policy for instruction using cloud-based learning platforms. (1) It protects students’ rights by allowing university registration to be frozen, according to the council of higher education’s instructions. (2) The flexibility of the online and in-person absence policies for students has encouraged them to pursue excellent education continuity. (3) To prevent confusion in class, the syllabus and course material should be made known to the students in advance. As a result, the council of higher education has sponsored both traditional and online higher education in Turkey.

Later, from March 2020 till today, understanding the actual use of cloud-based learning platforms has become significant. It can be inferred that effort expectancy (EE) was not correlated with behavioral intention but was correlated with the acceptance and use (AUB) of cloud-based learning platforms, as the online classes emerged swiftly, the ease of use of online learning during the COVID19 outbreak was not as important as the system usefulness at the first stage.

From effort expectancy —the acceptance and use (AUB) hypothesis (H1), as the students from the vocational school have applied classes, white blackboard extensions by Rahmadi (2021) and discussion forums by El Said (2021) were required with the file sharing and access feature for involving lab classes as two-way communications (Cavus, & Zabadi, 2014). It led the vocational school students to increase the perceived ease of use and decrease the complexity of accepting and using cloud-based learning platforms. From facilitating condition —the acceptance and use (AUB) hypothesis (H3), microlearning mentioned by Sozmen et al. (2021) was established for lecturers and students from vocational schools to share their experiences and keep track of students’ behavior for finding an effective human-friendly interface. Based on the feedback, the systems were upgraded by the IT help desk (van Houwelingen et al., 2018). It caused the vocational school students to increase their perceived behavioral control and the compatibility for accepting and using of system. From performance expectancy (PE) —the behavioral intention (BI) hypothesis (H2a), learning centers and the IT help desk worked collaboratively for contacting students and lecturers There is an urgent contact for students’ critical issues such as missing attending midterm and final exams on time. They also accompany lecturers to conduct online classes, video services, and real-time chatting declared by Karadag et al. (2021) for increasing productivity during and after classes. It induced the vocational school students to increase the efficiency of students’ behavioral intentions to adopt cloud-based learning platforms. From the social influence(SI)- behavioral intention (BI) hypothesis (H2b) lecturers conducted demo quizzes and virtual gamification classes by Sukarya et al. (2020) for students to acknowledge the systems’ use. It led the vocational school students to increase their awareness of students for behavioral intentions to adopt cloud-based learning platforms. From behavioral intention —the acceptance and use (AUB) hypothesis (H2) Most students have experienced using distance learning previously. Bozkurt (2017) stated that Anadolu University started open university education in 1993, Fırat University began remote certified programs in 1995, and Bilkent University held a video conference system education portal for lecturing from the US. They led the vocational school students to understand the subjective norms of accepting and using cloud-based learning platforms.

This research aims to investigate the behavioral intention of the acceptance and use of cloud-based learning platforms in the COVID-19 period to increase the quality of two-year higher education programs for vocational school students and build them ease of use and usability system by applying the UTAUT model. The indicators of UTAUT are performance expectancy and social influence which explained 46.3% of the variance of the behavioral intention of cloud-based learning platforms. Hence, this UTAUT model was constructed to indicate that effort expectancy, behavioral intentions and social influences are three important critical constructs for the actual acceptance and use of cloud-based learning platforms, which explained 75.4% of actual acceptance and use of cloud-based learning platforms. In this section, the results obtained will not be shown again, but the most outstanding ones will be highlighted based on citations from similar studies that complement those obtained.

Education was changed drastically in the spring term of 2020 during the pandemic by using cloud-based learning platforms. From that time to two years, as the new variants appeared, knowing the use and acceptance of cloud-based learning platforms from the perception of vocational school students is significant to preserving the education standards.

The study monitored the acceptance and use of cloud-based learning platforms tools by using a UTAUT theory, which was Effort expectancy (EE), Performance expectancy (PE), Social influence (SI), Facilitating condition (FC), Behavioural intention (BI), Use behavior (UB). The model provides vocational school students’ intentions for educational policymakers. Universities should especially create a communal learning platform manual for sharing their obstacles and hindrances over cloud-based learning platforms. Virtual learning centers for universities should also create student-oriented procedures for preparing manuals of the applied lectures. In addition, IT helpdesks should follow students’ behaviors by examining the system’s ease of user feedback for the classes and informing the board of universities’ executives to reduce the troubles. Lecturers should be taken as feedback to answer students’ instant requests to pervert the sufferer.

The research contributes to universities’ cloud-based learning platform providers by adopting the UTAUT model to how students have a perceived acceptance and use of cloud-based learning platforms. This will aid the students in avoiding unprecedented system issues in future pandemics.

5.1. Limitations and future lines of research

Despite the fact that the suggested approach has significant consequences for both students and higher education institutions, some drawbacks were discovered. First, different results will be obtained when the study is implemented in other places and time periods. The second step is to include students taking diverse courses in vocational schools, such as applied English translation, architectural restoration, foreign trade and tourism, and hotel management, to obtain the various samples. Third, the UTAUT model may be used in conjunction with other theories to create HOT-fit models that help us understand the technological, organizational, and human factors that influence the adoption of cloud based learning management adoption.

The research, writing, and/or publication of this work were all done without any financial assistance from the authors.

Adanir, G. A., & Cinar, M. (2021). The acceptance and use of an online exam system by online learners: Implementation of the UTAUT model. Sakarya University Journal of Education, 11(3), 412-430. https://doi.org/10.19126/suje.830529

Ahmed, R. R., Štreimikienė, D., & Štreimikis, J. (2022). The extended UTAUT model and learning management system during COVID-19: Evidence from PLS-SEM and conditional process modelling. Journal of Business Economics and Management, 23(1), 82-104. https://doi.org/10.3846/jbem.2021.15664

Akinnuwesi, B. A., Uzoka, F. M. E., Fashoto, S. G., Mbunge, E., Odumabo, A., Amusa, O. O., Okpeku, M., & Owolabi, O. (2022). A modified UTAUT model for the acceptance and use of digital technology for tackling COVID-19. Sustainable Operations and Computers, 3, 118-135. https://doi.org/10.1016/j.susoc.2021.12.001

Al-Fahim, N. H., Abdulgafor, R., & Qaid, E. H. (2021). Determinants of banks’ costumer’s intention to adopt Internet Banking Services in Yemen: Using the unified theory of acceptance and use of technology (UTAUT). In ICOTEN (Ed.), 2021 IEEE International Congress of Advanced Technology and Engineering (pp. 1-8). ICOTEN. https://doi.org/10.1109/ICOTEN52080.2021.9493448

Alvi, I. (2021). College students’ reception of social networking tools for learning in India: An extended UTAUT model. Smart Learning Environments, 8(19), e19. https://doi.org/10.1186/s40561-021-00164-9

Alwahaishi, S. (2021). Student use of e-learning during the coronavirus pandemic: An extension of UTAUT to trust and perceived risk. International Journal of Distance Education Technologies (IJDET), 19(42), 1-19. https://doi.org/10.4018/IJDET.286742

Alyoussef, I. Y. (2021). Factors Influencing Students’ Acceptance of M-Learning in Higher Education: An Application and Extension of the UTAUT Model. Electronics, 10(24), e3171. https://doi.org/10.3390/electronics10243171

Ariffin, N. H. M., Ahmad, F., & Haneef, U. M. (2020). Acceptance of mobile payments by retailers using UTAUT model. Indonesian Journal of Electrical Engineering and Computer Science, 19(1), 149-155. https://doi.org/10.11591/ijeecs.v19.i1.pp149-155

Ayaz, A., & Yanartaş, M. (2020). An analysis on the unified theory of acceptance and use of technology theory (UTAUT): Acceptance of electronic document management system (EDMS). Computers in Human Behavior Reports, 2(1), e100032. https://doi.org/10.1016/j.chbr.2020.100032

Batucan, G. B., Gonzales, G. G., Balbuena, M. G., Pasaol, K. R. B., Seno, D. N., & Gonzales, R. R. (2022). An extended UTAUT model to explain factors affecting online learning system amidst COVID-19 pandemic: The case of a developing economy. Frontiers Artificial Intelligence, 5, e768831. https://doi.org/10.3389/frai.2022.768831

Bautista, E., Quintana, N.L., & Sánchez, O.A. (2022). Distance education with students of the higher level in a period of quarantine by COVID-19. Innoeduca. International Journal of Technology and Educational Innovation, 8(2), 5-13. https://doi.org/10.24310/innoeduca.2022.v8i2.12257

Bommer, W. H., Rana, S., & Milevoj, E. (2022). A meta-analysis of eWallet adoption using the UTAUT model. International Journal of Bank Marketing, 40(4), 791-819. https://doi.org/10.1108/IJBM-06-2021-0258

Bozkurt, A. (2017). The Past, Present and Future of the Distance Education in Turkey. Journal of Open Education Applications and Researches, 3(2), 85-124. https://dergipark.org.tr/en/pub/auad/issue/34117/378446

Cavus, N., & Zabadi, T. (2014). Learning a comparison of open source learning management systems. Procedia - Social and Behavioral Sciences, 143, 521-526. https://doi.org/10.1016/j.sbspro.2014.07.430

Chan, T. J., Wok, S., Sari, N. N., & Muben, M. A. H. A. (2021). Factors influencing the intention to use mysejahtera application among Malaysian citizens during covid-19. Journal of Applied Structural Equation Modeling, 5(2), 1-21. https://doi.org/10.47263/JASEM.5(2)06

Chatterjee, S., Rana, N. P., Khorana, S., Mikalef, P., & Sharma, A. (2021). Assessing organizational users’ intentions and behavior to ai integrated CRM systems: A meta-UTAUT approach.  Information Systems Frontiers, 25, 1299-1313. https://doi.org/10.1007/s10796-021-10181-1

Chayomchai, A., Phonsiri, W., Junjit, A., Boongapim, R., & Suwannapusit, U. (2020). Factors affecting acceptance and use of online technology in Thai people during COVID-19 quarantine time. Management Science Letters, 10(2020), 3009-3016. https://doi.org/10.5267/j.msl.2020.5.024

Cohen, J. E. (1988). Statistical Power Analysis for the Behavioral Sciences. Lawrence Erlbaum Associates.

El Said, G. R. (2021). How did the COVID-19 pandemic affect higher education learning experience? An empirical investigation of learners’ academic performance at a university in a developing country. Advances in Human-Computer Interaction, 2021, e6649524. https://doi.org/10.1155/2021/6649524

Fornell, C., & Lacker, D. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18, 39-50. http://dx.doi.org/10.1108/15982688200700005

Hair, J. F. Jr., Anderson, R. E., Tatham, R. L. & Black, W. C. (1995). Multivariate Data Analysis (3rd ed). Macmillan.

Hair, J. F., Black, W.C., Babin, B. J., & Anderson, R. E. (2010), Multivariate data analysis: A global perspective (7th edition). Pearson Education.

Hair Jr, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2017). Advanced issues in partial least squares structural equation modelling. Sage publications.

Ikhsan, R. B., Prabowo, H. & Yuniarty Y. (2021). Drivers of the mobile-learning management system’s actual usage: Applying the UTAUT model. ICIC express letters. Part B, Applications: an international journal of research and surveys, 12(11), 1067-1074. https://doi.org/10.24507/icicelb.12.11.1067

Jameel, A. S., Karem, M. A., & Ahmad, A. R. (2022). Behavioral intention to use e-learning among academic staff during COVID-19 pandemic based on UTAUT model. In M. Al-Emran, M.A. Al-Sharafi, M.N. Al-Kabi, & K. Shaalan (Eds.), Proceedings of International Conference on Emerging Technologies and Intelligent Systems. ICETIS 2021. Lecture Notes in Networks and Systems (pp. 187-196). Springer. https://doi.org/10.1007/978-3-030-82616-1_17

Kacan, A., & Gelen, I. (2020). An overview of the distance education program in Turkey. International Journal of Education Science and Technology, 6(1), 411-434. Retrieved from https://doi.org/10.17478/jegys.1140286

Karadag, E., Ciftci, S. K., Gok, R., Su, A., Ergin-kocaturk, H., & Ciftci, S. S. (2021). Distance education capacities of universities during the Covid-19 pandemic process. Journal of University Research, 4(1), 8-22. https://dergipark.org.tr/tr/pub/uad/issue/60894/874799

Kirkan, B., & Kalelioglu, F. (2017). Status of distance education centers in Turkey: A descriptive study. Journal of Instructional Technologies & Teacher Education, 6(3), 88-98.

Li, Y., & Zhao, M. (2021). A study on the influencing factors of continued intention to use MOOCs: UTAUT model and CCC moderating effect. Frontiers in psychology, 12, e528259. https://doi.org /10.3389/fpsyg.2021.528259

Limna, P., Siripipatthanakul, S., Siripipattanakul, S., Woodeson, K., & Auttawechasakoon, P. (2022). Applying the UTAUT to explain factors affecting english learning intention via Netflix (English Subtitle) among Thai people. Asia-Pacific Review of Research in Education, 1(1), 1-19. https://ssrn.com/abstract=4046242

Lutfi, A. (2022). Factors Influencing the continuance intention to use accounting information system in Jordanian SMEs from the perspectives of UTAUT: Top management support and self-efficacy as predictor factors. Economies, 10(4), e75. https://doi.org/10.3390/economies10040075

MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130-149. https://doi.org/10.1037/1082-989X.1.2.130

Magsamen-Conrad, K., Wang, F., Tetteh, D, & Lee YI, (2019). Using technology adoption theory and a lifespan approach to develop a theoretical framework for eHealth literacy: extending UTAUT. Health Communication, 35(12), 1435-1446. https://doi.org/10.1080/10410236.2019.1641395

Marandu, E. E., Mathew, I. R., Svotwa, T. D., Machera, R. P., & Jaiyeoba, O. (2022). Predicting students’ intention to continue online learning post-COVID-19 pandemic: extension of the unified theory of acceptance and usage technology. Journal of Applied Research in Higher Education, 15(3), 681-697. https://doi.org/10.1108/JARHE-02-2022-0061

Namahoot, K. S., & Jantasri, V. (2022). Integration of UTAUT model in Thailand cashless payment system adoption: The mediating role of perceived risk and trust. Journal of Science and Technology Policy Management, 14(4), 634-658 https://doi.org/10.1108/JSTPM-07-2020-0102

Nunnally, J.C., & Bernstein, I.R. (1994). Psychometric theory. McGraw-Hill.

Nur, T., & Gosal, G. A. (2021). Mobile payment usage in online shopping among Gen Z in the Jabodetabek area: META-UTAUT approach. In ICIMTech (Ed.), 2021 IEEE International Conference on Information Management and Technology (pp. 464-469). ICIMTech. https://doi.org/10.1109/ICIMTech53080.2021.9535003

Ozbay, O. (2015). The Current status of distance education in The World And Turkey. The Journal of International Education Science, 2(5), 376-394. https://dergipark.org.tr/en/pub/inesj/issue/40015/475774

Popova, Y., & Zagulova, D. (2022). UTAUT Model for smart city concept implementation: Use of web applications by residents for everyday operations. Informatics, 9(1), e27. https://doi.org/10.3390/informatics9010027

Rahmadi, I. F. (2021). Teachers’ technology integration and distance learning adoption amidst the Covid-19 crisis: A reflection for the optimistic future. Turkish Online Journal of Distance Education, 22(2), 26-41. https://doi.org/10.17718/tojde.906472

Republic of Turkey Ministry of Health (2020). COVID-19 Weekly Situation Report 19/10/2020 – 25/10/2020 Turkey. https://covid19. saglik.gov.tr/Eklenti/39230/0/covid-19-weekly-situation-report---43pdf.pdf?_tag1=D3D202441F1F516 5A33D16981E6544EF7FC0A32F

Sabayleh, O., Alramamneh, A. L. K., & Al Etawi, N. A. (2020). Investigation of user requirement and UTAUT - Theory in web based learning for gifted students in developing country. Journal of Theoretical and Applied Information Technology, 98(8), 1312-1321.

Semiz, B. B., & Semiz, T. (2021). Examining consumer use of mobile health applications by the extended UTAUT model. Business & Management Studies: An International Journal, 9(1), 267-281. https://doi.org/10.15295/bmij.v9i1.1773

Serttas, Z., & Kasabali, A. (2020). Determining the english preparatory school students’ readiness for online learning. Near East University Online Journal of Education, 3(2), 67-78. https://dergipark.org. tr/en/pub/neuje/issue/57004/802693

Şimşek, A. S., & Ateş, H. (2022). El modelo de aceptación de tecnología extendida para las tecnologías Web 2.0 en la enseñanza. Innoeduca. International Journal of Technology and Educational Innovation, 8(2), 165-183. https://doi.org/10.24310/innoeduca.2022.v8i2.15413

Sozmen, E. Y., Karaca, O., & Bati, A. H. (2021). The effectiveness of interactive training and microlearning approaches on motivation and independent learning of medical students during the COVID-19 pandemic. Innovations in Education and Teaching International, 60(1), 70-79. https://doi.org/10.1080/14703297.2021.1966488

Sukarya, I. G. A., Pradnyana, I. M. A., & Sugihartini, N. (2020). Analisis faktor-faktor yang mempengaruhi perilaku penggunaan sistem e-learning undiksha dengan model unified theory of acceptance and use of technology (UTAUT).  Information System and Emerging Technology Journal, 1(2), 62-75. https://doi.org/10.23887/insert.v1i2.25940

The Council of Higher Education (2021). In the Global Epidemic to Education and Teaching Processes Guide to Applications. http://ubf.ankara.edu.tr/wp-content/uploads/sites/634/2021/09/kuresel-salginda-egitim-ve-ogretim-sureclerine-yonelik-uygulamalar-kilavuzu-2021.pdf

Thottoli, M. M., & Thomas, K. V. (2022). ERP software and practicing auditors: An empirical study applying UTAUT model. International Journal of Innovation in the Digital Economy (IJIDE), 13(1), 1-13. https://doi.org/10.4018/IJIDE.292009

Torras, M.E. (2021). Emergency Remote Teaching: ICT applied to education during confinement by COVID-19. Innoeduca. International Journal of Technology and Educational Innovation, 7(1), 122-136. https://doi.org/10.24310/innoeduca.2021.v7i1.9079

Tussardi, R. R., Izzati, B. M., & Saputra, M. (2021). Analysis of E-learning acceptance during distance learning using unified theory of acceptance and use of technology (UTAUT). JATISI (Jurnal Teknik Informatika dan Sistem Informasi), 8(2), 465-479. https://doi.org/10.35957/jatisi.v8i2.767

van Houwelingen, C. T., Ettema, R. G., Antonietti, M. G., & Kort, H. S. (2018). Understanding older people’s readiness for receiving telehealth: Mixed-method study. Journal of medical Internet research, 20(4), e123. https://doi.org/10.2196/jmir.8407

Vásquez, M.-S., Nuñez, P., & Cuestas, J. (2023). Teachers’ Digital Competences in the context of COVID-19. A quantitative approach. Pixel-Bit. Revista De Medios Y Educación, (67), 155-185. https://doi.org/10.12795/pixelbit.98129

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540

Yu, L., Chen, Z., Yao, P., & Liu, H. (2021). A study on the factors influencing users’ online knowledge paying-behavior based on the UTAUT model. Journal of Theoretical and Applied Electronic Commerce Research, 16(5), 1768-1790. https://doi.org/10.3390/jtaer16050099

Zeebaree, M., Agoyi, M., & Aqel, M. (2022). Sustainable adoption of E-Government from the UTAUT perspective. Sustainability, 14(9), 5370. https://doi.org/10.3390/su14095370

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