Skip to main content
Download PDF

Factors affecting medical students’ intention to use Rain Classroom: a cross-sectional survey

BMC Medical Education volume 24, Article number: 86 (2024) Cite this article

Abstract

Background

Rain Classroom was one of the most popular online learning platforms in Chinese higher education during the pandemic. However, there is little research on user intention under the guidance of technology acceptance and unified theory (UTAUT).

Objective

This research aims to determine factors influencing students' behavioural intention to use Rain Classroom.

Methods

In this cross-sectional and correlational investigation, 1138 medical students from five medical universities in Guangxi Province, China, made up the sample. This study added self-efficacy (SE), motivation (MO), stress (ST), and anxiety (AN) to the UTAUT framework. This study modified the framework by excluding actual usage variables and focusing only on intention determinants. SPSS-26 and AMOS-26 were used to analyze the data. The structural equation modelling technique was chosen to confirm the hypotheses.

Results

Except for facilitating conditions (FC), all proposed factors, including performance expectancy (PE), effort expectancy (EE), social influence (SI), self-efficacy (SE), motivation (MO), anxiety (AN), and stress (ST), had a significant effect on students' behavioural intentions to use Rain Classroom.

Conclusions

The research revealed that the proposed model, which was based on the UTAUT, is excellent at identifying the variables that influence students' behavioural intentions in the Rain Classroom. Higher education institutions can plan and implement productive classrooms.

Peer Review reports

Introduction

Countries have abandoned traditional education to combat the spread of the new coronavirus disease (COVID-19) [1]. Millions of students and educators worldwide have been affected, and various e-learning techniques have been deployed to promote learning in the epidemic age [2, 3]. The education system worldwide has begun looking for a new method of education. This has increased the use of e-learning, including MOOCs, Google Meet, Microsoft Teams, Zoom, GoToMeeting, and WebEx worldwide [4]. Higher academic institutions actively respond to the call of China's Ministry of Education (MoE) by suspending physical lessons and launching virtual classes such as MOOC and Rain Classroom. During the ongoing "lockdown" of the pandemic, these digital learning tools were crucial for carrying out education [5].

The Rain Classroom mobile app, created in 2016 in collaboration with the Online Education Office of Tsinghua University, is one of the most widely used electronic learning tools. Its purpose is to integrate teachers and students through smart terminals, give students new experiences before, during, and after class, improve teaching and learning, and push for education reform. [6]. Students can quickly enter the Rain Classroom by entering the lesson code on the projection screen or scanning the QR code displayed there using WeChat, China's most popular social media app [4].

Educators can share MOOC videos, coursework, and exercises with students via Rain Classroom before class, allowing communication and student feedback. Teachers can review lessons at any time, including preview time, duration, response rate, and preview exercise accuracy. The course facilitates teacher-student interactions, including sign-in, synchronized educational courseware, classroom testing, bullet screen interaction, red envelopes, multiscreen interaction, real-time instructional feedback, and classroom submissions. Students receive reports and general PowerPoint slides on their phones after class. In addition, teachers have access to teaching resources, student attendance, student performance, student participation in learning activities, answer analysis, answer details, class statistics, and data regarding the learning process. These resources can be utilized for educational improvement and reflection.

Although domestic researchers and teachers favour Rain Classroom, it is not as well known globally as Google Meet or Microsoft Teams are for similar online learning platforms [4].To successfully implement Rain Classroom in higher education, it is imperative that it is accepted. As a result, it is critical to comprehend and pinpoint the crucial elements that influence Rain Classroom acceptance. However, little research has been done on adopting and receiving Rain Classroom [4, 7].

In previous studies, researchers have used various technology acceptance models, particularly the UTAUT2 and the Technology Acceptance Mode, to identify the elements that affect users' adoption of specific information systems [8,9,10]. The UTAUT, which is typically used in fields such as e-learning systems [11], MOOCs [12], and health information systems [13], has recently been applied in the education sector to identify factors that affect user-specific acceptance of educational technologies [14].

Having to understand user acceptance, few researchers have attempted to apply the UTAUT paradigm to Rain Classroom (Yang & Yu, 2022) [22]. These studies have yet to uncover all the fundamental components that might have a significant impact on the behavioural intentions of students using Rain Classroom. Having to identify the variables that influence students' behavioural intentions toward Rain Classroom, this study combines the UTAUT with four new external constructs: self-efficacy (SE), motivation(MO), stress(ST), and anxiety(AN).

Review of the literature

Researchers have used human behaviour theory for many years to examine the intention of acceptance and use [15]. Various theories have been proposed to explain technology acceptance, including Theory of Reasoned Action (TRA), Theory of Planned Behavior (TPB), Social Cognitive Theory (SCT), Technology Acceptance Model (TAM), Extended Technology Acceptance Model (TAM2), Motivational Model (MM), Model of PC Utilization (MPCU), Innovation Diffusion Theory (IDT), and Unified Theory of Acceptance and Use of Technology (UTAUT)) [15,16,17]. The UTAUT, which combines eight of these theories, has been used to validate learners' behavioural intentions(Venkatesh et al., 2003). This model consists of four primary constructs: performance expectancy, effort expectancy, social influence, and facilitating conditions (Fig. 1) [18]. The UTAUT is among the most popular theoretical models for predicting technological acceptance and adoption. [17, 19]. The UTAUT model, which had an adjusted R2 of 69% and was superior to the eight individual models, could account for up to 70% of the variance in usage behaviour variance [18]. Research reveals that the UTAUT is a dependable model for understanding global technology adoption factors (Arifet al., 2018). As a result, the UTAUT serves as the theoretical foundation for our investigation. After reviewing the literature, the following relationships were proposed as hypotheses.

Fig. 1
figure 1

UTAUT model

Full size image

Performance expectancy

PE can be described as a student's level of optimism about using Rain Classroom to enhance academic achievement [18]. Some research has shown the positive impact of PE on behavioural intention [4, 20,21,22]. A pupil will have a favourable attitude in regard to the system that is being discussed and be more likely to use Rain Classroom if they anticipate improving their academic performance. Thus, the following is a statement of the hypothesis.

  • H1: Performance expectancy has a positive impact on medical students' behavioural intention to use Rain Classroom.

Effort expectancy

EE is the degree to which an individual perceives using a particular technology as easy or difficult [18]. This construct is analogous to the "ease-of-use" component of the Technology Acceptance Model [10]. Research has shown that EE can be a significant predictor of an individual's intention to use an online educational system [4, 21,22,23,24]. A meta-analysis of 52 studies found that ease of use had the strongest relationship with users' intentions to adopt new technologies [25]. This data allows us to provide a working hypothesis.

  • H2: Effort expectancy has a positive impact on medical students' behavioural intention to use Rain Classroom.

Social influence

SI refers to a student's perception of others' beliefs about their use of Rain Classroom (Venkatesh et al., 2003), which has been found to positively impact their behavioural intentions toward technology adoption in the workplace. Numerous studies have shown that SI significantly influences the behavioural intention to adopt online learning systems [24, 26,27,28]. C-M Chiu and ET Wang [29] suggested that a more favourable social influence on a behaviour, such as using an online course system, increases the likelihood of students carrying it out [30]. Hence, we postulate the hypothesis:

  • H3: Social influence has a positive impact on the behavioural intention to use Rain Classroom.

Facilitating conditions

The level of FC indicates a student's belief in the existence of an administrative and technological framework to enable the usage of an online course management system (Venkatesh et al., 2003). According to Venkatesh et al. (2003), FC has a favourable impact on how often people use technology at work. In this regard, many studies have indicated that FC has a considerable influence on users' intentions to use digital information systems [4, 15, 27, 28]. However, some research reports have indicated that FC has no significant impact on their intentions to use e-learning [24, 26, 31]. Based on what we already know, we have come up with the research hypothesis.

  • H4: Facilitating conditions have a positive impact on the behavioural intention of medical students to use Rain Classroom.

Self-efficacy

SE is the degree to which one believes in their ability to complete a task [32]. According to several studies, self-efficacy is one of the important factors determining the acceptance of an educational system [33,34,35]. Several studies have endorsed the SE variable as an integral part of promoting the adoption of MOOCs by students [16, 21, 36, 37]. These findings contribute to the extension of the UTAUT model related to the construct of SE on computers. Based on this, the study posits the following.

  • H5: Self-efficacy has a favorable impact on medical students' behavioral intention to use Rain Classroom.

Motivation

Self-determination theory (SDT) is a theory that explains human behaviour and motivation [38]. It focuses on fulfilling basic psychological needs and improving intrinsic motivation for learning [39]. The theory categorizes motivation into autonomous and controlled based on the level of autonomy or control [38]. Therefore, by integrating SDT and UTAUT, this study can address the limitations and improve the accuracy and comprehensiveness of its findings. Autonomous motivation, which meets individual autonomy needs, is a critical driver of behaviour [31]. Therefore, we assume the following:

  • H6: Motivation has a positive effect on medical students' behavioural intention to use Rain Classroom.

Stress

According to definitions, ST refers to a specific interaction between an individual and their surroundings in which the individual is draining or exceeding the environment's resources and endangering their wellbeing. [40]. Online learning has been used to assess the daily course and academic performance of students, and in some ways, students felt stressed [41]. MA Islam, SD Barna, H Raihan, MNA Khan and MT Hossain [42]noted that the pressure of online lecture tasks is a contributing factor to students' ST levels. These tasks require students to use online media that they may not be familiar with, leading to the need for immediate comprehension. As a result, the study proposes the following.

  • H7: Stress has a negative impact on medical students' behavioural intentions to use Rain Classroom.

Anxiety

AN is a negative emotional reaction that adversely affects a person's intention to perform a particular task [43]. In this study, task anxiety is defined as the fear of being unable to accomplish tasks [44]. The term "technology AN" refers to people's anxiety or unease when using computers or other forms of technology. [45]. Empirical research has indicated that anxiety directly affects behavioural intention negatively [46,47,48]. Some research has demonstrated the importance of predicting use behaviour [49]. Therefore, we propose the following hypothesis:

  • H8: Anxiety has a negative impact on the behavioural intention of medical students to use Rain Classroom.

Methods

Study design

This study utilized purposive sampling because defining the Rain Classroom user population is challenging. To the researcher's knowledge, no comprehensive census or complete list of all Rain Classroom exists in Guangxi, China. We developed a fitting model using a causal research design and verified the hypothesis proposed to understand students' behavioural intention to use Rain Classroom. An online survey platform was used to create and distribute a self-administered questionnaire. Confirmatory factor analysis and structural equation modelling were used to perform additional data analysis. The next section of the methodology is for participants, research instruments, and research procedures. To forecast how medical students will use Rain Classroom, a hypothetical model that uses the UTAUT was created, and its fit and validity were evaluated.

Participants

The selection of participants was based on four Inclusion criteria and five exclusion criteria to ensure they fulfilled the study's requirements. Inclusion criteria: current enrollment in medical university, smartphone users aged 18 years or above, and willingness to participate.Exclusion criteria: Students not enrolled in a medical course or program at a medical university in Guangxi Province, lack smartphone access, have no prior experience with Rain Classroom, and are underage and non-consenting individuals. The target participants were junior college, undergraduate and postgraduate students from five public medical universities of Guangxi in China, namely, Youjiang Medical University for Nationalities (YMUN), Guangxi Medical University(GXMU), Guangxi University of Chinese Medicine(GXUCM), Guangxi University of Science and Technology (GXUST), and Guilin Medical University(GLMU). These students were studying various medical disciplines, such as clinical medicine, nursing, and foundation medicine, and the students were currently using Rain Classroom.

Measurement

Utilizing Sojump, an online questionnaire was distributed to students at five medical universities with the objective of data collection. The survey was bifurcated into two sections. The initial section gathered demographic details, while the latter was designed to accumulate data on the research model, encompassing nine constructs with a total of 31 elements (Fig. 2 and Table 2). The constructs included PE, EE, SI, FC, SE, MO, ST, AN, and BI. To bolster the content validity of the questionnaire, all items were adapted from [18, 32, 41, 43]and were subsequently rephrased to align with the context of the Rain Classroom. These included PE (3 elements), EE (3 elements), SI (3 elements), FC (4 elements), SE (3 elements), MO (4 elements), ST (3 elements), AN (5 elements), and BI (3 elements). Participants were instructed to appraise each item employing a five-point Likert scale ranging from 1 (representing strong disagreement) to 5 (representing strong agreement). This measurement technique facilitated participants in expressing their degree of concurrence or discordance with each item on a graded scale, affording a more intricate comprehension of their attitudes and opinions. The reliability of the scales utilized in the study was evaluated using Cronbach's alpha coefficients, exceeding the acceptable threshold of 0.70 [50], thereby indicating the robust reliability of the measures.

Fig. 2
figure 2

Research model

Full size image

Data collection

The research used a questionnaire survey to implement the quantitative method. The data were collected from December 6, 2022, to February 7, 2023, by distributing self-administered online questionnaires in Sojump. In total, 1,074 questionnaires were distributed to students. For missing values, 35 questionnaires were discarded. Therefore, 1138 questionnaires were incorporated into the preliminary analysis, and the response rate was 97.02%. According to Hair et al. (2010), the minimum sample size for quantitative research is 354. Therefore, the sample size of this study (N = 1138) is sufficient. The data of the participants are shown in Table 1.

Table 1 Demographics of the participants (n = 1138)
Full size table

Data analysis

The accumulated data underwent meticulous analysis using the SPSS 21.0 and AMOS 26.0 software suites. The descriptive statistics methodology was leveraged to investigate the intrinsic characteristics of the data set in conjunction with the application of suitable tests designed to evaluate the reliability and validity of the deployed research instrument. A path analysis was conducted to ascertain the compatibility of theoretical models with the behavioural inclinations of medical students towards Rain Classroom usage. The effectiveness and path coefficient estimates of the model were subsequently calculated. An array of indices, including X2/df, RMSEA, GFI, NFI, IFI, CFI, PNFI, PCFI, and PGFI, were utilized to assess the model's fit [39]. The structural equation modelling technique was applied to evaluate the stipulated hypothesis. The p-value, used to denote significance, was ascertained to be P < 0.05.

Result

Features of the population

Of the 1138 participants involved in the study, 858 were women (75.4%), and 280 were men (24.6%) (Table 1). The mean age was 20.07 years. The participants came from five universities, including YMUN(n = 553,48.6%), GXUCM(n = 253,22.2%), GLMU(n = 159,14%), GXMU(n = 87,7.6%), GXUST(n = 86,7.6%). The majority of the students were undergraduate students (n = 772, 67.8%), followed by postgraduate students (n = 88, 7.7%) and junior students (n = 278, 24.4%). The average time spent on the internet for nonacademic purposes was 3.85 h, for using Rain Classroom was 1.76 h, and the use of the internet was mainly for communication, followed by Recreation, Learning knowledge, Shopping, Obtaining information, and Others.

Measurement model

This section scrutinizes the evaluation of measurement scales, focusing specifically on item reliability, internal consistency reliability, convergent validity, and discriminant validity [51]. In order to evaluate item reliability, the loadings of each item on its corresponding latent variable are examined, with an emphasis on standardized loadings of at least 0.50, preferably higher than 0.70, and statistical significance for all items [52]. Composite reliability (CR) is a common method to assess internal consistency, referencing a value of 0.70 or higher for each latent variable. Convergent validity is measured through average variance extracted (AVE) testing, requiring a value of 0.50 or higher for each latent variable. Sufficient reliability and convergent validity are reflected in measures that exhibit standardized factor loadings exceeding 0.70 (p < 0.001), AVE surpassing 0.50 (p < 0.001), and CR greater than 0.70 (p < 0.001) (Table 2). Discriminant validity, representing the significant difference between two factors [53], is assessed by scrutinizing the AVE of the UTAUT scale using the method proposed by C Fornell and DF Larcker [54]. The bold diagonal values in Table 3 indicate that the square root of AVE for each variable substantially exceeds the correlation with other variables, signifying the discriminant validity of the constructs and the superior performance of the questionnaire.

Table 2 Analyse of reliability and convergence validity
Full size table
Table 3 Discriminant validity results
Full size table

Structural model

Table 4 shows the results of the model-fit indices for a structural model. The absolute fit index is represented by X2/df, while the incremental fit index includes RMSEA, GFI, NFI, IFI, CFI, PNFI, PCFI, and PGFI. The X2/df value obtained is 2.588, below the recommended threshold of 3. The RMSEA value is 0.037, indicating an excellent fit to the model, as it is below the recommended threshold of 0.08. The GFI, NFI, IFI, and CFI values are above the recommended threshold of 0.90, indicating a good fit. The PNFI, PCFI, and PGFI values are also above the recommended threshold of 0.50, showing a good balance between model accuracy and parsimony. Based on these fit indices, the structural model appears to fit the observed data. Therefore, it can be recommended as a suitable model for the data under consideration.

Table 4 Model fit index
Full size table

In this study, the researchers used SEM path analysis to analyze the proposed hypotheses in the developed model, as shown in Fig. 3. Table 5 contains the results of the hypothesis testing. Of the eight hypotheses tested, seven were supported, and one was rejected. The study finds indicated that PE (β = 0.093, p < 0.01), EE (β = 0.206, p < 0.001), SI (β = 0.192, p < 0.001), SE (β = 0.111, p < 0.05), and MO (β = 0.16, p < 0.001) had a significant positive impact on BI, supporting hypotheses H1, H2, H3, H5, and H6. The study also found that the factors ST (β = -0.11, p < 0.001) and AN (β = -0.125, p < 0.001) factors were significant in affecting BI, supporting both H7 and H8. However, the effect of FC (β = 0.061, P > 0.01) on BI was negative but insignificant, so H4 was rejected. In general, the study supports the relationship between PE, EE, SI, SE, MO, ST, and AN constructs and BI, providing valuable information for researchers and practitioners to understand the factors that influence BI.

Fig. 3
figure 3

Structural model testing

Full size image
Table 5 Summary of path estimates
Full size table

Discussion

This study used the UTAUT framework to determine what factors are associated with medical students' intentions to use Rain Classroom. A structural model was created to analyze the causal relationships among several variables: PE, EE, SI, FC, SE, MO, ST, AN, and BI. A survey questionnaire was administered to gather empirical data. The results indicated that the use of Rain Classroom, PE, EE, and SE was positively affected, while AN and ST had negative effects. FC did not show any significant influence.

The results indicated that the student's behavioural intentions to use Rain Classroom were significantly positively impacted by PE. Students find Rain Classroom a learning experience that helps them complete things more effectively. As a result, this increases student output and improves their performance. [55]. This outcome was in line with the previous studies. [8, 30, 33, 56].

According to our study, EE significantly affected students' behavioural intentions to use Rain Classroom. Evidence from a variety of research shows that EE is a predictor of future technology adoption, particularly in the realms of mobile learning and services. [8]. The results of other studies confirm this finding [57,58,59,60]. Therefore, this finding indicates that the Rain Classroom was easy for students to use.

SI had a favourable effect on students' intentions to use Rain Classroom, it was discovered. This result is consistent with previous findings [30, 61], which also showed that SI positively affects the use of interactive online learning in virtual face-to-face technology [62]. Teachers are crucial in promoting technology use for students' learning, and the sociocultural environment may encourage and support students in using Rain Classroom.

FC did not significantly affect the students' behavioural intention to use Rain Classroom. Some previous research [9, 57, 63] obtained similar findings. However, we believe that both software and hardware are crucial factors that can influence students' intentions to use Rain Classroom. It is important to provide students with resources, including the fast internet and powerful computers, to effectively utilize the technological platform. In addition, students should have sufficient access to information and resources to make the most of the platform. Successful implementation of Rain Classroom necessitates good governance in higher education and meticulous planning.

This study showed that SE positively impacted students' behavioural intentions to use Rain Classroom. The most influential factor affecting acceptance intention is SE. The more self-efficacy students have, the more confident they are in their ability to address problems in the Rain Classroom process, and they are more likely to accept Rain Classroom. This result is consistent with previous findings [1, 56, 61]. Therefore, it is very important to train students to be able to operate the Rain Classroom.

Based on this study's findings, MO positively impacted students' intentions to use Rain Classroom. According to A Kaplan and ML Maehr [64], students who are intrinsically and extrinsically motivated tend to perform better academically. The intention to behave in a certain way is influenced by external MO. E Marlina, B Tjahjadi and S Ningsih [65] found that external factors such as parental involvement, student MO, learning strategies, and socioeconomic status (SES) can affect student behaviour in the teaching and learning process, which is consistent with the findings of several other studies [64, 66,67,68].

This research indicated that AN had a negative impact on students' behavioural intentions to use Rain Classroom. The more anxious a student is, the less willing he or she is to persist in the Rain Classroom. These confirm the findings of other studies [30, 69,70,71]. This finding and those of similar studies have acknowledged that student proficiency is improved due to reinforced confidence due to the effective reduction in the negative effect of AN due to a more comfortable environment provided for students by web-based instruction technology.

In this study, ST had a negative effect on students' intentions to use Rain Classroom. The main findings show that ST was critical to the behavioural intention to accept e-learning [72]. Rain Classroom has made an effort to improve the mental well-being of students in the digital learning environment. There is no ST shouldered by the students or teachers relevant to learning to use WeChat or PowerPoint.

Limitations

The present study has several limitations that require further investigation. First, the findings may not be generalizable due to the limited sample size of only five medical universities in China. Future research should aim to collect data from a more diverse range of universities in China and other countries to increase the validity and generalizability of the findings. Second, while the study adds four new variables based on the UTAUT model, there are likely more factors influencing students' intention to use Rain Classroom. Therefore, future research should adopt other variables to uncover more elements that influence medical students' intentions to use Rain Classroom. Third, this study relied solely on a cross-sectional survey conducted once, which may not provide a comprehensive understanding of the subject matter. It is recommended that future studies employ a range of data collection techniques, including interviews, qualitative research, and longitudinal studies, to gain a deeper insight into the determinants of medical students' willingness to utilize Rain Classroom. Finally, the research focuses primarily on a specific online learning system (Rain Classroom), so caution should be taken when generalizing the results to other technologies. Students' perceptions and experiences may differ when adopting different types of electronic learning. Therefore, future studies should duplicate this finding using various e-learning platforms, including Google Meet, Moocs, and m-learning.

Conclusion

The study indicate that the UTAUT-based model effectively identifies factors that influence students' intention to use the Rain Classroom in higher medical education. The research expanded the UTAUT model by including constructs of self-efficacy, motivation, stress, and anxiety. Performance expectancy, effort expectancy, social influence, self-efficacy, and motivation positively impacted students' intention to use Rain Classroom, while stress and anxiety had a negative impact. Facilitating conditions did not positively affect medical students' behavioural and intention to use Rain Classroom, as per the study. The literature review revealed that these findings align with some other research. This study can be a beneficial resource for future Rain Classroom research. The results suggest that addressing students' psychological attitudes regarding new technologies and offering social context support are essential for successfully implementing Rain Classroom. Furthermore, we believe these findings could be applied to other universities in China or elsewhere.

Availability of data and materials

The datasets used and analysed during the current study available from the corresponding author on reasonable request.

References

  1. Sakka YMH. Students’ acceptance of distance learning as a result of COVID-19 impact on higher education in Jordan. Educ Res Int. 2022.

  2. Nainggolan S. Evaluating of digital platforms related online learning during COVID-19 pandemic: students’ satisfaction view. AL-ISHLAH: Jurnal Pendidikan. 2021;13(2):1358–65.

    Google Scholar 

  3. Shahzad A, Hassan R, Aremu AY, Hussain A, Lodhi RN. Effects of COVID-19 in e-learning on higher education institution students: the group comparison between male and female. Qual Quant. 2021;55:805–26.

    Article  Google Scholar 

  4. Yang Y, Yu ZG. Examining users’ sustained attention to online learning by modifying a UTAUT model of rain Classroom. Int J Online Pedagog Course Des. 2022;12(1):1–20.

    Article  Google Scholar 

  5. Watermeyer R, Crick T, Knight C, Goodall J. COVID-19 and digital disruption in UK universities: afflictions and affordances of emergency online migration. High Educ (Dordr). 2021;81(3):623–41.

    Article  Google Scholar 

  6. Zeng RX. An online conference was held by school online to announce the launch of a smart teaching tool-rain classroom. Asia-Pacific Education. 2016;24:3.

  7. Yang CB, Huan SL, Yang Y. Application of big data technology in blended teaching ofcollege students: a case study on rain Classroom. Int J Emerg Technol Learn. 2020;15(11):4–16.

    Article  Google Scholar 

  8. Kwak Y, Seo YH, Ahn J-W. .Nursing students’ intent to use AI-based healthcare technology: path analysis using the unified theory of acceptance and use of technology. Nurse Educ Today. 2022;119: 105541.

    Article  Google Scholar 

  9. Azizi SM, Roozbahani N, Khatony A. Factors affecting the acceptance of blended learning in medical education: application of UTAUT2 model. BMC Med Educ. 2020;20(1):367.

    Article  Google Scholar 

  10. Tamilmani K, Rana NP, Nunkoo R, Raghavan V, Dwivedi YK. Indian travellers’ adoption of Airbnb platform. Inform Syst Front. 2022;24(1):77–96.

    Article  Google Scholar 

  11. Twum KK, Ofori D, Keney G, Korang-Yeboah B. .Using the UTAUT, personal innovativeness and perceived financial cost to examine student’s intention to use E-learning. J Sci Technol Policy Manage. 2022;13(3):713–37.

    Article  Google Scholar 

  12. Singh A, Sharma A. Acceptance of MOOCs as an alternative for internship for management students during COVID-19 pandemic: an Indian perspective. Int J Educational Manage. 2021;35(6):1231–44.

    Google Scholar 

  13. Bai B, Guo ZQ. Understanding users’ continuance usage behavior towards digital health information system driven by the digital revolution under COVID-19 context: an extended UTATU model. Psychol Res Behav Manage. 2022;15:2831–42.

    Article  Google Scholar 

  14. Abbad MMM. Using the UTAUT model to understand students’ usage of e-learning systems in developing countries. Educ Inf Technol (Dordr). 2021;26(6):7205–24.

    Article  Google Scholar 

  15. Li YL, Zhao M. A study on the influencing factors of continued intention to use MOOCs: UTAUT model and CCC moderating effect. Front Psychol. 2021;12: 528259.

    Article  Google Scholar 

  16. Fianu E, Blewett C, Ampong GOA, Ofori KS. Factors affecting MOOC usage by students in selected Ghanaian universities. Educ Sci. 2018;8(2): 70.

    Article  Google Scholar 

  17. Barrane FZ, Karuranga GE, Poulin D. Technology adoption and diffusion: a new application of the UTAUT model. Int J Innov Technol Manage. 2018;15(06):1950004.

    Google Scholar 

  18. Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: toward a unified view. MIS Q. 2003;27(3):425–78.

  19. Golam Sorwar CA, Penman O. Anastasia Ward.Factors that predict the acceptance and adoption of smart home technology by seniors in Australia: a structural equation model with longitudinal data. Inform Health Soc Care. 2023;48(1):80–94.

    Article  Google Scholar 

  20. Wut TM, Lee SW, Xu J. How do facilitating conditions influence student-to-student interaction within an online learning platform? A new typology of the serial mediation model. Educ Sci. 2022;12(5):337.

    Article  Google Scholar 

  21. Altalhi M. Toward a model for acceptance of MOOCs in higher education: the modified UTAUT model for Saudi Arabia. Educ Inf Technol. 2021;26(2):1589–605.

    Article  Google Scholar 

  22. Hamdan F, Nordin N, Khalid F, Muslimin MS, Norman H. Technology acceptance of online managerial finance training via massive Open Online courses for lifelong learning. Adv Sci Lett. 2018;24(4):2365–9.

    Article  Google Scholar 

  23. Li N, Purwanto E, Zhang XJ, Cao F, Wong KH, Chen XR. Understanding the perceived pedagogical value of JazzQuiz in interactive hybrid learning among university students: a technology acceptance analysis. Interact Learn Environ. 2022:1–17.

  24. Bazelais P, Binner G, Doleck T. Examining the key drivers of student acceptance of online labs. Interact Learn Environ. 2022:1–16.

  25. Venkatesh V, Bala HT. Acceptance Model 3 and a research agenda on interventions. Decis Sci. 2008;39(2):273–315.

    Article  Google Scholar 

  26. Wut TM, Lee SW. Factors affecting students’ online behavioral intention in using discussion forum. Interact Technol Smart Educ. 2022;19(3):300–18.

    Article  Google Scholar 

  27. Altalhi M. Towards understanding the students’ acceptance of MOOCs: a Unified Theory of Acceptance and Use of Technology (UTAUT). Int J Emerg Technol Learn. 2021;16(2):237–53.

    Article  Google Scholar 

  28. García Botero G, Questier F, Cincinnato S, He T, Zhu C. Acceptance and usage of mobile assisted language learning by higher education students. J Comput High Educ. 2018;30(3):426–51.

    Article  Google Scholar 

  29. Chiu C-M, Wang ET. Understanding web-based learning continuance intention: the role of subjective task value. Inf Manag. 2008;45(3):194–201.

    Article  Google Scholar 

  30. Lakhal S, Khechine H. Technological factors of students’ persistence in online courses in higher education: the moderating role of gender, age and prior online course experience. Educ Inf Technol. 2021;26(3):3347–73.

    Article  Google Scholar 

  31. Guo J. Influencing factors of college students’ use of sports apps in mandatory situations: based on UTAUT and SDT. Biomed Res Int. 2022;2022:9378860.

    Article  Google Scholar 

  32. Bandura A. Self-efficacy: toward a unifying theory of behavioral change. Psychol Rev. 1977;84(2): 191.

    Article  Google Scholar 

  33. Almaiah MA, Alamri MM, Al-Rahmi W. Applying the UTAUT model to explain the students’ acceptance of mobile learning system in higher education. Ieee Access. 2019;7:174673–86.

    Article  Google Scholar 

  34. Chavoshi A, Hamidi H. Social, individual, technological and pedagogical factors influencing mobile learning acceptance in higher education: a case from Iran. Telematics Inform. 2019;38:133–65.

    Article  Google Scholar 

  35. Almaiah MA, Alismaiel OA. Examination of factors influencing the use of mobile learning system: an empirical study. Educ Inf Technol. 2018;24(1):885–909.

    Article  Google Scholar 

  36. Lwoga ET, Komba M. Antecedents of continued usage intentions of web-based learning management system in Tanzania. Educ Training. 2015;57(7):738–56.

    Article  Google Scholar 

  37. Mikalef P, Pappas IO, Giannakos M. An integrative adoption model of video-based learning. Int J Inform Learn Technol. 2016;33(4):219–35.

    Article  Google Scholar 

  38. Ryan RM, Deci EL. Self-determination theory: basic psychological needs in motivation, development, and wellness. Guilford Publications; 2017.

  39. Haque MS, Isomursu M, Kangas M. Jämsä T.Measuring the influence of a persuasive application to promote physical activity. In: PPT@ PERSUASIVE: 2018; 2018: 43–57.

  40. Lazarus RS. & Folkman.Stress, appraisal, and coping. Springer publishing company.1984:19.

  41. Thandevaraj EJ, Gani NAN, Nasir MKM. A review of psychological impact on students online learning during COVID-19 in Malaysia. Creative Educ. 2021;12(06):1296–306.

    Article  Google Scholar 

  42. Islam MA, Barna SD, Raihan H, Khan MNA, Hossain MT. Depression and anxiety among university students during the COVID-19 pandemic in Bangladesh: a web-based cross-sectional survey. PLoS ONE. 2020;15(8): e0238162.

    Article  Google Scholar 

  43. Bandura A. The explanatory and predictive scope of self-efficacy theory. J Soc Clin Psychol. 1986;4(3):359–73.

    Article  Google Scholar 

  44. Russell G, Bradley G. Teachers’ computer anxiety: implications for professional development. Educ Inf Technol. 1997;2:17–30.

    Article  Google Scholar 

  45. Saadé RG, Kira D. Computer anxiety in e-learning: the effect of computer self-efficacy. J Inform Technol Educ Res. 2009;8(1):177–91.

    Google Scholar 

  46. Holzmann P, Schwarz EJ, Audretsch DB. Understanding the determinants of novel technology adoption among teachers: the case of 3D printing. J Technol Transf. 2018;45(1):259–75.

    Article  Google Scholar 

  47. Huang L. Acceptance of mobile learning in classroom instruction among college english teachers in China using an extended TAM. In: 2017 International Conference of Educational Innovation through Technology (EITT). 2017: 283–287.

  48. Maican CI, Cazan A-M, Lixandroiu RC. Dovleac L.A study on academic staff personality and technology acceptance: the case of communication and collaboration applications. Comput Educ. 2019;128:113–31.

    Article  Google Scholar 

  49. Khechine H, Lakhal S. Technology as a double-edged sword: from behavior prediction with utaut to students’ outcomes considering personal characteristics. J Inform Technol Education-Research. 2018;17:63–102.

    Article  Google Scholar 

  50. Nunnally JC. Psychometric theory—25 years ago and now. Educ Res. 1975;4(10):7–21.

    Google Scholar 

  51. Hair FJ, Black W, Babin B, Anderson R. Multivariate data analysis: a global perspective. New Jersey: Pearson Education Inc.; 2010. p. 7–11.

    Google Scholar 

  52. Anderson JC, Gerbing DW. Structural equation modeling in practice: a review and recommended two-step approach. Psychol Bull. 1988;103(3): 411.

    Article  Google Scholar 

  53. Gan C, Li H. Understanding continuance intention of mobile instant messaging: motivators and inhibitors. Industrial Manage data Syst. 2015;115(4):646–60.

    Article  Google Scholar 

  54. Fornell C, Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. J Mark Res. 1981;18(1):39–50.

    Article  Google Scholar 

  55. Dajani D, Hegleh AS. Behavior intention of animation usage among university students. Heliyon. 2019;5(10):e02536.

    Article  Google Scholar 

  56. Pan MH, Gao W. Determinants of the behavioral intention to use a mobile nursing application by nurses in China. BMC Health Serv Res. 2021;21(1):1–11.

    Article  Google Scholar 

  57. Abdekhoda M, Dehnad A, Ghazi Mirsaeed SJ. Zarea Gavgani V.Factors influencing the adoption of e-learning in Tabriz University of Medical Sciences. Med J Islam Repub Iran. 2016;30:457.

    Google Scholar 

  58. Hoque R, Sorwar G. Understanding factors influencing the adoption of mHealth by the elderly: an extension of the UTAUT model. Int J Med Inform. 2017;101:75–84.

    Article  Google Scholar 

  59. Lakhal S, Khechine H, Mukamurera J. Explaining persistence in online courses in higher education: a difference-in-differences analysis. Int J Educational Technol High Educ. 2021;18(1):1–32.

    Article  Google Scholar 

  60. Tarhini A, Masa’deh Re, Al-Busaidi KA, Mohammed AB, Maqableh M. Factors influencing students’ adoption of e-learning: a structural equation modeling approach. J Int Educ Bus. 2017;10(2):164–82.

    Article  Google Scholar 

  61. Sattari A, Abdekhoda M, Zarea Gavgani V. Determinant factors affecting the web–based training acceptance by health students. Appl UTAUT Model. 2017;12(10):112–26.

    Google Scholar 

  62. Suksakorn A, Tubtiang A. The acceptance and effectiveness of interactive online learning in virtual face-to-face learning technology. Int J Innov Learn. 2020;27(2):135–58.

    Article  Google Scholar 

  63. El-Masri M, Tarhini A. Factors affecting the adoption of e-learning systems in Qatar and USA: extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Education Tech Research Dev. 2017;65(3):743–63.

    Article  Google Scholar 

  64. Kaplan A, Maehr ML. Achievement goals and student well-being. Contemp Educ Psychol. 1999;24(4):330–58.

    Article  Google Scholar 

  65. Marlina E, Tjahjadi B, Ningsih S. Factors affecting student performance in e-learning: a case study of higher educational institutions in Indonesia. J Asian Finance Econ Bus. 2021;8(4):993–1001.

    Google Scholar 

  66. Wong LLC, Nunan D. The learning styles and strategies of effective language learners. System. 2011;39(2):144–63.

    Article  Google Scholar 

  67. Ahmar F, Anwar E. Socio economic status and its relation to academic achievement of higher secondary school students. IOSR J Humanit Social Sci. 2013;13(6):13–20.

    Article  Google Scholar 

  68. Ukut IIT, Krairit D. Justifying students’ performance. Interact Technol Smart Educ. 2019;16(1):18–35.

    Article  Google Scholar 

  69. Alenezi AR, Abdul Karim AM, Veloo A. An empirical investigation into the role of enjoyment, computer anxiety, computer self-efficacy and internet experience in influencing the students’ intention to use e-learning: a case study from Saudi Arabian governmental universities. Turkish Online J Educ Technol. 2010;9(4):22–34.

    Google Scholar 

  70. Cheng Y, Lin Y, Yeh R, Lou S. Examining factors affecting college students’intention to use web-based instruction systems: towards an integrated model. TOJET. 2013;12(2):111–21.

    Google Scholar 

  71. Van Raaij EM, Schepers JJL. The acceptance and use of a virtual learning environment in China. Comput Educ. 2008;50(3):838–52.

    Article  Google Scholar 

  72. Olasina G. Human and social factors affecting the decision of students to accept e-learning. Interact Learn Environ. 2019;27(3):363–76.

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Multimedia University Malaysia for the financial support and the staff of Youjiang Medical University for nationalities for their hard work and dedication.

Funding

This work was supported by the TM R&D, Telekom Malaysia, Malaysia (RDTC/221053), the 2021 Guangxi Higher Education Undergraduate Teaching Reform Project [Grant number 2021JGB302],the 2021 Guangxi Vocational Education Teaching Reform Research Project [Grant number GXGZJG2021B210],and The 2022 Guangxi Autonomous Region-Level New Engineering, New Medicine, New Agriculture, and New Liberal Arts Research and Practice Projects(Grand number:Gui Jiao Higher Education 2022,No.47:49).

Author information

Authors and Affiliations

  1. Faculty of Management, Multimedia University, Cyberjaya, Malaysia

    Hui Lv, Jinghong Low & Siow-kian Tan

  2. The Affiliated Hospital of Youjiang Medical Universily for Nationalities, Baise, China

    Xuebin Li

  3. College of Nursing, Youjiang Medical University for Nationalities, Baise, China

    Hui Lv & Lingjiao Tang

  4. Modern Industrial College of Biomedicine and Great Health, Youjiang Medical University for Nationalities, Baise, China

    Hui Lv

  5. School of Economics and Management, Xiamen University Malaysia, Sepang, Malaysia

    Siow-kian Tan

Authors
  1. Hui Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jinghong Low
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Siow-kian Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lingjiao Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xuebin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization, Jinghong Low, Siow-kian Tan, Hui Lv,and Xuebin Li. Methodology, Hui Lv.Search and literature review, Hui Lv. Investigation, Hui Lv,Lingjiao,Tang. Data curation, Hui Lv,Lingjiao,Tang. Formal analysis, Hui Lv. Writing—original draft preparation, Hui Lv. Review and editing, Hui Lv,Siow-kian Tan.

Corresponding authors

Correspondence to Jinghong Low or Xuebin Li.

Ethics declarations

Ethics approval and consent to participate

The present research was approved by the Ethics Committee of Youjiang Medical University for Nationalities. All methods were performed in accordance with the relevant guidelines and regulations. Also, written informed consent was obtained from participants to contribute to the study (grand number:2022111001).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Appendix 1. 

Please choose the appropriate response that reflects your opinion for each of the following statements.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lv, H., Low, J., Tan, Sk. et al. Factors affecting medical students’ intention to use Rain Classroom: a cross-sectional survey. BMC Med Educ 24, 86 (2024). https://doi.org/10.1186/s12909-024-05037-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12909-024-05037-6

Keywords

BMC Medical Education

ISSN: 1472-6920

Contact us