Application and effectiveness of blended learning in medical imaging via the technology acceptance model

doi:10.21203/rs.3.rs-4866975/v1

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Application and effectiveness of blended learning in medical imaging via the technology acceptance model

https://doi.org/10.21203/rs.3.rs-4866975/v1

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Blended learning, a combination of online learning and face-to-face instruction, is becoming an increasingly important component of higher education technology. However, there is currently insufficient research addressing students' perceptions of blended learning. Our study aims to investigate the satisfaction and behavioral intentions of students with blended learning in medical imaging. We employed the Technology Acceptance Model (TAM), which includes four independent variables, to evaluate students' satisfaction and behavioral intentions toward blended learning. The data were collected through the TAM survey, with questionnaires randomly distributed to the students participating in the "Medical Imaging" blended course at Hainan Medical University. A total of 145 valid questionnaires were returned and analysed via SPSS and Smart-PLS 3.3.3. Detailed results. Our results indicate that the practical application of blended learning has a positive and constructive impact and is worth promoting in higher education institutions. The empirical findings could also contribute to the integration of the TAM model to increase the effectiveness of blended learning for students.

Blended Learning

Technology Acceptance Model

Student Behavioral Intention

Higher Education Technology

Medical Imaging.

Currently, the emphasis on innovation in education, especially leveraging technology to adapt teaching methods to modern demands, is fascinating [1]. Especially in the context of the rapid development of information technology, teaching is vital, along with online training and mobile telecommunications [2]. While considerable research has been conducted on the impact of mobile learning on teaching effectiveness [3–5], studies investigating the sustainability of mobile learning are relatively limited. The blend of online and offline teaching, known as blended learning, enables teachers to play a leading role and monitor students' learning behaviors through mobile teaching via network platforms and internet technology while embodying a student-centered teaching philosophy [6]. It indeed offers significant potential for improving teaching effectiveness and meeting students' evolving needs [7]. Medical imaging, as a critical component bridging basic and clinical medicine, holds significant importance in medical education because of its systematic nature, practical applications, and reliance on information technology. Given the dynamic nature of healthcare education and the evolving demands of the field, traditional offline classroom teaching methods may no longer suffice in fully preparing students for the complexities of medical imaging practice. In response to these challenges, the adoption of a blended teaching model has emerged as a valuable supplement to traditional medical education practices [8–14]. By effectively integrating classroom teaching with information technology, the blended teaching model can revolutionize educational practices, cater to students' developmental needs, and yield optimal teaching outcomes. The focus of this study is to evaluate the application and effectiveness of blended teaching in medical imaging and perform cognitive analysis via the Technology Acceptance Model (TAM). Furthermore, we investigated the advantages and challenges of the blended teaching model in medical imaging education, along with exploring students' cognition and acceptance of this approach. Establishing a new model and conducting confirmatory factor analysis represent rigorous methods to gain deeper insights into how medical imaging students perceive and utilize the blended teaching model.

This study employed a TAM (Fig. 1) to evaluate the impact of the blended teaching mode on personal influence factors (PIFs), external influencing factors (EIFs), and students' behavioral intentions (BIs). Figure 1 also shows the relationships between personal innovation ability and student satisfaction, between task‒technology fit and mobile learning usage intention, and between perceived usefulness and ease of mobile learning in the context of practical student satisfaction (SS). On the basis of this relationship, we propose seven hypotheses concerning how blended teaching may affect the satisfaction and behavioral intentions of students using blended online and offline teaching in medical imaging higher education.

2.1 Personal influence factors (PIFs)

PIFs, consisting of individual innovation ability, learning style, and learning desire, have a great impact on blended learning. Individual innovation ability is a crucial factor influencing the success of the blended learning model [15, 16]. Students who possess strong innovation abilities are more likely to embrace new technologies and methods. Their sensitivity to innovation makes them more receptive to new technologies, leading to increased creativity and acceptance of new learning approaches [17, 18]. Different learning styles can affect students' attitudes and preferences toward blended learning. For example, students with a preference for independent learning may gravitate towards the use of online resources in a blended learning environment. Understanding students' diverse learning styles can help educators tailor their approach to better engage and support learners. The desire to learn through various methods is a significant determinant of the effectiveness of the blended learning model [19]. Students who exhibit a strong motivation to explore different learning approaches tend to be more engaged and proactive in their learning journeys. This willingness to experiment with diverse learning methods enhances the overall learning experience and outcomes in a blended learning setting.

2.2. External influencing factors (EIFs)

EIFs, including environmental pressure, direct experience with individual use, and technological infrastructure, greatly influence the use of blended learning [19–23]. Environmental pressure, which includes social influences and perceptions, can significantly impact the adoption of blended learning models. When individuals perceive social pressure to use specific technologies, such as when peers, school leaders, or teachers endorse certain tools, they may be more inclined to embrace these technologies. Support from the educational community can create an environment conducive to the acceptance and utilization of blended learning approaches [20]. Direct experience with technology plays a vital role in shaping individuals' attitudes toward digital tools in a blended learning context. Positive experiences with technology usage, supported by adequate training and technical assistance provided by schools, can enhance students' willingness to engage with blended learning resources [21]. Satisfactory user experiences contribute to increased acceptance and integration of technology into the learning process. Technological infrastructure serves as a critical external factor influencing the successful implementation and sustainability of blended learning initiatives. The availability of reliable and efficient technological resources directly impacts the effectiveness of blended learning programs and enhances the overall learning experience. A well-established technological infrastructure supports the seamless integration of digital tools, facilitating improved learning outcomes and student engagement [22, 23].

2.3 Perceived Ease of Use (PEU)

PEU refers to users' perceptions of how easily they can understand and use a particular system [24]. In the context of blended learning, high perceived ease of use indicates that students find the system easy to learn and operate without facing significant difficulties or complexities. This perception has a significant effect on shaping students' attitudes and behavioral intentions toward the learning platform. When students perceive a system as easy to use, they are more likely to develop a positive attitude towards it. This positive attitude can lead to continued use of the system and even recommendations to others [25]. Conversely, if they find the system difficult to use, they may develop negative emotions toward it, decrease their usage frequency, or look for alternative solutions. In the context of blended learning, the PEU can greatly enhance the learning experience. A system that is perceived as easy to use can help students more conveniently access learning resources, facilitate communication with teachers and classmates, and navigate the learning platform effectively. These aspects contribute to an overall increase in students' perceived ease of use, positively impacting their engagement with and satisfaction with the blended learning experience [25, 26].

2.4 Perceived usefulness (PU)

The PU refers to an individual’s subjective belief regarding the extent to which using a particular technology or system will be beneficial to their work efficiency [27]. When students perceive a certain technology or system as highly useful, they are more likely to develop a positive attitude towards it and are willing to accept and use this technology. The PU can be influenced by various factors, such as the actual effectiveness of technical functionalities, students’ expectations for work efficiency, and the extent to which the technology aids in task completion [28, 29].

2.5 Student Satisfaction (SS)

SS with blended learning refers to students' perceptions of how well the blended learning model aligns with their expectations and fulfils their needs [28, 30]. It encompasses students' contentment with learning resources, interactions, and support available within the blended learning environment. It serves as a critical measure of the effectiveness and impact of blended learning initiatives in multiple ways, including the alignment of acquired knowledge and skills with students' expectations, the quality of students' learning experiences during the blended learning process, and the extent to which blended learning addresses learning challenges [31–34]. It is one of the important indicators for evaluating the effectiveness of blended learning. By understanding the SS, teachers and educational institutions can adjust teaching strategies and improve teaching design promptly to enhance students' learning experience and outcomes [35]. In addition, the SS also serves as an important metric for decision-makers to evaluate the effectiveness and feasibility of blended learning schemes [36].

2.6 Behavioral Intention (BI)

Student BI in blended learning refers to students' willingness and inclination to use specific technologies or tools within the learning environment [37]. It reflects students' attitudes towards their interest and active engagement with these technologies or tools [38]. Student BI is one of the important factors influencing the actual adoption and utilization of blended learning technologies [39]. When students perceive the technology as easy to use and beneficial, they are more likely to show positive behavioral intentions. This means that they are more willing to accept and use the technologies in blended learning, thereby achieving better learning outcomes and experiences [40]. Therefore, understanding and promoting students' BI is crucial for effectively implementing blended learning.

The model in Fig. 1 addresses the relationships among different variables, proposing a close connection between perceived usefulness and ease of use with personal influencing factors and external influencing factors. Furthermore, the model traced the relationship between perceived usefulness and ease of use with student satisfaction. Ultimately, it focused on the relationship between student satisfaction and students' intention to use. Therefore, we propose the following hypotheses:

H1: Personal influencing factors have a positive effect on perceived ease of use.

H2: External influencing factors have a positive effect on perceived ease of use.

H3: Personally influencing factors have a positive effect on perceived usefulness.

H4: External influencing factors have a positive effect on perceived usefulness.

H5: Perceived ease of use has a positive effect on student satisfaction.

H6: Perceived usefulness has a positive effect on student satisfaction.

H7: Student satisfaction has a positive effect on students’ intention to use.

3.1 Research Design

Blended learning is a teaching model that uses online platforms and digital teaching resources, combining traditional face-to-face teaching with online learning to provide a more enriched, flexible, and personalized learning experience. The advantage of blended learning lies in its full utilization of network technology and digital resources, providing a more flexible and personalized learning method [41, 42]. Students can learn at their own pace and according to their learning needs while also interacting and collaborating with teachers and classmates [43]. Blended learning can also improve the development and utilization efficiency of educational resources and promote the development of educational informatization [44]. This study aims to investigate students' satisfaction with their intention to use blended learning and their perceptions of actual usage in higher education through online teaching.

The entire analysis in this study was divided into two stages. The first stage involved collecting data from university students through a questionnaire survey. The target student group includes undergraduate students majoring in medical imaging and clinical medicine who participate in medical imaging courses and have been fully exposed to blended learning. The questionnaire was designed with a five-point Likert scale [45, 46]. The second stage involved analysing the collected data with the structural equation modelling software Smart-PLS 3.3.3 and SPSS software [47]. The Smart-PLS was used to construct and evaluate the structural equation model on the basis of the survey responses. The validity and reliability of the data in the measurement model were assessed. SPSS was employed to calculate correlations between variables, descriptive statistics, regression analysis, and other statistical tests.

Figure 1 describes the proposed model, which consists of six components: PIF, EIF, PEU, PU, SS, and BI. Seven path lines were proposed between the six groups. Two path lines were proposed for PIF, EIF, PEU, PU, and SS, and one path line was proposed for BI. All the hypotheses were used to predict the six structures [48]. The original survey questionnaire is provided in Supplement Document 1.

3.2 Data collection and demographic characteristics

In this study, 256 surveys based on the TAM model (Table 1) were distributed to undergraduate students majoring in medical imaging and a small portion of clinical undergraduate students who had taken blended learning courses in medical imaging at Hainan Medical University via Questionnaire Star. The anonymous questionnaire focused on students’ opinions on the use of blended learning in terms of student satisfaction and behavioral intentions, as well as the impact of blended learning on sustainable learning measures. A total of 145 valid questionnaires were collected after screening [49]. The sample size can be used to evaluate the structural equation model [50]. Among the 145 collected questionnaires, 57 were from male participants and 88 were from female participants. The participants are between the ages of 18 and 25. The student grades (years of admission) ranged from 2018–2022 (Table 2).

Table 1

Questionnaire Design Based on the TAM
Question Design
Perceived Usefulness (PU)	PEU 1	The content of blended teaching is rich and practical.
	PEU 2	Blended teaching can improve learning efficiency.
	PEU 3	Blended teaching can enhance learning interest.
Perceived Ease of Use (PEU)	PU 1	Blended teaching makes it easier to find the necessary learning resources.
	PU 2	Blended teaching facilitates communication and interaction with teachers and classmates.
	PU 3	Blended teaching can improve learning effectiveness.
Personal Influencing Factors (PIF)	PIF 1	You are more willing to use new technologies and methods in daily learning.
	PIF 2	You prefer independent learning over collaborative learning.
	PIF 3	You hope to better grasp knowledge through different learning methods.
External Influencing Factors (EIF)	EIF 1	School leaders and teachers encourage the use of blended teaching.
	EIF 2	You have participated in blended teaching training and received technical support provided by the school.
	EIF 3	You believe that the technological infrastructure provided by the school makes blended teaching easier to implement.
Student Satisfaction (SS)	SS 1	Blended teaching improves learning effectiveness, increases learning enjoyment, and enhances learning motivation.
	SS 2	Blended teaching can enhance learning experience and efficiency.
	SS 3	Blended teaching provides sufficient technical support to help you solve problems encountered in learning.
Behavioral Intention (BI)	BI 1	You are willing to continue using this teaching method in the future.
	BI 2	You are willing to actively participate in learning in blended teaching.
	BI 3	You are willing to recommend the blended teaching model to others.

Table 2

Demographic characteristics
Category	Number	Percentage (%)
Gender
Male	57	39.31
Female	88	60.69
Age
18–19	35	24.14
20–21	35	24.14
22–23	57	39.31
23–25	18	12.41
Academic Year
2018	47	32.41
2019	26	17.93
2020	19	13.10
2021	15	10.34
2022	38	26.21
Major
Medical Imaging	134	92.41
Clinical Medicine	11	7.59

3.3 Data analysis

Smart-PLS 3.3.3 and IBM SPSS 26 software were used to perform structural equation modelling analysis on the collected data [51, 52]. To test the validity of the data, we reported the convergence validity and discriminant validity. Convergence validity was calculated via the average variance extracted (AVE) formula, with an AVE value of 0.500 indicating satisfactory convergence validity. Discriminant validity was obtained via the Fornell–Larcker criterion and cross-loading analysis. Moreover, internal consistency reliability (ICR), an essential measure of data reliability, was evaluated via two methods: composite reliability (CR) and Cronbach's alpha (CA). Both CA and CR values exceeding 0.700 indicated satisfactory reliability. For model evaluation, the significance of the relationship within the structural equation model was assessed through path coefficients, t values, and P values.

4.1 Loadings of reflective indicators

Confirmatory factor analysis was carried out by constructing a structural equation. Items with low standardized factor loading coefficients of measurement variables and latent variables were deleted [51]. The standardized factor loading coefficients of all the observed variables were greater than 0.7, indicating good reliability [53, 54]. Except for the loading of PIF2 (0.470), all the other indicators had loadings greater than 0.7 (Table 3). The 17 indicators were included for further analysis.

Table 3

Summary of Reliability and Validity Indicators
Constructs	Indicators	Cronbach's alpha	Composite reliability (rho_a)	Composite reliability (rho_c)	Average variance extracted (AVE)
BI	BI 1 - BI 3	0.925	0.934	0.952	0.869
EIF	EIF 1 - EIF 3	0.857	0.865	0.913	0.777
PEU	PEU 1 - PEU 3	0.928	0.932	0.954	0.875
PIF	PIF 1, PIF 3	0.810	0.818	0.913	0.840
PU	PU 1 - PU 3	0.942	0.943	0.963	0.896
SS	SS 1 - SS 3	0.962	0.963	0.976	0.930

4.2 Internal Consistency Reliability

Table 3 provides the CA and CR values of all the structures greater than 0.7 [51]. The CA and CR values of the PIF method were 0.810 and 0.818, as well as 0.857 and 0.865 for the EIF, 0.928 and 0.932 for the PEU, 0.942 and 0.943 for the PU, 0.962 and 0.963 for the SS, and 0.925 and 0.934 for the BI.

4.3 Convergent Validity Test

The AVE values of all the structures exceeded 0.500. Specifically, the AVE values of PIF, EIF, PEU, PU, SS, and BI were 0.840, 0.777, 0.875, 0.896, 0.930, and 0.869, respectively. (Table 3)

4.4 Discriminant validity test

According to the Fornell–Larcker criterion, to judge whether two latent constructs have discriminant validity, the following conditions must be met: (1) the AVE value of each latent structure must be greater than the square of its correlation coefficient with other constructs; (2) the AVE value of the latent structure must be greater than its cross-loadings with other constructs. The Fornell‒Larcker correlation matrix between each variable value is shown in Table 4.

Table 4

Fornell–Larcker correlation matrix.
	BI	EIF	PEU	PIF	PU	SS
BI	0.932
EIF	0.838	0.882
PEU	0.835	0.732	0.935
PIF	0.756	0.724	0.726	0.917
PU	0.869	0.710	0.874	0.685	0.946
SS	0.937	0.833	0.840	0.708	0.850	0.964

In addition, the discriminant validity of items can also be evaluated by calculating cross-loadings [51, 55], that is, correlating each item with its own latent variable and other latent variables to evaluate the relationships between the item and other latent variables. If the correlation coefficient between an item and its latent variable is significantly greater than the correlation coefficient with other latent variables, then the item can be considered to have good discriminant validity, as shown in Table 5.

Table 5

Summary of Cross-Loadings
	BI	EIF	PEU	PIF	PU	SS
BI1	0.954	0.775	0.797	0.681	0.809	0.899
BI2	0.949	0.811	0.782	0.676	0.827	0.933
BI3	0.893	0.759	0.757	0.770	0.796	0.776
EIF1	0.776	0.880	0.707	0.738	0.657	0.729
EIF2	0.686	0.863	0.532	0.605	0.579	0.672
EIF3	0.748	0.902	0.680	0.566	0.636	0.795
PEU1	0.734	0.615	0.915	0.624	0.785	0.749
PEU2	0.801	0.728	0.945	0.701	0.843	0.802
PEU3	0.804	0.706	0.945	0.709	0.822	0.804
PIF1	0.712	0.668	0.681	0.926	0.684	0.656
PIF3	0.671	0.660	0.650	0.907	0.566	0.643
PU1	0.799	0.644	0.782	0.659	0.940	0.751
PU2	0.843	0.689	0.830	0.652	0.960	0.837
PU3	0.823	0.683	0.867	0.635	0.939	0.824
SS1	0.876	0.812	0.772	0.653	0.801	0.955
SS2	0.905	0.792	0.832	0.714	0.834	0.976
SS3	0.928	0.807	0.825	0.681	0.825	0.963

4.5 Structural Model and Collinearity

The predictive ability of the structural model was tested as a part of the evaluation. Collinearity is represented by reporting the variance inflation factor (VIF) values. A VIF greater than 5 is usually considered to indicate multicollinearity [56]. In this study, the VIF values of all the structures were less than 5 (Table 6).

Table 6

Variance inflation factor (VIF).
	BI	PEU	PU	SS
BI
EIF		2.105	2.105
PEU				4.232
PIF		2.105	2.105
PU				4.232
SS	1.000

4.6 Structural Model Hypothesis Testing

Table 7 and Fig. 2 show the bootstrap calculation results for all the structures. Generally, an absolute value greater than 0.3 can be considered to have a significant impact, and an absolute value greater than 0.5 can be considered to have a major impact [57–60]. For PIF → PEU (PC = 0.412; t = 3.659; P < 0.001), the corresponding hypothesis (H1) was supported. For PIF → PU (PC = 0.359; t = 3.506; P < 0.001), the corresponding hypothesis (H2) was supported. For hypotheses H3 and H4, EIF → PEU (PC = 0.434; t = 4.132; P < 0.001) and EIF → PU (PC = 0.450; t = 4.028; P < 0.001), the corresponding hypotheses were supported. Moreover, there was a significant influence between PEU → SS (PC = 0.410; t = 2.737; P = 0.006) and PU → SS (PC = 0.492; t = 3.338; P = 0.001); thus, the corresponding hypothesis (H5, H6) was supported. The relationship between SS → BI (PC = 0.937; t = 68.891; P < 0.001) indicated that the corresponding hypothesis (H7) was supported.

Table 7

Model Hypothesis Testing
Path of Hypotheses	Path coefficients	Original sample (O)	Sample mean (M)	Standard deviation (STDEV)	T statistics (\|O/STDEV\|)	P values	Results
SS → BI	0.937	0.937	0.937	0.014	68.891	< 0.001	Supported
EIF → PEU	0.434	0.434	0.432	0.105	4.132	< 0.001	Supported
EIF → PU	0.450	0.45	0.446	0.112	4.028	< 0.001	Supported
PEU → SS	0.410	0.41	0.409	0.15	2.737	0.006	Supported
PIF → PEU	0.412	0.412	0.418	0.113	3.659	< 0.001	Supported
PIF → PU	0.359	0.359	0.366	0.102	3.506	< 0.001	Supported
PU → SS	0.492	0.492	0.492	0.147	3.338	0.001	Supported

The evolution of technology has profoundly influenced universities and the education system, necessitating the adoption of advanced technological systems. The digital transformation has revolutionized the flow of information, enabling innovative interactions among users through engaging multimedia applications [61–63]. Building on these technological advancements, this study developed a conceptual model to explore the continued use of teaching methods in the educational environment, with a particular focus on online training and mobile telecommunications. PEU is considered one of the key factors in the conceptual model, influencing both internal students and external teaching factors. It encompasses students' innovativeness, learning aspirations, environmental pressures, direct user experience, and technology infrastructure development [64–65]. On the other hand, it directly influences students’ satisfaction with continuing to use educational technologies, subsequently affecting their behavioral intentions toward blended learning [66]. By elucidating the relationships between perceived ease of use, student satisfaction, and behavioral intentions, this study sheds light on the critical role of technology acceptance and usability in promoting effective blended learning practices. Understanding these dynamics can inform educational strategies, instructional design, and technology integration efforts to optimize student learning outcomes and experiences.

The goal of this study was to evaluate students' perceptions of blended learning via the TAM framework. Despite its limited adoption in higher education, the findings indicated that students perceived value in blended learning and were open to embracing it. Constructs from the TAM model, such as PIF, EIF, PU, PEU, SS, and BI, were identified as positive factors driving the use of blended learning among university students, especially in medical imaging courses. Unlike previous research on mobile learning, this study highlighted the importance of investigating student satisfaction and intentions regarding blended learning. These findings suggest that universities should prioritize PU, PEU, and SS to foster student acceptance of blended learning. Future researchers should focus on the broad implementation of blended learning, explore planning strategies for PIFs and EIFs, and assess their potential in education. By integrating various internal and external factors with TAM structures, this study contributes to facilitating the adoption and acceptance of research. It distinguished itself by examining individual differences such as student satisfaction and behavioral intentions, thereby enhancing the understanding of blended learning models. Overall, this study advances empirical research in the application and promotion of blended learning.

The present study had several limitations. First, the conceptual model was confined to a specific set of internal and external factors directly linked to the TAM constructs. Future research could integrate additional influencing factors to provide a more comprehensive understanding of relevant technologies. Second, the sample is restricted to the students enrolled in a medical imaging course, which primarily consists of medical imaging majors. To gain insights into the attitudes of students from diverse majors towards blended learning, future investigations could consider exploring attitudinal differences across various courses and majors. Third, the survey was conducted via internet and social media platforms. There is potential for future research to explore alternative survey distribution methods to enhance data collection strategies and reach a more diverse participant pool.

Our results revealed that students exhibited belief in and readiness to accept the blended learning model, indicating a positive outlook towards this innovative approach to learning. The TAM has made significant contributions to understanding students' perceptions in this context. Future research should focus on investigating planning recommendations for PIFs and EIFs and evaluating the anticipated effectiveness of PIFs and EIFs in educational settings, considering the widespread adoption of blended learning.

Acknowledgements

The authors extend their appreciation to the teachers at Hainan Medical University who have conducted blended teaching in medical imaging.

Author contributions

Xiaofen Sun: Conceptualization, Data curation, Formal Analysis, Methodology, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. Jianghua Wan: Conceptualization, Data curation, Formal Analysis, Investigation, Software, Supervision, Writing – original draft, Writing – review & editing. Zhiqun Li: Formal Analysis, Investigation, Methodology, Validation.

Rong Tu: Conceptualization, Project administration, Supervision. Juan Lin: Data curation, Investigation. Xiaohua Li: Data curation, Investigation, Methodology. Jianqiang Chen: Conceptualization, Data curation, Project administration, Writing – review & editing.

Funding

This work was supported by the Education Department of Hainan Province (Grant Numbers: Hnjg2024ZC-70, Hnjgzc2023-20, 22A200069), and Hainan Medical University (Grant Numbers: HYYB202230, HYYB202380, HYKCPY202307, RZ2300005679). These institutions provided financial support but did not involve in study design, data collection, analysis, or interpretation. The financial supporters had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Data availability

The data used to support the findings of this study are included within the article.

Ethical approval and consent to participate

This study has been approved by the Biomedical Ethics Committee of the First Affiliated Hospital of Hainan Medical University (Approval No: 2024-KYL-176). To ensure confidentiality, the collection of participants’ questionnaires and the related analysis were conducted anonymously. We provided a detailed explanation of the study objectives to all students who participated in the blended teaching of medical imaging. Informed consent forms were distributed to all students, and written informed consent was obtained from all participants before their participation in the questionnaire survey. The survey was conducted under conditions that protected the privacy, confidentiality, and anonymity of participant information. Participants voluntarily chose whether or not to participate in the research and were informed of their right to withdraw from the study at any time without penalty.

Human Ethics and Consent to Participate declarations
The ethical aspects of this study have been reviewed and approved by the Biomedical Ethics Committee of the First Affiliated Hospital of Hainan Medical University, and informed consent was obtained from all individual participants involved in the study.

Competing interests

The authors declare no competing interests.

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Application and effectiveness of blended learning in medical imaging via the technology acceptance model

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Abstract

Figures

1. Introduction

2. Hypotheses and Model Building

2.1 Personal influence factors (PIFs)

2.2. External influencing factors (EIFs)

2.3 Perceived Ease of Use (PEU)

2.4 Perceived usefulness (PU)

2.5 Student Satisfaction (SS)

2.6 Behavioral Intention (BI)

3. Research Methodology

3.1 Research Design

3.2 Data collection and demographic characteristics

3.3 Data analysis

4. Results

4.1 Loadings of reflective indicators

4.2 Internal Consistency Reliability

4.3 Convergent Validity Test

4.4 Discriminant validity test

4.5 Structural Model and Collinearity

4.6 Structural Model Hypothesis Testing

5. Discussion

6. Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

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