Evaluation and Examination of Factors Influencing the Continued Effective use of E-learning Among Faculty Members of Medical Sciences Universities

doi:10.21203/rs.3.rs-5000046/v1

Background

E-learning facilitates the learning process for medical students by allowing them to enhance their knowledge, skills, and professional experiences, thereby contributing to the improvement of quality and standards in education. The objective is to investigate the sustainability of the usage of e-learning systems among the members of the academic faculty.

Methods

A researcher-constructed questionnaire was distributed to collect data from approximately 300 faculty members of medical sciences universities who have used the e-learning system for at least one year. Out of these, 120 questionnaires contained valid responses. The data were processed using statistical software and the statistical analysis technique based on (SEM).

Results

The results indicated that among the demographic factors of the members, age has a significant impact on the continued use of the electronic learning system. Among the two most influential factors on the continued use of the system, perceived usefulness and satisfaction, perceived usefulness was not influential in our study. This means that the members are aware of the benefits of face-to-face instruction compared to system use, which is due to the system being simple and basic without features.

Conclusion

In our study, incentive factor was not influential, which is partly due to the alignment of objectives between university management and faculty members for the use of the electronic system. By presenting more complex systems that incorporate decision support and virtual training, perceived usefulness could be somewhat increased. Additionally, the provision of blended systems, combining face-to-face and virtual modalities, could compensate for the perceived usefulness.

Electronic education

eLearning system

acceptance of eLearning system

Faculty members

The increasing need for education, lack of access to educational centers, economic limitations, scarcity of experienced instructors, and high education costs (1), as well as most importantly, the recent years' outbreak of contagious diseases like the coronavirus, have led to the widespread use of information technologies and concurrently brought a large population of learners under the ambit of e-learning. E-learning, as a novel method in education, has significantly impacted institutions and organizations, but its primary effect has been on universities and higher education centers (2).

The electronic learning system pertains to that segment of the educational system which utilizes information technology capabilities to implement learning programs. E-learning is one of the most popular learning environments and constitutes one of the most significant applications of information technology, offered in various formats such as Computer-Based Learning (CBL), Online Learning, Off-Line Learning, Network-Based Learning, and Web-Based Training. Electronic learning systems contribute to the effectiveness and flexibility of timing and location (3). Although it was first used in teachings that employed the internet for instruction, e-learning is a set of educational activities carried out with electronic tools, including audio and visual, computerized, and network resources.

The aim of electronic education is to integrate and coordinate new awareness regarding the new technologies used in higher education institutions. The framework of electronic education, through the utilization of web-based technologies and electronic learning activities, is growing to enhance active group learning among students. It is quite clear that the use of technology for learning and assessment allows students to carry out their tasks without the constraints of time and place, leading to peer collaboration, receiving feedback from instructors, and fostering cooperation that consider learning and assessment as an outcome for students (4, 5).

Although in recent years, the Ministry of Health and Medical Education has consistently emphasized the provision of medical sciences education in cyberspace, in reality, there has not been a widespread movement in this area across the country, and only some universities have initiated virtual courses. However, with the outbreak of COVID-19 since February 2020 in Iran, schools and universities were closed to break the chain of transmission and prevent further spread of the disease, and gradually, virtual education replaced in-person education (6).

In the field of medical sciences education, although initially, with the outbreak of COVID-19, education was offered in an unstructured manner through social networks, over time, medical sciences universities were required to use the Noorid system (a specialized academic learning software) as a centralized educational platform, and professors and students, after registration, could benefit from the capabilities of this system. This system had been designed before and had been used in some universities. Although it had not been universally employed across all medical sciences universities, the COVID-19 crisis led to its widespread implementation (6).

With the continuous growth and development of information technologies and efforts to implement electronic learning systems in universities and higher education centers, the issue of acceptance and continued use of these systems after being adopted by users (instructors and learners) has become a significant matter (7, 8). Non-continuance of the use of information systems, especially electronic learning systems, is one of the significant challenges facing university authorities and educational policymakers in higher education centers, and necessary planning and actions must be taken in this regard. To identify factors that affect the continued use of technologies and information systems, models have been presented.

To investigate and determine the factors influencing the continuance of the use of information systems in various organizations and sectors, models similar to those for the adoption and usage of technology have been presented. In recent years, many studies regarding the continuance of technology use have been conducted with the help of these models (9, 10). Past studies have emphasized the distinction and difference between technology acceptance models and technology continuance usage models (11).

The Information Systems Continuance Model, which was introduced by Bhattacherjee in 2001, is generally considered one of the most important models in university environments and electronic learning centers for determining factors influencing the intention to use information technology. In this model, four main variables are present: perceived usefulness, task confirmation, satisfaction, and the intention to continue use (Fig. 1) (12).

According to Nadri et al.'s definition of perceived usefulness, perceived usefulness means the degree to which a person believes that using a particular system will enhance their performance (13). The intention to continue use reflects the desire and intent of a user to continue using a system in the near future (14). Satisfaction occurs when feelings about unmet expectations are accompanied by the consumer's prior emotions regarding the experience. Satisfaction denotes a pleasant and positive emotional state that results from the evaluation of one's own work.

Expectation confirmation indicates the congruity between users' expectations of a technological system and their actual experiences from its continuous use (11). Moreover, recent studies have been conducted on the continuance use of electronic learning systems. Cheng, in 2014, examined the factors affecting the continuance usage of electronic learning among nurses and demonstrated that perceived usefulness had a significant impact on satisfaction, and that both perceived usefulness and satisfaction significantly affected the intention to continue using the system (15). In another study on this topic, quality confirmation, disconfirmation of quality, value, and disconfirmation of value had a positive effect on satisfaction, and disconfirmation of usability, innovation, and optimism about using the system ultimately led to continued use of e-learning in public organizations in Brazil (16). The study by Famoso et al. also showed that effectiveness, perceived ease of use, and perceived usefulness were the most important factors affecting the continuance of use of e-learning among students (17).

Considering the adoption of electronic learning systems in medical education as a result of COVID-19 in medical universities, the continued use of these information systems after the end of the COVID-19 pandemic with the resumption of in-person education at universities can, in addition to preventing the waste of resources spent on developing these systems (including design, development, and maintenance costs), assist in transforming technology-based learning (e-learning) into a modern educational culture in the field of medical sciences. Considering that preliminary studies by researchers have indicated that no study in this area has been conducted in Iranian medical universities, the present study aimed to determine the factors affecting the intention to continue using e-learning by employing a modified model for e-learning continuance from the perspective of faculty members at medical universities in the year 2022/2023.

For data collection, a researcher-developed questionnaire based on two models of technology continuance use will be utilized. After preliminary design, the face validity of the questionnaire was assessed by the opinions of 5 faculty members. Then, the content validity of the questionnaire, including the Content Validity Ratio (CVR) and Content Validity Index (CVI) of each of the questions in terms of necessity and relevance, was measured by the views of 10 faculty members. After verifying the validity of the questionnaire, its reliability was also measured using Cronbach's alpha statistical test. The study population includes all academic faculty members of the Ministry of Health. To determine the sample size, the sample size formula for estimating a population proportion was used, which resulted in a minimum sample size of 300 individuals with a 95% confidence level, a standard deviation of 0.5, and a margin of error of +/- 5%. The inclusion criterion for the professors was at least one year of experience using the electronic learning system. Upon reviewing the questionnaires, 120 of them met the entry criteria for the study. The data analysis tools used were the SMART PLS 4 software for structural equation modeling and the SPSS 20 software for statistical analysis.

Out of 300 questionnaires sent to faculty members of medical sciences universities, 120 were deemed valid for inclusion in the study. As shown in Table 1, among the participants, there were 60 women and 60 men. The majority of the faculty members were aged between 41–45 years. 103 (85.8%) of the participants were members of the basic sciences faculty, and the rest were clinical members of the university. The academic ranks of the members were as follows: instructor, assistant professor, associate professor, and full professor, with numbers at 19 (15.8%), 77 (64.2%), 18 (15%), and 6 (5%) respectively. Regarding the academic education level, about 19 people (15.8%) had a master's degree, 99 (82.5%) had a doctoral degree, and 2 (1.7%) had a subspecialty or fellowship qualification.

Table 1

The demographic information of the faculty members using the electronic learning system
Item	Category	Numbers and Percentage(%)	Item	Category	Numbers and Percentage(%)
Gender	Male	60 (50)	Education degree	Master	19 (15.8)
Gender	Female	60 (50)		Doctoral	99 (82.5)
Age	25–30 y old	6 (5)		Specialist	2 (1.7)
	31–35 y old	20 (16.6)	scientific rank	Instructor	19 (15.8)
	36–40 y old	46 (38.3)		Assistant professor	77 (64.2)
	41–45 y old	19 (15.8)		Associate professor	18 (15)
	46–50 y old	9 (0.7)		professor	6 (5)
	51–55 y old	12 (1)	Type faculty	Base science	103 (85.8)
	Over 55 old y	6 (0.5)	Type faculty	Clinical science	17 (14.2)

Table number 2 demonstrates the impact of demographic information on variables related to the continued use of the educational system. As detailed in the table, most demographic variables, except for the age of the members, do not affect the factors contributing to the continued use of the system. The age factor significantly influences expectations confirmation, goal congruence, and ultimately the continued use of the system.

Table 2. Examining the relationship between age, gender, academic rank, university rank and experience on latent variables

Type faculty	Gender	Experience	Level of Education	Scientific rank	Age	Variables
Independent Samples Test	Independent Samples Test	ANOVA (p-Value)	ANOVA (p-Value)	ANOVA (p-Value)	ANOVA (p-Value)	Variables
0.299	0.614	0.545	0.624	0.123	0.134	Perceived usefulness
0.566	0.863	0.482	0.513	0.558	0.030	Expectation confirmation
0.101	0.881	0.179	0.460	0.870	0.051	Satisfaction
0.607	0.174	0.212	0.117	0.248	0.718	Incentives
0.226	0.578	0.200	0.861	0.673	0.007	Goal congruence
0.096	0.773	0.038	0.467	0.244	0.059	Continuance intention

In structural equation modeling, the first step involved checking the fit of the research model to the sample population. Following this, a confirmatory factor analysis was conducted for the variables and their dimensions. After that, the measurement model was assessed. Before evaluating the measurement model, confirmatory factor analysis was conducted separately for all variables that had more than three items considered for their measurement.

In Smart PLS software, the factor loadings between manifest items (questions) and latent variables (constructs) are generally expected to exceed 0.7. If the factor loadings are less than this threshold, it suggests that the questions may not be consistent with the other items in the construct and might need to be removed from the questionnaire. As we can see in Fig. 2, all the factor loadings for the questionnaire items exceed this number. The loadings of both indicators and dimensions surpass the threshold of 0.707. Therefore, the indicators and dimensions are considered acceptable, which implies that our questions are valid and consistent.

The goodness-of-fit indices obtained from the model are shown in Table 3. The reliability of the model is evaluated through Cronbach's alpha and the composite reliability coefficient, while the convergent validity of the questionnaire is determined through the average variance extracted (AVE). Additionally, discriminant validity is obtained. The constructs demonstrate high internal consistency, as their composite reliability indices are above 0.7. Furthermore, convergent validity is achieved for all latent variables since the AVE exceeds the benchmark of 0.5. This implies that a significant amount of variance in the indicators is accounted for by the latent constructs, indicating adequate model reliability and validity.

Table 3

Goodness and fit indices as well as Cronbach's alpha of the model
Variables	Cronbach's Alpha	rho_A	Composite Reliability	Average Variance Extracted (AVE)	Q²
Perceived usefulness	0.945	0.947	0.958	0.82	0.438
continuance intention	0.916	0.932	0.939	0.756	0.438
Goal congruence	0.915	0.919	0.94	0.797
satisfaction	0.911	0.915	0.938	0.79	0.547
Expectation confirmation	0.86	0.879	0.892	0.544
Incentives	0.727	0.829	0.84	0.644

Finally, Table 4 shows that the square root of the Average Variance Extracted (AVE) for each latent variable is greater than the correlation of that latent variable with any other latent variable. This indicates that the latent variables have greater explanatory power over their own indicators than over those of other variables, affirming the presence of discriminant validity in the model. This ensures that each construct is adequately differentiated from the others, which is crucial for the structural model's validity.

Table 4

Discriminant validity
Variables	Expectation confirmation	Continence intention	Goal congruence	Satisfaction	Perceived usefulness	Incentives
Expectation confirmation	0.738
Continence intention	0.701	0.869
Goal congruence	0.726	0.75	0.893
Satisfaction	0.82	0.738	0.781	0.889
Perceived usefulness	0.64	0.687	0.753	0.734	0.906
Incentives	0.466	0.42	0.483	0.476	0.399	0.803

The t-statistic test results showed that the incentives variable has a negligible impact on the intention to use the e-learning system, and therefore hypothesis H8 is rejected. One of the most important variables that has been emphasized in most acceptance models is perceived usefulness, and in our study, it was shown that this variable has a negligible direct effect on the intention to continue using the e-learning system. However, it does have an indirect effect through satisfaction, which has a significant impact on continued use. Therefore, hypothesis H4 is rejected, while hypotheses H3 and H5 are accepted. The variable "goal congruence " directly influences the intention to use and has a significant direct effect on perceived usefulness. Therefore, in our statistical tests, hypotheses H6 and H7 are accepted. Another significant variable that has an indirect effect on the intention to use is " Expectation confirmation," which influences both satisfaction and perceived usefulness. As a result, hypotheses H1 and H2 are accepted.

Table 5

path coefficient, t-statistics, and research hypothesis results
Research hypothesis		Path coefficient	t-statistics	R-squared correlation (r²)	Sig	Results
Expectation confirmation → perceived usefulness	H1	0.198	2.190	0.585	0.05 > p	Confirm
Goal congruence → perceived usefulness	H6	0.609	3.737	0.585	0.01 > p	Confirm
Expectation confirmation → Satisfaction	H2	0.594	11.437	0.747	0.01 > p	Confirm
Perceived usefulness → Satisfaction	H3	0.354	5.759	0.747	0.01 > p	Confirm
Perceived usefulness →continence intention	H4	0.172	1.775	0.634	p > 0.05	Reject
Satisfaction → continence intention	H5	0.319	2.722		0.01 > p	Confirm
Incentives → continence intention	H8	0.026	0.475		p > 0.05	Reject
Goal congruence → continence intention	H7	0.359	3.437		0.01 > p	Confirm

The use of virtual learning can offer medical students easier access to a variety of up-to-date educational resources, time flexibility, increased interaction with virtual platforms, diversity in teaching methods, and immediate evaluation and feedback. However, these advantages are not one-sided and can conversely be utilized by educators, who in this context are faculty members. Yet, the adoption of this technology is often accompanied by behavioral challenges that are more significant than technical challenges, and this has been cited as an important factor in the lack of continued use by faculty members (10). Upon examining the impact of the demographic variables of the participants in the study, it was determined that most of the demographic factors of the members, except for age, have no effect on the continued use of the system. In other words, as age increases, the likelihood of continued use of the electronic learning system decreases.

Through the analysis of human factors using the established model for system learning acceptance and a questionnaire, it was determined which factors are impactful and which are not. Perceived usefulness is a crucial concept in the practical application of information technology, referring to how users evaluate the needs, desires, and expectations concerning an information technology system. This concept is significant because it aids in developing and adopting information technology systems that are better aligned with users' needs and desires. On the other hand, this variable has been effectively confirmed in most accepted systems. Although this variable was not directly confirmed in our study, it has an indirect effect by impacting user satisfaction and thus influences the continuity of system use. By strengthening this variable and gaining an accurate understanding of user needs and expectations, creating systems that meet these needs, we can improve the performance and effectiveness of technology systems and consequently assist users. Ultimately, this can lead to the successful adoption and use of these systems. Satisfaction and perceived usefulness are respectively the most important factors in the adoption process of learning systems. Chi-Min Hsu and colleagues (18) in their study of nurses' intentions to use a blended e-learning system (BELS), demonstrated that perceived usefulness and satisfaction have high predictive rates in usage behavior, with perceived usefulness being the strongest predictor of behavioral intent in the use of electronic learning systems.

Another important and comprehensive parameter in system acceptance is the satisfaction variable, which represents the level of contentment and pleasure that users derive from using an information system. According to Bhattacharjee's study, this variable is the strongest predictor of system acceptance (11). The satisfaction variable is determined based on the experiences of users and their reactions to the system. The degree of satisfaction provides us with a better understanding of the functioning and quality of information systems and prompts corrective and improvement actions to enhance system performance. This factor was confirmed in our study, with various elements influencing user satisfaction, including the system's robustness and efficiency, interface and user experience design, information quality, support and after-sales services, and system capabilities. By measuring, assessing, and improving the different factors that affect user satisfaction, we can facilitate continuous improvement in the performance and acceptance of information systems. Our study aligns with the findings of Ma, Li, and Kafashi (18–20). Alshurideh et al. demonstrated that the intention to continue using an e-learning system is determined by satisfaction and perceived usefulness, with satisfaction also being influenced by the expectations confirmation from the system in addition to perceived usefulness (21).

The variable of goal congruence is another key factor in system acceptance. This variable indicates the degree of alignment between the system's actual capabilities and the users' expectations (22). In other words, it reflects the extent to which faculty members support the higher-level goals set by university management. This variable allows faculty members to understand whether their information systems meet their needs and expectations. In our study, this variable has a direct impact on the level of acceptance and usage. Goal congruence may reduce the need for costly motivational mechanisms. If goal congruence is achieved, instructors will align themselves with the higher-level objectives defined by management. Faculty members will act in favor of management, as their inclination to feel effective in achieving valuable outcomes will have a positive impact (22). With less alignment between goals, there is a need for more incentives to reach the set objectives. In our study, goal congruence had a positive impact on both perceived usefulness and the continuity of system use. Therefore, it is evident that incentives have a lesser impact on system use continuity in our study and well demonstrate the alignment between university management and faculty.

Another key factor in system acceptance is the provision of incentives. The study indicated that incentives as a parameter in system acceptance were ineffectual and therefore rejected. As mentioned above, this factor is somewhat derived from the goal congruence factor; nonetheless, it has a significant impact on the level of motivation and users' inclination to use these systems. Offering appropriate incentives can accelerate improvements in the adoptability and usage of information systems, leading to increased participation and interaction of users with these systems. The importance of incentives in system adoption can increase user motivation, improve participation and interaction, enhance user satisfaction, stimulate action, and boost commitment and productivity. Providing benefits and rewards that satisfy users' needs and expectations can foster continuous improvement in activity and acceptance of these systems, thereby promoting the success of information technology projects. Dierck suggests that organizational incentives, such as rewards for electronic training, boost faculty motivation for the sustained use of electronic technologies for instructional courses (23). A good approach in providing incentives could involve incorporating a motivational mechanism within the e-learning system itself to record and monitor points for instructors. According to Rahimi et al. (24), a reward system is one of the five key motivational elements in education. Such mechanisms can provide immediate feedback and recognition for instructors' efforts, which can further encourage the adoption and continuous use of the e-learning platform.

Expectations confirmation is a vital aspect of how users interact with new technology. This sentence means that users feel the system has met or even exceeded their initial expectations. This concept is deeply rooted in the Expectation Confirmation Theory (ECT), which is derived from consumer behavior literature and adeptly applied in understanding user satisfaction and the intention to continue using information systems research (20). By meeting or surpassing initial expectations, a system can significantly influence a user's perception of its usefulness and their subsequent engagement with the technology. Expectations confirmation can act as a feedback loop that influences users' beliefs and attitudes towards an information system, consequently affecting subsequent use and leading to the overall success or failure of the system within the user community. The impact of this variable on perceived usefulness was confirmed in our study. Our findings are in alignment with the study by Alshurideh et al. (21), indicating the importance of satisfying users' initial expectations to foster a positive long-term engagement with the information system.

E-learning systems vary in scope and scale depending on the types of education and users they serve. Some systems include a range of educational resources, decision-support systems, gamification, virtual simulations, etc., while others might be essentially video conferencing applications with file-sharing capabilities and no additional features. A number of influencing factors based on the type of these systems could be incorporated into future studies, which are specific to certain aspects of education. This can help in tailoring e-learning platforms to better meet the specific needs and preferences of different educational contexts and user groups. Most educational systems for medical sciences were not advanced before the COVID-19 pandemic. With the outbreak of the coronavirus, systems were urgently designed to prevent interruption in education, which often lacked specific features and simply facilitated video or audio communication. The success of an e-learning system depends on various factors from different perspectives, such as the system itself, institutional support, instructors, and students (25). The pandemic expedited the adoption of these systems and has highlighted the need for continual enhancement to support the complexity of medical education. Indeed, our study has examined a general aspect of acceptance factors that can be applied to most interactive educational systems, regardless of their features. The broad applicability of these findings can help educators and system developers understand what drives user adoption and engagement across a variety of e-learning environments.

It is recommended that e-learning systems in medical sciences, which are much more complex than other types of education, should be more than just a communication tool and should be equipped with the necessary information and resources tailored to the type of content and the end-user. A variety of educational features, such as decision-support systems, gamification, virtual simulators, natural language processing capabilities, and integration with medical scientific databases, can significantly impact user satisfaction and perceived usefulness, consequently increasing the likelihood of continued use of the system by instructors and students alike. These enhancements would contribute to a more engaging and effective learning experience in medical education. It is advisable in the post-COVID era to design and evaluate an efficient system for virtual education using system evaluation models, taking into account surveys from end-users, namely educators and students. Such a system would ideally be hybrid, providing synchronous and asynchronous, as well as face-to-face and remote learning options. This would allow for flexibility in educational delivery and cater to different learning styles and preferences, while also being robust enough to adapt to any future disruptions or changes in learning environments.

Conflict of interest

There is no conflict of interest.

Consent for publication

Not applicable.

Ethical approval and consent to participate

Permission was obtained from the Lorestan University of Medical Science and Research Ethics Committee. Written informed consent was obtained from all participating students. Clinical trial number: not applicable. All methods were performed in accordance with relevant guidelines and regulations, including the principles outlined in the Declaration of Helsinki.

Competing interest

The authors declare no competing interests

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author Contribution

Study conception and design: A.G, N.A, H.N. Data collection: A.G, N.A, H.N. Data analysis and interpretation: A.G, N.A, P.A, H.N. Drafting of the article: H.N, A.G. Critical revision of the article: A.G, N.A, P.A, H.N.

Acknowledgement

We extend our gratitude and appreciation to everyone who contributed to this research and aided in the provision of e-learning education. Your efforts have been invaluable in advancing our understanding and implementation of educational technologies.

Availability of data and materials

The datasets generated and/or analysed during the current study are not publicly available due to ethical constraints but are available from the corresponding author on reasonable request.

Farhadi R. E-learning A new paradigm in the age of information. Iran J Inform Process Manage. 2005;21(1):49–66.
Nouri Hasanvand F, Nadri H, Nadri P, Alipouri F. Evaluation and factors of the continuation of the use of e-learning and education among student teachers of primary education in Lorestan province. Quartely J Teacher Educ. 2023;6(2):41–53.
Liaw S-S, Huang H-M, Chen G-D. Surveying instructor and learner attitudes toward e-learning. Comput Educ. 2007;49(4):1066–80.
Tawafak RM, Mohammed MN, Arshah RA, Romli A, editors. Review on the effect of student learning outcome and teaching Technology in Omani's higher education Institution's academic accreditation process. Proceedings of the 2018 7th International Conference on Software and Computer Applications; 2018.
Engelbrecht E. A look at e-learning models: investigating their value for developing an e-learning strategy. Progressio. 2003;25(2):38–47.
Ghafourifard M. The promotion of virtual education in Iran: The potential which turned into reality by Coronavirus. Iran J Med Educ. 2020;20:33–4.
Vululleh P. Determinants of students’e-learning acceptance in developing countries: An approach based on Structural Equation Modeling (SEM). Int J Educ Dev using ICT. 2018;14(1).
Kocaleva M, Stojanovic I, Zdravev Z. Model of e-learning acceptance and use for teaching staff in Higher Education Institutions. Int J Mod Educ Comput Sci (IJMECS). 2015;7(4):23–31.
Nabavi A, Taghavi-Fard MT, Hanafizadeh P, Taghva MR. Information technology continuance intention: A systematic literature review. Int J E-Business Res (IJEBR). 2016;12(1):58–95.
Rahimi B, Nadri H, Lotfnezhad Afshar H, Timpka T. A Systematic Review of the Technology Acceptance Model in Health Informatics. Appl Clin Inf. 2018;09(03):604–34.
Bhattacherjee A, Barfar A. Information technology continuance research: current state and future directions. Asia Pac J Inform Syst. 2011;21(2):1–18.
Bhattacherjee A. Understanding information systems continuance: An expectation-confirmation model. MIS Q. 2001:351–70.
Nadri H, Rahimi B, Lotfnezhad Afshar H, Samadbeik M, Garavand A. Factors Affecting Acceptance of Hospital Information Systems Based on Extended Technology Acceptance Model: A Case Study in Three Paraclinical Departments. Appl Clin Inf. 2018;09(02):238–47.
DeLone WH, McLean ER. The DeLone and McLean model of information systems success: a ten-year update. J Manage Inform Syst. 2003;19(4):9–30.
Cheng Y-M. What drives nurses' blended e-learning continuance intention? J Educational Technol Soc. 2014;17(4):203–15.
de Melo Pereira FA, Ramos ASM, Gouvêa MA, da Costa MF. Satisfaction and continuous use intention of e-learning service in Brazilian public organizations. Comput Hum Behav. 2015;46:139–48.
Saeed Al-Maroof R, Alhumaid K, Salloum S. The continuous intention to use e-learning, from two different perspectives. Educ Sci. 2020;11(1):6.
Ma C-M, Chao C-M, Cheng B-W. Integrating Technology Acceptance Model and Task-technology Fit into Blended E-learning System. J Appl Sci. 2013;13:736–42.
Lee M-C. Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model. Comput Educ. 2010;54(2):506–16.
Kafashi M, Nematollahi M. Effective Factors on continued use of the Picture Archiving and Communication System in Teaching Hospitals of Shiraz University of Medical Sciences. Iran J Med Inf. 2019;8(1).
Alshurideh M, Al Kurdi B, Salloum SA, editors. Examining the main mobile learning system drivers’ effects: A mix empirical examination of both the Expectation-Confirmation Model (ECM) and the Technology Acceptance Model (TAM). International conference on advanced intelligent systems and informatics; 2019: Springer.
Bøe T, Sandvik K, Gulbrandsen B. Continued use of e-learning technology in higher education: a managerial perspective. Stud High Educ. 2021;46(12):2664–79.
Schneckenberg D. Overcoming barriers for eLearning in universities—portfolio models for eCompetence development of faculty. Br J Edu Technol. 2010;41(6):979–91.
Rahimi S, Shute V, Kuba R, Dai C-P, Yang X, Smith G, et al. The use and effects of incentive systems on learning and performance in educational games. Comput Educ. 2021;165:104135.
Naveed QN, Qureshi MRN, Tairan N, Mohammad A, Shaikh A, Alsayed AO, et al. Evaluating critical success factors in implementing E-learning system using multi-criteria decision-making. PLoS ONE. 2020;15(5):e0231465.

No competing interests reported.

Evaluation and Examination of Factors Influencing the Continued Effective use of E-learning Among Faculty Members of Medical Sciences Universities

Status:

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Abstract

Background

Methods

Results

Conclusion

Figures

Background

Methods

Results

Discussion

Conclusion

Declarations

Conflict of interest

Consent for publication

Funding

Author Contribution

Acknowledgement

Availability of data and materials

References

Additional Declarations

Status:

Version 1