Electronic learning (eLearning) is an internet-based learning or training method, which comprises a set of instructions provided over electronic media systems (1–3). Nowadays, e-learning is one of the most cutting-edge teaching platforms in the educational system (2). eLearning in healthcare training is positioned as an important innovation education for healthcare professionals (3). According to a report issued by Global eLearning Statistics, the number of online learners reached 76 million in 2020 (4).
In Africa estimated the growth rate of eLearning was 15.3% in 2016 (5). The development of information and communication technology (ICT), makes ease the learning environment through interchangeable access when and where it suits you (6). Different initiatives are created to build staff capacity and fit healthcare professionals for purpose and practice. Continuing professional development (CPD) is healthcare professionals' lifelong learning to update the current knowledge, skills, and practices continuingly (7).
In Ethiopia, medical associations, profit healthcare organizations, academic institutions, Ministries of Health and other professional organizations initiate, provide and promote continuing professional development activities. But, the mode of CPD delivery was costly and inaccessible for most healthcare professionals (8).
E-Learning based continuing professional development is a technology that utilizes to train or learn by accessing modules, video, audio, and interactive class to improve healthcare professional practice throughout life (9). Healthcare professionals do not have access to continuing professional development, which missed opportunities for early updates of knowledge and skills. Thus eLearning aims to reduce geographic barriers, save time and cost, and optimize access to eLearning-based CPD for every healthcare professional (10).
Globally, healthcare challenges are experienced, which range from emerging or remerging health problems, fragmented CPD activities, and lack of CPD-trained healthcare professionals, which could affect the healthcare system (11–13). Healthcare professionals should take CPD to frequently update knowledge and skills which faces significant challenges due to financial constraints and tight schedules sending them away from the workplace as a result of low-quality healthcare due to a lack of adequate training (14). In this circumstance, the role of an effective digital health system is critical (15). Across the world, healthcare organizations are focusing on implementing eLearning technology (16). Many scholars suggest that the eLearning system is an essential technology to enhance continuing professional development and improve the quality of health care (17–20).
In Africa, a Global Health eLearning Center (GHeL) survey found that 47% of healthcare professionals owned eLearning technologies to update knowledge and skill, but there are still many hindrances to the intention to use eLearning-based CPD in the continent (21). In South Africa showed that social, cultural, and psychological factors may influence eLearning of medical education (22). Similarly, a study shows that 60% of healthcare professionals have not used continuing professional development (23). In another study in Morocco, the gap between the intention to use eLearning-based continuing professional development and healthcare professionals are caused by lack of experience, cost of technology, lack of motivation, and problems with an internet connection (7). A study shows in Tanzania the intention to use eLearning for continuing medical education was low (4%) (17). A study in Rwanda indicated that the intention and adoption of eLearning CPD are now increasing, particularly among health managers (18). In Ethiopia study reveals that the intention to use electronic health adoption is low among healthcare professionals (24).
Currently, in Ethiopia, Continuing professional education is an essential component of the Health Sector Transformation Plan (HSTP2) to improve the quality of health care (25). However, due to different challenges, the CPD program was inaccessible, inflexibility, time-consuming, expensive, and fragmented leading to low-quality healthcare (8). In this regard, one of the most important stages in assuring high-quality health service care is eLearning-based CPD.
Among the various initiatives planned by the Ethiopian Federal Ministry of Health, eLearning technology is one of the Digitalization projects (26, 27). However, the technological benefit obtained depends on the rate of use and adherence of users. Therefore, determining behavioral intention to use and its predictors of the adoption of technology is important and prevents implementation failure (24). To increase understanding of factors affecting behavioral intention, modification of the original Unified Theory of Acceptance and Use of Technology (UTAUT2) has been carried out by different scholars (17, 23, 28).
In this regard, increasing understanding of the predictors that influence the behavioral intention of healthcare professionals to use eLearning-based CPD by modifying UTAUT2 is important. To the extent of investigator knowledge information is limited on the proportion of intention to use eLearning-based CPD and its predictors. Therefore, this study is intended to fill this gap.
The findings of this study could help the Ethiopia Federal Ministry of Health by providing input on how to amend and revisit current policies and strategies, that will benefit healthcare professionals to use eLearning-based CPD for enhancing lifelong professional learning. According, to my review of the literature, limited research conducted on the intention of healthcare professionals in Ethiopia to use eLearning-based technologies to support continued professional development. Finally, the findings of this study would give insight to policymakers, CPD providers, and RHBS and it would facilitate further studies in the area
The theoretical background of the model and hypothesis
To explain technology adoption, models have been developed, with the UTAUT2 model explaining the majority of them (7, 17, 29, 30). Therefore, choosing the best theory or model to serve as the theoretical basis for explaining user behavior toward the technology under study is crucial to answering the research questions. Due to their greater explanatory power, this study suggests a theoretical framework based on the UTAUT2 to investigate the intention to use eLearning-based continuing professional development.
The UTAUT model was created after the examination of eight technology acceptance models, which include the theory of planned behavior, the theory of reason action, the technology acceptance model, the social cognitive theory, the motivational model, the model of personal computer utilization, the combined theory acceptance model and innovation diffusion theory, to incorporate out a unified perspective on technology acceptance (7, 17, 29, 31)..
Predictors of Intention to use eLearning-based continuing professional development
Various predictors influencing the intention to use eLearning based on continuing professional development are Performance Expectancy (PE), Effort Expectancy(EE), Social Influence(SI), Facilitating Condition(FC), Hedonic Motivation (HM), Habit (HT), and Price Value (PV) (17, 32). However, in this study, we adapted the UTAUT2 model by introducing computer literacy to include key predictors of intention to use eLearning-based CPD. Because users who are more familiar with IT are more likely to adopt and persist with new e-learning innovations than users who are less familiar with IT (33).
Performance Expectancy (PE): A degree to which a person believes that employing the system will assist him or her in enhancing job performance at work (29). A study conducted in Taiwan (34, 35), New Zealand (33), Saudi Arabia (36), Malaysia (37), Kenya (38), South Africa (23), and Morocco (7) are positively associated with behavioral intention to use eLearning based continuing professional development. We examine the hypothesis from the perspective of these findings.
H1. Performance expectancy has positively influenced healthcare professionals' intention to use eLearning based on continuing professional development.
Effort Expectancy(EE): The extent to which a person believes using a specific technology will not require spending a lot of effort (29). A study conducted in Sri Lanka(39), South Africa (40), and Tanzania (17) are positively associated with behavioral intention to use the eLearning system. On the other hand, a study conducted in Ethiopia on electron health adoption demonstrates that intention to use is strongly influenced by perceived ease of use (24). Due to this, we proposed the following hypothesis.
H2. Effort expectancy has positively influenced healthcare professionals' intention to use eLearning-based continuing professional development.
Social Influence (SI): The extent to which a people thinks important people think they should use a specific technology (29). A study done in Canada (41) and Morocco (7) are positively associated with behavioral intention to use the eLearning system. However, in a study conducted in the Philippines (32) and Indonesia (42), social influence does not influence behavioral intention to use the eLearning system. Thus, we proposed the following hypothesis based on the findings.
H3. Social Influence has positively influenced healthcare professionals toward the intention to use eLearning based on continuing professional development.
Facilitating condition (FC): The degree to which an individual thinks resources are accessible to help with technology use (29). A study conducted in Iran (22), and Malaysia (43) are positively associated with behavioral intention to use the eLearning system. This study looks into the following hypothesis in this context.
H4. Facilitating conditions has positively influenced healthcare professionals towards intention to use eLearning-based continuing professional development.
Hedonic motivation (HM): is the pleasure experienced after using a technological device (29). A study conducted in the USA (2), Sri Lanka (39), India (44) Indonesia (42), Libya (45), Zimbabwe (46), and Tanzania (17) had significant direct effects on behavioral intention to use eLearning technology. A comparable study conducted in the Philippines showed that the hedonic motivation condition does not influence the intention to use eLearning in medical education (32). Therefore, the following hypothesis was proposed.
H5. Hedonic motivation has positively influenced healthcare professionals towards intention to use eLearning-based continuing professional development.
Price value (PV): Cognitive trade-off between users' perceptions of the applications' benefits and their financial costs (29). A study conducted in Iran (22) had significant direct effects on behavioral intention to use eLearning technology. Another study conducted in Wales perceived value had significant effects on behavioral intention use of eLearning-based continuing professional development (47). A comparable study conducted in China (48), Tanzania (17), and Zimbabwe (46) showed that price value condition does not influence the intention to use eLearning in medical education. Therefore, we proposed the following hypothesis.
H6. Price value has positively influenced healthcare professionals towards intention to use eLearning-based continuing professional development.
Habit (HT): The degree to which people behave automatically is defined as their tendency to do so (29). A study conducted in Iran (49) are positively associated with behavioral intention to use mobile-based medical education device. Similarly, a study conducted in China is positively associated with blended learning-based medical education (48). A comparable study conducted in Zimbabwe (46) and Tanzania (17) showed that the habit condition does not influence the intention to use eLearning in medical education. Based on those findings we proposed the following hypothesis.
H7. Habit has positively influenced healthcare professionals toward the intention to use eLearning-based continuing professional development.
Computer literacy (CL): is defined as the ability to comprehend the relationship with digital technology and its uses, possibilities, and meanings (24, 33). A study done in Belgium (50) that computer literacy had positive influence the intention to use electronic health adoption. A study done in Dutch (51) and Malaysia (52) indicated that computer literacy is a determinant factor of intention to use health information technology. A study conducted in Ethiopia on electronic health record demonstrate that intention to use is strongly and favorably influenced by computer literacy (24). In the context of this, we proposed the hypothesis.
H8. Computer literacy has positively influenced healthcare professionals' intention to use eLearning-based CPD.
Moderating effects of intention to use eLearning continuing professional development
A moderator is a variable that can affect the strength, direction, and other characteristics of the relationship between exogenous and endogenous variables. In the UTAUT2 model of context, the variables of sex, experience, and age affect the direction or strength of the connection between the exogenous and endogenous factors of intention to use technology (24). But, in this study, the experience was not used as a moderator because the majority of participants were unfamiliar with eLearning-based continuing professional development (53–55).
Moderating effects of age
In a study done in Iran, age had a moderating effect on habit to behavioral intention to use mobile-based educational applications on pharmacy students (49). In a study conducted in Indonesia, age had a significant effect on habit in online learning of executive education (49). A study conducted in Morocco's age had a moderating effect on EE to behavioral intention to use eLearning-based continuing professional development (7). In Ethiopia study conducted on electron health adoption age had moderating effect on PE to behavioral intentions of healthcare professionals (24). Therefore, the following hypothesis was proposed.
H9. The influence of performance expectancy on healthcare professionals' behavioral intention to use eLearning-based continuing professional development has moderated by age.
H10. The influence of Effort expectancy on healthcare professionals’ behavioral intention to use eLearning-based continuing professional development has been moderated by age.
H11. The influence of Social influence on healthcare professionals’ behavioral intention to use eLearning-based continuing professional development has moderated by age.
H12. The influence of facilitated conditions on healthcare professionals’ behavioral intention to use eLearning-based continuing professional development has been moderated by age.
H13. The influence of Hedonic motivation on healthcare professionals’ behavioral intention to use eLearning-based continuing professional development has moderated by age.
H14. The influence of Price value on healthcare professionals’ behavioral intention to use eLearning-based continuing professional development has been moderated by age.
H15. The influence of habit on healthcare professionals’ behavioral intention to use eLearning-based continuing professional development has moderated by age.
H16. The influence of computer literacy on healthcare professionals’ intention to use eLearning-based CPD has been moderated by age.
Moderating effect of gender
In a study conducted in Saudi Arabia gender had a moderating effect on perceived ease of use and social norm on behavioral intention to use the eLearning system (30). Another study done in Iran gender had a moderating effect on habit to behavioral intention to use mobile-based educational applications in pharmacy students (49). A comparable study conducted in Morocco gender did not affect EE to behavioral intention to use eLearning based on continuing professional development (7). Based on those findings the following hypothesis was proposed.
H18. The influence of performance expectancy on healthcare professionals’ behavioral intention to use eLearning-based continuing professional development has been moderated by sex.
H19. The influence of effort expectancy on healthcare professionals’ behavioral intention to use eLearning based on continuing professional development has been moderated by sex.
H20. The influence of social influence on healthcare professionals’ behavioral intention to use eLearning-based continuing professional development has been moderated by sex.
H21. The influence of facilitated conditions on healthcare professionals’ behavioral intention to use eLearning-based continuing professional development has been moderated by sex.
H22. The influence of hedonic motivation on healthcare professionals’ behavioral intention to use eLearning-based continuing professional development has been moderated by sex.
H23. The influence of price value on healthcare professionals’ behavioral intention to use eLearning-based continuing professional development has been moderated by sex.
H24. The influence of habit on healthcare professionals’ behavioral intention to use eLearning-based continuing professional development has been moderated by sex.
The conceptual study model is composed of three components. Hence, eight adapted predictors as shown in Fig. 2 to measure the healthcare professionals’ intention in adopting eLearning are those from UTAUT2 which included: EE, PE, SI, HM, FC, HA, and PV and that added predictor included CL. The second part of the model is made up of endogenous variables. Since in Ethiopia, the suggested model is not yet implemented throughout the country, there is no actual usage of the eLearning-based continuing professional development by healthcare professionals, due to this, user behavior, which was considered a dependent variable in the original UTAUIT2 model, was not be measured in this research (2, 31). Given this, the last group is made up of the factors that affect both exogenous and endogenous variables like age and gender. Finally, the proposed model is presented as follows (Fig. 1).