Over the past few decades, digitization in the healthcare industry has evolved drastically with online resources and mobile applications now playing a significant role in healthcare (1). Healthcare organizations are under pressure to incorporate new scientific evidence into practice without compromising quality, access, or equity (2, 3). Information communication and technology (ICT) in healthcare service delivery is becoming more recognized for its potential benefits, with numerous small- and large-scale information system projects that have been initiated throughout African nations (4). One such ICT is telemedicine, which has been considered a necessary measure to alleviate the shortfall of skilled medical specialists in developing countries and discovered to be a quick solution to the shortage of health workers (5).

According to the definition given by the World Health Organization, telemedicine is defined as “the provision of health services, where distance is a critical factor, by all health professionals who use information and communication technologies (ICT) for valid exchange of information for the diagnosis, treatment, and prevention of diseases and injuries, research and evaluation, and for continuing education of healthcare providers, all in the interest of advancing the health of individuals and their communities” (6). Therefore, as it has been described in this definition, the promising potential of telemedicine is an ideal solution that attracts the attention of low-income countries struggling with health facilities and care provider shortages (7).

The World Health Organization (WHO) estimates that 57 countries worldwide have a critical shortage of health workers, with 36 of these countries in sub-Saharan Africa (8), indicating that consumers cannot get adequate and timely healthcare services due to the limited number of healthcare professionals (9). Health workforce shortages continue to be most severe in sub-Saharan African countries, which together bear 24% of the world's disease burden today (8). Ethiopia is one of the countries with a very low health workforce density, which is 0.96 per 1,000 population (9). This is much below five times the minimum level of 4.45 per 1,000 population specified by the WHO to reach the SDG health targets (10), and such shortages have resulted in fragmentation of care delivery (11).

Access to healthcare services is still an issue in Ethiopia, especially in remote or rural areas, due to the high population growth rate, increased demand for healthcare, slow economic growth, rising health costs, inadequate transportation, hard-to-reach geographical locations, and the scarcity of medical specialists, which forced patients to travel long distances to find specialists (12). To overcome this challenge, healthcare providers are looking to use telemedicine as an alternative solution (13). An option to allow access to healthcare services in a developing country such as Ethiopia is implementing telemedicine services and ensuring sustainability (12). Telemedicine is a growing field that can offer professional help over the internet via an app, email, video conferencing, online chat, or phone call (14).

In line with this, Salale University Comprehensive Specialized Hospital considers the potential of using telemedicine services currently in the North Shewa Zone to provide mentorship for local healthcare providers and also bridge the gap between communities and clinicians. The societies in Salale and its surroundings can call the Salale Digital Telemedicine Center (9824) and access the telemedicine services, most especially in general health education, non-communicable disease education, and treatment services; prescriptions; dermatology services; radiology services; ophthalmology services; dietary counseling; mental health counseling; maternal and child health counseling; and treatment services. However, many of such technology systems were not being used to their full potential due to factors such as project management failure, lack of post-implementation sustainability, user resistance, organizational culture, and national culture (15). The slow adoption of telemedicine in many low- and middle-income countries (LMICs) is related to factors such as insufficient infrastructure, slow acceptance, inadequate technological equipment, scarce financial resources, and inadequate skilled human resources (16, 17). Significant obstacles to the adoption of telemedicine may arise from patients' and healthcare providers' poor levels of digital literacy (DL), particularly in remote or low-resource areas (18). A number of issues, including ICT policies, intersectoral collaboration, e-health laws and regulations, telemedicine service-facilitating strategies, integrated supportive supervision on telehealth adoption, and ICT equipment, are associated with the diffusion of telemedicine in most sub-Saharan African nations, particularly Ethiopia (12).

However, user resistance has consistently been identified as a major obstacle to the successful implementation of telemedicine and system implementations, particularly serious in healthcare settings (19, 20). To successfully implement any telehealth services into current health systems, assessing their acceptance is a necessary and preliminary step (14).

Based on the lack of evidence of telemedicine acceptance by healthcare professionals, especially concerning the Salale Digital Telemedicine Center scenario, this study aimed to assess the acceptance to use (ATU) telemedicine services among healthcare professionals and to identify key predictors for facilitating and inhibiting telemedicine service acceptance through the Unified Theory of Acceptance and Use of Technology (UTAUT) model. This finding would be particularly important for the Ministry of Health (MOH), the regional health bureau, Salale University's specialized comprehensive hospital, policymakers, and future research, especially in Ethiopia.

The Unified Theory of Acceptance and Use of Technology (UTAUT) is still valid and is used to predict the acceptance behavior of remote care technologies by health professionals (21). The UTAUT model has been developed from a review, synthesis, and validation of eight theories/models of technology use including the theory of reasoned action (TRA), social cognitive theory (SCT), technology acceptance model (TAM), theory of planned behavior (TPB), motivational model, model of PC utilization (MPCU), combined TAM and TPB (C-TAM-TPB) and innovation diffusion theory (IDT), to propose a unified view of technology acceptance (22, 23). For our study, we chose the Unified Theory of Acceptance and Use of Technology (UTAUT) because it is the most comprehensive framework for understanding technology acceptance (23, 24). Several key reasons guided this decision. Firstly, the model has been proven to effectively predict the probability of customers intending to use telemedicine system services (25–28), which is directly relevant to our research focus. Secondly, the UTAUT model has been widely applied in the e-health domain to explore factors influencing the acceptance of telemedicine services (25, 29).

In our study, we assessed healthcare professionals' acceptance to use telemedicine services using the constructs of the modified UTAUT model, as it offers superior predictive power from an individual or consumer perspective (23). This approach ensures a comprehensive evaluation of healthcare information system (HIS) intentions and actual usage. Notably, UTAUT studies are not limited to consumer users alone. Researchers have applied UTAUT to examine various user types, including citizens adopting telemedicine services (30), and nurses’ willingness to embrace home telehealth technologies (31).

Additionally, emphasizing telemedicine as an integral part of health service systems in resource-limited settings such as Ethiopia has several benefits. The implication of using the UTAUT model to implement telemedicine systems in Ethiopia has allowed healthcare providers to convert patient health records into digital formats, enhance the management and quality of data, improve the workflow of supply chain management, make better clinical decisions by exchanging real-time patient data, improve interoperability, efficiently store and share electronic health information, boost customer satisfaction, and enhance the quality of care (32–34). The UTAUT model proposes four predictors, performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC), that explain behavioral intention (24). UTAUT identifies three direct determinants of behavioral intention to use technology (PE, EE, SI), two direct determinants of technology use (behavioral intention and facilitating conditions), and four moderators including gender, age, experience, and voluntariness that would alter the effect of the determinants on intention and/or behavior (25).

This study considered the four original constructs and one additional construct, a total of five independent constructs (PE, EE, SI, FC, and DL), two moderators (age and gender), and behavior intention to use (“acceptance”) as a dependent construct (26, 27). Because the telemedicine services from Salale Digital Telemedicine Center have not been widely used and are in an infancy stage, and due to the unclear status of actual use of telemedicine services currently, the construct “actual use” which was considered as a dependent variable in the original UTAUT was not used in this study (25, 27). Actual usage behavior should be measured over the 6-month post-training (24). The effect of moderator experience was excluded from our model because most of the study participants might not have had prior telemedicine experience at the time the data were collected. In addition, the system was voluntary, users were not forced to use the system, and the moderator “voluntariness to use” was excluded from our model (28). Finally, the proposed theoretical model is presented in Figure 1.

The theory was established on four original theoretical constructs performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC), and one additional construct digital literacy (DL).

Performance expectancy: the degree to which an individual believes that using the system will help him or her to attain gains in job performance (24, 31). In this context, performance expectancy is the degree to which healthcare professionals believe that using telemedicine services will help him or her manage health and enhance job performance. According to prior studies, performance expectance is the main significant predictor of acceptance and has significant positive effects on behavioral intention (24, 32). A study conducted in South Korea using the UTAUT model found that performance expectancy has positive effects on behavioral intention to use telemedicine service (p < 0.05) (33). However, studies conducted in Iran (28), Tanzania (34), and Ethiopia during COVID-19 (35) showed that there was no significant relationship between performance expectancy and behavioral intention. Therefore, to test the effect of PE on acceptance to use (ATU), the following hypothesis has been proposed:

✓ H1: Performance expectancy has positively influenced healthcare professionals' acceptance to use telemedicine services.

Studies conducted in the United States (27), Spain (41), Korea (37), and Nigeria (5) showed that the relationship between facilitating conditions and behavioral intention was significant. Moreover, a study conducted in South Korea showed that FC is an influential factor in the acceptance of telemedicine services (33). On the other hand, a study conducted in Ethiopia (35) revealed that there was no significant association between facilitating conditions and behavioral intention. To test the effect of FC on acceptance to use (ATU), the following hypothesis has been proposed:

✓ H4: Facilitating conditions have positively influenced healthcare professionals' acceptance to use telemedicine services.

Moderator is a variable that can strengthen, diminish, negate, or otherwise alter the association between exogenous and endogenous variables, and it can also change the direction of this relationship. The moderators that were included in the UTAUT model are gender, age, experience, and voluntariness of use (31). The gender and age moderators were described here.

Several studies have shown that gender had a moderating influence on the relationship between social influence and behavioral intention (33, 42) and is significant for the female group, indicating that women are more likely to be influenced by the opinions of others when deciding to use a new technology (36). A study conducted in South Korea using the UTAUT model reveals that the path between PE and behavioral intention to use telemedicine service is stronger for females than for males (33). In this model, to test the effect of the gender moderator on acceptance to use (ATU), the following hypothesis has been proposed:

✓ H6: The influence of performance expectancy (PE) on healthcare professionals' acceptance to use telemedicine services has been moderated by gender.

According to a study conducted in South Korea using the UTAUT model, the path between performance expectancy and behavioral intention to use telemedicine services is stronger for the younger group than the older group (33). A study conducted in Canada showed that older age healthcare professionals are less comfortable using technology and are more resistant to it (42). However, several studies showed that age did not moderate the relationships between the independent variables and behavioral intention (36, 37, 43). In this model, to test the effect of the age moderator on acceptance to use (ATU), the following hypothesis has been proposed:

✓ H11: The influence of performance expectancy (PE) on healthcare professionals' acceptance to use telemedicine services has been moderated by age.

This study aimed to investigate the acceptance to use telemedicine services and its predictors among healthcare professionals in public hospitals, which was examined using the UTAUT model. According to our finding, the proportion of healthcare professionals on acceptance to use telemedicine services was 315 (52.4%). This finding showed that approximately half of the healthcare professionals accepted the offered telemedicine services to provide healthcare for their patients and to improve their productivity. Although the finding is only approximately half, the offered telemedicine technology is a promising one for the future because the national digital blueprint has identified potential high-impact digital health interventions, particularly provider-to-provider telemedicine, that can be effectively and easily scaled up in the next 10 years (64). Additionally, Ethiopia currently has a low level of emerging technology development (65). Moreover, telemedicine technology is still in its infancy stage of development in Ethiopia and other sub-Saharan African countries (66), and considering this, our finding on the acceptance to use telemedicine services among healthcare professionals was promising for the future.

The predictors of acceptance to use telemedicine services explained 71% (R² = 0.71) of the variance in an endogenous construct, which indicated that the UTAUT was a substantial predictive model of healthcare professionals' acceptance to use telemedicine services. In this study, performance expectancy (PE) had a direct effect on healthcare professionals’ acceptance to use telemedicine services (β = 0.184, p = 0.001). This indicates that healthcare professionals have focused on the usefulness of telemedicine services in their daily work and are willing to increase their productivity by making it easier to diagnose difficult cases and improve patient care. This finding was consistent with some studies conducted in South Korea (β = 0.345, p < 0.01) (33), Slovenia (β = 0.25, p < 0.001) (67), Malaysia (β = 0.207, p < 0.05) (68), and Nigeria (β = 0.0901, p < 0.05) (5). This finding implicated that healthcare professionals are likely to accept a telemedicine technology (Salale Digital Telemedicine Center) when it is considered to be useful to their practice which might enhance their job performance and productivity. The possible explanation could be also that telemedicine can provide value through the redistribution of medical expertise, the incorporation of interdisciplinary providers' contributions, and the creation of new educational possibilities (69). In contrast, this study was contradicted by the study conducted on telemedicine during COVID-19 in Ethiopia (β = −0.11, p > 0.05) (35). This discrepancy may be accounted for by the fact that the investigation was conducted using online surveys.

The result of this study also showed that effort expectancy (EE) has a significant association with acceptance to use telemedicine services (β = 0.183, p < 0.001). According to this finding, healthcare professionals who believe in the ease of use of the provided telemedicine services can easily understand and accept such technologies. This result was in accordance with the findings of previous studies conducted in Pakistan (β = 0.148, p < 0.05) (70), Slovenia (β = 0.52, p < 0.01) (67), Ghana (β = 3.155, p < 0.01) (71), and Ethiopia (β = 0.27, p < 0.05) (35). The possible explanation could be that the users are motivated to accept these services as they perceive telemedicine as an easy technology (70), and healthcare professionals can easily carry out their duties by employing telemedicine services.

Another finding of this study showed that the social influence (SI) was insignificant in predicting healthcare professionals' acceptance to use telemedicine services (β = −0.028, p = 0.644). This finding indicates that healthcare professionals are highly motivated to accept telemedicine irrespective of the opinions of important others. This finding was congruent with studies conducted in Slovenia (β = −0.03, p > 0.05) (67), Nigeria (β = −0.0902, p > 0.05) (5), and Ethiopia (β = −0.18, p > 0.05) (35). This finding was consistent with the viewpoint healthcare professionals' telemedicine technology acceptance decisions might vary depending on their levels of autonomy, be made independently from other members of the healthcare team, and be evaluated in light of their routine clinical tasks and services (49). However, this finding was inconsistent with a study conducted in Pakistan (70); this might be due to population differences, as their study was conducted in a community where social influence is more important. Additionally, this is inconsistent with the study conducted in India (β = 0.690, p < 0.001) (72); it might be because cultural differences can play a role in how people perceive the opinions of others who are important to them (36). This disparity between our proposed hypothesis and the finding may be due to the possibility that the characteristics of our sample, such as demographics, professional background, or familiarity with telemedicine, may have played a role in diminishing the observed effect of social influence. Additionally, healthcare professionals' acceptance to use telemedicine services is influenced by a variety of factors other than social influence, such as performance expectancy, effort expectancy, facilitating conditions, or digital literacy with telemedicine.

This finding has also illustrated that facilitating condition (FC) has a direct effect on healthcare professionals’ acceptance to use telemedicine services (β = 0.249, p < 0.001). This result indicated that organizational conditions matter in shaping the acceptance of telemedicine services among healthcare professionals. This finding was consistent with studies conducted in Slovenia (β = 0.13, p < 0.01) (67). This might be because organizations identify important factors that need to be considered carefully when telemedicine technology is implemented, and healthcare practitioners might also have better access to facilitating conditions that make it easier for them to use the telemedicine system. The availability of resources, support, and knowledge are necessary to promote the acceptability of telemedicine services, and it is a prerequisite that medical professionals be able to monitor and provide feedback using adequate healthcare infrastructure (70). This is because healthcare professionals were highly influenced to accept telemedicine services based on their organizational and infrastructural preconditions. However, this study was inconsistent with the study conducted in Ethiopia (β = −0.18, p > 0.05) (35). This discrepancy may be attributed to the sample size and/or the fact that their research data were gathered via online surveys, which makes it possible that the healthcare professionals did not have sufficient knowledge about telemedicine technology.

This finding also showed that a strong relationship exists between digital literacy (DL) and acceptance to use telemedicine services (β = 0.403, p < 0.001), which was larger than the effects of other predictors on acceptance. This suggests that healthcare professionals who know about different digital technologies and need to use them for their jobs are likely to accept telemedicine technology. This study was consistent with studies conducted in Saud Arabia (β = 0.31, p < 0.001) (36) and Ethiopia (β = 0.087, p = 0.029) (62). This indicates that healthcare professionals who acquire adequate skills to search, understand, evaluate, and use health information available online can accept technology for better healthcare outcomes (73). Therefore, this finding suggests that the digital literacy predictor is a crucial factor influencing healthcare providers' acceptance to use telemedicine services. Accordingly, developing digital literacy training programs that focus on enhancing the digital literacy skills of healthcare workers at all levels, incorporating digital literacy as a fundamental part of healthcare workers' continuing professional development curriculum, and establishing mentorship programs where more tech-savvy professionals help their colleagues to develop digital skills might be strategies on how to improve the digital literacy of healthcare professionals. The digital literacy predictor is also implicated as an additional predictor for the theoretical understanding of the UTAUT model. Based on this, the finding might be used as input to policymakers, as they should encourage healthcare institutions to provide continuous digital literacy training to ensure healthcare professionals are proficient in using telemedicine platforms.

In this study, we also found that there was no significant difference between males and females in their acceptance to use telemedicine services. This outcome shows that telemedicine will be accepted regardless of a person's gender for use in their daily operations. This finding is supported by a study conducted in Saudi Arabia (74). There wasn’t much evidence to support the role of gender moderators in this study, despite the fact that they are crucial in the healthcare context (74). This study also found that the relationship between PE, EE, and SI with acceptance to use was not significant. This finding was supported by the study conducted in Saudi Arabia (43) and Australia (75).

Finally, we found that the relationship between facilitating conditions and healthcare professionals' acceptance to use telemedicine services was positively moderated by age (β = 5.422, p = 0.020). This effect was stronger for the younger group (<30 years). The possible explanation might be that younger healthcare professionals (<30 years) have the knowledge necessary to use telemedicine technology and they might get more technological support than the older age group (≥30 years). Moreover, the relationship between digital literacy and healthcare professionals' acceptance to use telemedicine services was positively moderated by age (β = 5.462, p = 0.019). This effect was stronger for the older group (≥30 years). A possible explanation could be that 90.7% of the respondents in the older group (≥30 years) have a BSc or above as their educational status.

This study examined factors affecting healthcare professionals' acceptance to use telemedicine services. The findings of the study showed that the acceptance of the provided telemedicine services was found to be promising from the perspective of healthcare professionals. Our research also found that performance expectancy, effort expectancy, facilitating conditions, and digital literacy constructs have significant positive effects on healthcare professionals’ acceptance to use telemedicine, while social influence did not. Digital literacy appeared to be the most significant factor affecting healthcare professionals' acceptance to use telemedicine services, as its path coefficient was the highest. Facilitating conditions and digital literacy with acceptance to use were moderated by age. Since healthcare professionals have shown promising acceptance to use telemedicine services, healthcare administrators should invest in ongoing training for healthcare professionals to ensure they are at ease using the technology. To increase accessibility and expand the use of telemedicine, policymakers should make sure that the technological infrastructure needed for it is robust, reliable, and easy to access. Based on the promising outcome in our study setting, policymakers could scale the telemedicine services technology to other regions, and efforts should be made to streamline the integration of telemedicine technology into the workflows of healthcare with particular emphasis on underserved areas such as Ethiopia. This could involve coordinating efforts between local hospitals, regional health authorities, and the Ministry of Health.