PhD Sample Hypothesis Development: Determinants of adoption of Electronic Health Records in emerging economies – Application of the UTAUT

Hypothesis Development

The growing application of electronic health record (EHR) systems in emerging economies has marked an important area of interest to determine factors associated with the acceptance and use of these systems. Technology not only helps in process improvement but also enhances strategic realisation in the healthcare sector. Recent studies have highlighted the rapid transformation in the healthcare industry driven by technology (Chen et al., 2020; Rahmani et al., 2023; Kumar et al., 2023). Research on the adoption of innovative technologies and analytical tools in the healthcare sector often depends on theoretical frameworks. These frameworks highlight the determinants influencing technology acceptance and usage behaviour. Among these models, the Unified Theory of Acceptance and Use of Technology (UTAUT) is considered one of the most unique and popular tools to understand the factors affecting the adoption of new technologies such as EHR in the healthcare sector (Khan et al., 2018).

Developed by Venkatesh et al. (2003), the UTAUT framework integrates important concepts from eight significant foundational technology adoption models to identify the factors influencing technology acceptance and usage behaviour (Frishammar et al., 2023). According to this framework, the possibility of adopting new technology is influenced by four important factors (Liu et al., 2022). These include performance expectancy (PE), effort expectancy (EE), facilitating conditions (FC) and social influence (SI). However, very few studies in the healthcare sector have focused on the adoption of technology in emerging economies. To address this gap, the current study uses the UTAUT model to get detailed insight into the determinants influencing the adoption of EHR in emerging economies. Due to the comprehensive nature of the UTAUT model, the framework helps in understanding the behavioural attitudes of healthcare workers and the technological factors influencing the adoption of the latest digital techniques. Identifying these factors can help policymakers in developing targeted strategies and interventions to improve the implementation of EHR in different healthcare settings.

Recent studies observed that UTAUT could explain up to 70% of the variance in a user’s behavioural intention of using a new system (Ayaz & Yanartaş, 2020; Bellet & Banet, 2023). Drawing from the UTAUT framework and its applications in healthcare settings, this study proposes several hypotheses regarding the adoption of EHR in emerging economies.

Performance Expectancy (PE)

PE represents the degree to which healthcare professionals believe that using EHR will help them attain gains in job performance. In healthcare settings, performance gains may include improved patient care quality, reduced medical errors and enhanced clinical decision-making. Venkatesh et al. (2003) stated that PE is “the degree to which the user can trust that using the technology will help to overcome difficulty and help him or her to achieve the desired goal in job performance”. Alam et al. (2019) noted that if the technology brings benefits, it can contribute to its adoption. Moreover, the study also highlighted a positive relationship between PE and the behavioural intention of healthcare workers. It was noted that as PE influences users’ intentions, it can help in the implementation of new technologies to promote healthcare information systems.

Other studies conducted in emerging economies have shown that PE significantly influences healthcare professionals’ intention to adopt new healthcare technologies. When healthcare professionals perceive EHR as beneficial for their clinical practice, they are more likely to adopt and use the system. For example, Turan and Koc (2022) noted that increased performance levels through technology strengthened the technology acceptance among Turkish physicians. Similarly, Hicks et al. (2021) highlighted the use of e-health tools among health workers in Nigeria that was influenced by technology adoption behaviour. These workers believed that adopting technology would enhance their job performance and overall effectiveness. Based on this rationale, we propose the following hypothesis:

H1: PE positively influences healthcare workers’ intention to adopt EHR systems in their practice.

Effort Expectancy (EE)

EE is another important construct introduced in the UTAUT framework. It refers to the degree of ease associated with using EHR systems. Moreover, it represents the perceptions and experiences of users regarding the ease of adopting and utilising new technologies in the healthcare systems. Venkatesh et al. (2003) define EE as “the degree of ease associated with the use of the system”. In healthcare contexts, this involves the perceived complexity of the system, the learning curve and the time required to become proficient (Bawack & Kamdjoug, 2018). Given the time constraints and high-pressure environment in healthcare settings, the ease of use becomes particularly crucial. This means that users are more likely to adopt new technology if they believe the technology can add value to their everyday lives.

Recent studies in developing countries have highlighted that complex systems with steep learning curves often face resistance from healthcare professionals. On the other hand, innovative technologies that are easy to use are often preferred by the users (Chereka et al., 2022; Chong et al., 2022). This study uses EE as a measure of the extent to which healthcare professionals in emerging economies prefer minimal effort for adopting and using EHR in their professional fields. The study also explores the various challenges faced by healthcare professionals in adopting new technologies and maintaining patient records. Thus, the study proposes the below hypothesis:

H2:EE positively influences healthcare professionals’ behavioural intention to adopt EHR systems.

Facilitating Conditions (FC)

FC represents the organisational and technical infrastructure available to support EHR use in healthcare settings. Especially in emerging economies, this includes IT support, training programs and technical resources. The availability of adequate infrastructure and support systems can play a crucial role in healthcare settings where system downtime can have serious consequences for both patients and staff members (Alam et al., 2020). It was also noted that FC has a positive impact on the behavioural intention of the users and adoption of technology in the healthcare sector. Venkatesh et al. (2003) defined FC as “the level to which an individual trusts that a structural and practical infrastructure exists to support the use of the system”. It emphasises the importance of organisational support, training, technical assistance, IT infrastructure and compatibility in enabling technology adoption and use among individuals. Venkatesh et al. (2003) highlighted that the availability of FC is essential for overcoming barriers to technology adoption and promoting successful implementation in organisational contexts.

Zhou et al. (2019) noted that FC, along with behavioural intention, served as the mediator that influenced the behaviour of nurses in Ghana. The study also highlighted the role of hospitals in Ghana to encourage and support nurses in adopting new technologies in the provision of quality healthcare services to their clients. Furthermore, Philippi et al. (2021) observed that access to FC conditions such as hardware or software, technical support, online tutorials and online demonstrations is important for developing the intention to use among healthcare workers. Thus, in the context of emerging economies, the study proposes the following hypothesis:

H3: FC positively influences healthcare professionals’ intention to adopt EHR systems.

Social Influence (SI)

SI reflects the extent to which healthcare professionals perceive the use of technology. It is introduced on the perception of using a new system based on common beliefs and influence from key stakeholders such as colleagues, supervisors or hospital administration (Schretzlmaier et al., 2022). The hierarchical nature of healthcare organisations in emerging economies makes SI particularly relevant. For example, healthcare workers are often affected by their community and their social networks when using the latest technology. Venkatesh et al. (2003) explained SI as “the degree to which an individual sees other important people believe he or she should use the new system”. The study also identified image, social factors and subjective norms as the three common concepts of SI. The way people act or behave in their immediate environment mostly depends on these three concepts, which have a significant influence on what people do or do not do.

Furthermore, Riva et al. (2022) noted that SI refers to both intentional and unintentional efforts made by others to affect a person’s thoughts, emotions or behaviour. As the social environment plays a crucial role in shaping behaviour and willingness to adopt new technology, individuals are more likely to follow prevailing norms in their environment. (Rajak & Shaw, 2021). This is more prevalent in sectors where senior actors are considered to have a higher level of knowledge and respect. In emerging economies where the use of technology is less relevant, individuals try to associate themselves with knowledgeable groups. Similarly, Ren & Zhou (2023) highlighted that EHR is a relatively new technology in the healthcare environment in emerging economies. Thus, it can serve as a foundation for researchers to understand the role of SI in the adoption of this technology. As the concept of EHR needs to gain acceptance in emerging economies, endorsement through verified channels and institutions can encourage healthcare workers to use this technology.

Existing studies also support and explain the positive effects of SI on users’ intentions in the use of technology-enabled services in the healthcare sector in developed nations (Ahmad & Khalid, 2017). Building on the existing literature, the current research proposes the following hypothesis:

H4: SI positively influences healthcare professionals’ behavioural intention to adopt EHR systems.

Technical Skills

Knowledge of appropriate technical skills is another common factor that influences the adoption of technology in the healthcare sector. Edo et al. (2003) noted that adopting digital technology requires individuals to possess digital competence. As technology is constantly evolving at a rapid rate, individuals will have to continuously update their knowledge and resources. Additionally, the level of an individual’s technological proficiency and their willingness to use innovative methods affect their performance on simple and complex tasks in an environment where technology is used (Rahmani et al., 2023). Furthermore, Lyles et al. (2020) noted that insufficient resources for developing and understanding new technologies can act as a major obstacle to technology adoption decisions. This can limit the capacity of EHR use in emerging economies and also lead to frustration among healthcare staff.

Thus, it becomes necessary for institutions to provide individuals with adequate resources and the necessary support to help further learning.  With the help of continuous training, the healthcare staff can be trained to enhance their technological skills and their unwillingness to adopt new technologies. However, Demsash et al. (2024) noted that in emerging economies, health care institutions often face a shortage of well-trained health professionals. Similarly, their educational and professional training qualifications may vary. Thus, regular training is important to improve the technical skills of healthcare staff. Based on this discussion, the study proposes the following hypothesis:

H5: Professional training adequacy positively influences healthcare professionals’ behavioural intention to adopt EHR systems.

Self-Efficacy

In addition to the common UTAUT constructs suggested by Venkatesh et al. (2003), self-efficacy is another key factor that can describe the adoption of technology in the healthcare sector (Yu & Chen, 2024). Self-efficacy is termed as “the belief that an individual holds in his or her capabilities to execute the course of action required to achieve specific performance goals” (Bandura, 1977). Abdalla et al. (2024) demonstrated a positive effect of perceived self-efficacy on users’ intention to utilise technology in the healthcare sector. By considering self-efficacy as a motivating factor in the UTAUT model, this study can gain a comprehensive understanding of the psychological motivators that influence the acceptance and usage behaviour of EHR in emerging economies.

Rahman et al. (2016) stated that perceived self-efficacy cannot be determined by the number of skills an individual possesses, but with their belief in doing what they can under different circumstances. People with a low sense of self-efficacy are more likely to have few aspirations and weak commitment to following their goals. This highlights the significance of the perceptions of healthcare workers in shaping their behaviour towards adopting new techniques in their professional practices. Thus, not considering self-efficacy as a direct determinant of behavioural intention may limit the overall UTAUT model used for the study. Based on this discussion, the study proposes the following hypothesis:

H6: Self-efficacy positively influences healthcare professionals’ behavioural intention to adopt EHR systems.

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