Adoption of Massive Open Online Courses (MOOCs) for Health Informatics and Administration Sustainability Education in Saudi Arabia

Abstract

Introduction: The educational landscape has been expanded to disadvantaged and distant areas through online courses. These online courses have gained extensive interest yet there are limited studies available in the literature. The emergence of massive open online courses (MOOCs) has allowed sustainability educators to glimpse the light. Online education is gaining popularity and, with the introduction of MOOCs, would be beneficial for knowledge building and sharing, and the development of learned society. Objective: This study investigated the mediating (indirect) effects of media richness and user-based use motives on the extended UTAUT model, use behavior (UB), and actual use (AU) of MOOCs on health informatics and administration sustainability education among educators and students in Saudi higher learning institutions (HLIs). A theoretical model based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and Channel Expansion Theory (CET) was used to investigate the factors that affect the adoption of MOOCs in health informatics and administration education. Methodology: A survey design approach was applied. Cross-sectional data were collected from health informatics educators and students from HLIs in Saudi Arabia. A non-probability convenience sampling technique was used for sampling. Data were collected online using Google Forms. A total of 145 completed questionnaires were used in the analysis. PLS-SEM(Version 4.1.1.2) was used for statistical analysis. To investigate the reliability and validity, a measurement model was developed and confirmatory factor analysis (CFA) was conducted. To test the hypotheses, a structural model was run using bootstrapping, coefficients, standard errors (SE) t-values, p values, and lower and upper-level confidence intervals. Results/Findings: The findings were that system quality and user satisfaction is an important factor in the UTAUT model and the inclusion of media richness and user-based use motives significantly mediated between the expanded UTAUT model and the UB and AU of MOOCs. Moreover, media richness and user-based use motives were found to be dominant factors in the overall study model to predict use behavior and actual use of health informaticians in Saudi Arabia. Conclusions: The combination of these two theories i.e., the UTAUT and CET, can effectively enhance the adoption, use behavior, and actual use of MOOCs in the emerging field of health informatics in Saudi Arabia.

1. Introduction

Massive open online courses are free with open access, they attract a large audience of learners, and are used to sustain education. There has been a significant shift from traditional learning to online learning with the emergence of MOOCs globally. Though MOOCs were introduced as informal learning mechanisms, higher learning institutions (HLIs) have started accepting them. Various colleges, institutions, and websites provide free access to MOOCs globally for sustainable education. The United Nation’s (UN) new agenda for 2023 sustainable development also recognized the importance of education in the transformation towards sustainability. Education is one of the seventeen sustainable development goals (SDGs) on the agenda. Education for sustainable development (ESD) should promote the transition to sustainability at all educational levels. MOOCs are opening doors and paving pathways towards sustainability in this regard [1]. The widespread adoption of information and communication technologies (ICTs) has profoundly impacted educational institutions, transforming the way we learn. Digitalization has revolutionized education by creating a collaborative socio-technical environment where students and teachers work together to develop and expand digital learning models. The rise of online learning, or e-learning, has led to a significant shift away from traditional teaching methods, with many courses now being offered online through platforms like edX and MiriadaX by subject matter experts [2,3]. Massive open online courses (MOOCs) are online courses accessible globally, taught by experienced academics and experts, providing students with valuable knowledge and experience [4,5]. Originating in Europe and the United States, MOOCs have gained worldwide recognition, particularly among distance education communities [6]. To integrate MOOCs into higher education, top universities now offer pedagogically designed learning resources, targeting large student populations [1]. Notably, Stanford University launched Coursera and Udacity, while Harvard and MIT introduced Harvardx and MITx, collectively reaching over 1.2 million students. Similarly, Jordan’s Edraak platform has provided online courses to over 12,000 students worldwide [7,8]. The extensive appeal of MOOCs stems from their ability to provide flexible and free online learning opportunities. By offering unparalleled accessibility in terms of scheduling, timing, and location, MOOCs have opened doors for learners worldwide, particularly those in remote areas or with limited access to traditional classroom education [9].

Despite the benefits of MOOCs, they face challenges that limit their adoption, including high dropout rates. Some learners only want to try online learning without completing courses, but sustained engagement is crucial for MOOCs’ long-term viability [10]. To address these issues, it is essential to investigate the factors influencing the adoption and acceptance of MOOCs globally. MOOC adoption rates are particularly low in developing countries, including Saudi Arabia [6]. Although research has explored MOOC growth and sustainability, few studies have focused on this issue in developing countries [10]. This study has investigated the mediating (indirect) effects of media richness and user-based use motives on the extended UTAUT model, use behavior (UB), and actual use (AU) of MOOCs among health informatics and administration educators and students in Saudi higher learning institutions (HLIs). Examining the adoption and utilization of MOOCs among educators and learners is crucial to understanding their potential to transform learning and teaching. The emerging field of health informatics and health administration can particularly benefit from MOOCs to enhance education. To design effective MOOCs for health informatics and health administration, it is essential to understand the underlying factors and theoretical issues influencing their adoption. The increase in the development of MOOCs increases sustainability and viability [2]. The model presented in this study contributes to the literature on MOOC adoption and how MOOC designers in higher learning institutions (HLIs) might develop the motivation to produce sustainable MOOCs. This study has filled the gap that was left by previous local studies on this topic [2]. To the best of researchers’ knowledge, this is one of the pioneer studies that has investigated the mediating effects of media richness and user-based use motives between the extended UTAUT model, use behavior, and actual use of MOOCs. The scope of this study is limited to the exploration the health informatics and administration students and faculty members from public and private higher education institutions (HEIs) in Saudi Arabia for MOOCs adoption. This study employed the extended UTAUT theory combined with CET to provide a comprehensive understanding of the factors affecting the acceptance and use of health informatics MOOCs. (see Figure 1)The main objective of this study is as follows:

⮚

To investigate the mediating (indirect) effects of media richness and user-based use motives on the extended UTAUT model, use behavior (UB), and actual use (AU) of MOOCs among health informatics and administration educators and students in Saudi higher learning institutions (HLIs).

1.1. MOOCs in the Kingdom of Saudi Arabia

Saudi Arabia has been investing heavily in new technologies to enhance sustained learning experiences. As part of this effort, several academic institutions have started exploring the potential of massive open online courses (MOOCs) to supplement traditional courses and prepare learners for the workforce. To achieve this, various platforms have been developed. For instance, King Khalid University’s KKUx provides high-quality digital content to equip learners with essential skills for their future careers [11]. Other platforms, such as Doroob and Rwaq, offer a range of open educational content targeting different audiences. Additionally, platforms like Maarefh and Future X have been introduced to boost MOOC adoption in Saudi Arabia [12,13]. Future X, launched by the Saudi National E-learning Center, supports the integration and delivery of diverse e-learning services and courses, including MOOCs. These initiatives demonstrate Saudi Arabia’s commitment to leveraging MOOCs for education and workforce development. Learning approaches such as programming, statistics, information technology, deep learning, big data analysis, machine learning, and opinion mining are increasingly used in MOOCs. The challenges mentioned above have been studied from a student perspective by overlooking the academicians. This study, however, has focused on both students and faculty members to cover the aforementioned needs, especially health informatics and administration education. Educators can benefit from the findings of this current study to improve the teaching standard, personal interest, rewards, incentives, growth, and value addition. By providing high-skill training through MOOCs, education systems can be altered and the Saudi workforce can develop a competitive advantage. As the adoption rate is low among students and faculty members, it is very important to study those factors that will help the HLIs to increase the adoption rate of MOOCs in Saudi Arabia. Student enrollment, student work evaluation, communication issues, delayed feedback, and performance are the issues highlighted that are responsible for the low rate of learners’ adoption of MOOCs. Health informatics and administration are rapidly growing fields that combine computer science, information technology, health insurance, management, health economics, and healthcare to improve patient care and outcomes. It involves the study and application of IT-based innovations in healthcare services, management, and planning [14]. As an interdisciplinary field, health informatics and administration requires students and professionals to have a broad understanding of the technical, clinical, and administrative aspects of healthcare. To advance health informatics and health administration education, academic institutions, and e-learning providers must develop resources that cater to the needs of learners in these emerging fields to modernize the Saudi labor workforce. Despite the importance of educational technology in health informatics and administration, few studies have explored its role in supporting education. A learning management system (LMS) is crucial for online and distance learning. Research has shown that students are interested in using LMS features to enhance their learning [15]. However, the potential benefits of massive open online courses (MOOCs) in health informatics education remain largely unexplored. A study by Paton [16] found that a health informatics MOOC attracted over 10,000 learners from 100 countries. The success of such systems depends on user acceptance. Previous studies have used models like TAM and the UTAUT to examine MOOC acceptance in developed economies [1,6,17]. For instance, Fianu et al. [18] employed the UTAUT model to identify factors influencing MOOC acceptance [18]. While MOOC acceptance has received significant attention, the adoption of health informatics MOOCs among learners and educators remains understudied [19].

1.2. World Experience on MOOCs

Globally, MOOCs are provided open access and are built on free educational resources, which are the most adaptive methods of providing high-quality education globally, especially for the people living in remote, disadvantaged, unattractive, distant, and underdeveloped areas. MOOCs have been designed effectively, though the graduation rates and teaching quality are still below average. To overcome these challenges, analytics on massive learning are required for forecasting, administration, and learners’ aid in MOOCs. As a result, elite HLIs around the globe have adopted MOOCs. Stanford University offers courses that are accessible to learners anywhere globally. Jordan offers Edraak, in which approximately twelve thousand students are enrolled from Arab nations. Likewise, more than a hundred and twenty thousand students from Arab nations are enrolled in MITx and Harvardx, as well as in Western nations like the United States [1,14]. To spread the adoption of MOOCs globally, it is essential to consider the critical success factors like culture, economic, and social contexts. This study responds to the aforementioned need.

2. Literature Review

The successful implementation of a new system hinges on user acceptance. To ensure the effective integration of MOOCs in higher education, researchers have investigated the crucial factors influencing student acceptance and how various communication channels convey information and achieve their goals. The Unified Theory of Acceptance and Use of Technology (UTAUT) and Channel Expansion Theory are widely employed frameworks for understanding academic technology adoption. An eclectic theoretical approach was used in this study because no single theory is able to clearly explain the factors responsible for use behavior and actual use of MOOCs, especially in emerging fields like health administration and informatics. Therefore, the UTAUT and channel expansion theory (CET) were bridged and combined to contribute to a body of knowledge on the adoption of MOOCs. According to Alharbi [11], the UTAUT has a high explanatory power compared to other models when combined with CET. The UTAUT consist of four constructs that can predict the use behavior and actual use of MOOCs, performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FCs), and a new construct was added in this study, system quality and user satisfaction (SQUS). PE refers to the degree to which an individual believes that using a particular system, technology, or process will help them achieve their goals or improve their performance, EE refers to ease of use of technology, SI refers to the opinions of others to adopt new technology, FCs refer to the infrastructure provided by the institutions, and SQUS refers to user satisfaction with the new system. CET consists of social influence theories, situational factors, and media richness (MR). MR refers to motivation to adopt new technology.

2.1. Theoretical Basis of the UTAUT and Channel Expansion Theory

The UTAUT model is a theoretical framework that explains the adoption and use of technology. The model was developed by Venkatesh et al. [20] and is based on the integration of eight previously established models of technology acceptance. This model posits that four key constructs influence an individual’s intention to use technology, which, in turn, affects their actual usage behavior. These constructs are 1. performance expectancy: the degree to which an individual believes that using the technology will improve their job performance; 2. effort expectancy: the degree to which an individual believes using the technology will be easy; 3. social influence: the degree to which an individual perceives that others (e.g., colleagues, supervisors) believe they should use the technology; and 4. facilitating conditions: the degree to which individuals believe they have the necessary resources and support to use the technology. Research by [21] suggests that factors like sex, age, involvement, and voluntary procedures can moderate the impact of these four key constructs on behavioral intentions and performance. The UTAUT model is adaptable to various technologies, allowing for rewording to fit specific contexts. Behavioral intention is defined as an individual’s subjective likelihood of performing a specific behavior [21]. Studies, such as Alharbi [11], have validated the UTAUT model, demonstrating its effectiveness in explaining user behavior. Human adaptation and change are influenced by technology and impact by peers. Human beings resist change; this resistance could be cognitive, affective, or behavioral. How can the use behavior of MOOCs and actual use of MOOCs be increased by using the extended UTAUT model and channel expansion theory? Users learn to form their experiences and create a perception and opinion on the effectiveness of technology based on its quality, ease of use, and recommendations from peers, and actual use would impact on post-consumption experience including satisfaction, quality, user-based use motives, and the selection of adequate channel [1].

Channel expansion theory is a combination of social influence theories, media richness, situational factors, and social presence. This theory states that media richness is a significant factor in media selection and its usage. The main theme and idea behind this theory is that learners’ experiences are the main facets that affect a channel’s richness perceptions. This theory categorizes experiences of knowledge building into four categories, channel, organizational context, communication partner, and topic. The communicators can rapidly send and receive feedback in case of an increase in their experiences. Experience with channels allows the learners to learn new features; experience with organizational context would bring social influences and use of media; experience with communication partners could enhance interactions; and experience with topics allow learners to apply common jargon, leading to high media richness perceptions.

2.1.1. Performance Expectancy

Performance expectancy refers to the degree to which an individual believes that using a particular system, technology, or process will help them achieve their goals or improve their performance [11]. Empirical research has shown that performance expectancy plays a significant role in determining intention to use information technology [22,23]. Behavioral intention is a direct determinant of the actual use of technology and indicates a desire or purpose to use it [1]. In the UTAUT, behavioral intention and actual use are two outcome variables. Behavioral intention refers to an individual’s plan or decision to perform a specific behavior or action. Actual use refers to the real-world application or utilization of a system, technology, or product by an individual or organization [24]. It is the outcome of the behavioral intention to use a system or technology, and it is often measured by observing or tracking the actual usage patterns of the system or technology. However, the intention to use information technology can change over time, and actual behavior is the ultimate measure of usage [24]. Performance expectancy has a significant impact on media richness and user-based use motives. Channel expansion theory suggests that performance expectancy influences an individual’s perception of media richness, affecting their behavioral intention to use a particular medium [25]. Studies have shown that performance expectancy has a positive effect on behavioral intention to use a system or technology. For instance, a study found that performance expectancy positively influenced behavioral intention to use m-commerce [26]. Another study revealed that performance expectancy had a positive impact on the use of information systems [27]. Research indicates that performance expectancy can impact an individual’s preference for media with varying levels of richness. Specifically, a study found that individuals with high-performance expectancy tend to prefer richer media, such as video conferencing, over leaner media, such as email [28]. User-based use motives are also influenced by performance expectancy. When individuals expect that using a particular system or technology will improve their performance, they are more likely to use it. A study found that performance expectancy was a significant predictor of user-based use motives, suggesting that individuals are motivated to use systems that they believe will enhance their performance experience [11].

Performance Expectancy: use behavior, actual use of MOOCs, media richness, and UBUMs.

H_1a:

Performance expectancy has a positive effect on the use behavior of MOOCs.

H_1b:

Performance expectancy has a positive effect on the actual use of MOOCs.

H_1c:

Performance expectancy has a positive effect on media richness.

H_1d:

Performance expectancy has a positive effect on user-based use motives.

2.1.2. Effort Expectancy

Effort expectancy refers to the degree to which an individual believes that using a particular system, technology, or process will be easy. In other words, it is the perceived ease of use or simplicity of a system or technology. It significantly influences university students’ continued intention to use MOOCs, with research indicating a positive relationship between the two [16]. However, a study in Thailand and Pakistan found no significant impact of effort expectancy on student intention to use MOOCs [29]. Understanding use behavior and actual use of MOOCs is crucial for evaluating their effectiveness. Research has shown that performance expectancy significantly affects students’ behavioral intentions to use MOOCs [30], and students are willing to incorporate e-learning systems, including MOOCs, into their study activities [31]. The user-based use motives (UBUMs) framework, an extension of the UTAUT model, helps explain user behavior and actual use of technology. Research has demonstrated the usefulness of UBUMs in understanding user behavior and the actual use of mobile technology, including MOOCs. User behavior in MOOCs refers to the actions, interactions, and engagement patterns of learners with the online course platform, content, and community. It encompasses various aspects, including learning engagement, communication, time management, and evaluation [31]. The actual use of MOOCs refers to the real-world utilization of MOOCs by learners, instructors, and educators. It encompasses various aspects, including enrollment and registration, course completion, course engagement, learning outcomes, and application and transfer [19,23]. Studies have found that effort expectancy is a significant predictor of the actual use of MOOCs among university students [23] and adult learners [16], with a moderate positive effect on the actual use of MOOCs [6]. These findings suggest that effort expectancy is a critical factor in determining the adoption and use of MOOCs.

Effort expectancy positively influences media richness. Media richness (MR) is defined as the capacity of a medium to convey complex and detailed information. Studies have found that MR positively affects user satisfaction and behavior [32]. Research has shown that when individuals perceive that using a technology or system requires less effort, they are more likely to view it as rich in information [10,32,33]. For example, studies in the context of online learning [11], mobile commerce [32], and social media [33] have found a positive relationship between effort expectancy and media richness. Additionally, effort expectancy has a significant impact on user-based use motives. Studies have found that when individuals perceive that using a technology or system requires less effort, they are more likely to use it for their own purposes [19,22,34]. This has been demonstrated in various contexts, including software adoption [34], technology adoption [19], and mobile commerce [22]. Overall, research suggests that effort expectancy plays a crucial role in shaping both media richness and user-based use motives

Effort Expectancy: use behavior, actual use of MOOCs, media richness, and UBUMs.

H_2a:

Effort expectancy has a positive effect on the use behavior of MOOCs.

H_2b:

Effort expectancy has a positive effect on the actual use of MOOCs.

H_2c:

Effort expectancy has a positive effect on media richness.

H_2d:

Effort expectancy has a positive effect on user-based use motives.

2.1.3. Facilitating Conditions

Facilitating conditions (FCs) are one of the four core constructs of the Unified Theory of Acceptance and Use of Technology (UTAUT) model [19]. FCs refer to the degree to which an individual believes that an organizational and technical infrastructure exists to support the use of technology [19]. FCs are conceptualized as a multidimensional construct that include three sub-dimensions: technical support, organizational support, and physical and technological infrastructure. Studies have shown that FCs have a significant impact on the use behavior of MOOCs and are a critical factor in determining the adoption and use of MOOCs [35]. A study by Alharbi [11] found that FCs were a significant predictor of MOOC adoption among university students. FCs also have a significant impact on the actual use of MOOCs. Studies have shown that FCs are positively related to the actual use of MOOCs [36] and found that FCs were a significant predictor of the actual use of MOOCs among adult learners. FCs also have a significant impact on media richness. Research has shown that FCs are positively related to media richness [10]. Fianu et al. [18] found that FCs were a significant predictor of media richness in the context of MOOCs. FCs also have a significant impact on user-based use motives. FCs are positively related to user-based use motives. Another study found that FCs were a significant predictor of user-based use motives in the context of MOOCs [37]. A few studies have also assessed the impact of FCs on the use behavior of MOOCs, actual use of MOOCs, media richness, and user-based use motives and found that they are moderated by several factors, including age, experience, and voluntariness. The impact of FCs on the use behavior of MOOCs is stronger among older adults [2]. Studies also found the impact of FCs on the actual use of MOOCs is stronger among individuals with less experience with MOOCs [10], and the impact of FCs on the use behavior of MOOCs is stronger when MOOCs use is voluntary [6].

Facilitating Condition: use behavior, actual use of MOOCs, media richness, and UBUMs.

H_3a:

Facilitating condition has a positive effect on the use behavior of MOOCs.

H_3b:

Facilitating condition has a positive effect on the actual use of MOOCs.

H_3c:

Facilitating condition has a positive effect on media richness.

H_3d:

Facilitating condition has a positive effect on user-based use motives.

2.1.4. Social Influence

Social influence (SI) is a critical factor in the UTAUT model, and explains user behavior and actual use of technology. SI refers to the degree to which an individual perceives that others important to them think they should use technology [20]. Research has shown that SI has a significant impact on behavioral intention (BI) to use technology. Venkatesh et al. [20] found that SI was a significant predictor of BI to use of technology among employees. Another study by Aldahmani et al. [36] found that SI was a significant predictor of BI to use of technology among university students. Social influence (SI) has a significant impact on the use behavior of MOOCs. Research has shown that SI is a critical factor in determining the adoption and use of MOOCs [35]. A study by Altahi [7] found that SI was a significant predictor of MOOC adoption among university students. SI also has a significant impact on the actual use of MOOCs. Research has shown that SI is positively related to the actual use of MOOCs [10]. A study by Wan et al. [17] found that SI was a significant predictor of the actual use of MOOCs among adult learners. SI also has a significant impact on media richness. Research has shown that SI is positively related to media richness. A study by Alturki and Aldraiweesh [1] found that SI was a significant predictor of media richness in the context of MOOCs. SI also has a significant impact on user-based use motives. Research has shown that SI is positively related to user-based use motives [37,38]. A study by Wan et al. [17] and Altahi [7] found that SI was a significant predictor of user-based use motives in the context of MOOCs. Research has shown that the impact of SI on the use behavior of MOOCs, actual use of MOOCs, media richness, and user-based use motives is moderated by several factors.

Social Influence: use behavior, actual use of MOOCs, media richness, and UBUMs.

H_4a:

Social influence has a positive effect on use behavior of MOOCs.

H_4b:

Social influence has a positive effect on the actual use of MOOCs.

H_4c:

Social influence has a positive effect on media richness.

H_4d:

Social influence has a positive effect on user-based use motives.

2.1.5. System Quality and User Satisfaction

System quality (SQ) is one of the four core constructs of the UTAUT model [20]. SQ refers to the degree to which a system is perceived as being reliable, efficient, and easy to use, whereas user satisfaction refers to the degree to which a user is pleased or satisfied with a system or product [36]. Research has consistently shown that SQ has a positive impact on user satisfaction (US) [19,38]. A study by Wan et al. [17] found that SQ was a significant predictor of US among employees using new technology. Studies have identified several key dimensions of system quality (SQ), including reliability (the perceived consistency and dependability of a system) [19], efficiency (the perceived speed and effectiveness of a system) [38], and ease of use (the perceived simplicity and navigability of a system) [39]. SQ and user satisfaction (US) play a crucial role in shaping use behavior, actual use of MOOCs, media richness, and user-based use motives (UBUMs). Research has consistently demonstrated that SQ and US have a positive influence on use behavior [19,38]. For example, a study by Vijai [39] found that SQ and US were significant predictors of use behavior among employees adopting new technology. Additionally, SQ and US have been shown to positively impact the actual use of MOOCs. A study by Rotar [33] found that SQ and US were significant predictors of the actual use of MOOCs among university students. Furthermore, SQ and US also have a positive impact on media richness. A study by Bae [40] and Shao [41] found that SQ and US were significant predictors of media richness in the context of MOOCs. SQ and US also have a positive impact on UBUMs. A study by Shao [41] found that SQ and US were significant predictors of UBUMs in the context of MOOCs. Research has also shown that the impact of SQ and US on use behavior, actual use of MOOCs, media richness, and UBUMs is moderated by several factors, including age—the impact of SQ and US on use behavior is stronger among older adults [20]; experience—the impact of SQ and US on actual use of MOOCs is stronger among individuals with less experience with MOOCs [35]; and voluntariness—the impact of SQ and US on use behavior is stronger when technology use is voluntary [16]. An understanding of the role of SQ and US in determining use behavior and actual use of MOOCs is essential for developing effective strategies to promote technology acceptance and use.

System Quality User Satisfaction Use behavior, actual use of MOOCs, Media Richness, and UBUMs.

H_5a:

System quality user satisfaction has a positive effect on the use behavior of MOOCs.

H_5b:

System quality user satisfaction has a positive effect on Actual use of MOOCs.

H_5c:

System quality user satisfaction has a positive effect on media richness.

H_5d:

System quality user satisfaction has a positive effect on user-based use motives.

2.2. Media Richness and User-Based Use Motives

Research has shown that UBUMs have a positive impact on the actual use of MOOCs. Use behavior (UB) is characterized by the patterns of usage exhibited by users [19]. Studies have identified various factors influencing UB, including MR, UBUMs, and system quality [10]. The actual use of MOOCs (AUM) is determined by the extent to which users engage with these platforms [35]. Research has found that AUM is influenced by factors such as MR, UBUMs, and system quality [1]. A study by Alharbi [11] discovered that MR positively affects UBUMs, which in turn positively affects UB [41], and ultimately, AUM [35]. The relationships between MR, UBUMs, UB, and AUM are influenced by moderating factors, including age [6], experience [36], and voluntariness [35]. Therefore, understanding the interplay between media richness, user-based use motives, use behavior, and actual use of MOOCs is crucial for developing effective strategies to promote MOOCs adoption and use.

Media Richness: user-based use motives, use behavior, and actual use of MOOCs.

H_6a:

Media richness has a positive effect on use behavior of MOOCs.

H_6b:

Media richness has a positive effect on the actual use of MOOCs.

H_7a:

User-based use motives have a positive effect on the use behavior of MOOCs.

H_7b:

User-based use motives have a positive effect on the actual use of MOOCs.

2.3. Mediating Effects of Media Richness and User-Based Use Motives

Research has shown that media richness (MR) and user-based use motives (UBUMs) play a mediating role in the relationships between various factors and outcomes. For instance, studies have found that MR mediates the links between system quality and user satisfaction [31], facilitating conditions and use behavior [10], and social influence and use behavior [42]. Similarly, UBUMs have been found to mediate the relationships between system quality and use behavior [21], facilitating conditions and use behavior, and social influence and use behavior [10]. Both MR and UBUMs have been shown to have a mediating effect on these relationships. Some studies have found that both MR and UBUMs mediate the relationships between system quality, facilitating conditions, and use behavior [43], as well as social influence, facilitating conditions, and use behavior [43]. Understanding the mediating effects of MR and UBUMs is crucial for developing effective strategies to promote technology acceptance and use.

Indirect Effects (Mediating Effects) Hypotheses.

H_8a:

Media richness and UBUMs significantly mediated the relationship between PE, use behavior of MOOCs, and actual use of MOOCs.

H_8b:

Media richness and UBUMs significantly mediated the relationship between EE, use behavior of MOOCs, and actual use of MOOCs.

H_8c:

Media richness and UBUMs significantly mediated the relationship between FCs, use behavior of MOOCs, and actual use of MOOCs.

H_8d:

Media richness and UBUMs significantly mediated the relationship between SI, use behavior of MOOCs, and actual use of MOOCs.

H_8E:

Media richness and UBUMs significantly mediated the relationship between SQUS, use behavior of MOOCs, and actual use of MOOCs.

H_8F:

Media richness and UBUMs significantly mediated the relationship between the UTAUT, use behavior of MOOCs, and actual use of MOOCs.

3. Material and Methods

3.1. Research Design and Type

The current study followed a quantitative cross-sectional research design approach. Cross-sectional means data were collected at one point in time. The nature of the data was primary i.e., first-hand data which were collected first time. The data were collected online using a survey. This is a cost-effective and time-saving approach to collecting data from a larger population. The structured questionnaire was adopted from past studies, and these studies have already established the reliability and validity of the questionnaire. The items in the questionnaire were related to the UTAUT, channel expansion theory, and user-based use motives. All items were measured on a seven-point scale. Before data collection, ethical approval was taken. The main aim for selecting the survey approach and use of partial least square structural equation modeling (PLS-SEM) was that it gives first-hand primary data. PLS-SEM is a 2nd generation variance-based software, which is used to analyze small data sets, non-normal data for testing hypotheses, and the development of measurement and structural models [44]. The constructs and variables were measured using scales already described in the past literature. All items were measured on a seven-point scale from 7 strongly agree to 1 = strongly disagree [45].

3.2. Population and Sampling

The population of this study was faculty members and students of health informatics and health administration programs from public and private universities in Saudi Arabia. A non-probability convenience sampling technique was used. Sekaran and Bougie [46] have defined this sampling as “population members who are willing to provide the required information”. It is one of the most common and best ways of collecting primary data efficiently, quickly, and inexpensively. Though this sampling has generalizability issues, researchers can collect the data from available and accessible respondents (Etikan et al. [47]). Given the size of the population, convenience sampling was the best method for our purpose. A total of 175 questionnaires were distributed to different universities. A total of 145 completed questionnaires were received and used in the analysis, thus yielding a response rate of 82.85%.

3.3. Technique and Instruments

The instruments were adopted from past studies. For the UTAUT, behavioral intention to use MOOCs, and actual use of MOOCs, items were adopted from Alharbi [11]. The sample items included “I would find MOOCs useful in my lectures”. The UTAUT comprises four constructs, and Alharbi [11] reported the composite reliability of all four constructs as 0.882, 0.898, 0.857, and 0.869, for behavioral intention to use MOOCs, 0.885; and for actual use, 0.856, respectively. For media richness, six items were originally developed and validated by Hew and Kadir [31], who reported the composite reliability meeting threshold, >0.70. In addition, Alharbi [10] reported composite reliability (CR) of media richness as 0.889. Two new constructs were added in this current study, one was system quality user satisfaction, which was added in the UTAUT model, and the other one was user-based use motives, which was added as the mediator in the model. The UTAUT has five constructs PE (4 items); EE (4 items); SI (5 items); FCs (5 items); and SQUS (5 items), for a total of 23 items for the UTAUT. Media richness (6 items); user-based use motives (4 items), use behvaior (3 items); and actual use (4 items). The constructs and variables were measured using scales already described in the past literature. All items were measured on a seven-point scale (7 = strongly agree and 1 = strongly disagree). See Appendix A (Appendix A.1, Appendix A.2 and Appendix A.3).

3.4. Data Collection and Informed Consent

Higher education institutions (HEIs) are the main source of learning and providing a workforce to institutions where they can contribute towards the economic development of the country. The existing study collected data from the faculty members and students of public and private HEIs in Saudi Arabia. There is limited empirical evidence available on the use behavior and actual use of MOOCs in Saudi Arabia with the extended model of the UTAUT. In addition, user-based use motives as a mediator is a valuable addition to the theoretical framework and contribution to the body of knowledge. Alharbi [11] conducted a study on the UTAUT and the actual use of MOOCs in Saudi Arabia, but system quality user satisfaction and user-based motives were missing links in the UTAUT and channel expansion theory. It was crucial to enhance the UTAUT model from the Saudi perspective to fill the theoretical gap. This study was conducted in 2024. The data were collected online using Google Forms. All HEIs were recognized universities by the Ministry of Education (MOE) in Saudi Arabia. Prior to data collection, informed consent was also obtained from respondents. Data collection was initiated in 2024 online using Google Forms. It was a challenging job and a difficult task to collect the data from faculty members and students of health informatics and health administration from Saudi Arabia. The convenience sampling technique is widely used in social sciences, management, learning sciences, and health sciences [36]. In addition, similar studies on MOOCs also used this sampling technique [15,16]. The questionnaire link was sent to respondents after obtaining informed consent and permission from them by phone and email. Respondents were given sufficient time to respond to the items in the questionnaire. It was ensured that the privacy and confidentiality of the respondents was preserved. They had 4–5 days to read and understand the items on the scale. In addition, the reputation of the individual and organization would not be harmed. The data were used only for academic purposes. The unit of analysis for this study were students and faculty members from respective HEIs’ health informatics and health administration departments from public and private HEIs’.

3.5. Data Analysis (Statistical Tools and Techniques)

Partial least square structural equation modeling (PLS-SEM) software was used for statistical data analysis. PLS-SEM is second-generation software that can run small and non-normal data sets. It is variance-based software. The current study tested the conceptual model using the PLS-SEM 4 version. PLS-SEM is used to investigate complex relationships. This software is used for exploratory research and theory development. It offers greater statistical power and goodness of fit for models. However, certain rules of thumb need to be met for evaluation of PLS-SEM results. This study developed a measurement model (confirmatory factor analysis) for the reliability and validity of the questionnaire. Likewise, hypotheses were investigated by testing structural models (direct and indirect effects) [48]. PLS-SEM is useful for prediction and exploratory frameworks and studies. Therefore, the aim of this study agrees with Hair et al.’s [48] recommendations, so it is useful and relevant to use PLS-SEM for this study. The reliability of the scale was investigated through Cronbach alpha (α) composite reliability (CR) and average variance extracted (AVE). As per Ringle et al. [49], the threshold for Cronbach alpha and CR was 0.70, for AVE 0.50, factor loadings must be greater than 0.50 but ideally should be greater than 0.70, and VIF must be less than 5. Discriminant validity was investigated through HTMT ratios.

4. Results

Table 1. Demographic Information.

Variables	Categories	n	%
Gender	Male	98	67.6
	Female	47	32.4
Age	18–30 Years	125	86.2
	30–35 Years	11	7.6
	36–45 Years	7	4.8
	46 to 60 and above	2	1.4
Sector	Public	131	90.3
	Private	14	9.7
Nationality	Saudi	128	88.27
	Non-Saudi	17	11.72
Education	Bachelor	34	23.44
	Masters	96	66.20
	PhD	15	10.34

presents the personal information of the respondents who took part in the online survey. The findings indicated that the majority of the respondents were male i.e., 98 (67.6%), followed by females i.e., 47 (32.4%); regarding age, most of them, one hundred and twenty-five, were between 18 and 30 years of age (86.2%), followed by eleven respondents who were 30–35 years of age, seven respondents who were 36–45 years, and two who were 46–60 and above. A total of 131 (90.3%) respondents belong to public sector universities, either as faculty members or students, and 14 (9.7%) belong to private HLIs in Saudi Arabia. A total of 128 (88.27%) were Saudi nationals and 17 (11.72%) were non-Saudi respondents. Regarding education, 96 (66.2%) had a masters degree, 34 (23.44%) had undergraduate bachelor degrees, and 15 (10.34%) were doctoral degree holders.

4.1. Measurement Model Assessment (Confirmatory Factor Analysis)

Internal consistency reliability was measured using Cronbach alpha. It means how well items in a measurement tool, such as a questionnaire, survey, or scale, measure the same concept. The range of alpha is between zero to one and a value of 0.7 and above is acceptable and considered reliable [50]. PLS-SEM is suitable software for predictive and variance-based studies. In this study, Cronbach alpha and composite reliabilities were used to investigate the indicator reliability; for convergent validity, average variance extracted was investigated; and for discriminant validity, HTMT ratios were calculated [51]. It is evident from

Table 2. Confirmatory Factor Analysis.

Variables	Items	Loadings	AVE	CR	Alpha α	VIF
Performance Expectancy	I would find MOOCs useful in my studies.	0.877				2.218
	Using MOOCs would help me solve problems in my studies.	0.849				2.168
	Using MOOCs would enable me to accomplish tasks more quickly.	0.761	0.627	0.827	0.800	2.348
	The use of MOOCs increases the efficiency of my study.	0.663				1.504
Effort Expectancy	My interaction with MOOC platforms is easy and understandable.	0.789				2.669
	It is easy for me to learn how to use MOOCs	0.841	0.688	0.850	0.849	2.897
	I find MOOCs easy to use.	0.862				2.817
	I have no difficulty in using MOOCs.	0.825				1.947
Facilitating Condition	It is convenient for me to study on an MOOCs platform.	0.703				2.512
	I have the hardware necessary to use MOOCs.	0.781	0.573	0.815	0.812	3.046
	I have the knowledge necessary to use MOOCs.	0.785				2.337
	MOOCs are compatible with other learning resources I use.	0.815				2.069
	Support from the platform is available when problems are encountered in MOOCs.	0.693				1.425
Social Influence	People who are important to me think that I should use MOOCs.	0.673	0.548	0.801	0.792	1.398
	I find that using MOOCs is a fashionable and popular way to study in universities.	0.759				1.606
	All my classmates/colleagues are using MOOCs.	0.664				1.362
	Professors/class fellows in my institution have been helpful in the use of MOOCs.	0.808				2.926
	Using MOOCs makes me feel that I belong to the learning community.	0.787				2.248
System Quality User Satisfy	I am satisfied with the functions provided by the MOOCs.	0.872	0.718	0.905	0.902	2.655
	I am satisfied with the services provided by the MOOCs.	0.868				3.872
	I am satisfied with the contents of MOOCs.	0.803				2.466
	I am satisfied with the quality of MOOCs.	0.829				3.019
	Overall, I am satisfied with the MOOCs I use.	0.862				3.468
Medica Richness	The MOOC features allow me to give and receive timely feedback.	0.715	0.574	0.864	0.851	1.644
	The MOOC features allow me to tailor my teaching/learning to my own personal requirements.	0.712				1.572
	The MOOC features allow me to communicate a variety of different cues (such as emotional tone, attitude, or formality).	0.722				1.646
	The MOOC features allow me to use a rich and varied language in learning and teaching.	0.74				1.72
	I could easily explain concepts using the MOOC features.	0.801				1.962
	The MOOC features help me to communicate quickly and understand other.	0.845				2.253
User-Based Use Motives	I participate in an MOOC because it is my preferred way to acquire knowledge and skills.	0.851	0.660	0.838	0.828	2.036
	I participate in an MOOC because it suits my tendency to try new things out.	0.753				1.637
	I participate in an MOOC because it suits my ambition to go with the times.	0.787				1.712
	I participate in an MOOC because it aligns with how I want to learn.	0.854				2.093
Behavior Intention of MOOCs	I intend to continue to use MOOCs for learning in the future.	0.869	0.719	0.805	0.805	1.929
	I plan to use MOOCs for learning in the future.	0.832				1.62
	I will insist on using MOOCs to study the courses I registered for.	0.843				1.761
Actual use of MOOCs	I often use MOOCs to manage my tasks.	0.779	0.598	0.776	0.776	1.535
	I usually use MOOCs.	0.788				1.614
	I regularly use MOOCs.	0.769				1.51
	I frequently complete courses from MOOCs site.	0.756				1.448

that all items and their respective factor loadings are between 0.60 and 0.869 [52], and AVE values are all >0.50; convergent validity implies measures of related constructs align as expected [53]. CR and Cronbach alpha values are >0.70, the established indicator of reliability, and VIF values are less than 5, the threshold given by Hair et al. [48]. In Table 2, all values met their threshold, and therefore the measurement model is considered good fit.

4.2. Discriminant Validity

Discriminant validity refers to the extent to which constructs differ from each other in the model [53]. Based on the threshold given by Henseler et al. [51], discriminant validity is established if HTMT_0.90 is less than or equal to the cut-off level.

Table 3. Discriminant Validity HTMT Ratios.

Variables	1	2	3	4	5	6	7	8	9	10
Actual Use	0.773
Effort Expectancy	0.472	0.83
Facilitating Condition	0.533	0.775	0.757
Media Richness	0.671	0.522	0.643	0.757
Performance Expectancy	0.506	0.697	0.722	0.511	0.792
Social Influence	0.53	0.672	0.733	0.664	0.69	0.74
System Quality Satisfaction	0.525	0.684	0.813	0.641	0.64	0.74	0.847
UTAUT	0.588	0.857	0.923	0.687	0.835	0.874	0.902	0.698
Behavior Intention MOOCs	0.719	0.542	0.697	0.752	0.598	0.698	0.657	0.733	0.848
User-Based Use Motives	0.644	0.467	0.639	0.715	0.526	0.624	0.614	0.663	0.783	0.812

presents findings of HTMT ratios, and it is evident that discriminant validity is established.

4.3. Structural Model Assessment (Direct Effects)

Table 4. Direct Effects.

Relationship	β	SE	t	p	LLCI	ULCI
PE→Actual Use of MOOCs (H1a)	0.110	0.015	7.364	0.000	0.084	0.143
PE→Use Behavior of MOOCs (H1b)	0.137	0.012	10.994	0.000	0.114	0.163
PE→Media Richness (H1c)	0.129	0.013	9.682	0.000	0.104	0.157
PE→User-Based Use Motives (H1d)	0.124	0.013	9.738	0.000	0.100	0.151
EE→Actual Use of MOOCs (H2a)	0.112	0.013	8.888	0.000	0.088	0.137
EE→Use Behavior of MOOCs (H2b)	0.139	0.013	10.534	0.000	0.112	0.164
EE→Media Richness (H2c)	0.131	0.014	9.383	0.000	0.103	0.158
EE→User-Based Use Motives (H2d)	0.126	0.014	8.833	0.000	0.098	0.154
FCs→Actual Use of MOOCs (H3a)	0.138	0.015	9.227	0.000	0.11	0.17
FCs→Use Behavior of MOOCs (H3b)	0.172	0.015	11.667	0.000	0.144	0.201
FCs→Media Richness (H3c)	0.162	0.015	11.076	0.000	0.134	0.191
FCs→User-Based Use Motives (H3d)	0.156	0.016	9.85	0.000	0.126	0.188
SI→Actual Use of MOOCs (H4a)	0.135	0.015	9.061	0.000	0.108	0.166
SI→Use Behavior of MOOCs (H4b)	0.168	0.012	13.627	0.000	0.145	0.193
SI→Media Richness (H4c)	0.157	0.015	10.529	0.000	0.129	0.188
SI→User-Based Use Motives (H4d)	0.152	0.014	10.965	0.000	0.126	0.18
SQUS→Actual Use of MOOCs (H5a)	0.171	0.02	8.619	0.000	0.135	0.213
SQUS→Use Behavior of MOOCs (H5b)	0.214	0.017	12.295	0.000	0.181	0.25
SQUS→Media Richness (H5c)	0.201	0.016	12.369	0.000	0.17	0.234
SQUS→User-Based Use Motives (H5d)	0.193	0.018	10.671	0.000	0.16	0.23
Media Richness→Actual Use(H6a)	0.367	0.099	3.697	0.000	0.184	0.577
Media Richness→UB of MOOCs (H6b)	0.271	0.088	3.06	0.002	0.088	0.438
UBUMs→Actual Use of MOOCs (H7a)	0.283	0.108	2.612	0.009	0.039	0.469
UBSMs→BI of MOOCs (H7b)	0.405	0.088	4.607	0.000	0.241	0.598
Overall R2 UTAUT→Actual Use = 0.515
Overall R2 UTAUT→UB = 0.725

presents the direct effects of predictors upon criterion variables. Performance expectancy (PE) had a positive and significant effect on the actual use (AU) of MOOCs (0.110 ** p < 0.01); use behavior of MOOCs (UB) (0.137 **, p < 0.01); media richness (MR) (0.129 **, p < 0.01); and user-based use motives (UBUMs) (0.124 **, p < 0.01), respectively. This means that a percent change in PE could bring an 11%, change in AU, a 13.7% change in UB, a 12.9% change in MR, and a 12.4% change in UBUMs. Hence, H_1a to H_1d are substantiated. Further analysis of results revealed that effort expectancy (EE) had a positive and significant effect on actual use (AU) of MOOCs (0.112 ** p < 0.01); use behavior of MOOCs (UB) (0.139 **, p < 0.01); media richness (MR) (0.131 **, p < 0.01); and user-based use motives (UBUMs) (0.126 **, p < 0.01), respectively. This means that a percent change in EE could bring an 11.2% change in AU, a 13.9% change in UB, a 13.1% change in MR, and a 12.6% change in UBUMs. Hence, H_2a to H_2d are accepted. Likewise, facilitating conditions (FCs) had a positive and significant effect on the actual use (AU) of MOOCs (0.138 ** p < 0.01); use behavior of MOOCs (UB) (0.172 **, p < 0.01); media richness (MR) (0.162 **, p < 0.01); and user-based use motives (UBUMs) (0.156 **, p < 0.01), respectively. This means that a percent change in FCs could bring a 13.8% change in AU, a 17.2% change in UB, a 16.2% change in MR, and a 15.6% change in UBUMs. Hence, H_3a to H_3d are accepted. Moreover, social influence (SI) had a positive and significant effect on actual use (AU) of MOOCs (0.135 ** p < 0.01); use behavior of MOOCs (UB) (0.168 **, p < 0.01); media richness (MR) (0.157 **, p < 0.01); and user-based use motives (UBUMs) (0.152 **, p < 0.01), respectively. This means that a percent change in SI could bring a 13.5% change in AU, 16.8% change in UB, 15.7% change in MR, and 15.2% change in UBUMs. Hence, H_4a to H_4d are acknowledged. In addition, a new variable was added in the UTAUT model to extend this model. System quality user satisfaction (SQUS) was added, and the findings revealed that SQUS had a positive and significant effect on actual use (AU) of MOOCs (0.171 ** p < 0.01); use behavior of MOOCs (UB) (0.214 **, p < 0.01); media richness (MR) (0.201 **, p < 0.01); and user-based use motives (UBUMs) (0.193 **, p < 0.01), respectively. This means that a percent change in SQUS could bring a 17.1% change in AU, a 21.4% change in UB, a 20.1% change in MR, and a 19.3% change in UBUMs. Hence, H_5a to H_5d are accepted. Furthermore, media richness also explained the significant impact of MOOCs on AU and US, with the findings revealing the MR impact on AU (0.367 **) and UB (0.271 **); a 36.7% and 27.1% change is possible in AU and UB, respectively, due to change in media richness. Moreover, UBUMs also explained a significant impact on AU (0.283 **) i.e., 28.3%, and UB (0.405 **) i.e., 40.5% change. Overall, the UTAUT explained variance upon actual use R² = 0.515, i.e., 51.5%, and UB R² = 0.725, i.e., 72.5% upon use behavior of MOOCs.

4.4. Mediating (Indirect) Effects

Media richness and user-based use motives were added as mediators in the UTAUT model to investigate the AU and UB of MOOCs among Saudi students and faculty members. It is evident from

Table 5. Mediation Effects Indirect Effects.

Indirect Effects	β	SE	T	p	LLCI	ULCI
PE→Media Richness→Use Behavior of MOOCs	0.035	0.013	2.738	0.006	0.011	0.061
PE→User-Based Use Motives→Use Behavior of MOOCs	0.05	0.014	3.682	0.000	0.028	0.082
PE→Media Richness→Actual Use	0.047	0.014	3.48	0.001	0.024	0.077
PE→User-Based Use Motives→Actual Use	0.035	0.014	2.47	0.014	0.005	0.062
EE→Media Richness→Use Behavior of MOOCs	0.035	0.012	2.865	0.004	0.011	0.061
EE→User-Based Use Motives→Use Behavior of MOOCs	0.051	0.013	3.901	0.000	0.029	0.08
EE→Media Richness→ Actual Use	0.048	0.014	3.475	0.001	0.024	0.079
EE→User-Based Use Motives→Actual Use	0.036	0.014	2.48	0.013	0.005	0.063
FCs→Media Richness→Use Behavior of MOOCs	0.044	0.015	2.988	0.003	0.015	0.073
FCs→User-Based Use Motives→Use Behavior of MOOCs	0.063	0.016	3.879	0.000	0.036	0.101
FCs→Media Richness →Actual Use	0.059	0.017	3.519	0.000	0.031	0.098
FCs→User-Based Use Motives→Actual Use	0.044	0.018	2.503	0.012	0.006	0.076
SI→Media Richness→Use Behavior of MOOCs	0.043	0.016	2.736	0.006	0.014	0.076
SI→User-Based Use Motives→Use Behavior of MOOCs	0.061	0.016	3.96	0.000	0.035	0.097
SI→Media Richness → Actual Use	0.058	0.017	3.335	0.001	0.029	0.098
SI→User-Based Use Motives→Actual Use	0.043	0.017	2.555	0.011	0.006	0.074
SQUS→Media Richness→Use Behavior of MOOCs	0.054	0.019	2.914	0.004	0.018	0.092
SQUS→User-Based Use Motives→Use Behavior of MOOCs	0.078	0.019	4.038	0.000	0.046	0.123
SQUS→Media Richness→Actual Use	0.074	0.021	3.521	0.000	0.037	0.121
SQUS→User-Based Use Motives→Actual Use	0.055	0.021	2.572	0.01	0.008	0.093
UTAUT→Media Richness→Use Behavior of MOOCs	0.186	0.063	2.938	0.003	0.061	0.311
UTAUT→User-Based Use Motives→Use Behavior of MOOCs	0.268	0.067	4.005	0.000	0.156	0.425
UTAUT→Media Richness→Actual Use	0.252	0.07	3.589	0.000	0.13	0.408
UTAUT→User-Based Use Motives→Actual Use	0.188	0.074	2.545	0.011	0.028	0.323

that MR significantly mediates the relationships between PE and AU (0.047 **), and PE and UB (0.035 **). In addition, UBUMs also mediated between PE and AU (0.035 *), and PE and UB (0.05 **), respectively. MR mediates between EE and AU (0.048 **), and EE and UB (0.035 **). UBUMs also mediates between EE and AU (0.036 **), and EE and UB (0.051 **). MR mediates between FCs and AU (0.059 **), and FCs and UB (0.044 **). UBUMs mediated between FCs and AU (0.044 **), and FCs and UB (0.063 **). MR has an indirect effect between SI and AU (0.058 **), and SI and UB (0.043 **). UBUMs mediate between SI and AU (0.043 **), and SI and UB (0.061 **). SQUS was added as a new predictor in the UTAUT, hence MR also mediates between SQUS and AU (0.074 **), and SQUS and UB (0.054 **). UBUMs also have an indirect effect on SQUS and AU (0.055 **) and SQUS and UB (0.078 **), respectively. Therefore, H8a to H8E are accepted. Moreover, combined MR and UBUMs mediating effects were also investigated on the combined model of the UTAUT on AU (0.252 **); (0.186 **); UBUMs had an indirect effect the UTAUT and AU (0.188, p < 0.05); and the UTAUT and UB (0.268 **). Hence, UBUMs do mediate between the UTAUT and actual use.

5. Discussion

Teaching and learning have been revolutionized and digitalized through ICT and connectivity, which is a practical alternative to traditional classrooms. MOOCs engage students in a new form of learning collaboration. With the high cost of education and accessibility problems, MOOCs are the alternative practical model of learning and teaching. MOOCs have been added to the United Nation’s education agenda for sustainable education. This study established the connection between the UTAUT and channel expansion theory by adding system quality user satisfaction as a predictive variable in the UTAUT, and media richness and user-based use motives as mediating variables in the framework. Moreover, the current study investigated the impact of the UTAUT model on use behavior and actual use of MOOCs acceptance. This study extends the UTAUT model to include media richness, user-based use motives, and system quality user satisfaction. This study has also validated the positive relationship between constructs and aligned with the past studies of Altahi [7]. Moreover, this study also successfully added media richness and UBUMs as mediating variables in the UTAUT model to investigate the use behavior and actual use of MOOCs. Performance expectancy, (PE), effort expectancy, (EE), facilitating conditions (FCs), and social influence (SI) were found to have positive effects on use behavior (UB) as well as actual use (AU) of MOOCs. Further, an analysis of the results revealed that the new construct, system quality and user satisfaction (SQUS), also has a positive and significant effect on UB and AU of MOOCs, and moreover, these variables also positively predict media richness (MR) and user-based use motives (UBUMs). A positive effect of PE on use behavior, AU, MR, and UBUMs implies that using new learning systems would enhance their behavior and actual use of MOOCs. Moreover, efficient communication and high perception of media richness are also enhanced with the use of new learning systems and channels. These findings are in line with the findings of Altahi [7], Alharbi [11], and Hew and Kadir [31]. Thus, H_1a to H_1d are accepted. In addition, EE also positively and significantly predicts UB and AU of MOOCs, and moreover, also has a positive influence on MR and UBUMs, which implies that ease of use of MOOCs would enhance UB and AU of MOOCs. Likewise, a selection of channel (i.e., media) could also be enhanced through ease of use of new learning system and fulfill their usage motives. These findings of EE on UB, MR, and UBUMs are in line with findings of [10,16,32], but for EE on AU of MOOCs, these findings contradict with the findings of Alharbi [11], who did not report a significant impact of EE on AU; however, these findings are in line with [16]. Thus, H_2a to H_2d are accepted. Likewise, facilitating conditions positively and significantly predicts, UB, AU, MR, and UBUMs of MOOCs. This means that faculty members and health informatics students perceived that their educational institutions have the infrastructure to support the new system for learning to enhance their UB and AU of MOOCs, as well as MR and UBUMs. These findings agree with the findings of [34,54], who reported that FCs could enhance the UB and AU of MOOCs, and the appropriate selection of platform also plays an important role in enhancing the UB and AU of MOOCs. Thus, H_3a to H_3d are accepted. In addition, social influence also significantly predicts the UB and AU of MOOCs, as well as MR, and UBUMs, and they are also influenced by peers, colleagues, and other relatives, which enhances their UB and actual use of MOOCs. These findings are in line with past studies [1,6,10,54], which also reported positive and significant effects of SI up on UB and AU of MOOCs in Saudi Arabia. Thus, H_4a to H_4d are accepted. In addition, system quality user satisfaction also significantly predicts UB and AU of MOOCs, as well as MR and UBUMs, thus SQUS has been successfully added to the UTAUT model in the Saudi context. These findings are in line with Vijai [39], who also reported that system quality and user satisfaction have a positive effect on the UB and AU of MOOCs. Likewise Wan, Xie, and Shu [17] also reported the positive impact on system quality and user satisfaction upon UB and AU of MOOCs. Furthermore, Hew and Kadir [33] also reported positive effects on media richness on UB and AU of MOOCs. The current study findings are also supported by the findings of Hew and Kadir [31], and Alharbi [11]. Thus, H_5a to H_5d are accepted. Moreover, MR significantly predicted UB and AU, and UBUMs also significantly predicted UB and AU of MOOCs in Saudi Arabia. These findings are aligned with findings of past studies of Alharbi [11] and Wan et al. [17]. Thus, H_6a to H_6b and H_7a to H_7b are accepted. To investigate the mediating effects of media richness and user-based use motives, hypotheses H_8a to H_8F were developed. From the findings, it is evident that MR and UBUMs significantly mediated between PE, EE, FCs, SI, and SQUS, thus substantiating the H_8a to H_8f. The combined effects of the UTAUT were investigated on UB and AU of MOOCs with mediating effects of MR and UBUMs, and it was identified that MR and UBUMs mediated the UTAUT, UB and MR significantly mediated between the UTAUT and AU, and UBUMs mediated between the UTAUT and AU of MOOCs. Thus, H_8F is also accepted. The proposed model in this study explains the factors required for the main problem i.e., (use behavior and actual use of MOOCs) and integrates MOOCs into the educational domain for better academic performance. Use behavior and actual use of MOOCs are not only beneficial for full-time students, but also workers and faculty members who do not have sufficient time for learning a particular technology. This study’s findings are equally significant for higher learning institutions’ policymakers who seek to establish quality standards and provide MOOCs that fit with the demands of the learners. The extended UTAUT model explains how well the factors of the UTAUT, along with user-based use motives and media richness, explain the use behavior and actual use of MOOCs by students and faculty members. As proposed by Alharbi [11], the adoption of new technology increases performance expectancy i.e., the individual performance in every aspect of technology acceptance. The extended UTAUT model is explained in a reasonable way to understand to adopt MOOCs in an educational environment, where not only students, but also faculty members are sensitive about learning. The original UTAUT model has gone through many extensions but the current UTAUT model has integrated the system quality and user satisfaction suggested by Wang et al. [55](with constructs of channel expansion theory, i.e., media richness by Hew and Kadir [31] and Alharbi [11] and user-based use motives as mediators) and offers additional significant contributions to the literature about online learning courses. This expanded UTAUT model revealed that students of health informatics and health administration, as well as faculty members, can improve their performance by adopting MOOCs. They perceive the adoption and use of new technology as easy, indicating a high level of effort expectancy. Additionally, they are influenced and motivated by their universities and peer groups, demonstrating the impact of social influence. The facilitating conditions, including the infrastructure provided by their institutions, are also deemed sufficient to support new technologies. Furthermore, they express satisfaction with the quality of the content offered in the MOOCs. Previous models of Altahi [7] explained 67% variance upon use behavior and actual use of MOOCs while the current extended model has explained 72.5% variance. Media richness and user-based use motives also play a significant role in the adoption of MOOCs. Appropriate platforms and UBUMs can enhance not only use behavior, but the actual use of MOOCs by students and faculty members. Performance, ease of use, influence from peers, satisfaction, use behavior, and actual use of MOOCs could be enhanced through media richness and UBUMs. This implies that as long as students and faculty members realize the usefulness and benefits of MOOCs, such as effectively completing learning tasks, improving learning performances, and adding value to their physical skills, they will be more inclined towards the use of MOOCs. In addition, PE is students’ and faculty members’ internal demand while social influence they receive from the external environment. The findings of this study indicate that use behavior and actual use of MOOCs by students and faculty of health informatics and health administration are influenced by people around them such as family, friends, peers, teacher recommendations, class-fellow recommendations, support from peers, sense of recognition, and feeling to be part of the learning communities, and will contribute to the continued intention and use of MOOCs. The present MOOCs platform looks pretty good, is easy to use, the interface is user-friendly, and learners do not need to put much effort into mastering it. The current generation belongs to the digital generation. They are well equipped with internet use skills and technology adoption, hence there are no obstacles for university students and faculty members in using MOOCs to conduct learning. In addition, facilitating conditions refers to provisions of software, hardware, internet, wifi, and portable devices to help the smooth learning process for learners. The most dominant factors in this model are media richness and user-based use motives. Media richness has a direct impact on use behavior and actual use of MOOCs, and it could enhance learning and instructional effectiveness, and support interactivity, content design, attitudes towards knowledge, and educational level. Media richness enhanced teachers’ experience, students’ learning experience, and interactions with channel, and organizational and communication partners may lead to improved use behavior and actual use of MOOCs. The significant impact of media richness has proved that timely feedback, variety of cues, and languages have enabled the learners to communicate quickly, receive timely feedback, enhance their learning and teaching performances, absorb the concepts easily, and become motivated to use new system easily and bring them out of the psychological stress [56].

5.1. Theoretical Contributions

The current study has investigated whether media richness and user-based use motives can mediate the relationship between the extended model of the UTAUT, use behavior, and actual use of MOOCs from the Saudi perspective. Moreover, system quality and user satisfaction were added as new constructs in the UTAUT theory. The current study applied the unified theory of acceptance and use of technology (UTAUT) and channel expansion theory (CET) to study the adoption, use behavior, and actual use of MOOCs by Saudi Health informaticians. The findings of this study have extended the body of knowledge on the UTAUT and CET. Therefore, the findings of this study add to the current literature by offering a broader insight into how attributes of the UTAUT independently and collectively enhance use behavior and actual use of massive open online courses (MOOCs) in higher learning institutions (HLIs) settings in Saudi Arabia. Another contribution of this study is the original model, which explained 26% of the variance in the use behavior of MOOCs [20]. This model has explained 72.5% variance in the use behavior of MOOCs. The third contribution of this study is that it explained 51.5% of the actual use of MOOCs.

5.2. Practical Contributions

HLIs have an intense need to adopt new approaches to learning; however, the emergence of MOOCs in developing countries is a thrilling new approach to learning and teaching, particularly in the Saudi context. There have been limited studies conducted on MOOCs from the Saudi perspective, but studies that have been conducted report the direct effects of the UTAUT and TAM model by overlooking the importance of indirect mediating effects of channel expansion theory and user-based used motives in the UTAUT model. This study has contributed practically in numerous ways, highlighting the importance of adding system quality and user satisfaction in the UTAUT model as well as bridging the UTAUT and channel expansion theory by

Adding media richness as a mediator. HLI policymakers must recognize the importance of MOOCs so that MOOCs can be penetrated into higher learning institutions to uplift education. Media richness can make MOOCs more attractive and power users can provide a positive image of MOOCs, thus increasing its use by HLI professionals and students. In collaboration with the Ministry of Education (MOE) of Saudi Arabia, HLIs can develop instructional material and activities to be integrated into the education system. When there is a breakthrough in educational technologies and multimedia, the advanced technologies can be incorporated to enrich media richness. HLIs and MOEs can provide rewards to students and faculty members for interactive activities. This might be achieved by using star ratings. Where teachers and students are rated on a five-star rating based on their level of interaction with each other. Rewards could be certificates, storage space, and highlighting their names in the interactive system as the best teachers for interactive sessions. Likewise, for knowledge-sharing sessions, HLIs and MOEs may organize seminars, workshops, colloquiums, conferences, and symposiums to raise awareness of MOOCs.

5.3. Conclusions

This study has found that the scales used are reliable and valid as all the threshold values for reliability (Cronbach alpha, composite reliability, average variance extracted, and factor loadings) met the cut off level. Hence, the indicated reliabilities and validities were established. In addition, a structural model was developed to test hypotheses, in which a bootstrap was used. It was found that direct and indirect effects were all positive and significant. PE, EE, SI, FCs, and SQUS significantly predicted UB, AU, MR, and UBUMs. Moreover, MR and UBUMs significantly mediated the relationships between PE < EE < SI, FCs, SQUS, UB, and AU. These findings could benefit the policymakers in the Ministry of Education (MOE) of Saudi Arabia, who could implement this model to increase the adoption of MOOCs in HEIs. The proposed model explained 72.5% and 51.5% variance in use behavior and actual use of MOOCs. Moreover, the predictors positively and significantly predicted the use behavior and actual use of MOOCs as well and they are indirectly related via media richness and UBUMs. Therefore, this study contributes to the UTAUT and channel expansion theory literature, as well as the adoption of MOOCs from a Saudi perspective. This is one of the pioneer studies that has not only extended the UTAUT model, but also bridged the extended UTAUT with channel expansion theory to investigate the use behavior and actual use of MOOCs. Limited empirical evidence is available on the indirect effects of media richness and UBUMs on the relationship between the UTAUT and use behavior and the actual use of MOOCs. From the findings, it is revealed that use behavior and actual use of MOOCs could be enhanced through performance expectancy, effort expectancy, facilitating conditions, social influence, system quality and user satisfaction, media richness, and user-based use motives. Moreover, mediating effects also confirmed that media richness and user-based use motives play a parallel mediating role between predictors (performance expectancy, effort expectancy, facilitating conditions, social influence, system quality, and user satisfaction) and criterion variables (use behavior and actual use of MOOCs). It is concluded, based on findings, that MOOCs provide students with access to learning content anytime, anywhere. MOOCs provide storage and the facility of sharing content with peers; most importantly, health informatics and health administration students can apply this knowledge in problem identification, problem-solving, and decision-making activities. Online learning not only enhances the behavior and actual use of MOOCs, but also, adopting technology gives learners a competitive advantage and a sustainable learning environment in the academic field for growing in their academic career. Further, the role of access to knowledge, application, and sharing in MOOCs should be investigated particularly in terms of adaptability and sustainability in HLIs in developing economies. This study has combined the expanded UTAUT model and channel expansion theory (CET), and added user-based use motives as an intervening mechanism to investigate the use behavior and actual use of MOOCs by health informaticians and health administration students and faculty in Saudi Arabia’s context. The findings indicate that all factors, including the newly added constructs such as system quality and user satisfaction, significantly predicted use behavior and actual use of MOOCs. Moreover, media richness and UBUMs significantly mediated the relationship between attributes of the UTAUT and the UB and AU of MOOCs. This means that practitioners not only pay attention to ease of use, infrastructure, and influence from colleagues to adopt the use of MOOCs and continued intention of using MOOCs, but also to system quality, user satisfaction, media richness, and user-based use motives, which are also important in increasing use behavior and actual use of MOOCs. Adding system quality and user satisfaction has verified the suggestion of Wan et al. [16] in the UTAUT model. This study has proposed eight main hypotheses and thirty sub-hypotheses. All are accepted. It was concluded that media richness and user-based use motives are found to be the most dominant constructs, explaining about 21% to 40% of the increase in use behavior and actual use of MOOCs in Saudi Arabia. Regarding indirect mediating effects, again, media richness and user-based use motives could increase the UB and AU of MOOCs by 18% to 25%.

5.3.1. Limitations of This Study

This study has offered several contributions, but it is very important to mention the limitations of this study as well. A past study of Altahi [7] explained a 63% variance in use behavior, while this study had an overall variance of 72.5%, thus extending the model effectiveness by adding a new construct and bridging the UTAUT with CET, leaving 27.5% unexplained variance. Further, one of the limitations of this study was that it has used a small sample size, Moreover, this study included only health informaticians and health administrators from Saudi Arabia, which might restrict the generalizability of the findings. In addition, this study used non-probability convenience sampling, and this sampling method has generalizability issues. Finally, this study did not use any moderator variable.

5.3.2. Future Lines of Research

It is recommended that future studies use a large sample size. In addition, future studies could add more constructs such as self-determination theory constructs, and constructs from the task-technology fit model. Likewise, on the basis of our findings, it is recommended to include respondents from other fields such as engineering, computer sciences, business and management sciences, economics, and medicine. Furthermore, it is recommended that simple random sampling is used in the future to overcome generalization issues. Future studies may add moderating variables like digital skills to enhance use behvaior and actual use of MOOCs.