Adoption and use of eLearning platforms by universities in developing countries: Evidence from Zimbabwe

Abstract

This article examined the adoption and use of eLearning platforms by universities in Zimbabwe using the Delphi technique. Using the Delphi method, professionals in the area were asked for their in-depth opinions on how Zimbabwean institutions could embrace and utilise eLearning systems. ODeL specialists, academic consultants, university professors, and doctors made up a panel of 10 experts. When it came to the adoption and usage of eLearning platforms by institutions in Zimbabwe, four rounds of questionnaires were sent out one after the other to gain expert feedback. These questionnaires were derived from the Unified Theory of Acceptance and Use of Technology and the Theory of Planned Behavior. Latent variables and measurement items in these theories served as the basis for the questions. The main findings of this study were that although eLearning is highly valued and well-known in Zimbabwe, nothing much has been done to embrace and use eLearning systems by the nation’s universities. Major obstacles holding back Zimbabwean universities’ adoption and use of eLearning platforms include slow or nonexistent internet connection, high data costs, a lack of hardware and software options, and bad connectivity. Additionally, it has become abundantly evident that teachers and learners adopting and using eLearning systems is a planned behavior. This study intends to resolve various ambiguities and gaps in Zimbabwean universities’ adoption and use of eLearning while also informing policy design and implementation.

Keywords:

• eLearning
• online learning
• ODeL
• Delphi
• UTAUT
• UTAUT2
• higher education

JEL Classification:

• D01
• D03
• D80
• D83

PUBLIC INTEREST STATEMENT

The purpose of this article was to examine the adoption and use of eLearning platforms by universities in developing countries with a particular focus on Zimbabwe. A Delphi approach consisting of 10 experts, drawn from the academia, educationists and ODeL experts, was used to examine the adoption and use of eLearning platforms by universities in Zimbabwe. The results showed that despite consensus opinion regarding the importance and positive impact of eLearning on the efficiency and effectiveness of knowledge dissemination and access to learning material, there still exists a huge gap in the adoption and use of eLearning platforms by universities in Zimbabwe. Concerted effort by both the public and private sector is needed to narrow down the gap. Policy makers are therefore recommended to consider the role of Public-Private Partnerships (PPPs) in higher education.

1. Introduction

Zimbabwe established public elementary and secondary education in 1980 as a fundamental human right. By 2030, the nation aims for universal, free education in line with the Sustainable Development Goal. The 2030 Agenda for Sustainable Development is universal, holistic and indivisible, with a special imperative to leave no one behind. The achievement of SDG 4 – ensure inclusive and equitable quality education and promote lifelong learning opportunities for all—plays a central role in building sustainable, inclusive and resilient societies. Education has become a basic right and elementary to human dignity. Postsecondary education was established in 1957, and by 1990, 9,017 students enrolled at the University of Zimbabwe. In 2006, the country had 12 universities, including five private and seven state-owned.

Zimbabwe has 16 universities, including 10 state-owned and six private institutions. To enhance education, universities must produce graduates with employment prospects. Higher education is a vital economic engine, impacting economic development and growth. Despite government efforts since 1980, universities are struggling to attract top talent and maintain a competitive educational system. A country’s human and physical capital is crucial for its economic prosperity and well-being (Olaniyan & Okemakinde, 2008). Human capital, which includes knowledge, competence, and ability acquired through formal education, can significantly impact civilization evolution. Raising human capital can be expensive due to the long educational process in industrial societies (Maune, 2016). Skolnik and Berenbaum (2007) further define human capital as knowledge, competence, and ability obtained through formal education, highlighting the importance of balancing education and economic growth.

Intellectual capital, including customer capital and knowledge artifacts, has become a crucial factor in economic and corporate growth. Stewart (2001) asserts that intellectual human capital surpasses other competitive advantages, as knowledge is the fundamental source of competitive advantage for firms and governments. Skolnik and Berenbaum (2007) cited by Maune (2016) argue that the rapid accumulation of human capital may be partly responsible for Israel’s prosperity and productivity growth, which was evident by 1960. Skolnik and Berenbaum (2007) show immigrants’ education and experience reflect their industrial countries’ state-of-the-art, significantly contributing to the growth of host countries’ human capital and intellect.

Ndinguri et al. (2012) assert that technological advancements have enhanced learning and, consequently, human capital development in general. Technology is crucial in the development of human capital as the demand for new skills and relationship requirements in enterprises increases (Maune, 2016). This development is becoming more significant as a paradigm for economic development in the modern period. People are essential elements of economic growth, acting as agents and intangible assets. Technology has significantly influenced the approaches to creating human capital, which is crucial for economic success. Higher education today has a responsibility and a right to adapt to and exploit technology.

Socioeconomic classes in Africa, especially in Zimbabwe, have influenced the adoption of technology in education. The wealth gap between the affluent and poor has led to distinct educational systems catering to the affluent and impoverished. Wealthy individuals have access to resources, while impoverished children lack them. The colonial system, which divided white and black schools, has resulted in inadequate facilities in remote and underprivileged regions, impacting schooling during and after the COVID-19 pandemic. Low-income students in African countries face challenges in accessing online learning due to limited resources, including power, internet connection, computers, laptops, and mobile phones. Higher education students, particularly in Zimbabwe, face challenges due to high data costs and inadequate funding. State-owned universities struggle to provide online learning, while local colleges struggle to purchase or subscribe to platforms like Microsoft Teams, Wiseup, and Moodle. These issues significantly impact students’ ability to access and utilize online learning platforms.

The purpose of this study is to examine the adoption and use of eLearning platforms in developing countries with a particular focus on Zimbabwe. This study is significant in policy formulation and implementation in higher education. It is the author`s conviction that this study will advance knowledge in the subject through invoking meaningful debates and pushbacks in some of the thoughts brought in by this study. Further future research will follow from this researcher and others in the field. The findings of this study have valuable theoretical and practical implications for HEIs, researchers and developers of eLearning platforms.

The next sections of this article will be as follows: section 2 will review relevant literature; section 3 will detail the research methods; section 4 will provide the study findings; and section 5 will offer conclusions and suggestions.

2. Literature review

The Theory of Planned Behavior (TPB) (Ajzen, 1991), the UTAUT (Venkatesh et al., 2003), the UTAUT2 (Venkatesh et al., 2012), and the Extended UTAUT2 (Maune & Milind, 2022) are the theories that guide this investigation. They are briefly discussed in this section of the study. The researcher used these theories as he investigates the uptake and utilization of online platforms by universities in developing nations. The extended UTAUT2 framework was used in an effort to address inquiries about the phenomena that was being researched. Given the context-dependent nature of eLearning, the framework was applied and extended with constructs related to TPB and Theory of Reasoned Action (Al-Adwan et al., 2022). The Delphi method was applied in this process.

2.1. Theoretical review

The Theory of Reasoned Action (TRA) (Fishbein & Ajzen, 1975), the Theory of Planned Behavior (TPB) (Ajzen, 1991), the UTAUT (Venkatesh et al., 2003), the UTAUT2 (Venkatesh et al., 2012), and the Extended UTAUT2 (Maune & Milind, 2022) are the theoretical foundations for this work. The TPB asserts that institutions’ adoption and utilization of online learning platforms is a deliberate behavior. This school of thought is motivated by the complexity and technological advancements in higher education, notably in the wake of COVID-19. People are unlikely to accept and use online learning platforms without giving them considerable thought because of its influence on them. Therefore, participating in online learning is regarded as a deliberate action. According to research, there are a number of reasons why people are either included or excluded in online learning systems. Particular factors affecting a person’s willingness to use online learning platforms include involuntary or voluntary, socioeconomic, trust, cost, information, literacy, documentation, and eligibility (Beck et al., 2009; Ellis et al., 2010; Kempson & Whyley, 1999). According to Shneor and Munim (2019), they must precede behavioral intentions and behavior. They see volitional control and intentionality as the building blocks of individual behavior when it comes to embracing and utilizing technology. As a result, using the TPB model can assist forecast users’ intents and behavior while using online learning.

According to Sethi et al. (2018) and Shneor and Munim (2019), TPB developed as an extension of Fishbein and Ajzen’s (1975) theory of reasoned action (TRA), which failed to adequately explain individual behaviors that were not under volitional control.

The so-called “rational choice models” category includes the TPB (Maune et al., 2021). This idea, created by Icke Ajzen and Martin Fishbein, is now arguably the most well-known reasoned action method (Maune et al., 2021). The TPB has become a significant paradigm for comprehending, forecasting, and modifying social behavior in humans. The TPB’s central tenet contends that an individual’s desire to participate in a certain behavior affects the chance that they will do so (Ajzen, 1991). According to Sethi et al. (2018) and Shneor and Munim (2019), the basic thrust of the TPB is that perfect perceived or volitional control is uncommon and that some behaviors need for specialized knowledge and abilities. How can intention be described, assuming intention can explain behavior? Three factors, as identified by Ajzen (1991) and Fishbein and Ajzen (1975), account for behavioral intention: (1) attitude (one’s own opinions about the behavior); (2) subjective norms (the opinions of others); and (3) perceived behavioral control (one’s self-efficacy toward the behavior).

This paradigm states that perceptions of behavioral control, attitudes, and subjective standards all influence intention, which in turn predicts behavior. Prior to the behavior occurring, the motive behind the behavior is explained by attitudes, subjective norms, and perceived behavioral control. Shneor and Munim (2019) redesigned the TPB and dubbed their model the expanded TPB framework. According to the new model, attitudes, PBC, self-efficacy, subjective norms, and societal norms all function as antecedents of intents. According to Smith and McSweeney (2007), who cite Armitage and Conner (2001), the TPB was a frugal explanation of the attitude-behavior link in its initial version. Researchers were given latitude by Ajzen (1991) to add discovered predictors to the TPB.

The Unified Theory of Acceptance and Use of Technology (UTAUT), created in 2003 by Venkatesh et al., was later renamed Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) following minor revisions in 2012. The UTAUT2 increased the number of structures from the initial four to three. Price value, hedonic motivation, and habit are the constructions. In 2022, Maune and Milind made more modifications to the UTAUT2 by adding new elements and combining the theories (TRA, TPB, UTAUT, UTAUT2) to create a single model that influences deliberate behavior and use behavior while adopting and employing technology. This model is referred to as UTAUT3. After a thorough assessment of the literature, this was carried out (Maune, 2021). Perceived risk and trust have been shown to be excellent indicators of behavioral intention in the acceptance and usage of technology (Maune, 2021). In online learning, risk has been identified as a significant motivator of behavioral intention and usage. Both subjective norm and self-efficacy were taken from the Theory of Planned Behavior (TPB) (Ajzen, 1991) and the Theory of Reasoned Action (TRA) (Fishbein & Ajzen, 1975). The notion that behavior is influenced by intentions is fundamental to the TPB and other reasoned action models (Ajzen, 2012).

According to Venkatesh et al. (2012), intentionality is a crucial underlying theoretical process that governs behavior in UTAUT and comparable models. Many have suggested that it is crucial to include additional theoretical mechanisms, including critics of this class of models (Venkatesh et al., 2012). Perceived risk, trust, subjective norms, and self-efficacy were incorporated into UTAUT2 as shown in Figure 1 based on the research by Maune (2021) and the justifications therein.

Figure 1. Research model.

Source: Adopted and adapted from Maune and Milind (2022).

2.2. Online learning in higher education institutions

The COVID-19 pandemic forced global institutions to close in mid-April 2020, affecting more than 1.3 billion students worldwide (Ahmad et al., 2023). This forced educators to adapt to alternate teaching strategies, leading to the adoption of web-based education, or e-Learning, as a common form of instruction. Educators worldwide now integrate e-Learning, as a common form of instruction (Ahmad et al., 2023). E-learning has become a popular method for institutions to continue teaching and providing students with necessary learning materials (Shams et al., 2022). With the rapid development of technology, distance learning has become widely available across all subject areas. Students appreciate its adaptability, ease, and personalized learning environment (Arpaci et al., 2020). Al-Adwan and Al-Debei (2023) argue that the interest in metaverse technology has risen considerably in higher education learning contexts. The two further argue that, due to the global spread of the COVID-19 pandemic, HEIs are now increasingly emphasizing online interactive learning. The adoption of eLearning technology in HEIs aims to increase the efficiency of learning processes and to transform the delivery of university education (Hujran et al. (2021).

E-learning, is the use of computer network technology, primarily through the internet, to provide knowledge and instructions to people (Wang et al., 2010). It involves the transfer of knowledge and skills through electronic means like the internet, intranets, and extranets with accredited accreditations (Akbari et al., 2022). Curtain (2002) defines online learning as the use of the internet to improve student-teacher interaction, including synchronous and asynchronous engagement methods like chat rooms, newsgroups, and email. Online learning, or e-learning, has become more creative, adaptable, and autonomous due to the use of technology. E-learning is also referred to as computer-mediated learning, online learning, M-learning, open learning, and web-based learning. These terms refer to technical tools connected to a network, allowing students to study anywhere and anytime (Al-Maroof & Al-Emran, 2021; Cojocariu et al., 2014). E-learning is a formal online platform used for providing educational programs to remote learners (Arkorful & Abaidoo, 2015). It uses multimedia tools and is supported by electronic hardware and software (Al Rawashdeh et al., 2021). A personal computer is typically required for computer-enhanced learning.

Online learning and e-Learning are often used interchangeably, but online learning is a type of e-Learning, along with blended learning, mobile learning, and remote learning (Basak et al., 2018). Other communication technologies like tutorials, learning support systems, and online lectures also contribute to e-learning. This system is supported by both online and offline hardware and software. E-learning, a technology-based approach, aims to boost student engagement in the classroom by utilizing various tools such as texts, videos, sounds, collaborative sharing, and interactive graphics. This approach enhances the quality of instruction, underscores the need for higher education institutions to maintain a competitive edge, and provides students with access to education and training in a globalized world, ultimately improving the overall learning experience (Islam et al., 2015).

E-learning, an integration of information technology has reduced student costs and improved teaching and learning quality (Songkram, 2015). Students can save money and use their free time for other activities. Both instructors and students must adapt educational technology to the study’s setting. This has made teaching and learning more creative, adaptable, and autonomous (Al-Nuaimi & Al-Emran, 2021; Indira & Sakshi, 2017). Synchronous online learning involves real-time dialogue between teachers and students during live lectures, providing immediate feedback. Asynchronous learning is flexible and student-centered, with instructional materials available on various platforms and discussion boards. Planning ahead and gathering materials is crucial for online learning. Students require more help than in-person learning, and online platforms allow teachers to teach students with limited computer experience in additional skills development. Despite being a novel teaching method for over 10 years, online learning remains relevant in the literature (Cojocariu et al., 2014). The European Data Portal reports that prior to the current epidemic, digitization of instructional content was less frequent, with only 10% of nations having digital resources outside schools and 20% having online learning materials (European Data Portal, 2020).

The global crisis has led to a significant shift towards digital learning, with governments worldwide implementing measures to ensure uninterrupted education (Al-Nuaimi & Al-Emran, 2021; The World Bank, 2020). Understanding learners’ perspectives and presenting platforms and technology effectively are crucial for successful online learning systems (Williams et al., 2020). Video conferences and immediate conversations are available on the best digital learning platforms, allowing for immediate interaction. Any digital device, including phones, can support content, provide rapid feedback, record lectures, and submit assignments. This study, however, focuses on the adoption and use of online learning platforms by universities in developing countries rather than understanding various teaching and learning forms.

3. Research methodology

3.1. Material and methods

The study utilized the Delphi method, involving experts in the field of higher education and online learning to reach a consensus on the adoption and use of online platforms by universities in developing countries (Adler & Ziglio, 1996; Cantrill et al., 1996; Dalkey & Helmer, 1963; Grisham, 2008; Linstone & Turoff, 1975; McKenna, 1994). A panel of 10 experts (6 males & 4 females) drawn from lecturers, consultants and ODeL experts was assembled (see Table 1) (Delbecq et al., 1975; Linstone, 1978; Skinner et al., 2015). The Delphi method ensured a reliable consensus on the adoption and use of online platforms by universities in developing countries. Rowe and Wright (1999) used rigorous questions and controlled opinion feedback, while Walker and Selfe (1996) employed open-ended rounds. A series of intensive questionnaires interspersed with controlled opinion feedback were used. The researcher conducted four rounds. The rounds and questions were informed by the theories around the adoption and use of technology which are, Extended TPB (Fishbein & Ajzen, 1975; Shneor & Munim, 2019), UTAUT and UTAUT2 (Venkatesh et al., 2003, 2012) and the Extended UTAUT2 (Maune, 2021; Maune & Milind, 2022; Venkatesh et al., 2012) (see Table 2). The first round of questions focused on the adoption and use of online platforms by universities. The second, third and fourth rounds focused on the themes defined by the said theories. In the final round (4), expert consensus was established, allowing participants to modify their prior decisions (Hsu & Sandford, 2007). This round provided a final opportunity for participants to revise their judgments. The researcher followed a four-round process.

Table 1. Delphi experts’ panel size

Academia (4)	Educationists (Consultants) (2)	ODeL Experts (4)
This included lecturers drawn from local universities, both private and state owned. Two Professors and two Doctors, three (3) male and 1 female. These were drawn from various fields. The informants had more than 10 year’s professional experience in higher education.	Two educationists, a male and a female, one a PhD holder and another a Master’s degree holder based in Zimbabwe formed part of the panel. The two have international experience in higher education spanning for more than 15 years.	The panel comprised of 2 female and 2 male participants with more than 10 year professional experience in higher education. The four are recognized experts in ODeL. Two of the experts have international experience in higher education and some of them have published work on ODeL and some are regular speakers on ODeL.

Note: This information was derived from public platforms such as LinkedIn and other public websites.

Table 2. Round, Theory, themes and source

Round	Theory/Basis	Theme/Variables	Source
1	General eLearning Questions	Definition of eLearning, Types of eLearning platforms, Ranking, Adoption, Use and Challenges.	Literature review
2	ETPB	Attitude, Perceived Behaviour Control, Self-efficacy, Social norms, Subjective norms.	Fishbein and Ajzen (1975), Ajzen (1991), Shneor and Munim (2019)
3	UTAUT2	Performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, habit	Venkatesh et al. (2003), Venkatesh et al. (2012)
4	EUTAUT2	Perceived risk, Trust	Venkatesh et al. (2012), Maune (2021), Maune and Milind (2022)

There have been studies on the adoption and usage of online learning platforms in Zimbabwe, but none of them, as far as the researcher is aware, employed the Delphi method to examine the problem. The independence of individuals with experience-based backgrounds was preserved, which minimized personal bias (Grisham, 2008; Robinson, 2004; Rowe & Wright, 1999; Rudy, 1996). By making sure that participants were independent, personal prejudice was minimized. The Delphi method was the optimal technique due to Linstone and Turoff (2002)‘s reasons. The use of a Delphi approach was considered because it served as both a collaborative learning and research technique tool. Moreover, the approach was considered because many a times collective opinion from reliable experts surpasses a statistically supported opinion derived, for example, from large samples of less qualified informants. Table 2 shows the question creation process, sorted by the specific theory where necessary.

4. Presentation and discussion of results

Four rounds of questionnaires were used in the study, the first of which sought the opinions of experts on their knowledge of eLearning platforms, adoption and usage by universities in Zimbabwe. Below are the discussions of the outcomes of these rounds.

4.1. Round one results

In the first round, an open-ended questionnaire with questions centered on the definition of eLearning, types of eLearning platforms, ranking of eLearning platforms, adoption and use of eLearning platforms, as well as the challenges faced in adopting and using eLearning platforms in Zimbabwean universities, was sent to 10 selected experts, as shown in Tables 1 and 2. The experts demonstrated a good degree of understanding of the definition of online or eLearning platforms. The broad definitions offered in the literature (Akbari et al., 2022; Al Rawashdeh et al., 2021; Curtain, 2002; Wang et al., 2010) were in accordance with what they defined. The versatility, simplicity, and individualized learning environment provided by these eLearning platforms were highly regarded by the experts (Arpaci et al., 2020). Some of the key themes that emerged from the 10 experts in relation to the definition of online learning or eLearning include the following: electronic means of learning, internet-based learning, computer-mediated learning, technical tools connected to a network that allow students to study from anywhere and anytime, computer-enhanced learning, mobile learning, remote learning, learning systems supported by both hardware and software, and technology-based learning approach. The professionals expressed their admiration for the many eLearning platforms that are available both domestically and abroad.

In addition to WhatsApp, these platforms also include Moodle, Google Classroom, Google Meet, Zoom, Acacia, Wiseup, and MyVista. The experts concurred that Zimbabwean institutions have a high acceptance and usage rate for the Moodle eLearning platform. Other experts, however, countered that due to the high expenses associated with implementing these platforms, many have turned to WhatsApp group platforms to disseminate knowledge and get access to instructional resources like audios, videos, and other articles. Owing to the high cellphone penetration rate in Zimbabwe, this platform has gained a lot of use owing to its accessibility and low cost compared to other platforms. More than 88.2% of Zimbabweans currently own a mobile phone (Maune et al., 2022). A significant portion of society now trusts and accepts the use of mobile phones for transactions, brief messaging, and spoken information exchange. There are now greater prospects for eLearning due to the widespread use of mobile phones as reliable communication tools and the country’s population of 12.9 million users (Maune et al., 2022).

Although there was expert agreement on the meaning, adoption, and use of different eLearning platforms by universities in Zimbabwe, there appear to be a number of obstacles preventing their full adoption and use, which has hampered the transition from face-to-face teaching methods to online teaching. Costs related to various eLearning platforms, the availability of dependable internet connections, and access to computers by both students and lecturers are some of the issues mentioned. In order to address the lack of electricity in some places and loading shedding, some colleges have turned to the usage of solar-powered devices. According to one expert, the adoption and usage of eLearning platforms in Zimbabwe’s higher education appears to be influenced by the Toiling Class Theory. The issue of socioeconomic classes has had an influence on the adoption and use of technology in the educational system in Africa in general and in Zimbabwe in particular. In African society, there are several social classes, including the lower class, middle class, and upper class. These classes sparked the creation of distinct educational systems, one catered to the affluent and the other to the impoverished. Because they lack access to power, the internet, computers and laptops, and mobile phones, many students from low-income households and communities in Zimbabwe cannot afford online education. Most state-owned colleges are unable to provide students viable online learning systems that enable them to access study materials and attend lectures online due to a lack of funds. Many colleges have completely abandoned such programs as a result of these obstacles, rather than adopting eLearning to supplement the conventional face-to-face learning style.

4.2. Round two results

A follow-up questionnaire, this time based on the Extended Theory of Planned Behaviour (ETPB), was inspired by the expert comments in round one. Fishbein and Ajzen (1975), Ajzen (1991), and Shneor and Munim (2019) all contributed to the development of this theory. In essence, this theory asserts that use behavior is a planned action or behavior. As a result of attitude, perceived behavior control, self-efficacy, social norms, and subjective standards, the adoption and usage of eLearning platforms by universities may be viewed in this situation as a planned behavior (see Table 2). Based on the questions that were derived from the ETPB, there was agreement among the 10 experts about the adoption and usage of eLearning platforms. The adoption and usage of eLearning systems by universities in Zimbabwe was viewed favorably by all experts. They contend that eLearning is suitable, beneficial, and something they would use if given the chance. They generally agreed that its acceptance and use are desirable and beneficial since it increases both the accessibility of study resources for students and the efficacy and efficiency of instruction. As it draws in a bigger pool of students from both domestic and international sources, it benefits the learner and the instructor as well as the institution as a whole.

Additionally, there was broad agreement on the need for behavior management in Zimbabwean universities’ adoption and usage of eLearning. The experts held that it is solely up to the individual to decide whether to accept and use eLearning platforms. The common view was that they controlled the adoption and usage of these platforms, i.e., they control the behavior to utilize them. They have complete discretion over whether or not to use these sites. They have the effectiveness to utilize these eLearning platforms, experts agreed. They all had faith in their knowledge of how to use these eLearning tools. They also concurred that the mainstream media, press, and news portray uptake and use of these eLearning platforms favorably and portray these platforms favorably. The 10 experts responded that significant individuals believe that the adoption and usage of eLearning platforms is a good thing when given questions about subjective norms. The experts concurred that eLearning is beneficial for students and university professors in the eyes of their peers and influential individuals in their communities.

According to the 10 experts, one’s own ideas about the conduct, the opinions of others, and one’s self-efficacy toward the behavior all have an impact on behavioral intention (Ajzen, 1991; Fishbein & Ajzen, 1975; Shneor & Munim, 2019). They contend that perceptions of behavioral control, attitudes, social norms, and subjective standards all influence intention, which in turn affects behavior prediction. Prior to the conduct taking place, attitudes, subjective norms, societal standards, and perceived behavioral control all describe the motivation for the behavior. The experts also agreed that, as stated by Ajzen (1991), an individual’s desire to participate in a certain behavior affects that individual’s chance of completing that behavior. Additionally, they claimed that while the TPB’s major focus is on the rarity of fully perceived or volitional control, some behaviors—most notably the adoption and use of eLearning platforms—require specialized knowledge and abilities (Sethi et al., 2018; Shneor & Munim, 2019). For instance, in Zimbabwe, a lack of resources has prevented colleges from adopting and utilizing eLearning technologies.

4.3. Round three results

The UTAUT2 created by Venkatesh et al. (2003) and Venkatesh et al. (2012) served as the foundation for round three. After the second round of discussions, the questionnaire was distributed to specialists. The UTAUT2 measuring items for seven latent variables served as the basis for the questions’ adaptation and adoption. Table 2 lists these factors. The questions were designed with regard to performance expectations, effort expectations, social influence, facilitating conditions, hedonic motivation, price value, and the habit of Zimbabwean institutions adopting and using eLearning systems in mind. All of the experts agreed that the amount of behavior intentions to utilize eLearning platforms in higher education increased with an individual’s performance and effort expectations about their adoption and utilization. The same is true for habit, price value, hedonic drive, facilitating situations, and social influence. These findings were in line with findings by Al-Adwan et al. (2022) and Al-Adwan and Al-Debei (2023).

Dodds et al. (1991), who contend that price value is the customers’ cognitive tradeoff between the perceived advantages of the application and the financial cost of utilizing it, had an impact on the experts’ arguments on pricing value. According to Venkatesh et al. (2012), this pricing value turns positive when people believe the advantages of utilizing a technology outweigh the cost, and this price value has a favorable effect on intention. The experts concurred that the perceived advantages of implementing and utilizing eLearning systems outweigh the associated expenses. Regarding hedonic motivation, the experts concurred with Brown and Venkatesh (2005), who claim that using eLearning platforms is enjoyable or pleasant and that this factor has been demonstrated to be crucial in deciding technology adoption and use. The experts concurred that eLearning usage in the past is a reliable indicator of future use of comparable platforms when it comes to habits. This was consistent with the justifications offered by Kim et al. (2005) for operationalizing habit as past usage. The two discovered that past technology use has a significant impact on present and future use. According to Limayem et al. (2007), habit has a direct impact on how people use technology in addition to how people want to use it. Habit also moderates how people intend to use technology, making intention less significant as habit levels rise.

4.4. Round four results

This time, the 10 experts were given a questionnaire based on the questions created from the two latent variables provided by EUTAUT2 and their measurement items. This was the fourth and last phase of the study. Perceived danger and trust were the latent variables in question. The experts expressed partial agreement that they wouldn’t feel secure sharing and providing their personal information via these eLearning platforms when questioned about the perceived risk connected with the adoption and usage of eLearning platforms. According to one expert, he is concerned about how these eLearning platforms will be used in the future since other individuals could be able to access his data. Concerning the security of private information provided through eLearning platforms, there was broad agreement.

Since consumers would have little influence over these platforms, questions have been raised about the security and confidentiality of information shared through them. Although these platforms are more valuable for teaching and learning, the experts concurred that data security and privacy were a top priority for them. They contend that these eLearning platforms expose students to cyberbullying. The experts concurred that these two latent factors were important predictors of how Zimbabwean institutions will embrace and use eLearning systems. They support Maune’s (2021) claim that the two were effective markers of behavioral intention in the acceptance and use of technology. Furthermore, it was acknowledged that risk was a key driver of behavior in the usage of e-learning platforms. The professionals also thought that the creators of these e-learning platforms were sincere and reliable. They concurred that these platforms offer their consumers high-quality services, show concern for them, and listen to their complaints. Regarding the subject of user security and privacy, there was a feeling of consensus. According to experts, platform developers consider users’ privacy and security while creating new platforms.

4.5. Implications for practice

This study looked at the adoption and use of eLearning platforms by universities in developing countries with a particular focus on Zimbabwe. The results showed that as much as there was consensus among the panelists regarding the importance and criticality of eLearning in higher education, very little has been done with regards to adoption and use of such platforms in Zimbabwe`s higher education due to reasons such as lack of adequate funding, infrastructure as well as internet provision and connectivity. Perhaps one of the most significant finding from this study for ODeL experts and other stakeholders in higher education was the gap in adoption and use of eLearning platforms in Zimbabwe. This gap exists despite research having shown the positive impact eLearning has in improving efficiency and effectiveness of knowledge dissemination to learners. This was proven during and after COVID-19 globally. The impact according to research was huge. E-learning was proven to be a success and a critical tool in knowledge dissemination as well as providing access to study materials. A concerted effort is therefore required from both private and public sector to cover the existing gap through funding eLearning projects in higher education. All stakeholders are critical in closing the gap given the fact that eLearning comes with different facets, such as internet connectivity, softwares, hardware, servers and electricity to mention but a few. There are training costs which are also involved. There is also need for provision of sustainable energy given the rampant electricity outages especially in developing countries. Investment in solar powered systems must be prioritized. Universities, therefore needs to prioritize the adoption and use of eLearning platforms by its lecturers and learners. This will also help capture and keep experienced and seasoned professors and learners from the region and beyond. This area provides serious investment opportunities and there is need to promote and facilitate public-private partnerships focusing on the provision of eLearning at higher education. Notwithstanding ongoing projects around technological hubs across all universities, there is also need to expand these synergies towards promoting the adoption and use of eLearning platforms in higher education in the country.

4.6. Limitations

This study used the Delphi method to examine how Zimbabwean institutions adopted and used eLearning systems. As may be seen in Table 1, the Delphi panel included 10 academic experts. Future study should use a mixed-methods approach and a larger sample size to prevent biases and ensure field sensitivity. Future research might compile a list of e-learning resources and assess their impact on Zimbabwe’s higher education standards. Comparative studies should highlight trends and geographical differences. But this investigation’s complexity and normative nature made the Delphi technique beneficial.

5. Conclusion

In conclusion, this study used a Delphi methodology to examine how Zimbabwean institutions adopted and used eLearning systems. Four sets of questionnaires were distributed to a panel of 10 experts, as indicated in Table 1, to get their input on the adoption and use of eLearning platforms in Zimbabwe. As indicated in Table 2, the questions were created using a variety of latent variables and measuring items from theories of planned behavior, including UTAUT, UTAUT2, and EUTAUT2. The results revealed broad agreement that using and adopting eLearning systems was a deliberate action, and that behavioral intentions have a significant impact on these actions. According to the numerous theories explored in this paper, behavioral intention is a function of several latent factors. Determining the impact of these latent factors on behavioral intention in more future investigations is therefore crucial. Using factor analysis and structural equation modeling, it is necessary to evaluate the importance of these interactions. The Ministry of Higher Education, Science and Technology should take eLearning seriously and allocate finances for its acceptance and usage, especially in state-owned universities, in order for it to continue to be useful and effective. Additionally, it will make it easier to draw in and keep skilled and seasoned instructors, as well as students from the region and overseas. Since COVID-19, e-learning has gained significant importance on a global scale. E-learning has gained popularity as a way for institutions to go on with teaching and supply students with essential learning resources. Technology has advanced quickly, and distant learning is now widely accessible across all academic areas.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary data

Supplemental data for this article can be accessed online at https://doi.org/10.1080/2331186X.2023.2287905

Additional information

Notes on contributors

Alexander Maune

Alexander Maune is a seasoned researcher, academic and strategic competitive intelligence expert with more than 15 years of practical experience. His substantial academic contributions include over 40 journal articles, two books, one book chapter and more than 37 newspaper and magazine papers. His vast expertise extends to mentoring and supporting academic students worldwide. He has examined several proposals, dissertations and thesis worldwide. His area of research spans from competitive intelligence, alternative finance, development finance, taxation, accounting, banking & finance and corporate governance. He is also a Talmudic and Zoharic scholar.