1 Introduction

For the past three decades, we have witnessed a rapid development and proliferation of information and communication technologies (ICTs), which have undoubtedly provided numerous opportunities for the higher education sectors to apply new methods of delivering learning programs (Li et al. 2012; Teo et al. 2018b). Electronic learning (e-learning), as one of such educational innovations has changed the landscape of learning by offering various opportunities to students (Salloum et al. 2019). Given its huge impact in education, much research has been conducted to examine the integration of e-learning systems in teaching and learning and understanding the key drivers that facilitate its adoption (Tarhini et al. 2014). In comparison with the traditional learning systems, e-learning has received prominent attention due to its convenience, flexibility, and low cost (Li et al. 2012).

For higher educational institutions, it is imperative to encourage a collaborative learning environment to leverage learners’ performance and promote higher levels of knowledge acquisition. The evolution of e-learning systems in higher education institutions has altered the pedagogical programs, in which emphasis is no longer placed on the instructor but on the learner (Valencia-Arias et al. 2018). The distinctive features in e-learning have facilitated learners ability to acquire and disseminate knowledge through the practices of knowledge management (KM) in anytime anywhere settings (Salloum et al. 2018).

KM processes were believed to be the main predictors of technology adoption (Ali et al. 2018). These processes include “knowledge acquisition”, “knowledge sharing”, and “knowledge application” (Tiwana 2000). Under these processes, e-learning is regarded to be the potential lever that enables the acquisition and sharing of knowledge through the online delivery of learning (Wild et al. 2002). In a recent review of KM processes and its interrelation with technologies, Al-Emran et al. (2018b) reported a lack of research regarding the impact of these processes on educational technologies, including e-learning. It was also argued that knowledge acquisition and knowledge sharing were the significant determinants that affected the usage and adoption of various technologies compared to other KM processes. These two determinants might have an influential role in explaining the intention to adopt e-learning systems (Lau and Tsui 2009). Further, at the university level, educators are encountered with the challenge of providing attention to tens of learners. The large number of registered students could raise issues regarding the acquisition of learning materials (i.e., knowledge acquisition) and the sharing of these materials between the instructors and their students (i.e., knowledge sharing) (Al-Emran et al. 2019). These issues were minimized with the delivery of education through e-learning systems. Although e-learning systems facilitate the process of knowledge acquisition and knowledge sharing through their considerable features, little is known about the impact of these two factors on e-learning adoption.

To overcome these limitations and promote a higher level of e-learning adoption in education, it is imperative to understand the roles played by knowledge acquisition and knowledge sharing in explaining e-learning adoption by students. It is hoped that this study would provide empirical evidence in support of the factors affecting the students’ behavioral intention to adopt e-learning through the lenses of an extended Technology Acceptance Model (TAM) that included knowledge acquisition and knowledge sharing as additional variables.

2 Literature review

From the extant literature on e-learning, the main focus was to determine the technical characteristics of e-learning systems (Teo et al. 2011), and to examine the learners’ and educators’ experiences with various features including student-instructor interaction, course curriculum, and learning management systems (Alexander and Golja 2007; Saekow and Samson 2011). However, the degree to which learners are enthusiastic to use e-learning in their learning agenda could be varied according to their usage experiences and beliefs about teaching and learning (Teo 2010). Under these conditions, e-learning scholars anticipated that more studies have to be carried out to understand the determinants that affect e-learning adoption (Abdullah and Ward 2016; Salloum et al. 2019).

It is argued that users in developing countries are less familiar with e-learning systems (Ngampornchai and Adams 2016). Research concerning the impact of e-learning systems implementation in these countries is moving slowly, as most of the scholars tend to give more attention examining the drivers affecting these systems (Tarhini et al. 2014). For many countries in the Arab world, the adoption rate of e-learning systems is low (Tarhini et al. 2017). This may be due to a preference by most of the Arab countries to use the traditional learning system over e-learning (Dagher and Boujaoude 2011).

In recent years, e-learning in Oman has been featured in a significant way in the higher educational sector and this is reflected in the substantial increase of students in these learning environment (Al Musawi and Yousif Abdelraheem 2004). However, another research suggested that many users still did not use these systems, and sometimes resist the usage of technology (Al-Musawi 2007). Hence, understanding the determinants that influence the e-learning systems adoption is essential in developing and promoting e-learning in the Arab world (Tarhini et al. 2017), with a particular focus on the Omani context (Al-Maroof and Al-Emran 2018).

Concerning the importance of KM processes in learning activities, Al-Emran et al. (2019) highlighted the application of KM processes in m-learning systems. It was suggested that knowledge acquisition can be achieved through the process of students’ acquisition of the course material upon uploading that material by their instructor. It was also argued that knowledge sharing can be accomplished through the process of sharing the course material between the students themselves and their instructor. Further, the students can collaborate and communicate with each other by sending messages and sharing questions and posts regarding the course. Since e-learning is the main umbrella of m-learning (Alzaza and Yaakub 2011), the features of KM processes can also be extended to the domain of e-learning.

From the literature, a number of theoretical models were developed to examine the factors affecting the adoption of new technologies. Of the more commonly cited are the “Technology Acceptance Model (TAM)” (Davis 1989), the “Unified Theory of Acceptance and Use of Technology (UTAUT)” (Venkatesh et al. 2003), “Innovation Diffusion Theory (IDT)” (Rogers 1962), the “Theory of Reasoned Action (TRA)” (Fishbein and Ajzen 1975), and the “Theory of Planned Behavior (TPB)” (Ajzen 1985). Among these models, the TAM has been widely validated and found to be efficient in explaining technology acceptance and adoption (Al-Qaysi et al. 2018; Marangunić and Granić 2015).

In line with the presented literature, the aim of this study is to extend the TAM by adding knowledge acquisition and knowledge sharing as variables to understand e-learning adoption in Oman. Specifically, the present study has two main objectives. First, to determine whether knowledge acquisition, knowledge sharing, perceived usefulness, and perceived ease of use have an impact on students’ behavioral intention to use e-learning systems in Oman. Second, to identify the nature of the relationships among these factors and to determine the significant factors that are critical in affecting e-learning adoption in Oman.

3 Research model and hypotheses development

This study develops a research model through the extension of TAM with knowledge acquisition and knowledge sharing factors. It is argued that the behavioral intention to adopt e-learning can be influenced by multiple significant factors, namely knowledge acquisition, knowledge sharing, perceived usefulness, and perceived ease of use. It is also suggested that perceived usefulness and perceived ease of use are affected by knowledge acquisition and knowledge sharing. It is worth mentioning that the original conceptualization of TAM and its subsequent-based research has modified the model by eliminating the “attitudes towards use” construct due to the reason of simplifying the model and reducing the number of indicators in the instrument (Davis and Venkatesh 1996). In keeping with this, the “attitudes towards use” construct has also been eliminated in this study as it does not mediate the impact of perceived usefulness on behavioral intention.

3.1 Knowledge acquisition (KA)

Knowledge Acquisition (KA) refers to the process of acquiring new knowledge and building upon that knowledge when a new one is derived. Previous research in technology adoption indicated that KA positively influences the perceived usefulness and perceived ease of use (Al-Emran et al. 2018a). Some studies also revealed that KA does positively affect individuals’ behavioral intention to use technology (Chong et al. 2013; García-Sánchez et al. 2017). In the context of this study, it is posited that a higher knowledge acquired through e-learning systems would be associated with a higher level of perceived usefulness, perceived ease of use, and behavioral intention to use the e-learning system by students. Thus, the following hypotheses are formulated:

  • H1: KA has a positive effect on the perceived usefulness of e-learning.

  • H2: KA has a positive effect on the perceived ease of use of e-learning.

  • H3: KA has a positive effect on the behavioral intention to use e-learning.

3.2 Knowledge sharing (KS)

Knowledge Sharing (KS) refers to the process of disseminating various resources among the individuals taking part in specific activities. Evidence from the literature points to the positive relationship between KS and perceived usefulness and perceived ease of use of a particular technology (Al-Emran et al. 2018a; Cheung and Vogel 2013; Kim 2012). It was found that KS is a significant predictor of behavioral intention to use various technologies (Aboelmaged 2018; Chong et al. 2013; Iglesias-Pradas et al. 2015; Salloum et al. 2019). From the preceding evidence, the following hypotheses are formulated:

  • H4: KS has a positive effect on the perceived usefulness of e-learning.

  • H5: KS has a positive effect on the perceived ease of use of e-learning.

  • H6: KS has a positive effect on the behavioral intention to use e-learning.

3.3 Perceived usefulness (PU)

PU is generally defined as “the degree to which a person believes that using a particular system would enhance his/her job performance” (Davis 1989). For many e-learning research, PU has been found as a prominent predictor of behavioral intention to use (Kanwal and Rehman 2017; Li et al. 2012; Tarhini et al. 2015; Teo et al. 2014). In this study, the following hypothesis was proposed:

  • H7: PU has a positive effect on the behavioral intention to use e-learning.

3.4 Perceived ease of use (PEOU)

PEOU refers to “the degree to which the person believes that using a particular system would be free of effort” (Davis 1989). From the literature, PEOU has a positive association with perceived usefulness and behavioral intention to use a specific technology (Almaiah et al. 2016; Böhm and Constantine 2016; Li et al. 2012). In this study, we hypothesize the following:

  • H8: PEOU has a positive effect on the perceived usefulness of e-learning.

  • H9: PEOU has a positive effect on the behavioral intention to use e-learning.

Given the above-formulated hypotheses, Fig. 1 shows the developed research model for this study.

Fig. 1
figure 1

Research model

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4 Methodology

4.1 Participants

This study used a quantitative approach with the questionnaire as a tool for data collection. The participants were 453 students enrolled at Al Buraimi University College (BUC) in Oman. In line with the policies of scientific research at BUC, any research study should get an approval prior to being undertaken. Based on that, an ethical approval was obtained from the department of scientific research after indicating the target participants and the purpose of the study. This study was carried out at the beginning of the academic year 2018–2019. The participation was voluntary, and the participants were introduced to the purpose of the research before filling out the questionnaire. The filling process of the questionnaire took around 10–15 min. They were selected using convenience sampling and came from different majors in different departments. After accounting for missing data and non-completed questionnaires, a total of 403 cases were retained for data analysis.

4.2 Instrument

A questionnaire survey was developed by the authors for this study. Comprising two parts, the first contained items to solicit demographic information of the respondents including gender, age, and year of study. The second part comprises items to measure each of five constructs: knowledge acquisition (5 items), knowledge sharing (5 items), perceived usefulness (5 items), perceived ease of use (5 items), and behavioral intention (3 items). These constructs were measured using a 5-point Likert scale with “1 = strongly disagree” and “5 = strongly agree”. Items measuring knowledge acquisition and knowledge sharing were adopted from previously published resources with further modifications to fit the scope of this study (Al-Emran et al. 2018a). Those for perceived usefulness, perceived ease of use, and behavioral intention were adopted from the seminal study on TAM (Davis 1989) and previously published studies on technology acceptance (Mohammadi 2015; Teo et al. 2018a). The constructs and their corresponding items are shown in the Appendix.

5 Results

5.1 Data analysis

In this study, the data analysis was undertaken with the partial least squares-structural equation modeling (PLS-SEM) using the SmartPLS V.3.2.7 software (Ringle et al. 2015). Data were analyzed using a two-step approach in which the measurement and structural model were evaluated in sequence (Hair Jr et al. 2016). The choice of PLS-SEM in this study is attributed to several reasons. First, PLS-SEM is best suited for use when a study aims to develop an existing theory (Urbach and Ahlemann 2010). Second, PLS-SEM provides concurrent analysis for both measurement and structural model, which in turn, leads to more accurate estimations (Barclay et al. 1995).

5.2 Descriptive analysis

From the 403 responses, 57.6% were females, and 42.4% were males. Participants’ age ranges between 18 and 22 years old. In addition, 24.1% of the participants were in year one, 26.6% in year two, 25.6% in year three, and 23.8% in year four. Finally, 46.7% of the participants indicated their preference in using e-learning as a study mode and 50.1% indicated a preference to using dual mode of study (i.e., e-learning and traditional learning).

5.3 Measurement model assessment

In assessing the measurement model, Hair and his colleagues suggested estimating the construct reliability (including Cronbach’s alpha and composite reliability) and validity (including convergent and discriminant validity) (Hair Jr et al. 2016). Table 1 shows the Cronbach’s alpha and composite reliability (CR) to be well above the recommended threshold value of 0.7 (Gefen et al. 2000; Kannan and Tan 2005). On this basis, construct reliability was established.

Table 1 Measurement model results
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For the measurement of convergent validity, the factor loadings and average variance extracted (AVE) were examined (Hair Jr et al. 2016). The results in Table 1 indicate that all factor loadings and AVEs are higher than the suggested value of 0.7 and 0.5, respectively, thus lending support for the presence of convergent validity in the measurement model.

Discriminant validity was assessed using the Fornell-Larker criterion, cross-loadings, and the Heterotrait-Monotrait ratio (HTMT) (Hair Jr et al. 2016). Table 2 shows the results of the Fornell-Larker criterion test in which the square roots of all AVEs are greater than its correlation with other constructs (Fornell and Larcker 1981). Table 3 contains evidence to support the cross-loadings criterion since the indicator loadings on each construct are higher than the loadings of its corresponding constructs.

Table 2 Fornell-Larker criterion
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Table 3 Cross-loadings results
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Table 4 shows the HTMT ratio results, in which the value of each construct does not exceed the threshold value of 0.85, hence establishing discriminant validity (Henseler et al. 2015). The analysis results provide an evidence that there were no issues regarding the assessment of the measurement model in terms of its reliability and validity, and therefore it would be proper to proceed with the evaluation of the structural model.

Table 4 Heterotrait-Monotrait ratio (HTMT) results
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5.4 Structural model assessment

For the assessment of the structural model, we examined the path coefficients and coefficient of determination (R2) through a bootstrapping procedure of 5000 resamples (Hair et al. 2017). As shown in Table 5, knowledge acquisition was a positive predictor of perceived usefulness, perceived ease of use, and behavioral intention to use e-learning. Therefore, H1 (β = 0.254, t = 5.019), H2 (β = 0.306, t = 5.926), and H3 (β = 0.102, t = 2.003) were supported. Furthermore, knowledge sharing had positively predicted perceived usefulness, perceived ease of use, and behavioral intention to use e-learning thus supporting H4 (β = 0.113, t = 2.120), H5 (β = 0.405, t = 7.064), and H6 (β = 0.152, t = 2.742). Perceived usefulness was found to positively affect the behavioral intention to use e-learning, supporting H7 (β = 0.333, t = 5.875). Perceived ease of use was found to have a positive influence on perceived usefulness and behavioral intention to use e-learning. Therefore, H8 (β = 0.421, t = 8.329) and H9 (β = 0.284, t = 4.986) were also supported.

Table 5 Structural path analysis results
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In Fig. 2, the results revealed that knowledge acquisition, knowledge sharing, and perceived ease of use had explained 47% of the variance in perceived usefulness. It is also revealed that both knowledge acquisition and knowledge sharing explained 41.5% of the variance in perceived ease of use. Notably, knowledge acquisition, knowledge sharing, perceived usefulness, and perceived ease of use had explained 54% of the variance in behavioral intention to use e-learning. In line with the recommendation by Chin (1998), the obtained R2 values in this study were regarded to be acceptable.

Fig. 2
figure 2

PLS algorithm results

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6 Discussion

The present study examined the factors affecting students’ adoption of e-learning in the higher educational institutions in Oman by extending the TAM with knowledge acquisition and knowledge sharing as external variables to the model. The results indicated that the extended TAM is effecient in explaining the significant factors influencing the behavioral intention to adopt e-learning systems. This could be seen from the supported hypothesized relationships among the model constructs.

The results also indicated that knowledge acquisition has a significant direct effect on perceived usefulness, perceived ease of use, and behavioral intention to use e-learning systems. This is consistent with prior studies in the literature where knowledge acquisition was found to have a positive relationship with perceived usefulness and perceived ease of use (Al-Emran et al. 2018a), and those which postulated that knowledge acquisition had a positive relationship with the behavioral intention to use technology (Chong et al. 2013; García-Sánchez et al. 2017). These results also suggested that when learners are capable of acquiring knowledge through the e-learning system, the strength of their perceived usefulness, perceived ease of use, and behavioral intention to use such a system would increase correspondingly.

The results of this study found that knowledge sharing has a significant direct influence on perceived usefulness, perceived ease of use, and behavioral intention to use e-learning systems. These findings were also in agreement with previous studies which concluded that knowledge sharing had a significant positive relationship with perceived usefulness and perceived ease of use (Al-Emran et al. 2018a; Cheung and Vogel 2013; Kim 2012), and those which revealed that knowledge sharing has a significant positive effect on the behavioral intention to use technology (Aboelmaged 2018; Chong et al. 2013; Iglesias-Pradas et al. 2015; Salloum et al. 2019).

In terms of the TAM’s core constructs (i.e., perceived usefulness and perceived ease of use), the results pointed out that perceived usefulness has the strongest direct effect on behavioral intention to use e-learning systems. This result was also consistent with previous studies that found perceived usefulness to have the strongest impact on behavioral intention to use e-learning systems (Li et al. 2012). Specifically, this study suggests that students in Oman were concerned about the usefulness of e-learning system, and how e-learning would enhance their learning performance.

In addition, perceived ease of use was found to have a significant relationship with perceived usefulness and behavioral intention to use e-learning systems. In keeping with the e-learning literature, these results were in agreement with Li et al. (2012) who pointed out that perceived ease of use had a significant positive influence on perceived usefulness and behavioral intention to use. In the context of this study, when students in Oman perceived that e-learning was useful, easy to use, and user-friendly, their behavioral intention to use e-learning would be strengthened.

6.1 Implications for theory and practice

The findings drawn from this study have implications for theory and practice. Theoretically, this study contributes to furthering our understanding of technology acceptance models through the extension of TAM with two prominent factors from the knowledge management field, namely knowledge acquisition, and knowledge sharing. Arguably, this study is among the early works to determine the relationship between knowledge management factors and technology acceptance models in general, and the extension of TAM with these factors in the e-learning context in specific.

Practically, policy-makers and educators could take a leaf from the results of this study to implement an innovative e-learning system that could enable the students to acquire and share knowledge in anytime anywhere settings. Additionally, the significant relationship between perceived ease of use and both perceived usefulness and behavioral intention to use suggests the need to provide students with opportunities to experience the e-learning system usefulness in education in order to achieve higher levels of acceptance.

6.2 Limitations of this study and future research

Despite the significant results yielded in the present study, there are several limitations, and these should be considered in future research. First, the data were collected from a sample of students enrolled in only one higher educational institution in Oman. Thus, caution should be exercised when generalizing the results to the entire higher educational institutions in Oman. For future research, it would be more useful to validate the model using a sample of students from different academic institutions. Second, the data were collected from a sample of students only. Further research should attempt to collect data from a wider sample of educators across the country to validate the research model proposed in this study and examine their intentions to adopt e-learning systems.

7 Conclusion

A large number of research articles were carried out to examine the factors affecting the e-learning adoption. However, it is worth mentioning that the adoption of e-learning systems in general, and the Arab countries in particular, is still an issue that requires further examination (Tarhini et al. 2017). This means that students are not using these systems at the optimal level. Given these limitations in literature, examining the factors affecting the e-learning systems adoption is still an important research direction that has attracted the attention of many scholars.

The main contribution of this study was to propose and validate a theoretical research model based on TAM and knowledge management practices (i.e., knowledge acquisition and knowledge sharing) to examine students’ behavioral intention to use e-learning systems in Oman. The proposed research model has been validated using the PLS-SEM through a two-step approach, namely measurement model and structural model. The empirical results indicated that knowledge acquisition, knowledge sharing, perceived usefulness, and perceived ease of use have a positive direct influence on the behavioral intention to use e-learning systems. The evidence from these results provides holistic insights which could assist the policy-makers and educators to better understand the factors affecting the adoption of e-learning systems in Oman.