1. INTRODUCTION

The rapid development of information technology (IT) helps provide rich educational content over the Internet, which is known as online learning. This refers to the integration of education and the learning process with modern technologies. Flexibility and accessibility are the main advantages of this learning technology (Wu et al., 2010). Moreover, online learning facilitates the learning process, reduces costs, and provides accessible education (Mahande & Malago, 2019). Besides these advantages, several issues and challenges need more effort to achieve the desired goals of online learning. In its early application, limited access and communication represented its key drawbacks (Chin, 1999). With time, this was replaced with other issues such as isolation, lack of motivation and direct guidance, and lack of experience (Abbad, 2021; Bouhnik & Marcus, 2006; Dutton et al., 2001).

Despite the spreading use of online learning technologies around the world, developing countries still face many challenges. This is due to the low rate of technology acceptance, the unavailability of adequate technical and human infrastructure, and the lack of information sharing and institutional cooperation (Kim & Park, 2018). Accordingly, online learning systems are not gaining popularity in developing countries (Farid et al., 2015). In Iraq, for example, after the partial and full shutdown due to the COVID-19 pandemic, all educational institutions adopted distance and blended education. However, many obstacles still prevent the successful application of this technology in Iraq (Al-Azawei et al., 2016; Al‐Radhi, 2008). To address such issues, it is necessary to understand what can affect users’ acceptance and adoption of online learning technology (Ashraf et al., 2016). Thus, identifying factors that may affect online learning adoption can help educational institutions apply specific strategies to attract students towards this technology (Park, 2009).

Consequently, it is necessary to make further efforts to expose the common factors that may influence learners’ decisions in adopting online learning technology, particularly in developing countries. This present study, therefore, aims to (1) investigate learners’ adoption of online learning in Iraq as a case of developing nations, (2) extend the Unified Theory of Acceptance and Use of Technology (UTAUT) model to understand the influence of other theories on technology acceptance, (3) improve the predictability of UTAUT by integrating new variables, and (4) highlight constructs that can help predict effort expectancy and performance expectancy, as this has been neglected in the original UTAUT. The research draws upon UTAUT (Venkatesh et al., 2003). However, it proposes an integrated model based on four theories, namely UTAUT, compatibility, online self-efficacy, and knowledge sharing and knowledge acquisition. Two motivations are behind this extension. First, this can address the deficit of UTAUT by investigating the influence of the integrated theories on online learning adoption. Second, these theories can complement each other, as they look towards technology acceptance from different angles, particularly in online learning systems.

2. PREVIOUS WORK AND THE PROPOSED MODEL

Implementing IT relies on user acceptance (Davis, 1989). The acceptance may refer to user satisfaction with this particular technology to accomplish the activities and tasks for which the technology was intended (Al-Azawei, 2019; Walldén et al., 2016; Wixom & Todd, 2005). Ignoring learners’ perceptions, on the other hand, can negatively affect the acceptance of educational technologies (Alowayr & Al-Azawei, 2021).

To reach the desired acceptance, many efforts have been made in several domains including psychology, sociology, and information system (IS), to develop theoretical models for predicting and explaining user acceptance of IT or IS (Chao, 2019). The technology acceptance model (TAM) of Davis (1986) is a widely cited theory in this field. However, some studies have pointed out many disadvantages of TAM related to external variables of its perceived usefulness and perceived ease of use (Chao, 2019; Tsai et al., 2018). Therefore, Venkatesh et al. (2003) proposed another theory which is the so-called UTAUT. It has attracted considerable attention in online learning acceptance research (Abbad, 2021; Alowayr & Al-Azawei, 2021; Mahande & Malago, 2019).

Although UTAUT has been widely used and adopted, there is controversy and doubt about its capability to explain users’ technology acceptance. This may indicate that the factors and variables used in UTAUT may not be sufficient to determine the required level of technology adoption. Accordingly, previous research modified and extended UTAUT (Chao, 2019; Cimperman et al., 2016; Khalilzadeh et al., 2017; Mtebe et al., 2016). Such literature suggested that adding several different external variables could improve the model’s predictability. In online learning, Alowayr and Al-Azawei (2021) extended the model based on the self-determination and expectation-confirmation theories. Based on UTAUT, Kim and Lee (2020) built a conceptual framework of effective information communication technologies-based instruction.

To address some of UTAUT’s limitations, this present study extends it by constructing an integrated framework based on four well-known theories. Here, four external variables are integrated with UTAUT, which are compatibility, online self-efficacy, knowledge sharing, and knowledge acquisition. This study, therefore, investigates the effect of these four external constructs as depicted in the proposed research model (see Fig. 1).

Fig. 1

Proposed research model.

3. RESEARCH METHODOLOGY

This study adopts a survey research design to examine the cause and effect relationships among the proposed research model. One of the advantages of this research is that the subjectivity issues are not found, as researchers rely solely on the statistical findings to understand the possible causality associations between different constructs.

4. RESULTS

The proposed theoretical model was investigated using partial least squares (PLS). In comparison to traditional statistics, such as regression, this method has many advantages. First, it can be used to examine the association among a series of constructs (Al-Azawei, 2017). Moreover, according to Chin (1998), structural equation modeling (SEM) is a superior method for theory development and prediction. Finally, previous research on predicting users’ behavior has widely adopted this technique (Al-Azawei & Alowayr, 2020; Ameen et al., 2019; Shin & Kang, 2015). The collected data were analyzed in two steps. The first was validating the research survey, whereas the second was examining the predictability of the independent variables to the dependent constructs.

4.2. Findings of the Proposed Model

Table 6 shows that ten out of twelve hypotheses were supported based on the path association among the proposed variables, whereas Fig. 2 illustrates R² and β values of the model after performing the PLS. According to this analysis, the results indicate that performance expectancy (β_PE→BI=0.371, p<0.001) and compatibility (β_Comp→BI=0.514, p<0.001) were significant predictors of behavioral intention to explain 76.1% (R²=0.761) of the variance of this construct, supporting hypotheses H3 and H7. On the other hand, effort expectancy (β_EE→BI=-0.043, p=0.428) and social influence (β_SI→BI=0.100, p=0.055) were not determinants of behavioral intention, and so hypotheses H1 and H4 were rejected.

 

Fig. 2

Findings of the proposed model.

 

Table 6

Research findings

Hypothesis	β	Mean	SD	T-value	p-value	Findings
H1: Effort expectancy -> Behavioral intention	-0.043	-0.043	0.055	0.793	0.428	Rejected
H2: Effort expectancy -> Performance expectancy	0.193	0.193	0.062	3.117	0.002	Supported
H3: Performance expectancy -> Behavioral intention	0.371	0.371	0.067	5.547	0.000	Supported
H4: Social Influence -> behavioral intention	0.100	0.100	0.052	1.923	0.055	Rejected
H5: Facilitating conditions -> Actual use	0.152	0.156	0.057	2.683	0.007	Supported
H6: Behavioral intention -> Actual use	0.345	0.341	0.067	5.147	0.000	Supported
H7: Compatibility -> Behavioral intention	0.514	0.513	0.054	9.538	0.000	Supported
H8: Compatibility -> Actual use	0.416	0.416	0.070	5.951	0.000	Supported
H9: Knowledge acquisition -> Performance expectancy	0.392	0.393	0.074	5.321	0.000	Supported
H10: Knowledge sharing -> Performance expectancy	0.186	0.185	0.070	2.661	0.008	Supported
H11: Online self-efficacy -> Effort expectancy	0.691	0.692	0.040	17.358	0.000	Supported
H12: Online self-efficacy -> Performance expectancy	0.198	0.197	0.068	2.922	0.003	Supported

 

Regarding the predictors of actual online learning use, the findings show that facilitating conditions (β_FC→AU=0.152, p=0.007), behavioral intention (β_BI→AU= 0.345, p<0.001), and compatibility (β_Comp→AU=0.416, p<0.001) were determinants of this construct to explain 71.8% (R²=0.718) of its overall variance. Such results supported hypotheses H5, H6, and H8.

The results also confirm that four constructs were predictors of performance expectancy, namely effort expectancy (β_EE→PE=0.193, p=0.002), knowledge acquisition (β_KA→PE=0.392, p<0.001), knowledge sharing (β_KS→PE=0.186, p=0.008), and online self-efficacy (β_OSE→PE=0.198, p=0.003). These variables explained 75.1% (R²=0.751). Thus, hypotheses H2, H9, H10, and H12 were confirmed. Finally, online self-efficacy (β_OSE→EE=0.691, p<0.001) was also a significant predictor of effort expectancy to explain 47.7% of its variance. This confirms hypothesis H11.

5. DISCUSSION

This research extends UTAUT to investigate the acceptance of online learning. It takes into account compatibility as a direct predictor of behavioral intention. The study also assumes that knowledge acquisition and knowledge sharing are determinants of performance expectancy, whereas online self-efficacy is proposed as a predictor of effort expectancy and performance expectancy. Thus, this research addresses some of the weaknesses in the original UTAUT, as it did not explain what can affect effort and performance expectancies. It also ignored the role of compatibility in examining online learning adoption. Table 7 summarizes the key findings of the proposed hypotheses. From Fig. 2, it is clear that knowledge acquisition was the strongest predictor of performance expectancy (β_KA→PE=0.392). Compatibility, on the other hand, was the best predictor of both behavioral intention (β_Comp→BI=0.514) and actual use (β_Comp→AU=0.416). This reveals that extending UTAUT with the new constructs such as knowledge acquisition and compatibility has a significant influence on improving its predictability.

As for the predictors of performance expectancy, all proposed variables, namely effort expectancy, knowledge acquisition, knowledge sharing, and online self-efficacy, were determinants of this construct, supporting hypotheses H2, H9, H10, and H12. These four constructs explained 75.1% of the variance of performance expectancy. In agreement with our results, earlier research revealed that effort expectancy is a predictor of performance expectancy (Al-Emran et al., 2020; Davis, 1989), indicating that users can perceive the usefulness of technology if its use does not require a high mental effort. Moreover, other studies also found that both knowledge acquisition and knowledge sharing were predictors of performance expectancy (Al-Emran & Teo, 2020). This means that online courses should be designed in a way that allows participants to share knowledge and improve its acquisition. Thus, when students are capable of sharing and acquiring knowledge through online learning technology, their perceptions about technology usefulness in improving their overall performance can be enhanced as well. The research analysis also shows that online self-efficacy had a direct and positive relationship with performance expectancy. This may indicate that when learners are self-confident about their personal abilities in using online learning technology, their perceptions regarding its usefulness will be increased accordingly.

Online self-efficacy as a reference of learners’ self-expectation was a predictor of effort expectancy to explain 47.7% of its variance, confirming hypothesis H11. Bandura (1986) assumes that people’s expectations of efficacy can determine subsequent behavior in terms of the beginning and continuous coping behavior. According to Lin (2021), the self-efficacy of people may reveal the motivational level of effort invested in an endeavor and this, in turn, could indicate that an individual with a low self-efficacy level will not persist in adopting or using technology. Based on our analysis, it is obvious that when students have a high level of self-efficacy, a low effort will be required to perform a particular action.

It was also found that two constructs out of four were determinants of behavioral intention, namely performance expectancy and compatibility, confirming hypotheses H3 and H7 to explain 76.1% of the variance of behavioral intention towards online learning use. On the other hand, this analysis rejects hypotheses H1 and H4. Overall, our findings are in agreement with earlier research that performance expectancy is a determinant of behavioral intention (Alowayr & Al-Azawei, 2021; Wang & Lin, 2021). This means that students are willing to continue using online learning because of its usefulness in improving their performance. The research analysis also shows that compatibility had a significant effect on intention to use. Such a finding is consistent with the outcomes of Wang and Lin (2021). This may indicate that students had a high level of compatibility in online learning settings and this, in turn, reflected their positive attitudes towards online classes. On the contrary, social influence and effort expectancy had no significant influence on behavioral intention. This may indicate that students had positive perceptions of the advantages of online learning regardless of the effort that is required to adopt it or the perspectives of other people who are important to the students. In an earlier research study (Al-Azawei & Alowayr, 2020), it was also found that both effort expectancy and social influence did not affect students’ decision or perception to use mobile learning technology in the Iraqi context. A possible explanation for the weak influence of effort expectancy is that as students are part of the youth, they use online technologies daily, so their experience in dealing with them is great. Moreover, Ameen and Willis (2018) also revealed that the social influence of Iraqi students did not affect behavioral intention.

The research findings suggest that facilitating conditions, behavioral intention, and compatibility were determinants of online learning’s actual use, which explained 71.8% of the variance of this construct and supported hypotheses H5, H6, and H8. In UTAUT, Venkatesh et al. (2003) assume that facilitating conditions could be a predictor of technology’s actual use. This assumption was confirmed in our research to indicate that educational institutions have to provide reliable information and communication technologies infrastructure, and support all users to ensure the successful application of online learning. Furthermore, based on previous literature, behavioral intention towards a particular action can indicate that people will actually perform it (Ajzen & Fishbein, 1980). Finally, in this research, compatibility was the strongest predictor of online learning usage. This means that students might perceive the consistency of this technology with their individual needs, particularly after the spread of the COVID-19 pandemic worldwide. It also suggests that when learning technology is compatible with learners’ individual needs and practice styles, their actual use will increase accordingly. Such an outcome is in line with the findings of earlier literature that real usage of mobile learning can be predicted by compatibility (Cheng, 2015). In agreement with the UTAUT hypotheses (Venkatesh et al., 2003), technology’s actual use can be positively enhanced if users feel that there is direct organizational support for technical issues that they may face.

Based on the research findings and analysis, many theoretical and practical implications can be drawn. Theoretically, this research extends previous work on online learning adoption, particularly in the case of developing countries. Furthermore, it extends current technology acceptance theories according to the integration of UTAUT with variables from the social cognitive theory (self-efficacy), compatibility theory (compatibility), and knowledge management topic (knowledge sharing and knowledge acquisition). Accordingly, this research is among the early literature that investigates the extension of UTAUT in an online learning context. From the practical view, this research provides clear guidance for decision-makers, teachers, and online technology designers to apply innovative means in online learning to enhance knowledge sharing and acquisition among learners. Moreover, educational institutions should ensure that both academic staff and students have high confidence in their personal abilities to use the technology, as this could affect their decision to adopt it. Finally, as compatibility was a predictor of behavioral intention and actual use, online learning courses should be designed in a manner that responds to learners’ individual expectations and preferences.

6. CONCLUSION

This research aimed at extending UTAUT to understand online learning acceptance. To achieve this aim, four constructs were integrated with UTAUT, namely compatibility, knowledge acquisition, knowledge sharing, and online self-efficacy. Although UTAUT suggested that effort expectancy and performance expectancy are determinants of adoption behavior, it does not explain what can affect these factors. Accordingly, the present study confirmed that knowledge acquisition and/or sharing as well as online self-efficacy had a significant role in predicting such constructs. Moreover, this research supports the necessity of considering compatibility in predicting both behavioral intention and actual behavior.

In regard to future research directions, it is recommended to carry out a longitudinal research design to understand the phenomenon under investigation over a longer period. Furthermore, choosing a sample from different disciplines and cultures should be another aim of future research. Finally, researchers also need to analyze the role of knowledge sharing and knowledge acquisition in other contexts of the learning process.