Factors influencing postgraduate students’ intention to use learning management system

Kamaludeen Samaila, Mas Nida Md. Khambari,
Jeya Amantha Kumar, and Mona Masood[*]

doi: https://doi.org/10.18543/tjhe.2177

Received: 13 August 2021
Accepted: 11 April 2022
E-published: May 2022

Abstract: Learning management systems (LMS) are continuously being implemented in tertiary institutions to manage and strengthen educational activities. LMS such as Moodle facilitates the management of learning content, collaboration, and communication. However, there have been limited studies examining factors influencing postgraduate students’ intention to use LMS in Malaysian universities, as studies mainly concentrate on undergraduates’ use intentions. Therefore, this study investigates factors influencing the behavioural intention to use LMS based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The results indicated performance expectancy (PE) followed by social influence (SI) and effort expectancy (EE) as the factors influencing the behavioural intention (BI) to use LMS. Concurrently, facilitating conditions (FC) were found not to influence BI, and we denote that socio-economy standings and maturity influence their overall acceptance of the LMS. Practical and theoretical implications are discussed accordingly.

Keywords: LMS; UTAUT; technology acceptance; postgraduate; Malaysia.

I. Introduction

The recent advancement of technological innovations has triggered a gradual change from the conventional teaching method towards modern methods that adopt online learning. These methods range from learning management systems (LMS), blended learning, mobile learning, and flipped learning as a means to introduce technology in the classroom. Henceforth, most higher education institutions has resorted to benefit from this need by integrating a platform to support multiple online resources such as a LMS.[1] LMS is a web-based application used to organize, implement, manage, and assess learning content.[2] It has been widely used to support e-learning worldwide[3] and successfully implemented in Malaysian HEIs.[4] It is a system created to assist administrators, teachers, and students in accessing and managing online learning services,[5] data and contents, and are available as an open source platform (e.g. Moodle, Google Classroom, Dokeos, and Claroline) or commercially (e.g. Blackboard and WebCT).

In Malaysia, most public universities use Moodle as their official LMS[6]^,[7] due to its scalability and free access.[8] LMS allows instructors to share, upload, and interact with students, whereas students at the receiving end have easy access to these learning interactions[9] and communicate with their lecturers.[10] These attributes facilitate collaboration,[11] engagement,[12] create a flexible learning environment,[13] and monitor progress and assess performance.[14] Despite these benefits, some challenges still hindered students from using LMS effectively. LMS has been found to have implementation issues such as technical setbacks, lack of proper implementation policies, unengaging,[15] and mobile accessibility.[16] Moreover, LMS is also perceived as a course-centered platform with high reliability to the internet connection for successful implementation.[17] Furthermore, instructors have been found to treat LMS as a learning content repository and henceforth lack initiatives to design interactive content that promotes interaction through the platform.[18] Due to this, empirical findings in Malaysia indicated that HEI students are more favourable towards conventional learning because LMS has been perceived as incapable of offering physical-emotional interaction, especially for a vast number of learners simultaneously.[19] Therefore, we theorised that even if the acceptance of e-learning has been universal, there is not much understanding of the factors affecting the intention and use of LMS.[20] Similarly, these assessments have been consistent with empirical findings on postgraduates in Malaysia as reported by[21] highlighting limited studies that warrants further investigation.

Furthermore, LMS are usually adopted as a formal learning platform to enhance content delivery, assessment, and manage learning activities for postgraduates.[22] Therefore,[23] claims that identifying factors influencing LMS intention and use for postgraduate students may be novel in improving any existing e-learning system in higher education. Moreover, this is further amplified with limited studies on postgraduates’ use of LMS and the tendency to assume homogeneity of use behaviour between undergraduates and postgraduates.[24] In addition such investigations, especially using the adoption model, may highlight their intention to exploit LMS functions and use it effectively.[25] For this purpose, we used the Unified Theory of Acceptance and Use of Technology (UTAUT) model.

II. Unified Theory of Acceptance and Use of Technology (UTAUT) Model

UTAUT and Technology Acceptance Model (TAM) are two prominent models used in assessing users’ acceptance and adoption of technology. TAM was one of the earliest models used for evaluating users’ intention, acceptance, and adoption of new technology. It has been applied in much scientific research,[26] however, researchers have debated the implication of TAM due to its limited insight into users’ perspectives.[27]^,[28] Therefore, we considered UTAUT as a better alternative. UTAUT was introduced by,[29] who proposed a combination of TAM, Theory of Planned Behaviour (TPB), Social Cognitive Theory (SCT), and the model of PC utilization (MPCU). The model is predicted to be able to explain 50% of the variance in user intention[30] and is a reliable model for measuring the level of technology acceptance, adoption, and actual usage. Primarily, the model comprises six core variables, namely performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating condition (FC), and behavioural intention (BI), and actual use (USE). In addition, the model also considers moderating variables such as gender, age, experience, and voluntariness. Nevertheless, moderating variables were defered and only main constructs were used to determine behavioural intention as validated in other empirical LMS studies conducted in Malaysian HEI.[31]^,[32]^,[33]

III. Hypotheses development

The hypotheses development is discussed based on the main latent variables used to predict behavioural intention

III.1. Behavioural Intention (BI)

BI is defined as intentions or motivational factors that reflect efforts to perform a behaviour.[34] In terms of technology usage, BI can be refered to as the users’ intention to use technology.[35] For this study, BI is defined as the postgraduates’ interest in using the LMS. Previous studies had shown that LMS provided a convenient avenue for higher education students to access online learning materials and improve their academic performance.[36]^,[37]

III.2. Performance Expectancy (PE)

Performance expectancy has been one of the core constructs of the UTAUT model and refers to the extent to which people are convinced that technology helps enhance their activities and improve their job performance.[38] Previous literature indicated that PE and BI were correlated in both intended and compulsory settings[39]^,[40] Furthermore, PE was found to strongly influence learners’ intention to use technology.[41] Besides, PE has been found to effect postgraduate students’ BI,[42] but[43]^,[44] claimed otherwise. Nevertheless, due to the novelty of this study, we hypothesized a significant effect on BI as the use of the LMS is an important aspect that facilitates teaching and learning. Hence, this study projected the following hypothesis: 

H1: Performance expectancy has a significant effect on postgraduate students’ behavioural intention to use LMS.

III.3. Effort Expectancy (EE)

Effort expectancy is a critical factor cited in the UTAUT model. It has been defined as “the degree of ease associated with the use of the system”.[45] It is an important variable that profoundly contributes towards determining users’ behaviour to use technologies[46] and significantly correlated with students’ use of LMS.[47] In contrast, EE has also been found to have no significant effect on postgraduates’ behavioural intention to use LMS as reported by.[48] Nevertheless,[49]^,[50]^,[51] described EE as the core determinant of behavioural intention to use an information system. Therefore, we stipulate the need to explore this relationship based on the original context of the UTAUT model based on the following hypothesis is proposed:

H2: Effort expectancy has a significant effect on the postgraduate students’ behavioural intention to use LMS.

III.4. Social Influence (SI)

Social influence is a crucial determinant for predicting users’ behavioural intention to use technology. It is referred to as the extent to which a person perceives colleagues/superiors can influence him or her to use technology.[52] SI significantly impacted the adoption and acceptance of technology in both intended and compulsory settings.[53] Furthermore, SI was among the core factors that influenced postgraduates’ use of LMS[54] and boosted their intention to use it.[55] Study reported that SI had a strong relationship with users’ technology usage and strongly influenced students’ intention to use technology in HEI.[56] Consequently, the following hypothesis was formulated:

H3: Social influence has a significant effect on postgraduate students’ behavioural intention to use LMS.

III.5. Facilitating condition (FC)

Facilitating condition (FC) is defined as the extent to which a person trusts that technical and organizational infrastructure exists to encourage the use of technology.[57] It has been found to influence postgraduates’ intention to use technology.[58] Nonetheless, FC was also found to not significantly affect students’ intention to use LMS[59] but was not specific for postgraduate students. Therefore, the present study hypothesized that facilitating conditions could significantly affect students’ behavioral intention to use of LMS. Therefore, the following hypothesis was proposed:

H4: Facilitating condition has a significant effect on the postgraduate students’ behavioural intention to use LMS.

Consequently, this study aims to investigate postgraduate students’ use of LMS in Malaysia’s public university, based on PE, EE, SI, and FC towards BI. Therefore, we decided to exclude moderating variables as we focus the study on the intention to use the LMS. Various studies did not include moderating variables to identify use behaviour as these factors were uncontrollable aspects of usage in their context.[60]^,[61] Therefore, we adopted the same strategy as we deemed these factors unreasonable as a future consideration towards the platform’s design, especially when the use is compulsory. The conceptual model proposed in this study is represented in Figure 1.

IV. Methodology

In this study, we define the postgraduate students as mandatory users of the university’s LMS to complete compulsory courses for their Master of Education. This study adopts a quantitative approach where the survey questionnaire was distributed electronically to all postgraduate students in the education faculty through email using Google Forms. In the email, students were informed about the research purpose, and by answering the questionnaire, they provide consent to be part of the study. Furthermore, all participation is based on a voluntary basis, and we explained that the study abides by the university’s ethical standards to ensure anonymity and confidentiality. The study was conducted based on the approval of the research committee of the institute.

The questionnaires were distributed twice with an interval of three weeks to ensure a better response rate. There were two sections in the questionnaire where the first section was related to the demographic details of the respondents, whereas the second part reflected the factor influencing the intention to use LMS. The items for PE, EE, SI, FC, and BI were adapted from.[62] The 41-item questionnaire was measured based on a Likert scale ranging from 1-strongly disagree to 5-strongly agree and will take 15 to 20 minutes to complete. The data collected were analysed using the Statistical Package for the Social Sciences version 22 (SPSS), in which the 297 respondents showed high reliability[63] based on the Cronbach’s alpha value (Table 1). Next, to predict factors influencing postgraduate BI to use LMS, a multiple regression analysis was employed to investigate the influence of PE, EE, SI, and FC relationship with the intention to use LMS. Multiple regression is able to analyse the relationship between BI and the other factors simultaneously.[64]

V. Results

Based on the total population of 921 postgraduate students, 297 students participated in this study. As shown in Table 2, the majority of the students were female (n = 211, 76.2%), while the rest were male (n = 66, 23.8%). 52.0% of the respondents were between the ages of 26-35 years old (n=144), while 3.6%, the smallest group, were above 46 years old.

Based on the analysis, PE (β = .436, p <.000) was the most crucial factor in predicting postgraduate students’ intention to use LMS, followed by SI (β = .232, p <.000) and EE (β = .193, p <.003) (Table 3). The value of the standardized beta coefficient (β) determines the strength of the relationships between both variables. Meanwhile, the result also showed that FC had no impact on postgraduate students’ intention to use LMS (β = -.037, p <.505). Figure 2 reflects graphical representation of the standard regression analysis. The model was also found to be highly significant at F(1, 4) = 2490.753, p < 000 as reflected in Table 4.

Next, a stepwise regression analysis (Table 5) describes model 1 representing PE as explaining 48% of the variance (R² change = .483), model 2 representing SI explained 5% of the variance (R² change = .050), and model 3 representing EE explaining less than 2% of the variance (R² change = .015). Finally, the conceptual model was found to explained 54% of the variance (adjusted R² = .541). According to,[65] the total variance values can be categorized as weak (0.25), medium (0.5) and substantial (0.75). Therefore, the model’s in-sample explanatory power has been found to be moderate. Furthermore, Table 6 showed that the three variables predicted the students’ intention to use LMS at a 0.05 level of significance. Therefore, the absolute values of the standardized estimate (β) of these predictors were presented as follows: PE (β = .695, t =16.015, p <.05), SI (β = .291, t = 5.392p <.05), and EE (β = .174, t = 2.997, p <.05). The predictor that explained the highest variance in postgraduate students’ intention to use LMS was PE, followed by SI and lastly EE.

Figure 3 demonstrates that PE has the maximum influence in predicting postgraduate students’ intention (BI) to use LMS. In the same vein, postgraduate students’ intention to use LMS was influence by SI. The figure further explains that EE is the third factor that significantly affects the postgraduates’ decisions on using LMS.

VI. Discussion

This study reveals that not only PE but SI and EE are also among the factors that contribute to postgraduate students’ intention to use LMS. These findings indicate that postgraduate students relate the platform’s usefulness to access learning content as their main reason to use the LMS. The findings of this study correspond with that in,[66] who argued that PE plays a vital role in determining students’ intention to use technology. As for LMS, empirical findings have also indicated that PE cannot be overlooked as an important factor.[67] It is understood that postgraduate students perceived the LMS as an essential instrument that can enhance their collaboration, learning activities, efficiency, and effectiveness in completing their course work.

At the same time, SI was the second strongest influencer for postgraduate students’ intention to use LMS. SI relates to postgraduates perception of their peers and lecturers need to use the LMS. According to,[68] LMS should not only be viewed as a database of learning contents but also as a platform that supports interaction and collaboration, especially as postgraduate are autonomous learners. In this study, the weak significant relationship may have been attributed to less awareness of their peers’ need for using the platform for teaching and learning. Moreover, postgraduates have been found to welcome the idea of independent learning due to the flexibility that fits with their work and family schedule[69]^,[70] which may have contributed towards the insignificant relationship. Furthermore, empirical findings also described postgraduate students as not having emotional relationship or connectedness when using the LMS and tend to ignore such non-formal relationships.[71] Hence, they tend to view the LMS as just a learning tool and not a tool for socialising and communicating with their peers. However, such relationships are more successfully built through social media platforms such as WhatsApp or Facebook, where there is the ease of accessibility through mobile devices that permits non-restricting and informal communication compared to a Moodle-based LMS.[72]

Subsequently, EE, which denotes ease of using the LMS to achieve their learning goals, was also found to influence postgraduate students’ intention to use LMS. We deemed this outcome as related to the mandatory nature of using the LMS and not having a choice in selecting the learning platform. The results of this study are in congruence with the findings in,[73] indicating a significant relationship between EE and learners’ intention to use technology.[74] added that even if postgraduate students appreciate LMS’s flexibility, ease of navigation, and managing their learning, they still favour face-to-face teaching and view the LMS only as a supplementary learning tool. We also agree with,[75] indicating that the mandatory use of e-learning platforms acts as a conditioning of behaviour that may have influenced how they perceive EE’s relationship with intention. Furthermore, questioning if EE or habit is a better predictor of BI in a mandatory setting.[76]

We also observed that FC, which was significant in determining BI,[77] was non-significant in our study. Nevertheless, the findings of this study did not differ from,[78] indicating that FC was ineffective in determining students’ use of modern technology. Subsequently, even if evidence shows that technical infrastructures, Internet, computer, and wireless facilities are the primary resources to access LMS,[79] it did not influence postgraduate students’ intention to use LMS. Furthermore, according to,[80] FC determines behavioral expectation and not BI. Besides, the demographic profile indicated that most respondents were above 26 years old, indicating mature students who may also have better financial standing in supporting their technical needs than undergraduates. Additionally, they might have the means to obtain these functionalities on their own by purchasing a laptop and subscribing to internet access independently. Nevertheless, FC is not always related to technology access and computers; it also relates to academic support that the student perceives that they receive to support their learning. Another study conducted by,[81] also indicated that undergraduates do not relate FC towards BI but towards the actual use of the system.

Therefore, based on these findings, the original model indicated that 54% of students’ behavioural intention to use LMS to be influenced by PE, SI, and EE but not FC. While the study indicated medium predictive power, this did not vary from another study similar study in the Malaysian context indicating 52% of variance.[82] PE strength was further strengthened when FC was removed from the model where the path coefficient value increased from β = .436 to β = .695, indicating that the primary influence is the usefulness in the mandatory setting. Nevertheless,[83] claim that postgraduate students usually have better acceptance of LMS than undergraduates due to having high regards on the value of the LMS regardless of EE. Nevertheless,[84] added that the LMS value is only seen as a repository for course material without pedagogical implications. When compared to undergradute’s intention to use LMS, a study by[85] indicated that SI followed by FC were better predictors compared to PE in which the model predicted 70.1% of BI. The difference can be attributed towards the perceived value that the postgraduates have on the LMS compared to the undergraduates. Nevertheless, the R² value is often determined by the number of predictive variables[86] and medium predictive variance may stipulate the need for additional variables to be added towards the model.

VII. Practical and theoretical implication

This study provides findings that LMS managers, faculty members, and university management might use to improve LMS for lifelong learning. While, postgraduate students’ behavioural intention to use LMS is influenced mainly by PE and not EE or SI, there is a need to consider how the system’s usefulness can be further improved to aid postgraduate students in achieving their learning goals. Undoubtedly, most lecturers and students view the LMS as a repository of learning contents; however, with the availability of new integrations and APIs, the possibilities of creating an engaging learning experience is more realistic.

Moreover, there is no doubt that social influence contributes positively to determining postgraduates’ behavioural intention to use LMS. The LMS is capable of providing access to developing an online learning community. Therefore, university management should introduce policies that will encourage both course instructors and students to use the LMS to promote cognitive, social, and affective learning outcomes. Next, with respect to the theoretical implication, this study identified that only 54% of the variance in the dependent variable is explained by the three predictors (i.e., performance expectancy, social influence, and effort expectancy). This implies that other elements such as convinence, personal innovativeness, and technology fit could be explored in the future as it relates to PE.

VIII. Conclusion, limitation, and future research

The result highlighted that PE, SI, and EE as factors that influence behavioural intention to use LMS among postgraduate students. While, FC had no significant influence, we deduced this to the expected positive financial standing of a postgraduate student and the affordance of technology and mobile learning. However, we also recommend further investigation by exploring other factors such as perceived enjoyment, family support, and other constructs in influencing their intention. Next, as this study only reflects postgraduates from one faculty and does not compare the use of LMS between full-time and part-time students, future studies should include various backgrounds and compare the different learning modes. The authors also recommend that for students to fully understand the importance of LMS and accept the use of LMS, teachers/instructors play a significant role in promoting the successful use of the platform. Furthermore, a mixed-method approach comprising observation and interviews could be employed in further studies to obtain data in dissimilar ways. We also agree with[87] that research should also look into the personal experience and socio-economic background and warrants further exploration. As such, the result of this study may not be generalized to all backgrounds.

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