Examining predictors of perceived usefulness of learning management system from the perspectives of distance learning students: an application of the information success model Open Access

A learning management system (LMS) is an online, interactive platform that facilitates the design, organization and real-time evaluation of the learning process (Chaubey and Bhattacharya, 2015). It also serves as a robust network for the efficient sharing of information between administrators, educators and students (Arfeen and Noor, 2017). An ideal LMS works as an inclusive and learning platform for educational purposes, enabling online collaborative groupings, academic discussions, professional trainings and coordination among users. Fathema and Sutton (2013), Park et al. (2012) and Watson and Watson (2007), among others, suggest that LMS is an effective system that can manage various learning activities, including the dissemination of educational content, resource tracking, submission of assignments, allocation of academic tasks, hosting of virtual classrooms, assigning resource personnel and conducting assessments and grading. The adoption of LMSs by academic institutions has been driven by the need for efficient information exchange and the ability to meet the academic requirements of both instructors and students (Zhang and Goel, 2011).

Recent studies have indicated that LMSs are integral to the digital transformation of educational practices. Pappas (2020) identified a range of LMSs adopted by educational institutions, including Docebo, Adobe Captivate Prime, Talent LMS, SAP Litmos and Canvas, among others. These platforms are evaluated based on several criteria, such as software features, customer support, innovation and user satisfaction (US), with the LMS market expected to reach a value of $15.72bn (Pappas, 2020). Moreover, eLearning is becoming increasingly prevalent, with nearly half of academic institutions' classes expected to transition to eLearning formats soon. Additionally, over 41.7% of global companies have integrated educational technology into their employee training programs (Pappas, 2020). According to Mwase and Kissaka (2017), the efficiency of LMS and the satisfaction of users are important components for the adoption of LMS in educational settings through both synchronous and asynchronous methods (Courtney and Wilhoite-Mathews, 2015). Thus, LMSs are essential tools for enhancing the efficiency and accessibility of education, supporting the achievement of desired learning outcomes.

Allama Iqbal Open University (AIOU), as a premier online and distance learning (ODL) university, offers educational programs to over 1.3 million students currently. To accommodate these huge numbers of students is a challenging job, which is impossible without a capable support system. To accommodate and facilitate these huge and growing numbers of students, the LMS known as Aaghi LMS (a customized form of Moodle) was developed in 2020 to facilitate distance learning students (AIOU, 2021). The system bridges together students, facilitators, tutors, educational materials and administrators. It informs the students about their semester-level assignment submission and workshop schedule. The students can submit their assignments and check their results through this portal. The students’ complete record or biodata is saved under this system. Every registered student is given her unique credentials to access the Aaghi LMS. The Aaghi LMS provides an active learning environment for the students. It overcomes the barriers of time and geographical location as it is an Internet-based system and is considered an important learning tool, particularly in a distance learning environment.

LMS is a robust system for managing and controlling the entire learning process, including the sharing of e-content, resource tracking, tutorials, assignments, academic tasks, virtual classrooms and assessments. Several studies advocated the adoption of LMS for both traditional and online distance learning environments, recognizing its effectiveness in coping with the academic needs of both stakeholders (Ghilay, 2019). This technological shift in learning has been embraced worldwide, adopting LMS to enhance educational delivery. However, evaluating the system’s quality, information accuracy and overall effectiveness from the perspectives of organizations, faculty, students and administrators is crucial (Ghilay, 2019). Serdyukov (2017) emphasizes that an LMS must not only be comprehensive and dynamic but also continually evolve to address the challenges posed by an increasingly interconnected and volatile world. Resultantly, numerous evaluation theories and models are introduced to examine various factors affecting the implementation of technologies in academia, with studies linking LMS usage to increased perceived usefulness (PU) and US (Aldiab et al., 2019; Cavus and Alhih, 2014; Deepak, 2017; Ghilay, 2019; Jafari et al., 2015; Martin and Tapp, 2019). In the context of Pakistan, various studies have examined the adoption and use of various LMS platforms. Arfeen and Noor (2017) analyzed the usage of LMS at the Virtual University of Pakistan, while Memon et al. (2019) compared the adoption of Google Classroom, Moodle and Blackboard in medical universities. Shah et al. (2017) explored the factors influencing students' attitudes toward LMS use in Pakistan, and other research has supported LMS adoption in Pakistani academic institutions (Bakhsh et al., 2019; Kalhoro and Mallah, 2017; Mir, 2017). Nevertheless, there is an increasing need for exploring the attitudes of Pakistani ODL students regarding the adoption of LMS, particularly in the context of AIOU. The current empirical research, therefore, seeks to explore the usefulness of LMS from the viewpoint of AIOU ODL students.

This study adopted Jafari et al.'s (2015) model considering DeLone and McLean’s information system success (ISS) model (2004) (Figure 1). This model was adopted due to its strong theoretical foundation and is more suitable to assess various dimensions of students’ PU of LMS. Furthermore, several researchers have advocated that the Delone and McLean ISS model is an important model for understanding the value and efficacy of LMS from students’ perspectives. According to recent Google Scholar citations, the DeLone and McLean (2004) ISS model has been implemented and adopted by more than 12,408 researchers for the evaluation of various LMS and/or Content Management System systems in different cultural contexts of the world.

The present theoretical framework consists of the following six variables.

System quality (SQ) is the desirable output generated by using a system from an individual perspective. SQ involves the combined performance of both software and hardware (Halawi et al., 2008). Key components include user interface consistency, ease of use, content quality and program code reliability. The quality of an LMS is linked to its functions, features, content and interactivity. It encompasses factors such as durability, ease of use, reliability and response times (Gorla et al., 2010; Petter and McLean, 2009). The connection between SQ and the effectiveness of LMS is well-documented in the literature, and it is reported that the quality of a system affects PU (Fathema and Sutton, 2013; Park et al., 2012). Similarly, other studies such as Fathema and Sutton (2013) and Park et al. (2012) have shown that SQ positively influences users' attitudes and intentions toward e-learning systems, commerce platforms and technology in the context of LMS. Therefore, an LMS that incorporates essential quality features fosters positive user attitudes and increases system usage. Consequently, prior research also confirms a positive association between SQ and PU, further contributing to an increase in PU of the LMS.

Information quality (IQ) is the output of an information system, particularly the accuracy and reliability of reports produced by the system, and it is a significant determinant of student success in online learning environments (Adeyinka and Mutula, 2010). Wu and Wang (2006) support the notion that higher IQ positively influences US, while Michnik and Lo (2009) and Michnik and Lo (2009) further demonstrate that IQ is crucial for the effective use of information systems. Students in ODL environments prioritize the quality of learning materials and lectures over hardware and software, underscoring the importance of content accuracy and comprehensiveness (Adeyinka and Mutula, 2010). IQ is typically assessed based on factors such as correctness, reliability, durability and comprehensiveness, all of which contribute to the system’s overall effectiveness (Petter and McLean, 2009). Michnik and Lo (2009) categorized IQ into intrinsic, contextual, representational and availability aspects. Studies by Gorla et al. (2010) study the role that IQ plays in the effective use of information systems. IQ should be measured in terms of accuracy, consistency and reliability. Thus, it can be theorized that high-quality information, particularly accurate and understandable lecture materials, has a strong positive correlation with US and LMS usage, reinforcing the position of IQ in online learning environments.

Readiness for online learning (ROL) encompasses the preparedness of students, educators and institutions to effectively engage in digital education. Recent studies have highlighted the critical role of students' perceptions and self-regulation in online learning environments. For instance, Wei and Chou (2020) examined how students' readiness and perceptions influence their online learning performance and satisfaction. Their research, involving 356 university students in Taiwan enrolled in an asynchronous online course, found that technical competencies significantly impacted students' satisfaction with the course. Taşkın and Erzurumlu (2021) investigated the online learning readiness of 1,963 students during COVID-19. Their research revealed that while students generally felt prepared for online learning, there were notable differences based on demographic variables. Studies have examined various factors influencing the ROL, including digital literacy, self-efficacy and infrastructure. For instance, a study by Nurhikmah et al. (2024) identified that teachers' attitudes toward online learning were the most influential factor in their readiness, accounting for 93% of the variance in readiness levels.

System use (SU) refers to the extent and nature of the use of an IS and is considered a significant construct of ISS, including period, frequency, relevancy and purpose of use (DeLone and McLean, 2004). Recent studies have continued to explore the relationship between SU and PU, building upon the foundational technology acceptance model. Fridkin et al. (2024) examined a mandatory information system and found that perceived ease of use significantly influences PU, which in turn positively affects the symbolic adoption of the system. Their research highlights the mediating role of PU between ease of use and user adoption, suggesting that enhancing users' perceptions of a system’s usefulness can lead to higher acceptance rates. Al-Jumaili et al. (2021) conducted a cross-sectional study assessing healthcare students' use of informational technologies over five years. The study revealed that while students utilized electronic course management systems, Internet resources and other digital tools, challenges such as inadequate technical support and unstable Internet connections hindered effective SU. The findings underscore the necessity for continuous technical support and training to enhance students' adoption of educational technologies. In the context of education, Maxwell (2022) developed a systems-based approach to research use, emphasizing the importance of integrating research findings into educational practices. This approach highlights the need for comprehensive frameworks that facilitate the effective use of research in educational settings, promoting evidence-informed decision-making.

Service quality (SrQ) pertains to service meeting user expectations from an LMS, which significantly influences system adoption and acceptance (DeLone and McLean, 2003). According to Martinez-Arguelles and Batalla-Busquets (2016), SrQ represents the reliability, responsiveness, assurance and availability of an information system, fostering US and adoption. Key attributes of SrQ include promptness, fairness, expertise, tangibility, empathy and reliability (Lee-Post, 2009). In the process of digital learning, SrQ enhances students' understanding of the system and its PU, as reflected in its responsiveness, empathy and reliability (Roca et al., 2006). Recent research on SrQ has expanded our understanding of its dimensions, measurement and impact across various sectors. A systematic literature review by Saleem et al. (2024) analyzed publications from 1984 to 2023, identifying the evolution of SrQ research. The study highlighted the increasing relevance of SrQ and focused on the skills of business professionals and their activities in dynamic environments. Furthermore, Ighomereho et al. (2022) developed seven dimensions of standard LMS: website display, use, consistency, security, personalization, fulfillment and responsiveness. The research emphasized the importance of these dimensions in evaluating and improving e-service quality, providing valuable insights for managers in the digital service industry. These studies collectively underscore the SrQ and its role in customer satisfaction and organizational performance across various industries.

US is positively correlated with PU and is closely linked to information and SQ (Yang et al., 2003). Recent research has extensively examined the interplay between PU, US, and their collective impact on user behavior across various domains. A study by Amalia and Fahrudi (2021) explored that perceived ease of use and PU positively influence US. Notably, perceived ease of use had a stronger effect, underscoring its critical role in US within mandatory settings. Similarly, Wilson et al. (2021) investigated the computer industry in China and find that PU and perceived ease of use considerably impact users’ satisfaction and trust, which in turn affect customer loyalty. This research shows the significance of these perceptions in fostering trust and loyalty among end users. Collectively, these studies emphasize the role of PU and ease of use in determining US and subsequent behaviors, offering valuable insights for designing user-centric systems and services.

PU is the degree to which a user expects that using a system would increase her job performance and satisfaction (Abdallah et al., 2019). Usefulness is used by researchers as a predictor of satisfaction in the context of online learning (Yang et al., 2003). PU is the evaluation of the benefits of LMS to the students (Jafari et al., 2015). Researchers have used users’ satisfaction as a strong predictor of PU in e-learning environments (Yang et al., 2003). Furthermore, PU of online reviews also influences trust and purchase intentions among users in Mexico (Ventre and Kolbe, 2020). Similarly, Malhan et al. (2023) explored the impact of artificial intelligence constructs, including PU, on consumer behavior in the sports shoe market in India. Their research suggests that while PU positively affects customer satisfaction and trust, it does not directly influence brand trust or customer satisfaction. This highlights the complex interplay between PU and other factors in shaping consumer loyalty and repurchase intentions. These studies underscore the contribution of PU in transforming consumer behavior across various markets and technologies. Understanding how users assess the utility of products, services or information could give intuitions for businesses aiming to increase customer engagement and satisfaction.

This study used a positivist research paradigm. According to Creswell (2012), the positivist research paradigm is the application of natural science strategies by obtaining data quantitatively through direct observation of social phenomena to examine a social reality and expand human knowledge (Bryman, 2012). This research approach is a widely adopted strategy in the field of social and management science to establish an association between the causes and effects of social problems (Sekaran, 2003). Furthermore, this study used a cross-sectional survey approach for data collection from target respondents regarding the study variables, as it is economical, easy to understand (Babbie, 2010; Schutt, 2006), effective in examining social issues, provides valid, precise and reliable data (Creswell, 2012; Neuman, 2013).

Unit of analysis, population and sampling procedure: The present study analyzed MPhil and Ph.D. scholars from four faculties – Social Sciences and Humanities, Pure Sciences, Education and Arabic and Islamic Studies – enrolled in the spring and autumn semesters of 2020 at AIOU, Islamabad. The total population (N = 836) was stratified into four groups, each comprising 70 individuals, resulting in a targeted sample size of 280 postgraduate students. Data on student enrollment were obtained from the Directorate of Board of Advanced Studies and Research at AIOU. A convenient sampling technique was employed to ensure accessibility and manageability while providing equal participation opportunities across the faculties. A closed-ended questionnaire was developed in Google Forms, and its link was distributed via students’ email addresses, WhatsApp contacts and WhatsApp groups obtained through course coordinators. Printed copies of the questionnaire were also provided to participants during departmental visits. The participants were provided with informed consent and confidentiality forms, guaranteeing the privacy and anonymity of their responses. The survey remained open for two months (January–February 2021), with periodic reminders sent via WhatsApp to encourage participation. This approach successfully achieved an 88.2% response rate (n = 247), with all respondents voluntarily contributing to the study.

Scale validity and reliability: The statements of the data collection questionnaire were adapted from past reliable and validated measurement scales (i.e. Adeyinka and Mutula, 2010; Alshare et al., 2011; Hair et al., 2006; Jafari et al., 2015; Mtebe and Raisamo, 2014; Stapleton et al., 2009). However, to confirm the face as well as content validity of the measurement instruments, it was validated by ten professionals in the discipline of information systems, information and communication technologies (ICTs) and Library and Information Sciences who had suggested several changes or modifications. Consequently, for a pretesting instrument, it was tested on 45 postgraduate students who were not part of the final survey and found appropriate. The study evaluated various constructs using Cronbach’s alpha (CA) to ensure reliability, achieving high values across all measures. IQ and Aaghi LMS Use (LU) each had 5 items with a CA of 0.95 and 0.94, respectively. SQ and ROL, comprising 7 items each, both recorded a CA of 0.94. Similarly, SrQ, US and PU, all with 06 items, also demonstrated a CA of 0.94, reflecting robust internal consistency across the constructs.

Respondents’ demographics: Out of 247 respondents, 167 (67.6%) were male, whereas 80 (32.4%) female students participated in the survey. The majority (n = 101, 40.9%) of them were between 27 and 32 ages year old, 83 (33.6%) had 33–38 years old, 41 (16.6%) had 39 and above and only 22 (8.9%) had 21–26 year old. Most (n = 190, 76.1%) of the students were enrolled in the MPhil program, followed by 57 (23.1%) in Ph.D. programs. In addition, out of 247 respondents, 63 (25.5%) respondents were from the faculty of science, 62 (25.1%) from the faculty of Arabic and Islamic studies and 61 (24.7%) from the faculty of education, whereas 61 (24.7%) respondents from the faculty of social sciences and humanities participated in the survey (Table 1).

Frequency of respondents’ views about the dimensions of the study: The results in Table 2 depict that the respondents agreed with the “information quality” (M = 3.73, standard deviation (SD) = 0.81), use (M = 3.73, SD = 0.85), “user satisfaction (M = 3.55, SD = 1.02)”, “readiness for online learning” (M = 3.52, SD = 0.83) and “perceived usefulness (M = 3.50, SD = 1.02)” of Aaghi LMS. Though, they were neutral with the dimensions of “service quality” (M = 3.13, SD = 0.98) and “SQ” (M = 3.13, SD = 0.98).

Regression results: Before executing the standard multiple regression analysis test, the data normality of the PU (dependent variable [DV]) was checked using the normal probability plot (P–P) of the regression standard residual. This exercise give hope to the researcher that the data point will set in a reasonable straight diagonal line from bottom left to top right (Pallant, 2011). Consequently, straightway line recommends the distribution of data normality. Thus, this study found that the data have been normally distributed (Figure 2) (see Table 3)

A regression test was executed to assess the effect of IS, SQ, SrQ, US and ROL (independent variables [IVs]) on PU (DV) of Aaghi LMS in the context of study. This test was considered with a linear association of IVs (SrQ, IQ, Aaghi LMS use-LU, US-US and S and ROL) that was statistically correlated with DV (PU-PU). It allowed the researchers to predict well DV (Y) from IVs (X) in the study context. The results of the enter method test were also significant (F = 190.653), with a p-value = 0.000 (Table 4). Furthermore, the data showed that a linear combination of all six IVs positively predicted postgraduate students’ PU with Aaghi LMS. Next, results in Table 5 show that the correlation coefficient (R = 0.893) depicts a positive correlation of all six independent variables with dependent variables, and thus the model had a strong influence on the dependent variable (Table 3). Further, the Rs value = 0.798 shows that about 80% variance rate is examined in the model for respondents’ PU using all six predictor variables with adjusted R² = 0.794 variance value. Next, a regression equation was applied to predict for PU, i.e. Y = a +b1x + b2x, explained as Y = −0.381 = 0.224 (IQ) + 193 (RoL) + 0.070 (LU) + 0.301 (US) + 0.330 (SrQ) (Table 5). Y showed PU as DV. Moreover, to investigate those IVs that confirmed the prediction of the dependent variable, the correlation of each independent variable was examined by executing a standardized regression coefficient. The results depicted that the highest beta score was 0.316 for the SQ of the Aaghi LMS system. It shows that SQ establishes the strongest correlation by confirming the dependent variable in comparison with the beta coefficient value of other IVs (Table 5). Next, the result of the sample t-test depicts that five IVs were statistically significant with the probability of beta value = 0.000 in the study population. On the other hand, the beta value for Aaghi LMS use (LU) was not statistically significant at 0.324. Thus, the explored beta values have confirmed that the five IVs supported in the model predicted scores for PU except for the “LMS use” variable (Table 5).

This empirical study investigated the effects of IQ, SQ, SrQ, US and ROL (IVs) on PU (DV) from the viewpoint of distance learning students in Pakistan. For data collection, a structured questionnaire with a five-point Likert Scale (from strongly disagree to strongly agree) was applied to the target respondents. The findings of regression analysis showed that there were statistically positive effects of independent variables (i.e. IQ, SQ, SrQ, RoL and US) on dependent variables (PU). The beta values have confirmed that the five IVs supported in the model predicted scores for PU except for the “LMS use” variable. This statistical variation might be due to a lack of training and awareness to effectively use Aaghi LMS for academic needs. The results of the present study confirmed Michnik and Lo (2009) and Gorla et al.'s (2010) study, which reported that IQ had a strong effect on the usage of IS. The influence of IQ on instructors’ satisfaction was also identified in past studies (Al-Busaidi and Al-Shihi, 2012; Almarashdeh, 2016).

The results of the present study also support the earlier study where they noted that SQ had a significant positive effect on instructors’ satisfaction (Al-Busaidi and Al-Shihi, 2012; Almarashdeh, 2016). Moreover, the findings of the present study depict that postgraduate students are willing to implement LMS for academic and learning purposes in Pakistan. Consequently, it is also confirmed from the findings of this research that these students have equal opportunities for online learning, and they possess computer and digital literacy at the postgraduate level. This study also verifies Shah et al.'s (2017) study, which reported positive attitudes of students towards the adoption and usage of LMS for academic tasks in Pakistan. This study also emphasizes the necessity of a high-quality system for distance learning students. Furthermore, the findings of research also validate Islam and Azad (2015), Islam (2011), Mouakket and Bettayeb's (2015) studies, which empirically revealed that PU had a significantly positive impact on users’ satisfaction.

Consequently, this study also confirms Almarashdeh’s (2016) findings, where he found that SrQ, PU, SQ and IQ are significant positive predictors of users’ satisfaction. Other studies have also advocated the usage of LMS by academics in Pakistan (Bakhsh et al., 2019; Kalhoro and Mallah, 2017; Khalil, 2013; Mir, 2017; Shah et al., 2017). Thus, this study reveals that Aaghi LMS is an interactive platform used to facilitate both instructors and students in online education, provides 24/7 access to education beyond geographical and social obstacles and connects instructors and students with learning resources together on a single platform. This study also demonstrates that most respondents agreed on the significance of IQ, Aaghi LMS usage, ROL and US and the PU of Aaghi LMS. However, participants expressed a neutral stance on SrQ and SQ. The analysis further demonstrated that the dimensions of IQ, SQ, SrQ, ROL and US significantly affect postgraduate students' PU of Aaghi LMS.

This study investigates the factors impacting the adoption of LMS from the perspective of postgraduate students at AIOU, Islamabad. Specifically, it examines the impact of various independent variables including IQ, SQ, SrQ, US and ROL on PU (DV). The research follows a quantitative approach within the positivist paradigm. The study highlights the benefits of Aaghi LMS in facilitating interactions, discussions and feedback between students and teachers. It proves particularly beneficial for students unable to visit or reside on campus, as it eliminates the constraints of time and location. LMSs, including Aaghi LMS, provide a virtual platform that supports teaching and learning, even when students are distributed across multiple campuses of the same university. This empirical investigation depicts the contribution of LMS in enhancing accessibility, interactivity and flexibility in education. This empirical study concludes that while students recognize the advantages of using Aaghi LMS, their attitudes towards its adoption remain inconclusive. The results suggest that Aaghi LMS is a valuable tool for addressing the challenges of time and location in distance education, promoting student engagement and fostering collaborative learning environments. Further research is recommended to explore strategies for enhancing service and SQ to maximize the effectiveness of LMS adoption.

To further improve the efficiency and effectiveness of LMS adoption at AIOU, the administration must address the gaps between students’ perceptions and the system’s current usage in online learning. AIOU should prioritize recruiting competent information technology professionals to design a user-friendly interface that meets the information needs of postgraduate students. In addition, the university must implement policies aimed at increasing awareness about Aaghi LMS among students to promote its usage. Publicizing the system’s benefits through social media campaigns, seminars and workshops tailored for both students and faculty can significantly improve its adoption and utilization. The introduction of short courses focused on the appropriate use of Aaghi LMS, at both the main campus and sub-campuses, will further equip students with the necessary skills to maximize their learning experience.

To ensure the long-term and productive use of Aaghi LMS, AIOU should focus on strengthening its ICT infrastructure and providing continuous technical support to students. Administrators should conduct regular studies to evaluate advancements in LMS technology and incorporate relevant updates into Aaghi LMS. Additionally, fostering collaboration between the ICT department and faculty members will encourage students to actively engage with the platform for academic purposes. Training programs on information literacy and LMS usage, particularly for research scholars, can enhance their ability to access and utilize resources effectively. By developing a more interactive and modern LMS interface, enriched with advanced tools and features, AIOU can ensure a seamless user experience, thereby promoting the LMS as an integral component of the university’s academic ecosystem. Hence, this research proposes that the LMS must be designed to support efficient and effective access to learning materials, enhancing the overall learning experience of end users.

This empirical research suggests the need for further research to optimize the use of LMS and its impact on academic performance. Comprehensive surveys should be conducted to evaluate the LMS’s influence on students’ learning outcomes at AIOU. Future research should focus on developing a robust model for effective LMS utilization, addressing diverse academic needs. Replicating this study in other universities across Pakistan and internationally will provide comparative insights and validate the proposed models. Moreover, mixed-methods research can offer a deeper understanding of end-users' perspectives regarding LMS usage. Expanding the scope of future studies to include a larger population and sufficient resources will yield more comprehensive findings. Lastly, integrating LMS into AIOU’s curriculum should be explored to ensure its alignment with the institution’s pedagogical goals.