Vol.:(0123456789) Education and Information Technologies (2025) 30:19051–19073 https://doi.org/10.1007/s10639-025-13544-2 Correlates of factors on students’ use behavior of E‑learning management systems in Ghanaian Public Universities Daniel Amadiok1   · Winston Kwame Abroampa2   · Eric Opoku Osei3  Received: 5 August 2024 / Accepted: 19 March 2025 / Published online: 5 April 2025 © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025 Abstract Numerous studies highlight that the high adoption rates of e-learning management systems (e-LMSs) are not necessarily matched by their actual use among students in sub-Saharan African higher education. Nevertheless, factors such as perceived behavioral control of e-LMS, teaching activities in e-LMS, administrative activities in e-LMS, and effectiveness of e-LMS appeared to have influenced students’ use behavior of e-LMSs during the COVID-19 pandemic, although empirical research on these influences remains scarce. This study, therefore, empirically examined the impact of these factors on students’ use behavior of e-LMSs in Ghanaian pub- lic universities. Data were collected through a paper-based questionnaire adminis- tered to 531 students across three public universities in Ghana. The data were coded, converted into a comma-delimited file, and analyzed using SmartPLS 3 software. Employing the partial least squares structural equation modeling, the study revealed insights into the factors driving e-LMS use in Ghanaian public universities. The results indicate that the independent variables explained 69.5% of the variance in students’ use behavior of e-LMS. These findings suggest that higher education insti- tutions (HEIs) should focus on activating key factors such as perceived behavioral control of e-LMS, teaching activities in e-LMS, administrative activities in e-LMS, and effectiveness of e-LMS to enhance students’ use behavior of these systems. This study provides HEIs with valuable insights and actionable recommendations to sup- port effective e-LMS utilization. Keywords  Use behavior · E-learning management systems · Partial least squares structural equation modeling · COVID-19 pandemic · Cross-sectional survey · Higher education institutions · Ghanaian public universities Extended author information available on the last page of the article http://orcid.org/0009-0005-2717-6342 http://orcid.org/0000-0002-0753-4553 http://orcid.org/0000-0002-9871-3900 http://crossmark.crossref.org/dialog/?doi=10.1007/s10639-025-13544-2&domain=pdf 19052 Education and Information Technologies (2025) 30:19051–19073 1  Introduction Information and communication technologies have significantly altered the way humans live and are now essential in every endeavor (Juhanak et al., 2019). Edu- cation has not been spared from the rippling effects of technological advance- ments (Bradley, 2021). These advancements ensure easy access to pedagogical content, making knowledge and learning resources readily available (Imran & Kowalski, 2014). The methods by which we source knowledge and information are continually improving (Kelentric et  al., 2017). Online learning, collabora- tion, and assessments have transcended traditional classroom boundaries. Schools increasingly use technology to create, store, and distribute educational materials to students. Furthermore, technologies such as digital whiteboards, laptops, and smartphones are integrated to create virtual classrooms that support education anytime and anywhere. These tools enhance 21st-century education by promoting activities that stimulate critical thinking, problem-solving, and creativity among students. Virtually all higher education institutions worldwide employ some form of technology to support teaching and learning (Machajewski et al., 2019). These tools positively impact higher education by enhancing the effectiveness and effi- ciency of teaching and learning processes. Education has become accessible any- time and anywhere, provided students have access to a digital device with internet connectivity  (Sharma et  al., 2017). As digital natives, students are increasingly reliant on technological tools in their learning processes (Buthelezi & Wyk, 2020). The integration of technology into education has given rise to the term "e-learning," a term that has gained significant popularity among 21st-century students. E-learning involves using the internet to deliver educational content. It has become the primary medium for educational disseminations, particularly dur- ing the COVID-19 pandemic. A significant component of e-learning is e-LMSs, which are software platforms designed to provide learning resources, track stu- dent performance, and generate reports. E-LMSs facilitate the administration of quizzes, examinations, and grading (Patil, 2012). They also support planning, evaluation, and execution of learning activities. Furthermore, these systems man- age users, including students, teachers, and administrators, and also store files, monitor learning progress, and enable communication between learners and instructors. Despite the widespread adoption of e-LMSs like Sakai, Moodle, Edumondo, and Blackboard in higher education institutions across Sub-Saharan Africa, these platforms remain underutilized. Several studies (Soursa & Eskilson, 2014; Choga, 2015; Webbstock & Fisher, 2016; Kanwal & Rehman, 2017; Washington, 2019) indicate infrequent use by students. For instance, Choga (2015) reported that fewer than 5% of students at the University of Science and Technology, Zim- babwe, accessed their institutional e-LMS consistently. Similarly, Darko-Adjei and Ankrah (2020) found that students at the University of Ghana rarely used the institution’s e-LMS. Studies by Dampson (2021), Bervell and Umar (2020) also 19053Education and Information Technologies (2025) 30:19051–19073 highlighted this trend, emphasizing the need for further research into contextual factors influencing student behavior toward e-LMSs in Sub-Saharan Africa. A literature review and bibliometric analysis of studies in the domain of e-LMSs indicate that the combined effect of perceived behavioral control of e-LMS, teaching activities in e-LMS, administrative activities in e-LMS, and effectiveness of e-LMS on students’ use behavior of the system remain largely unexplored. Meanwhile, dur- ing the COVID-19 pandemic, most higher education institutions in Sub-Saharan Africa deployed effective e-LMSs to ensure that teaching and administrative activi- ties continued (Agyapong et al., 2020). During the COVID-19 pandemic, however, most higher education institutions in Sub-Saharan Africa deployed effective e-LMSs to ensure the continuity of teaching and administrative activities (Agyapong et al., 2020). Students’ behavioral control over these learning platforms played a critical role in the success of online learning during this period. Nevertheless, the extent to which these factors contribute to students’ use behavior of e-LMS remains unclear. Therefore, the purpose of this study was to empirically examine the influence of the factors (perceived behavioral control of e-LMS, teaching activities in e-LMS, administrative activities in e-LMS, and effectiveness of e-LMS) on students’ use behavior of e-LMSs in public universities in Ghana. 2 � Literature review Use behavior refers to the frequency with which a particular technology is uti- lized for various activities (Kim, 2008). It signifies a commitment to using a sys- tem (Black, 1982) and is often used interchangeably with the term "actual use" (Davis, 1989). Numerous studies have highlighted the low utilization of e-LMSs in higher education institutions across sub-Saharan Africa. For instance, Mtani and Mbelwa (2022) conducted a quantitative study examining the factors influencing e-LMS usage in Tanzanian higher education and found that, despite the advantages of e-LMSs, both students and instructors underutilize them. Similarly, Cavus et al. (2021) observed that although the adoption of e-LMSs has increased in Nigeria and other sub-Saharan African countries, actual usage remains low, with a slow uptake in colleges and universities. Furthermore, Mtebe (2015) reported that a significant portion of universities’ resources in sub-Saharan Africa is dedicated to acquiring and maintaining e-LMSs, yet actual usage remains minimal. In Ghana, Asamoah et al. (2023) asserted that despite substantial investments in e-LMS adoption, lecturers do not effectively utilize these systems, thereby nega- tively impacting the teaching and learning process. Darko-Adjei and Ankrah (2020) investigated the factors influencing students’ use of e-LMS at the University of Ghana, employing a convenience sampling technique to gather data from 230 stu- dents. Their analysis revealed very low use behavior among students. Similarly, Ansong et  al. (2016) highlighted the underutilization of e-LMS in Ghanaian uni- versities. Adu and Biljon (2024) also noted the persistent low utilization of e-LMS among both lecturers and students in Ghana’s higher education institutions. Damp- son (2021) demonstrated that although e-LMS platforms are initially embraced with enthusiasm upon installation, they often experience significant underutilization over 19054 Education and Information Technologies (2025) 30:19051–19073 time, despite the training provided to students. Even with free internet access and a generally stable electricity supply in Ghana, the issue of low e-LMS utilization per- sists. Finally, Bervell and Umar (2020) reinforced this finding by demonstrating the lack of actual use of e-LMS in the country. Numerous studies have investigated various factors as antecedents to use behav- ior. Davis (1989) developed the Technology Acceptance Model (TAM), which integrates behavioral intention, attitudes, perceived ease of use, and perceived use- fulness to predict use behavior. Similarly, Venkatesh et  al. (2003) introduced the Unified Theory of Acceptance and Use of Technology (UTAUT), incorporating per- formance expectancy, effort expectancy, social influence, and facilitating conditions as predictors of use behavior. Ajzen (1991) advanced the Theory of Planned Behav- ior (TPB), which combines attitudes, subjective norms, and perceived behavioral control to assess use behavior. Several studies have expanded these theories (TPB, UTAUT, TAM) or integrated variables from them with novel conceptual variables to enhance their predictive power. For instance, Mtani and Mbelwa (2022) incor- porated perceived usefulness, self-efficacy, and intrinsic motivation as antecedents of e-LMS use behavior. Cavus et  al. (2021) developed a model predicting e-LMS use behavior by including facilitating conditions, attitudes toward e-LMS, perceived enjoyment, user satisfaction, perceived usefulness, social influence, system quality, and ease of use. Similarly, Bervell and Arkorful (2020) combined facilitating condi- tions, voluntariness of use, and actual use to formulate a model for assessing e-LMS use. The combined effects of teaching activities in e-LMS, administrative activities in e-LMS, behavioral control of e-LMS, and the effectiveness of e-LMS on system use behavior remain empirically unexplored. Dampson et al. (2020) acknowledged the positive role of e-LMS in facilitating teaching and learning during the COVID- 19 pandemic but did so without empirical data to support their assertion. Similarly, while teaching activities, administrative activities, and perceived behavioral control in an effective e-LMS may significantly influence use behavior, empirical evidence on this relationship is lacking. Therefore, a conceptual model that integrates sys- tem factor (effectiveness of e-LMS), student factor (perceived behavioral control of e-LMS), and institutional factors (teaching and administrative activities in e-LMS) could provide a more comprehensive explanation of e-LMS use behavior, particu- larly in the sub-Saharan African context, where e-LMS utilization remain notably low. 3 � Hypotheses development The Technology Utilization Theory (TUT) and the Theory of Planned Behavior (TPB) underpin this study. The TUT (Fig. 1) is designed to examine users’ actual use of a system by combining the concepts of effectiveness and efficiency (Almaiah et  al., 2021). This theory was proposed by Ghapanchi and Talltaei-Khoei (2018). Effectiveness is achieved when technology enables a person to accomplish a goal or perform a task, while efficiency refers to achieving a goal without wasting much time (Eltahir et al., 2019). 19055Education and Information Technologies (2025) 30:19051–19073 The Theory of Planned Behavior (Fig.  2) is rooted in the Theory of Reasoned Action (TRA) (Ajzen, 1991) and was developed to enhance the predictive capacity of the TRA. According to TPB, intentions to engage in a behavior are influenced by subjective norms and attitudes. The performance of the activity is then facilitated by behavioral control and the intention to perform the behavior. Both the Technology Utilization Theory and the Theory of Planned Behavior underpin this study because they encompass variables that explain technology use behavior. We believe that these theories will help elucidate some of the variables in our conceptualized model (Fig. 3). This model was developed by integrating four main Fig. 1   Technology utilization theory (Ghapanchi and Talltaei-Khoei, 2018) Fig. 2   Theory of planned behavior (Ajzen, 1991) 19056 Education and Information Technologies (2025) 30:19051–19073 factors: teaching activities in e-LMS, administrative activities in e-LMS, students’ behavioral control of e-LMS, and effectiveness of e-LMS —all of which appeared to influence e-LMS use behavior during the COVID-19 pandemic. Therefore, we hypothesize that these factors may impact students’ e-LMS use behavior. 3.1 � Perceive behavioral control of e‑learning management systems (PBCE) To understand students’ behavior regarding the use of information systems, Ajzen (1991) expanded the theory of reasoned action by introducing the concept of per- ceived behavioral control. This variable has been widely explored in studies focus- ing on technology use among students. According to Ajzen (1991), perceived behavioral control refers to an individual’s perception of the ease or difficulty of performing a specific task and their sense of control over the task. It reflects the belief that an activity can be completed without significant obstacles and represents an individual’s control over their behavior (Zhang, 2023). Additionally, the sense of ability and confidence in using a system strongly influences users’ decisions to adopt and use it (Chiou, 1998). Perceived behavioral control enhances students’ autonomy in performing tasks, thereby granting them greater control over their engagement with information systems. Several studies have established a positive relationship between perceived behavioral control and the use of information systems. For exam- ple, Khalaf et al. (2022), Wang et al. (2022), and Lee et al. (2020) found that per- ceived behavioral control significantly influences students’ use of information sys- tems. This concept also shapes students’ behavioral intentions before system use (Ly et al., 2022). Hou et al. (2022) further highlighted that perceived behavioral control impacts pre-service teachers’ intentions to integrate technology into their learning environments. Similarly, Tagoe and Abakah (2014) concluded that students are more likely to utilize technology when they feel a sense of control over its use. Research by Menozzi (2017) and Kam et al. (2018) also identified perceived behavioral con- trol as a significant predictor of user behavior. However, contrasting findings exist. Fig. 3   The research model (SUBE) and hypotheses 19057Education and Information Technologies (2025) 30:19051–19073 Koc and Memduhoghlu (2020) observed that perceived behavioral control did not significantly influence technology use behavior. This discrepancy contrasts with the results of Lee et al. (2020), Khalaf et al. (2022), and Tagoe and Abakah (2014). It is worth noting that few studies have specifically examined students’ preparedness, knowledge, confidence, and ability to use e-LMSs in Ghana’s higher education insti- tutions. This gap highlights the need for further research to address this contextual factor. Thus, the following hypothesis is proposed: H1 Perceived behavioral control of e-LMS has a statistically significant influence on students’ use behavior of e-LMS. 3.2 � Teaching activities in e‑learning management systems (TAE) E-learning management systems (e-LMSs) create virtual environments that enable effective interaction between teachers and students. In higher education, teachers play a pivotal role in utilizing technology to enhance the learning experience (Shin, 2015). Through e-LMSs, educators conduct online teaching, which profoundly influ- ences how students engage with these platforms. By extending traditional class- room activities into the digital realm, e-LMSs provide a flexible medium for learn- ing. Many universities require lecturers to deliver courses online using customized e-LMSs, ensuring compliance with institutional guidelines on their use (Dampson et  al., 2020). Through e-LMSs, teachers can create online courses that encourage collaboration among students by fostering group activities (Zanjani et  al., 2016). These platforms also support various teaching functions, including communica- tion, uploading educational materials, and assessing student performance (Atukunda et  al., 2024; Chow et  al., 2018; Sackstein et  al., 2019). A critical factor enabling effective use of e-LMSs is teachers’ self-efficacy, which significantly impacts their ability to leverage these platforms for teaching activities (Zanjani et al., 2016). Dur- ing the COVID-19 pandemic, e-LMSs became indispensable for delivering teaching activities online (Sulaiman et al., 2022). Incorporating activities such as problem- solving exercises and group projects within e-LMSs can enhance teachers’ role in the learning process. Well-designed teaching strategies motivate students to engage with e-LMSs regularly, while frequent feedback and encouragement of peer interac- tions foster sustained involvement. Despite the benefits of e-LMSs, there is limited empirical data on teaching activities in e-LMS, particularly in the context of sub- Saharan Africa. Therefore, this study proposes the following hypothesis: H2 Teaching activities in e-LMS have a statistically significant influence on stu- dents’ use behavior of e-LMS. 3.3 � Administrative activities in e‑learning management systems (AAE) The administration of students in higher education institutions requires signifi- cant effort, particularly given the large student populations involved. To manage this complexity, universities increasingly rely on e-learning management systems 19058 Education and Information Technologies (2025) 30:19051–19073 (e-LMSs) to streamline student information management. These platforms support various functions, including course registration, enrollment, and academic perfor- mance monitoring. They also aid in planning, implementing, and evaluating stu- dents’ progress throughout their courses (Sanchez et al., 2024; Wright et al., 2014). Additionally, e-LMSs facilitate documentation and reporting while serving as a central hub for university administration (Kumar & Sharma, 2021). Key activities such as course registration, transcript requests, and course scheduling can enhance students’ interaction with these systems. Features like regular updates, reminders, tutoring support, and performance analytics can further motivate students to engage consistently with e-LMSs. Therefore, this study proposes the following hypothesis: H3 Administrative activities in e-LMS have a statistically significant influence on students’ use behavior of e-LMS. 3.4 � Effectiveness of e‑learning management systems (EE) Effectiveness refers to the extent to which a goal or task is successfully achieved. It also involves evaluating how well a technology or system performs (Onacan & Erturk, 2016). Effectiveness is achieved when there is a balance between usability and functionality. A system becomes usable, and thus promotes effective use, when it has simple and easy-to-use features. E-learning management systems (e-LMSs) serve as effective tools for lecturers and students who possess the competency to use them (Ghilay, 2019). Studies by Rahrouh et  al. (2018), Holmes and Prieto- Rodriguez (2018), and Syaad and Hidayat (2018) show that students generally per- ceive e-LMSs as highly effective. The platform’s effectiveness plays a crucial role in encouraging regular usage (Cavus et al., 2021). Furthermore, Alturki et al. (2016) investigated the usability and accessibility of the Blackboard e-LMS at King Saud University. The study found that Blackboard was effective due to its ease of use and high interactivity. Similarly, Atukuda et al. (2024) reported that only two out of the fifty-one lecturers in their study expressed dissenting opinions about the effective- ness of e-LMSs. However, despite these findings, there remains a lack of extensive research on the effectiveness of e-LMSs in universities worldwide. Thus, it is vital to collect empirical data on this factor to assess its impact on students’ use behavior of e-LMS in sub-Saharan African universities. Consequently, this study proposes the following hypothesis: H4 The effectiveness of e-LMS has a statistically significant influence on stu- dents’ use behavior of e-LMS. 19059Education and Information Technologies (2025) 30:19051–19073 4 � Methodology 4.1 � Research design The nature of the research problem and the research objective situate the study within the positivism paradigm, leading to the collection of quantitative data. To gather a large dataset for structural equation modeling at a specific point in time, a cross-sectional survey design was adopted to account for the influence of the factors on students’ use behavior of e-LMS (Creswell, 2013; Hair et al., 2017). 4.2 � Participants The population of this study consisted of all continuing undergraduate and post- graduate students in public universities in Ghana. They are expected to have had sufficient experiences with their universities’ e-LMSs. Several studies have high- lighted the general underutilization of these platforms in Ghana’s public universities (Asamoah, 2020; Dampson, 2021; Sahoo et al., 2020; Tagoe & Cole, 2020). There- fore, including all or a subset of these students will provide valuable insights into the objectives of this study. The accessible population comprised 208,070 students from three public universities. These universities have suitable and unrestricted structures for efficient data collection for academic purposes. A sample of students was selected from these universities using a multistage random sampling strategy. The faculties (divisions within a college) and the departments within the universities served as the natural clusters. In the first stage, one faculty was randomly selected from each of the three sampled public universities. In the second stage, one depart- ment was randomly chosen from each selected faculty using randomization soft- ware, as recommended by Creswell and Creswell (2018). The sample size for each department was determined using Krejcie and Morgan’s (1970) table, resulting in a total of 825 respondents randomly selected. 4.3 � Measurement instrument Empirical data were collected using a paper-based questionnaire. This type of ques- tionnaire is known to enhance response rates (Sekaran & Bougie, 2016). It consists of two sections: the first section gathered data on respondents’ personal characteris- tics, while the second section focused on the factors. The items of the factors were adapted from prior studies. Respondents rated their agreement with these items on a five-point Likert-type scale, where 1 = strongly disagree, 2 = disagree, 3 = unsure, 4 = agree, and 5 = strongly agree. The items assessed the effectiveness of e-LMS, perceived behavioral control of e-LMS, teaching activities in e-LMS, administrative activities in e-LMS, and use behavior of e-LMS, all of which were derived from the literature. Specifically, items for effectiveness of e-LMS were adapted from Almaiah et  al. (2021), perceived behavioral control of e-LMS from Ajzen (1991), teaching activities in e-LMS from Zanjani et  al. (2016), Chen and Almunawar (2019), and Fathema et al. (2015), administrative activities in e-LMS from Wright et al. (2014), 19060 Education and Information Technologies (2025) 30:19051–19073 and Kumar and Sharma (2021), and use behavior of e-LMS from Venkatesh et al. (2003). After developing the questionnaire, three professors specializing in ICT edu- cation reviewed the items for validity. Additionally, the reliability of the question- naire was assessed using Cronbach’s coefficient alpha. A total reliability score of 0.948 for the 22 items was obtained. This value indicates that the questionnaire is reliable. A summary of the study’s items can be found in Appendix 1. 4.4 � Data collection and analysis The paper-based questionnaires were distributed and collected between March 20, 2023, and May 28, 2023. This distribution method was chosen due to its high response rate (Binyamin, 2019). A total of 825 questionnaires were distributed, and 598 were retrieved. After data entry into the Statistical Package for the Social Sci- ences (SPSS), some questionnaires were excluded from the analysis due to missing responses or lack of engagement. Ultimately, 531 questionnaires were included in the analysis, resulting in a response rate of 64.4%. For data analysis, Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed using SmartPLS 3 software. This technique was selected because it is commonly used for testing new models (Hair et al., 2017). Prior to analysis, the data were screened for outliers, and the skewness and kurtosis values were calculated. Values fell within the ± 1 range which indicate that the data were normally distributed and suitable for structural equation modeling. The analysis was conducted in three stages: demographic data analysis, measurement model assessment, and structural model assessment. The demographic data were analyzed using SPSS, while the measurement model and structural model assessments were carried out in SmartPLS 3. Table 1   Demographic profiles of the respondents Participants Frequency (f) Percentage (%) Gender Male 289 54.4 Female 242 45.6 Students’ Education Undergraduate 449 84.6 Postgraduate 82 15.4 Age Below 18 0 0 18–22 245 46.1 23–27 211 39.7 28–32 49 9.2 33–37 20 3.8 38–42 5 0.9 43–47 1 0.2 Above 47 0 0 19061Education and Information Technologies (2025) 30:19051–19073 5 � Findings 5.1 � Demographic profiles Table  1 provides a demographic breakdown of the respondents involved in the study. Among the participants, 289 (54.4%) were male, while 242 (45.6%) were female. The majority, 449 (84.6%), were undergraduates, with 82 (15.4%) being postgraduates. Regarding age, 302 (56.9%) of the respondents were between 19 and 23  years old, while 229 (43.1%) were between 24 and 47  years old. These findings indicate that the study primarily included more males, undergraduates, and respondents under the age of 24. 5.2 � Collinearity statistics (VIF) The Variance Inflation Factor (VIF) was used to assess the degree to which the regression coefficients are inflated due to collinearity. VIF value of 5 or higher suggests the presence of multicollinearity (Hair et al., 2017). In Table 2, the VIF values are all below the recommended threshold value of 5, indicating that multi- collinearity is not present among the independent variables. 5.3 � Measurement model assessment The model was evaluated for validity through discriminant validity measures, including the Fornell-Larcker criterion and the Heterotrait-Monotrait Ratio (HTMT). Reliability was assessed using indicator reliability via factor loadings, as well as construct reliability through Cronbach’s alpha and composite reliabil- ity. The results of the measurement model evaluation are presented in Tables 3, 4, and 5. The outer loading for the indicator EE5 was 0.644, which was below the recommended threshold of 0.7; therefore, this factor was eliminated (Hair et al., 2017). In Table 3, the factor loadings, Cronbach’s alpha (CA), and composite reli- ability (CR) values were all greater than the threshold value of 0.7, confirming Table 2   Collinearity statistics (VIF) PBCE Perceived behavioral control of e-LMS, TAE Teaching activi- ties in e-LMS, AAE Administrative activities in e-LMS, EE Effec- tiveness of e-LMS, SUBE Students’ use behavior of e-LMS PBCE TAE AAE EE SUBE PBCE 2.724 TAE 3.245 AAE 2.110 EE 2.929 SUBE 19062 Education and Information Technologies (2025) 30:19051–19073 the model’s reliability. Additionally, the average variance extracted (AVE) for all variables exceeded the recommended value of 0.5 (Hair et al., 2017). The bolded values in Table  4 are the square root of the Average Variance Extracted (AVE) of the constructs. The table shows that all the constructs correlate better with themselves than the others. Thus, discriminate validity was established. In Table 5, all the correlations among the constructs were below the threshold value of 0.90 (Henseler et al., 2015). Thus, discriminate validity was established. Table 3   Result of the confirmatory factor analysis (CFA) CA Cronbach’s alpha, CR composite reliability, AVE average variance extracted Constructs Item Loading > 0.7 CA > 0.7 CR > 0.7 AVE > 0.5 Perceived behavioral control of e-LMS (PBCE) PBCE1 0.823 0.884 0.92 0.743 PBCE2 0.905 PBCE3 0.896 PBCE4 0.819 Teaching activities in e-LMS (TAE) TAE1 0.824 0.89 0.924 0.752 TAE2 0.887 TAE3 0.854 TAE4 0.902 Administrative activities in e-LMS (AAE) AAE1 0.859 0.893 0.922 0.702 AAE2 0.860 AAE3 0.797 AAE4 0.878 AAE5 0.792 Effectiveness of e-LMS (EE) EE1 0.874 0.872 0.909 0.669 EE2 0.924 EE3 0.889 EE4 0.798 Students’ use behavior of e-LMS (SUBE) SUBE1 0.828 0.865 0.908 0.712 SUBE2 0.84 SUBE3 0.811 SUBE4 0.895 Table 4   Discriminant validity (Fornell-Larcker) PBCE Perceived behavioral control of e-LMS, TAE Teaching activi- ties in e-LMS AAE Administrative activities in e-LMS, EE Effective- ness of e-LMS, SUBE Students’ use behavior of e-LMS AAE EE PBCE SUBE TAE AAE 0.838 EE 0.449 0.873 PBCE 0.512 0.768 0.862 SUBE 0.696 0.659 0.743 0.844 TAE 0.715 0.694 0.667 0.691 0.867 19063Education and Information Technologies (2025) 30:19051–19073 5.4 � Structural model assessment The validity and reliability of the measurement model have been established, allow- ing for the evaluation of the structural model. Assessing the structural model defines the relationships between the factors and students’ use behavior of e-LMS. This assessment was conducted using the bootstrapping technique in SmartPLS 3, with 5,000 samples drawn (Hair et al., 2017). Bootstrapping generates randomized sub- samples from the dataset. It allows iterative calculation of the model’s parameters. The bootstrapping technique produces path coefficients (β), t-statistics, and p-values, which explain the relationships between the variables in the model. The results of the structural model assessment are presented in Fig. 4 and Table 6. Figure 4 illus- trates the path diagram, path coefficients, and p-values. Table 5   Discriminant validity (Heterotrait-Monotrait Ratio) PBCE Perceived behavioral control of e-LMS, TAE Teaching activi- ties in e-LMS, AAE Administrative activities in e-LMS, EE Effec- tiveness of e-LMS, SUBE Students’ use behavior of e-LMS AAE EE PBCE SUBE TAE AAE EES 0.502 PBCE 0.572 0.863 SUBE 0.784 0.745 0.849 TAE 0.800 0.782 0.750 0.783 Fig. 4   Structural model evaluation 19064 Education and Information Technologies (2025) 30:19051–19073 Table  6 reveals that three relationships PBCE—> SUBE, EE—> SUBE, and AAE—> SUBE were significant in the model (p < 0.05). Specifically, perceived behavioral control of e-LMS (β = 0.404; t = 10.174; p < 0.05), the effectiveness of e-LMS (β = 0.144; t = 3.419; p < 0.05), and administrative activities in e-LMS (β = 0.397; t = 11.019; p < 0.05) positively influence students’ use behavior of e-LMS. These results support the acceptance of hypotheses H1, H3, and H4. How- ever, teaching activities in e-LMS (β = 0.037; t = 0.727; p > 0.05) did not signifi- cantly influence students’ use behavior, leading to the rejection of hypothesis H2. Among the paths, PBCE—> SUBE emerged as the most significant, followed closely by AAE—> SUBE. The relationships AAE—> SUBE and PBCE—> SUBE had moderate effect sizes of 0.247 and 0.198, respectively, aligning with Cohen’s (1988) suggestion that effect sizes of 0.35 indicate large effects, 0.15 indicate medium effects, and 0.02 indicate small effect. The path TAE—> SUBE, with an effect size of 0.001, showed a small effect. Table  7 presents the R-square and R-square adjusted value for students’ use behavior of e-LMS (SUBE). From Table 7, the R-square and R-square adjusted values for SUBE are 0.697 and 0.695, respectively. The R-square adjusted value of 0.695 indicates that the model has a strong explanatory power, as suggested by Chin (1998). This means that 69.5% of the variance in students’ use behavior of e-LMS can be attributed to perceived behavioral control of e-LMS, effectiveness of e-LMS, teaching activities in e-LMS, and administrative activities in e-LMS. Table 6   Structural model evaluation PBCE Perceived behavioral control of e-LMS, TAE Teaching activities in e-LMS, AAE Administrative activities in e-LMS, EE Effectiveness of e-LMS, SUBE Students’ use behavior of e-LMS * = significant at α = 0.05 Hypotheses Paths Coefficients (β) F2 T-statistics P-Values Results of hypotheses testing H1 PBCE—> SUBE 0.404 0.198 10.174 0.000* Accepted H2 TAE—> SUBE 0.037 0.001 0.727 0.458 Rejected H3 AAE—> SUBE 0.397 0.247 11.019 0.000* Accepted H4 EE—> SUBE 0.144 0.023 3.419 0.000* Accepted Table 7   Coefficient of determination (R2) SUBE student’ use behavior of e-LMS Variable R-square R-square adjusted SUBE 0.697 0.695 19065Education and Information Technologies (2025) 30:19051–19073 6 � Discussion A bibliometric analysis of existing studies indicates that factors such as the effective- ness of e-LMS, teaching activities in e-LMS, administrative activities in e-LMS, and perceived behavioral control of e-LMS have collectively not been sufficiently explored in relation to their influence on e-LMS use behavior. Although these factors seem to have influenced e-LMS usage during the COVID-19 pandemic, empirical data on their effects on use behavior remain lacking. To address this gap, a model was developed to assess the impact of these factors on students’ e-LMS use behavior in Ghana’s public universities. Partial least squares structural equation modeling was used to examine the relationships in the model, which are expressed as hypotheses. The results show that perceived behavioral control of e-LMS has a direct and sta- tistically significant influence on students’ use behavior of the platform. This finding suggests that students are more likely to use the platform when they feel confident and capable. However, limited perceived behavioral control over e-LMS usage nega- tively affects engagement. Building self-efficacy, along with the necessary beliefs, skills, and abilities, over time enhances students’ engagement with e-LMSs. This can be achieved through social support and reinforcement of its use. The result aligns with prior studies by Lee et al. (2020) and Tagoe and Abakah (2014), which found that perceived behavioral control positively impacts technology use. Addition- ally, the outcome corroborates the studies by Hou et al. (2022), Zhang (2023), and Wang et al. (2022), which confirm that perceived behavioral control over technology promotes its use in the learning environment. Moreover, this result supports Ajzen’s (1991) Theory of Planned Behavior, which postulates that perceived behavioral con- trol positively impacts information system use. Furthermore, Songkram et al. (2023) reveal that technologies are used when their users have behavioral control over them. A possible explanation for this finding is the increasing prevalence of digital natives among students, who are inherently comfortable with technology. Given the central role of technology in modern education, students must develop behavioral control over it to engage effectively with learning tools (Amin et al., 2024). Thus, humanizing e-learning management systems and making them more user-friendly can help remove cognitive and physical barriers, thereby promoting their equitable access and use by all students (Pacansky-Brook et al., 2020). However, this study reveals that there was no statistically significant influence of teaching activities in e-LMS on students’ use behavior of the system. This result is inconsistent with studies by Bradley (2021) and O’Dwyer et  al. (2015), which demonstrated that teaching activities in e-LMS increase students’ use of the system. Furthermore, it contradicts the study by Sulaiman et  al. (2022), which confirmed that teachers’ pedagogical activities in e-LMS ensured its use during the COVID-19 pandemic. This result is surprising but might be attributed to the claim of Atukuda et al. (2024), who suggested that poor internet connectivity, login difficulties, limited smartphone compatibility, system crashes, slow response times, and complex inter- faces may negatively affect teaching activities in e-LMSs. Additionally, e-LMSs in sub-Saharan Africa are often viewed merely as information repositories, with most teaching occurring face-to-face (Asamoah, 2020; Sahoo et  al., 2020). Moreover, 19066 Education and Information Technologies (2025) 30:19051–19073 the return to in-person classes post-COVID-19 may have reduced teachers’ reliance on these platforms for instructional purposes (Sanchez et al., 2024). Nevertheless, teaching activities in e-LMSs are beneficial because they increase engagement, collaboration, personalization of learning, and provide analytics of learning. Fur- thermore, multimedia resources can be injected into a lesson when teaching is con- ducted in e-LMSs (Bancoro, 2024). While disruptions may occur, teaching in virtual environments like e-LMSs remains unaffected due to their flexibility, accessibility, and broader reach, unlike the traditional brick-and-mortar education model (Makda, 2024). The outcomes of the study also indicate that administrative activities in e-LMSs positively influence students’ use behavior of the system. This implies that per- forming administrative duties, such as posting academic schedules, course materi- als, announcements, and hosting webinars in e-LMS, promotes its use. This result aligns with findings by Wright et  al. (2014) and Koh and Kan (2020), who noted that timely and relevant administrative updates in e-LMS encourage its use. Addi- tionally, the result corroborates studies by Sanchez et  al. (2024) and Kumar and Sharma (2021), which found that the administrative use of e-LMS as productivity and communication tools promotes its use. Higher education institutions in develop- ing countries, such as Ghana, are required to have effective e-LMSs to support their activities. Finally, the study reveals that the effectiveness of e-LMS has a statistically signif- icant impact on students’ use behavior of the platform. Students are more motivated to use e-LMSs that are efficient and user-friendly. Such platforms provide function- alities such as uploading assignments, downloading resources, and participating in online quizzes and exams. In line with the study by Al-Khresheh (2022), the e-LMSs deployed in this study’s setting are highly effective. Additionally, this finding aligns with previous research by Atukunda et al. (2024), Rahrouh et al. (2018), Cavus et al. (2021), Alturki et al. (2016), and Ghapanchi and Talaei-Khoei (2018), which found that the e-LMSs they investigated were highly effective. Effective e-LMSs have a positive impact and offer substantial benefits for the teaching and learning process, as they ensure the seamless delivery and access of academic services online. 7 � Conclusion This study has demonstrated that perceived behavioral control over e-LMS, admin- istrative activities in e-LMS, and the effectiveness of e-LMS are significant fac- tors influencing students’ use behavior of the platform. To maximize use, students should receive adequate orientation, training, and technical support to enhance their confidence, skills, and competencies in using e-LMS. Furthermore, students should be motivated to use the platform autonomously, with clear expectations regarding its use communicated to them. These measures would collectively enhance their behav- ioral control over the platform. Key administrative activities, such as announce- ments, course management, user management, content management, assessments, grading, reporting, and the dissemination of academic calendars, should consistently be conducted through e-LMS. This consistency fosters regular interactions with the 19067Education and Information Technologies (2025) 30:19051–19073 platform. Additionally, the sustained usage of e-LMS heavily depends on its effec- tiveness. Developers and designers must prioritize creating robust, user-friendly systems tailored to the specific needs of higher education institutions. Interestingly, contrary to expectations, teaching activities conducted in e-LMS did not have a notable impact on students’ use behavior. Nevertheless, university administrators should encourage lecturers to adopt the platform for online teaching to boost student engagement. Teaching activities within e-LMS should not be optional but manda- tory, ensuring uninterrupted classes during crises such as pandemics or natural dis- asters. Emphasis should also be placed on improving teachers’ pedagogical practices within e-LMS. When instructors actively engage with the system, students are more likely to follow suit, particularly when sufficient support is provided. Comparatively, while perceived ease of use and perceived usefulness in the Technology Acceptance Model (TAM) account for 40% of the variance in technology use behavior (Ly et al., 2022), the variables in this study explain 69.5% of the variance. This finding sug- gests that incorporating these variables into future studies could offer a more com- prehensive understanding of technology use behavior, potentially uncovering greater predictive power. Institutionalizing these recommendations will enable higher edu- cation institutions to achieve high levels of sustained e-LMS use, ensuring both stu- dent and faculty engagement in an increasingly digital academic environment. 8 � Implications The conceptual model of this study contributes significantly to theory building in e-LMS utilization. It identifies key factors: perceived behavioral control of e-LMS, effectiveness of e-LMS, teaching activities in e-LMS, and administrative activities in e-LMS as predictors of e-LMS use behavior. Therefore, these factors can be fur- ther extended or disaggregated into other models to explore technology use. This research holds practical significance for higher education institutions since it high- lights factors that should be institutionalized to promote the effective use of e-LMS. 9 � Limitation and future research direction This study has some notable limitations. First, the data were collected exclusively from students in public universities in Ghana, which restricts the generalizability of the findings to the broader tertiary education sector. To address this limitation, future research should expand the scope to include private universities, colleges, and technical universities within Ghana and beyond. This would enable a more com- prehensive understanding of e-LMS use across various types of institutions. Sec- ond, the study employed a quantitative approach, which, although valuable, did not capture the qualitative insights necessary to deepen the understanding of students’ e-LMS use behavior. Future studies should consider integrating qualitative methods alongside quantitative data collection. This mixed-methods approach would provide a more nuanced and holistic perspective on students’ use behavior of e-LMSs. 19068 Education and Information Technologies (2025) 30:19051–19073 Appendix 1 Questionnaire Acknowledgements  We acknowledge all the staff and students of the Department of Teacher Education, Kwame Nkrumah University of Science and Technology for their encouragement and support. We also thank the three universities that allowed us to collect data from their students. 19069Education and Information Technologies (2025) 30:19051–19073 Funding  There was no funding for this study. Data availability  The dataset used for the analysis is not publicly available, however, upon reasonable request the corresponding author will make it available. Declarations  Ethical approval  Ethical approval was obtained from the ethics board of the Kwame Nkrumah University of Science and Technology, Kumasi with approval number HuSSREC/AP/B2/VOL. 1, dated 24/03/2023 and all necessary ethics were observed throughout the study. Conflict of interest  The authors do not have any conflict interest to disclose. References Adu, A. S. Y. spsampsps Biljon, J. V. (2024). Factors Influencing Learning Management Systems Use Among Lecturers in Ghanaian Higher Education Institutions. IFIP Advances in Information and Communication Technology Implications of Information and Digital Technologies for Development, 215–229. https://​doi.​org/​10.​1007/​978-3-​031-​66982-8_​15 Agyapong S, Asare S, Essah P, Heady L, Munday G. (2020). Learning in Crisis: COVID-19 pandemic response and lessons for students, faculty, and Vice Chancellors in sub-Saharan Africa. 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Higher Education, 87, 567–590. https://​doi.​org/​10.​1007/​ s10734-​023-​01024-w Publisher’s Note  Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Authors and Affiliations Daniel Amadiok1   · Winston Kwame Abroampa2   · Eric Opoku Osei3  * Daniel Amadiok danielamadiok@gmail.com Winston Kwame Abroampa wynxtin@yahoo.com Eric Opoku Osei eric.opoku.osei@knust.edu.gh 1 Department of Mathematics & ICT, St. Joseph’s College of Education, Bechem, Ghana 2 Department of Teacher Education, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana 3 Department of Computer Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana http://ijedict.dec.uwi.edu/viewarticle.php?id=1731 https://doi.org/10.1080/02680513.2019.1704232 https://doi.org/10.2307/30036540 https://doi.org/10.3390/ijerph191710758 https://doi.org/10.3390/ijerph191710758 https://doi.org/10.1177/0047239519874037 https://doi.org/10.1177/0047239519874037 https://er.educause.edu/articles/2014/4/selecting-alearning-management-system-advice-from-an-academic-perspective https://er.educause.edu/articles/2014/4/selecting-alearning-management-system-advice-from-an-academic-perspective https://doi.org/10.1007/s40299-016-0277-2 https://doi.org/10.1007/s10734-023-01024-w https://doi.org/10.1007/s10734-023-01024-w http://orcid.org/0009-0005-2717-6342 http://orcid.org/0000-0002-0753-4553 http://orcid.org/0000-0002-9871-3900 Correlates of factors on students’ use behavior of E-learning management systems in Ghanaian Public Universities Abstract 1 Introduction 2 Literature review 3 Hypotheses development 3.1 Perceive behavioral control of e-learning management systems (PBCE) 3.2 Teaching activities in e-learning management systems (TAE) 3.3 Administrative activities in e-learning management systems (AAE) 3.4 Effectiveness of e-learning management systems (EE) 4 Methodology 4.1 Research design 4.2 Participants 4.3 Measurement instrument 4.4 Data collection and analysis 5 Findings 5.1 Demographic profiles 5.2 Collinearity statistics (VIF) 5.3 Measurement model assessment 5.4 Structural model assessment 6 Discussion 7 Conclusion 8 Implications 9 Limitation and future research direction Appendix 1 Questionnaire Acknowledgements References