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\title{A Study on the Factors Influencing Student's Acceptance of Learning Management Systems: A Case of}
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             \author[1]{Ace N.  Bombaes}

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\date{\small \em Received: 9 February 2021 Accepted: 28 February 2021 Published: 15 March 2021}

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\begin{abstract}
        


The utilization of Learning Management System (LMS) has been given much emphasis especially during the COVID-19 pandemic period and has become a common phenomenon during the new normal. This study examined the factors that influence LMS adoption among the students. Drawing upon the model of technological acceptance (TAM), five factors have been identified such as facilitating conditions (FC), perceived enjoyment (PE), perceived service quality (SQ), perceived value (PV), and satisfaction (SAT) in successful acceptance of LMS as an observational viewpoint from the of the University of Perpetual Help System â??" Pueblo de Panay. The study used quantitative with the support of SPSS, SEM, multi-level linear regressions, and regression analysis. The study will add value to the growing figures of investigation on TAM by measuring the factors and their impact on LMS adoption.

\end{abstract}


\keywords{learning management system (LMS), moodle, technology acceptance model (TAM), }

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
nnovation is the keystone and foundation of change especially in education and it increases effectiveness, competitiveness and efficiency of services and establish unique forms of participation which can heard, influence the success of a learning institution towards achieving its national and global agendas \hyperref[b10]{(Haas et al., 2009)}.\par
During the COVID-19 pandemic, many schools had to adapt to the new normal. In order to make it happen, they invested in learning management system that can support students' learning activities by providing an extensive and integrated range of services and tools for learners \hyperref[b18]{(Zanjani et al., 2017)}. It has become very crucial since this period disabled many functions and movements were limited. Students had to stay at home in a secure environment for them to be safe from any harm brought by this contagious virus. Through LMS, teachers could still facilitate learning and organise learning contents; students could still learn and submit their learning outputs, requirements like projects, and assignments. What is more, both teachers and students could provide feedback and insights to one another for the betterment of all learning contents and outcomes towards achieving one goal which is learning and applying all the learned concepts. Moreover, LMS helps teachers in providing and organising new teaching and learning contents and methods which help students to learn and perform better \hyperref[b14]{(Panay et al., 2019)}.\par
This study delves on the identified factors that influence student's LMS adoption. Technology Acceptance Model (TAM) is used as its guiding model that explains as to how adoption is made possible. Five factors have been identified like facilitating conditions (FC), perceived enjoyment (PE), perceived service quality (SQ), perceived value (PV), and satisfaction (SAT) in successful acceptance of LMS as an observational viewpoint from the of the University of Perpetual Help System -Pueblo de Panay. All data were collected from students of the University of Perpetual Help System-Pueblo de Panay Campus, Roxas City, Capiz, Philippines. 
\section[{a) Learning Management System}]{a) Learning Management System}\par
Learning Management Systems (LMS), also known as Course Management Systems (CMS) or Virtual Learning Environment (VLE) \hyperref[b2]{(Bahsh \& Daoud, 2016}) \& \hyperref[b0]{(Asiri et al., 2012)}, is a set of software tools that are specifically designed to support the educational process. The University has been using its own Moodle  {\ref (Rakic et al., n.d.)} that supports learning and training. It provides custom learning environments for students as well as to teachers in creating, providing, and organising learning contents. It a learning platform where students and teacher can collaboratively and actively interactive with each other. It has all the important features that enable students to review the class calendar, submit students to have a great access to all available learning materials and also enables the University to monitor student's learning progress and evaluate the learning process.\par
The effectiveness of this LMS has already been studied and proven effective in teaching and training. As studied by \hyperref[b9]{(Goyal \& Tambe, 2015)}, their findings revealed that there was a favourable feedback from the students and a successful adoption of MOODLE by the teachers. A similar study was conducted by \hyperref[b5]{(Costa et al., 2012)} which also reveals that Moodle has a great potential and has been recognized by many student the importance of the use of other functionalities of this platform in order to promote the success of the teaching/learning process. 
\section[{b) Research Model}]{b) Research Model}\par
The study adopted Technology Adoption Model (TAM) by \hyperref[b7]{Davis (1986)} since it further explains and describes the adoption process and as to how the identified factors influence and enable the success LMS adoption. By using this model, the study is able to point out and explore the factors that determine and predict student's intention towards LMS adoption.\par
The figure shows the relationship of PE to SAT and PV, the relationship of SQ to SAT and PV, the relationship of FC to SAT and PV, the relationship of SAT towards Bi, and the relationship of PV towards Bi. Two key moderating factors (PV, SAT) have been identified for they have significant in conjunction with this model. 
\section[{II.}]{II.} 
\section[{Literature Review a) Perceived Enjoyment (PE)}]{Literature Review a) Perceived Enjoyment (PE)}\par
Perceived enjoyment is regarded as the level of pleasure as to how individuals perceive something they receive or from using an application. This is further explored and explained by previous studies which support that perceived enjoyment influences technology-aided adoption thus giving the affirmative effect on satisfaction and perceived value \hyperref[b16]{(Venkatesh, 2000)}. PE in this investigation is regarded as the level of pleasure that consumers perceive from using M-coupon applications and can predict student's degree of enjoyment to do or repeat an amusing activity. PE has a favorable link to student's motive to learn and perform thus defining the capability of a certain system to keep student from utilizing the system \hyperref[b1]{(Asmi et al., 2017)}. Moreover, student perceives using something e.g. LMS when it meets the expectations and value of how much he pays for it. 
\section[{b) Perceived Service Quality (PSQ)}]{b) Perceived Service Quality (PSQ)}\par
It is included as a factor in this study in order to gauge the student's degree of perception for it leads to student's LMS adoption. Previous studies defined SQ as an influencing factor that leads to successful LMS adoption when student feels like the expectations are met and the service it provides pleases students thus contributing to the overall judgment or assessment which lead to favorable response based on the given service (A. N. \hyperref[b4]{Bombaes et al., 2019)}. Furthermore, service quality refers to the quality of supports provided to the system's end-users \hyperref[b8]{(Ghazal et al., 2018)}. 
\section[{c) Perceived Facilitating Conditions (PFC)}]{c) Perceived Facilitating Conditions (PFC)}\par
Facilitating conditions can be any technical, instructional support and infrastructure needed for LMS \hyperref[b17]{(Venkatesh et al., 2012)}. Previous investigations display that FC have an essential impact on student's behavioral intention towards the adoption of technology \hyperref[b12]{(Jeng \& Tzeng, 2012)}. FC is a strong predictor for forecasting technology acceptance and usage. 
\section[{d) Perceived Satisfaction (SAT)}]{d) Perceived Satisfaction (SAT)}\par
It is defined as the overall affective acknowledgment built upon the performance of a service after expenditure \hyperref[b13]{(Oliver, 1980)}. Moreover, satisfaction is defined as the individual's perceptions of the extent to which the system meets their needs and expectations \hyperref[b8]{(Ghazal et al., 2018)}. Therefore, this is a strong determinant which can predict the success of student's LMS adoption. 
\section[{e) Perceived Value (PV\} )}]{e) Perceived Value (PV\} )}\par
It is a powerful determining factor that leads to LMS adoption. An investigation recommends that PV may be a superior predictor of reacquiring than either SQ or SAT \hyperref[b6]{(Cronin et al., 2000)}. Students feel that it is worth the amount of they paid for since the expectations and needs of the students have been met. It is a strong that leads to students LMS adoption. 
\section[{f) Behavioural Intention (Bi)}]{f) Behavioural Intention (Bi)}\par
It refers to individual motives to use a particular technology just like LMS that directly affects actual usage \hyperref[b11]{(Hussein, 2018)}. It is an individual's intention to act on a certain situation after receiving, judging or evaluating the benefits, advantages and gains from a certain application, technology, object or activity (A. Bombaes, 2017). 
\section[{III.}]{III.} 
\section[{Research Design and Method}]{Research Design and Method}\par
The study is a conclusive research since the study wants to reveal the links or relationships of the identified towards student's intention. It is a quantitative research since it utilises numerical and experimental tools in coming up with results. The study uses survey as its strategy in gathering data. The structured questionnaire is used to reveal the strong foundation of building behavioural intention towards LMS adoption. 
\section[{a) Data Collection}]{a) Data Collection}\par
All questionnaires were done and conducted via google form and were given to students of the University of Perpetual Help System-Pueblo de Panay; items focused on measuring the attitude and is equipped with seven-point Likert Scale as part of quantitative instrument. Quantitative was also used to analyze hypotheses in experiments because of its ability to measure data using statistics.\par
For statistical analysis the data were collected from Senior High School students who are active users of LMS. There were 160 participants and they were given a structured questionnaire. The questionnaire was used to perform correlation analysis to test the relationship of every variable and Cronbach's Alpha was used to determine the scale reliability. This study is quantitative with the support of SPSS. 
\section[{b) Data Analysis}]{b) Data Analysis}\par
As part of statistical approach, this study used SPSS which aims to understand the relationships and behavior of respondents as regards LMS adoption. Descriptive statistical tools were used to describe the relations among the identified factors available in research. 
\section[{c) Analysis and Findings}]{c) Analysis and Findings}\par
The analysis aims to seek answers that validate the proposed hypotheses and model of study. The proposed hypotheses are shown below.  This part presents construct reliability and convergent reliability, and then followed by discriminant validity analysis. The investigation was assessed by examining the value of Cronbach's Alpha, composite reliability (CR) and the average variance extracted (AVE). The corresponding construct of Cronbach's Alphas were all higher than 0.80 thresholds. Both CR and AVE achieved a satisfactory level.\par
Table  {\ref 5}-1 presents the outcomes of reliability. Convergent Reliability was used, for it is a measurable factor forming one of a set that categorizes a system or sets of its operation which is often used in sociology, psychology, and other behavioral sciences. CR is tested using the three criteria of all factors such as CR, AVE, and the item loading on all corresponding components should be big enough. All hypotheses in the current investigation at least at the 0.5 significance level except for H1b which is unsupported. The structural model demonstrates the insignificant impact of perceived enjoyment on satisfaction. The rest of the hypotheses are supported where H1a demonstrates a strong impact of perceived enjoyment on satisfaction (?=0.235, t= 4.493, P<00.1), h2a demonstrates a strong impact of service quality on satisfaction (?=.225, t=2.844, P<00.4), h2b demonstrates a strong impact of service quality on satisfaction (?=.589, t=5.565, P<00.1), h3a demonstrates a strong impact of facilitating condition on satisfaction (?=0.258, t=5.058, P<00.1), h3b demonstrates a strong impact of facilitating condition on perceived value (?=.361, t=5.27, P<00.1), h4 demonstrates a strong impact of satisfaction on behavioral intention (?=.407, t=4.884, P<00.1), h5 demonstrates a strong impact of perceived value on behavioral intention (?=.257, t=3.976, P<001) as regards LMS adoption.\par
Table  {\ref 1}: Factor Analysis, Composite Reliability, Average Variance Extracted, and Correlation Both tables 5-1 and 5-2 indicate the outcomes of convergent validity whereas based on the overall model the relationship of PE between SAT is statistically significant (?=0.235, t= 4.493, P<00.1); the relationship between FC and SAT is statistically significant (?= 0.258, t= 5.058, P<00.1); the relationship between PE and PV is statistically insignificant (?= -0.041, t= -.032, P=0.527); the relationship between FC and PV is statistically significant (?= .361, t= 5.627, P<00.1); the relationship between SQ and SAT is statistically significant (?= .225, t= 2.844, P<00.4); the relationship between PE and PV is statistically significant (?= .589, t= 5.565, P<00.1); the relationship between SAT and Bi is statistically significant (?= .407, t= 4.884, P<00.1); the relationship between PV and Bi is statistically significant (?= .257, t= 3.976, P<00.1). The study came up with conclusive summary about the proposed hypotheses that H1a, H2a, H2b, H3a, and H4 are all supported except for H1b. The researcher therefore concludes that PE is insignificant towards Perceived Value (PV). Although it is an important component, but it is insufficient to define its effect and impact on Perceived Value (PV). All tests were done at the 0.01 significance level. PE has a low correlation to SAT which can be partly attributed to student's perceptions of LMS, and will invariably fluctuate as they use it extensively. However, what is worthy to note is that PE has a negative correlation with PV, meaning that they are mutually exclusive of each other. Even if there is high PV, it does not equate to high PE. The results state that SQ has a low correlation to SAT but a high correlation to PV. This can be explained by students preconceived notions towards LMS adoption. Student service quality can be heightened and exaggerated through grassroots campaigning and word-of-mouth advertising. However, the reality of the matter depends on after use and actual service quality rendered. This is subjective and has too much variance, which explains the low correlation between SQ and SAT. 
\section[{d) Structural Model}]{d) Structural Model}\par
FC puts simply as external conditions (i.e. infrastructure, qualified personnel, ICT facilities, related equipment, and qualified technical support) has a positive relationship with SAT and PV. FC goes hand-in-hand with SAT and PV, although PV slightly edges out SAT. In the end from the model, the researcher ascertain that SAT has a bigger impact (0.407) on Behavioral Intent than Perceived Value (0.250). SAT is most optimally mediated by FC, SQ, and PE. PV is based upon student confidence in LMS, and therefore innately has many problems due to high levels of variance and instability. In conclusion, SAT has the edge over PV in influencing Bi. This can be accounted for due to students trying the platform, investing time in to using LMS and giving school valuable feedback on what they thought was good and what they thought was bad. PV of LMS is excellent but has an innate drawback in that it is subjective and not able to be accurately qualified due to high variance. SAT on the other hand can be quantified due to its robust flexibility and most importantly due to the fact that it relies not on a student's initial feelings towards the platform, but is based on usage time. .861 PE3 This activity (LMS) used to hold my attention.\par
.866 PE4 I would describe (LMS experience) as very interesting activity.\par
.883 SQ1 The likely quality of this site is extremely high.\par
.825 0.565 .794 SQ2 This site appears to be of very poor quality.\par
.768 SQ3 This site must be of very good quality.\par
.737 SAT1 I feel pleased with my overall experience of LMS use.\par
. Exploratory factor analysis is performed using SPSS to evaluate and assess the values of factor loadings higher than 0.5. The outcomes of exploratory factor analysis present that indices are according to benchmark values which validate the proposed model; the values are between 0.729 and 0.897 . The outcomes do not report the issue of cross-loadings, excepts six items. These components are removed from the construct. Confirmatory Factor Analysis (CFA) is used for measuring the model that consists of validity and reliability test. The first item is done through factor analysis. All factors are tested to be higher than 0.7 that are acceptable to run SEM technique. Secondly, the major component analysis is used in order to analyze all the components. In the reliability test, Cronbach's Alpha value is determined and found to be higher than .7. 
\section[{IV.}]{IV.} 
\section[{Conclusion}]{Conclusion}\par
This part mainly deliberates the primary study as to what factors mainly influence LMS adoption. This provides more details about adoption and other key determinant components that mainly influence adoption of LMS and to offer vivid perception of the whole adoption process by starting from the key determining factors perceived enjoyment (PE), facilitating conditions (FC), service quality (SQ), satisfaction (SAT), and perceived value(PV) that dominate student's motive towards LMS adoption. Among three key determining variables, PE, FC, and SQ by which SQ possesses the most influence which contributes directly to student's perception of using the platform. However, based on the overall findings PE has an insignificant impact on PV. It means that whatever is felt and received, the better the appraisal will be for the students. The findings show that the better SQ the better the level of value received is. It is vital to enhance and enrich SQ to keep on maintaining its long lasting effect on student's motive to use LMS.\par
Therefore, exploring the factors under SQ should be improved for there has a fundamental influence and impact on PV. Facilitating conditions (FC) are components in classifying student's eagerness and fascination to function a certain task. It is emphasized that skills training, information or materials available, information access and connection, and administrative support are the influencing factors of facilitating conditions. These are labeled and quoted as highly critical that drive students from technology adoption. It is therefore, the absence of accessibility to computers; inadequate technical support extended and online support services provided hinder the acceptability of technology. If these challenges are met, there would be a heavy and strong impact on satisfaction which defines the intention to use. Satisfaction (SAT) is received and felt by the users which determines student's desires, expectations, and needs in line with the adoption whereas the PV is noted as student's overall appraisal of the total worth of the experience taken from student's judgment of what is provided and received. It shows that in case of LMS adoption at the University of Perpetual Help System-Pueblo de Panay Campus, SAT defines Bi whereas PV acts a more significant role which signifies that it has more influence on the intentions to use than what satisfaction does.\begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-2.png}
\caption{\label{fig_0}Figure 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-3.png}
\caption{\label{fig_1}Figure}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2}\includegraphics[]{image-4.png}
\caption{\label{fig_2}Figure 2 :}\end{figure}
  \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.06460807600950119\textwidth}P{0.31900237529691206\textwidth}P{0.31900237529691206\textwidth}P{0.14738717339667456\textwidth}}
Hypotheses\tabcellsep Path\tabcellsep Path Coefficient\tabcellsep Conclusion\\
H1a\tabcellsep \multicolumn{2}{l}{PE to SAT (?= 0.235, t= 4.493, P<00.1)}\tabcellsep Supported\\
H1b\tabcellsep PE to PV\tabcellsep \multicolumn{2}{l}{(?= -0.041, t= -.032, Not supported}\\
H2a\tabcellsep \multicolumn{2}{l}{SQ to SAT (?= .225, t= 2.844, P<00.4)}\tabcellsep Supported\\
H2b\tabcellsep SQ to PV\tabcellsep (?= .589, t= 5.565, P<00.1)\tabcellsep Supported\\
H3a\tabcellsep \multicolumn{2}{l}{FC to SAT (?= 0.258, t= 5.058, P<00.1)}\tabcellsep Supported\\
H3b\tabcellsep FC to PV\tabcellsep (?= .361, t= 5.27, P<00.1)\tabcellsep Supported\\
H4\tabcellsep SAT to Bi\tabcellsep (?= .407, t= 4.884, P<00.1)\tabcellsep Supported\\
H5\tabcellsep PV to Bi\tabcellsep (?= .257, t= 3.976, P<00.1)\tabcellsep Supported\end{longtable} \par
 
\caption{\label{tab_0}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.7117886178861789\textwidth}P{0.05528455284552846\textwidth}P{0.06449864498644986\textwidth}P{0.018428184281842817\textwidth}}
Rotated Component Matrix a\tabcellsep \tabcellsep \tabcellsep \\
Component\tabcellsep \tabcellsep \tabcellsep \\
QUESTIONS\tabcellsep \tabcellsep \multicolumn{2}{l}{AVE CRONBACH APLHA}\\
FC1 I have control over using LMS.\tabcellsep .869\tabcellsep \tabcellsep \\
FC2 I have the resources necessary to use LMS.\tabcellsep .805\tabcellsep 0.722\tabcellsep .885\\
FC3 I have the knowledge necessary to use LMS\tabcellsep .839\tabcellsep \tabcellsep \\
BI1 I intend to continue using LMS in the future.\tabcellsep .851\tabcellsep \tabcellsep \\
BI2 I will always try to LMS in my daily life.\tabcellsep .758\tabcellsep 0.568\tabcellsep .789\\
BI3 I plan to continue to use LMS frequently.\tabcellsep .793\tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_1}Table 3}\end{figure}
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