The Enhancement of Learning Management System in Teaching Learning Process with the UTAUT2 and Trust Model

Meyliana, Meyliana; Widjaja, Henry Antonius Eka; Santoso, Stephen Wahyudi; Petrus, Simon; Jovian,; Jessica, .

doi:10.1109/icimtech.2019.8843828

Cited by 11 publications

(11 citation statements)

References 16 publications

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“…Statistically, the non-significance of the direct effect of facilitating conditions on behavioural intention is explained by the total absorption of the mediation effects of both habit and hedonic motivation towards behavioural intention. The insignificance of facilitating conditions on behavioural intention for GC acceptance is reflective of other GC studies (Kumar & Bervell, 2019;Raman & Rathakrishnan, 2020) and information system studies (Meyliana et al, 2019). Furthermore, UTAUT's proposition of facilitating conditions relates to external factors, which is distinct from the proposition of behavioural intention as an internal factor (Venkatesh et al, 2008).…”

Section: Discussionmentioning

confidence: 57%

“…Ajzen (1985) described behavioural intention as a factor that has limited predicting capabilities of any selfimposed behaviour which is similar to the use of GC for teaching and learning. With respect to social influence, since students were experienced in using GC, the social effect of referent others did not influence psychological intention formations (Arain et al, 2019;Chávez Herting et al, 2020;Meyliana et al, 2019). Maruping et al (2017) explained that both social influence and facilitating conditions are related to external factors and are better predictors of BE than behavioural intention.…”

Section: Discussionmentioning

confidence: 99%

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Remodelling the role of facilitating conditions for Google Classroom acceptance: A revision of UTAUT2

Bervell

Kumar

Arkorful

et al. 2021

AJET

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Online learning environments have become a contemporary component of global tertiary education due to their affordances. These environments are hinged on internet-based learning management systems and one such tool is Google Classroom. However, empirical studies have indicated that gaps exist in determining how Google Classroom influences students’ behavioural intention to use it for online learning. Accordingly, this study defines a model based on the unified theory of acceptance and technology 2 (UTAUT2) to examine the relationship between facilitating conditions and other variables towards intention formation for Google Classroom usage. Based on a mixed method using the explanatory sequential design, survey data from 163 students were initially analysed using partial least squares structural equation modelling followed by a qualitative approach based on open-ended questions for thematic analysis. Results from the partial least squares structural equation modelling approach validated the hypothesised model confirming the significant predictive relationship of facilitating conditions with effort expectancy, hedonic motivation, habit and social influence but had an insignificant relationship with behavioural intention. Furthermore, hedonic motivation and habit were the main predictors of behavioural intention by masking the role of facilitating conditions. Subsequently, the qualitative outcome indicated that habit and perceived control of using Google applications influence hedonic motivation. Finally, recommendations were made to universities towards policy formation and practice of virtual learning using Google Classroom. Implications for practice or policy: Higher education institutions should create an environment for students to be motivated and enjoy Google Classroom use. Universities should provide Internet, technical and pedagogical support for students’ Google Classroom use. Universities should explain to students the affordances of using Google Classroom. Universities should encourage students to acquire access gadgets (smartphones or tablets) for Google Classroom use.

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Section: Discussionmentioning

confidence: 57%

Section: Discussionmentioning

confidence: 99%

Remodelling the role of facilitating conditions for Google Classroom acceptance: A revision of UTAUT2

Bervell

Kumar

Arkorful

et al. 2021

AJET

View full text Add to dashboard Cite

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“…Huang (2018), Bower et al (2020), Raman & Don (2013), and Amadin et al (2018) reached similar results with the findings of this study. In addition, Tseng et al (2019), Mtebe et al (2016), Kim & Lee (2020), Mohammad-Salehi et al (2021), andWidjaja, Santoso, andPetrus (2019) found that it did not affect BI. This study determined that PE was a significant variable for teachers' www.mojet.net intention to use digital learning resources.…”

Section: Discussionmentioning

confidence: 95%

Examining the factors affecting teachers’ use of digital learning resources with UTAUT2

Avci¹

2022

MOJET

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Digital learning resources include various tools and applications that effectively plan and evaluate instructions, increase student learning, and make the teaching process more efficient. Although digital learning resources are compatible with Generation Z students’ learning paths and are used to make the teaching planning, implementation, and evaluation process more practical, teachers do not frequently use them. This study aimsto determine the predictive powers of variables within the framework of the UTAUT2 model for teachers’ intention to use and their use behavior of Digital Teaching Resources. In line with the research purpose, data from 355 teachers working at different education levels were collected through the UTAUT2 scale. In the UTAUT2 model, Social Influence, Facilitating Conditions, Hedonic Motivation, Performance Expectancy, Effort Expectancy, Price Value, and Habit are exogenous variables. Furthermore, Use Behavioris endogenous and Behavioral Intention is the mediator variable. Path analysis was used for determining the predictive level of the exogenous variables and the mediating variable. According to the results, teachers’ Intention to Use digital learning materials is predicted by Hedonic Motivation, Performance Expectancy, and Habit. Similarly, the Use Behavior is predicted by Behavioral Intention and Habit. Extrinsic variables directly explain 81% of the variance in Behavioral Intention, while exogenous variables and Behavioral Intention directly or indirectly explain 67% of the variance in Use Behavior.

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“…Use Behavior explains the constancy of the technology usage and the degree to which technology may improve a user's knowledge and skills [12]. Use Behavior also is referred to the level of technology use among students such as their use of mobile devices as learning aids [13].…”

Section: A Models Related To Technology Acceptance and Usagementioning

confidence: 99%

“…The adherence of the user to keep using the system and how long the user intends to use the system is referred to as Behavioral Intention [12]. Furthermore, Behavioral Intention is the extent to which students expect to utilize technology such as smartphones in their studies and how long they will continue to do so [13].…”

Section: A Models Related To Technology Acceptance and Usagementioning

confidence: 99%

Student Acceptance Towards Online Learning Management System based on UTAUT2 Model

Musa¹,

Ismail²,

Tahir³

et al. 2022

IJACSA

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Recently, education has changed from physical learning to online and hybrid learning. Furthermore, the outbreak of COVID-19 makes them more significant. An online learning management system (LMS) is one of the most prevalent approaches to online and distance learning. The acceptance of the students towards the LMS is significant and it can give either bad or good responses to ensure the success of LMS. However, the Universiti Tun Hussein Onn Malaysia (UTHM) has not yet implemented any study to examine their LMS. The Unified Theory of Acceptance and Usage of Technology (UTAUT2) model is used in this study to investigate students' Behavioral Intention and Use Behavior when using the LMS in UTHM. This study also introduces a new construct in UTAUT2 named Online Learning Value. 376 respondents took part in this survey. Descriptive Statistics, Reliability Analysis, Pearson Correlation Coefficient, and Multiple Linear Regression analysis were all used to analyze survey data. The outcome of this research is Performance Expectancy (β=0.129, p=0.014), Hedonic Motivation (β=0.221, p=0.000), Online Learning Value (β=0.109, p=0.036) and Habit (β=0.513, p=0.000) has influence on students' intention to use LMS. Besides that, Facilitating Conditions (β=0.481, p=0.000) are the most important factors in students' use behavior toward the LMS followed by Habit (β=0.343, p=0.000) and Behavioral Intention (β=0.239, p=0.000). By utilizing the UTAUT2 model, the constructs of technology acceptance related to students' adoption of LMS have been identified and may become a reference to the stakeholders for future enhancement.

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The Enhancement of Learning Management System in Teaching Learning Process with the UTAUT2 and Trust Model

Cited by 11 publications

References 16 publications

Remodelling the role of facilitating conditions for Google Classroom acceptance: A revision of UTAUT2

Remodelling the role of facilitating conditions for Google Classroom acceptance: A revision of UTAUT2

Examining the factors affecting teachers’ use of digital learning resources with UTAUT2

Student Acceptance Towards Online Learning Management System based on UTAUT2 Model

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