scholarly journals Students’ use behavior towards e-learning tools during COVID-19 pandemics: Case study of higher educational institutions of Thailand

2021 ◽  
Vol 10 (4) ◽  
pp. 1166
Author(s):  
Chaiyawit Muangmee ◽  
Sebastian Kot ◽  
Nusanee Meekaewkunchorn ◽  
Nuttapon Kassakorn ◽  
Somyos Tiranawatananun ◽  
...  
Keyword(s):  
Social Distance ◽  
Behavioral Intention ◽  
Educational Institutions ◽  
Intention To Use ◽  
Learning Tools ◽  
Higher Educational Institutions ◽  
E Learning ◽  
Use Behavior ◽  
Behavioral Intention To Use

<p><span>The research purpose was to conduct an empirical investigation into students’ use behavior of e-learning tools during the COVID-19 pandemic, a case study of higher educational institutions in Thailand. The study applied the UTAUT2 theoretical model. Primary data was collected using a structured questionnaire from a total of 1,493 students across institutions of higher learning in Thailand. Structural equation modelling (SEM) was conducted using AMOS. The findings indicated that student’s behavioral intention to use e-learning tools is positively and significantly influenced by performance expectancy (β=0.22, p&lt;0.05); effort expectancy (β=0.14, p&lt;0.05); social influence (β=0.20, p&lt;0.05); facilitating condition (β=0.50, p&lt;0.05); hedonic motivation (β=0.35, p&lt;0.05); learning value (β=0.51, p&lt;0.05); and social distance (β=0.46, p&lt;0.05). Similarly, behavioral intention by the students to use e-learning tools have a positive and significant effect on actual use of e-learning tools (β=0.82, p&lt;0.05). Learning value and social distance had the largest positive effects on the student’s behavioral intention to use e-learning tools. It is important for the higher education institutions in Thailand to consider them. They are major factors driving students towards adopting e-learning tools during disruptions as witnessed during the COVID-19 pandemic period.</span></p>

scholarly journals USE OF E-LEARNING TOOLS ON HIGHER EDUCATION INSTITUTIONS

2020 ◽  
Vol 8 (12) ◽  
pp. 805-810
Author(s):  
Alka Sharma ◽  
◽  
Hina Jain Gupta ◽  
Keyword(s):  
Higher Education ◽  
Quality Education ◽  
Educational Institutions ◽  
Learning Tools ◽  
Higher Educational Institutions ◽  
E Learning ◽  
Higher Educational ◽  
Almost All ◽  
The Impact ◽  
Quantitative And Qualitative Methods

In the last two decades, technology has evolved at a great pace and has influenced almost all spheres of life and education is no exception to it. Nowadays, most of the educational institutions are using various tools and equipments to impart education to the students. This paper has tried to explore the impact of e-education tools on thestudents in higher educational institutions. The sample consists of students enrolled in higher educational institutions. Both quantitative and qualitative methods have been adopted for data collection including questionnaires, semi-structured &open-ended interviews. Use of computer and internet was found to be one of the most important e-learning tools. The findings are expected to assist the higher educational institutions in framing their policies to impart quality education to the students.

scholarly journals Analysis of Factors Affecting Behavioral Intention to Use E-Learning Uses the Unified Theory of Acceptance and Use of Technology Approach

2020 ◽  
Author(s):  
Ramllah . ◽  
Ahmad Nurkhin
Keyword(s):  
Social Influence ◽  
Behavioral Intention ◽  
Intention To Use ◽  
Performance Expectancy ◽  
Learning Intention ◽  
Effort Expectancy ◽  
E Learning ◽  
Facilitating Conditions ◽  
Behavioral Intention To Use ◽  
Descriptive Statistical Analysis

The purpose of this study isto analyze the influence of performance expectancy, effort expectancy, social influence, facilitating conditions, perceived creadibility, and anxiety on e-learning behavioral intention to use who are moderated by experience and voluntariness of use.The study population was 215 students who used e-learning in the Accounting Department of SMK N 1 Karanganyar. The sample selection using Slovin method with an error rate of 5% and sampling area technique obtained by respondents as many as 140 students. The technique of collecting data using a questionnaire. Data analysis techniques used descriptive statistical analysis and SEM-PLS. Data analysis tool using WarpPLS 5.0.The results of the descriptive statistical analysis show that the behavioral intention to use e-learning, performance expectancy, effort expectancy, social influence, facilitating conditions, perceived creativity, anxiety, experience and voluntariness of use are in the sufficient category. Hypothesis test results show the influence of performance expectancy on e-learning behavioral intention to use, effort expectancy does not affect the behavioral e-learning intention to use, social influence has an effect on behavioral e-learning intention to use, facilitating conditions have no effect on behavioral intention to Using e-learning, perceived creativity does not affect e-learning behavior, anxiety influences the behavioral intention to use e-learning, voluntary moderating negative social influences the behavioral e-learning intention to use, experience moderates the effect of effort expectancy on The behavior of e-learning intention to use, experience does not moderate the influence of social influence on the behavioral e-learning intention to use, experience does not moderate the effect of facilitating conditions on e-learning behavioral intention to use e-learning the conclusion of this study states that of the ten hypotheses proposed there are five types of hypotheses accepted. Keywords: E-learning, Behavioral Intention, UTAUT.

scholarly journals THE EFFECT OF USERS’ EFFORT EXPECTANCY ON USERS’ BEHAVIORAL INTENTION TO USE M-COMMERCE APPLICATIONS: CASE STUDY IN LIBYA

2021 ◽  
Vol 5 (4) ◽  
Author(s):  
Salah M. M. Dagnoush ◽  
Gamal S. A. Khalifa
Keyword(s):  
Behavioral Intention ◽  
Positive Influence ◽  
The Body ◽  
Intention To Use ◽  
Use Of Technology ◽  
Effort Expectancy ◽  
Using Data ◽  
Behavioral Intention To Use ◽  
The Relationship

The object of this study is to examine the relationship between effort expectancy and behavioral intention to use m-commerce in the Libyan context. It also aimed to determine the effect of effort expectancy on users’ behavioral intention. Using data from 310 respondents, the model of this study is supported by Unified theory of acceptance and use of technology (UTAUT). The findings of the study suggested that there is a positive relationship between users’ effort expectancy and users’ behavioral intention. The findings also proposed that effort expectancy has a positive influence on user's behavioral intention to use m-commerce in the Libyan context. This study contributes to the body knowledge on m-commerce usage while also providing practical guidance for the Libyan government on how to improve user usage of m-commerce systems. Particularly, it confirms that the user's effort expectancy increases the user's behavioral intention in the Libyan context.

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