scholarly journals Predicting behavioral intention to use e-learning system: A case-study in Begum Rokeya University, Rangpur, Bangladesh

2021 ◽  
Author(s):  
Thasnim Humida ◽  
Md Habib Al Mamun ◽  
Pantea Keikhosrokiani
Keyword(s):  
Behavioral Intention ◽  
Learning System ◽  
Intention To Use ◽  
System A ◽  
E Learning ◽  
Behavioral Intention To Use

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 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 FRAMEWORK AND VERIFICATION OF A BLENDED E-LEARNING SYSTEM BEHAVIORAL INTENTION MODEL AMONG CLINICAL NURSES

2015 ◽  
Vol 14 (6) ◽  
pp. 733-743
Author(s):  
Tsui-Fen Chang ◽  
Cheng-Min Chao ◽  
Bor-Wen Cheng
Keyword(s):  
Technology Acceptance ◽  
Behavioral Intention ◽  
Perceived Risk ◽  
Structural Equation ◽  
Ease Of Use ◽  
Learning System ◽  
Perceived Ease Of Use ◽  
E Learning ◽  
Acceptance Model ◽  
Behavioral Intention To Use

As the various applications of Internet and information communication technology (ICT) grow rapidly, the education and hospital institutions provide more distance learning programs, which also makes the research on e-learning more important. However, disadvantages in e-learning have been identified. Blended e-learning systems (BELSs) are considered effective alternative learning approaches. This research proposes a conceptual model to explain the factors affecting nurses’ behavioral intention to use a blended e-learning system (BELS). This research integrated perceived risk and the technology acceptance model to hypothesize a theoretical model for explaining and predicting learners’ behavioral intention to use a BELS. Self-report questionnaires were distributed to local community hospitals, regional hospitals, and medical centers in Central Taiwan. To confirm this research hypothesis, data were collected 682 nurses, with a response rate of 97.4%. Using structural equation modeling (SEM), the results show that perceived risk, perceived ease of use, and attitude influenced BELS behavioral intention. Perceived ease of use, and perceived usefulness substantially influenced use attitude. In addition, the path coefficient of perceived risk on attitude was non-significant. On the basis of the results, hospital institutions can devise better strategies for developing their blended e-learning system. Key words: nurses education, blended learning, technology acceptance model, nurse perceptions, structural equation model.

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