Factors influencing students’ adoption of e-learning: a structural equation modeling approach

Purpose This research aims to examine the factors that may hinder or enable the adoption of e-learning systems by university students. Design/methodology/approach A conceptual framework was developed through extending the unified theory of acceptance and use of technology (performance expectancy, effort expectancy, hedonic motivation, habit, social influence, price value and facilitating conditions) by incorporating two additional factors, namely, trust and self-efficacy. Data were collected from students at two universities in England using a cross-sectional questionnaire survey between January and March 2015. Findings The results showed that behavioral intention (BI) was significantly influenced by performance expectancy, social influence, habit, hedonic motivation, self-efficacy, effort expectancy and trust, in their order of influencing the strength and explained 70.6 per cent of the variance in behavioral intention. Contrary to expectations, facilitating conditions and price value did not have an influence on behavioral intention. Originality/value The aforementioned factors are considered critical in explaining technology adoption but, to the best of the authors’ knowledge, there has been no study in which all these factors were modeled together. Therefore, this study will contribute to the literature related to social networking adoption by integrating all these variables and the first to be tested in the UK universities.

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Analisis Behavioral Intention Pada Layanan Aplikasi RCTI+

Jurnal Manajemen Bisnis dan Kewirausahaan ◽

10.24912/jmbk.v4i5.9212 ◽

2020 ◽

Vol 4 (5) ◽

pp. 199

Author(s):

Anggit Mardiana Permatasari ◽

Hetty Karunia Tunjungsari

Keyword(s):

Social Influence ◽

Behavioral Intention ◽

Structural Equation ◽

Negative Influence ◽

Equation Modeling ◽

Moderator Variable ◽

Performance Expectancy ◽

Digital Era ◽

Effort Expectancy ◽

Facilitating Conditions

The current era is called the information age, where humans really need information. The existence of the internet on smartphones makes it easier for humans to get information and enjoy content wherever and whenever. One of the content services in Indonesia is the MNC Group's RCTI + application. Although RCTI + is a new company, RCTI + has an active number of users of 302,569 until November 2019. RCTI + has a fairly high market share because the digital era is growing rapidly. This study measures the interest of users of RCTI + applications in Indonesia by using a modified UTAUT2 research model, where researchers analyze the variables Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Hedonic Motivation, Habit, and Content on Behavioral Intention. The data used in this study were 89 valid respondents obtained online using a questionnaire. Respondents are users of the RCTI + application. Researchers used Structural Equation Modeling (SEM) with SmartPLS software version 3.0 to test hypotheses. The results showed that Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Habit and Content had an influence on Behavioral Intention. However, Hedonic Motivations has a negative influence on Behavioral Intention. The Age variable as a moderator variable influences Content on Behavioral Intention, while Gender has no effect. This study resulted in an R2 of 0,900 and included in the moderate category. This research, has found that the variable that most influences Behavioral Intention is Habit.

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The Moderating Effects of Gender on E-Commerce Systems Adoption Factors

International Journal of Strategic Decision Sciences ◽

10.4018/ijsds.2018100106 ◽

2018 ◽

Vol 9 (4) ◽

pp. 86-104

Author(s):

Frederick Pobee ◽

Daniel Opoku

Keyword(s):

Behavioral Intention ◽

Structural Equation ◽

Equation Modeling ◽

Moderating Effects ◽

Performance Expectancy ◽

Adoption Factors ◽

Use Of Technology ◽

Effort Expectancy ◽

Actual Use ◽

Facilitating Conditions

The purpose of this article was to investigate the moderating effects of gender on e-commerce systems adoption factors among university lecturers in Ghana. In order to achieve this purpose, the unified theory of acceptance and use of technology (UTAUT) was used as the theoretical lens for the study. Eight hypotheses were developed and tested. Data analysis was performed with a structural equation modeling (SEM) technique using SmartPLS Application. Using a survey of 223 respondents, the study showed that factors such as performance expectancy, effort expectancy, and facilitating conditions positively and significantly influenced Ghanaian lecturers' behavioral intention and ultimately the actual use of e-commerce systems. As for the moderating effects of gender, this study discovered that gender insignificantly moderated the effects of performance expectancy, effort expectancy and social influence on behavioral intention.

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Modeling Factors Affecting Student’s Usage Behaviour of E-Learning Systems in Lebanon

International Journal of Business and Management ◽

10.5539/ijbm.v11n2p299 ◽

2016 ◽

Vol 11 (2) ◽

pp. 299 ◽

Cited By ~ 20

Author(s):

Ra'ed (Moh'd Taisir) Masa'deh ◽

Ali Tarhini ◽

Ashraf Bany Mohammed ◽

Mahmoud Maqableh

Keyword(s):

Social Influence ◽

Learning Systems ◽

Behavioural Intention ◽

Equation Modeling ◽

Positive Effects ◽

Performance Expectancy ◽

Effort Expectancy ◽

E Learning ◽

Facilitating Conditions ◽

The Impact

<p>This study seeks to explore the factors that influence students’ usage behaviour of e-learning systems. Based on the strong theoretical foundation of the TAM, UTAM and using structural equation modeling (SEM) via AMOS 20.0, this research paper examines the impact of performance expectancy, effort expectancy, hedonic motivation, habit, social influence, and trust on student’s behavioural intention, which is later examined along with facilitating conditions on student’s usage behaviour of e-learning systems. Data was collected from students at two universities in Beirut (capital of Lebanon) using a cross-sectional questionnaire survey between January and March 2015. The results revealed direct positive effect of performance expectancy, hedonic motivation, habit, and trust on student’s behavioural intention to use e-learning explaining around 71% of overall behavioural intention. Meanwhile, behavioural intention and facilitating conditions accounted for 40% with strong positive effects on student’s usage behviour of e-learning systems. However, both effort expectancy and social influence did not impact student’s behavioural intention.</p>

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The Moderating Effects of Gender on E-Commerce Systems Adoption Factors

Research Anthology on E-Commerce Adoption, Models, and Applications for Modern Business ◽

10.4018/978-1-7998-8957-1.ch017 ◽

2021 ◽

pp. 319-340

Author(s):

Frederick Pobee ◽

Daniel Opoku

Keyword(s):

Behavioral Intention ◽

Structural Equation ◽

Equation Modeling ◽

Moderating Effects ◽

Performance Expectancy ◽

Adoption Factors ◽

Use Of Technology ◽

Effort Expectancy ◽

Actual Use ◽

Facilitating Conditions

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Validation of Non-Linear Relationships-Based UTAUT Model on Higher Distance Education Students’ Acceptance of WhatsApp for Supporting Learning

TEXILA INTERNATIONAL JOURNAL OF ACADEMIC RESEARCH ◽

10.21522/tijar.2014.07.02.art004 ◽

2020 ◽

Vol 7 (2) ◽

pp. 27-39

Author(s):

Douglas Yeboah

Keyword(s):

Distance Education ◽

Social Influence ◽

Self Efficacy ◽

Structural Equation ◽

Equation Modeling ◽

Education Students ◽

Cape Coast ◽

Utaut Model ◽

Effort Expectancy ◽

Facilitating Conditions

This study examined relationships among the exogenous constructs of the Unified Theory of Acceptance and Use of Technology (UTAUT) model to identify those that significantly predict others. Questionnaires were used to collect data from 273 distance education students pursuing various diploma, bachelor’s degree and post-graduate diploma programs at the Cape Coast study center of the Institute for Distance and e-Learning (IDeL) of the University of Education, Winneba in Ghana. Proportional stratified random sampling technique was employed to obtain the sample of students. The data were analyzed using Partial Least Squares – Structural Equation Modeling (PLS-SEM). The results indicated that in acceptance of WhatsApp for supporting higher distance learning, effort expectancy and social influence predict performance expectancy; mobile self-efficacy and facilitating conditions predict effort expectancy; and facilitating conditions predict social influence. Also, mobile self-efficacy was found to significantly predict behavioral intention. We recommend that prior to introduction of a new technology such as WhatsApp for supporting learning, necessary resources and training should be provided by educational administrators and faculty to the students. This would make the students perceive that they can use the technology effectively to bring about gains in their learning; and subsequently accept the technology.

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

KnE Social Sciences ◽

10.18502/kss.v4i6.6658 ◽

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.

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Investigating consumer intention to accept mobile payment systems through unified theory of acceptance model

South Asian Journal of Business Studies ◽

10.1108/sajbs-03-2019-0037 ◽

2019 ◽

Vol 9 (1) ◽

pp. 88-114 ◽

Cited By ~ 2

Author(s):

Kanishk Gupta ◽

Nupur Arora

Keyword(s):

Social Influence ◽

Behavioral Intention ◽

Mobile Payment ◽

National Capital Region ◽

Payment Systems ◽

Content Type ◽

Performance Expectancy ◽

Effort Expectancy ◽

Facilitating Conditions ◽

Use Behavior

Purpose The purpose of this paper is to examine the impact of key antecedents of unified theory of acceptance and use of technology model 2 on behavioral intention to accept and use mobile payment systems in National Capital Region, India. Design/methodology/approach A sample of 267 mobile payment system users in National Capital Region was obtained through an online survey. A partial least squares method was used to find out whether key antecedents of UTAUT2 predict behavioral intention to accept mobile payment systems which further predicts use behavior toward mobile payment systems. Findings The research substantiates that performance expectancy, effort expectancy, habit and facilitating conditions significantly predict behavioral intention, which in turn significantly predict use behavior to use mobile payment systems. Both social influence and hedonic motivation were weak predictors of behavioral intention. Research limitations/implications The research substantiates that performance expectancy, effort expectancy, habit and facilitating conditions significantly predict behavioral intention, which in turn significantly predict use behavior to use mobile payment systems. Both social influence and hedonic motivation were weak predictors of behavioral intention. Originality/value The research substantiates that performance expectancy, effort expectancy, habit and facilitating conditions significantly predict behavioral intention, which in turn significantly predict use behavior to use mobile payment systems. Both social influence and hedonic motivation were weak predictors of behavioral intention.

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Individuals' intention to use sports wearables: the moderating role of technophobia

International Journal of Sports Marketing and Sponsorship ◽

10.1108/ijsms-08-2019-0083 ◽

2020 ◽

Vol 21 (2) ◽

pp. 225-245 ◽

Cited By ~ 1

Author(s):

Nilsah Cavdar Aksoy ◽

Alev Kocak Alan ◽

Ebru Tumer Kabadayi ◽

Alican Aksoy

Keyword(s):

Structural Equation ◽

Digital Age ◽

Equation Modeling ◽

Content Type ◽

Psychological Constructs ◽

Performance Expectancy ◽

Use Of Technology ◽

Effort Expectancy ◽

Facilitating Conditions ◽

The Relationship

PurposeThis study aims to examine the wearable devices market as an essential representative of the digital age using a framework based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and the context of sports wearables.Design/methodology/approach411 people, are both users and non-users of this technology were surveyed online, and the obtained data analyzed using structural equation modeling.FindingsThe results support the effects of performance expectancy, effort expectancy, facilitating conditions, and social influence on attitude toward sports wearables and attitude of usage intention. Further, technophobia moderates the relationship between performance expectancy and attitude. However, a moderating effect of technophobia on the relationship between effort expectancy and attitude was not observed.Originality/valueDue to innovative technologies in the digital age we live in, the devices we use in everyday life have gained intelligence. As more developments take place, and related products enter the market, understanding how people react to these products becomes an important issue. While investigating this issue in the context of sports wearables in this study, an important psychological construct, technophobia, was included in the research model in order to explore the usage intention of individuals through the effects of psychological constructs, such as paranoia, fear, anxiety, cybernetic revolt and cellphone avoidance, and the strong combination of important constructs of phobia to go against technology.

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Analysis of Behaviour of E-learning Users by Unified Teory of Acceptance and Use of Technology (UTAUT) Model A Case Study of Vocational Education in Halu Oleo University

Jurnal Vokasi Indonesia ◽

10.7454/jvi.v5i2.109 ◽

2017 ◽

Vol 5 (2) ◽

Author(s):

St. Nawal Jaya ◽

Muh. Naidzirin Anshari Nur ◽

Arman Faslih ◽

Muh. Nadzirin Anshari Nur

Keyword(s):

Social Influence ◽

Vocational Education ◽

Behavioral Intention ◽

Performance Expectancy ◽

Use Of Technology ◽

Effort Expectancy ◽

E Learning ◽

Supporting Tool ◽

Facilitating Conditions

E-learning (EL) as a supporting tool in learning process has increasingly developed because the implementation of the tool will be helpful for both lecturers and students to be more interactive in delivering materials and to evaluate learning outcomes. Analysis of the user’s behavior was required to measure the success rate of the implementation of e-learning. One of the models used in the present study was Unified Theory of Acceptance and Use of Technology (UTAUT). The model was designed to explain the behavior of the users on the information technology. The main variable was behavioral intention with four elements namely performance expectancy, effort expectancy, social influence and facilitating conditions. Data were collected randomly from the students of vocational education, university of Halu Oleo by distributing questioners to the e-learner users. The data were then validated and analyzed using regression analysis. The results showed EL affected the behavioral intention, performance expectancy, effort expectancy, social influence and facilitating conditions.

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Predictors of behavioral intention to adopt e-AgriFinance app among the farmers in Sarawak, Malaysia

British Food Journal ◽

10.1108/bfj-04-2021-0449 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Quistina Omar ◽

Ching Seng Yap ◽

Poh Ling Ho ◽

William Keling

Keyword(s):

Social Influence ◽

Behavioral Intention ◽

Structural Equation ◽

Agricultural Finance ◽

Content Type ◽

Intention To Adopt ◽

Use Of Technology ◽

Effort Expectancy ◽

Facilitating Conditions ◽

Practical Implications

PurposeThis study examines the predictors of behavioral intention of farmers to adopt a mobile agricultural finance application called e-AgriFinance using the Unified Theory of Acceptance and Use of Technology (UTAUT) and perceived cost as an additional predictor.Design/methodology/approachUsing a questionnaire survey, data are collected from 337 farmers in Sarawak, Malaysia. The collected data are analyzed using partial least squares structural equation modelling (PLS-SEM).FindingsThe research finds that performance expectancy, effort expectancy, social influence and facilitating conditions are positively related to behavioral intention to adopt the e-AgriFinance app, with social influence being the strongest predictor. Perceived cost is also found to be positively related to behavioral intention which contradicts the prediction of the model.Research limitations/implicationsThis study contributes to the use of UTAUT in predicting the adoption of mobile agricultural finance applications among farmers.Practical implicationsFor practice, this study provides implications for the Sarawak government to promote digital and financial inclusivity for all communities. This study also provides insights into important features of the e-AgriFinance app for digital finance providers to develop the apps that will be well accepted by farmers in the future.Originality/valueThis research is one of the few studies that focused on farmers' mobile technology adoption in agribusiness from the perspective of an emerging economy.

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