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

2016 ◽  
Vol 11 (2) ◽  
pp. 299 ◽  
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>

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

2017 ◽  
Vol 10 (2) ◽  
pp. 164-182 ◽  
Author(s):  
Ali Tarhini ◽  
Ra’ed Masa’deh ◽  
Kamla Ali Al-Busaidi ◽  
Ashraf Bany Mohammed ◽  
Mahmoud Maqableh
Keyword(s):  
Social Influence ◽  
Behavioral Intention ◽  
Self Efficacy ◽  
Structural Equation ◽  
Equation Modeling ◽  
Content Type ◽  
Performance Expectancy ◽  
Effort Expectancy ◽  
E Learning ◽  
Facilitating Conditions

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.

scholarly journals Analisis Behavioral Intention Pada Layanan Aplikasi RCTI+

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.

scholarly journals Factors Determine the Behavioural Intention in Adopting the Blockchain Technology by Malaysian Public Sector Officers

2020 ◽  
Vol 20 (1) ◽  
pp. 34-43
Author(s):  
Mohd Izzat Latifa ◽  
Zukarnain Zakaria
Keyword(s):  
Public Sector ◽  
Social Influence ◽  
New Technology ◽  
Behavioural Intention ◽  
The Public ◽  
Performance Expectancy ◽  
Blockchain Technology ◽  
Effort Expectancy ◽  
The Government ◽  
Facilitating Conditions

Recently, Blockchain technology has attracted great attention in both private organisations and the public sector around the world. However, not many are aware and understand this new technology. Thus, lack of understanding of the Blockchain technology could influence the intention in adopting the technology. Therefore, the objective of this paper is to identify the behaviour intention towards adopting Blockchain Technology in the Malaysian Public Sector. Data were gathered using a questionnaire to analyse the relationship between factors such as Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions and Trust with Behavioural Intention to adopt Blockchain Technology. The data were gathered from 100 officers in various government department. The survey revealed that majority of the government officers are aware about the Blockchain technology. However, most of them have inadequate exposure and knowledge about the technology. Findings from the regression analysis found that Trust, Performance Expectancy and Social Influence positively and significantly influence the behaviour intention of government officers in adopting Blockchain technology. Meanwhile, Effort Expectancy and Facilitating Conditions were found not significant. The findings from this study suggest that it is essential to develop strategies to implement a suitable Blockchain application in the public sector. Prior to such implementation, it is imperative for government officers to be equipped with knowledge, skills and resources related to the Blockchain technology.

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 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

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.

Engaging m-commerce adopters in India

2019 ◽  
Vol 32 (1) ◽  
pp. 191-210 ◽  
Author(s):  
Shweta Pandey ◽  
Deepak Chawla
Keyword(s):  
Social Influence ◽  
Perceived Risk ◽  
Content Delivery ◽  
Content Type ◽  
Performance Expectancy ◽  
Effort Expectancy ◽  
Perceived Enjoyment ◽  
Facilitating Conditions ◽  
Adoption Level ◽  
The Impact

PurposeThe purpose of this paper is to identify the impact of factors derived from the unified theory of user acceptance of technology (performance expectancy, effort expectancy, social influence, facilitating conditions, age, gender) and of those drawn from literature (perceived risk, perceived enjoyment and innovativeness) on the adoption of m-commerce in India. It also suggests implications of these for the consumer behavior theory practitioners and marketers.Design/methodology/approachData were collected using an online survey from 321 respondents, split into two groups (high and low adoption level users) based on the usage scores of the four categories of m-commerce- location-based, transaction-based, entertainment and content delivery. Logistic regression technique was used to identify the prominent factors among the nine identified influencers to understand the differences between the two groups.FindingsThe findings of this paper are sample biasness, self-reported m-commerce adoption level, limited m-commerce categories and specific context.Research limitations/implicationsExcept the two factors of performance expectancy and facilitating conditions, all other variables discriminate between low and high adoption levels of m-commerce services in India. Social influence, perceived enjoyment and innovativeness were the three main factors that were found to have the most significant impact on the discrimination levels of m-commerce service users in India. Further, it was found that women and the younger generation users of m-commerce showed a greater propensity for adopting m-commerce practices.Practical implicationsMarketers need to focus on key factors like social influence, perceived enjoyment, perceived risk and effort expectancy to persuade the young and innovative consumer target groups increase their adoption of m-commerce services.Originality/valueThis is the first study of its kind to explore factors that distinguish users with low and high levels of m-commerce adoption, by taking into consideration all four categories of m-commerce (transaction-based, content delivery, location-based and entertainment). In doing so, it highlights the need for marketers to focus on factors beyond facilitating conditions and performance expectancy, to enhance the adoption of m-commerce practices.

Penerapan Metode Unified Theory Of Acceptence And Use Of Technology (UTAUT) Dalam Memprediksi Behavioral Intentions Pada Penggunaan E-Commerce Shopee Dikalangan Mahasiswa (Studi Kasus Di Fakultas Tarbiyah Dan Keguruan UIN Ar-Raniry)

2019 ◽  
Vol 10 (2) ◽  
pp. 9-14
Author(s):  
Hanifah Oktana Putri ◽  
Bustami Yusuf
Keyword(s):  
Teacher Training ◽  
Social Influence ◽  
Behavioral Intention ◽  
Behavioral Intentions ◽  
Unified Theory ◽  
Positive Effects ◽  
Performance Expectancy ◽  
Use Of Technology ◽  
Effort Expectancy ◽  
Facilitating Conditions

This study aims to determine the effect of performance expectancy, effort expectancy, social influence, facilitating conditions on Behavioral intentions among students Tarbiyah and Teacher Training Faculty (FTK), State Islamic University of Ar-Raniry (UIN Ar-Raniry), in using e-commerce Shopee. The sample used in this study were students who had used e-commerce Shopee. We used the Unified Theory of Acceptance and Use of Technology (UTAUT) methodology in this study by distributing questionnaires both in paper based and by utilizing the google form application. The results of this study indicate that the four factors studied has the positive effects on Behavioral Intention of FTK’s students by 42%.

Modeling Millennials’ Adoption Intentions of E-banking: Extending UTAUT with Perceived Risk and Trust

2021 ◽  
pp. 231971452110526
Author(s):  
Akansha Mer ◽  
Amarpreet Singh Virdi
Keyword(s):  
Social Influence ◽  
Perceived Risk ◽  
Structural Equation ◽  
Sampling Technique ◽  
Positive Association ◽  
Behavioural Intention ◽  
Primary Data ◽  
Equation Modeling ◽  
Performance Expectancy ◽  
Effort Expectancy

The rationale of this research is to analyse and observe the factors that persuade millennials’ acceptance, adoption and usage of e-banking services in the banking industry in India. This research used primary data gathered from millennials in India. The sampling technique used is judgment sampling. Statistical analysis is conducted using structural equation modeling. The study’s findings indicate that effort expectancy, trust, social influence, performance expectancy and perceived risk are the factors that impinge on millennials’ behavioural intention of e-banking. The study suggests that effort expectancy, trust, social influence and performance expectancy posit a positive association with the e-banking behavioural intention of millennials in India. On the contrary, perceived risk is in a negative association with the e-banking behavioural intention of the millennials. The study makes a novel contribution to the literature with reference to India, being a pioneer attempt to investigate the factors that affect e-banking acceptance by millennials in Indian banks by extending UTAUT with perceived risk and trust. The present study contributes to the literature on e-banking adoption in India by highlighting that trust plays a crucial role in Indian millennials’ intention to adopt e-banking technology in high-power distance country like India.

scholarly journals User Acceptance of Mobile Apps for Restaurants: An Expanded and Extended UTAUT-2

2019 ◽  
Vol 11 (4) ◽  
pp. 1210 ◽  
Author(s):  
Ramon Palau-Saumell ◽  
Santiago Forgas-Coll ◽  
Javier Sánchez-García ◽  
Emilio Robres
Keyword(s):  
Social Influence ◽  
Structural Equation ◽  
Mobile Apps ◽  
Group Analysis ◽  
Equation Modeling ◽  
Perceived Credibility ◽  
Performance Expectancy ◽  
Effort Expectancy ◽  
Facilitating Conditions ◽  
Experiential Quality

This paper examines the adoption of mobile applications for restaurant searches and/or reservations (MARSR) by users, as part of their experiential quality. Following an extended and expanded version of UTAUT-2, this research proposes eight determinants of intentions to use: performance expectancy, effort expectancy, facilitating conditions, hedonic motivation, price-saving orientation, habit, social influence, and perceived credibility. The data were collected from Spanish users of MARSR applications (n = 1200), and analyzed using structural equation modeling (SEM). The findings confirm the need to extend and expand UTAUT-2 by incorporating perceived credibility and the social norm approach. The results gathered from SEM indicate that the drivers of intentions to use MARSR are, in order of impact: habit, perceived credibility, hedonic motivation, price-saving orientation, effort expectancy, performance expectancy, social influence, and facilitating conditions. Habit, facilitating conditions, and intentions to use are significantly related to use. Additionally, the moderating effects of gender, age, and experience were tested by means of a multi-group analysis. The users’ experience was seen to exert a moderating effect in some of the relationships hypothesized in the model, while gender and age did not play a significant role. The findings have both research and practical implications.

scholarly journals Factors Influencing the Intentions to Adopt Technology of the Broiler Farmer in Livestock Region 3, Thailand

2022 ◽  
Vol 19 (1) ◽  
pp. 1707
Author(s):  
Apisara Wichean ◽  
Mullika Sungsanit
Keyword(s):  
Social Influence ◽  
Structural Equation ◽  
Likelihood Estimation ◽  
Growth Mindset ◽  
Equation Modeling ◽  
Fixed Mindset ◽  
Performance Expectancy ◽  
Intention To Adopt ◽  
Effort Expectancy ◽  
Facilitating Conditions

          This research aimed to study the types and influence of mindsets, performance expectancy, effort expectancy, social influence and facilitating conditions on farmers' intention to adopt a technology. The research participants were 110 broiler farmers in livestock region 3. The research used a questionnaire to collect quantitative data and analyse the data using frequency, percentage, mean, standard deviation,  correlation coefficient and structural equation modeling with maximum likelihood estimation to analyse path coefficienct and structural relationships. The result showed that most of the participants have a growth mindset more than a fixed mindset. Performance expectancy, effort expectancy, social influence and facilitating conditions have directly affected boiler farmers' intention to adopt the technology. Effort expectancy has a total effect on attitude toward using technology. Interestingly, facilitating conditions have shown the most considerable influence on attitude toward adopting the technology. Mindsets have an influence on effort expectancy and facilitating conditions. HIGHLIGHTS Most broiler farmers in livestock region 3 possess a growth mindset than a fixed mindset Performance expectancy, effort expectancy, social influence and facilitating conditions have direct effect on boiler farmers' intention to adopt the technology Mindsets influence farmers' perception of effort expectancy and facilitating conditions of adopting the technology

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