scholarly journals Investigating Effort Expectancy and Facilitating Conditions Affecting Behavioral Intention to Use Mobile Learning

2020 ◽  
Vol 55 (5) ◽  
Author(s):  
Nahla Aljojo ◽  
Bashair Alsuhaimi
Keyword(s):  
University Students ◽  
Mobile Learning ◽  
Behavioral Intention ◽  
Quantitative Research ◽  
Rapid Change ◽  
Intention To Use ◽  
Effort Expectancy ◽  
Facilitating Conditions ◽  
Behavioral Intention To Use ◽  
The Impact

The availability of internet access has created a rapid change in learning. This paper aims to investigate the impact of effort expectancy and facilitating conditions on behavioral intention to use mobile learning for Taibah University students in Saudi Arabia by using the unified theory of acceptance and use of technology model. Quantitative research methodology is used, so that the research-proposed formulated hypothesis will be tested. A sample of 110 Taibah University students was drawn. A survey questionnaire was designed for data collections to measure the impact of effort expectancy and facilitating conditions on behavioral intention to use mobile learning for Taibah University students. The independent variables of the research model are effort expectancy and facilitating conditions. The dependent variable is behavioral intention to use. The data was analyzed using statistical techniques, including reliability, validity, and regression analysis. The results indicate that effort expectancy and facilitating conditions were significant and directly influenced students' behavioral intention to use mobile learning.

scholarly journals Investigating the Determinants of Students’ Intention to Use Business Simulation Games

2019 ◽  
Vol 58 (2) ◽  
pp. 433-458 ◽  
Author(s):  
Yu-Yin Wang ◽  
Yi-Shun Wang ◽  
Shi-En Jian
Keyword(s):  
Social Influence ◽  
Behavioral Intention ◽  
Partial Least Square ◽  
Intention To Use ◽  
Business Simulation ◽  
Simulation Games ◽  
Performance Expectancy ◽  
Effort Expectancy ◽  
Facilitating Conditions ◽  
Behavioral Intention To Use

Business simulation games (BSGs) are educational tools that help students develop business management knowledge and skills. However, to date, relatively little research has investigated the factors that influence students’ BSG usage intention. Grounded on the extended unified theory of acceptance and use of technology, this study helped to fill this gap by exploring intention to use BSGs. Specifically, this study investigated the influence of performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, and price value on behavioral intention to use BSGs. Data collected from 141 useful respondents were tested against the research model using partial least square approach. The results of this study indicated that behavioral intention to use BSGs was influenced by facilitating conditions, hedonic motivation, and price value. Unexpectedly, performance expectancy, effort expectancy, and social influence were not predictive of students’ behavioral intention to use BSGs. These findings enhanced our understanding of students’ BSG usage behavior and provided several important theoretical and practical implications for the application of BSG in the context of business and management education.

scholarly journals The Determinants of Use and Acceptance of Mobile Assisted Language Learning: The Case of EFL Students in Morocco

2019 ◽  
Author(s):  
Nabil Morchid
Keyword(s):  
Language Learning ◽  
Technology Acceptance ◽  
Behavioral Intention ◽  
Teacher Feedback ◽  
Intention To Use ◽  
Performance Expectancy ◽  
Mobile Assisted Language Learning ◽  
Effort Expectancy ◽  
Behavioral Intention To Use ◽  
The Impact

The intent of this paper is to research the factors that determine students’ acceptance of mobile assisted language learning (MALL) in Morocco. This study emphasizes the inclusive character of the Unified Theory of Acceptance and Use of Technology (UTAUT). After careful assessment of the multiple relationships within UTAUT, a modified version of the theory was hypothesized then researched for the impact it has on the English as Foreign Language (EFL) context in Morocco. The technology acceptance model in this paper emphasized four directions connecting performance expectancy, effort expectancy, teacher feedback and compatibility to behavioral intention, also referred to as the determinants of behavioral intention to use MALL. For the purpose of this study, a technology enhanced environment was created. A total number of 156 EFL common core students were brought to interact on a WhatsApp-based platform by means of text-messaging. The WhatsApp treatment was optimized to synchronize with the institutionalized character of the teaching of English in Moroccan public schools. The questionnaire method was used for data collection. The data were screened for missingness, normality and outliers. Then, multiple reliability and validity tests were performed to substantiate the legitimacy of the dataset. Structural equation modelling (SEM) was used in the assessment of the measurement model and the structural model. The outputs of structural modelling corroborated the hypothesized directions connecting teacher feedback and compatibility to behavioral intention to use MALL while there was lack of support for the relationships linking performance expectancy and effort expectancy to behavioral intention to use MALL.

Smartphone Alcohol Use Disorder Recovery Apps: an Survey of Behavioral Intention to Use using UTAUT (Preprint)

2021 ◽  
Author(s):  
Rijuta Menon ◽  
Julien Meyer ◽  
Pria Nippak ◽  
Housne Begum
Keyword(s):  
Social Influence ◽  
Behavioral Intention ◽  
Alcohol Use Disorder ◽  
Survey Study ◽  
P Value ◽  
Intention To Use ◽  
Performance Expectancy ◽  
Effort Expectancy ◽  
Facilitating Conditions ◽  
Behavioral Intention To Use

BACKGROUND Alcohol Use Disorder (AUD) carries a huge health and economic cost to society. Effective interventions exist but numerous challenges limit their adoption, especially in a pandemic context. AUD recovery apps (AUDRA) have emerged as a potential complement to in-person interventions. They are easy to access and show promising results in terms of efficacy. However, they rely on individual adoption decision and remain underused. OBJECTIVE The aim of this survey study is to explore the beliefs that determine the intention to use AUDRA. METHODS We conducted a cross-sectional survey study of people suffering from AUD. We used the Unified Theory of Acceptance and Use of Technology, which predicts use and behavioral intention to use based on performance expectancy, effort expectancy, social influence and facilitating conditions. Participants were recruited directly from two sources: first, respondents at addiction treatment facilities in Ontario, Canada were contacted in person and filled a paper form; second, members from AUD recovery support groups on social media were contacted and invited to fill an online sruvey. The survey was conducted between October 2019 and June 2020. RESULTS The final sample was comprised of 159 participants (124 online and 35 paper based) self-identifying somewhat or very much with AUD. Most participants (85.5%) were aware of AUDRA and those participants scored higher on performance expectancy, effort expectancy and social influence. Overall, the model explains 35.4% of the variance in behavioral intention to use AUDRA and 11.1% of the variance in use. Social influence (p-value 0.314), especially for women (p-value 0.227) and effort expectancy (p value 0.247) were key antecedents of behavioral intention. Facilitating conditions was not significant overall but was moderated by age (p value 0.231) suggesting that it matters for older participants. Performance expectancy did not predict behavioral intention, which is unlike many other technologies but confirms other findings with mhealth. Open-ended questions suggest that privacy concerns may play a significant role for AUDRA. CONCLUSIONS This study suggests that unlike many other technologies, the adoption of AUDRA is not mainly determined by utilitarian factors such as performance expectancy. Rather, effort expectancy and social influence play a key role in determining the intention to use AUDRA.

scholarly journals Auto Mechanic Students’ Perceptions and Readiness toward Mobile Learning in Thailand

2018 ◽  
Vol 12 (5) ◽  
pp. 28
Author(s):  
Ekkalak Issaramanoros ◽  
Jintavee Khlaisang ◽  
Pakawan Pugsee
Keyword(s):  
Mobile Learning ◽  
Behavioral Intention ◽  
Teaching And Learning ◽  
Intention To Use ◽  
Personal Innovativeness ◽  
Use Of Technology ◽  
Rural And Urban ◽  
Effort Expectancy ◽  
Education Gap ◽  
Behavioral Intention To Use

Access to quality education is now a huge challenge in Thailand with ever-increasing inequality between rural and urban populations. Existing teaching and learning facilities are no longer adequate. Mobile learning has been suggested as a sustainable and appropriate delivery mechanism to reduce this rural/urban education gap. Students are supplied with their own mobile device at no cost to learners or their families. Opportunities offered through mobile learning to auto mechanic education in Thailand were explored. Data from 384 auto mechanic students were collected and descriptive and multiple regression analyses were performed based on the unified theory of acceptance and use of technology 2 (UTAUT2) model. Results showed that performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation and personal innovativeness were positively related to behavioral intention to use mobile learning. Furthermore, effort expectancy, hedonic motivation and personal innovativeness were the most significant predictors of behavioral intention to use mobile learning. Auto mechanic students in Thailand had positive perceptions toward mobile learning and the effect of students’ effort expectancy provided a better explanation for the adoption of mobile learning in auto mechanic education.

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.

User Behavioral Intention toward Using Mobile Healthcare System

2015 ◽  
pp. 128-143 ◽  
Author(s):  
Pantea Keikhosrokiani ◽  
Norlia Mustaffa ◽  
Nasriah Zakaria ◽  
Ahmad Suhaimi Baharudin
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Healthcare System ◽  
Behavioral Intention ◽  
Measurement Model ◽  
Healthcare Systems ◽  
Intention To Use ◽  
Mobile Healthcare ◽  
Effort Expectancy ◽  
Behavioral Intention To Use

This chapter introduces Mobile Healthcare Systems (MHS) and employs some theories to explore the behavioral intention of Smartphone users in Penang, Malaysia to use MHS. A survey was conducted in the form of questionnaire to Smartphone users in Penang, Malaysia for the duration of three weeks starting in September 2013. A total number of 123 valid surveys out of 150 were returned, which is equivalent to a response rate of 82%. The authors use Partial Least Squares (PLS) for analyzing the proposed measurement model. The factors that are tested are self-efficacy, anxiety, effort expectancy, performance expectancy, attitude, and behavioral intention to use. The results indicate which factors have a significant effect on Smartphone users' behavioral intention and which factors are not significant. The results assist in assessing whether MHS is highly demanded by users or not, and will assist in development of the system in the future.

Investigating the Factors Influencing Pre-Service Teachers' Acceptance to Use Mobile Devices for Learning

2020 ◽  
pp. 183-208
Author(s):  
Georgios K. Zacharis
Keyword(s):  
Data Collection ◽  
University Students ◽  
Social Influence ◽  
Mobile Devices ◽  
Behavioral Intention ◽  
Study Results ◽  
Data Collection Instrument ◽  
Faculty Of Education ◽  
Facilitating Conditions ◽  
Behavioral Intention To Use

This chapter determines the factors that significantly influence pre-service teachers' acceptance to use mobile devices as resources for learning in a university context. Based on the methodological framework of the UTAUT, a modified contextualized model of evaluation was created. A data collection instrument was designed, validated contextually, and optimized for mobile learning in higher education. A total of 320 Greek university students from a Faculty of Education participated in the study. Results demonstrated that the instrument constructed showed a notable internal consistency, with a high validity for data collection in 8 of its 9 factors. Results indicated Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Hedonic Motivation, and Empowerment in Learning as factors which affected participants' Behavioral Intention to use mobile technology for learning. Behavioral Intention, Social Influence and Empowerment in Learning affected university students' behavior to use mobile devices for learning.

scholarly journals E-Government Services Adoption: An Extension of the Unified Model of Electronic Government Adoption

SAGE Open ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 215824402093359
Author(s):  
Isaac Kofi Mensah ◽  
Guohua Zeng ◽  
Chuanyong Luo
Keyword(s):  
Structural Equation ◽  
Unified Model ◽  
Electronic Government ◽  
Equation Modeling ◽  
Intention To Use ◽  
Perceived Service Quality ◽  
Government Services ◽  
Effort Expectancy ◽  
Facilitating Conditions ◽  
Behavioral Intention To Use

This study proposed and validated an extension of the unified model of electronic government adoption (UMEGA). The data analysis was conducted with a structural equation modeling technique using Smart PLS 3.0. The results have demonstrated contrary to expectations that performance expectancy, effort expectancy, and social influence do not predict the attitude toward the use of e-government services. Facilitating conditions, however, were found to significantly determine both the behavioral intention to use and effort expectancy of e-government services. Also, perceived service quality and trust in government were found to positively predict, respectively, the intention to use and recommend the adoption of e-government services. The implications of these and other result findings of this study are thoroughly interrogated.

scholarly journals The Roles of Self-Efficacy and Domain Knowledge on Users Behavioral Intention to use Online Distance Learning

2021 ◽  
Vol 8 (2) ◽  
pp. 63-67
Author(s):  
Mohamad Rahimi Mohamad Rosman ◽  
Izzatil Husna Arshad ◽  
Mohamad Sayuti Md Saleh ◽  
Nurulannisa Abdullah ◽  
Faizal Haini Fadzil ◽  
...  
Keyword(s):  
Distance Learning ◽  
Behavioral Intention ◽  
Self Efficacy ◽  
Domain Knowledge ◽  
Quantitative Research ◽  
Descriptive Analysis ◽  
Intention To Use ◽  
Online Distance Learning ◽  
Online Distance ◽  
Behavioral Intention To Use

Novel Coronavirus 2019 (COVID-19) has shifted the educational landscape for the past year. Face-to-face interaction has become a distant memory. It signals the emergence of digital landscape with the dependency on online distance learning (ODL) application such as Google Meet, WebEx, Zoom, and Microsoft Team. The dependencies on this software raise the issue of the willingness and user behavioral intention to use such application. Therefore, this study investigated the roles of self-efficacy and domain knowledge on the user behavioral intention to use ODL. A quantitative research methodology was adopted; instrument was adopted from previous study before following rigorous testing, pretest, pilot study, and actual data collection. The findings were then analyze based on relationship or inferentil and descriptive analysis.

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