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.

User Behavioral Intention Toward Using Mobile Healthcare System

2019 ◽  
pp. 429-444 ◽  
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.

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.

scholarly journals Predicting Patients’ Intention to Use a Personal Health Record Using an Adapted UTAUT Model: Secondary Data Analysis (Preprint)

2021 ◽  
Author(s):  
Consuela Cheriece Yousef ◽  
Teresa M. Salgado ◽  
Keisha Burnett ◽  
Laura E McClelland ◽  
Abin Thomas ◽  
...  
Keyword(s):  
Health Care ◽  
Behavioral Intention ◽  
Positive Attitude ◽  
Personal Health ◽  
Intention To Use ◽  
Performance Expectancy ◽  
Effort Expectancy ◽  
Centered Care ◽  
Behavioral Intention To Use ◽  
Health Applications

BACKGROUND With the rise in the use of information and communication technologies in health care, there has been a push for patients to accept more responsibility for their health and well-being using eHealth tools such as personal health records (PHRs). PHRs support patient-centered care and patient engagement. To support the achievement of the Kingdom of Saudi Arabia’s Vision 2030 ambitions, the National Transformation program provides a framework to use PHRs in meeting the triple aim for health care – increased access, reduced cost, and improved quality of care – and to provide patient- and person-centered care. However, there has been limited research on PHR uptake within the country. OBJECTIVE The aim of this study was to identify predictors of patient intention to utilize the Ministry of National Guard-Health Affairs (MNG-HA) PHR (MNGHA Care) using an adapted model of the Unified Theory of Acceptance and Use of Technology (UTAUT) as the theoretical framework. METHODS This cross-sectional study utilized a survey developed based on the UTAUT to measure behavioral intention to use MNGHA Care among adults visiting MNG-HA facilities in Riyadh, Jeddah, Dammam, Madinah, Al Ahsa, and Qassim. The main theory constructs performance expectancy, effort expectancy, social influence, facilitating conditions, and positive attitude toward using the PHR were collected as independent variables. Age, gender, experience with health applications, and health status were tested as moderators between the main theory constructs and behavioral intention using hierarchical multiple regression. RESULTS Of the eligible population, a total of 261 adult patients were included in the analysis with a mean age of 35.07 years (± 9.61), male (n=132, 50.6%), university-educated (n=118, 45.2%), and at least one chronic medical condition (n=139, 53.3%). The model explained 48.9% of the variance in behavioral intention to use the PHR (P=.377). Performance expectancy, effort expectancy, and positive attitude were significantly associated with behavioral intention to use the PHR (P<.05). Prior experience with health applications moderated the relationship between social influence and behavioral intention to use the PHR (P=.043). CONCLUSIONS This research contributes to the existing literature on PHR adoption broadly as well as in the context of the Kingdom of Saudi Arabia. Understanding which factors are associated with patient adoption of PHRs can guide future development and support the country’s aim of transforming the health care system. Similar to other studies on PHR adoption, performance expectancy, effort expectancy, and positive attitude are important factors, and practical consideration should be given to how support these areas.

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.

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.

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.

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