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.

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 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.

scholarly journals Wearable Technology Acceptance in Health Care Based on National Culture Differences: Cross-Country Analysis Between Chinese and Swiss Consumers (Preprint)

2020 ◽  
Author(s):  
Dong Yang Meier ◽  
Petra Barthelmess ◽  
Wei Sun ◽  
Florian Liberatore
Keyword(s):  
Health Care ◽  
Social Influence ◽  
Technology Acceptance ◽  
National Culture ◽  
Behavioral Intention ◽  
Wearable Devices ◽  
Performance Expectancy ◽  
Intention To Adopt ◽  
Health Consciousness ◽  
Effort Expectancy

BACKGROUND The advancement of wearable devices and growing demand of consumers to monitor their own health have influenced the medical industry. Health care providers, insurers, and global technology companies intend to develop more wearable devices incorporating medical technology and to target consumers worldwide. However, acceptance of these devices varies considerably among consumers of different cultural backgrounds. Consumer willingness to use health care wearables is influenced by multiple factors that are of varying importance in various cultures. However, there is insufficient knowledge of the extent to which social and cultural factors affect wearable technology acceptance in health care. OBJECTIVE The aims of this study were to examine the influential factors on the intention to adopt health care wearables, and the differences in the underlying motives and usage barriers between Chinese and Swiss consumers. METHODS A new model for acceptance of health care wearables was conceptualized by incorporating predictors of different theories such as technology acceptance, health behavior, and privacy calculus based on an existing framework. To verify the model, a web-based survey in both the Chinese and German languages was conducted in China and Switzerland, resulting in 201 valid Chinese and 110 valid Swiss respondents. A multigroup partial least squares path analysis was applied to the survey data. RESULTS Performance expectancy (β=.361, <i>P</i>&lt;.001), social influence (β=.475, <i>P</i>&lt;.001), and hedonic motivation (β=.111, <i>P</i>=.01) all positively affected the behavioral intention of consumers to adopt wearables, whereas effort expectancy, functional congruence, health consciousness, and perceived privacy risk did not demonstrate a significant impact on behavioral intention. The group-specific path coefficients indicated health consciousness (β=.150, <i>P=</i>.01) as a factor positively affecting only the behavior intention of the Chinese respondents, whereas the factors affecting only the behavioral intention of the Swiss respondents proved to be effort expectancy (β=.165, <i>P</i>=.02) and hedonic motivation (β=.212, <i>P</i>=.02). Performance expectancy asserted more of an influence on the behavioral intention of the Swiss (β=.426, <i>P</i>&lt;.001) than the Chinese (β=.271, <i>P</i>&lt;.001) respondents, whereas social influence had a greater influence on the behavioral intention of the Chinese (β=.321, <i>P</i>&lt;.001) than the Swiss (β=.217, <i>P</i>=.004) respondents. Overall, the Chinese consumers displayed considerably higher behavioral intention (<i>P</i>&lt;.001) than the Swiss. These discrepancies are explained by differences in national culture. CONCLUSIONS This is one of the first studies to investigate consumers’ intention to adopt wearables from a cross-cultural perspective. This provides a theoretical and methodological foundation for future research, as well as practical implications for global vendors and insurers developing and promoting health care wearables with appropriate features in different countries. The testimonials and support by physicians, evidence of measurement accuracy, and easy handling of health care wearables would be useful in promoting the acceptance of wearables in Switzerland. The opinions of in-group members, involvement of employers, and multifunctional apps providing credible health care advice and solutions in cooperation with health care institutions would increase acceptance among the Chinese.

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 Determinants of Fitness App Usage and Moderating Impacts of Education-, Motivation-, and Gamification-Related App Features on Physical Activity Intentions: Cross-sectional Survey Study (Preprint)

2020 ◽  
Author(s):  
Yanxiang Yang ◽  
Joerg Koenigstorfer
Keyword(s):  
Physical Activity ◽  
Behavioral Intention ◽  
Unified Theory ◽  
Physically Active ◽  
Performance Expectancy ◽  
Use Of Technology ◽  
Effort Expectancy ◽  
And Gender ◽  
Facilitating Conditions ◽  
Behavioral Intention To Use

BACKGROUND Smartphone fitness apps are considered promising tools for promoting physical activity and health. However, it is unclear which user-perceived factors and app features encourage users to download apps with the intention of being physically active. OBJECTIVE Building on the second version of the Unified Theory of Acceptance and Use of Technology, this study aims to examine the association of the seven determinants of the second version of the Unified Theory of Acceptance and Use of Technology with the app usage intentions of the individuals and their behavioral intentions of being physically active as well as the moderating effects of different smartphone fitness app features (ie, education, motivation, and gamification related) and individual differences (ie, age, gender, and experience) on these intentions. METHODS Data from 839 US residents who reported having used at least one smartphone fitness app were collected via a web-based survey. A confirmatory factor analysis was performed, and path modeling was used to test the hypotheses and explore the influence of moderators on structural relationships. RESULTS The determinants explain 76% of the variance in the behavioral intention to use fitness apps. Habit (<i>β</i>=.42; <i>P</i>&lt;.001), performance expectancy (<i>β</i>=.36; <i>P</i>&lt;.001), facilitating conditions (<i>β</i>=.15; <i>P</i>&lt;.001), price value (<i>β</i>=.13; <i>P</i>&lt;.001), and effort expectancy (<i>β</i>=.09; <i>P</i>=.04) were positively related to behavioral intention to use fitness apps, whereas social influence and hedonic motivation were nonsignificant predictors. Behavioral intentions to use fitness apps were positively related to intentions of being physically active (<i>β</i>=.12; <i>P</i>&lt;.001; <i>R<sup>2</sup></i>=0.02). Education-related app features moderated the association between performance expectancy and habit and app usage intentions; motivation-related features moderated the association of performance expectancy, facilitating conditions, and habit with usage intentions; and gamification-related features moderated the association between hedonic motivation and usage intentions. Age moderated the association between effort expectancy and usage intentions, and gender moderated the association between performance expectancy and habit and usage intentions. User experience was a nonsignificant moderator. Follow-up tests were used to describe the nature of significant interaction effects. CONCLUSIONS This study identifies the drivers of the use of fitness apps. Smartphone app features should be designed to increase the likelihood of app usage, and hence physical activity, by supporting users in achieving their goals and facilitating habit formation. Target group–specific preferences for education-, motivation-, and gamification-related app features, as well as age and gender differences, should be considered. Performance expectancy had a high predictive power for intended usage for male (vs female) users who appreciated motivation-related features. Thus, apps targeting these user groups should focus on goal achievement–related features (eg, goal setting and monitoring). Future research could examine the mechanisms of these moderation effects and their long-term influence on physical activity.

scholarly journals Artificial Intelligence in Human Resources Information Systems: Investigating its Trust and Adoption Determinants

2020 ◽  
Vol 5 (1) ◽  
pp. 749-765
Author(s):  
Bilal Ibrahim Hmoud ◽  
László Várallyai
Keyword(s):  
Artificial Intelligence ◽  
Human Resources ◽  
Significant Influence ◽  
Behavioral Intention ◽  
Intention To Use ◽  
Technological Readiness ◽  
Performance Expectancy ◽  
Human Resources Information Systems ◽  
And Performance ◽  
Behavioral Intention To Use

With the rapidly emerging trend of employing Artificial Intelligence technologies within modern economics. This study is an attempt to fill the research gap associated with the factors that have influence with the adoption of artificial intelligence in human resources information systems on HR-leaders intention to use it. It empirically investigates the influences that trust, technological readiness, facilitating condition and performance expectancy on HR-professional’s behavioral intention to use AI in HRM. Besides, examine the moderating effect of age and experience on the proposed associations. Data were collected from by online questionnaire from 185 HR managers. A structural framework was introduced to test the relationship between study latent variables. Result exhibited that trust and performance expectancy has a significant influence on HR-professionals behavioral intention to use AI-HRIS. Trust and technological readiness showed a significant influence on HR-professionals performance expectancy of using AI-HRIS. While facilitating condition, organizational size and technological readiness did not show a significant influence on HR-professionals behavioral intention toward using AI-HRIS. Lastly, Age and Experience did not have a moderating effect on trust and performance expectancy association with the behavioral intention toward using AI-HRIS. The findings of this study contribute to the theory development of information technology diffusion in HRM.

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