scholarly journals The Online Technology Acceptance Model of Generation-Z People in Thailand during COVID-19 Crisis

2020 ◽  
Vol 15 (s1) ◽  
pp. 496-512
Author(s):  
Ampol Chayomchai
Keyword(s):  
Technology Acceptance ◽  
Personal Innovativeness ◽  
Online Technology ◽  
Performance Expectancy ◽  
Generation Z ◽  
Use Of Technology ◽  
Effort Expectancy ◽  
Thai People ◽  
Acceptance Model ◽  
Behavioral Intention To Use

AbstractThis research aims to study the acceptance of the online technology of Thai people in Generation-Z during the incidence of COVID-19 disease. During this period, Thai people must quarantine themselves at home or work from home to prevent the outbreak of this disease and must comply with the laws of the Thai government. The researchers are interested in the Generation Z population because they are highly interested in technology. Previous literature and research used multiple models of acceptance and use of technology such as the Technology Acceptance Model, Unified Theory of Acceptance and Use of Technology model. This study adapted various variables from many models in the past, including personal innovativeness, performance expectancy, effort expectancy, social influence, trust, and behavioral intention to use technology. The research uses questionnaires as a research tool. 457 usable questionnaires from online data collection were used for data analysis. Descriptive and inferential statistical analysis was performed. The researcher tested the hypothesis by assessment of the Partial Least Square-Structural Equation Model. Research findings found that the behavioral intention to use online technology during COVID-19 disease is predicted by three key factors including performance expectancy, effort expectancy, and trust. Effort expectancy positively influences performance expectancy. In addition, personal innovativeness and the trust of users directly significantly affect performance expectancy and effort expectancy. The researcher suggests that the management level can use the findings in the planning of the organization’s management or marketers can utilize the results for the marketing strategy of the organization.

scholarly journals Acceptance of matchmaking tools in coworking spaces: an extended perspective

2021 ◽  
Author(s):  
Cristopher Siegfried Kopplin ◽  
Till Marius Gantert ◽  
Julia Verena Maier
Keyword(s):  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Sense Of Community ◽  
Unified Theory ◽  
Personal Innovativeness ◽  
Face To Face ◽  
Use Of Technology ◽  
Effort Expectancy ◽  
Coworking Spaces ◽  
Acceptance Model

AbstractAn extended technology acceptance model for matchmaking tools in coworking spaces is presented and tested among 92 German coworkers. Advancing previous research, hedonic and community-related aspects are integrated into a framework based on the extended unified theory of acceptance and use of technology (UTAUT2). Coworkers emphasize a matchmaking tool’s productivity aspects, which are positively moderated by their sense of community. Hedonic motivation (HM) and personal innovativeness contribute to usage intention, while effort expectancy is not a driver. The influence of HM is negatively moderated by sense of community, suggesting that a favorable social atmosphere that is explorable in person acts as a partial substitute for the enjoyment of tool usage. Surprisingly, satisfaction with face-to-face activities does not affect their perception of a matchmaking tool.

scholarly journals THE INFLUENCE OF UTAUT FACTORS ON E-RETENTION WITH E-SATISFACTION AS MEDIATING VARIABLE IN E-LEARNING

2018 ◽  
Vol 2 (1) ◽  
pp. 19
Author(s):  
Fibria Indriati ◽  
Priyanka Agustina
Keyword(s):  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
New Technology ◽  
Unified Theory ◽  
Performance Expectancy ◽  
Use Of Technology ◽  
Effort Expectancy ◽  
E Learning ◽  
Acceptance Model ◽  
Facilitating Conditions

Factors on Unified Theory of Acceptance and Use of Technology (UTAUT) are factors derived from UTAUT technology acceptance model, which is used to describe the factors that influence the acceptance of individuals on the implementation of a new technology in the enterprise. This study aimed to analyze the influence of UTAUT factors on E-retention with E-satisfaction as mediating variable. This study uses a quantitative approach with the total samples. The results showed that: (1) Performance Expectancy influences E-retention significantly, (2) Effort Expectancy influences E-retention significantly, (3) Social Influence influences E-retention not significantly, (4) Facilitating Conditions influences E-retention significantly, (5) E-satisfaction influences E-retention significantly, and (6) UTAUT Factors influence E-retention with E-satisfaction as mediating variable. However, the bigger impact is shown through the influence of UTAUT Factors on E-retention directly without going through the E-satisfaction. 

Analyzing Factors Influencing Behavior Intention to Use Snapchat and Instagram Stories

2017 ◽  
Vol 4 (2) ◽  
pp. 75-81 ◽  
Author(s):  
Vincent Valiant Coa ◽  
Johan Setiawan
Keyword(s):  
Structural Equation Modeling ◽  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Social Presence ◽  
Structural Equation ◽  
Equation Modeling ◽  
Intention To Use ◽  
Generation Z ◽  
Acceptance Model ◽  
Behavioral Intention To Use

Snapchat, and Instagram are two social networks which recently gain their users after adopting such a feature called "Story" which allows a certain post to be disappeared after a certain time. This research takes up this technology trends analyzing the factors that probably affect the behavioral intention to use Snapchat and Instagram stories among generation Z. Factors are analyzed using Structural Equation Modeling, with basis model and variables from Technology Acceptance Model. Data collection was targeted to finished within 1 week using online questionnaire with respondent from Jakarta and Tangerang for 100 respondent that are using both Snapchat stories and Instagram Stories. There are two tools researcher usually use to analyze Structural Equation Modeling: SPSS AMOS and LISREL. In this research, researchers choose AMOS. From six hypothesis proposed for Snapchat analysis, four hypothesis is accepted, while the other two are rejected. Meanwhile, on Instagram Stories analysis, five hypothesis is accepted and one hypothesis is rejected. This study finds out the Social Presence is an exogenous variable which has a major role in affecting other variables. While Perceived Enjoyment influenced the behavioral intention to use Snapchat and Instagram Stories the most. Index Terms—Structural Equation Modeling, Technology Acceptance Model, influence, generation Z, Snapchat, Instagram REFERENCES [1] L. Chin and Z. Ahmad, "Perceived Enjoyment and Malaysian Consumers’ Intention to Use a Single Platform EPayment", SHS Web of Conferences, vol. 18, 2015. [2] M. Ariff, T. Shan, N. Zakuan, N. Ishak and M. Wahi, "Examining Users' E-Satisfaction in the Usage of Social Networking Sites; Contribution from Utilitarian and Hedonic Information Systems", IOP Conference Series: Materials Science and Engineering, vol. 58, 2014. [3] K. Hassanein and M. Head, "Manipulating perceived social presence through the web interface and its impact on attitude towards online shopping", International Journal of HumanComputer Studies, vol. 65, no. 8, pp. 689-708, 2007. [4] P. Surendran, "Technology Acceptance Model: A Survey of Literature", 2012. [5] F. Davis, "Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology", MIS Quarterly, vol. 13, no. 3, p. 319, 1989

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 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 ANALISIS MINAT DAN PERILAKU PENGGUNAAN APLIKASI SHAREIT DENGAN PENDEKATAN UNIFIED THEORY OF ACCEPTANCE AND USE OF TECHNOLOGY

2021 ◽  
Vol 4 (2) ◽  
pp. 170-184
Author(s):  
Qona'ah El Hasan ◽  
Budi Permana
Keyword(s):  
Technology Acceptance ◽  
Behavioral Intention ◽  
Structural Equation ◽  
Positive Influence ◽  
Unified Theory ◽  
Personal Innovativeness ◽  
Use Of Technology ◽  
User Communities ◽  
Effort Expectancy ◽  
Facilitating Conditions

Technology acceptance is important for identifying elements that influence the user, so it is easy to use apps. The evaluation of technology reception will help to improve the system better in the future and indicate whether the system succeeded or not, so the users will keep using the apps. SHAREit is a program that can be downloaded on the Android Smartphone platform and on personal computers. It is able to transfer documents, pictures, music, and videos rapidly. Even so, the applications are faster than Bluetooth and Wifi. Then, Indonesia is the biggest second market for SHAREit in the world. The research is proposed to detect how the user accepts SHAREit in Kuningan Regency by using the Unified Theory of Acceptance and Use of Technology Method. It has been modified. The instrument of research is a questionnaire then distributed to user communities of SHAREit in Kuningan Regency by using Incidental Sampling, instrument testing and data processing Structural Equation Modelling by AMOS apps. Furthermore, the hypothesis is arranged through moderator variables; age, gender, and experience. The result of research indicates that there are three factors possessing positive influence on Behavioral Intention of usage SHAREit, such as Performance Expectancy, Social Influence, and Facilitating Conditions. A factor influencing use of behavioral is behavioral intention. Whereas, Personal Innovativeness in the Domain of Information Technology and Effort Expectancy do not influence the Behavioral Intention of SHAREit Apps in Kuningan Regency.

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.

Acceptability and Adoption of mHealth Interventions for Medication Adherence

2021 ◽  
pp. 114-134
Keyword(s):  
Medication Adherence ◽  
Technology Acceptance ◽  
User Acceptance ◽  
Mobile Technologies ◽  
Daily Lives ◽  
Use Of Technology ◽  
Technological Intervention ◽  
Acceptance Model ◽  
Behavioral Intention To Use ◽  
Mhealth Apps

The success of medication adherence apps depends on user acceptance and usage of the apps in their daily lives. It is essential for app developers to gain an in-depth understanding of the factors that can influence the acceptance of mHealth apps for adherence. It has been suggested that end users' acceptance of a technology-based health intervention can be studied through evaluation of their behavioral intention to use the intervention. Hence, it is important to continuously engage the target audience (e.g. clinicians, patients, caregivers) for their perceptions, experiences and feedback about the technological intervention, in this case, medication adherence apps. Factors such as socio-demographics and prior use of mobile technologies can influence user acceptance and adoption of mHealth apps. This chapter introduces the evolution of the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). In addition, the application of the UTAUT2 model is described through a case study in Singapore that determined the factors which influenced the acceptability of mHealth-based adherence interventions among healthcare institutions in Singapore.

Financial Technology as a Future Game-Changer

2022 ◽  
pp. 14-30
Author(s):  
Nelson Lajuni ◽  
Avnner Chardles Wellfren ◽  
Noraini Binti Abdullah ◽  
Salumah Binti Nain
Keyword(s):  
Structural Model ◽  
Intention To Use ◽  
Performance Expectancy ◽  
Generation Z ◽  
Use Of Technology ◽  
Effort Expectancy ◽  
Endogenous Variables ◽  
Engagement Process ◽  
Financial Technology ◽  
Game Changer

Financial technology has emerged as a game-changer to complement and enhance how the Millennial and Generation Z conduct transactions. Responding to calls of prior research, this study used the unified theory of acceptance and use of technology (UTAUT) to understand how performance expectancy and effort expectancy factors influence consumers' intention to use financial technology. Performance expectancy (PE) and effort expectancy (EE) were investigated as exogenous variables towards the intention to use financial technology (FinTech) that act as endogenous variables in this study. The authors collected 284 Millennial and Generation Z respondents and analysed the data using PLS-SEM. The result from the study's structural model suggested that FinTech continuance use intention was dependent on performance expectancy and effort expectancy on engaging with young vibrant consumers and establishing attractive FinTech elements. In addition, Millennial and Generation Z consumers with a high-performance expectancy were found to emphasize FinTech components in the engagement process.

scholarly journals The influence of Internet of Things on employee’s engagement among generation Y at the workplace

2018 ◽  
Vol 56 ◽  
pp. 03003
Author(s):  
Nur ‘Izzah Mohamad Nor ◽  
Lawrence Arokiasamy ◽  
Rani Ann Balaraman
Keyword(s):  
Internet Of Things ◽  
Technology Acceptance ◽  
Employee Engagement ◽  
Generation Y ◽  
Mediating Role ◽  
Use Of Technology ◽  
Effort Expectancy ◽  
Spss Software ◽  
Acceptance Model ◽  
Facilitating Conditions

This study provides the analysis of the influences Internet of Things (IoT) have towards employee engagement among generation Y in the workplace. Due to the declining levels of engagement among employees, especially among generation Y, a study of the influences towards the engagement levels are crucial. IoT is used for our daily activities thus, it acts as an influencer. Using the Technology Acceptance Model (TAM), United Theory of Acceptance and Use of Technology (UTAUT) and employee engagement model, the conceptual model for the research is developed. A total of 384 respondents are targeted for the research. The responses will be analyzed using the Statistical Package for Social Sciences (SPSS) Software. The aspects of performance expectancy of IoT, effort expectancy of IoT, facilitating conditions for IoT, attitude towards using IoT and the perceived enjoyment towards IoT are the focus of this research. The use of IoT has a mediating role in the model. The research will contribute towards the development and performances of organizations.

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